The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.
A Novel Three-Dimensional Adaptive Localization (T-Dial) Algorithm for Wirele...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Enhanced Predictive Proportion using TMP Algorithm in WSN NavigationIJCERT
visit http://www.ijcert.org for more research journals it offers discount for Indian Research scholars
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
Multiagent based multipath routing in wireless sensor networksijwmn
This paper proposes a Multiagent Based Multipath Routing (MBMR) using a set of static and mobile agents
by employing localization technique. The operation of proposed routing technique can be briefly explained
as follows. (1) Anchor nodes are deployed evenly over the network environment. (2) Unknown sensor nodes
are deployed randomly over network environment and these nodes perform localization. (3) Source node
computes the shortest route to destination node through arbitrary midpoint node and intermediate nodes.
(4) Source node generates mobile agents for partial route discovery, which traverses to destination node
through the midpoint and intermediate nodes by carrying information. (5) Mobile agents update the
destination node with carried information. (6) Destination node computes route weight factor for all the
routes discovered by mobile agents. (7) Destination node computes the node disjoint routes and it selects
routes depending on the criticalness of event for communication. The performance of the proposed scheme
is evaluated in terms of performance parameters such as localization error, network lifetime, energy
consumption, cost factor, packet delivery ratio, and latency.
LOCALIZATION ALGORITHM USING VARYING SPEED MOBILE SINK FOR WIRELESS SENSOR NE...ijasuc
Localization of sensor nodes is important in many aspects in wireless sensor networks. The known
location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the
event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel
range based localization algorithm which helps the mobile sink to compute the location of the sensor
nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the
sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for
localization. The localization mechanism has been implemented in TOSSIM. The simulation results show
that our scheme performed better than other range-based schemes.
A Novel Three-Dimensional Adaptive Localization (T-Dial) Algorithm for Wirele...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Enhanced Predictive Proportion using TMP Algorithm in WSN NavigationIJCERT
visit http://www.ijcert.org for more research journals it offers discount for Indian Research scholars
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
Multiagent based multipath routing in wireless sensor networksijwmn
This paper proposes a Multiagent Based Multipath Routing (MBMR) using a set of static and mobile agents
by employing localization technique. The operation of proposed routing technique can be briefly explained
as follows. (1) Anchor nodes are deployed evenly over the network environment. (2) Unknown sensor nodes
are deployed randomly over network environment and these nodes perform localization. (3) Source node
computes the shortest route to destination node through arbitrary midpoint node and intermediate nodes.
(4) Source node generates mobile agents for partial route discovery, which traverses to destination node
through the midpoint and intermediate nodes by carrying information. (5) Mobile agents update the
destination node with carried information. (6) Destination node computes route weight factor for all the
routes discovered by mobile agents. (7) Destination node computes the node disjoint routes and it selects
routes depending on the criticalness of event for communication. The performance of the proposed scheme
is evaluated in terms of performance parameters such as localization error, network lifetime, energy
consumption, cost factor, packet delivery ratio, and latency.
LOCALIZATION ALGORITHM USING VARYING SPEED MOBILE SINK FOR WIRELESS SENSOR NE...ijasuc
Localization of sensor nodes is important in many aspects in wireless sensor networks. The known
location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the
event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel
range based localization algorithm which helps the mobile sink to compute the location of the sensor
nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the
sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for
localization. The localization mechanism has been implemented in TOSSIM. The simulation results show
that our scheme performed better than other range-based schemes.
Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
ENERGY EFFICIENT MULTIHOP QUALITY PATH BASED DATA COLLECTION IN WIRELESS SENS...Editor IJMTER
In recent years there has been an increased focus on the use of sensor networks to sense and measure
the environment. This leads to a wide variety of theoretical and practical issues on appropriate protocols for data
sensing and transfer. Recent work shows sink mobility can improve the energy efficiency in wireless sensor
networks (WSNs). However, data delivery latency often increases due to the speed limit of mobile sink. Most of
them exploit mobility to address the problem of data collection in WSNs. The WSNs with MS (mobile Sink) and
provide a comprehensive taxonomy of their architectures, based on the role of the MS. An overview of the data
collection process in such a scenario, and identify the corresponding issues and challenges. A protocol named
weighted rendezvous planning (WRP) which is a heuristic method that finds a near-optimal traveling tour that
minimizes the energy consumption of sensor nodes. Focus on the path selection problem in delay-guaranteed
sensor networks with a path-constrained mobile sink. Concentrate an efficient data collection scheme, which
simultaneously improves the total amount of data and reduces the energy consumption. The optimal path is chosen
to meet the requirement on delay as well as minimize the energy consumption of entire network. Predictable sink
mobility is exploited to improve energy efficiency of sensor networks.
Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides
fundamental support for many location-aware protocols and applications. Constraints on cost and power
consumption make it infeasible to equip each sensor node in the network with a global position system
(GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use
anchor nodes, which are equipped with GPS units among unknown nodes and broadcast their current
locations to help nearby unknown nodes with localization. In this paper we can proposed a novel algorithm
of cuboid localization with the help of central point precision method. Simulation shows that the results are
far better then existing cuboid methods and gain accuracy of up to 83% with a localization error of 1.6m
and standard deviation of 2.7.
AN EFFICIENT DEPLOYMENT APPROACH FOR IMPROVED COVERAGE IN WIRELESS SENSOR NET...csandit
Wireless Sensor Networks (WSNs) are experiencing a revival of interest and a continuous advancement in various scientific and industrial fields. WSNs offer favorable low cost and readily deployable solutions to perform the monitoring, target tracking, and recognition of physical events. The foremost step required for these types of ad-hoc networks is to deploy all the sensor nodes in their positions carefully to form an efficient network. Such network should satisfy the quality of service (QoS) requirements in order to achieve high performance levels. In
this paper we address the coverage requirement and its relation with WSN nodes placement problems. In fact, we present a new optimization approach based on the Flower Pollination Algorithm (FPA) to find the best placement topologies in terms of coverage maximization. We have compared the performance of the resulting algorithm, called FPACO, with the original practical swarm optimization (PSO) and the genetic algorithm (GA). In all the test instances, FPACO performs better than all other algorithms.
Location Fingerprinting is a very familiar Wi-Fi positioning method, which determines a device by retrieving the information recorded containing the location fingerprint. These methods deploy the signal strength (RSS) to predict the coordinate. There are feedbacks for using the absolute RSS either the absolute RSS in a time interval may not be representable of the IEEE 802.11 signal, as the signal may fluctuate or a manual error prone calibration is needed across different mobile platform. The main target is to propose the use of Fourier descriptors in LF. We convert the IEEE 802.11b Wi-Fi signal into a Fourier domain. Then, the Fourier descriptors are used to predict the location by applying the K-Nearest Neighbor algorithm. The results show that the effectiveness of LF methods based on Fourier descriptors lead to substantially more accurate and robust localization.
FINGERPRINT LOCATION METHODS USING RAY-TRACINGmarcelonog29
Mobile location methods that employ signal fingerprints are becoming increas- ingly popular in a number of wireless positioning solutions. A fingerprint is a spatial database, created either by recorded measurement or simulation, of the radio envi- ronment. It is used to assign signal characteristics such as received signal strength or power delay profiles to an actual location. Measurements made by either the handset or the network, are then matched to those in the fingerprint in order to determine a location. Creation of the fingerprint by an a priori measurement stage is costly and time consuming. Virtual fingerprints, those created by a ray-tracing radio propagation prediction tool, normally require a lengthy o↵-line simulation mode that needs to be repeated each time changes are made to the network or built environment. An open research question exists of whether a virtual fingerprint could be created dynamically via a ray-trace model embedded on a mobile handset for positioning purposes.
The key aim of this thesis is to investigate the trade-o↵ between complexity of the physics required for ray-tracing models and the accuracy of the virtual fingerprints they produce. The most demanding computational phase of a ray-trace simulation is the ray-path finding stage, whereby a distribution of rays cast from a source point, interacting with walls and edges by reflection and di↵raction phenomena are traced to a set of receive points. Due to this, we specifically develop a new technique that decreases the computation of the ray-path finding stage. The new technique utilises a modified method of images rather than brute-force ray casting. It leads to the creation of virtual fingerprints requiring significantly less computation e↵ort relative to ray casting techniques, with only small decreases in accuracy.
Our new technique for virtual fingerprint creation was then applied to the devel- opment of a signal strength fingerprint for a 3G UMTS network covering the Sydney central business district. Our main goal was to determine whether on current mo- bile handsets, a sub-50m location accuracy could be achieved within a few seconds timescale using our system. The results show that this was in fact achievable. We also show how virtual fingerprinting can lead to more accurate solutions. Based on these results we claim user embedded fingerprinting is now a viable alternative to a priori measurement schemes.
Effective range free localization scheme for wireless sensor networkijmnct
Location aware sensors can be used in many areas such as military and civilian applications. Wireless
Sensor Networks help to identify the accurate location of the event. In this paper a cost effective schema for
localization has been proposed. It uses two beacon nodes to identify the location of unknown nodes. It
also uses flooding and estimating method to accurately identify the location of other nodes. Available area
is divided into zones and beacons are provided for each zone. Beacon nodes are placed in appropriate
locations normally two in a zone to provide location information. Using the two nodes location of unknown
nodes can be calculated accurately.
A Comparative Analysis for Hybrid Routing Protocol for Wireless Sensor NetworksIJERA Editor
Wireless Sensor Networks (WSNs) consist of smallnodes with sensing, computation and wireless
communicationscapabilities. These sensor networks interconnect a several othernodes when established in large
and this opens up severaltechnical challenges and immense application possibilities.These wireless sensor
networks communicate using multi-hopwireless communications, regular ad hoc routing techniquescannot be
directly applied to sensor networks domain due tothe limited processing power and the finite power available
toeach sensor nodes hence recent advances in wireless sensornetworks have developed many protocols
depending on theapplication and network architecture and are specificallydesigned for sensor networks where
energy awareness is anessential consideration. This paper presents routingprotocols for sensor networks and
compares the routingprotocols that are presently of increasing importance.
In this paper, we propose Hybrid Routing Protocol whichcombines the merits of proactive and reactive approach
andovercome their demerits.
NETWORK PERFORMANCE ENHANCEMENT WITH OPTIMIZATION SENSOR PLACEMENT IN WIRELES...ijwmn
From one side, sensor manufacturing technology and from other side wireless communication technology
improvement has an effect on the growth and deployment of Wireless Network Sensor (WSN). The
appropriate performance of WSN has abundant necessity which has dependent on the different parameters
such as optimize sensor placement and structure of network sensor. The optimized placement in WSN not
only would optimize number of sensors, but also help to reach to the more precise information. Therefore
different solutions are proposed to reduce cost and increase life time of sensor networks that most of them
are concentrated in the field of routing and information transmission. In this paper, places which they need
new sensors placement or sensor movements are determined and then with applying these changes,
performance of WSN will calculate. To achieve the optimum placement, the network should evaluate
precisely and effective criteria on the performance should extract. Therefore the criteria should be ranked
and after weighting with using AHP algorithms, with use of Geographical Information System (GIS), these
weighted criteria will combined and in the locations which WSN doesn’t have enough performance, new
sensor placement will create. New proposed method, improve 21.11% performance of WSN with sensor
placement in the low performance locations. Also the number of added sensor is 26.09% which is lowest
number of added sensors in comparison with other methods.
NETWORK PERFORMANCE ENHANCEMENT WITH OPTIMIZATION SENSOR PLACEMENT IN WIRELES...ijwmn
From one side, sensor manufacturing technology and from other side wireless communication technology
improvement has an effect on the growth and deployment of Wireless Network Sensor (WSN). The
appropriate performance of WSN has abundant necessity which has dependent on the different parameters
such as optimize sensor placement and structure of network sensor. The optimized placement in WSN not
only would optimize number of sensors, but also help to reach to the more precise information. Therefore
different solutions are proposed to reduce cost and increase life time of sensor networks that most of them
are concentrated in the field of routing and information transmission. In this paper, places which they need
new sensors placement or sensor movements are determined and then with applying these changes,
performance of WSN will calculate. To achieve the optimum placement, the network should evaluate
precisely and effective criteria on the performance should extract. Therefore the criteria should be ranked
and after weighting with using AHP algorithms, with use of Geographical Information System (GIS), these
weighted criteria will combined and in the locations which WSN doesn’t have enough performance, new
sensor placement will create. New proposed method, improve 21.11% performance of WSN with sensor
placement in the low performance locations. Also the number of added sensor is 26.09% which is lowest
number of added sensors in comparison with other methods.
NETWORK PERFORMANCE ENHANCEMENT WITH OPTIMIZATION SENSOR PLACEMENT IN WIRELES...ijwmn
From one side, sensor manufacturing technology and from other side wireless communication technology
improvement has an effect on the growth and deployment of Wireless Network Sensor (WSN). The
appropriate performance of WSN has abundant necessity which has dependent on the different parameters
such as optimize sensor placement and structure of network sensor. The optimized placement in WSN not
only would optimize number of sensors, but also help to reach to the more precise information. Therefore
different solutions are proposed to reduce cost and increase life time of sensor networks that most of them
are concentrated in the field of routing and information transmission. In this paper, places which they need
new sensors placement or sensor movements are determined and then with applying these changes,
performance of WSN will calculate. To achieve the optimum placement, the network should evaluate
precisely and effective criteria on the performance should extract. Therefore the criteria should be ranked
and after weighting with using AHP algorithms, with use of Geographical Information System (GIS), these
weighted criteria will combined and in the locations which WSN doesn’t have enough performance, new
sensor placement will create. New proposed method, improve 21.11% performance of WSN with sensor
placement in the low performance locations. Also the number of added sensor is 26.09% which is lowest
number of added sensors in comparison with other methods.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
ENERGY EFFICIENT MULTIHOP QUALITY PATH BASED DATA COLLECTION IN WIRELESS SENS...Editor IJMTER
In recent years there has been an increased focus on the use of sensor networks to sense and measure
the environment. This leads to a wide variety of theoretical and practical issues on appropriate protocols for data
sensing and transfer. Recent work shows sink mobility can improve the energy efficiency in wireless sensor
networks (WSNs). However, data delivery latency often increases due to the speed limit of mobile sink. Most of
them exploit mobility to address the problem of data collection in WSNs. The WSNs with MS (mobile Sink) and
provide a comprehensive taxonomy of their architectures, based on the role of the MS. An overview of the data
collection process in such a scenario, and identify the corresponding issues and challenges. A protocol named
weighted rendezvous planning (WRP) which is a heuristic method that finds a near-optimal traveling tour that
minimizes the energy consumption of sensor nodes. Focus on the path selection problem in delay-guaranteed
sensor networks with a path-constrained mobile sink. Concentrate an efficient data collection scheme, which
simultaneously improves the total amount of data and reduces the energy consumption. The optimal path is chosen
to meet the requirement on delay as well as minimize the energy consumption of entire network. Predictable sink
mobility is exploited to improve energy efficiency of sensor networks.
Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides
fundamental support for many location-aware protocols and applications. Constraints on cost and power
consumption make it infeasible to equip each sensor node in the network with a global position system
(GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use
anchor nodes, which are equipped with GPS units among unknown nodes and broadcast their current
locations to help nearby unknown nodes with localization. In this paper we can proposed a novel algorithm
of cuboid localization with the help of central point precision method. Simulation shows that the results are
far better then existing cuboid methods and gain accuracy of up to 83% with a localization error of 1.6m
and standard deviation of 2.7.
AN EFFICIENT DEPLOYMENT APPROACH FOR IMPROVED COVERAGE IN WIRELESS SENSOR NET...csandit
Wireless Sensor Networks (WSNs) are experiencing a revival of interest and a continuous advancement in various scientific and industrial fields. WSNs offer favorable low cost and readily deployable solutions to perform the monitoring, target tracking, and recognition of physical events. The foremost step required for these types of ad-hoc networks is to deploy all the sensor nodes in their positions carefully to form an efficient network. Such network should satisfy the quality of service (QoS) requirements in order to achieve high performance levels. In
this paper we address the coverage requirement and its relation with WSN nodes placement problems. In fact, we present a new optimization approach based on the Flower Pollination Algorithm (FPA) to find the best placement topologies in terms of coverage maximization. We have compared the performance of the resulting algorithm, called FPACO, with the original practical swarm optimization (PSO) and the genetic algorithm (GA). In all the test instances, FPACO performs better than all other algorithms.
Location Fingerprinting is a very familiar Wi-Fi positioning method, which determines a device by retrieving the information recorded containing the location fingerprint. These methods deploy the signal strength (RSS) to predict the coordinate. There are feedbacks for using the absolute RSS either the absolute RSS in a time interval may not be representable of the IEEE 802.11 signal, as the signal may fluctuate or a manual error prone calibration is needed across different mobile platform. The main target is to propose the use of Fourier descriptors in LF. We convert the IEEE 802.11b Wi-Fi signal into a Fourier domain. Then, the Fourier descriptors are used to predict the location by applying the K-Nearest Neighbor algorithm. The results show that the effectiveness of LF methods based on Fourier descriptors lead to substantially more accurate and robust localization.
FINGERPRINT LOCATION METHODS USING RAY-TRACINGmarcelonog29
Mobile location methods that employ signal fingerprints are becoming increas- ingly popular in a number of wireless positioning solutions. A fingerprint is a spatial database, created either by recorded measurement or simulation, of the radio envi- ronment. It is used to assign signal characteristics such as received signal strength or power delay profiles to an actual location. Measurements made by either the handset or the network, are then matched to those in the fingerprint in order to determine a location. Creation of the fingerprint by an a priori measurement stage is costly and time consuming. Virtual fingerprints, those created by a ray-tracing radio propagation prediction tool, normally require a lengthy o↵-line simulation mode that needs to be repeated each time changes are made to the network or built environment. An open research question exists of whether a virtual fingerprint could be created dynamically via a ray-trace model embedded on a mobile handset for positioning purposes.
The key aim of this thesis is to investigate the trade-o↵ between complexity of the physics required for ray-tracing models and the accuracy of the virtual fingerprints they produce. The most demanding computational phase of a ray-trace simulation is the ray-path finding stage, whereby a distribution of rays cast from a source point, interacting with walls and edges by reflection and di↵raction phenomena are traced to a set of receive points. Due to this, we specifically develop a new technique that decreases the computation of the ray-path finding stage. The new technique utilises a modified method of images rather than brute-force ray casting. It leads to the creation of virtual fingerprints requiring significantly less computation e↵ort relative to ray casting techniques, with only small decreases in accuracy.
Our new technique for virtual fingerprint creation was then applied to the devel- opment of a signal strength fingerprint for a 3G UMTS network covering the Sydney central business district. Our main goal was to determine whether on current mo- bile handsets, a sub-50m location accuracy could be achieved within a few seconds timescale using our system. The results show that this was in fact achievable. We also show how virtual fingerprinting can lead to more accurate solutions. Based on these results we claim user embedded fingerprinting is now a viable alternative to a priori measurement schemes.
Effective range free localization scheme for wireless sensor networkijmnct
Location aware sensors can be used in many areas such as military and civilian applications. Wireless
Sensor Networks help to identify the accurate location of the event. In this paper a cost effective schema for
localization has been proposed. It uses two beacon nodes to identify the location of unknown nodes. It
also uses flooding and estimating method to accurately identify the location of other nodes. Available area
is divided into zones and beacons are provided for each zone. Beacon nodes are placed in appropriate
locations normally two in a zone to provide location information. Using the two nodes location of unknown
nodes can be calculated accurately.
A Comparative Analysis for Hybrid Routing Protocol for Wireless Sensor NetworksIJERA Editor
Wireless Sensor Networks (WSNs) consist of smallnodes with sensing, computation and wireless
communicationscapabilities. These sensor networks interconnect a several othernodes when established in large
and this opens up severaltechnical challenges and immense application possibilities.These wireless sensor
networks communicate using multi-hopwireless communications, regular ad hoc routing techniquescannot be
directly applied to sensor networks domain due tothe limited processing power and the finite power available
toeach sensor nodes hence recent advances in wireless sensornetworks have developed many protocols
depending on theapplication and network architecture and are specificallydesigned for sensor networks where
energy awareness is anessential consideration. This paper presents routingprotocols for sensor networks and
compares the routingprotocols that are presently of increasing importance.
In this paper, we propose Hybrid Routing Protocol whichcombines the merits of proactive and reactive approach
andovercome their demerits.
NETWORK PERFORMANCE ENHANCEMENT WITH OPTIMIZATION SENSOR PLACEMENT IN WIRELES...ijwmn
From one side, sensor manufacturing technology and from other side wireless communication technology
improvement has an effect on the growth and deployment of Wireless Network Sensor (WSN). The
appropriate performance of WSN has abundant necessity which has dependent on the different parameters
such as optimize sensor placement and structure of network sensor. The optimized placement in WSN not
only would optimize number of sensors, but also help to reach to the more precise information. Therefore
different solutions are proposed to reduce cost and increase life time of sensor networks that most of them
are concentrated in the field of routing and information transmission. In this paper, places which they need
new sensors placement or sensor movements are determined and then with applying these changes,
performance of WSN will calculate. To achieve the optimum placement, the network should evaluate
precisely and effective criteria on the performance should extract. Therefore the criteria should be ranked
and after weighting with using AHP algorithms, with use of Geographical Information System (GIS), these
weighted criteria will combined and in the locations which WSN doesn’t have enough performance, new
sensor placement will create. New proposed method, improve 21.11% performance of WSN with sensor
placement in the low performance locations. Also the number of added sensor is 26.09% which is lowest
number of added sensors in comparison with other methods.
NETWORK PERFORMANCE ENHANCEMENT WITH OPTIMIZATION SENSOR PLACEMENT IN WIRELES...ijwmn
From one side, sensor manufacturing technology and from other side wireless communication technology
improvement has an effect on the growth and deployment of Wireless Network Sensor (WSN). The
appropriate performance of WSN has abundant necessity which has dependent on the different parameters
such as optimize sensor placement and structure of network sensor. The optimized placement in WSN not
only would optimize number of sensors, but also help to reach to the more precise information. Therefore
different solutions are proposed to reduce cost and increase life time of sensor networks that most of them
are concentrated in the field of routing and information transmission. In this paper, places which they need
new sensors placement or sensor movements are determined and then with applying these changes,
performance of WSN will calculate. To achieve the optimum placement, the network should evaluate
precisely and effective criteria on the performance should extract. Therefore the criteria should be ranked
and after weighting with using AHP algorithms, with use of Geographical Information System (GIS), these
weighted criteria will combined and in the locations which WSN doesn’t have enough performance, new
sensor placement will create. New proposed method, improve 21.11% performance of WSN with sensor
placement in the low performance locations. Also the number of added sensor is 26.09% which is lowest
number of added sensors in comparison with other methods.
NETWORK PERFORMANCE ENHANCEMENT WITH OPTIMIZATION SENSOR PLACEMENT IN WIRELES...ijwmn
From one side, sensor manufacturing technology and from other side wireless communication technology
improvement has an effect on the growth and deployment of Wireless Network Sensor (WSN). The
appropriate performance of WSN has abundant necessity which has dependent on the different parameters
such as optimize sensor placement and structure of network sensor. The optimized placement in WSN not
only would optimize number of sensors, but also help to reach to the more precise information. Therefore
different solutions are proposed to reduce cost and increase life time of sensor networks that most of them
are concentrated in the field of routing and information transmission. In this paper, places which they need
new sensors placement or sensor movements are determined and then with applying these changes,
performance of WSN will calculate. To achieve the optimum placement, the network should evaluate
precisely and effective criteria on the performance should extract. Therefore the criteria should be ranked
and after weighting with using AHP algorithms, with use of Geographical Information System (GIS), these
weighted criteria will combined and in the locations which WSN doesn’t have enough performance, new
sensor placement will create. New proposed method, improve 21.11% performance of WSN with sensor
placement in the low performance locations. Also the number of added sensor is 26.09% which is lowest
number of added sensors in comparison with other methods.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
K-centroid convergence clustering identification in one-label per type for di...IAESIJAI
Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
Plant leaf detection through machine learning based image classification appr...IAESIJAI
Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
Backbone search for object detection for applications in intrusion warning sy...IAESIJAI
In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Deep neural network for lateral control of self-driving cars in urban environ...IAESIJAI
The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray i...IAESIJAI
Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions. Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 4, December 2023, pp. 2022~2032
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i4.pp2022-2032 2022
Journal homepage: http://ijai.iaescore.com
High uncertainty aware localization and error optimization of
mobile nodes for wireless sensor networks
Raja Thejaswini Nandyala, Muthupandi Gandhi
Department of ECE, School of Engineering, Presidency University, Bangalore, India
Article Info ABSTRACT
Article history:
Received Oct 11, 2022
Revised Jan 25, 2023
Accepted Mar 10, 2023
The localization of mobile sensor nodes in a wireless sensor network (WSN)
is a key research area for the speedy development of wireless communication
and microelectronics. The localization of mobile sensor nodes massively
depends upon the received signal strength (RSS). Recently, the least squared
relative error (LSRE) measurements are optimized using traditional
semidefinite programming (SDP) and the location of the mobile sensor nodes
was determined using the previous localization methods like least squared
relative error and semidefinite programming (LSRE-SDP), and approximate
nonlinear least squares and semidefinite programming (ANLS-SDP).
Therefore, in this work, a novel high uncertainty aware-localization error
correction and optimization (HUA-LECO) model is employed to minimize
the aforementioned problems regarding the localization of mobile sensor
nodes and enhance the performance efficiency of root mean square error
(RMSE) results. Here, the position of target mobile sensor nodes is evaluated
based on the gathered measurements while discarding faulty data. Here, an
iterative weight updation approach is utilized to perform localization based on
Monte Carlo simulations. Simulation results show significant improvement in
terms of RMSE results in comparison with traditional LSRE-SDP and
ANLS-SDP methods under high uncertainty.
Keywords:
High uncertainty
Least squared relative error
Localization
Mobile sensor nodes
Radio signal strength
Wireless sensor networks
This is an open access article under the CC BY-SA license.
Corresponding Author:
Raja Thejaswini Nandyala
Department of ECE, School of Engineering, Presidency University
Bengaluru, India
Email: phdtej2k21@rediffmail.com
1. INTRODUCTION
Wireless sensor networks (WSNs) are a collection of a large number of sensor nodes that can
communicate with each other in varied environmental conditions through wireless channels [1], [2] to measure
physical changes. The distributed sensor nodes gather data related to their adjacent activities and events and
share gathered information with the other nodes to the base station through cluster head (CH) node for further
processing [3]. Moreover, there are numerous applications of WSNs in different fields and some of those fields
are animal tracks, medical fields, environmental tracking, military task handling, oil exploration, fire
identification, intruder identification, infrastructure management, disaster detection and situation handling, and
tracking of sea animals and creatures [4]–[7]. In most of these mentioned applications, sensor nodes are placed
in very harsh, unpredictable, unapproachable, and in such environments that humans cannot reach easily [8].
In these types of applications, sensor nodes are distributed in a random scattering style through aerial vehicles
or some other way at the desired location. However, in these types of applications, accurate knowledge of node
location is extremely important to detect the source of sensed information and to take required actions based
on this obtained sensed information [9].
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Furthermore, it is highly essential to have knowledge of the source of data generation to analyze
sensed data. For an instance, in some cases, node positions remain static in a WSN environment and knowledge
of installation points will make analysis easier and better. However, in some cases, node positions remain
mobile, and these nodes change their position dynamically. Moreover, the use of mobile sensor nodes can
enhance the scope of applications with several benefits like coverage and scalability enhancement and
improves packet transmission rate. However, there can be a few limitations as well in the case of mobile sensor
node utilization in terms of energy decay and uncertainty regarding the source of sensed data. Moreover, precise
knowledge of the location of source data is a base for several applications like fraud detection systems, routing
analysis, surveillance, and tracking. In dynamic WSNs, the sensor node position and location need to be
identified dynamically, which can be an extremely complicated and tiresome task. Thus, localization in
dynamic WSNs can be a significant research direction.
Therefore, precise geographic and sensor node position information has become an essential need in
WSNs to perform multiple operations like environmental tracking, indoor navigation, and data gathering
[10]–[12]. Additionally, multiple networking protocols, sensor node tracking, association, and geographical
routing require precise knowledge of sensor node locations. However, adding global positioning system (GPS)
receivers for every node is challenging and complicated due to their massive cost, hardware constraints, high
power dissipation, and reduced performance due to shadowing effects. Besides, multiple localization detection
techniques are developed in recent years by numerous researchers [13]. Some location detection techniques
utilize few reference nodes and based on these adopted reference nodes, the location of an unknown node is
identified [14]. Some of the measurement methods are angle of arrival (AoA), time of arrival (ToA), connectivity,
and radio signal strength (RSS) [15], [16]. Among all these measurement or source location identification
methods, RSS-based measurement is extremely effective due to the advantage of the ease of deployment and is
less complex in nature. However, there are a few limitations regarding RSS-localization methods, which may
cause high localization errors. The main limitation of the RSS method is the use of transmit power as a priori
knowledge to identify node locations. However, the obtained transmit power is massively dependent on antenna
gain and the battery status of sensor nodes. Thus, in real-time applications, transmit power may not remain steady
which can lead to poor estimation regarding transmitted power [17], [18].
Recently, in [19], a fault-filtering method is adopted to identify fault-reference nodes so that
localization error can be minimized. This technique mainly focuses on distance estimation for the
implementation of the fault-filtering method based on the communication range of the nodes. In [20], a
robustness enhanced sensor assisted Monte Carlo localization (RESA-MCL) method is adopted to identify the
accurate position of mobile sensor nodes. Based on the node movements, RESA-MCL enhances the
performance of the localization of sensor nodes. In [21], an extension of the distance vector-hop algorithm
(DV-Hop) localization method is introduced as a regularized least square DV-Hop localization algorithm to
enhance node localization accuracy based on statistical filtering optimization and the least square localization
method. In [22], an anchor-assisted localization method is adopted to detect precise positions of mobile sensor
nodes based on the transmission power control mechanism. However, a major limitation related to least square
relative error (LSRE) approach is the presence of high uncertainty and high noise can change measured values
drastically and remains extremely sensitive to outliers. Moreover, the traditional localization methods remain
sensitive to the different environmental conditions like path loss and fading which can further enhance the
noise factor. To overcome this challenge, a novel high uncertainty aware-localization error correction and
optimization (HUA-LECO) model is adopted in this article to precisely identify the location of mobile sensor
nodes. This work is an extension of the dynamic noisy measurement aware localization (DNMAL) model [23],
in which the major focus was the minimization of the localization error of static sensor nodes with varying
noise levels.The contribution of using the proposed HUA-LECO model is described.
− The HUA-LECO model performs extremely well in the case of inducing high uncertainty considering the
dynamic mobility pattern of sensor nodes in the measurement model.
− Minimize localization error of mobile sensor node with a higher convergence rate. Thus, the HUA-LECO
model can be utilized in different WSNs applications that require precise node localization data with higher
accuracy and better performance in cases of high uncertainty regarding mobile sensor node position to
accurately identify their position.
− The proposed HUA-LECO model shows a high-accuracy performance in terms of localization error
minimization for both indoor and outdoor complex WSNs scenarios.
− Comparatively higher localization accuracy of mobile sensor nodes is achieved considering varied
simulation parameters than state-of-art-localization error minimization methods like LSRE and
semidefinite programming (SDP), even when some mobile sensor nodes provide faulty measurements and
the impact on localization errors considering varied noise levels and the size of Monte Carlo simulations is
also observed.
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The paper is structured in the following style. Section 1, discusses the estimation of mobile sensor
node localization and the significance of the proposed HUA-LECO model in WSNs. In section 2, a literature
survey regarding the proposed HUA-LECO model and limitations of the existing localization detection
methods are presented. In section 3, the methodology to reduce localization errors in dynamic WSNs is
discussed. In section 4, the performance results of the HUA-LECO model are discussed and compared with
traditional localization error minimization methods. Lastly, the percentage enhancement of the proposed
HUA-LECO model is concluded and future enhancement of the HUA-LECO model is discussed.
2. LITERATURE SURVEY
This section studies some recent research works for performing localization in WSNs. In [24], node
localization is achieved using uncertain data mapping in WSN applications based on RSS. Performance results
are evaluated considering absolute mean localization error and compared against traditional localization
methods. However, four beacon nodes are made static at the edges of the experimental WSN environment
which may heavily affect accuracy results in the case of RSS attenuation and uncertainty of node position.
In [25], a localization method in asynchronous WSNs is adopted based on time-of-flight (ToF). Here,
localization accuracy results are obtained to compare against varied state-of-art-localization methods.
However, in this method, beacon nodes are kept static in WSN. The calculation of the clock skews and
localization capacity performance measurement is heavily dependent upon the centralized server. As a result,
breaking down at one single centralized point can cause a system or server failure, and the complete localization
process in WSN can be compromised.
In [26], a combined localization and synchronization method is introduced using time difference of
arrival (TDoA) in asynchronous WSNs. Here, the localization process mainly depends upon the centralized
server. The experimental results are performed on the basis of uncertainty in the position of anchor nodes.
Therefore, similar to [25], breaking down centralized points can cause server failure. In [27], a node localization
approach is performed on the basis of measurement of noise weighted least squares and error variance in an
indoor environment. Here, a weighted approach is adopted based on the hybrid RSS/AoA measurements and
utilizes three beacon nodes for localization measurements. However, faults in beacon node measurements can
give faulty measurements. Another disadvantage is that the multipath fading and non-line of sight (NLoS)
effects are not considered.
In [28], a Monte-Carlo localization (MCL) method is presented which is a range-free localization with
no additional hardware complexities. Further, all the sensor nodes and beacon nodes move freely over time in
a random manner. Here, a particle filter is used to enhance localization accuracy based on node mobility.
Similarly, in [29], [30], MCL methods are adopted to enhance localization efficiency. However, the possibility
of localization failure in these methods is high due to loss in connectivity and lower density of beacon nodes.
This problem can be sorted by the sensor-assisted Monte Carlo localization (SA-MCL) method [31] which
enhances the connectivity of beacon nodes in WSNs and handles the limitations of MCL methods. To improve
localization accuracy [29] an enhanced MCL method is presented based on the differential evolution
optimization (DEO) approach under low beacon density of sensor nodes. Here, the MCL-DE objective
functions through sample weights instead of a regular sampling method. This method estimates valid samples
for localization measurements. However, communication and computation cost are reasonably high. In [32], a
neural network-based localization system is presented for WSNs in indoor scenarios to evaluate the position
of Alzheimer’s patients. Here, beacon nodes pre-dominantly utilize RSS to get the experimental results
considering mean localization error. Here, beacon nodes are static and RSS samples are recorded to enhance
connectivity. However, hardware needs to make this design more complex and the transformation of this
approach for mobile nodes is a very complicated process. In [33], data aggregation and clustering-based
methods are presented to improve the lifetime of the network based on the node localization and routing
mechanism. In [34], a grid clustering method is presented to identify sensor node locations based on the genetic
algorithm. In [35], a detailed review is performed to analyze localization problems and their performance. In
the next section, solutions to avoid these addressed research limitations are presented in this paper namely a
novel HUA-LECO model.
3. HUA-LECO MODEL FOR WIRELESS SENSOR NETWORKS
This section provides details of the methodology used to study the proposed HUA-LECO model for
WSNs. First of all, the foremost aim of this work is an estimation of the mobile sensor nodes by accurately
predicting their position based on the root mean square error (RMSE) when there is high uncertainty regarding
the position of the sensor nodes. The second aim is the optimization of uncertainty-aware errors to precisely
estimate the location of sensor devices. Here, mobile sensor nodes are used inside WSN which continuously
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changes its position due to which strength of the radio signal varies with an unknown decay factor. These
continuous RSS variations make localization of mobile sensor nodes quite difficult and challenging. Therefore,
detailed mathematical modeling is presented in this section to achieve the desired objectives. Initially, the
system model for the HUA-LECO model is discussed. Then, the mathematical modeling and working
methodology of the HUA-LECO model is presented. At last, an algorithm for localization of sensor devices in
case of high uncertainty through the HUA-LECO model is discussed in multiple steps.
3.1. System model
In this section, a system model is discussed for the detection of mobile sensor node location within
WSN when the exact position of the sensor node is highly uncertain. Consider a WSN is formed and the number
of sensor nodes deployed is S whose initial position is known prior, and later on; The position of the mobile
sensor nodes is measured on the basis of radio signal strength (RSS) measurement computation from the sensor
devices. All the measured information is gathered by a node namely the sink node and this node helps to predict
the exact position of the desired node based on the RSS measurements. Then, the computation of RSS
measurements from sensor nodes in a highly uncertain WSN environment is given by (1),
𝑚𝑙 = ‖𝑘 − 𝑎𝑙‖2 + 𝜑𝑙, 𝑙 = 1, … , 𝑆, (1)
Where the coordinates of the desired sensor node are given by 𝒌 ∈ 𝕄𝒐
, the position of the 𝒍𝒕𝒉
sensor node is
given by 𝒂𝒍 ∈ 𝕄𝒐
, uncertainty-aware errors are given by 𝝋𝒍 and these errors are identically distributed with
the help of a random distribution parameter, Euclidean distance is given by the function ‖∙‖𝟐. Modeling
considering highly uncertain mobile sensor node position can be handled using probability density function to
analyze uncertainty-aware error correction estimates which can be defined using (2),
𝑇(𝜑) = (1 − 𝜆)Ψ(𝜑; 0, 𝜇2) + 𝜆Γ(𝜑). (2)
Where uncertainty-aware measurement errors can be computed with the help of two different probability
distributions such as one distribution is represented by Γ(𝜑) whose probability is defined by 𝜆 and the second
distribution is defined by Ψ(𝜑; 0, 𝜇2) whose probability is expressed by 1 − 𝜆. A highly uncertain position of
sensor nodes can be modeled based on uncertainty-aware error correction estimates from Γ(𝜑) and
uncertainty-aware error correction estimates with zero means and 𝜇2
variance from Ψ(𝜑; 0, 𝜇2). Assume that
𝜆 is the ratio of uncertainty-aware error correction estimates to the overall measurement estimates from all the
sensor devices. Furthermore, consider that the design of the proposed sensor node localization model relies
minimum upon the uncertainty-aware error correction estimate value from probability distribution Γ(𝜑).
Assume that only uncertainty-aware error correction estimates are considered to determine ℎ from the
measurements 𝑚𝑙 where 𝑙 = 1, … … . , 𝑀. And the rest of the uncertainty-aware error measurement estimates
are discarded. Moreover, the processing node does not have any idea about the sensor nodes which are giving
error measurements and how their measurement distribution is processed in WSN. Consider that, the overall
measurements obtained from all the sensor nodes, whether they are uncertainty-aware error correction
estimates or uncertainty-aware error measurement estimates, or inappropriate estimates, are positive
measurement values.
In several cases, the position of sensor nodes in WSN is determined using the least square bounding
approach as discussed in [36], [37]. However, measured data in [36], [37] were noisy. Thus, these methods
failed to provide an optimum solution to minimize measurement errors. Thus, an optimum solution is achieved
to minimize measurement errors using the proposed dynamic noisy measurement aware localization (DNMAL)
model which is our previous work [23] with the help of an improved least square bounding model. However,
this work is an extension of the DNMAL model where the main objective is to obtain the optimum solution to
get the exact position of the desired node in case of high uncertainties regarding their accurate localization due
to the dynamic sensor node mobility patterns. This objective can be achieved with the help of the proposed
HUA-LECO model by obtaining optimized weights. Then, the improved least square bounding model is given
by the following equation. The problem is optimized to get the constrained minimization problem
with the help of (3).
min
ℎ
∑ (‖ℎ − 𝑎𝑙‖2
2
− 𝑚𝑙
2
)2
.
𝑀
𝑙=1 (3)
min
ℎ,
∑ (Ω − 2𝑎𝑙
𝑊
ℎ + ‖𝑎𝑙‖2
− 𝑚𝑙
2)2
,
𝑀
𝑙=1 𝑠. 𝑡 ‖𝑎𝑙‖2
= Ω. (4)
The traditional model utilizes Gaussian noise distribution as the noise distribution model. Here, Ω can be
obtained through an optimization process and 𝜆 is obtained as the value of the uncertainty-aware error
correction estimates with respect to the overall measurement estimates from (2). However, the main aim of the
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proposed HUA-LECO model is to estimate the exact position of mobile sensor nodes based on the estimated
error reduction which can be achieved by modeling the uncertainty-aware measurement error distribution
Γ(𝜑) in WSN. Then, the optimal solution related to the sensor node localization can be obtained by estimating
(4) iteratively with the help of (5).
Γ(𝑦, 𝑓) = ∑ 𝑓𝑙(𝑎
̃𝑙
𝑊
𝑦 − 𝑏𝑙)2
+ ∑ 𝛽2
𝑓𝑙 − ln 𝑓𝑙
𝑀
𝑙=1
𝑀
𝑙=1 (5)
Where the coefficient 𝑎
̃𝑙
𝑊
is determined using (6),
𝑎
̃𝑙
𝑊
= [−2𝑎𝑙
𝑊
1], (6)
Then, parameter 𝑧 is computed using (7),
𝑦 = [ℎ Ω]𝑊
, (7)
In the same way, 𝑏𝑙 is determined using (8),
𝑏𝑙 = 𝑚𝑙
2
− ‖𝑎𝑙‖2
, (8)
Here, the weight vector is defined by the parameter 𝑓 where 𝑓 ∈ 𝕄𝑀
and 𝑓𝑙 is computed as 𝑓𝑙 > 0, ∀𝑙. The aim
of the proposed localization model is to optimize (5) using (9) respective to the parameters 𝑦 and 𝑓,
𝑚𝑖𝑛
𝑦,𝑓
𝛤(𝑦, 𝑓),
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑦𝑊
𝐶𝑦 + 2𝑗𝑖
𝑦 = 0, (9)
Where 𝑓𝑙 > 0, ∀𝑙 and 𝐶 is determined using (10),
𝐶 = [
𝐿𝑧 𝐸𝑧∗1
𝐸1∗𝑧 𝐸
] (10)
Where values of the weight matrix range from 0 to 1 and are defined with the help of 𝑧 × 1 and 1 × 𝑧 and 𝑗 are
determined using (11),
𝑗 = [
𝐸𝑧∗1
−𝐷
]. (11)
Where the weights and the coefficient 𝑦 can be updated with the help of 𝑓𝑙
(0)
= 1, ∀𝑙, and the solution to update
the coefficient 𝑦 for a specified 𝑔 − 𝑡ℎ iteration is given by (12),
𝑦(𝑔+1)
= 𝑎𝑟𝑔 𝑚𝑖𝑛 𝛤(𝑦, 𝑓(𝑔)
), (12)
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑓𝑙 > 𝑦𝑊
𝐶𝑦 + 2𝑗𝑊
𝑦 = 0.
Similarly, the weights are updated using (13),
𝑓(𝑔+1)
= arg min Γ(𝑦(𝑔+1)
, 𝑓), (13)
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑓𝑙 > 0, ∀𝑙.
3.2. HUA-LECO model
This section provides details related to the mathematical representation of uncertainty-aware
localization error optimization. This can be achieved by optimizing the problem of (12) with the help of the
proposed HUA-LECO model. The optimization of (12) is performed by employing some constraints, objective
functions, and gradients of the objective function on the basis of the Lipschitz continuity [38]. The optimization
of the problem in (12) requires large computation and several iterations. Thus, the proposed HUA-LECO model
is adopted to perform faster computations and handle a large number of iterations. Then, the value of coefficient
𝒚(𝒈)
in every iteration gets updated using (14).
𝑦(𝑔)
= arg min
𝑦
〈χ𝑦Γ(𝑦
̂(𝑔)
, 𝑓(𝑔−1)
), 𝑦 − 𝑦
̂(𝑔)
〉 +𝑛(𝑔)
‖𝑦 − 𝑦
̂(𝑔)
‖2
2
, (14)
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑓𝑙 > 𝑦𝑊
𝐶𝑦 + 2𝑗𝑊
𝑦 = 0.
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Where parameter 𝑦
̂(𝑔)
is determined by (15),
𝑦
̂(𝑔)
= 𝑦(𝑔−1)
+ 𝑞(𝑔)
(𝑦(𝑔−1)
− 𝑦(𝑔−2)
) (15)
Where constant for the Lipschitz continuity is given by 𝑛(𝑔)
of the function [χ𝑦Γ(𝑦, 𝑓(𝑔−1)
)] for 𝑔 − 𝑡ℎ
iteration using (16),
𝑛(𝑔)
‖𝜌 − 𝜛‖ ≥ ‖χ𝑦Γ(𝜌, 𝑓(𝑔−1)
) − χ𝑦Γ(𝜛, 𝑓(𝑔−1)
)‖ (16)
Where first term represents gradient magnitude controller of the objective function and second term represents
descent of the objective function. Here, the Lipschitz constant is used as a constraint on the step size of the
objective function. Here, parameter 𝑦
̂(𝑔)
is represented as a prediction estimate for the estimate 𝑦(𝑔)
. With the
help of prior iterations and trend estimation parameters, a prediction estimate 𝑦
̂(𝑔)
is obtained. Then, the trend
estimation parameter is given by (17),
𝑞(𝑔)
=
1
12
(
𝑛(𝑔−1)
𝑛(𝑔) ) (17)
Then, weights of 𝑓 get updated by using (18),
𝑓𝑔
(𝑙)
=
1
(𝑗𝑙
(𝑔)
)
2
+𝛽2
(18)
Where 𝑗𝑙
(𝑔)
is evaluated using (19),
𝑗𝑙
(𝑔)
= 𝑎
̃𝑙
𝑊
𝑦(𝑔)
− 𝑏𝑙. (19)
Then, the optimization problem in (14) can be minimized with the help of prediction estimate 𝑦
̂(𝑔)
, parameter
𝑛(𝑔)
, and coefficient 𝐶 using (20),
(𝑛(𝑔)
𝐿𝑧+1 + 𝜉𝐶)𝑦(𝑔)
= −𝐷𝑊
𝑃(𝑔−1)
(𝐷𝑦
̂(𝑔)
− 𝑎) + 𝑛(𝑔)
𝑦
̂(𝑔)
− 𝜉𝑗 (20)
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑦(𝑔)𝑊
𝐶𝑦(𝑔)
+ 2𝑗𝑊
𝑦(𝑔)
= 0
Where parameter 𝜉 is defined using (21),
𝜉 ≥ max {−𝑛(𝑔)
, − (𝜉1(𝐶, 𝐷𝑊
𝑃(𝑔−1)
𝐷))
−1
} (21)
Where parameter 𝜉 is adopted to update the value of 𝑦 and 𝑓 after finding the value of prediction estimate 𝑦
̂(𝑔)
in each iteration. Furthermore, a detailed step-by-step execution process for the exact localization of mobile
sensor nodes by predicting and minimizing errors using the proposed HUA-LECO model is presented in
Algorithm 1.
Algorithm 1. High uncertainty aware-localization error correction and optimization model
Input. 𝑎𝑙, 𝑚𝑙 for 𝑙 = 1, … , 𝑀, 𝛽, Λ↑
, & 𝜓
Step 1. Start.
Step 2. Estimate 𝐶, 𝑗, 𝑦
̂(𝑔)
, & 𝑞(𝑔)
utilizing Eq. (10), (11), (15) and (17).
Step 3. Initialize 𝑃(0)
, 𝑦(−1)
= 𝑦(0)
= 𝐷↑
𝑎, 𝑛(0)
= 0 & 𝑔 = 1.
Step 4. Iterate
Step 5. 𝑛(𝑔)
= 2‖𝐷𝑊
𝑃(𝑔−1)
𝐷‖𝐽
Step 6. 𝑞(𝑔)
=
1
12
(
𝑛(𝑔−1)
𝑛(𝑔) )
Step 7. 𝑦
̂(𝑔)
= 𝑦(𝑔−1)
+ 𝑞(𝑔)
(𝑦(𝑔−1)
− 𝑦(𝑔−2)
)
Step 7. Establish
𝜉∗
using 𝑦(𝜉) = (𝑛(𝑔)
𝐿𝑧+1 + 𝜉𝐶)
−1
− 𝐷𝑊
𝑃(𝑔−1)
(𝐷𝑦
̂(𝑔)
− 𝑎) + 𝑛(𝑔)
𝑦
̂(𝑔)
− 𝜉𝑗
Step 8. 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑦(𝑔)𝑊
𝐶𝑦(𝑔)
+ 2𝑗𝑊
𝑦(𝑔)
= 0
Step 8. Update 𝑦: 𝑦(𝑔)
= 𝑦( 𝜉∗).
Step 8. Update 𝑓𝑔
(𝑙)
utilizing Eq. (18).
Step 9. Until convergence ‖𝑦(𝑔)
− 𝑦(𝑔−1)
‖ < 𝜓 or when Λ↑
is reached.
Step 10. Stop.
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In this way, the proposed HUA-LECO Model determines the localization performance of mobile sensor nodes
in case of high uncertainty regarding sensor node positions by estimating uncertainty-aware localization errors
and optimizing uncertainty-aware localization errors. The minimization of uncertainty-aware localization
errors is achieved and the impact of varying noise levels and size of Monte Carlo Simulations is discussed
through the simulation study in next section. Further, results analysis is performed.
4. SIMULATION ANALYSIS AND RESULT
This section demonstrates performance results using the proposed HUA-LECO Model regarding the
exact localization of mobile sensor nodes by minimizing localization RMSE error and compared against varied
traditional localization models like approximate nonlinear least squares and semidefinite programming
(ANLS-SDP) [39] and least squared relative error and semidefinite programming (LSRE-SDP) [36].
Furthermore, the performance of the proposed HUA-LECO Model in terms of localization error minimization
is validated considering the baseline cramer-rao lower bound (CRLB) model for WSNs. MATLAB 2018a
software is utilized for the implementation of the WSN models and the adopted system is configured with the
specifications of 16 GB RAM and an Intel I-5 processor with NVIDIA graphics.
A generalized system is considered in this article for both indoor and outdoor applications and follows
a uniform distribution into WSNs under a high-uncertainty environment and is used to track targets through
cellular radio networks in WSNs. Localization of mobile sensor nodes under high uncertainty regarding their
position is demonstrated in Figure 1 through a network model. Details of experimental configuration are kept
the same as [36] for better analysis against the previous best localization methods. Here, all the sensor devices
are placed in a uniform manner using a random distribution approach where the area of the WSN field is
considered as [−50, 50] × [50,50]𝑚2
. A shadowing effect is also considered to determine RSS based on the
approach presented in [36]. Additionally, Gaussian distribution is adopted with standard deviation as
𝜇 = 55 𝑚. All the sensor measurements are taken using Gaussian distribution and sensor geometry is gathered
using a location-operating network. The location of sensor nodes is measured under high uncertainties by
varying sensor node size from 8 to 16. Here, the number of Monte Carlo Simulations used as 4,000 to generate
a system model for WSN. It can be seen from the network model diagram that 2 sensor devices show faulty
measurements and can be referred as outliers.
Figure 1. Localization of unknown mobile sensor node (i.e., red) in network model with multiple sensor
nodes (i.e., blue)
The localization error is determined using (22),
𝑅𝑀𝑆𝐸 = √
1
ℵ𝑏
∑ ‖𝑓
̂𝜕 − 𝑓𝜕‖
2
,
ℵ𝑏
𝜕=1
(22)
where Monte Carlo Simulations are denoted by ℵ𝑏 and the number of Monte Carlo Simulations are used as
default are 4,000. Actual positions of sensor devices are given by 𝑓𝑖 and estimated positions of sensor devices
are given by 𝑓
̂𝑖.
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High uncertainty aware localization and error optimization of mobile… (Raja Thejaswini Nandyala)
2029
4.1. Scenario 1: performance by varying size of Monte Carlo simulations
Performance of the proposed HUA-LECO model is determined in terms of localization root mean
square error with respect to the number of sensor nodes under high certainties regarding the position of desired
unknown mobile sensor node and compared against existing localization error optimization methods like
ANLS-SDP and LSRE-SDP. Here, the number of sensor devices and the number of Monte Carlo simulations
are varied to get the performance efficiency and compared against traditional ANLS-SDP and LSRE-SDP
methods. The main objective of this work is to identify position of an unknown mobile sensor node as shown
in Figure 1 by varying the size of Monte Carlo simulations. In this experiment, the size of Monte Carlo
Simulations is changed to 4,000, 6,000, and 8,000. At the same time, the number of sensor nodes varies from
8 to 16, and the noise level is kept at 8db. The localization error regarding the position of the unknown mobile
sensor node is demonstrated in Figure 2 against varied number of sensor nodes when Monte Carlo Simulations
are kept at 4,000. Similarly, when the size of ℵ𝑏 is changed to 6,000 iterations, the evaluated RMSE plot is
demonstrated in Figure 3 for localizing the unknown mobile sensor device. Further, Figure 3 demonstrates the
localization of the unknown mobile sensor node when the size of ℵ𝑏 is changed to 8,000 iterations. From all
these graphs such as Figures 2 to 4 it is clear that the proposed HUA-LECO model minimizes the localization
of unknown mobile sensor nodes significantly in comparison with existing localization methods ANLS-SDP
and LSRE-SDP under high uncertainties and also satisfy the condition of generalized Cremer-lower bound. It
is evident from Figures 2 to 4 results that RMSE is quite low in case of sensor node increment.
Figure 2. Comparison for RMSE against the number
of sensor nodes considering different methods for
Monte Carlo simulations as 4,000 iterations
Figure 3. Comparison for RMSE against the
number of sensor nodes considering different
methods for Monte Carlo simulations as 6,000
iterations
Figure 4. Comparison for RMSE against the number of sensor nodes considering different methods for
Monte Carlo simulations as 8,000 iterations
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4.2. Scenario 2: under varying noise levels
Here performance of the proposed HUA-LECO model is discussed based on the varying noise levels
and keeping number of Monte Carlo simulations fixed in terms of RMSE against number of sensor nodes.
Moreover, a simulation is performed by keeping Monte Carlo simulations as 4,000 and considering a noise
level of 3dB, 5dB, 7dB, 9dB and number of sensor devices changes from 8 to 16. All the experiments are
performed by setting up environment similar to [36]. Here, Figure 5 demonstrates the localization of mobile
sensor nodes by evaluating RMSE for different number of sensor devices at noise level as 3dB. Similarly,
similar to [36], Figure 6 shows the localization of mobile sensor nodes by determining RMSE for different
number of sensor devices (8 to 16) at noise level as 5dB. In the same way, with reference to [36], Figure 7
demonstrates the localization of unknown mobile sensor nodes by determining RMSE for the different number
of sensor devices (8 to 16) at noise levels as 7dB. Finally, the last simulation is performed considering a noise
level of 9dB to evaluate mobile sensor node position in terms of RMSE against the varying number of sensor
nodes from 8 to 16 as shown in Figure 8. It can be observed from Figure 5 to Figure 8 that with the increase in
noise level, the localization errors vary for all the localization models. However, the variations in localization
error with the increase in noise level using the proposed HUA-LECO model are significantly lower than in
comparison with the previous best localization models ANLS-SDP and LSRE-SDP. Whereas, the localization
error varies using ANLS-SDP and LSRE-SDP models is quite high. Therefore, it is evident from the
performance results that the proposed HUA-LECO model provides significant results and minimizes
localization errors compare to ANLS-SDP and LSRE-SDP models in terms of RMSE by varying noise levels
to estimate the location of the mobile sensor device.
Figure 5. Comparison of RMSE versus the number
of sensor nodes for different methods considering
noise level of 3dB
Figure 6. Comparison of RMSE versus the number
of sensor nodes for different methods considering
noise level of 5dB
Figure 7. Comparison of RMSE versus the number
of sensor nodes for different methods considering
noise level of 7dB
Figure 8. Comparison of RMSE versus the number
of sensor nodes for different methods considering
noise level of 9dB
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5. CONCLUSION
The significance of mobile sensor node localization is extremely high in WSN. The continuous change
in mobile sensor node position can vary strength of radio signals based on an unknown decay factor under high
uncertainties regarding position of mobile sensor node. Moreover, few works are presented related to
localization of sensor nodes in terms of error minimization. However, localization of mobile sensor nodes
under high uncertainties using these models is not that efficient due to complexity of continuous varying RSS.
Therefore, a detailed study is conducted in this work to determine location of unknown mobile sensor node
using the proposed HUA-LECO model. Moreover, varied works are presented related to error minimization to
determine sensor node location such as LSRE through SDP optimization and ANLS-SDP model. However, in
this work, a study to determine the localization of mobile sensor nodes and the impact of noise factor and
Monte Carlo iterations on localization error is observed against the varied number of sensors from the obtained
multi-measurements using the proposed HUA-LECO model. An iterative weight updation approach is used
based on the Monte Carlo Simulations to minimize the root mean square error and achieve fast convergence.
Simulation performance results demonstrate a significant improvement in terms of mobile sensor node
localization with minimum localization error in comparison with ANLS-SDP and LSRE-SDP models. The
major contribution of this study is the estimation of mobile sensor node location when RSS is continuously
varying under high uncertainties and minimization of localization error in comparison with ANLS-SDP and
LSRE-SDP models. Another contribution is the observation of impact of varying noise levels and Monte Carlo
iterations on localization error. In future work, impact of several environmental conditions like multi-path loss
and fading can be considered.
REFERENCES
[1] R. Rathna, “Simple clustering for wireless sensor networks,” Cybernetics and Information Technologies, vol. 16, no. 1, pp. 57–72,
Mar. 2016, doi: 10.1515/cait-2016-0004.
[2] A. Tripathi, H. P. Gupta, T. Dutta, R. Mishra, K. K. Shukla, and S. Jit, “Coverage and connectivity in WSNs: a survey, research
issues and challenges,” IEEE Access, vol. 6, pp. 26971–26992, 2018, doi: 10.1109/access.2018.2833632.
[3] Z. Sun, Y. Liu, and L. Tao, “Attack localization task allocation in wireless sensor networks based on multi-objective binary particle
swarm optimization,” Journal of Network and Computer Applications, vol. 112, pp. 29–40, Jun. 2018,
doi: 10.1016/j.jnca.2018.03.023.
[4] J. Won and E. Bertino, “Robust sensor localization against known sensor position attacks,” IEEE Transactions on Mobile
Computing, vol. 18, no. 12, pp. 2954–2967, Dec. 2019, doi: 10.1109/tmc.2018.2883578.
[5] S. Savazzi, U. Spagnolini, L. Goratti, D. Molteni, M. Latva-aho, and M. Nicoli, “Ultra-wide band sensor networks in oil and gas
explorations,” IEEE Communications Magazine, vol. 51, no. 4, pp. 150–160, Apr. 2013, doi: 10.1109/mcom.2013.6495774.
[6] G. Werner-Allen et al., “Deploying a wireless sensor network on an active volcano,” IEEE Internet Computing, vol. 10, no. 2,
pp. 18–25, Mar. 2006, doi: 10.1109/mic.2006.26.
[7] R. Bhatt, P. Maheshwary, P. Shukla, P. Shukla, M. Shrivastava, and S. Changlani, “Implementation of fruit y optimization algorithm
(FFOA) to escalate the attacking efciency of node capture attack in wireless sensor networks (WSN),” Computer Communications,
vol. 149, pp. 134–145, Jan. 2020, doi: 10.1016/j.comcom.2019.09.007.
[8] X. Liu, S. Su, F. Han, Y. Liu, and Z. Pan, “A range-based secure localization algorithm for wireless sensor networks,” IEEE Sensors
Journal, vol. 19, no. 2, pp. 785–796, Jan. 2019, doi: 10.1109/jsen.2018.2877306.
[9] S. A. AlRoomi, I. Ahmad, and T. Dimitriou, “Secure localization using hypothesis testing in wireless networks,” Ad Hoc Networks,
vol. 74, pp. 47–56, May 2018, doi: 10.1016/j.adhoc.2018.03.008.
[10] A. Allouch, O. Cheikhrouhou, A. Koubaa, M. Khalgui, and T. Abbes, “MAVSec: securing the MAVLink protocol for ardupilot/PX4
unmanned aerial systems,” in 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Jun.
2019, pp. 621–628, doi: 10.1109/iwcmc.2019.8766667.
[11] O. Cheikhrouhou, G. M. Bhatti, and R. Alroobaea, “A hybrid DV-hop algorithm using RSSI for localization in large-scale wireless
sensor networks,” Sensors, vol. 18, no. 5, p. 1469, May 2018, doi: 10.3390/s18051469.
[12] S. Messous, H. Liouane, and N. Liouane, “Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor
networks,” Telecommunication Systems, vol. 73, no. 1, pp. 75–86, Jul. 2019, doi: 10.1007/s11235-019-00592-6.
[13] D. Han, Y. Yu, K.-C. Li, and R. F. de Mello, “Enhancing the sensor node localization algorithm based on improved DV-hop and
DE algorithms in wireless sensor networks,” Sensors, vol. 20, no. 2, p. 343, Jan. 2020, doi: 10.3390/s20020343.
[14] A. P. Das and S. M. Thampi, “Fault-resilient localization for underwater sensor networks,” Ad Hoc Networks, vol. 55, pp. 132–142,
Feb. 2017, doi: 10.1016/j.adhoc.2016.09.003.
[15] S. J. Bhat and K. V Santhosh, “Is localization of wireless sensor networks in irregular elds a challenge?,” Wireless Personal
Communications, vol. 114, no. 3, pp. 2017–2042, May 2020, doi: 10.1007/s11277-020-07460-6.
[16] J. Kumari, P. Kumar, and S. K. Singh, “Localization in three-dimensional wireless sensor networks: a survey,” The Journal of
Supercomputing, vol. 75, no. 8, pp. 5040–5083, Feb. 2019, doi: 10.1007/s11227-019-02781-1.
[17] K. Gao, J. Zhu, and Z. Xu, “Majorization-minimization-based target localization problem from range measurements,” IEEE
Communications Letters, vol. 24, no. 3, pp. 558–562, Mar. 2020, doi: 10.1109/lcomm.2019.2963834.
[18] A. P. Behera, A. Singh, S. Verma, and M. Kumar, “Manifold learning with localized procrustes analysis based WSN localization,”
IEEE Sensors Letters, vol. 4, no. 10, pp. 1–4, Oct. 2020, doi: 10.1109/lsens.2020.3025360.
[19] S. J. Bhat and K. V Santhosh, “A method for fault tolerant localization of heterogeneous wireless sensor networks,” IEEE Access,
vol. 9, pp. 37054–37063, 2021, doi: 10.1109/access.2021.3063160.
[20] A. Bochem and H. Zhang, “Robustnessenhanced sensor assisted Monte Carlo localization for wireless sensor networks and the
internet of things,” IEEE Access, vol. 10, pp. 33408–33420, 2022, doi: 10.1109/access.2022.3162288.
[21] H. Liouane, S. Messous, O. Cheikhrouhou, M. Baz, and H. Hamam, “Regularized least square multi-hops localization algorithm
for wireless sensor networks,” IEEE Access, vol. 9, pp. 136406–136418, 2021, doi: 10.1109/access.2021.3116767.
[22] K. Sabale and S. Mini, “Transmission power control for anchor-assisted localization in wireless sensor networks,” IEEE Sensors
11. ISSN: 2252-8938
Int J Artif Intell, Vol. 12, No. 4, December 2023: 2022-2032
2032
Journal, vol. 21, no. 8, pp. 10102–10111, Apr. 2021, doi: 10.1109/jsen.2021.3054372.
[23] N. R. Thejaswini and G. Muthupandi, “Dynamic noisy measurement aware localization model for wireless sensor networks,”
Journal of Interconnection Networks, vol. 22, no. 04, May 2022, doi: 10.1142/s0219265921500328.
[24] Q. Luo, Y. Peng, J. Li, and X. Peng, “RSSI-based localization through uncertain data mapping for wireless sensor networks,” IEEE
Sensors Journal, vol. 16, no. 9, pp. 3155–3162, May 2016, doi: 10.1109/jsen.2016.2524532.
[25] T. Wang, H. Ding, H. Xiong, and L. Zheng, “A compensated multi-anchors TOF-based localization algorithm for asynchronous
wireless sensor networks,” IEEE Access, vol. 7, pp. 64162–64176, 2019, doi: 10.1109/access.2019.2917505.
[26] T. Wang, H. Xiong, H. Ding, and L. Zheng, “TDOA-based joint synchronization and localization algorithm for asynchronous
wireless sensor networks,” IEEE Transactions on Communications, vol. 68, no. 5, pp. 3107–3124, May 2020,
doi: 10.1109/tcomm.2020.2973961.
[27] W. Ding, S. Chang, and J. Li, “A novel weighted localization method in wireless sensor networks based on hybrid RSS/AoA
measurements,” IEEE Access, vol. 9, pp. 150677–150685, 2021, doi: 10.1109/access.2021.3126148.
[28] L. Hu and D. Evans, “Localization for mobile sensor networks,” in Proceedings of the 10th annual international conference on
Mobile computing and networking, Sep. 2004, pp. 45–57, doi: 10.1145/1023720.1023726.
[29] M. Qin and R. Zhu, “A Monte Carlo localization method based on differential evolution optimization applied into economic
forecasting in mobile wireless sensor networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2018,
no. 1, pp. 1–9, Feb. 2018, doi: 10.1186/s13638-018-1037-1.
[30] Y. Zhang, L. Cui, and S. Chai, “Energy-ef cient localization for mobile sensor networks based on RSS and historical information,”
in The 27th Chinese Control and Decision Conference (CCDC), May 2015, pp. 5246–251, doi: 10.1109/ccdc.2015.7162860.
[31] S. Hartung, A. Kellner, K. Rieck, and D. Hogrefe, “Applied sensorassisted Monte Carlo localization for mobile wireless sensor
networks,” in 2016 IEEE Wireless Communications and Networking Conference, Apr. 2016, pp. 181–192,
doi: 10.1109/wcnc.2016.7564906.
[32] Z. Munadhil, S. K. Gharghan, A. H. Mutlag, A. Al-Naji, and J. Chahl, “Neural network-based Alzheimer’s patient localization for
wireless sensor network in an indoor environment,” IEEE Access, vol. 8, pp. 150527–150538, 2020,
doi: 10.1109/access.2020.3016832.
[33] P. Rajanikanth and K. S. Reddy, “An efficient routing mechanism for node localization, cluster based approach and data aggregation
to extend WSN lifetime,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 1, Feb. 2022,
doi: 10.22266/ijies2022.0228.28.
[34] J. Chen, S. H. Sackey, J. A. Ansere, X. Zhang, and A. Ayush, “A neighborhood grid clustering algorithm for solving localization
problem in WSN using genetic algorithm,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–13, Jun. 2022,
doi: 10.1155/2022/8552142.
[35] S. P. Racharla and J. Kalaivani, “A review on localization problems in wireless sensor network: algorithmic and performance
analysis,” in 2021 3rd International Conference on Signal Processing and Communication (ICPSC), May 2021, pp. 31–35,
doi: 10.1109/icspc51351.2021.9451770.
[36] J. Shi, G. Wang, and L. Jin, “Least squared relative error estimator for RSS based localization with unknown transmit power,” IEEE
Signal Processing Letters, vol. 27, pp. 1165–1169, 2020, doi: 10.1109/lsp.2020.3005298.
[37] J. Shi, G. Wang, and L. Jin, “Robust semidefinite relaxation method for energy-based source localization: known and unknown
decay factor cases,” IEEE Access, vol. 7, pp. 163740–163748, 2019, doi: 10.1109/access.2019.2952641.
[38] Y. Xu and W. Yin, “A globally convergent algorithm for nonconvex optimization based on block coordinate update,” Journal of
Scientific Computing, vol. 72, no. 2, pp. 700–734, Feb. 2017, doi: 10.1007/s10915-017-0376-0.
[39] S. Tomic, M. Beko, and R. Dinis, “RSS-based localization in wireless sensor networks using convex relaxation: noncooperative
and cooperative schemes,” IEEE Transactions on Vehicular Technology, vol. 64, no. 5, pp. 2037–2050, May 2015,
doi: 10.1109/tvt.2014.2334397.
BIOGRAPHIES OF AUTHOR
Raja Thejaswini Nandyala currently working as assistant professor at Gopalan
college of engineering & management, Bangalore. Overall, 15 years of teaching experience.
Areas of interest are wireless sensor network, VLSI design. Received B. E. and M. Tech
degree form JNTU, Hyderabad. Published journal “Dynamic Noisy Measurement Aware
Localization Model” in Journal of Interconnection Networks. She can be contacted at this
email: thejas.nannyal@gmail.com.
Dr. G. Muthupandi received a B.E. degree in Electronics and Communication
Engineering from MK University, TN (2001). and M.E. degree in Computer and
Communication Engineering from PSNACET-Anna University, TN (2007). Completed his
Ph.D. degree in 2018 in Information and Communication Engineering from Anna University,
He is currently working as an Associate Professor in the Department of Electronics and
Communication Engineering at Presidency University, Bangalore. His area of research
includes Digital Image Signal Processing and VLSI signal Processing, wireless sensor
networks. He can be contacted at this email: muthupandi@presidencyuniversity.in.