In this paper, node localization algorithms in wireless sensor networks are researched, the traditional
algorithms are studied, and some meaningful results are obtained. For the localization algorithm and route
planning of WSN exists a big localization error in wireless communication. WSN communication system is
researched. According to the anchor nodes and unknown nodes, a new localization algorithm and route
planning method of WSN are proposed in this paper. At the same time, a new genetic algorithm of route
planning of WSN is proposed. The performance of the node density and localization error is simulated and
analyzed. The simulation results show that the performance of proposed WSN localization algorithm and
route planning method are better than the traditional algorithms.
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.
Vertex covering has important applications for wireless sensor networks such as monitoring link failures,
facility location, clustering, and data aggregation. In this study, we designed three algorithms for
constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the
Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the
nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted
matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then
constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the
operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM
environment. Finally we have implemented, compared and assessed all these approaches. The transmitted
message count of the first algorithm is smallest among other algorithms where the third algorithm has
turned out to be presenting the best results in vertex cover approximation ratio.
Computational Geometry based Remote Networkingidescitation
In recent years wireless sensor networks (WSNs) have become one of the most
active research areas due to the bright and interesting future promised to the world of
information technology. It is an emerging field which is accomplishing much importance
because of its vast contribution in varieties of applications. Coverage is one of the important
aspects of WSNs and many approaches are introduced to maximize it. It is the key research
issue in WSN as it can be considered as the measure of the Quality of Service (QoS) of
sensing function for a sensor network. The goal of coverage is to have each location in the
physical space of interest within the sensing range of at least one sensor. By combining
computational geometry and graph theoretic techniques, specifically the Voronoi Diagram
(VD), Delaunay Triangulation (DT) and Graph Search algorithms, can solve the problem.
This paper defines some recent research approaches on coverage of WSNs using VD and
DT. Also shows how they are being utilized in various research works.
Minimization of Localization Error using Connectivity based Geometrical Metho...Dr. Amarjeet Singh
Many localization schemes are designed for finding
the geographical coordinates of the unlocalized node in the
network. Still, it is a difficult problem to find accurate and
efficient localization schemes in the Wireless Sensor Networks
(WSNs). We proposed a new method, connectivity based WSN
node localization using one of the geometrical method namely
centroid of a triangle. By developing the centroid of a triangle
from the WSN network model in terms of localization
requirements. The simulation outcomes have shown that the
modified centroid (centroid_T) performs marginally better
than the existing centroid method with a marginally increase
in the computation process. We also observe the variation of
localization error with various anchor nodes, radio range, and
network size.
CODE AWARE DYNAMIC SOURCE ROUTING FOR DISTRIBUTED SENSOR NETWORKIJNSA Journal
Sensor network facilitates monitoring and controlling of physical environments. These wireless networks consist of dense collection of sensors capable of collection and dissemination of data. They have application in variety of fields such as military purposes, environment monitoring etc. Typical deployment of sensor network assumes central processing station or a gateway to which all other nodes route their data using dynamic source routing (DSR). This causes congestion at central station and thus reduces the efficiency of the network. In this work we will propose a better dynamic source routing technique using network coding to reduce total number of transmission in sensor networks resulting in better efficiency.
COMPARATIVE ANALYSIS OF ROUTE INFORMATION BASED ENHANCED DIVIDE AND RULE STRA...ijsc
In wireless sensor network, routing data efficiently to the base station is a big issue and for this purpose, a
number of routing algorithms are invented by researchers. Clustering plays a very important role in the
design and as well as development of wireless sensor networks for well distribution of network and also to
route data efficiently. In this paper, we had done the enhancement of divide and rule strategy that is
basically route information protocol based upon static clustering and dynamic cluster head selection.
Simulation results show that our technique outperforms DR, LEACH, and AODV on the basis of packet
loss, delay, and throughput.
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.
Vertex covering has important applications for wireless sensor networks such as monitoring link failures,
facility location, clustering, and data aggregation. In this study, we designed three algorithms for
constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the
Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the
nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted
matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then
constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the
operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM
environment. Finally we have implemented, compared and assessed all these approaches. The transmitted
message count of the first algorithm is smallest among other algorithms where the third algorithm has
turned out to be presenting the best results in vertex cover approximation ratio.
Computational Geometry based Remote Networkingidescitation
In recent years wireless sensor networks (WSNs) have become one of the most
active research areas due to the bright and interesting future promised to the world of
information technology. It is an emerging field which is accomplishing much importance
because of its vast contribution in varieties of applications. Coverage is one of the important
aspects of WSNs and many approaches are introduced to maximize it. It is the key research
issue in WSN as it can be considered as the measure of the Quality of Service (QoS) of
sensing function for a sensor network. The goal of coverage is to have each location in the
physical space of interest within the sensing range of at least one sensor. By combining
computational geometry and graph theoretic techniques, specifically the Voronoi Diagram
(VD), Delaunay Triangulation (DT) and Graph Search algorithms, can solve the problem.
This paper defines some recent research approaches on coverage of WSNs using VD and
DT. Also shows how they are being utilized in various research works.
Minimization of Localization Error using Connectivity based Geometrical Metho...Dr. Amarjeet Singh
Many localization schemes are designed for finding
the geographical coordinates of the unlocalized node in the
network. Still, it is a difficult problem to find accurate and
efficient localization schemes in the Wireless Sensor Networks
(WSNs). We proposed a new method, connectivity based WSN
node localization using one of the geometrical method namely
centroid of a triangle. By developing the centroid of a triangle
from the WSN network model in terms of localization
requirements. The simulation outcomes have shown that the
modified centroid (centroid_T) performs marginally better
than the existing centroid method with a marginally increase
in the computation process. We also observe the variation of
localization error with various anchor nodes, radio range, and
network size.
CODE AWARE DYNAMIC SOURCE ROUTING FOR DISTRIBUTED SENSOR NETWORKIJNSA Journal
Sensor network facilitates monitoring and controlling of physical environments. These wireless networks consist of dense collection of sensors capable of collection and dissemination of data. They have application in variety of fields such as military purposes, environment monitoring etc. Typical deployment of sensor network assumes central processing station or a gateway to which all other nodes route their data using dynamic source routing (DSR). This causes congestion at central station and thus reduces the efficiency of the network. In this work we will propose a better dynamic source routing technique using network coding to reduce total number of transmission in sensor networks resulting in better efficiency.
COMPARATIVE ANALYSIS OF ROUTE INFORMATION BASED ENHANCED DIVIDE AND RULE STRA...ijsc
In wireless sensor network, routing data efficiently to the base station is a big issue and for this purpose, a
number of routing algorithms are invented by researchers. Clustering plays a very important role in the
design and as well as development of wireless sensor networks for well distribution of network and also to
route data efficiently. In this paper, we had done the enhancement of divide and rule strategy that is
basically route information protocol based upon static clustering and dynamic cluster head selection.
Simulation results show that our technique outperforms DR, LEACH, and AODV on the basis of packet
loss, delay, and throughput.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications.
Energy efficient approach based on evolutionary algorithm for coverage contro...ijcseit
Coverage and connectivity are two important requirements in Wireless Sensor Networks (WSNs). In this
paper, we address the problem of network coverage and connectivity and propose an energy efficient
approach based on genetic evolutionary algorithm for maintaining coverage and connectivity where the
sensor nodes can have different sensing ranges and transmission ranges. The proposed algorithm is
simulated and it' efficiency is demonstrated via different experiments.
Comparison of Proposed Kite Architecture with P- Hexagon for Directional Sens...idescitation
While considering the Wireless Sensor Network
(WSN), many problemswere encountered related to connected
coverage in directional sensor networks. The idea is to deploy
directional sensors which work on ultra wide bands, thereby,
making wireless electronic data communication possible
across a network. In this paper,a consideration on the problems
of a connected network to cover either a set of point locations
(Connected Point-Coverage Deployment ->CPD) or the entire
sensing region (Connected Region-Coverage Deployment -
>CRD) has been done. An Introduction has been made to KITE
architecture in sectors like, sensing range to cover the entire
coverage region. A validation of the merits of the proposal has
been analysed and compared to the existing work with the
help of extensive simulation result.
Broad-Spectrum Model for Sharing Analysis between IMTAdvanced Systems and FSS...IOSRJECE
An appraisal of orthogonal frequency division multiplexing (OFDM) accredited for IMT-Advanced has been well thought-out in this letter. Derivation of the power spectral density (PSD) produce new model which easily assess the interfering signal power that appears in the band of a victim system without a spectrum emission mask. Furthermore, the broad-spectrum investigative model (BIM) can assess the interference from the 4G systems into FSS systems, when transmit power is unallocated to some sub-carriers overlapping the band of the victim system. Closed form is derived to create the model.
Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using A...ijtsrd
This study proposes Artificial Neural Network ANN based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN based models were created on bases of the Generalized Regression Neural network GRNN and the Multi Layer Perceptron Neural Network MLP NN . These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations BTSs distributed across the rural areas. Results indicate that the GRNN and MLP NN based models with Root Mean Squared Error RMSE values of 4.78dBm and 5.56dBm respectively, offer significant improvement over the empirical Hata Okumura counterpart, which overestimates the signal strength by an RMSE value of 20.17dBm. Deme C. Abraham ""Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30228.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30228/mobile-network-coverage-determination-at-900mhz-for-abuja-rural-areas-using-artificial-neural-networks/deme-c-abraham
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.
Ameliorate the performance using soft computing approaches in wireless networksIJECEIAES
Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.
Limit energy is a severe bottleneck of wireless sensor networks (WSNs), and limits network lifetime of WSNs. To extend network lifetime, buffer zone has been proposed. Sensors send their data packets to buffer zone. The sensors in buffer zone buffer the data packets. And then the sink visits the buffer zone to collect the data ackets. This leads to that the loads of the sensors in buffer zone are too high and the sensors die quickly. To further extend network lifetime, an algorithm based on dynamic buffer zone has been proposed in this paper. The algorithm divides the whole network area into some areas, and lets all areas act as the buffer zone in turn. And the reasonable times each zone acts as the buffer zone are computed with linear programming. The simulation results have shown that our proposed algorithm notably extends network lifetime.
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.
Energy Efficient Modeling of Wireless Sensor Networks using Random Graph Theoryidescitation
This paper deals with the discussion of an innovative and a design for the
efficient power management and power failure diagnosis in the area of wireless sensors
networks. A Wireless Network consists of a web of networks where hundreds of pairs are
connected to each other wirelessly. A critical issue in the wireless sensor networks in the
present scenario is the limited availability of energy within network nodes. Therefore,
making good use of energy is necessary in modeling a sensor network. In this paper we have
tried to propose a new model of wireless sensors networks on a three-dimensional plane
using the percolation model, a kind of random graph in which edges are formed between the
neighbouring nodes. An algorithm has been described in which the power failure diagnosis
is made and solved. The concepts of Electromagnetics, Wave Duality, Energy model of an
atom is linked with wireless networks. A model is prepared in which the positioning of
nodes of sensors are decided. Also the model is made more efficient regarding the energy
consumption, power delivery etc. using the concepts of graph theory concepts, probability
distribution.
A SIMULATION-BASED PERFORMANCE COMPARISON OF MANETS CDS CREATION ALGORITHMS U...cscpconf
Mobile Ad Hoc networks (MANETs) are gaining increased interest due to their wide range of potential applications in civilian and military sectors. The self-control, self-organization,
topology dynamism, and bandwidth limitation of the wireless communication channel make implementation of MANETs a challenging task. The Connected Dominating Set (CDS) has been proposed to facilitate MANETs realization. Minimizing the CDS size has several advantages; however, this minimization is NP complete problem; therefore, approximation algorithms are
used to tackle this problem. The fastest CDS creation algorithm is Wu and Li algorithm; however, it generates a relatively high signaling overhead. Utilizing the location information of
network members reduces the signaling overhead of Wu and Li algorithm. In this paper, we compare the performance of Wu and Li algorithm with its Location-Information-Based version
under two types of Medium Access Control protocols, and several network sizes. The MAC protocols used are: a virtual ideal MAC protocol, and the IEEE 802.11 MAC protocol. The use of a virtual ideal MAC enables us to investigate how the real-world performance of these algorithms deviates from their ideal-conditions counterpart. The simulator used in this research
is the ns-2 network simulator.
Backtracking Search Optimization for Collaborative Beamforming in Wireless Se...TELKOMNIKA JOURNAL
Due to energy limitation and constraint in communication capabilities, the undesirable high
battery power consumption has become one of the major issues in wireless sensor network (WSN).
Therefore, a collaborative beamforming (CB) method was introduced with the aim to improve the radiation
beampattern in order to compensate the power consumption. A CB is a technique which can increase the
sensor node gain and performance by aiming at the desired objectives through intelligent capabilities. The
sensor nodes were located randomly in WSN environment. The nodes were designed to cooperate among
each other and act as a collaborative antenna array. The configuration of the collaborative nodes was
modeled in circular array formation. The position of array nodes was determined by obtaining the optimum
parameters pertaining to the antenna array which implemented by using Backtracking Search Optimization
Algorithm (BSA). The parameter considered in the project was the side-lobe level minimization. It was
observed that, the suppression of side-lobe level for BSA was better compared to the radiation
beampattern obtained for conventional uniform circular array.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications.
Energy efficient approach based on evolutionary algorithm for coverage contro...ijcseit
Coverage and connectivity are two important requirements in Wireless Sensor Networks (WSNs). In this
paper, we address the problem of network coverage and connectivity and propose an energy efficient
approach based on genetic evolutionary algorithm for maintaining coverage and connectivity where the
sensor nodes can have different sensing ranges and transmission ranges. The proposed algorithm is
simulated and it' efficiency is demonstrated via different experiments.
Comparison of Proposed Kite Architecture with P- Hexagon for Directional Sens...idescitation
While considering the Wireless Sensor Network
(WSN), many problemswere encountered related to connected
coverage in directional sensor networks. The idea is to deploy
directional sensors which work on ultra wide bands, thereby,
making wireless electronic data communication possible
across a network. In this paper,a consideration on the problems
of a connected network to cover either a set of point locations
(Connected Point-Coverage Deployment ->CPD) or the entire
sensing region (Connected Region-Coverage Deployment -
>CRD) has been done. An Introduction has been made to KITE
architecture in sectors like, sensing range to cover the entire
coverage region. A validation of the merits of the proposal has
been analysed and compared to the existing work with the
help of extensive simulation result.
Broad-Spectrum Model for Sharing Analysis between IMTAdvanced Systems and FSS...IOSRJECE
An appraisal of orthogonal frequency division multiplexing (OFDM) accredited for IMT-Advanced has been well thought-out in this letter. Derivation of the power spectral density (PSD) produce new model which easily assess the interfering signal power that appears in the band of a victim system without a spectrum emission mask. Furthermore, the broad-spectrum investigative model (BIM) can assess the interference from the 4G systems into FSS systems, when transmit power is unallocated to some sub-carriers overlapping the band of the victim system. Closed form is derived to create the model.
Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using A...ijtsrd
This study proposes Artificial Neural Network ANN based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN based models were created on bases of the Generalized Regression Neural network GRNN and the Multi Layer Perceptron Neural Network MLP NN . These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations BTSs distributed across the rural areas. Results indicate that the GRNN and MLP NN based models with Root Mean Squared Error RMSE values of 4.78dBm and 5.56dBm respectively, offer significant improvement over the empirical Hata Okumura counterpart, which overestimates the signal strength by an RMSE value of 20.17dBm. Deme C. Abraham ""Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30228.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30228/mobile-network-coverage-determination-at-900mhz-for-abuja-rural-areas-using-artificial-neural-networks/deme-c-abraham
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.
Ameliorate the performance using soft computing approaches in wireless networksIJECEIAES
Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.
Limit energy is a severe bottleneck of wireless sensor networks (WSNs), and limits network lifetime of WSNs. To extend network lifetime, buffer zone has been proposed. Sensors send their data packets to buffer zone. The sensors in buffer zone buffer the data packets. And then the sink visits the buffer zone to collect the data ackets. This leads to that the loads of the sensors in buffer zone are too high and the sensors die quickly. To further extend network lifetime, an algorithm based on dynamic buffer zone has been proposed in this paper. The algorithm divides the whole network area into some areas, and lets all areas act as the buffer zone in turn. And the reasonable times each zone acts as the buffer zone are computed with linear programming. The simulation results have shown that our proposed algorithm notably extends network lifetime.
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.
Energy Efficient Modeling of Wireless Sensor Networks using Random Graph Theoryidescitation
This paper deals with the discussion of an innovative and a design for the
efficient power management and power failure diagnosis in the area of wireless sensors
networks. A Wireless Network consists of a web of networks where hundreds of pairs are
connected to each other wirelessly. A critical issue in the wireless sensor networks in the
present scenario is the limited availability of energy within network nodes. Therefore,
making good use of energy is necessary in modeling a sensor network. In this paper we have
tried to propose a new model of wireless sensors networks on a three-dimensional plane
using the percolation model, a kind of random graph in which edges are formed between the
neighbouring nodes. An algorithm has been described in which the power failure diagnosis
is made and solved. The concepts of Electromagnetics, Wave Duality, Energy model of an
atom is linked with wireless networks. A model is prepared in which the positioning of
nodes of sensors are decided. Also the model is made more efficient regarding the energy
consumption, power delivery etc. using the concepts of graph theory concepts, probability
distribution.
A SIMULATION-BASED PERFORMANCE COMPARISON OF MANETS CDS CREATION ALGORITHMS U...cscpconf
Mobile Ad Hoc networks (MANETs) are gaining increased interest due to their wide range of potential applications in civilian and military sectors. The self-control, self-organization,
topology dynamism, and bandwidth limitation of the wireless communication channel make implementation of MANETs a challenging task. The Connected Dominating Set (CDS) has been proposed to facilitate MANETs realization. Minimizing the CDS size has several advantages; however, this minimization is NP complete problem; therefore, approximation algorithms are
used to tackle this problem. The fastest CDS creation algorithm is Wu and Li algorithm; however, it generates a relatively high signaling overhead. Utilizing the location information of
network members reduces the signaling overhead of Wu and Li algorithm. In this paper, we compare the performance of Wu and Li algorithm with its Location-Information-Based version
under two types of Medium Access Control protocols, and several network sizes. The MAC protocols used are: a virtual ideal MAC protocol, and the IEEE 802.11 MAC protocol. The use of a virtual ideal MAC enables us to investigate how the real-world performance of these algorithms deviates from their ideal-conditions counterpart. The simulator used in this research
is the ns-2 network simulator.
Backtracking Search Optimization for Collaborative Beamforming in Wireless Se...TELKOMNIKA JOURNAL
Due to energy limitation and constraint in communication capabilities, the undesirable high
battery power consumption has become one of the major issues in wireless sensor network (WSN).
Therefore, a collaborative beamforming (CB) method was introduced with the aim to improve the radiation
beampattern in order to compensate the power consumption. A CB is a technique which can increase the
sensor node gain and performance by aiming at the desired objectives through intelligent capabilities. The
sensor nodes were located randomly in WSN environment. The nodes were designed to cooperate among
each other and act as a collaborative antenna array. The configuration of the collaborative nodes was
modeled in circular array formation. The position of array nodes was determined by obtaining the optimum
parameters pertaining to the antenna array which implemented by using Backtracking Search Optimization
Algorithm (BSA). The parameter considered in the project was the side-lobe level minimization. It was
observed that, the suppression of side-lobe level for BSA was better compared to the radiation
beampattern obtained for conventional uniform circular array.
Adaptive Monitoring and Localization of Faulty Node in a Wireless Sensor Netw...Onyebuchi nosiri
Abstract
This work seeks to solve the problem that is being experienced in most existing remote monitoring systems by coming up with an enhanced monitoring system called Wireless Sensor Network. A Personal Area Network was evolved to increase the coverage area by spatially distributing Sensor nodes to capture and transmit physical parameters like temperature and Carbon monoxide in an indoor local cooking environment. Faulty node detection and localization was also realized, this was achieved by coming up with an algorithm that logically considers the receive signal strength value of -100 dbm as threshold, Result of data transmitted were viewed via a C-Sharp interface for Adaptive monitoring. The result from the Visual Basic plot shows that the Sensor nodes were able to capture temperature range of between 250C to 510C while the result of the CO emission shows an interval of 0.01g/m3 to 30.0 g/m3. A comparison between data transmitted at source and data received at the destination (sink) was carried out, a ranking test was used to validate the data received, a 0.9325 correlation value was obtained which shows a high level of integrity of 93.25% .
Shortest path algorithm for data transmission in wireless ad hoc sensor networksijasuc
Wireless sensor networks determine probable in military, environments, health and commercial
applications. The process of transferring of information from a remote sensor node to other nodes in a
network holds importance for such applications. Various constraints such as limited computation, storage
and power makes the process of transferring of information routing interesting and has opened new arenas
for researchers. The fundamental problem in sensor networks states the significance and routing of
information through a real path as path length decides some basic performance parameters for sensor
networks. This paper strongly focuses on a shortest path algorithm for wireless adhoc networks. The
simulations are performed on NS2 and the results obtained discuss the role of transferring of information
through a shortest path.
Localization based range map stitching in wireless sensor network under non l...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A SIMULATION-BASED PERFORMANCE COMPARISON OF MANETS CDS CREATION ALGORITHMS U...csandit
Mobile Ad Hoc networks (MANETs) are gaining increased interest due to their wide range of
potential applications in civilian and military sectors. The self-control, self-organization,
topology dynamism, and bandwidth limitation of the wireless communication channel make
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NONLINEAR MODELING AND ANALYSIS OF WSN NODE LOCALIZATION METHOD
1. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
DOI: 10.5121/ijwmn.2018.10106 61
NONLINEAR MODELING AND ANALYSIS OF WSN
NODE LOCALIZATION METHOD
Xiaoyang Liu1,2
,Ya Luo1
, Chao Liu1
,Hengyang Liu1
1
School of Computer Science and Engineering, Chongqing University of Technology,
Chongqing, 400054, China
2
College of Engineering, The University of Alabama, Tuscaloosa, Alabama,35401,USA
Correspondence author: Ya Luo; E-mail:158010342@qq.com
ABSTRACT
In this paper, node localization algorithms in wireless sensor networks are researched, the traditional
algorithms are studied, and some meaningful results are obtained. For the localization algorithm and route
planning of WSN exists a big localization error in wireless communication. WSN communication system is
researched. According to the anchor nodes and unknown nodes, a new localization algorithm and route
planning method of WSN are proposed in this paper. At the same time, a new genetic algorithm of route
planning of WSN is proposed. The performance of the node density and localization error is simulated and
analyzed. The simulation results show that the performance of proposed WSN localization algorithm and
route planning method are better than the traditional algorithms.
KEYWORDS
wireless sensor network;anchor node; localization algorithm; route planning
1. INTRODUCTION
So far, in the process of studying and developing of wireless sensor networks(WSN), security has
been concentrated less. As a new network,the wireless sensor network is a multi-discipline,
highlyintersecting researched hot field, which is of both military and business values.Topology
control is one of the most fundamental problems in wireless sensor networks.[1-2]
. WSN,which are
the newest technology of information collecting and processing, have a wide range of application
including military and business. But the node localization information has a key role in the
application of wireless sensor network.Research of the wireless sensor network gradually gets the
focus from the industrial and academe[3-4]. It has a great application future in the military and
civil area. Reducing power consumption to extend network lifetime is one of the most important
challenges in designing wireless sensor networks. In order to improve the positioning accuracy of
wireless sensor networks, references [5-7] proposed a localization algorithm DAC-ND based on
aggregation, collinearity and connectivity of anchors. The relationship between the positioning
accuracy and the distribution of anchors were studied. The experimental shows that the anchor
nodes in collinear or concentration distribution can lead to poor positionig accuracy for WSN
localization algorithms based on distance measurement. References [8-15] prolonged network
lifetime, good scalability and proper load balancing are important requirements for many sensor
2. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
62
network applications. Clustering sensor nodes is an effective technique for achieving these goals.
Clustering Algorithm based on Node Correlation (CANC) is proposed.CANC utilizes received
signal strength, residual energy and connectivity to choose cluster-heads. It takes the node
correlation into account to determine cluster members. Analysis and simulation results show
preliminarily that, the new CANC algorithm can make cluster-heads well distributed and achieve
good performance in terms of system lifetime, scalability and LBF (load balancing factor).
Classi`c clustering algorithms in wireless sensor networks are studied ,which are fixed operation
periods and too much information exchanged in cluster-heads selection. Then an Energy-Efficient
Clustering Algorithm (EECA) is proposed, whose kernels are adaptive operation period model
and a new cluster-heads selection method[16-18]
. Simulation results show that the proposed
protocol can adjust operation period adaptively and reduce the information exchanging in
choosing cluster-heads, is more energy-efficient and suitable for wireless sensor network. A new
localization algorithm and route planning for use in wireless sensor network are studied in
References[19-20].References[21-22]presents the analytic and simulation results of the
performance of UWB relative localization estimation in wireless sensor networks. References
[23-24]propose resolving schemes of data collection in wireless sensor networks of both plane
model and linear and nonlinear mathematics model,and proposed a new node route planning
method.
The main contributions of this paper are listed as follows.(1) A new WSN node localization
algorithm is proposed to reduce the localization error and and the number of anchor nodes.(2) A
new genetic algorithm of route planning of WSN is proposed Some novel synchronization results
are proposed.These results are more practical.(3) The novel route planning method is proposed
for prolonging network lifetime and obtaining shortest path in WSN.
The rest of this paper is organized as follows. In Section 2,the wireless sensor network system
model node route planning is researched. In Section 3,the localization algorithm and route
planning is built up for the wireless sensor network communication system. In Section
4,simulation results are presented.Finally,conclusions are drawn in Section 5.
II. TRADITIONAL WSN NODE ROUTE PLANNING
Wireless sensor network network includes an anchor nodes and unknown nodes. It can be
implemented with a laser or microwave communication between them.It is a challenging problem
about the localization algorithm. The traditional localization algorithm of SCAN[25-26]
for
wireless sensor network can be described as follows:
The beacon node’s position can be noted as 1 1( , )x y ,A is the unknown node, the position is
( , )x y ,We suppose 1d=AB ,so we can get:
The traditional localization algorithm of CIRCLES[27-28]
for wireless sensor network [20-21]
is noted
as follows.The relationship of multilateral localization of the unknown node is shown in Fig.1.
3. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
63
p
1p
2p
3p
4p
X
Y
Z
Fig.1. Multilateral localization of the unknown node
There are two beacon nodes in the wireless sensor network system. The position of beacon node
C 2 2 2( , , )x y z , 2AC d= , the mathematics model can be denoted as[28-29].
There are three beacon nodes in the wireless sensor network system, the mathematics model can
be expressed as
The distance of anchor nodes can be calculated as follows:
III. WSN NODE LOCALIZATION ALGORITHM
For wireless sensor network node localization, due to the Localization of the mobile node insert
uncertain nodes, so the inserted into the virtual anchor nodes, which helps to limit the
4. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
64
localization error. The node system model is shown in Fig.2.
( , )x y
1 1( , )x y
2 2( , )x y
3 3( , )x y
Anchor node
Unknown node
Fig.2. The node system model
The node movement route plan are obtained.So we can get:
0
0
cos(2 )
sin(2 )
x x r t t
y y r t t
π ϕ
π ϕ
= + × × +
= + × × +
(6)
Similarly,in the triangle ,we can get:
21
2
0
2
1
2
2 )4/(
cos
dd
ddd −+
=θ (7)
Combined with formula (6) and (7),d0 can be expressed as:
2
22 2
2
2
3
2
1
0
ddd
d
−+
= (8)
The total path length can be expressed as
20
2 2 2 2 2 2
t=1
4 4 sin(4 ) 4 cos(4 )D r r t r t t r t tπ π π= + + +∑ (9)
Node relative positioning and the radial error can be expressed respectively:
( ) ( ) ( )
2 2 2' ' '
1
100%
n
i i i i i i
i
avg
x x y y z z
error
n R
=
− + − + −
= ×
×
∑
(10)
%100
)(
1
'
×
×
−
=
∑=
Rn
xxabs
error
n
i
ii
x
(11)
5. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
65
IV. WSN NODE ROUTE PLANNING
In WSN, the uneven distribution of nodes and the different amount of perception data will lead to
the imbalance of energy consumption and hotspot problem.To solve this key problem of WSN,a
route planning algorithm of mobile WSN sink node is proposed to prolong network lifetime and
travel shorter route in wireless sensor networks. By defining the grids in the network area, several
candidate sites of mobile sink are distributed in each grid, and then sink node select a site for
sojourning and collecting data of nodes in each grid. Based on the relationship between network
lifetime and the selection of sink sites, the network model is proposed in Fig.3.
Network grid
Sink Station Node of high amount data
Node of low amount data
Fig.3. The network model
As can be seen from Fig.4.The monitoring area is divided into multiple same grid size. The
length of grid is L ( L R< , R is the communication radius of WSN). Wireless sensor nodes
( n ) are distributed randomly in the monitoring area. gridV denotes the grid set. nodeV is the sensor
node set. According to the actual circumstance of the network. Sink station nodes ( m ) are
distributed in each grid. siteV m G= × and sel siteV G− = .
The sensor nodes to send data to a node of one hop routing is considered. The energy
consumption of node i send 1 bit data to the sink node is as follows:
( )
( )
2
0
4
0
,
,
else fs i MS i ms
i
else mp i MS i ms
f E d d d
E
f E d d d
ε
ε
→ →
→ →
× + <
=
× + ≥
(12)
Where, f is the sensor node data transmission rate.
The network life cycle netT can be defined by Network began to run into any one node energy
exhausted by the time. survival time of network nodei can be expressed as
6. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
66
, 1,2, ,
i i
e data
i i
i i
S S
T t i n
E t f
= = =
K (13)
Where, i
eS is residual energy of node i . i
dataS is the sense data
volume of node i . it is the consuming time by accessing sink nodes.
Based on the above analysis. Purpose is to maximize the network life cycle, and to minimize sink
moving path length.
The optimization model can be formulated as
( )max min /
node
i TSP
i V
T d
∈
(14)
( )2
. . i
i i else fs i MS es t T t E d S iε →× + ≤ ∀ (15)
i
i dataft S i= ∀ (16)
0, 0, 1,2,i i MST d i n→> ≥ = K (17)
Where, formula (14) is the ratio of min
node
i
i V
T
∈
(maximize the network lifetime) and TSPd (The only site
selection in each grid node traversal path length in their wake).Constraint formula (15) ensures
that each sensor node in the network life cycle energy consumption is less than the initial energy
of data transmission. Constraint formula (16) ensure that each sensor node in the mobile sink to
access data transmission time is equal to the volume of the data of perception. Constraint formula
(17) ensures the network life cycle and the distance of the sensor nodes to the mobile sink node is
not negative.
3 1 6 4 5 2
1 00 1 1 00 1
Network grid
Sink node
Combination
0
1
1
1
0
1
1
6
0
1
1
4
0
1
1
5
0
1
1
2
0
1
1
3
Gene
Sub-chain 1
Sub-chain 2
Chromosome
Fig.4. The relationship between the sub-chain and the chromosome
The optimization model can be solved by the following steps:
Step 1: Initialization
Initializes a double-stranded chromosomes,the number of chromosomes is C .The number of
iterations g is equal to 0.Chromosome operands 0c = , 1 2a a= .
7. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
67
Step 2: Chromosome assessment
Calculate all the fitness of chromosomes, those have biggest fitness will be selected to the next
generation of populations.
Step 3: Selection
According to the roulette strategy, select two chromosomes which need to cross.
Step 4: Cross
Generate a random number between0 1 .If it is greater than the value of 1a .Crossover operation
was carried out on the selected two chromosomes. Crossover operation is used by using partial
matches the crossover. First randomly generated two intersections, definition of these two areas
as the matching area. And the exchange of two elder matching area.As can be seen from Fig.9.
0
1
1
1
0
1
1
6
0
1
1
4
0
1
1
5
0
1
1
2
0
1
1
3
0
1
1
3
1
0
1
1
1
0
0
5
0
1
1
2
0
1
1
4
1
0
1
6
0
1
1
1
0
1
1
1
0
1
1
5
0
1
1
5
0
1
1
2
0
0
1
3
0
1
1
3
0
0
1
6
1
1
0
4
0
1
1
2
0
1
1
2
0
1
0
6
Elder A
Elder B
TEMP A
TEMP B
Fig. 5. The exchange of parental matching regions
The TEMP A,TEMP B of matching area in digital duplication. According to match the location
of the area one by one to replace.Matching relationship is{ }3 2,1 4↔ ↔ .Generation individual A
and B.
Step 5: Mutation
Generate a random number between0 1 .If it is greater than the value of 1a .Crossover operation
was carried out on the selected two chromosomes.Randomly generated two variants. Exchange of
chromosome two variants of genes, Variation of pair to sub-chain 2(sink station chain) a
corresponding value for a variable.
Step 6: Return (End)
1c c= + ,if 1c C< − , skip to step 3.Otherwise 1g g= + and 0m = .If maxg g< , return to step 2.
Otherwise, double chain of genetic algorithm is termination. Obtain largest fitness of
chromosomes. The chromosome decoding available mobile sink node traverses the entire optimal
8. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
68
path grid to collect data.
The congestion prediction of WSN is defined as:
1 1
2 2
j j jfc α β= ⋅ + (18)
Where, jα is cache utilization of the node j , jβ is the congestion factor of the node j . /j jl Lβ = . L
indicates the total number of links in the current network.
Node forwarding goodness is defined as:
( )
2
1
1 j
j
j
fc
fs
e
ε−
−
=
−
(19)
Where, jε denotes the minimum number of hops from the node to the target node.
In order to evaluate the quality of the path calculated by the multipath routing algorithm, the path
fitness function from the source node s to the destination node d is defined based on the
parameters defined as follows:
( ) ( ),
1
/
n
s d ij ij
j
fitness i fcµ ε
=
= ⋅∑ (20)
Subject to:
{ } ( )
( )
, 1, 2,... ..., ,
0,1 1,2,...,ij
s i i ij d path s d i
j nµ
∈ →
= =
(21)
V. SIMULATION ANALYSIS
A. Main parameters setting
Main simulation parameters setting of WSN node localization and route planning are shown in
Table 1.
Table 1. Main simulation parameters setting
9. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
69
B.Simulation results analysis
To verify the convergence of the proposed algorithm, the largest fitness value and the average
fitness is simulated and calculated. Its convergence speed, the optimization results are shown in
Fig.6.
Fig.6. The convergence of proposed algorithm
As can be seen from Fig.8,the proposed algorithm has good convergence by comparing the
optimal solution and the average solution.
In order to compare network life cycle performance of the proposed algorithm and the traditional
algorithms(MS-LEACH-RN algorithm and LEACH algorithm).Sensor
nodes(50,100,150,100,250,300) are distributed randomly in the area of WSN.
Fig.7. Network lifetime
As shown in Fig.7, the proposed algorithm of the network life cycle is 1~2 times the traditional
algorithm of MS-LEACH-RN, it is 8 times of the LEACH algorithm. It is proved that the
proposed algorithm can extend the network survival time considering the 150 nodes to be
mounted on the random uniform topology. The relation between node radius and localization
error is shown in Fig.8.
0 100 200 300 400 500 600 700 800 900 1000
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
Evolution generations
Fitnessvalue
Max fitness value
Average fitness value
50 100 150 200 250 300
0
100
200
300
400
500
600
700
800
900
The number of sensor nodes
Networklifecycle(rounds)
Proposed
MS-LEACH-RN
LEACH
10. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
70
Fig.8. Relation between node density and localization error
As can be seen from Fig.8.It shows different changing tendency along with different localization
error.In all,the proposed algorithm is better than the traditional SCAN and CIRCLES methods.
When the number of nodes n is equal 50.The relation between average localization error and
ranging radius is shown in Fig.9.
Fig.9. The average localization error under different ranging radius
In Fig.9.The ranging radius is increased from 30 to 70.The average localization error of the
algorithms with the increase of ranging radius is becoming less.Compared with traditional
methods(SCAN and CIRCL), the proposed node localization algorithm was 21.5 % and 11.6%
decreased, respectively.
VI. SUMMARY
Based on the analysis of the wireless sensor network,some conclusions are obtained.First of all,
the wireless sensor network communication system is set up.Then,localization algorithm and
node route planning of wireless sensor network are proposed. Some mathematics model is built
according to the wireless sensor network communication system, a new genetic algorithm of
route planning of WSN is proposed.Last,WSN node localization algorithm and route planning
method are simulated. The performance of the proposed localization algorithm and route
planning method is better than the traditional methods.
2 4 6 8 10 12 14 16 18 20
0.8
1
1.2
1.4
1.6
1.8
2
Node density
Locationerror(m)
Proposed algorithm
Traditional SCAN
Traditional CIRCLES
30 35 40 45 50 55 60 65 70
0
1
2
3
4
5
6
7
8
Ranging Radius(m)
AverageLocalizationError(s)
Proposed algorithm
Traditional SCAN
Traditional CIRCL
11. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 1, February 2018
71
Though this work is targeted at the node localization of WSN, the methods presented here could
be applied for other applications such as the localization of nodes through Internet of Things
(IOT) and Internet of Vehicles (IOV).
In the future,we intend to study the spatial localization of nodes in WSN, which is an active area
of research,with many applications in sensing from distributed systems,such as micro aerial
vehicles, smart dust sensors, and mobile robotics.
ACKNOWLEDGEMENT
The paper was supported by science and technology research program of chongqing municipal
education commission(kj1600923,kj17092060),young fund project of humanities and social
sciences research of ministry of education of china (16yjc860010),national social science fund of
china west project(17xxw004),social science of humanity of chongqing municipal education
commission(17skg144),natural science foundation of china (61571069,61501065,61502064,6150
3052).2017 annual open project of chongqing university network public opinion and ideological
dynamic research center of consultation (kfjj2017024).the author xiaoyang liu thanks for the
financial support from csc(china scholarship council)(no.201608505142).
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