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.     

Maximizing lifetime of event-unobservable wireless sensor networks

In wireless sensor networks (WSNs) contextual information such as the information regarding whether, when, and where the data is collected cannot be protected using only traditional measures (e.g., encryption). Contextual information can be protected against global eavesdroppers by periodic packet transmission combined with dummy traffic filtering at proxy nodes. In this paper, through a Linear Programming (LP) framework, we analyze lifetime limits of WSNs preserving event-unobservability with different proxy assignment methodologies. We show that to maximize the network lifetime data flow should pass through multiple proxies that are organized as a general directed graph rather than as a tree.     

基于模拟退火的无线传感器网络最大生命期路由*

将节点功率控制与数据聚合有机结合,为进一步降低网络能耗提供了可能,但是也给路由算法的设计带来了新的挑战。为此,针对WSNs中结合数据聚合的节能数据传递方式进行了研究,提出了一种新的最大化网络生命期的路由算法。该算法采用模拟退火算法最优化数据聚合点的选择,均衡节点能耗,最大化网络生命期。仿真结果表明该算法性能明显优于现有算法,达到了提高网络生命期的目的。     

基于双效用函数的无线传感器网络拓扑博弈算法

传统拓扑控制算法采用单个效用函数,无法适应网络性能需求的动态变化.在拓扑控制中引入了博弈论,提出了两个具有不同优化目标的效用函数.当节点剩余能量较高时,选择一个全面考虑能量均衡度、网络能耗、网络连通性等因素的效用函数;否则,为了尽可能降低节点能耗而选择另一个更趋向于较低功率的效用函数.实验表明,采用双效用函数的拓扑博弈算法在网络寿命、能量均衡度等方面具有较好的性能.     

mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks

Knowledge of the battery lifetime of the wireless sensor network is important for many situations, such as in evaluation of the location of nodes or the estimation of the connectivity, along time, between devices. However, experimental evaluation is a very time-consuming task. It depends on many factors, such as the use of the radio transceiver or the distance between nodes. Simulations reduce considerably this time. They allow the evaluation of the network behavior before its deployment. This article presents a simulation tool which helps developers to obtain information about battery state. This simulator extends the well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications using an accurate model of the battery consumption and its relation to the radio power transmission. Although an specific indoor scenario is used in testing of simulation, the simulator is not limited to this environment. It is possible to work in outdoor scenarios too. Experimental results validate the proposed model.     

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.     

Energy efficient algorithms for enhancing lifetime in wireless sensor networks

Microsystem Technologies - A crucial research problem in the field of wireless sensor network is to maximize its lifetime. One approach to solve this problem is to group the nodes in clusters or...     

基于区域分簇的大规模无线传感器网络生命周期优化策略

针对环境监测、电网冰灾监测等大规模监测系统中监测区域覆盖广、传感器数量大等特性,为节约网络能耗以延长生命周期,提出了一种基于区域分簇的大规模无线传感器网络生命周期优化策略(RCS).该策略首先利用传感器节点的位置信息进行凝聚的层次聚类(AGNES)算法将大规模网络分区以优化簇首的分布;其次,候选簇首节点竞选簇首成功后进行不均匀分簇,同时加入时间阈值来均衡簇首节点的能耗;最后,采用簇间多跳路由,根据节点剩余能量、与汇聚点距离计算网络能耗代价来构建最小生成树进行路由选择.在仿真实验中,该策略与经典的低功耗自适应分簇(LEACH)协议和能量高效的非均匀分簇(EEUC)算法比较,簇首能耗平均分别减少了45.1%和2.4%,网络生命周期分别延长了38%和3.7%.实验结果表明,RCS在大规模网络中能有效均衡整体网络能耗,显著延长了网络的生命周期.     

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.     

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。     

Maximizing network lifetime based on transmission range adjustment in wireless sensor networks

In a wireless sensor network (WSN), the unbalanced distribution of communication loads often causes the problem of energy hole, which means the energy of the nodes in the hole region will be exhausted sooner than the nodes in other regions. This is a key factor which affects the lifetime of the networks. In this paper we propose an improved corona model with levels for analyzing sensors with adjustable transmission ranges in a WSN with circular multi-hop deployment (modeled as concentric coronas). Based on the model we consider that the right transmission ranges of sensors in each corona is the decision factor for optimizing the network lifetime after nodes deployment. We prove that searching optimal transmission ranges of sensors among all coronas is a multi-objective optimization problem (MOP), which is NP hard. Therefore, we propose a centralized algorithm and a distributed algorithm for assigning the transmission ranges of sensors in each corona for different node distributions. The two algorithms can not only reduce the searching complexity but also obtain results approximated to the optimal solution. Furthermore, the simulation results of our solutions indicate that the network lifetime approximates to that ensured by the optimal under both uniform and non-uniform node distribution.     

A chain-cluster based routing algorithm for wireless sensor networks

Wireless sensor networks (WSNs) are an emerging technology for monitoring physical world. Different from the traditional wireless networks and ad hoc networks, the energy constraint of WSNs makes energy saving become the most important goal of various routing algorithms. For this purpose, a cluster based routing algorithm LEACH (low energy adaptive clustering hierarchy) has been proposed to organize a sensor network into a set of clusters so that the energy consumption can be evenly distributed among all the sensor nodes. Periodical cluster head voting in LEACH, however, consumes non-negligible energy and other resources. While another chain-based algorithm PEGASIS (power- efficient gathering in sensor information systems) can reduce such energy consumption, it causes a longer delay for data transmission. In this paper, we propose a routing algorithm called CCM (Chain-Cluster based Mixed routing), which makes full use of the advantages of LEACH and PEGASIS, and provide improved performance. It divides a WSN into a few chains and runs in two stages. In the first stage, sensor nodes in each chain transmit data to their own chain head node in parallel, using an improved chain routing protocol. In the second stage, all chain head nodes group as a cluster in a self- organized manner, where they transmit fused data to a voted cluster head using the cluster based routing. Experimental results demonstrate that our CCM algorithm outperforms both LEACH and PEGASIS in terms of the product of consumed energy and delay, weighting the overall performance of both energy consumption and transmission delay.     

Optimal energy allocation in heterogeneous wireless sensor networks for lifetime maximization

We consider the problem of optimal energy allocation and lifetime maximization in heterogeneous wireless sensor networks. We construct a probabilistic model for heterogeneous wireless sensor networks where sensors can have different sensing range, different transmission range, different energy consumption for data sensing, and different energy consumption for data transmission, and the stream of data sensed and transmitted from a sensor and the stream of data relayed by a sensor to a base station are all treated as Poisson streams. We derive the probability distribution and the expectation of the number of data transmissions during the lifetime of each sensor and the probability distribution and the expectation of the lifetime of each sensor. In all these analysis, energy consumption of data sensing and data transmission and data relay are all taken into consideration. We develop an algorithm to find an optimal initial energy allocation to the sensors such that the network lifetime in the sense of the identical expected sensor lifetime is maximized. We show how to deal with a large amount of energy budget that may cause excessive computational time by developing accurate closed form approximate expressions of sensor lifetime and network lifetime and optimal initial energy allocation. We derive the expected number of working sensors at any time. Based on such results, we can find the latest time such that the expected number of sensors that are still functioning up to that time is above certain threshold.     

Network lifetime maximization for time-sensitive data gathering in wireless sensor networks

Energy-constrained sensor networks have been widely deployed for environmental monitoring and security surveillance purposes. Since sensors are usually powered by energy-limited batteries, in order to prolong the network lifetime, most existing research focuses on constructing a load-balanced routing tree rooted at the base station for data gathering. However, this may result in a long routing path from some sensors to the base station. Motivated by the need of some mission-critical applications that require all sensed data to be received by the base station with minimal delay, this paper aims to construct a routing tree such that the network lifetime is maximized while keeping the routing path from each sensor to the base station minimized. This paper shows that finding such a tree is NP-hard. Thus a novel heuristic called top-down algorithm is presented, which constructs the routing tree layer by layer such that each layer is optimally extended, using a network flow model. A distributed refinement algorithm is then devised that dramatically improves on the load balance for the routing tree produced by the top-down algorithm. Finally, extensive simulations are conducted. The experimental results show that the top-down algorithm with balance-refinement delivers a shortest routing tree whose network lifetime achieves around 85% of the optimum.     

Multilayer cluster designing algorithm for lifetime improvement of wireless sensor networks

Cluster-based network is a proven architecture for energy-aware routing, but more attention is required to ameliorate the energy consumption aspect of its cluster designing process. In this research work, we introduce a novel design of clustered network architecture. The proposed design technique is innovative in its idea. The general trend in this scene is either centralized decision at base station for cluster head selection and its members or distributed decision by exchanging information between neighboring nodes until the cluster head and its members are selected. Both the techniques drastically create mess in energy consumption due to too much broadcasting, especially in large networks as well as message exchange until some final decision is made. Our novel layer-based hybrid algorithm for cluster head and cluster member selection comes up to novel communication architecture. Since its substantial constituent is cluster designing, we named it Multilayer Cluster Designing Algorithm (MCDA). The proposed design not only has effect on lessening blind broadcasting, but also on decreasing the message exchange in a passionate way. It also encapsulates the beauty of efficient centralized decision making for cluster designing and energy-aware distributed cluster head selection and cluster member allocation process. Comprehensive experimentations have been performed on the comparative analysis of MCDA with state-of-the-art centralized and distributed cluster designing approaches present in published literature. Calculation of energy consumption in various operational parametric values, number of clusters designed and the number of packets broadcasted during cluster designing are the main performance evaluation parameters. It has been found that MCDA outperforms compared to its three competing algorithms with respect to the aforementioned parameters due to its multilayered synergistic mating approach.     

Satisfying the target network lifetime in wireless sensor networks

Generally, the lifetime of a wireless sensor network (WSN) is defined as the duration until any sensor node dies due to battery exhaustion. If the traffic load is not properly balanced, the batteries of some sensor nodes may be depleted quickly, and the lifetime of the WSN will be shortened. While many energy-efficient routing schemes have been proposed for WSNs, they focus on maximizing the WSN lifetime. In this paper, we propose a scheme that satisfies a given ‘target’ lifetime. Because energy consumption depends on traffic volume, the target lifetime cannot be guaranteed through energy-efficient routing alone. We take an approach that jointly optimizes the sensing rate (i.e., controlling the sensor-traffic generation or duty cycle) and route selection. Satisfying the target lifetime while maximizing the sensing rate is a NP-hard problem. Our scheme is based on a simple Linear Programming (LP) model and clever heuristics are applied to compute a near-optimal result from the LP solution. We prove that the proposed scheme guarantees a 1/2-approximation to the optimal solution in the worst case. The simulation results indicate that the proposed scheme achieves near-optimality in various network configurations.     

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。     

Distributed routing in wireless sensor networks using energy welfare metric

There are several requirements for a routing algorithm in wireless sensor networks. First, it should achieve both energy-efficiency and energy-balancing together, in order to prolong the lifetime of sensor networks. Second, the algorithm should follow a distributed control scheme so that it is applicable to large-scale networks. Third, it needs to be robust to diverse potential event generation patterns. The routing algorithm, MaxEW, designed in this study satisfies such requirements. It adopts the social welfare function from social sciences to compute energy welfare as a goodness measure for energy populations. When each sensor tries to maximize energy welfare of its local society, it collectively leads to globally efficient energy-balancing. This emergent property consequently supports preparedness and hence robustness to diverse event generation patterns. We demonstrate the effectiveness of the proposed routing algorithm through extensive simulation-based experiments, by comparing with other existing algorithms as well as optimal routing solutions.     

一种无线传感器网络链式传输分簇路由协议

由于周围环境对无线传感器网络(WSNs)的影响,在布设到特殊环境下时会产生信号的衰减与损耗,导致通信不畅。针对此问题,提出一种以LEACH路由协议为基础适应特殊环境(长直空间)的新型路由算法。本算法采用链式传输,即从内部逐一将信号传输给距空间最外端且距基站位置较近的簇头,克服了内部节点死亡过快的问题。同时簇内采用链式传输并且改进簇头阈值与成簇半径,减小了能量消耗,提高了稳定性,克服了LEACH算法的不足。