Local adaptive sampling for wireless sensor network powered by energy harvesting

For a sensor network, energy limitation is always a key factor to affect the continuous work of a sensor node. A good idea is harvesting energy from the environment to support the node to work continuously. However, energy from environment is varied with time, weather and season. So in order to use the varied environment energy, it is necessary to find a way to achieve real-time monitoring and adaptive working. In this paper, an algorithm called “Adaptive Sampling” was proposed to adapt the sample mode to the present energy condition. Simulation proves that the algorithm can make the CO₂ sensor flexibly achieve adaptive sampling under different energy condition with the least MSE 2.7767. This algorithm can be widely used in wireless sensor network power by energy harvesting for local adaptive sampling.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

一种能耗均衡的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均衡问题,通过对节点生命期建模,得出节点生命期受节点剩余能量和通信距离的影响,进而将两端节点生命期作为构建拓扑时边权重的影响因子,通过边权重控制节点权重,最终得出了一种能耗均衡的无线传感器网络加权无标度拓扑模型,并理论证明了该模型的点权、边权和节点度均服从幂律分布.实验结果表明,该模型具有无标度拓扑的强容错性,并有效的均衡了网络中的节点能耗,延长了网络的生命期.     

基于生命期模型的无线传感器网络信道分配博弈算法

针对无线传感器网络中节点因干扰过大导致重传能耗增加, 进而节点过早失效、网络生命期缩短的问题, 根据网络拓扑信息和路由信息设计节点的负载模型, 从而构建了节点的生命期模型. 然后利用博弈论将路径增益、交叉干扰和节点生命期等性能参数融入到效益函数中, 构建信道分配博弈模型. 理论分析证明该博弈模型存在纳什均衡. 进而运用最佳回应策略, 在所构建的信道分配博弈模型的基础上, 设计了一种优化网络生命期的抗干扰信道分配算法. 该算法使节点在选择信道时避免与网络中交叉干扰较大的节点和生命期较小的节点使用相同信道, 实现干扰小、能耗低且均衡的信道选择. 理论分析与仿真结果证明该算法最终能够快速地收敛到纳什均衡, 且具有较小的信息复杂度, 从而减小算法本身的通信能耗. 同时, 该算法具有良好的抗干扰性和信道均衡性, 能够有效地延长网络生命期.     

An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird Algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios.     

Modeling wireless sensor networks using random graph theory

A critical issue in wireless sensor networks (WSNs) is represented by limited availability of energy within network nodes. Therefore, making good use of energy is necessary in modeling sensor networks. In this paper we proposed a new model of WSNs on a two-dimensional plane using site percolation model, a kind of random graph in which edges are formed only between neighbouring nodes. Then we investigated WSNs connectivity and energy consumption at percolation threshold when a so-called phase transition phenomena happen. Furthermore, we proposed an algorithm to improve the model; as a result the lifetime of networks is prolonged. We analyzed the energy consumption with Markov process and applied these results to simulation.     

Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network

Inadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed.     

Adaptive coding clustered routing protocol for energy efficient and reliable WSN

In Wireless Sensor Networks (WSNs), the sensor nodes are expected to remain autonomous for a long time and the communication must be successful. So, energy efficiency and communication reliability are the main challenges for WSN. Routing protocols and, in particular, clustering is the key to energy constraints in WSNs. In a loss milieu, data shared between nodes is susceptible to corruption by errors caused by signal fading, random noise, and some other forces. For this reason, channel coding is necessary to assure efficient and reliable transmission. Therefore, energy efficiency and reliability of communications are the most crucial factors in the design of an efficient cluster-based routing protocol. In this context, to raise the reliability of the communication link and minimize the necessary transmission energy, a new adaptive coding routing protocol is proposed, which implements RS and LDPC codes to a routing protocol. Simulation results show that our proposed protocol improves energy consumption, network lifetime, stability, and throughput compared to LEACH and BRE-LEACH. In addition, it reaches a good BER performance and coding gain by using the LDPC code.     

一种优化无线传感器网络生命周期的容错拓扑研究

<正>由于无线传感器网络的节点能量受限,优化网络生命周期成为设计网络拓扑时首要考虑的问题.通过分析节点的剩余能量和负载量对节点生命周期的影响,提出了一种可延长无线传感器网络生命期的容错拓扑演化模型,并得出了在节点满足网络生存时间的条件下负载调节系数的取值范围.仿真实验结果表明,基于无标度网络的演化拓扑结构具有较好的容错性,并能够均衡网络节点能耗和延长网络生命周期.     

能量异构的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均和如何高效获得节点和边的负载问题,提出一种局域范围内能量异构的加权无标度拓扑演化模型.通过对节点能量与负载、能耗的关系建模,建立节点能量与点权和边权的联系,进而结合点权和加权模型给出网络的演化方式,推出点权、度和边权的幂率分布规律,最终根据网络获得的点权和边权来分析负载和能耗.仿真结果表明,提出的模型不仅能够准确计算点边的负载,而且缓解了无标度网络的节点能耗不均衡问题.     

基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配

提出了一种基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配方法,该方法运用混沌序列和并行遗传算法来动态调整传感器网络节点的探测目标及通信时隙等参数,对资源分配方式进行跨层整体优化.在多目标无线传感器网络环境下,将本文方法与传统的随机分配方法、动态规划方法、T-MAC协议及S-MAC协议等资源分配算法进行了仿真比较.仿真结果表明,本文提出的混沌并行遗传算法具有通信时延小,目标检测成功率高等优点,在降低了无线传感器网络功率消耗的同时提高了对目标检测的实时性. 关键词： 无线传感器网络 无线资源管理 Henon映射 并行遗传算法     

Analysis of scalability for AODV routing protocol in wireless sensor networks

This paper presents preliminary work to address scalability concern over AODV protocol in wireless sensor network. Firstly, we discussed the scalability design issues with related work in context of wireless sensor networks (WSN). Following, we designed and illustrated wireless sensor network model. Finally, significance of scalability on the behaviour of application, MAC, transport and physical layer performance is described.     

基于质心迭代估计的无线传感器网络节点定位算法

针对无线传感器网络非测距定位方法的应用,提出了基于质心迭代估计的节点定位算法.该算法首先计算当前连通信标节点所围成的平面质心的坐标及其与未知节点间的接收信号强度,然后用计算所得质心节点替代距离未知节点最远的连通信标节点,缩小连通信标节点所围成的平面,并通过多次迭代的方法提高节点定位精度.仿真实验结果表明,该算法的各项指标均为良好,适用于无线传感器网络的节点定位.     

Mobile sink-based data collection in event-driven wireless sensor networks using a modified ant colony optimization

The hotspot problem is one of the primary challenges in the wireless sensor networks (WSNs) because it isolates the sink node from the remaining part of the WSN. A mobile sink (MS)-based data acquisition strategy mitigates the hotspot problem, but the traditional MS-based data gathering approaches do not resolve the issue. However, the conventional techniques follow a fixed order of visits and static traversal of the MS. In this context, this paper uses a modified version of the ant colony optimization strategy for the data collected through a MS to mitigate the hotspot problem in the WSNs while improving the energy efficiency, network lifetime, throughput by reducing the packet loss and delay. In our work, we initially construct a forwarded load spanning tree to estimate the freight of each node in the WSN. Further, we choose RPs and their path simultaneously using the modified ACO algorithm by considering the forward loads, remaining energy, distance, etc. The proposed work also adopts the virtual RP selection strategy void unnecessary data exchanges between the nodes and RPs. Hence, it reduces the burden on relay nodes and optimize the energy usage among the nodes. We compare our approach with the recent ACO-based algorithms, and our approach outperforms them.     

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network(WSN), the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption(PCCAGM). This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption(PCCAA) is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.     

Multi-radius geographical spatial networks: Statistical characteristics and application to wireless sensor networks

In this paper, a new kind of complex network model named multi-radius geographical spatial networks is proposed. We investigate statistical characteristics of this model and then map wireless sensor networks (WSNs) to it based on an efficient mechanism of broadcasting radius adjustment. Analysis and simulation show that WSNs working under this mechanism obtain longer lifetime and faster data delivering speed than those in traditional uniform radius WSNs.     

一种基于势博弈的无线传感器网络拓扑控制算法

在实际的应用中,无线传感器网络常常由大量电池资源有限的传感器节点组成.如何降低网络功耗,最大化网络生存时间,是传感器网络拓扑控制技术的重要研究目标.随着传感节点的运行,节点的能量分布可能越来越不均衡,需要在考虑该因素的情况下,动态地调整节点的网络负载以均衡节点的能耗,达到延长网络生存时间的目的.该文引入博弈理论和势博弈的概念,综合考虑节点的剩余能量和节点发射功率等因素,设计了一种基于势博弈的拓扑控制模型,并证明了该模型纳什均衡的存在性.通过构造兼顾节点连通性和能耗均衡性的收益函数,以确保降低节点功耗的同时维持网络的连通性.通过提高邻居节点的平均剩余能量值以实现将剩余能量多的节点选择作为自身的邻居节点,提高节点能耗的均衡性.在此基础上,提出了一种分布式的能耗均衡拓扑控制算法.理论分析证明了该算法能保持网络的连通性.与现有基于博弈理论的DIA算法和MLPT算法相比,本算法形成的拓扑负载较重、剩余能量较小的瓶颈节点数量较少,节点剩余能量的方差较小,网络生存时间更长.     

A target group tracking algorithm for wireless sensor networks using azimuthal angle of arrival information

<正>In this paper,we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network.Since a group of targets moves collectively and is restricted within a limited region,it is not worth consuming scarce resources of sensors in computing the trajectory of each single target.Hence,in this paper,the problem is modeled as tracking a geographical continuous region covered by all targets.A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period.Based on the locations of sensors and the azimuthal angle of arrival(AOA) information,the estimated region covering all the group members is obtained.Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group.Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error,which is between 10%and 40%of the hull algorithm,with a similar density of sensors.And when the density of sensors increases,the localization accuracy of the proposed algorithm improves dramatically.     

Fault-tolerant topology in the wireless sensor networks for energy depletion and random failure

Nodes in the wireless sensor networks(WSNs) are prone to failure due to energy depletion and poor environment,which could have a negative impact on the normal operation of the network. In order to solve this problem, in this paper, we build a fault-tolerant topology which can effectively tolerate energy depletion and random failure. Firstly, a comprehensive failure model about energy depletion and random failure is established. Then an improved evolution model is presented to generate a fault-tolerant topology, and the degree distribution of the topology can be adjusted. Finally, the relation between the degree distribution and the topological fault tolerance is analyzed, and the optimal value of evolution model parameter is obtained. Then the target fault-tolerant topology which can effectively tolerate energy depletion and random failure is obtained. The performances of the new fault tolerant topology are verified by simulation experiments. The results show that the new fault tolerant topology effectively prolongs the network lifetime and has strong fault tolerance.     

无线传感网中基于质心的高效坐标压缩算法

鉴于无线传感网中有限的通信带宽和计算资源, 传感器节点传送压缩后的数据对于系统节能具有十分重要的意义. 本文提出一种利用反正切函数构建的非线性坐标压缩策略来降低系统的数据传输量. 得益于反正切函数的非线性特点, 在质心附近压缩率低, 离质心越远, 压缩率越高. 仿真结果显示, 所提算法特别适应于在质心附近具有高频分量的信号类型. 所提算法具有以下几个特点: 1)采样间隔是非均匀的; 2)可以在采样前实现压缩, 类似于压缩感知; 3)计算复杂度低, 算法简单易实现, 在实际应用中更具有优势. 关键词： 无线传感器网络 坐标压缩 非线性 质心