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.     

利用传感器距离和能耗约束的无线传感器网络路由优化

针对大多数现有的无线传感器网络设计方法通常仅找到给定图的最短路径而导致只能优化单个用户性能的问题,提出了一种寻找节点之间的最短路径和最低能耗的路由优化模型,该模型约束WSNs的特定资源,考虑多种约束条件：多周期、最短距离和低能耗。本文按照混合整数线性规划,使用11.0 ILOG CPLEX优化引擎的ILOG OPL开发工具5.5进行编码和求解本文的优化模型。实验结果表明,相比一般可行网络,本文模型的节点的总距离和能耗均有明显降低,此外,在三个不同周期和约束条件数下,每组实验花费的时间均少于10s,完全满足资源有限的无线网络应用要求。     

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.     

回归的能源有效网络大数据流汇聚算法研究

为了降低传感器网络数据流汇聚时的能源消耗,提出了一种基于回归的能源有效数据流汇聚算法。首先,将传感器节点分为活跃节点和能源有效节点。然后,以活跃节点为中心点将所有节点进行聚类,并应用回归方法通过活跃节点的数据流对能源有效节点的数据进行预测。接下来,通过节点预测值的累积误差不断修正活跃节点集。最后,应用活跃节点的数据流信息对能源有效节点的数据进行预测。实验表明,本文提出的算法与其它相关算法相比具有更好的预测准确性。     

A local-world heterogeneous model of wireless sensor networks with node and link diversity

In this paper, we present a novel local-world model of wireless sensor networks (WSN) with two kinds of nodes: sensor nodes and sink nodes, which is different from other models with identical nodes and links. The model balances energy consumption by limiting the connectivity of sink nodes to prolong the life of the network. How the proportion of sink nodes, different energy distribution and the local-world scale would affect the topological structure and network performance are investigated. We find that, using mean-field theory, the degree distribution is obtained as an integral with respect to the proportion of sink nodes and energy distribution. We also show that, the model exhibits a mixed connectivity correlation which is greatly distinct from general networks. Moreover, from the perspective of the efficiency and the average hops for data processing, we find some suitable range of the proportion p of sink nodes would make the network model have optimal performance for data processing.     

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.     

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.     

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.     

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.     

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.     

A random walk evolution model of wireless sensor networks and virus spreading

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations.     

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.     

一种无线传感器网络健壮性可调的能量均衡拓扑控制算法

无线传感器网络中, 应用环境的干扰导致节点间距不能被准确度量. 所以利用以节点间距作为权重的闭包图(EG)模型构建的拓扑没有考虑环境的干扰, 忽略了这部分干扰带来的能耗, 缩短了网络生存时间. 针对无线传感器网络拓扑能量不均的特点和EG模型的缺陷, 首先引入节点度调节因子, 建立通信度量模型和节点实际生存时间模型; 其次量化网络节点度, 从而获取满足能量均衡和网络生命期最大化需求的节点度的取值规律; 然后利用该取值规律和函数极值充分条件解析推导出网络最大能量消耗值和最长生存时间, 并获得最优节点度; 最后基于以上模型提出一种健壮性可调的能量均衡拓扑控制算法. 理论证明该拓扑连通且为双向连通. 仿真结果说明网络能利用最优节点度达到较高的健壮性, 保证信息可靠传输, 且算法能有效平衡节点能耗, 提高网络健壮性, 延长网络生命周期.     

无线传感器网络的分布式竞争路由算法

在WSNs体系结构中,路由算法是网络层的关键,分簇技术在路由算法中可提高WSNs的扩展性。提出一种竞争簇头,非均匀分簇和多跳路由相结合的WSNs 分布式竞争路由算法(DSA)．它的核心是构建一个高效的非均匀分簇算法,通过竞争的方式候选簇头,靠近基站的簇头为簇间的数据转发预留能量,根据节点的剩余能量、簇内和簇间通信代价,每个簇头在邻居簇头集中用贪心法确定中继节点,仿真表明,DSA算法能有效平衡了簇头的能耗、均衡了网络能耗、显著延长网络生存周期。     

基于非均匀分簇的无线传感器网络路由协议

在无线传感器网络中,分簇技术是一种有效延长网络生命周期的方法。但是这种多跳的网络模型,如果节点均匀分布并且簇的大小相等,则靠近基站的簇头由于要中继更多的数据,则会导致能量空洞现象。因此提出一种非均匀分簇方法来缓解能量空洞问题。首先,通过节点的剩余能量、到基站的距离以及邻居节点数量来选择簇头。簇一旦形成之后,通过单跳和多跳的混合机制将数据发送到基站。实验结果表明,此协议能有效的延长网络的生命周期,均衡网络能耗,有效延缓能量空洞的形成速度。     

Exploiting perturbed and coalescent anchor node geometry with semidefinite relaxation for sensor network localization

Localization in wireless sensor networks (WSNs) suffer from performance issues whenever the anchor nodes (which are aware of their location) are subjected to motion from their usual position. Moreover, accurate localization demands more anchor nodes which is a scarce resource and needs to be used judiciously. In the current work, we propose a novel framework that addresses these two prime concerns by harnessing the inter relationship of anchor node geometry. For an unknown source node surrounded by anchor nodes, the anchors lying on the inner boundary of the deployment geometry may be carrying closely related information about source node, leading to redundancy and inefficient utilization. By anticipating the level of correlation between these anchors, localization can be made more frugal. Rigorous mathematical analysis is carried out to derive lower bounds on estimated locations. Based on fisher information from two proposed models, a convex estimation objective function is formulated using semidefinite programming (SDP) approach to validate the theoretical proceedings. Based on the findings, the proposed method is able to successfully extract useful information about the unknown source node location with limited number of anchor nodes, hence achieving superior localization.     

Cascading failure in the wireless sensor scale-free networks

In the practical wireless sensor networks(WSNs), the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free topology in WSNs. Firstly,a cascading failure model for scale-free topology in WSNs is studied. Through analyzing the influence of the node load on cascading failure, the critical load triggering large-scale cascading failure is obtained. Then based on the critical load,a control method for cascading failure is presented. In addition, the simulation experiments are performed to validate the effectiveness of the control method. The results show that the control method can effectively prevent cascading failure.     

Connectivity and percolation behaviour of grain boundary networks in three dimensions

Grain boundary networks are subject to crystallographic constraints at both triple junctions (first-order constraints) and quadruple nodes (second-order constraints). First-order constraints are known to influence the connectivity and percolation behaviour in two-dimensional grain boundary networks, and here we extend these considerations to fully three-dimensional microstructures. Defining a quadruple node distribution (QND) to quantify both the composition and isomerism of quadruple nodes, we explore how the QNDs for crystallographically consistent networks differ from that expected in a randomly assembled network. Configurational entropy is used to quantify the relative strength of each type of constraint (i.e., first- and second-order), with first-order triple junction constraints accounting for at least 75% of the non-random correlations in the network. As the dominant effects of constraint are captured by considering the triple junctions alone, a new analytical model is presented which allows the 3-D network connectivity to be estimated from data on 2-D microstructural sections. Finally, we show that the percolation thresholds for 3-D crystallographically consistent networks differ by as much as ±0.07 from those of standard percolation theory.     

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.     

A Distributed and Collaborative Beamforming algorithm for a self-organizing Wireless Sensor Network

In this paper the scenario where sensors of a Wireless Sensor Network (WSN) are able to process and transmit monitored data to a far collector is considered. The far collector may be a Base Station (BS) that gathers data from a certain number of deployed WSNs, in applications such as earthquake, tsunami, or pollution monitoring. In this paper, the possible use of Distributed and Collaborative BeaMForming (DC-BMF) technique is analyzed, with the goal of enhancing the capability of a single sensor to communicate its data to the far collector. This technique considers nodes as elements of a phased array, where the phases of the signals at each antenna node are linearly combined in order to adjust the directional gain of the whole array. In particular, a novel self-localization technique for WSNs performing DC-BMF is studied, a closed form solution for beamforming gain degradation is derived and the evaluation of the power consumption of the proposed DC-BMF algorithm is provided.