A new genetic-based approach for maximizing network lifetime in directional sensor networks with adjustable sensing ranges

In recent years, the directional sensor networks have been attractive to researchers due to their wide and different applications. These networks normally contain a number of self-configurable directional sensors holding adjustable spherical sectors with limited angle. One of the most significant problems in such networks is how to monitor the targets scattered in these networks using sensors with adjustable sensing range and, at the same time, maximize the network lifetime. This problem is recognized as Maximum Network Lifetime With Adjustable Ranges; it has been already proved as an NP-complete problem. As an efficient solution to this problem, the present paper proposes a target-oriented GA-based algorithm that can form cover sets comprising sensors with appropriate directions and sensing ranges in a way to desirably monitor all targets in the network. We examined the efficiency of the proposed algorithm by comparing its obtained results with those of a greedy-based one introduced recently in literature. The comparative results confirmed the efficient performance of the proposed algorithm and also its superiority over the greedy-based algorithm in terms of extending the network lifetime.     

Maximizing lifetime in wireless sensor networks with multiple sensor families

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.     

Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.     

Decomposition algorithms for maximizing the lifetime of wireless sensor networks with mobile sinks

We address the problem of maximizing the lifetime of a wireless sensor network with energy-constrained sensors and a mobile sink. The sink travels among discrete locations to gather information from all the sensors. Data can be relayed among sensors and then to the sink location, as long as the sensors and the sink are within a certain threshold distance of each other. However, sending information along a data link consumes energy at both the sender and the receiver nodes. A vital problem that arises is to prescribe sink stop durations and data flow patterns that maximally prolong the life of the network, defined as the amount of time until any node exhausts its energy. We describe linear programming and column generation approaches for this problem, and also for a version in which data can be delayed in its transmission to the sink. Our column generation approach exploits special structures of the linear programming formulations so that all subproblems are shortest path problems with non-negative costs. Computational results demonstrate the efficiency of the proposed algorithms.     

Data censoring with network lifetime constraint in wireless sensor networks

In wireless sensor networks (WSNs), senor nodes are usually battery-powered with limited energy budget. The network lifetime is directly related to the energy consumption of each node. Online censoring is an effective approach to reduce the overall energy consumption by only transmitting statistical informative data. However, the network lifetime is not proportionally extended with online censoring, since individual sensor may still suffer from energy shortage due to frequent transmission of informative data or transmission over long distance. In this paper, a parameters estimation problem is considered in WSNs, where the goal is to minimize the estimation error under the network lifetime constraint. Two censoring algorithms are developed, which allow sensor nodes to make decisions locally on whether to transmit the sampled data. The proposed algorithms can extend the network lifetime with little performance loss. Simulation results validate their effectivenesses.     

A two-hop clustered image transmission scheme for maximizing network lifetime in wireless multimedia sensor networks

In traditional wireless sensor networks, normal sensor nodes which measure scalar physical phenomena like temperature, pressure and humidity usually compress the data before sending them out to minimize the communication energy consumption. However, this strategy may not be suitable for image transmission in wireless multimedia sensor networks. In the traditional clustering structure, when the camera-equipped node or the cluster head compresses the images, an energy hole will appear. This is a key factor that affects the lifetime of the network. To avoid the energy hole problem, a two-hop clustered image transmission scheme is proposed in this paper. In the proposed scheme, many redirectors are used to compress and forward the images for the purpose of reducing energy consumption of the camera-equipped node and the cluster head. With adaptive adjustment of the transmission radius in the camera cluster and tasks allocation based on the residual energy of the normal sensor nodes by the camera-equipped node, the energy consumption of the nodes in the network is balanced. The experimental results show that the proposed scheme can prolong the network lifetime dramatically in the case of the sensor nodes deployed densely.     

Tradeoff between utility and lifetime in energy-constrained wireless sensor networks

We study the tradeoff between network utility and network lifetime using a cross-layer optimization approach. The tradeoff model in this paper is based on the framework of layering as optimization decomposition. Our tradeoff model is the first one that incorporates time slots allocation into this framework. By using Lagrangian dual decomposition method, we decompose the tradeoff model into two subproblems: routing problem at network layer and resource allocation problem at medium access control (MAC) layer. The interfaces between the layers are precisely the dual variables. A partially distributed algorithm is proposed to solve the nonlinear, convex, and separable tradeoff model. Numerical simulation results are presented to support our algorithm.     

基于节点协同覆盖的传感器网络寿命最大化模型

针对保证网络连通覆盖和最小能量消耗的优化目标,建模了基于节点协同覆盖的传感器网络寿命最大化模型.提出一种基于多目标优化遗传算法的求解方案,设计了基于链路状态的分簇机制以及基于NSGA-Ⅱ的簇内覆盖控制算法.仿真结果表明该方案能快速收敛于最优解,在高密度和低密度布撒环境下表现出优越的性能,且具有良好的适应性.     

A column generation approach to extend lifetime in wireless sensor networks with coverage and connectivity constraints

This paper addresses the maximum network lifetime problem in wireless sensor networks with connectivity and coverage constraints. In this problem, the purpose is to schedule the activity of a set of wireless sensors, keeping them connected while network lifetime is maximized. Two cases are considered. First, the full coverage of the targets is required, and second only a fraction of the targets has to be covered at any instant of time. An exact approach based on column generation and boosted by GRASP and VNS is proposed to address both of these problems. Finally, a multiphase framework combining these two approaches is built by sequentially using these two heuristics at each iteration of the column generation algorithm. The results show that our proposals are able to tackle the problem efficiently and that combining the two heuristic approaches improves the results significantly.     

Optimal number of annuli for maximizing the lifetime of sensor networks

The most effective way to maximize the lifetime of a wireless sensor network (WSN) is to allocate initial energy to sensors such that they exhaust their energy at the same time. The lifetime of a WSN as well as an optimal initial energy allocation are determined by a network design. The main contribution of the paper is to show that the lifetime of a WSN can be maximized by an optimal network design. We represent the network lifetime as a function of the number m$m$ of annuli and show that m$m$ has significant impact on network lifetime. We prove that if the energy consumed by data transmission is proportional to d^α+c$d^{\alpha }+c$, where d$d$ is the distance of data transmission and α$\alpha$ and c$c$ are some constants, then for a circular area of interest with radius R$R$, the optimal number of annuli that maximizes the network lifetime is m=R((α−1)/c)^1/α$m=R{((\alpha -1)/c)}^{1/\alpha }$ for an arbitrary sensor density function.     

Using mobile data collectors to improve network lifetime of wireless sensor networks with reliability constraints

In this paper, we focus on maximizing network lifetime of a Wireless Sensor Network (WSN) using mobile Data Collectors (DCs) without compromising on the reliability requirements. We consider a heterogeneous WSN which consists of a large number of sensor nodes, a few DCs, and a static Base Station (BS). The sensor nodes are static and are deployed uniformly in the terrain. The DCs have locomotion capabilities and their movement can be controlled. Each sensor node periodically sends sensed event packets to its nearest DC. The DCs aggregate the event packets received from the sensor nodes and send these aggregate event packets to the static BS. We address the following problem: the DCs should send the aggregate event packets to the BS with a given reliability while avoiding the hotspot regions such that the network lifetime is improved. Reliability is achieved by sending each aggregate event packet via multiple paths to the BS. The network lifetime is maximized by moving the DCs in such a way that the forwarding load is distributed among the sensor nodes. We propose both centralized and distributed approaches for finding a movement strategy of the DCs. We show via simulations that the proposed approaches achieve the required reliability and also maximize the network lifetime compared to the existing approaches.     

无线传感器网络中基于遗传算法的优化覆盖机制

覆盖作为无线传感器网络应用的一个基本问题．反映了网络监测和实现目标跟踪的质量效果．针对传感器节点的高密度部署情况,研究了工作节点集选取问题．提出两种基于加权遗传算法和基于约束遗传算法的优化覆盖机制．根据生成的适值函数进行遗传算法操作．并计算传感器网络充分覆盖区域所需的近似最优工作节点集．仿真结果表明该算法能快速收敛于最优解．完成工作节点集的优化选取,从而降低网络冗余,延长网络生存时间．     

Theoretical analysis of the lifetime and energy hole in cluster based wireless sensor networks

Cluster based wireless sensor networks have been widely used due to the good performance. However, in so many cluster based protocols, because of the complexity of the problem, theoretical analysis and optimization remain difficult to develop. This paper studies the performance optimization of four protocols theoretically. They are LEACH (Low Energy Adaptive Clustering Hierarchy), MLEACH (Multi-hop LEACH), HEED (Hybrid Energy-Efficient Distributed Clustering Approach), and UCR (Unequal Cluster based Routing). The maximum FIRST node DIED TIME (FDT) and the maximum ALL node DIED TIME (ADT) are obtained for the first time in this paper, as well as the optimal parameters which maximize the network lifetime. Different from previous analysis of network lifetime, this paper analyzes the node energy consumption in different regions through the differential analysis method. Thus, the optimal parameters which maximize the lifetime can be obtained and the detailed energy consumption in different regions at different time can be also obtained. Moreover, we can obtain the time and space evolution of the network, from a steady state (without any death) to a non-steady state (with some death of nodes), and then to the final situation (all nodes die). Therefore, we are fully aware of the network status from spatial and temporal analysis. Additionally, the correctness of the theoretical analysis in this paper is proved by the Omnet++ experiment results. This conclusion can be an effective guideline for the deployment and optimization of cluster based networks.     

Improved sensor network lifetime with multiple mobile sinks

A critical issue for data gathering in wireless sensor networks is the formation of energy holes near the sinks. Sensors near the sinks have to participate in relaying data on behalf of other sensors and thus will deplete their energy very quickly, resulting in network partitioning and limitation of the network lifetime. The solution that we propose in this paper is to use mobile sinks that change their location when the nearby sensors’ energy becomes low. In this way the sensors located near sinks change over time. In deciding a new location, a sink searches for zones with richer sensor energy.First, we study the improvement in network lifetime when sinks move on a predetermined path, along the perimeter of a hexagonal tiling. Two cases are considered for data gathering when sinks stop in the hexagon’s corners and when the sinks stop on multiple locations on the hexagon perimeter. This study shows an improvement of up to 4.86 times in network lifetime. Second, we design a distributed and localized algorithm used by the sinks to decide their next movement location such that the virtual backbone formed by the sinks remains interconnected at all times. Two extensions of the distributed algorithm, coverage requirement and limitation of the time-delivery requirement, are also addressed. Simulation results are presented to verify our approaches.     

A distributed energy-efficient clustering protocol for wireless sensor networks

Minimizing energy dissipation and maximizing network lifetime are among the central concerns when designing applications and protocols for sensor networks. Clustering has been proven to be energy-efficient in sensor networks since data routing and relaying are only operated by cluster heads. Besides, cluster heads can process, filter and aggregate data sent by cluster members, thus reducing network load and alleviating the bandwidth. In this paper, we propose a novel distributed clustering algorithm where cluster heads are elected following a three-way message exchange between each sensor and its neighbors. Sensor’s eligibility to be elected cluster head is based on its residual energy and its degree. Our protocol has a message exchange complexity of O(1) and a worst-case convergence time complexity of O(N). Simulations show that our algorithm outperforms EESH, one of the most recently published distributed clustering algorithms, in terms of network lifetime and ratio of elected cluster heads.     

Optimal replicator factor control in wireless sensor networks

For TDMA MAC protocols in wireless sensor networks (WSNs), redundancy and retransmission are two important methods to provide high end-to-end transmission reliability. Since reliable transmissions will lead to more energy consumption, there exists an intrinsic tradeoff between transmission reliability and energy efficiency. For each link, we name the number of its reserved time slots in each MAC superframe as a replicator factor. In the following paper, we propose a reliability-lifetime tradeoff framework (RLTF) for WSNs to study replicator factor control problem. First, for the redundancy TDMA MAC, we formulate replicator factor control problem as convex programming. By the gradient projection method, we develop a fully distributed algorithm to solve the convex programming. Second, for the retransmission TDMA MAC, we set the retransmission upper bound for each link according to the optimal replicator factors under the redundancy MAC and compute the total communication overhead for the retransmission MAC. Finally, we compare the communication overhead of these two MAC protocols under different channel conditions.     

圆环形传感器网络生命周期最大化模型与求解

传感器网络由大量能量有限的微型传感器节点组成,如何延长网络的生命周期是一个需要解决的重要问题。针对圆环形传感器网络,给出了求解网络生命周期最大化模型,得出了圆环的宽度,并定量规划了每个圆环中节点的数目。模拟结果显示:网络生命周期终止时,网络中的节点几乎达到了能量均衡,从而延长了网络的生命周期。     

A soft computing approach to localization in wireless sensor networks

In this paper, we propose two intelligent localization schemes for wireless sensor networks (WSNs). The two schemes introduced in this paper exhibit range-free localization, which utilize the received signal strength (RSS) from the anchor nodes. Soft computing plays a crucial role in both schemes. In the first scheme, we consider the edge weight of each anchor node separately and combine them to compute the location of sensor nodes. The edge weights are modeled by the fuzzy logic system (FLS) and optimized by the genetic algorithm (GA). In the second scheme, we consider the localization as a single problem and approximate the entire sensor location mapping from the anchor node signals by a neural network (NN). The simulation and experimental results demonstrate the effectiveness of the proposed schemes by comparing them with the previous methods.     

优化无线传感器网络寿命的动态路由算法

针对多跳无线传感器网络能量受限的特点,以提高网络寿命为目标,建立基于最大最小节点寿命的线性规划网络模型。当传感器节点失效时,根据网络的拓扑结构动态更新节点的路由。仿真分析结果表明：基于网络拓扑结构变化动态更新节点路由的方法能够拓展网络寿命,大幅度地增加基站接收信息的数量和提高节点能量的使用效率。     

无线传感器网络最大生命期聚合树路由算法

提出了一种无线传感器网络最大生命期聚合树路由算法,根据能量等限制条件建立线性规划模型。考虑到网络最大生命期是NP难问题,在算法复杂度较低情况下,将网络最大生命期问题转化为网络最小归一化负载问题,在建立最大归一化负载聚合树过程中,不断调整负载较重节点的数据转发压力,最终建立一棵负载较轻的数据融合树,实现了网络生命期的最大化。通过仿真验证了算法的性能,并表明所提出算法可以有效延长网络生命期。