In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Senso...In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.展开更多
Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this...Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this process,a biased random walk method is used to determine the next position of the sink.Then,a rendezvous point selection with splitting tree technique is used to find the optimal data transmission path.If the sink moves within the range of the rendezvous point,it receives the gathered data and if moved out,it selects a relay node from its neighbours to relay packets from rendezvous point to the sink.Proposed algorithm reduces the signal overhead and improves the triangular routing problem.Here the sink acts as a vehicle and collect the data from the sensor.The results show that the proposed model effectively supports sink mobility with low overhead and delay when compared with Intelligent Agent-based Routing protocol(IAR) and also increases the reliability and delivery ratio when the number of sources increases.展开更多
基金Research Supporting Project Number(RSP2024R421),King Saud University,Riyadh,Saudi Arabia.
文摘In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
文摘Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this process,a biased random walk method is used to determine the next position of the sink.Then,a rendezvous point selection with splitting tree technique is used to find the optimal data transmission path.If the sink moves within the range of the rendezvous point,it receives the gathered data and if moved out,it selects a relay node from its neighbours to relay packets from rendezvous point to the sink.Proposed algorithm reduces the signal overhead and improves the triangular routing problem.Here the sink acts as a vehicle and collect the data from the sensor.The results show that the proposed model effectively supports sink mobility with low overhead and delay when compared with Intelligent Agent-based Routing protocol(IAR) and also increases the reliability and delivery ratio when the number of sources increases.
