Review: A survey on coverage and connectivity issues in wireless sensor networks
01 Mar 2012-Journal of Network and Computer Applications (JOURNAL OF NETWORK AND COMPUTER APPLICATIONS)-Vol. 35, Iss: 2, pp 619-632
TL;DR: The coverage problem is classified from different angles, the evaluation metrics of coverage control algorithms are described, the relationship between coverage and connectivity is analyzed, typical simulation tools are compared, and research challenges and existing problems in this area are discussed.
About: This article is published in Journal of Network and Computer Applications.The article was published on 2012-03-01. It has received 523 citations till now. The article focuses on the topics: Key distribution in wireless sensor networks & Wireless sensor network.
Citations
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TL;DR: Characteristics of four recent energy-efficient coverage strategies are analyzed by carefully choosing four representative connected coverage algorithms to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications.
Abstract: Recent breakthroughs in wireless technologies have greatly spurred the emergence of industrial wireless sensor networks (IWSNs). To facilitate the adaptation of IWSNs to industrial applications, concerns about networks’ full coverage and connectivity must be addressed to fulfill reliability and real-time requirements. Although connected target coverage (CTC) algorithms in general sensor networks have been extensively studied, little attention has been paid to reveal both the applicability and limitations of different coverage strategies from an industrial viewpoint. In this paper, we analyze characteristics of four recent energy-efficient coverage strategies by carefully choosing four representative connected coverage algorithms: 1) communication weighted greedy cover; 2) optimized connected coverage heuristic; 3) overlapped target and connected coverage; and 4) adjustable range set covers. Through a detailed comparison in terms of network lifetime, coverage time, average energy consumption, ratio of dead nodes, etc., characteristics of basic design ideas used to optimize coverage and network connectivity of IWSNs are embodied. Various network parameters are simulated in a noisy environment to obtain the optimal network coverage. The most appropriate industrial field for each algorithm is also described based on coverage properties. Our study aims to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications.
195 citations
Cites background from "Review: A survey on coverage and co..."
...This is often referred to as the connected target coverage (CTC) problem, where each discrete target in the network must be within the sensing range of at least one sensor node, and where at least one routing path must be found to connect any source node to the sink node [5]....
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01 Oct 2016
TL;DR: The limits of this technology are quantified and the merits of using LoRaWAN for IoT communications in the context of 5G are exposed.
Abstract: The last decade saw the emergence of the Internet of Things (IoT) paradigm, which aims to connect any object to the Internet. In this context, a new type of wireless communication network emerged known as Low-Power Wire-Area Network (LPWAN). By contrast to well-known short range and multi-hop wireless networks, LPWAN networks allow long range communications at a low bit rate. Furthermore, LPWAN networks are considered to be integrated into 5G. Among LPWAN networks, the LoRaWAN technology gains more and more interest from the research and industrial communities. In this article, we have led a thorough experimental performance evaluation of LoRaWAN in an indoor environment. From this study, we quantify the limits of this technology and expose the merits of using LoRaWAN for IoT communications in the context of 5G.
163 citations
Cites background from "Review: A survey on coverage and co..."
...Still, 1http://www.cisco.com/c/en/us/solutions/internet-of-things/overview.html serious scientific challenges remain such as the deployment complexity [3], the need of an energy consuming routing protocol [4] or the lack of security [5]....
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TL;DR: A tree-cluster-based data-gathering algorithm for WSNs with a mobile sink that can significantly balance the load of the whole network, reduce the energy consumption, alleviate the hotspot problem, and prolong the network lifetime is proposed.
Abstract: Wireless sensor networks (WSNs) have been widely applied in various industrial applications, which involve collecting a massive amount of heterogeneous sensory data. However, most of the data-gathering strategies for WSNs cannot avoid the hotspot problem in local or whole deployment area. Hotspot problem affects the network connectivity and decreases the network lifetime. Hence, we propose a tree-cluster-based data-gathering algorithm (TCBDGA) for WSNs with a mobile sink. A novel weight-based tree-construction method is introduced. The root nodes of the constructed trees are defined as rendezvous points (RPs). Additionally, some special nodes called subrendezvous points (SRPs) are selected according to their traffic load and hops to root nodes. RPs and SRPs are viewed as stop points of the mobile sink for data collection, and can be reselected after a certain period. The simulation and comparison with other algorithms show that our TCBDGA can significantly balance the load of the whole network, reduce the energy consumption, alleviate the hotspot problem, and prolong the network lifetime.
143 citations
Cites background from "Review: A survey on coverage and co..."
...A WSN consists of a large number of tiny sensor nodes with low power, limited storage and transmission abilities [1]....
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TL;DR: An integer linear programming model is developed to solve the problem of deploying a minimum number of wireless sensors while maintaining the connectivity between the deployed sensors and it is verified by computational experiments that it is able to provide an optimal solution of all small and medium size problems.
139 citations
TL;DR: The deployment problem is classified based on few important factors and four deployment strategies and their related results are studied in each class and the advantages and disadvantages along with important challenges of several strategies have been discussed so that more efficient deployment strategies can be developed in future.
138 citations
References
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TL;DR: The Voronoi diagram as discussed by the authors divides the plane according to the nearest-neighbor points in the plane, and then divides the vertices of the plane into vertices, where vertices correspond to vertices in a plane.
Abstract: Computational geometry is concerned with the design and analysis of algorithms for geometrical problems. In addition, other more practically oriented, areas of computer science— such as computer graphics, computer-aided design, robotics, pattern recognition, and operations research—give rise to problems that inherently are geometrical. This is one reason computational geometry has attracted enormous research interest in the past decade and is a well-established area today. (For standard sources, we refer to the survey article by Lee and Preparata [19841 and to the textbooks by Preparata and Shames [1985] and Edelsbrunner [1987bl.) Readers familiar with the literature of computational geometry will have noticed, especially in the last few years, an increasing interest in a geometrical construct called the Voronoi diagram. This trend can also be observed in combinatorial geometry and in a considerable number of articles in natural science journals that address the Voronoi diagram under different names specific to the respective area. Given some number of points in the plane, their Voronoi diagram divides the plane according to the nearest-neighbor
4,236 citations
"Review: A survey on coverage and co..." refers background in this paper
...The Voronoi diagram (Aurenhammer, 1991; Fortune, 1997) is an important data structure in computational geometry....
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12 Nov 2000
TL;DR: Key requirements are identified, a small device is developed that is representative of the class, a tiny event-driven operating system is designed, and it is shown that it provides support for efficient modularity and concurrency-intensive operation.
Abstract: Technological progress in integrated, low-power, CMOS communication devices and sensors makes a rich design space of networked sensors viable. They can be deeply embedded in the physical world and spread throughout our environment like smart dust. The missing elements are an overall system architecture and a methodology for systematic advance. To this end, we identify key requirements, develop a small device that is representative of the class, design a tiny event-driven operating system, and show that it provides support for efficient modularity and concurrency-intensive operation. Our operating system fits in 178 bytes of memory, propagates events in the time it takes to copy 1.25 bytes of memory, context switches in the time it takes to copy 6 bytes of memory and supports two level scheduling. The analysis lays a groundwork for future architectural advances.
3,648 citations
"Review: A survey on coverage and co..." refers background in this paper
...The authors in Hill et al. (2000) also present both centralized and distributed version algorithms....
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05 Nov 2003
TL;DR: TOSSIM, a simulator for TinyOS wireless sensor networks can capture network behavior at a high fidelity while scaling to thousands of nodes, by using a probabilistic bit error model for the network.
Abstract: Accurate and scalable simulation has historically been a key enabling factor for systems research. We present TOSSIM, a simulator for TinyOS wireless sensor networks. By exploiting the sensor network domain and TinyOS's design, TOSSIM can capture network behavior at a high fidelity while scaling to thousands of nodes. By using a probabilistic bit error model for the network, TOSSIM remains simple and efficient, but expressive enough to capture a wide range of network interactions. Using TOSSIM, we have discovered several bugs in TinyOS, ranging from network bit-level MAC interactions to queue overflows in an ad-hoc routing protocol. Through these and other evaluations, we show that detailed, scalable sensor network simulation is possible.
2,281 citations
16 Jul 2001
TL;DR: A randomized algorithm where coordinators rotate with time is given, demonstrating how localized node decisions lead to a connected, capacity-preserving global topology.
Abstract: This paper presents Span, a power saving technique for multi-hop ad hoc wireless networks that reduces energy consumption without significantly diminishing the capacity or connectivity of the network. Span builds on the observation that when a region of a shared-channel wireless network bag a sufficient density of nodes, only a small number of them need be on at any time to forward traffic for active connections.Span is a distributed, randomized algorithm where nodes make local decisions on whether to sleep, or to join a forwarding backbone as a coordinator. Each node bases its decision on an estimate of how many of its neighbors will benefit from it being awake, and the amount of energy available to it. We give a randomized algorithm where coordinators rotate with time, demonstrating how localized node decisions lead to a connected, capacity-preserving global topology.Improvement in system lifetime due to Span increases as the ratio of idle-to-sleep energy consumption increases, and increases as the density of the network increases. For example, our simulations show that with a practical energy model, system lifetime of an 802.11 network in power saving mode with Span is a factor of two better than without. Span integrates nicely with 802.11—when run in conjunction with the 802.11 power saving mode, Span improves communication latency, capacity, and system lifetime.
1,854 citations
TL;DR: This article presents a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network.
Abstract: This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy-aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude.
1,820 citations
"Review: A survey on coverage and co..." refers background in this paper
...Zhou, Das, and Gupta develop both centralized and distributed algorithms for connected k-coverage in Raghunathan et al. (2002)....
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