Existing works on data aggregation in wireless sensor networks (WSNs) usually use a single channel which results in a long latency due to high interference, especially in high-density networks. Therefore, data aggre- ...Existing works on data aggregation in wireless sensor networks (WSNs) usually use a single channel which results in a long latency due to high interference, especially in high-density networks. Therefore, data aggre- gation is a fundamental yet time-consuming task in WSNs. We present an improved algorithm to reduce data aggregation latency. Our algorithm has a latency bound of 16R + Δ – 11, where Δ is the maximum degree and R is the network radius. We prove that our algorithm has smaller latency than the algorithm in [1]. The simulation results show that our algorithm has much better performance in practice than previous works.展开更多
To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel c...To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.展开更多
Random distribution of sensor nodes in large scale network leads redundant nodes in the application field. Sensor nodes are with irreplaceable battery in nature, which drains the energy due to repeated collection...Random distribution of sensor nodes in large scale network leads redundant nodes in the application field. Sensor nodes are with irreplaceable battery in nature, which drains the energy due to repeated collection of data and decreases network lifetime. Scheduling algorithms are the one way of addressing this issue. In proposed method, an optimized sleep scheduling used to enhance the network lifetime. While using the scheduling algorithm, the target coverage and data collection must be maintained throughout the network. In-network, aggregation method also used to remove the unwanted information in the collected data in level. Modified clustering algorithm highlights three cluster heads in each cluster which are separated by minimum distance between them. The simulation results show the 20% improvement in network lifetime, 25% improvement in throughput and 30% improvement in end to end delay.展开更多
As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance scenario.Based on multiple agents＇ technique,this article introduces a...As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance scenario.Based on multiple agents＇ technique,this article introduces a series of intelligent algorithms such as simulated annealing algorithm（SA）,genetic algorithm（GA）,and ant colony optimization algorithm（ACO） or their mixed algorithms,to resolve the optimization of tasks schedule and data transmission.This article analyzes the performance of abovementioned algorithms and verifies their feasibility associated with agents.The simulations demonstrates that the mixed algorithms based on SA and GA obtain the optimal solution to tasks schedule,and those combined with SA-ACO show advantages on multimedia sensor networks routing optimization.展开更多
任务分配是高性能计算领域中的一个广泛研究的经典问题,然而,传感器网络资源严重受限,现有的算法不能直接应用.提出一种基于遗传算法的嵌套优化技术,在多跳聚簇网络中进行能源高效的任务分配.一般化的优化目标既可以满足应用的实时性要...任务分配是高性能计算领域中的一个广泛研究的经典问题,然而,传感器网络资源严重受限,现有的算法不能直接应用.提出一种基于遗传算法的嵌套优化技术,在多跳聚簇网络中进行能源高效的任务分配.一般化的优化目标既可以满足应用的实时性要求,也可以实现能源的高效性.优化解通过结合基于遗传算法的任务映射、路由路径分配、任务调度以及动态电压调制(dynamic voltage scaling,简称DVS)这几个过程而获得.随机产生任务图模拟实验,结果表明,嵌套优化技术与随机优化技术相比,具有较好的实时性和能源高效性.展开更多
文摘Existing works on data aggregation in wireless sensor networks (WSNs) usually use a single channel which results in a long latency due to high interference, especially in high-density networks. Therefore, data aggre- gation is a fundamental yet time-consuming task in WSNs. We present an improved algorithm to reduce data aggregation latency. Our algorithm has a latency bound of 16R + Δ – 11, where Δ is the maximum degree and R is the network radius. We prove that our algorithm has smaller latency than the algorithm in [1]. The simulation results show that our algorithm has much better performance in practice than previous works.
基金supported by Postdoctoral Science Foundation of China(No.2021M702441)National Natural Science Foundation of China(No.61871283)。
文摘To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.
文摘Random distribution of sensor nodes in large scale network leads redundant nodes in the application field. Sensor nodes are with irreplaceable battery in nature, which drains the energy due to repeated collection of data and decreases network lifetime. Scheduling algorithms are the one way of addressing this issue. In proposed method, an optimized sleep scheduling used to enhance the network lifetime. While using the scheduling algorithm, the target coverage and data collection must be maintained throughout the network. In-network, aggregation method also used to remove the unwanted information in the collected data in level. Modified clustering algorithm highlights three cluster heads in each cluster which are separated by minimum distance between them. The simulation results show the 20% improvement in network lifetime, 25% improvement in throughput and 30% improvement in end to end delay.
基金sponsored by the National Natural Science Foundation of China (60973139, 60773041)the Natural Science Foundation of Jiangsu Province (BK2008451)+4 种基金the Hi-Tech Research and Development Program of China (2007AA01Z404, 2007AA01Z478)Special Fund for Software Technology of Jiangsu ProvinceFoundation of National Laboratory for Modern Communications (9140C1105040805)Postdoctoral Foundation (0801019C, 20090451240)the six kinds of Top Talent of Jiangsu Province (2008118)
文摘As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance scenario.Based on multiple agents＇ technique,this article introduces a series of intelligent algorithms such as simulated annealing algorithm（SA）,genetic algorithm（GA）,and ant colony optimization algorithm（ACO） or their mixed algorithms,to resolve the optimization of tasks schedule and data transmission.This article analyzes the performance of abovementioned algorithms and verifies their feasibility associated with agents.The simulations demonstrates that the mixed algorithms based on SA and GA obtain the optimal solution to tasks schedule,and those combined with SA-ACO show advantages on multimedia sensor networks routing optimization.
基金Supported by the Key Program of the National Natural Science Foundation of China under Grant No.60533110(国家自然科学基金重点项目)the National Natural Science Foundation of China under Grant No.60473075(国家自然科学基金)+3 种基金the National Grand Fundamental Research973Program of China under Grant No.2006CB303000(国家重点基础研究发展计划(973))the Key Program of the Natural Science Foundation of Heilongjiang Province of China under Grant No.ZJG03-05(黑龙江省自然科学基金重点项目)the Program for New Century Excellent Talents in University of China under Grant No.NCET-05-0333(新世纪优秀人才支持计划)the Heilongjiang Province Scientific and Technological Special Fund for Young Scholars of China under Grant No.QC06C033(黑龙江省青年科技专项资金)
文摘任务分配是高性能计算领域中的一个广泛研究的经典问题,然而,传感器网络资源严重受限,现有的算法不能直接应用.提出一种基于遗传算法的嵌套优化技术,在多跳聚簇网络中进行能源高效的任务分配.一般化的优化目标既可以满足应用的实时性要求,也可以实现能源的高效性.优化解通过结合基于遗传算法的任务映射、路由路径分配、任务调度以及动态电压调制(dynamic voltage scaling,简称DVS)这几个过程而获得.随机产生任务图模拟实验,结果表明,嵌套优化技术与随机优化技术相比,具有较好的实时性和能源高效性.
