A pragmatic approach to area coverage in hybrid wireless sensor networks

Success of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. This paper seeks to address the problem of determining the current coverage achieved by the non‐deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. We identify three key elements that are critical for ensuring effective area coverage in Hybrid WSN: (i) determining the boundary of the target region and evaluating the area coverage (ii) locating coverage holes and maneuvering mobile nodes to fill these voids, and (iii) maintaining the desired coverage over the entire operational lifetime of the network. We propose a comprehensive solution that addresses all of the aforementioned aspects of the area coverage, called MAPC (mobility assisted probabilistic coverage). MAPC is a distributed protocol that operates in three distinct phases. The first phase identifies the boundary nodes using the geometric right‐hand rule. Next, the static nodes calculate the area coverage and identify coverage holes using a novel probabilistic coverage algorithm (PCA). PCA incorporates realistic sensing coverage model for range‐based sensors. The second phase of MAPC is responsible for navigating the mobile nodes to plug the coverage holes. We propose a set of coverage and energy‐aware variants of the basic virtual force algorithm (VFA). Finally, the third phase addresses the problem of coverage loss due to faulty and energy depleted nodes. We formulate this problem as an Integer Linear Program (ILP) and propose practical heuristic solutions that achieve similar performance as that of the optimal ILP solution. A guiding principle in our design process has been to ensure that the MAPC can be readily implemented in real‐world applications. We implemented the boundary detection and PCA algorithm (i.e., Phase I) of the MAPC protocol on off‐the‐shelf sensor nodes and results show that the MAPC can successfully identify boundary nodes and accurately determine the area coverage in the presence of real radio irregularities observed during the experiments. Extensive simulations were carried out to evaluate the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage, while reducing the total energy consumption by up to 70% as compared with the basic VFA. Copyright © 2010 John Wiley & Sons, Ltd.     

基于人工蜂群寻优算法的WSN中继节点布局方案

无线传感器网络(WSN)环境下,中继节点位置布局性能优劣是影响网络寿命的关键因素之一。目前,针对三维空间高密度WSN,缺少能够在硬件成本和连通性双重约束条件下提高网络寿命的中继节点位置布局方案。基于网格布局方式,提出了一种基于人工蜂群优化算法的中继节点布局方案(ABC-RNDS)。ABC-RNDS方案采用双层网络拓扑结构,首先使用最小生成树法构建骨干网络,再使用人工蜂群优化算法通过网络参数寻优和限制中继节点总数的方法实现网络寿命的延长。实验验证分析表明,在成本和连通性受约束的条件下,ABC-RNDS算法与传统方案相比能够显著提高网络寿命。     

Relay Node Deployment Strategies in Heterogeneous Wireless Sensor Networks

In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multihop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large-scale heterogeneous WSN.     

A Novel Sensor Node Deployment using Low Discrepancy Sequences for WSN

The foremost challenge in designing Wireless Sensor Networks (WSNs) is careful node placement. Sensor Node placement is a one of the powerful optimization technique for accomplishing the anticipated goals. This paper focuses on categories of node deployment in WSN. The paper highlights various problems and identifies the different objectives in sensor node deployment. The paper proposes a novel node deployment strategy based on Quasi- random method of low-discrepancy sequences to increase the lifetime and the coverage of the network. The aim of the paper is to study how the node deployment affects the different QoS parameters such as packet delivery ratio, average energy consumption, delay, etc. with various multi-objective routing algorithms WSN. To validate the proposal simulation, results are presented in this paper. The paper concludes with the future outlook.     

A study on one‐dimensional k‐coverage problem in wireless sensor networks

In this paper, we study the one‐dimensional coverage problem in a wireless sensor network (WSN) and consider a network deployed along a one‐dimensional line according to a Poisson distribution. We analyze three important parameters that are related to the problem, i.e., expected k‐coverage proportion, full k‐coverage probability, and partial k‐coverage probability, and derive mathematical models that describe the relationships between the node density in the network and these parameters. The purpose is to calculate or estimate the node density required for achieving a given coverage probability, which is useful in the deployment of a one‐dimensional network for many applications. We first analyze the expected k‐coverage proportion, then analyze the full k‐coverage probability for k = 1 and the lower bound to the full k‐coverage probability for k > 1, and finally analyze the partial k‐coverage probability for k = 1 and give a brief discussion of the partial k‐coverage probability for k > 1. The mathematical models are validated through simulation. Copyright © 2011 John Wiley & Sons, Ltd.     

An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks

In this paper we investigate the expected lifetime and information capacity, defined as the maximum amount of data (bits) transferred before the first sensor node death due to energy depletion, of a data-gathering wireless sensor network. We develop a fluid-flow based computational framework that extends the existing approach, which requires precise knowledge of the layout/deployment of the network, i.e., exact sensor positions. Our method, on the other hand, views a specific network deployment as a particular instance (sample path) from an underlying distribution of sensor node layouts and sensor data rates. To compute the expected information capacity under this distribution-based viewpoint, we model parameters such as the node density, the energy density and the sensed data rate as continuous spatial functions. This continuous-space flow model is then discretized into grids and solved using a linear programming approach. Numerical studies show that this model produces very accurate results, compared to averaging over results from random instances of deployment, with significantly less computation. Moreover, we develop a robust version of the linear program, which generates robust solutions that apply not just to a specific deployment, but also to topologies that are appropriately perturbed versions. This is especially important for a network designer studying the fundamental lifetime limit of a family of network layouts, since the lifetime of specific network deployment instances may differ appreciably. As an example of this model's use, we determine the optimal node distribution for a linear network and study the properties of optimal routing that maximizes the lifetime of the network.     

Lifetime enhancement of wireless sensor networks by avoiding energy-holes with Gaussian distribution

Wireless sensor networks (WSNs) are receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In WSNs, preserving energy requires utmost attention, as they are highly resource constrained. One fundamental way of conserving energy is judicious deployment of nodes within the network for balancing energy flow throughout the network. Node deployment using Gaussian distribution is a standard practice and is widely acceptable when random deployment is used. Initially, an analysis is done to establish that Gaussian distribution based node deployment is not energy balanced. Standard deviation of Gaussian distribution is identified as the parameter responsible for energy balancing. A deployment strategy is proposed for energy balancing using customized Gaussian distribution by discretizing the standard deviation. Performance of the scheme is evaluated in terms of energy balance and network lifetime. Exhaustive simulation is performed to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing. The simulation results show that our scheme also provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with three competing schemes and the results confirm our scheme’s supremacy in terms of both design performance metrics as well as network performance metrics.     

On Balancing Energy Consumption in Wireless Sensor Networks

Wireless sensor networks (WSNs) require protocols that make judicious use of the limited energy capacity of the sensor nodes. In this paper, the potential performance improvement gained by balancing the traffic throughout the WSN is investigated. We show that sending the traffic generated by each sensor node through multiple paths, instead of a single path, allows significant energy conservation. A new analytical model for load-balanced systems is complemented by simulation to quantitatively evaluate the benefits of the proposed load-balancing technique. Specifically, we derive the set of paths to be used by each sensor node and the associated weights (i.e., the proportion of utilization) that maximize the network lifetime.      

基于改进萤火虫算法的WSN覆盖优化

针对无线传感器网络节点部署不均所导致的网络覆盖率较低问题,以无线传感器网络覆盖率最大化为目标,提出一种基于改进萤火虫算法(IFA)的网络覆盖优化方法。该方法运用佳点集方法初始化种群,提高种群的多样性,奠定全局搜索基础;利用具有非线性指数递减的变形Sigmoid函数作为惯性权重,平衡算法的全局搜索和局部开发能力;采用高斯扰动策略对个体位置扰动更新,避免算法早熟。仿真结果表明,该算法与人工鱼群算法(AFSA)、种子杂交粒子群算法(HSPSO)和混沌萤火虫算法(CGSO)相比,能有效提高网络覆盖率,使节点部署分布更均匀。     

Enhancing Coverage Using Weight Based Clustering in Wireless Sensor Networks

Energy conservation in wireless sensor networks (WSNs) is a fundamental issue. For certain surveillance applications in WSN, coverage lifetime is an important issue and this is related to energy consumption significantly. In order to handle these two interlinked aspects in WSN, a new scheme named Weight based Coverage Enhancing Protocol (WCEP) has been introduced. The WCEP aims to obtain longer full coverage and better network life time. The WCEP is based on assigning different weight values to certain governing parameters which are residual energy, overlapping degree, node density and degree of sensor node. These governing parameters affect the energy and coverage aspects predominantly. Further, these four different parameters are prime elements in cluster formation process and node scheduling mechanisms. The weight values help in selection of an optimal group of Cluster Heads and Cluster Members, which result in enhancement of complete coverage lifetime. The simulation results indicate that WCEP performs better in terms of energy consumption also. The enhancement of value 24% in full coverage lifetime has been obtained as compared to established existing techniques.

     

Design of Probability Density Function Targeting Energy Efficient Network for Coalition Based WSNs

Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodically sending of the sensed data to the base station. Therefore, coverage and the lifetime are the two important issues that need to be addressed. Effective deployment of wireless sensors is a major concern as the coverage and lifetime of any wireless sensor network depends on it. In this paper, we propose the design of a Probability Density Function (PDF) targeting the desired coverage, and energy efficient node deployment scheme. The suitability of the proposed PDF based node distribution to model the network architecture considered in this work has been analyzed. The PDF divides the deployment area into concentric coronas and provides a probability of occurrence of a node within any corona. Further, the performance of the proposed PDF is evaluated in terms of the coverage, the number of transmissions of packets and the lifetime of the network. The scheme is compared with the existing node deployment schemes based on various distributions. The percentage gain of the proposed PDF based node deployment is 32\(\%\) more than that when compared with the existing schemes. Thus, the simulation results obtained confirm the schemes superiority over the other existing schemes.     

无线传感器网络部署及其覆盖问题研究

无线传感器网络是近几年发展起来的一种新兴技术,在条件恶劣和无人坚守的环境监测和事件跟踪中显示了很大的应用价值。节点部署是无线传感器网络工作的基础,对网络的运行情况和寿命有很大的影响。部署问题涉及覆盖、连接和节约能量消耗3个方面。该文重点讨论了网络部署中的覆盖问题,综述了现有的研究成果,总结了今后的热点研究方向,为以后的研究奠定了基础。     

Cost Modelling and Studies with Different Deployment Strategies for Wireless Multimedia Sensor Network Over Flat and Elevated Terrains

Wireless multimedia sensor networks (WMSNs) is widely used for surveillance application. These multimedia (audio and video) nodes are distributed according to different deployment strategies in a multi-tier heterogeneous architecture environment. In this paper we have modelled the deployment cost of WMSN considering the sensor type (audio or video), sensor configuration such as remaining energy of battery, deployment point, and terrain characteristics for surveillance applications. Using our proposed cost models we have studied the effects of different deployment strategies of WMSN over flat and elevated terrains. Our cost models helps in minimizing the cost of deployment while maintaining Quality-of-Service i.e., the coverage and connectivity of the audio and video sensors separately. We have formulated an integer linear program and proposed a heuristic solution to minimize the placement costs subject to network coverage requirements using our first cost model. Our second cost model is used to propose a scheme that will ensure connectivity of the network. We have done simulations with three network deployment strategies, namely deterministic, random and hybrid and show that the hybrid deployment of sensor nodes yields a balance of performance and cost as compared to the other two. Our study provides guidelines for the network architect to select a particular deployment strategy under performance and cost requirements.     

Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes

Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes.     

Mathematical Evaluation of Environmental Monitoring Estimation Error through Energy-Efficient Wireless Sensor Networks

In this paper, the estimation of a scalar field over a bidimensional scenario (e.g., the atmospheric pressure in a wide area) through a self-organizing wireless sensor network (WSN) with energy constraints is investigated. The sensor devices (denoted as nodes) are randomly distributed; they transmit samples to a supervisor by using a clustered network. This paper provides a mathematical framework to analyze the interdependent aspects of WSN communication protocol and signal processing design. Channel modelling and connectivity issues, multiple access control and routing, and the role of distributed digital signal processing (DDSP) techniques are accounted for. The possibility that nodes perform DDSP is studied through a distributed compression technique based on signal resampling. The DDSP impact on network energy efficiency is compared through a novel mathematical approach to the case where the processing is performed entirely by the supervisor. The trade-off between energy conservation (i.e., network lifetime) and estimation error is discussed and a design criterion is proposed as well. Comparison to simulation outcomes validates the model. As an example result, the required node density is found as a trade-off between estimation quality and network lifetime for different system parameters and scalar field characteristics. It is shown that both the DDSP technique and the MAC protocol choice have a relevant impact on the performance of a WSN.     

Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs

In the wireless sensor networks, sensor deployment and coverage are the vital parameter that impacts the network lifetime. Network lifetime can be increased by optimal placement of sensor nodes and optimizing the coverage with the scheduling approach. For sensor deployment, heuristic algorithm is proposed which automatically adjusts the sensing range with overlapping sensing area without affecting the high degree of coverage. In order to demonstrate the network lifetime, we propose a new heuristic algorithm for scheduling which increases the network lifetime in the wireless sensor network. Further, the proposed heuristic algorithm is compared with the existing algorithms such as ant colony optimization, artificial bee colony algorithm and particle swarm optimization. The result reveals that the proposed heuristic algorithm with adjustable sensing range for sensor deployment and scheduling algorithm significantly increases the network lifetime.     

基于ERPMT改进启发式方法的WSN寿命最大化算法

针对传感器节点的电池容量限制导致无线传感网络寿命低的问题,基于容量最大化(CMAX)、线上最大化寿命(OML)两种启发式方法以及高效路由能量管理技术(ERPMT),提出了基于ERPMT改进启发式方法的无线传感网络寿命最大化算法。首先,通过启发式方法初始化每个传感器节点,将节点能量划分为传感器节点起源数据和其它节点数据延迟;然后利用加入的一种优先度量延迟一跳节点的能量消耗;最后,根据路径平均能量为每个路由分配一个优先级,并通过ERPMT实现最终的无线传感网络优化。针对不同分布类型网络寿命的实验验证了本文算法的有效性及可靠性,实验结果表明,相比较为先进的启发式方法CMAX及OML,本文算法明显增大了无线传感网络的覆盖范围,并且大大地延长了网络的寿命。     

Optimized mobile sink based grid coverage-aware sensor deployment and link quality based routing in wireless sensor networks

Mobile sink (MS) has been used in wireless sensor networks (WSN) to increase the network lifetime by changing the location over time. The major quality of service given by WSN is coverage energy consumption (EC) and network lifetime. There are many methods implemented for enhance the coverage hole restoration and reduce the EC. We propose a novel MSCOLER (MS based Coverage Optimization and Link-stability Estimation Routing) protocol for Optimal Coverage restoration and Link stability Estimation. An optimization algorithm is used to optimize the coverage hole and move the redundant node besides the hole. During the routing process, link quality based routing is used to discover the relay nodes with the estimation of link stability to enhance the entire network lifetime and practically make the perfect transmission distance for energy saving. Experimental results demonstrate that proposed protocol can solve the coverage restoration problem, decrease the EC and reduce the network lifetime. The performance is evaluated regarding Average of residual energy (ARE), Receiving packets ratio (RPR), Moving energy consumption (MEC), Network lifetime (NL), Percentage of coverage (%C) and Average Energy Consumption (AEC).     

3D node deployment strategies prediction in wireless sensors network

ABSTRACT

3D Deployment plays a fundamental role in setting up efficient wireless sensor networks (WSNs) and IoT networks. In general, WSN are widely utilised in a set of real contexts such as monitoring smart houses and forest fires with parachuted sensors. This study focus on planned 3D deployment in which the sensor nodes must be accurately positioned at predetermined locations to optimise one or more design objectives under some given constraints. The purpose of planned deployment is to identify the type, the number, and the locations of nodes to optimise the coverage, the connectivity and the network lifetime. There have been a large number of studies that proposed algorithms resolving the premeditated problem of 3D deployment. The objective of this study is twofold. The first one is to present the complexity of 3D deployment and then detail the types of sensors, objectives, applications and recent research that concerns the strategy used to solve this problem. The second one is to present a comparative survey between the recent optimisation strategies solving the problem of 3D deployment in WSN. Based on our extensive review, we discuss the strengths and limitations of each proposed solution and compare them in terms of WSN design factors.     

Node clustering in wireless sensor networks: recent developments and deployment challenges

The large-scale deployment of wireless sensor networks (WSNs) and the need for data aggregation necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering has proven to be an effective approach for organizing the network into a connected hierarchy. In this article, we highlight the challenges in clustering a WSN, discuss the design rationale of the different clustering approaches, and classify the proposed approaches based on their objectives and design principles. We further discuss several key issues that affect the practical deployment of clustering techniques in sensor network applications.