Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an extreme learning machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade-ELM to increase localization accuracy in range-free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
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 Novel Range-Free Localization Scheme for Wireless Sensor NetworksGiselleginaGloria
This paper present a low-cost yet effective localization scheme for the wireless sensor networks. There are many studies in the literature of locating the sensors in the wireless sensor networks. Most of them require either installing extra hardware or having a certain amount of sensor nodes with known positions. The localization scheme we propose in this paper is range-free, i.e., not requiring extra hardware devices, and meanwhile it only needs two anchor nodes with known position. Firstly, we install the first anchor node at the lower left corner (Sink X) and the other anchor node at the lower right corner (Sink Y). Then we calculate the minimum hop counts for each unknown node to both Sink X and Sink Y. According to the minimum hop count pair to Sink X and Sink Y of each node, we can virtually divide the monitored region into zones. We then estimate the coordinate of each sensor depending on its located zone. Finally, we adjust the location estimation of each sensor according to its relative position in the zone. We simulate our proposed scheme and the well-known DV-Hop method. The simulation results show that our proposed scheme is superior to the DV-Hop method under both low density and high density sensor deployments.
Recent advances in radio and embedded systems for completing the procedure of location estimation most
of the time sensor networks are fully dependent on the distance measurements that is present between the
sensor neighbourhood node. Techniques used for the localization can be categorized differently.
Techniques used for the measurement of the distance between the wireless sensor nodes, dependent upon
the physical means are divided into three broader categories namely Received signal strength (RSS), Angle
of Arrival (AOA) and propagation base on time measurements. This paper discusses the most of the
approached of WSN and IoT based positioning system.
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
Wide-band spectrum sensing with convolution neural network using spectral cor...IJECEIAES
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
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.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
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 Novel Range-Free Localization Scheme for Wireless Sensor NetworksGiselleginaGloria
This paper present a low-cost yet effective localization scheme for the wireless sensor networks. There are many studies in the literature of locating the sensors in the wireless sensor networks. Most of them require either installing extra hardware or having a certain amount of sensor nodes with known positions. The localization scheme we propose in this paper is range-free, i.e., not requiring extra hardware devices, and meanwhile it only needs two anchor nodes with known position. Firstly, we install the first anchor node at the lower left corner (Sink X) and the other anchor node at the lower right corner (Sink Y). Then we calculate the minimum hop counts for each unknown node to both Sink X and Sink Y. According to the minimum hop count pair to Sink X and Sink Y of each node, we can virtually divide the monitored region into zones. We then estimate the coordinate of each sensor depending on its located zone. Finally, we adjust the location estimation of each sensor according to its relative position in the zone. We simulate our proposed scheme and the well-known DV-Hop method. The simulation results show that our proposed scheme is superior to the DV-Hop method under both low density and high density sensor deployments.
Recent advances in radio and embedded systems for completing the procedure of location estimation most
of the time sensor networks are fully dependent on the distance measurements that is present between the
sensor neighbourhood node. Techniques used for the localization can be categorized differently.
Techniques used for the measurement of the distance between the wireless sensor nodes, dependent upon
the physical means are divided into three broader categories namely Received signal strength (RSS), Angle
of Arrival (AOA) and propagation base on time measurements. This paper discusses the most of the
approached of WSN and IoT based positioning system.
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
Wide-band spectrum sensing with convolution neural network using spectral cor...IJECEIAES
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
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.
Performance Analysis of DV-Hop Localization Using Voronoi ApproachIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Ant Colony Optimization for Wireless Sensor Network: A Reviewiosrjce
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.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
Performance Evaluation of DV-HOP and Amorphous Algorithms based on Localizati...TELKOMNIKA JOURNAL
In the field of high-risk observation, the nodes in Wireless Sensor Network (WSN) are distributed
randomly. The result from sensing becomes meaningless if it is not known from where the originating node
is. Therefore, a sensor node positioning scheme, known as the localization scheme, is required. The
localization scheme consists of distance estimation and position computing. Thus, this research used
connectivity as distance estimation within range free algorithm DV-Hop and Amorphous, and then trilateral
algorithm for computing the position. Besides that, distance estimation using the connectivity between
nodes is not needed for the additional hardware ranging as required by a range-based localization
scheme. In this research compared the localization algorithm based on range free localization, which are
DV-Hop algorithm and Amorphous algorithm. The simulation result shows that the amorphous alg orithm
have achieved 13.60% and 24.538% lower than dv-hop algorithm for each parameter error localization and
energy consumption. On node density variations, dv-hop algorithm gained a localization error that is
26.95% lower than amorphous algorithm, but for energy consumption parameter, amorphous gained
14.227% lower than dv-hop algorithm. In the communication range variation scenario, dv-hop algorithm
gained a localization error that is50.282% lower than amorphous. However, for energy consumption
parameter, amorphous algorithm gained 12.35%. lower than dv-hop algorithm.
Accurate indoor positioning system based on modify nearest point techniqueIJECEIAES
Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).
Investigations on real time RSSI based outdoor target tracking using kalman f...IJECEIAES
Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn‟t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the nonanchor node is estimated after finding the distance between beacon and nonanchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.
A New Approach for Error Reduction in Localization for Wireless Sensor Networksidescitation
Localization is one of the most challenging and
important issues in wireless sensor networks (WSNs),
especially if cost effective approaches are demanded. Distance
measurement based on RSSI (Received Signal Strength
Indication) is a low cost and low complexity of the distance
measurement technique, and it is widely applied in the range-
based localization of the WSN. The RSS (Received Signal
Strength) used to estimate the distance between an unknown
node and a number of reference nodes with known co-ordinates.
Location of the target node is then determined by trilateration.
Log-normal shadowing model, can better describe the
relationship between the RSSI value and distance. Non-line
of sight and multipath transmission effects as the indoor
environment, the distance error or ranging error is large. In
this paper, experimental results that are carried out to analyze
the sensitivity of RSSI measurements in an indoor
environment for various power levels are presented. Location
error influenced by distance measure error and network
connectivity is analyzed.
Index Terms—
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.
Smart parking is common in contemporary cities. These smart parking lots are outfitted mostly with wireless sensor networks (WSNs), which are used to detect, monitor, and collect data on the availability status of all existing parking spaces in a given area. Sensors make up WSN, which may gather, process, and transmit informations to the sink. However, the power and
communication limitations of the sensors have an effect on the performance and quality of the WSNs. The decrease in the battery and the energy of the
nodes causes a decrease in the life of the nodes and also of the entire WSN network. In this article, we present a routing protocol that implements an
efficient and robust algorithm allowing the creation of clusters so that the base station can receive data from the entire WSN network. This protocol
adopts a reliable and efficient algorithm allowing to minimize the energy dissipation of the sensors and to increase the lifetime of the WSN. In
comparison to alternative parking lot management protocols already in use,
the simulation results of the proposed protocol are effective and robust in terms of power consumption, data transmission reliability, and WSN network longevity.
Spatial Correlation Based Medium Access Control Protocol Using DSR & AODV Rou...IOSR Journals
Abstract : In Wireless sensor network sensor nodes have a limited battery life and their efficient utilization is
a very much importent task. Their are many ways are proposed for efficient utilization of energy.For efficient
energy utilization many topologies,protocals are proposed by the help of which we can maximize the battery
life. In this paper we propesed a methode in which a correlation is made between all the sensor nodes including
ME(mobile element). A Vector Quantization methode are used for distance calculation between all the sensor
nodes and mobile element. After finding the corrélation we used the DSR & AODV routing Protocol. The
performance of the proposed protocol has been examined and evaluated with the NS-2 simulator in terms of
packet drop ratio and energy consumption. The simulation result shows that the proposed protocol with AODV
routing gives a batter result compared with same protocol with DSR routing.
Keywords: ME, DVT, DSR, AODV, Wireless Sensor Network, Efficient Energy Utilization
In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.
Agriculture has since become a major source of livelihood for Nigerians. It also accounts for over 85% of the total food consumed within her borders. The sector has maintained improved productivity and profitability via a concerted effort to address critical issues such as an unorganized regulatory system, lack of food safety data, no standards in agricultural produce, non-adaptation to precision farming, and non-harmony via inventory trace supports. This study proposes blockchain-based trace-support in a continued effort to ensure food quality, consumer safety, and trading of food assets. It uses the radio frequency identification (RFID) sensor to register and track livestocks, farms/farmers, and abattoir processes as well as provisions a databank to trace livestock data. Results show the model adequately perform about 1,101 transactions per seconds with a response time of 0.21 s for queries and 0.28 s for https pages respectively for 2,500 users. Also, it yields a slightly longer time of 0.32 s for queries and 0.38 s for https pages respectively with an increased 5,000 users via the world-state as stored in the blockchain’s hyper-fabric ledger. Overall, the framework can directly query and retrieve data without it traversing the whole ledger. This, in turn, improves the efficiency and effectiveness of the traceability system.
Performance Analysis of DV-Hop Localization Using Voronoi ApproachIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Ant Colony Optimization for Wireless Sensor Network: A Reviewiosrjce
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.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
Performance Evaluation of DV-HOP and Amorphous Algorithms based on Localizati...TELKOMNIKA JOURNAL
In the field of high-risk observation, the nodes in Wireless Sensor Network (WSN) are distributed
randomly. The result from sensing becomes meaningless if it is not known from where the originating node
is. Therefore, a sensor node positioning scheme, known as the localization scheme, is required. The
localization scheme consists of distance estimation and position computing. Thus, this research used
connectivity as distance estimation within range free algorithm DV-Hop and Amorphous, and then trilateral
algorithm for computing the position. Besides that, distance estimation using the connectivity between
nodes is not needed for the additional hardware ranging as required by a range-based localization
scheme. In this research compared the localization algorithm based on range free localization, which are
DV-Hop algorithm and Amorphous algorithm. The simulation result shows that the amorphous alg orithm
have achieved 13.60% and 24.538% lower than dv-hop algorithm for each parameter error localization and
energy consumption. On node density variations, dv-hop algorithm gained a localization error that is
26.95% lower than amorphous algorithm, but for energy consumption parameter, amorphous gained
14.227% lower than dv-hop algorithm. In the communication range variation scenario, dv-hop algorithm
gained a localization error that is50.282% lower than amorphous. However, for energy consumption
parameter, amorphous algorithm gained 12.35%. lower than dv-hop algorithm.
Accurate indoor positioning system based on modify nearest point techniqueIJECEIAES
Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).
Investigations on real time RSSI based outdoor target tracking using kalman f...IJECEIAES
Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn‟t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the nonanchor node is estimated after finding the distance between beacon and nonanchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.
A New Approach for Error Reduction in Localization for Wireless Sensor Networksidescitation
Localization is one of the most challenging and
important issues in wireless sensor networks (WSNs),
especially if cost effective approaches are demanded. Distance
measurement based on RSSI (Received Signal Strength
Indication) is a low cost and low complexity of the distance
measurement technique, and it is widely applied in the range-
based localization of the WSN. The RSS (Received Signal
Strength) used to estimate the distance between an unknown
node and a number of reference nodes with known co-ordinates.
Location of the target node is then determined by trilateration.
Log-normal shadowing model, can better describe the
relationship between the RSSI value and distance. Non-line
of sight and multipath transmission effects as the indoor
environment, the distance error or ranging error is large. In
this paper, experimental results that are carried out to analyze
the sensitivity of RSSI measurements in an indoor
environment for various power levels are presented. Location
error influenced by distance measure error and network
connectivity is analyzed.
Index Terms—
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.
Smart parking is common in contemporary cities. These smart parking lots are outfitted mostly with wireless sensor networks (WSNs), which are used to detect, monitor, and collect data on the availability status of all existing parking spaces in a given area. Sensors make up WSN, which may gather, process, and transmit informations to the sink. However, the power and
communication limitations of the sensors have an effect on the performance and quality of the WSNs. The decrease in the battery and the energy of the
nodes causes a decrease in the life of the nodes and also of the entire WSN network. In this article, we present a routing protocol that implements an
efficient and robust algorithm allowing the creation of clusters so that the base station can receive data from the entire WSN network. This protocol
adopts a reliable and efficient algorithm allowing to minimize the energy dissipation of the sensors and to increase the lifetime of the WSN. In
comparison to alternative parking lot management protocols already in use,
the simulation results of the proposed protocol are effective and robust in terms of power consumption, data transmission reliability, and WSN network longevity.
Spatial Correlation Based Medium Access Control Protocol Using DSR & AODV Rou...IOSR Journals
Abstract : In Wireless sensor network sensor nodes have a limited battery life and their efficient utilization is
a very much importent task. Their are many ways are proposed for efficient utilization of energy.For efficient
energy utilization many topologies,protocals are proposed by the help of which we can maximize the battery
life. In this paper we propesed a methode in which a correlation is made between all the sensor nodes including
ME(mobile element). A Vector Quantization methode are used for distance calculation between all the sensor
nodes and mobile element. After finding the corrélation we used the DSR & AODV routing Protocol. The
performance of the proposed protocol has been examined and evaluated with the NS-2 simulator in terms of
packet drop ratio and energy consumption. The simulation result shows that the proposed protocol with AODV
routing gives a batter result compared with same protocol with DSR routing.
Keywords: ME, DVT, DSR, AODV, Wireless Sensor Network, Efficient Energy Utilization
In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.
Agriculture has since become a major source of livelihood for Nigerians. It also accounts for over 85% of the total food consumed within her borders. The sector has maintained improved productivity and profitability via a concerted effort to address critical issues such as an unorganized regulatory system, lack of food safety data, no standards in agricultural produce, non-adaptation to precision farming, and non-harmony via inventory trace supports. This study proposes blockchain-based trace-support in a continued effort to ensure food quality, consumer safety, and trading of food assets. It uses the radio frequency identification (RFID) sensor to register and track livestocks, farms/farmers, and abattoir processes as well as provisions a databank to trace livestock data. Results show the model adequately perform about 1,101 transactions per seconds with a response time of 0.21 s for queries and 0.28 s for https pages respectively for 2,500 users. Also, it yields a slightly longer time of 0.32 s for queries and 0.38 s for https pages respectively with an increased 5,000 users via the world-state as stored in the blockchain’s hyper-fabric ledger. Overall, the framework can directly query and retrieve data without it traversing the whole ledger. This, in turn, improves the efficiency and effectiveness of the traceability system.
Diabetic retinopathy (DR) is one of the most common causes of blindness. The necessity for a robust and automated DR screening system for regular examination has long been recognized in order to identify DR at an early stage. In this paper, an embedded DR diagnosis system based on convolutional neural networks (CNNs) has been proposed to assess the proper stage of DR. We coupled the power of CNN with transfer learning to design our model based on state-of-the-art architecture. We preprocessed the input data, which is color fundus photography, to reduce undesirable noise in the image. After training many models on the dataset, we chose the adopted ResNet50 because it produced the best results, with a 92.90% accuracy. Extensive experiments and comparisons with other research work show that the proposed method is effective. Furthermore, the CNN model has been implemented on an embedded target to be a part of a medical instrument diagnostic system. We have accelerated our model inference on a field programmable gate array (FPGA) using Xilinx tools. Results have confirmed that a customized FPGA system on chip (SoC) with hardware accelerators is a promising target for our DR detection model with high performance and low power consumption.
As many countries experience the emergence of new waves of Covid-19, many governments around the world have reminded their citizens of the need for an engaging intervention that could improve compliance with Covid-19 safe behaviors using the media general public or social media. In the face of the serious threat of Covid-19, immunity issues are currently the subject of various research and studies. A promising approach is to use video game culture to educate and train citizens to healthily adopt eating habits to strengthen the immune system. The objective of this study is to develop a prototype of a serious game (SG) on how to strengthen the immune defenses in order to be able to fight a coronavirus infection and to constitute an antivirus barrier. After defining the learning objectives by interviewing the stakeholders, we searched the scientific literature to establish the relevant theoretical bases. The learning contents have been validated by biology teachers. The learning mechanisms were then determined based on the learning objectives. The obtained experimental results show that 92% of the participants in the study have appreciated the quality of the scenario and the way in which the concept of interaction between the different game elements was presented.
The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and humanmachine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligencebased systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic dysarthric speech recognition (DSR) using machine learning (ML) and deep learning (DL) paradigms. It focuses on the methodology, database, evaluation metrics, and major findings from the study of previous approaches. From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR. The performance of the various machine and DL schemes is evaluated for the DSR on UASpeech dataset based on accuracy, precision, recall, and F1- score. It is observed that the DL based DSR schems outperforms the ML based DSR schemes.
The router is a network device that is used to connect subnetwork and packet-switched networking by directing the data packets to the intended IP addresses. It succeeds the traffic between different systems and allows several devices to share the internet connection. The router is applicable for the effective commutation in system on chip (SoC) modules for network on chip (NoC) communication. The research paper emphasizes the design of the two dimensional (2D) router hardware chip in the Xilinx integrated system environment (ISE) 14.7 software and further logic verification using the data packets transmitted from all input/output ports. The design evaluation is done based on the pre-synthesis device utilization summary relating to different field programmable gate array (FPGA) boards such as Spartan-3E (XC3S500E), Spartan-6 (XC6SLX45), Virtex-4 (XC4VFX12), Virtex-5 (XC5VSX50T), and Virtex-7 (XC7VX550T). The 64-bit data logic is verified on the different ports of the router configuration in the Xilinx and Modelsim waveform simulator. The Virtex-7 has proven the fast-switching speed and optimal hardware parameters in comparison to other FPGAs.
In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both userfriendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.
The use of a convolutional neural network (CNN) to analyze and classify electroencephalogram (EEG) signals has recently attracted the interest of researchers to identify epileptic seizures. This success has come with an enormous increase in the computational complexity and memory requirements of CNNs. For the sake of boosting the performance of CNN inference, several hardware accelerators have been proposed. The high performance and flexibility of the field programmable gate array (FPGA) make it an efficient accelerator for CNNs. Nevertheless, for resource-limited platforms, the deployment of CNN models poses significant challenges. For an ease of CNN implementation on such platforms, several tools and frameworks have been made available by the research community along with different optimization techniques. In this paper, we proposed an FPGA implementation for an automatic seizure detection approach using two CNN models, namely VGG-16 and ResNet-50. To reduce the model size and computation cost, we exploited two optimization approaches: pruning and quantization. Furthermore, we presented the results and discussed the advantages and limitations of two implementation alternatives for the inference acceleration of quantized CNNs on Zynq-7000: an advanced RISC machine (ARM) software implementation-based ARM, NN, software development kit (SDK) and a software/hardware implementation-based deep learning processor unit (DPU) accelerator and DNNDK toolkit.
A number of benefits have been reported for computer-based assessments over traditional paper-based exams, both in terms of IT support for question development, reduced distribution and test administration costs, and automated support. Possible for the ranking. However, existing computerized assessment systems do not provide all kinds of questions, namely open questions that require writing solutions. To overcome the challenges of the existing, the objective of this work is to achieve an intelligent evaluation system (IES) responding to the problems identified, and which adapts to the different types of questions, especially open-ended questions of which the answer requires sentence writing or programming.
Convolutional neural networks (CNN) trained using deep learning (DL) have advanced dramatically in recent years. Researchers from a variety of fields have been motivated by the success of CNNs in computer vision to develop better CNN models for use in other visually-rich settings. Successes in image classification and research have been achieved in a wide variety of domains throughout the past year. Among the many popularized image classification techniques, the detection of plant leaf diseases has received extensive research. As a result of the nature of the procedure, image quality is often degraded and distortions are introduced during the capturing of the image. In this study, we look into how various CNN models are affected by distortions. Corn-maze leaf photos from the 4,188-image corn or maize leaf Dataset (split into four categories) are under consideration. To evaluate how well they handle noise and blur, researchers have deployed pre-trained deep CNN models like visual geometry group (VGG), InceptionV3, ResNet50, and EfficientNetB0. Classification accuracy and metrics like as recall and f1-score are used to evaluate CNN performance.
In this research, we present a low phase noise (PN) and wide tuning range 175 GHz inductors and capacitors (LC) voltage-controlled oscillator (VCO) based on a differential Colpitts oscillator that was designed using a 0.13 μm bipolar complementary metal oxide semiconductor (BiCMOS) and simulated. The square of the tank Q-factor and the square of the oscillation amplitude were both maximized to reduce PN. With an extensive examination of parasitic, mathematical analysis of load impedances, and implementation of differential design, the PN was reduced, and the output power was enhanced. Using a supply voltage of 1.6 V, the VCO consumes 41.9 mA, resulting in a total power usage of 67 mW to prevent undesirable PN deterioration, an inter-stage LC filter at the VCO-buffer interface increases the swing at the buffer input. To make a better output, a buffer is used to isolate the load from the VCO core. In addition, the VCO has a high linearity and the overall, the VCO presented in this study demonstrates excellent performance and has the potential to be used in a wide range of applications that require a high-performance, low-power VCO.
Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pretrained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
Wirelessly based security applications have exploded as a result of modern technology. To build and/or implement security access control systems, many types of wireless communication technologies have been deployed. quick response (QR code) is a contactless technology that is extensively utilised in a variety of sectors, including access control, library book tracking, supply chains, and tollgate systems, among others. This paper combines QR code technology with Arduino and Python to construct an automated QR code-based access management system. After detecting a QR code, the QR scanner at the entry collects and compares the user's unique identifier (UID) with the UID recorded in the system. The results show that this system is capable of granting or denying access to a protected environment in a timely, effective, and reliable way. Security systems can protect physical and intellectual property by preventing unauthorized persons from entering the area. Many door locks, such as mechanical and electrical locks, were created to meet basic security needs but it also helps to create a data files structure of the authorized persons.
The study evaluated interference in a dense heterogeneous network using third-generation universal mobile telecommunication systems (UMTS) and fourth-generation long term evolution (LTE) networks LTE. The UMTs/LTE heterogeneous network determines the level of interference when the two communication systems coexist and how to improve the network by migrating from UMTs to LTE, which has a faster download speed and larger capacity. Techno lite 8 on third generation (3G) and Infinix Pro 6 on fourth generation (4G) were used to measure network the received signal strength (RSS) during site investigation. UE interference was detected and traced using a spectrum analyzer. UMTS and LTE path loss exponents are 2.6 and 3.2. Shannon's capacity theorem calculated LTE and UMTS capacity. When signal to interference and noise ratio (SINR) was used as a quality of service (QoS) indicator, MATLAB channel capacity plots did not match Shannon's due to neighboring interference. UMTS had an R2 of 0.54 and LTE 0.57 for the Shannon channel capacity equation. Adjacent channel interference (ACI) user devices reduce network capacity, lowering QoS for other customers.
The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. The instructor may directly examine the log or use more complex method such as data mining to conduct a quicker and more in-depth examination of the student's behaviors. Most previous studies on analyzing this log data rely on predictive analysis. Instead of predictive analysis, this study investigates cluster analysis and association analysis. Cluster analysis based on k-means++ is utilized to organize students into groups, given their engagement in the learning course module. Association analysis based on apriori is utilized to extract the relationships between various student activities. A dashboard presentation of the findings is provided to facilitate clearer comprehension. Based on the analysis findings, it can be concluded that the structure of the student cluster is medium. In contrast, the association between student activities is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.
The software-defined network (SDN) controller adds and removes the contents of the flow table through secure channels to determine how packets are processed and how the flow table is managed. The controller pays attention to network intelligence and becomes the middle part, where the network manages the transfer data of the aircraft delivered via the OpenFlow (OF) switch. To this end, the controller provides an interface for managing, controlling, and managing this switch flow table. Run tests to calculate controller throughput and latency levels and test using the cbance tool, which can test transmission control protocol (TCP) and user datagram protocol (UDP) protocols. The tests are run by forcing the controller to run at maximum without any additional settings (default settings) in order to use the correct information about the controller’s capabilities. Because of this need, you need to test the performance of your controller. In this study, the tests were run on three popular controllers. Test results show that flowed controllers are more stable than open network operating dystem (ONOS) and open daylight (ODL) controllers in managing switch and host loads.
This study examined the influence of electronic government (e-government) implementation for the Ministry of Transport on fulfilling Saudi vision 2030 by transforming the Kingdom of Saudi Arabia (KSA) into a logistics center linking three continents. Saudi vision 2030 aims to cut transportation costs by improving infrastructure, shorten importing and exporting times by streamlining and automating operations, and increase supply chain transparency through sector reform. Implementing e-government would improve government services and engagement through information and communication technology (ICT). This article focuses on four primary areas: i) making KSA a logistics center; ii) increasing the chance of living throughout the Kingdom; and iii) promoting long-term financial sustainability. The study is founded on the idea that logistics is a crucial component for competitive advantage and transportation (by land, sea, or air) is a logistical sub-process for Saudi enterprises that benefit from transport networks similar to the best in the world. The Kingdom's strategic location at the junction of three continents gives its transport sector a geographical competitive advantage that provides access to important emerging markets and critical sea lanes. Despite the optimistic future of the transport and logistics industries in KSA, some important hurdles must be overcome.
Chronic disease (CD) such as kidney disease and causes severe challenging issues to the people all around the world. Chronic kidney disease (CKD) and diabetes mellitus (DM) are considered in this paper. Predicting the diseases in earlier stage, gives better preventive measures to the people. Healthcare domain leads to tremendous cost savings and improved health status of the society. The main objective of this paper is to develop an algorithm to predict CKD occurrence using machine learning (ML) technique. The commonly used classification algorithms namely logistic regression (LR), random forest (RF), conditional random forest (CRF), and recurrent neural networks (RNN) are considered to predict the disease at an earlier stage. The proposed algorithm in this paper uses medical code data to predict disease at an earlier stage.
Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size (EDNSS) algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original EDNSS algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real-world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
1. International Journal of Informatics and Communication Technology (IJ-ICT)
Vol. 12, No. 2, August 2023, pp. 140~149
ISSN: 2252-8776, DOI: 10.11591/ijict.v12i2.pp140-149 140
Journal homepage: http://ijict.iaescore.com
Novel DV-Hop algorithm-based machines learning technics for
node localization in range-free wireless sensor networks
Oumaima Liouane1,3
, Smain Femmam2,3
, Toufik Bakir4
, Abdessalem Ben Abdelali1
1
Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia
2
Department of Networks and Communications, Haute-Alsace University, Mulhouse, France
3
EPF Engineering School, Paris-Cachan, France
4
Laboratory ImViA EA 7535, Université Bourgogne, Dijon, France
Article Info ABSTRACT
Article history:
Received Apr 30, 2022
Revised Jul 23, 2022
Accepted Aug 17, 2022
Localization is a critical concern in many wireless sensor network (WSN)
applications. Furthermore, correct information regarding the geographic
placements of nodes (sensors) is critical for making the collected data
valuable and relevant. Because of their benefits, such as simplicity and
acceptable accuracy, the based connectivity algorithms attempt to localize
multi-hop WSN. However, due to environmental factors, the precision of
localisation may be rather low. This publication describes an extreme
learning machine (ELM) technique for minimizing localization error in
range-free WSN. In this paper, we propose a Cascade-ELM to increase
localization accuracy in range-free WSNs. We tested the proposed
approaches in a variety of multi-hop WSN scenarios. Our research focused
on an isotropic and irregular environment. The simulation results show that
the proposed Cascade-ELM algorithm considerably improves localization
accuracy when compared to previous algorithms derived from smart
computing approaches. When compared to previous work, isotropic
environments show improved localization results.
Keywords:
Deep extreme learning machine
Irregularity
Localization
Machine learning
Range free
Wireless sensors network
This is an open access article under the CC BY-SA license.
Corresponding Author:
Oumaima Liouane
Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir
University of Monastir
Monastir, Tunisia
Email: Oumaima_Liouane@etu.u-bourgogne.fr
1. INTRODUCTION
Recent applications of wireless sensor networks (WSN) show one of the most recent developments in
wireless communication and industry 4.0 technology. The WSN consist of a collection of sensor devices that
are both compact and affordable. These intelligent sensors are able to interact with one another through multi-
hop transmission so that they may acquire physical data and phenomena from their surroundings. Each
individual device is responsible for data collection and transmission over the network architecture, based on the
sensing it possesses. In order to make the data that was obtained usable, it is necessary for the sensor nodes that
collected the data to have location awareness so that they can determine where the event is taking place. A few
examples of applications in the WSN sector include tracking, supervision, and the internet of things security
industry. Recent research efforts have been dedicating to investigating the localization challenges in WSN.
In the most recent decade, there has been a heightened focus on the application of machine learning
techniques (MLT), in WSN. Artificial neural network (ANN), support vector machine (SVM), and deep
learning (DL) approach models are utilized in many domains for the purpose of solving classification
problems, estimating densities, or identifying processes, respectively. In point of fact, the MLT method was
2. Int J Inf & Commun Technol ISSN: 2252-8776
Novel DV-Hop algorithm-based machines learning technics for node localization in … (Oumaima Liouane)
141
implemented in a wide variety of WSN setups, including range-based, range-free, isotropic, and anisotropic
settings (see Figure 1). In the range-based scenarios, the ANN inputs shown in Figure 2 present some of the
physical features of the signals that were received. These include the received signal strength indicator
(RSSI), time of arrival (ToA), time difference of arrival (TDoA), and/or angle of arrival (AoA). These ANN-
range-based models have excellent performance when it comes to localization, but they require additional
hardware equipment. The location of unknown nodes may be inferred using ANN-range-free approaches,
which are based on the connection of WSN and the placements of anchors. These techniques do not require
any extra devices. The ANN-range-free localization model is applicable to any kind of isotropic WSN, and it
provides accuracy that is satisfactory.
Recently, ANN, SVM, extreme learning machine (ELM), and DL have been utilized in order to
overcome the challenges of localization in WSNs [1]–[5]. For instance, Javadi et al. [4] utilizes the SVM and a
variation of it called twin-SVM in order to localize sources in WSN. Research by Hatami et al. [6] believe that
the twin-SVM employs the distributed learning method in order to localize the region surrounding the
predicted node position throughout the process of localization. The position of the event that has to be found
might be assumed to be the position of the node that has an average position inside the sensing region [4], [7].
Regularized ELM-WSN was utilized by [8]–[11] in order to tackle the multi-hop localization problem. The
proposed technique is comprised of three stages: sensing learning data through the correlation between the
number of hops and the physical distances separating known and unknown nodes; the trilateration algorithm
for the purpose of carrying out the process of localization; and finally [5]. The hybrid localization models that
in [12]–[16] the authors described were based on fuzzy logic and the ELM model. In order to achieve the
highest possible degree of precision in localisation, the PSO works to mitigate the impact of irregular
deployments. A localization approach for large-scale WSNs was reported by [17], [18] using a fast-SVM. The
location estimate position of the WSNs is converted into a multiclass problem by the localization method that
has been provided, and the binary SVM for localization is utilized in order to find a solution to this problem.
The similarity measure is brought to the table by the fast-SVM that has been presented, and the support vectors
may be segmented into groups according to the maximal similarity measure [19].
Moreover, Pule et al. [20] proposed combination of the genetic algorithm metaheuristic and the
distance vector-hop (DV-Hop) algorithm to compute unknown node coordinates in WSNs. In fact, by using the
feasible population region defined by the max-min techniques, the optimization localization process via the
genetic algorithm is applied for minimizing the localization errors [21]–[23]. Research by Payal et al. [24]
exploit the machine learning ELM to find the appropriate sub-anchor nodes for localization process via the
improved DV-Hop localization algorithm. Firstly, the called DV-Hop-ELM upgrades several virtual unknown
nodes to sub-anchor nodes via the ELM process. The sub-anchor and real anchor nodes are used together to
locate the remaining unknown nodes by the classic DV-Hop algorithm [25]. Recently, Wang et al. [26]
proposed the exploitation of the Kernel extreme learning machines based on Hop-count quantization (KELM-
HQ) for localization problem in range-free WSNs. The suggested method computes the expected real number of
hop-counts between anchors and unknown nodes. For the training phase, the inputs and target outputs of the
KELM are respectively the hop-counts number (between anchors and unknown nodes) and the anchors
locations. Using the linear-kernel, the proposed method uses the real quantized hop-counts between unknown
nodes as the test samples for the localization process in the exploitation phase [27]–[32].
In this work, a novel ANN-range-free model based on Cascade-ELM algorithms for WSN localization
is proposed. The Cascade-ELM algorithm is a novel method based on range-free techniques to tackle the
localization in WSN. Different scenarios in isotropic environments will be considered to experiment the
suggested algorithm and to show the efficacy of the proposed technique. The rest of this paper is presented as
follows. Section 2 is dedicated to the review of the state of the art on localization problem in WSN. In sections 3
and 4 we present respectively the basic single hidden layer ELM and the Cascade-ELM and their application for
the localization task. Section 5 is dedicated to the analysis of simulation results and the comparison of the
proposed ELM architectures performances. Finally, the conclusion and future works are given in section 6.
Figure 1. Isotropic WSN deployment (without obstacles) and anisotropic WSN deployment (with obstacles)
3. ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 12, No. 2, August 2023: 140-149
142
Figure 2. Range based deep-ANN localization modes
2. CASCAD-E ELM ALGORITHM DESCRIPTION
In WSN, it has been proved that there exists a direct correlation between minimum hop count and
the corresponding physical distance. By using the ELM model, the localization of unknown nodes in the
WSN can be done by the exploitation of this. In the first learning phase, a beacon packet is broadcasted by
each anchor node within the sensing network to inform other nodes about anchors information (ID and
Hop-count values). Once a node received this packet the sensor node increments its hop-count. Then, each
node computes its cumulative minimum hops counts between them and the anchor nodes.
a. Step 1: WSN discovery
Like the first step of the basic DV-Hop algorithm called flooding phase, in the first learning phase, a
beacon packet is broadcasted by each anchor node within the sensing network to inform other nodes about
anchors information (ID and hop-count values). Once a node received this packet the sensor node increments
its hop-count. Then, each node computes its cumulative minimum hops counts between them and the anchor
nodes. As a result, the minimum hops accounts between all nodes are given and provide the global hop count
matrix HC. The HC matrix is divided into two sub-matrix Hca and Hcn designing the anchors connectivity
and the unknown nodes connectivity. Moreover, the anchors connectivity Hca matrix play a reference node
for the learning phase then the coordinates Xa of all anchor nodes are known the distances matrix Da of the
anchor nodes can be directly calculated.
b. Step 2: WSN localization learning phase via the ELM model
The 2-dimentionnal WSN is composed by (n) randomly deployed nodes which divided in two groups
respectively (na) anchors nodes and (nn) unknown nodes. The global hop counts matrix, the global distance
matrix between all nodes and the coordinate matrix of all nodes are represented respectively by HC, D and XY.
𝐻𝐶 = (
[𝐻𝑐𝑎]
[𝐻𝑐𝑛]
) ∈ 𝑅(𝑛𝑎+𝑛𝑛)×𝑛𝑎
, 𝐷 = (
[𝐷𝑎]
[𝐷𝑛]
) ∈ 𝑅(𝑛𝑎+𝑛𝑛)×𝑛𝑎
, 𝑋𝑌 = (
[𝑋𝑎]
[𝑋𝑛]
) ∈ 𝑅(𝑛𝑎+𝑛𝑛)×2
− Step 2.1: the first layer of the ELM interpretation
We suppose that the relation between hop account matrix HC and the distance matrix D can be
expressed by the machine learning process via the ELM Model as shown by Figure 3.
Figure 3. The first ELM learning phase
The hidden layer matrix can be expressed by:
𝐻 = [
𝑔(𝐻𝐶1𝑊1 + 𝑏1) 𝐿 𝑔(𝐻𝐶1𝑊
𝑧 + 𝑏𝑧)
𝑀 𝑂 𝑀
𝑔(𝐻𝐶𝑛𝑎+𝑛𝑛𝑊1 + 𝑏1) 𝐿 𝑔(𝐻𝐶𝑛𝑎+𝑛𝑛𝑊
𝑧 + 𝑏𝑧)
] (1)
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𝐻 = (
[𝑔(𝐻𝑐𝑎, 𝑊, 𝐵)]
[𝑔(𝐻𝑐𝑛, 𝑊, 𝐵)]
) = (
[𝐻𝑎]
[𝐻𝑛]
)
with 𝐻 ∈ 𝑅(𝑛𝑎+𝑛𝑛)×𝑧
, 𝛽1 ∈ 𝑅𝑧×𝑛𝑎
and 𝐷 ∈ 𝑅(𝑛𝑎+𝑛𝑛)×𝑛𝑎
(2)
where g represents the sigmoid activation function. According to the ELM theory, the output layer is given
by the least square method:
𝐻 ⋅ 𝛽1 = 𝐷 ⇒ (
[𝐻𝑎]
[𝐻𝑛]
) ⋅ 𝛽1 = (
[𝐷𝑎]
[𝐷𝑛]
)
⇒ {
[𝐻𝑎] ⋅ 𝛽1 = [𝐷𝑎] ⇒ 𝑙𝑒𝑟𝑎𝑛𝑖𝑛𝑔 𝑝ℎ𝑎𝑠𝑒
[𝐻𝑛] ⋅ 𝛽1 = [𝐷𝑛] ⇒ exp𝑙𝑜𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑝ℎ𝑎𝑠𝑒
(3)
where 𝛽1 represent the output wheigt matrix of the ELM model and can be calculated in the learning phase
via the least square optimization method:
𝛽1 = (𝐻𝑎𝑇
𝐻𝑎)−1
𝐻𝑎𝑇
𝐷𝑎 (4)
where:
𝐻𝑎 = 𝑔(𝐻𝐶𝑎, 𝑊, 𝐵) (5)
The reduced number of the anchor nodes for the training phase introduces the problem of
underfitting or overfitting. Then to reduce these problems and ameliorate the generalization error of the
localization process via ELM, we use the regularization factor “α” for the output weight 𝛽1 estimation. The
“α” parameter controls how much we adjusting the weights of the ELM with respect the generalization error
in the exploitation phase. Then the regularized ELM gives the 𝛽1 equal to:
𝛽1 = (𝐻𝑎𝑇
𝐻𝑎 + 𝛼 𝐼𝑑)−1
𝐻𝑎𝑇
𝐷𝑎 (6)
where Id denotes (z×z) identity matrix.
− Step 2.2: the second layer of the ELM interpretation
Moreover, we suppose that the relation between distance matrix D and the coordinate XY of all
nodes can be computed by ELM model, in Figure 4 we present the Cascade-ELM learning phase model and
this can be calculated in the second learning phase via the least square optimization method:
𝐷 𝛽2 = 𝑋𝑌 ⇒ (
[𝐷𝑎]
[𝐷𝑛]
) ⋅ 𝛽2 = (
[𝑋𝑎]
[𝑋𝑛]
)
⇒ {
[𝐷𝑎] ⋅ 𝛽2 = [𝑋𝑎] ⇒ 𝑙𝑒𝑟𝑎𝑛𝑖𝑛𝑔 𝑝ℎ𝑎𝑠𝑒
[𝐷𝑛] ⋅ 𝛽2 = [𝑋𝑛] ⇒ exp𝑙𝑜𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑝ℎ𝑎𝑠𝑒
(7)
where 𝛽2 𝑅𝑛𝑎×2
represent the output weight matrix of the second hidden layer and can be calculated via the
ELM model:
𝛽2 = (𝐷𝑎𝑇
𝐷𝑎)−1
𝐷𝑎𝑇
𝑋𝑎 (8)
Figure 4. The Cascade-ELM learning phase
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144
c. Step 3: the WSN localization phase
− Step 3.1: the distance estimation
Then the expected distances between all unknown nodes and anchor nodes are given by:
𝐷𝑛 = 𝐻𝑛 𝛽1
𝐷𝑛 = 𝐻𝑛(𝐻𝑎𝑇
𝐻𝑎)−1
𝐻𝑎𝑇
𝐷𝑎 (9)
where:
𝐻𝑛 = 𝑔(𝐻𝐶𝑛, 𝑊, 𝐵) (10)
− Step 3.2: the localization process
Then the expected position Xn of all unknown nodes are given by:
𝑋𝑛 = 𝐷𝑛 𝛽2
𝑋𝑛 = 𝐷𝑛(𝐷𝑎𝑇
𝐷𝑎)−1
𝐷𝑎𝑇
𝑋𝑎 (11)
The resume of the WSN localization process approximation as Cascade-ELM model is depicted in Figure 5.
Figure 5. The Cascade-ELM model for WSN localization process
3. RESULTS AND DISCUSSION
In this section we conduct simulation to check the localization accuracy of our Cascade-ELM
algorithm in isotropic case with N=300 unknown nodes. The localization errors of the proposed
Cascade-ELM algorithm are compared with those of KELM-HQ, the fast-SVM, the GADV-Hop and the
DV-Hop-ELM algorithms. These least algorithms, issued from soft computing technics, are chosen for
comparison thanks to their good localization accuracy compared with the improved traditional DV-Hop
heuristic. MATLAB tools are used for the implementation and the simulations of Cascade-ELM. We conduct
50 times randomly deployment scenarios simulation, and we computed the average values of these
simulations. In the first part of simulation, the unknown nodes are deployed in a 2-D sensing field of surface
S=100×100 m. All nodes have the same communication range R=10 m. The number of anchor nodes is set to
5, 10, 15, 20, 25, 30, and 35. During the localization phase, we assume as well that every node in the network
communicates with the others by the multi-hop routing protocol. The first hidden layer uses 200 neurons as
well as the sigmoidal activation function. Moreover, during the exploitation step, the weight matrix W and
Bias remain the same as those used in the learning step. We use the normalized localization error (NLE) to
measure the accuracy of our proposed localization schemes.
𝑁𝐿𝐸 =
1
𝑁×𝑅
∑ √(𝑥𝑖
𝑒𝑠𝑡
− 𝑥𝑖)2 + (𝑦𝑖
𝑒𝑠𝑡
− 𝑦𝑖)2
𝑁
𝑖=1 (12)
where N=300 and R=10 m are the total amount of the unknown nodes and the communication radio, respectively.
The (xi, yi) present the real position and the (xest
, yest
) are estimated position of the i’th unknown node.
3.1. Results and comparison for isotropic wireless sensor network
Figures 6 to 9 give an example of localization results using the Cascade-ELM for different anchor
deployment scenarios. The deployed environment is made up of 300 sensor nodes and the adopted
communication range is of 10 m. The actual position of each unknown node is indicated by the red point and
the error between the exact position and the estimated one is represented by the blue line.
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Figure 6. Localization results for 5 and 20 anchors
Figure 7. Localization results for 35 and 50 anchors
As expected in Figure 8, for the KELM-HQ, the fast-SVM, the GADV- Hop, the DV-Hop-ELM and
the Cascade-ELM algorithms, the accuracy of localization is ameliorating as the number of anchor nodes
increasing. In fact, the increase of the number of anchors leads to increase the number of reference nodes
which improving the information for the training phase. Furthermore, the localization error of the proposed
Cascade-ELM algorithm was largely smaller than that of its counterparts. In fact, the proposed Cascade-ELM
algorithm accuracy increased over 5%, 25%, 15%, and 10% when compared with KELM-HQ, fast-SVM,
GADV-Hop the DV-Hop-ELM respectively. Therefore, boosted by the first layer for real distance estimation
between anchors and unknown nodes our Cascade-ELM algorithm outperforms in terms of localization
accuracy in comparison with the other four algorithms. Consequently, the expected distance between anchors
and unknown node corresponds more to the real distance. Hence, the average positioning error will decrease.
Figure 9 and Table 1 show the histogram of repartition errors and the statistical results of localization error
with different number of anchors. The results are for 50 simulation runs.
Figure 8. Localization results of the Cascade-ELM algorithms for 5-35 anchors
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Figure 9. Histogram of the average localization errors for 50 simulation runs
Table 1. Performance results of the Cascade-ELM localization process
5 10 15 20 25 30 35
Min 0.27 0.21 0.23 0.20 0.24 0.20 0.20
Max 1.65 1.61 1.56 1.45 1.3 0.97 0.96
Mean 0.69 0.53 0.51 0.48 0.45 0.42 0.40
Std 0.44 0.38 0.21 0.31 0.24 0.30 0.28
3.2. Degree if irregularity signal effects
Practically, in sensor networks the sensed environment is affected by many irregularity effects like
the electromagnetic noise and the RSSI variation, thus the radio communication of the radio frequency (RF)
sensor nodes will take the form of an irregular elliptic form instead of a standard circle. The impact of radio
irregularity on routing protocol can affect the minimum hop counts for localization process in range free
WSN. Many researches investigate on the characterization of degree of radio irregularity signal. Indeed, the
degree of irregularity (DOI) model draws the maximal variation of radio range per unit degree change within
different directions of radio propagation antenna.
In the following simulation phase, we exploit the most used DOI model to study the impact of
communication irregularity phenomena. In fact, the probability that two nodes can communicate with each
other is controlled by a parameter (d). The next model, according to Xiao et al. [13] describes the
connectivity probability for two nodes separated by the distance (d) and the ideal communication range R. In
this model the probability of the connectivity described by:
𝑃𝑑 =
{
1
𝑑
𝑅
< 1 − DOI,
1
2×DOI
(
𝑑
𝑅
− 1) +
1
2
, 1 − DOI ≤
𝑑
𝑅
≤ 1 + DOI
0,
𝑑
𝑅
> 1 + DOI
(13)
As shown in Figure 10, the transmission radio changes with the value of DOI. When DOI=0, the
transmission radio R takes the form of an ideal circle. Moreover, as the value of DOI increases as the
irregularity of the transmission range increases and affects the number of hops between anchor nodes and the
localized nodes. In our simulation, the DOI signal permits to represent the propagation irregularities in WSN
localization process.
To study the DOI effect on the Cascade-ELM localization process and find the correlation between
NLE and the DOI, we implement the localization algorithm with the model of radio range irregularity, and
we suppose that sensor nodes have the same transmission range of radius R=100 m. DOI is varied between
[0, 0.07]. In the simulation cases, 300 unknown nodes are deployed in a 2-D area of a surface
S=1,000×1,000 m with the average communication range R=100 m and the number of anchor nodes is equal
to 50. Figure 11 gives the simulation results of the NLE for different anchors deployment and for different
values of the DOI. Figure 12 shows an example of localization results given by the proposed Cascade-ELM
localization process.
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Figure 10. DOI effects on the
irregularity radio communication
Figure 11. The NLE for different DOI values
Figure 12. Sample simulation run for deployment zone 1,000×1,000 m², 50 anchors, 300 unknown nodes and
the average communication range of each node is R=100 m
As expected, if the DOI increases then the connectivity of the wireless sensors network is perturbed
and the hop counts between anchors and unknown nodes is affected, then the localization accuracy will be
deteriorated. For example, for 35 anchors nodes, if the DOI is 0 then the localization error is near to 0.4×R
but if the DOI=0.07 the localization errors is near to 0.7×R because the hop count value is affected and
introduces the localization errors. Moreover, for 5 anchors nodes, if the DOI is 0 then the localization error is
near to 0.75×R but if the DOI=0.07 the localization errors is near to 1.35×R because the hop count value is
largely affected and introduces a large localization error.
4. CONCLUSION
In this work, a deep neural network algorithm based on the Cascade-ELM has been suggested in
order to improve the node localization performance in WSN. The proposed Cascade-ELM algorithms are
based on range free technique in isotropic cases. The Cascade-ELM represents a new way to tackle the WSN
localization problem. They have been experimented via simulation for many scenarios in isotropic
environments. The NLE has been applied to evaluate the performance of the localization model. The
performance of the proposed localization algorithms is well shown through simulation results when
compared with the other soft-computing algorithms in term of average NLE. Boosted by the expected first
layer of the ELM for hop-size estimation and the second layer for the positions estimation, the experimental
results demonstrate that the Cascade-ELM localization algorithm for localization in WSNs minimizes the
average localization error of nodes and has higher location accuracy compared with its counterparts.
ACKNOWLEDGEMENTS
Authors thank the ImVia laboratory University of Burgundy Franche-Comté, at Dijon, France, for
their sponsor and financial support acknowledgments.
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BIOGRAPHIES OF AUTHORS
Oumaima Liouane received Master Degree in embedded system and
instrumentation from the High Institute of Informatics and Mathematics of Monastir (ISIMM)
and Ph.D. student at the University of Burgundy Franche-Comte, Dijon, France and National
Engineering School of Monastir (ENIM), University of Monastir, Tunisia. Her research
interests include the wireless sensors network optimization, machine learning tools, and
embedded system. She can be contacted at email: liouaneoumay@gmail.com.
Smain Femmam is SMIEEE, IEEE-CA-France section, SMSEE, M-Fellow IETI,
he is Professor at the University of Haute-Alsace, France. He received the MS and Ph.D.
Degrees in Signal Processing and Computers from Versailles University in 1997 and 1999,
respectively. His main research area is signal processing, safety of WSNs and wireless
communication. He has a strong interest in perception and characterization of WSN signals,
optimal filtering, spectral analysis, wavelets and perception-haptics. He can be contacted at
email: smain.femmam@uha.fr.
Toufik Bakir received his Ph.D. degree in industrial automatics from the
University of Claude Bernard Lyon I, Lyon, France, in 2006. He is currently assistant
professor at the laboratory ImVia in the University of Burgundy Franche-Comte, Dijon,
France. His research interests include WSN optimization, modeling, and control of dynamic
systems. He can be contacted at email: toufik.bakir@u-bourgogne.fr.
Abdessalem Ben Abdelali received his degree in Electrical Engineering and his
DEA in industrial informatics from the National School of Engineering of Sfax (ENIS),
Tunisia, respectively, in 2001 and 2002. He received his Ph.D. from ENIS and Burgundy
University (BU), France, in 2007. Since 2017 he has been working as Professor in digital
embedded electronic at the High Institute of Informatics and Mathematics of Monastir
(ISIMM). His current research interests include reconfigurable architectures and hardware
deep learning implementation of image and video processing for WSN applications. He can be
contacted at email: abdelalienis@yahoo.fr.