Wireless Sensor Networks(WSNs)are an integral part of the Internet of Things(IoT)and are widely used in a plethora of applications.Typically,sensor networks operate in harsh environments where human intervention is of...Wireless Sensor Networks(WSNs)are an integral part of the Internet of Things(IoT)and are widely used in a plethora of applications.Typically,sensor networks operate in harsh environments where human intervention is often restricted,which makes battery replacement for sensor nodes impractical.Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network.Without a connectivity restoration mechanism,node failures ultimately lead to a network partition,which affects the basic function of the sensor network.Therefore,the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks.Since energy is a scarce resource in sensor networks,it becomes the focus of research,and researchers strive to propose new solutions that are energy efficient.The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization.This paper introduces a Clusterbased Node Recovery(CNR)connectivity restoration mechanism based on the concept of clustering.Clustering is a well-known mechanism in sensor networks,and it is known for its energy-efficient operation and scalability.The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes,while Cluster Heads(CHs)play a significant role in the restoration of connectivity.Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques.The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms.The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.展开更多
In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-ti...In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-tion for Cluster Head and Gateway Selection(NQCAFFFOCHGS)has the best network performance because it uses the Improved Weighted Clustering Algo-rithm(IWCA)to cluster the network and the FFO algorithm,which uses fuzzy-based network metrics to select the best CH and entryway.However,the major drawback of the fuzzy system was to appropriately select the membership func-tions.Also,the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway.When learning fuzzy sets,this algorithm employs a new Continuous Action-set Learning Automata(CALA)approach that correctly modifies and chooses the fuzzy membership functions.Despite the fact that it extends the network’s lifespan,it does not assist in the detection of defective nodes in the routing route.Because of this,a new Fault Tolerance(NQCAEFFFOCHGS-FT)mechanism based on the Distributed Connectivity Restoration(DCR)mechanism is proposed,which allows the net-work to self-heal as a consequence of the algorithm’s self-healing capacity.Because of the way this method is designed,node failures may be utilised to rebuild the network topology via the use of cascaded node moves.Founded on the fractional network information and topologic overhead related with each node,the DCR is suggested as an alternative to the DCR.When compared to the NQCAFFFOCHGS algorithm,the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay,energy consumption,Packet Loss Ratio(PLR),Normalized Routing Overhead(NRO),and Balanced Load Index(BLI).展开更多
基金This research is funded by Najran University Saudi Arabia,under the research Project Number(NU/ESCI/17/093).URL:www.nu.edu.sa。
文摘Wireless Sensor Networks(WSNs)are an integral part of the Internet of Things(IoT)and are widely used in a plethora of applications.Typically,sensor networks operate in harsh environments where human intervention is often restricted,which makes battery replacement for sensor nodes impractical.Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network.Without a connectivity restoration mechanism,node failures ultimately lead to a network partition,which affects the basic function of the sensor network.Therefore,the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks.Since energy is a scarce resource in sensor networks,it becomes the focus of research,and researchers strive to propose new solutions that are energy efficient.The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization.This paper introduces a Clusterbased Node Recovery(CNR)connectivity restoration mechanism based on the concept of clustering.Clustering is a well-known mechanism in sensor networks,and it is known for its energy-efficient operation and scalability.The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes,while Cluster Heads(CHs)play a significant role in the restoration of connectivity.Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques.The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms.The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.
文摘In Mobile Ad Hoc Networks(MANET),Quality of Service(QoS)is an important factor that must be analysed for the showing the better performance.The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimiza-tion for Cluster Head and Gateway Selection(NQCAFFFOCHGS)has the best network performance because it uses the Improved Weighted Clustering Algo-rithm(IWCA)to cluster the network and the FFO algorithm,which uses fuzzy-based network metrics to select the best CH and entryway.However,the major drawback of the fuzzy system was to appropriately select the membership func-tions.Also,the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway.When learning fuzzy sets,this algorithm employs a new Continuous Action-set Learning Automata(CALA)approach that correctly modifies and chooses the fuzzy membership functions.Despite the fact that it extends the network’s lifespan,it does not assist in the detection of defective nodes in the routing route.Because of this,a new Fault Tolerance(NQCAEFFFOCHGS-FT)mechanism based on the Distributed Connectivity Restoration(DCR)mechanism is proposed,which allows the net-work to self-heal as a consequence of the algorithm’s self-healing capacity.Because of the way this method is designed,node failures may be utilised to rebuild the network topology via the use of cascaded node moves.Founded on the fractional network information and topologic overhead related with each node,the DCR is suggested as an alternative to the DCR.When compared to the NQCAFFFOCHGS algorithm,the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay,energy consumption,Packet Loss Ratio(PLR),Normalized Routing Overhead(NRO),and Balanced Load Index(BLI).
