Sensor networks, which consist of sensor nodes each capable of sensing environment and transmitting data, have lots of applications in battlefield surveillance, environmental monitoring, industrial diagnostics, etc. Coverage which is one of the most important performance metrics for sensor networks reflects how well a sensor field is monitored. Individual sensor coverage models are dependent on the sensing functions of different types of sensors, while network-wide sensing coverage is a collective performance measure for geographically distributed sensor nodes. This article surveys research progress made to address various coverage problems in sensor networks. We first provide discussions on sensor coverage models and design issues. The coverage problems in sensor networks can be classified into three categories according to the subject to be covered. We state the basic coverage problems in each category, and review representative solution approaches in the literature. We also provide comments and discussions on some extensions and variants of these basic coverage problems.

Coverage problems in sensor networks: A survey

The coverage optimization problem has been examined thoroughly for omni-directional sensor networks in the past decades. However, the coverage problem in directional sensor networks (DSN) has newly taken attraction, especially with the increasing number of wireless multimedia sensor network (WMSN) applications. Directional sensor nodes equipped with ultrasound, infrared, and video sensors differ from traditional omni-directional sensor nodes with their unique characteristics, such as angle of view, working direction, and line of sight (LoS) properties. Therefore, DSN applications require specific solutions and techniques for coverage enhancement. In this survey article, we mainly aim at categorizing available coverage optimization solutions and survey their problem definitions, assumptions, contributions, complexities and performance results. We categorize available studies about coverage enhancement into four categories. Target-based coverage enhancement, area-based coverage enhancement, coverage enhancement with guaranteed connectivity, and network lifetime prolonging. We define sensing models, design issues and challenges for directional sensor networks and describe their (dis)similarities to omni-directional sensor networks. We also give some information on the physical capabilities of directional sensors available on the market. Moreover, we specify the (dis)advantages of motility and mobility in terms of the coverage and network lifetime of DSNs.

On coverage issues in directional sensor networks: A survey

Unlike convectional omni-directional sensors that always have an omni-angle of sensing range, directional sensors may have a limited angle of sensing range due to technical constraints or cost considerations. A directional sensor network consists of a number of directional sensors, which can switch to several directions to extend their sensing ability to cover all the targets in a given area. Power conservation is still an important issue in such directional sensor networks. In this paper, we address the multiple directional cover sets problem (MDCS) of organizing the directions of sensors into a group of non-disjoint cover sets to extend the network lifetime. One cover set, in which the directions cover all the targets, is activated at one time. We prove the MDCS to be NP-complete and propose three heuristic algorithms for the MDCS. Simulation results are also presented to demonstrate the performance of these algorithms.

Target-Oriented Scheduling in Directional Sensor Networks

Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

In directional sensor networks (DSNs), motility capability of a directional sensor node has a considerable impact on the coverage enhancement after the initial deployment. Since random deployment may result in overlapped field of views (FoVs) and occluded regions, directional sensor nodes with rotatable mechanisms may reorganize their working directions to improve the coverage. Our proposed algorithm, Attraction Forces of Uncovered Points (AFUP), aims at both minimizing the overlapped areas and facing the working directions towards the area of interest. AFUP is a distributed iterative algorithm and exploits the repel forces exerted by the uncovered points around the sensor nodes. The proposed algorithm improves the coverage by 18%-25% after the initial deployment. Moreover, AFUP outperforms three well-known area coverage enhancement methods [15] [19] [16] in terms of coverage improvement and overlap minimization. Our simulation results show that AFUP converges in five iterations in most of the scenarios.

A new coverage improvement algorithm based on motility capability of directional sensor nodes

Unlike convectional omnidirectional sensors that always have an omni-angle of sensing range, directional sensors may have a limited angle of sensing range due to the technical constraints or cost considerations. A directional sensor network consists of a number of directional sensors, which can switch to several directions to extend their sensing ability to cover all the targets in a given area. Power conservation is still an important issue in such directional sensor networks. In this paper, we address the multiple directional cover sets (MDCS) problem of organizing the directions of sensors into a group of non-disjoint cover sets to extend the network lifetime. One cover set in which the directions cover all the targets is activated at one time. We prove the MDCS to be NP-complete and propose several algorithms for the MDCS. Simulation results are presented to demonstrate the performance of these algorithms.

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Energy Efficient Target-Oriented Scheduling in Directional Sensor Networks

A directional sensor network consists of a number of directional sensors, which can switch to several directions to extend their sensing ability to cover the interested targets in a given area. Because a directional sensor has a smaller angle of sensing range or even does not cover any target when it is deployed, how to cover the interested targets becomes a major problem in directional sensor networks. In this paper, we address the directional cover set problem (DCS) of finding a cover set in a directional sensor network, in which the directions cover all the targets. We propose both centralized and distributed algorithms for the DCS. We also introduce two applications that utilize these algorithms to extend the network work time while maximizing the coverage of the targets. Simulation results are presented to demonstrate the performance of these algorithms and the applications.

Cover Set Problem in Directional Sensor Networks

A surveillance application requires sufficient coverage of the protected region while minimizing the energy consumption and extending the lifetime of sensor networks. This can be achieved by putting redundant sensor nodes to sleep. In this paper, we propose a precise and energy-aware coverage control protocol, named Area-based Collaborative Sleeping (ACOS). The ACOS protocol, based on the net sensing area of a sensor, controls the mode of sensors to maximize the coverage, minimize the energy consumption, and to extend the lifetime of the sensor network. The simulation shows that our protocol has better coverage of the surveillance area while waking fewer sensors than other state-of-the-art sleeping protocols. It extends the lifetime of sensor networks significantly.

ACOS: An Area-based Collaborative Sleeping Protocol for Wireless Sensor Networks.

A surveillance application requires sufficient coverage of the protected region while minimizing the energy consumption and extending the lifetime of sensor networks This can be achieved by putting redundant sensor nodes to sleep In this paper, we propose a precise and energy-aware coverage control protocol, named Area-based Collaborative Sleeping (ACOS) The ACOS protocol, based on the net sensing area of a sensor, controls the mode of sensors to maximize the coverage, minimize the energy consumption, and to extend the lifetime of the sensor network The simulation shows that our protocol has better coverage of the surveillance area while waking fewer sensors than other state-of-the-art sleeping protocols

Yanli Cai

Papers

Target-Oriented Scheduling in Directional Sensor Networks

Energy Efficient Target-Oriented Scheduling in Directional Sensor Networks

Cover Set Problem in Directional Sensor Networks

ACOS: An Area-based Collaborative Sleeping Protocol for Wireless Sensor Networks.

ACOS: a precise energy-aware coverage control protocol for wireless sensor networks