Review
A survey on coverage and connectivity issues in wireless sensor networks

https://doi.org/10.1016/j.jnca.2011.11.016Get rights and content

Abstract

A wireless sensor network (WSN) is composed of a group of small power-constrained nodes with functions of sensing and communication, which can be scattered over a vast region for the purpose of detecting or monitoring some special events. The first challenge encountered in WSNs is how to cover a monitoring region perfectly. Coverage and connectivity are two of the most fundamental issues in WSNs, which have a great impact on the performance of WSNs. Optimized deployment strategy, sleep scheduling mechanism, and coverage radius cannot only reduce cost, but also extend the network lifetime. In this paper, we classify the coverage problem from different angles, describe the evaluation metrics of coverage control algorithms, analyze the relationship between coverage and connectivity, compare typical simulation tools, and discuss research challenges and existing problems in this area.

Introduction

The advancing in the sensor technology, micro-electromechanical systems, modern networking and wireless communications technology has greatly promoted the emergence and development of modern WSNs (Pottie, 1998, Stankovic, 2008). WSNs are currently of concern in the world wide involving a high degree of cross-multidisciplinary, highly integrated, cutting-edge knowledge of hot research field. WSNs extend the ability of people to access information, communicate physical information of the objective environment with transmission networks, and the next generation network will provide people with the most direct, effective and authentic information. Thus, WSN technology has very broad application prospects, which can be used in military, industrial and agricultural control, urban management, biomedical, environmental testing, disaster relief and other fields. Academia and industry of many countries attach great importance to WSN technology, which is considered as one of the most influential technologies in the 21st century.

An important problem receiving increased consideration recently is the coverage problem, which focuses on how well the sensors observe the physical space they deployed. Coverage is one of the measurements of WSNs QoS (Quality of Service), and it is closely related with energy consumption. In some cases it is possible to obtain energy from external environment (Want et al., 2005) (e.g., by using solar cells as power source). However, in many applications and scenarios, nodes of WSNs are often dropped or thrown into the sensor field randomly. External power supply sources often exhibit a non-continuous behavior so that an energy buffer is needed as well, and even no power supply at all. In any case, energy is a very critical resource and must be used very sparingly. Therefore, energy conservation is a key issue in the design of systems based on WSNs. Due to the limited energy resource in each sensor node, we need to utilize the sensors in an efficient manner so as to increase the lifetime of the network.

The researches about coverage and connectivity issues in WSNs have been involved a lot, which mainly confined to explaining “what is” and “how to realize”. In this article, not only “what is” and “how to realize” are concerned, but also “why” is taken into consideration. Here, “why” is described by energy consumption, which shows the basis of classification. There are three different approaches to the problem of conserving energy in WSNs, and all of the approaches must keep the initial coverage QoS. The first approach is to optimize coverage deployment strategy. The second approach is to plan a schedule of active sensors that enables other sensors to go into a sleep mode. The third approach is adjusting the sensing range of sensors for energy conservation.

In this paper, we analyze coverage and connectivity issues primarily based on the angle of energy consumption, especially in coverage deployment strategy, sleep scheduling mechanism and adjustable coverage radius. In Section 2, we introduce the basic knowledge of coverage concepts, such as node properties, sensing models, and evaluation metrics. Section 3 coverage deployment strategy is described. Recent research issues about static coverage and dynamic coverage are described here as well as the solution to optimize coverage deployment. Section 4 discusses sleep scheduling mechanism through some typical examples. It is a very efficient solution of energy conservation to coverage problem. In Section 5, we focus on adjusting the sensing range of each sensor in order to reduce the overlaps among sensing ranges while maintain the QoS of coverage above a predefined detection level. In Section 6, we introduce the relationship between coverage and connectivity, and prove that a network is connective when Rc2Rs. Section 7 introduces the simulation tools on coverage and connectivity, compares the differences among several popular simulators. In Section 8, we summarize typical issues on coverage and connectivity in WSNs, and discuss existing problems and research challenges in this area. Finally, in Section 9, a simple conclusion is given.

Section snippets

Preliminaries

The solutions to coverage and connectivity issues in WSNs involve a lot of basic theories and assumptions. The basic knowledge of coverage concepts is essential. In this section, we describe sensor node properties, sensing models, centralized/distributed algorithms, and the evaluation indicators of coverage quality.

Coverage deployment strategy

Coverage has attracted a great deal of research attention due to its relation to optimization of resources in a sensing field. Maximizing the coverage and maintaining a lower cost of deployment have always been a challenge, especially when the monitoring region is unknown and possibly hazardous. An effective approach for energy conservation in WSNs is coverage deployment strategy. Many simulation results show that optimal deployment strategy can achieve a certain degree of coverage results with

Sleep scheduling mechanism

Energy is paramountly concern in WSN applications that need to operate for a long time with the limited battery power. Another effective approach for energy conservation in WSNs is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity.

In order to understand the sleep scheduling mechanisms, one first needs to

Adjustable coverage radius

Many existing networks assume that the sensing range of a sensor is fixed. However, adjusting the transmission or sensing range of the wireless senor nodes is another power saving techniques. To the best of our knowledge, such a radius adaptive mechanism is mainly used for solving target coverage problems. The main idea of radius adaptive mechanism is reducing the overlaps among sensing ranges while maintain the QoS of coverage above a predefined detection level. How to extend the mechanism to

Overview

The above mentioned are mainly deal with coverage sensing, however, it is connectivity which determines the effective transmission of data. In Wang et al. (2003), it's clear that connectivity only requires that the location of any active node be within the communication range of one or more active nodes such that all active nodes can form a connected communication backbone, while coverage requires all locations in the coverage region be within the sensing range of at least one active node. Once

Simulation tools

Nowadays simulation tools of WSNs have been widely used, due to two serious limitations of the physics experimental platform (Shu et al., 2008). The first one is large scale, for what it is very expensive to buy a large number of sensor nodes until today. Especially, the cost for building a large scale platform of WSNs is also not acceptable for most academic researchers. The second one is non-replicable environment, for some specific applications, e.g., monitoring an erupting volcano,

Typical issues summary

As described in Section 1, there are three categories of approaches to the issues of conserving energy in WSNs, including coverage deployment strategy, sleep scheduling mechanism, and adjustable coverage radius. Many researchers have investigated these approaches, and proposed their own solutions. We summarize the typical issues on coverage and connectivity in WSN as shown in Table 2. This summary aims to be more convenient to grasp the coverage and connectivity issues from macro.

Research challenges

A great number

Conclusion

Coverage and connectivity are two of the most fundamental issues in WSNs, which have a great impact on QoS of WSNs. Many algorithms, strategies and mechanisms have been proposed by researchers around the world to solve these problems. First, a brief introduction to the basic knowledge of coverage concepts is given in this paper. Second, we take energy efficient factors into consideration, and describe the coverage and connectivity issues from three aspects: coverage deployment strategy, sleep

Acknowledgment

The research in this paper is supported by “the Fundamental Research Funds for the Central Universities, Nos. 2010B22914, 2010B22814, 2010B24414”, and “the research fund of Jiangsu Key Laboratory Of Power Transmission & Distribution Equipment Technology, No. 2010JSSPD04”.

Lei Shu's research in this paper was supported by Grant-in-Aid for Scientific Research (S) (21220002) of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Lei Shu is the corresponding author of this

References (70)

  • M. Locatelli et al.

    Packing equal circles in a square: a deterministic global optimization approach

    Discrete Applied Mathematics

    (2002)
  • H. Qi et al.

    Distributed sensor networks—a review of recent research

    Journal of the Franklin Institute

    (2001)
  • B. Wang et al.

    Layered diffusion-based coverage control in wireless sensor networks

    Computer Networks

    (2009)
  • N. Ahmed et al.

    The holes problem in wireless sensor networks: a survey

    SIGMOBILE Mobile Computing and Communications Review

    (2005)
  • F. Aurenhammer

    Voronoi diagrams—a survey of a fundamental geometric data structure

    ACM Computing Surveys

    (1991)
  • Y.U. Cao et al.

    Cooperative mobile robotics: antecedents and directions

    Autonomous Robots

    (1997)
  • M. Cardei et al.

    Wireless sensor networks with energy efficient organization

    Journal of Interconnection Networks

    (2002)
  • M. Cardei et al.

    Energy-efficient target coverage in wireless sensor networks

  • M. Cardei et al.

    Maximum network lifetime in wireless sensor networks with adjustable sensing ranges

  • J.-F. Chamberland et al.

    Decentralized detection in sensor networks

    IEEE Transactions on Signal Processing

    (2003)
  • R.-S. Chang et al.

    Self-deployment by density control in sensor networks

    IEEE Transactions on Vehicular Technology

    (2008)
  • A. Chen et al.

    Designing localized algorithms for barrier coverage

  • A. Chen et al.

    Measuring and guaranteeing quality of barrier-coverage in wireless sensor networks

  • B. Chen et al.

    Span: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks

    Wireless Networks

    (2002)
  • W. Cheng et al.

    Sweep coverage with mobile sensors

  • X. Cheng et al.

    Relay sensor placement in wireless sensor networks

    Wireless Networks

    (2008)
  • T. Clouqueur et al.

    Sensor deployment strategy for target detection

  • S. Dhillon et al.

    Sensor placement for effective coverage and surveillance in distributed sensor networks

  • S. Dhillon et al.

    Sensor placement for effective coverage and surveillance in distributed sensor networks

  • A. Efrat et al.

    Approximation algorithms for two optimal location problems in sensor networks

  • S. Fortune

    Handbook of discrete and computational geometry

  • A. Ghosh

    Estimating coverage holes and enhancing coverage in mixed sensor networks

  • J. Harada et al.

    Path coverage property of randomly deployed sensor networks with finite communication ranges

  • J. Hill et al.

    System architecture directions for networked sensors

    SIGPLAN Notices

    (2000)
  • A. Howard et al.

    An incremental self-deployment algorithm for mobile sensor networks

    Autonomous Robots

    (2002)
  • Howard A, Matari MJ, Sukhatme GS. Mobile sensor network deployment using potential fields: a distributed, scalable...
  • C.-F. Huang et al.

    The coverage problem in a wireless sensor network

    Mobile Networks and Applications

    (2005)
  • S. Kumar et al.

    Barrier coverage with wireless sensors

    Wireless Networks

    (2007)
  • P. Levis et al.

    Tossim: accurate and scalable simulation of entire tinyos applications

  • B. Liu et al.

    Mobility improves coverage of sensor networks

  • B. Liu et al.

    Strong barrier coverage of wireless sensor networks

  • L.-H. Loo et al.

    Cooperative multi-agent constellation formation under sensing and communication constraints

  • V. Lumelsky et al.

    Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

    Algorithmica

    (1987)
  • S. Megerian et al.

    Exposure in wireless sensor networks: theory and practical solutions

    Wireless Networks

    (2002)
  • S. Nath et al.

    Communicating via fireflies: geographic routing on duty-cycled sensors

  • Cited by (496)

    View all citing articles on Scopus
    View full text