A Location-free Algorithm of Energy-Efficient Connected Coverage for High Density Wireless Sensor Networks

Bai, Hongliang; Chen, Xi; Li, Bin; Han, Dianfei

doi:10.1007/s10626-006-0005-9

Cited by 23 publications

(11 citation statements)

References 19 publications

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“…We proposed a novel node scheduling algorithm in [20], StanGA, which can provide any degree of coverage as well as connectivity. Furthermore, we proved the conditions for StanGA to guarantee any degree of coverage and network connectivity.…”

Section: Location-free Scheduling Methods For Maintaining Sensing Covmentioning

confidence: 99%

Energy-Efficient Sensing Coverage and Communication for Wireless Sensor Networks

Chen

Zhao

Guan

2007

Jrl Syst Sci & Complex

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Wireless sensor networks have a wide range of applications. Sensing coverage and communication coverage are two fundamental quality of service. In this paper, we present our work on energy efficient sensing coverage and communication. We design several schemes for sensing coverage subject to different requirements and constraints respectively. We also propose a broadcasting communication protocol with high energy efficiency and low latency for large scale sensor networks based on the Small World network theory. Simulation and experiment results show that our schemes and protocol have good performance.

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Section: Location-free Scheduling Methods For Maintaining Sensing Covmentioning

confidence: 99%

Energy-Efficient Sensing Coverage and Communication for Wireless Sensor Networks

Chen

Zhao

Guan

2007

Jrl Syst Sci & Complex

Self Cite

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“…This problem has been addressed in many articles on wireless sensor networks. References [1,3] proposed GPS-free scheduling when the only available information is the number of neighbors, and the sensor uses a probabilistic algorithm for its scheduling decisions. In some scenarios, a sensor measures the distance to each neighbor using the received signal strength (RSS), the time of arrival (ToA), the time difference of arrival (TDoA), and even measures the direction of the neighbor based on the angle of arrival (AoA) capability.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, some sensors could move to cover other gaps. An example line 3-covered overlap 3 3 is shown in Fig. 2, which is covered by sensors x 3 , x 4 , x 5 , and x 6 respectively.…”

Section: Problem Statementmentioning

confidence: 99%

Minimizing mobile sensor movements to form a line K-coverage

Wang

Gao

et al. 2016

Peer-to-Peer Netw. Appl.

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In wireless sensor networks and social networks, distributed nodes usually form a network with coverage ability for a lot of applications, such as the intrusion detection. In this paper, a new kind of coverage problem with mobile sensors is addressed, named Line K-Coverage. It guarantees that any intruder trajectory line cutting across a region of interest will be detected by at least K sensors. For energy efficiency, we aim to schedule an efficient sensor movement to satisfy the line K-coverage while minimizing the total sensor movements, which is named as LK-MinMovs problem. We firstly construct two timeefficient heuristics named LK-KM and LK-KM+ based on the famous Hungarian algorithm. By sacrificing optimality a little bit, these two algorithms have better time efficiency.Then we propose a pioneering layer-based algorithm LLKMinMovs to solve LK-MinMovs in polynomial time. Here, we assume that all sensors are initially located in a closed region. We validate its correctness by theoretical analysis. Later, the more general situation are considered that all sensors are allowed to locate outside of the region. We improve LLK-MinMovs algorithm to the general version: GenLLKMinMovs. More importantly, our GenLLK-MinMovs fixes a critical flaw for MinSum algorithm which was proposed by previous literature to solve line 1-coverage problem. We show the flaw using a counter example. Finally, we validate the efficiency of all our designs by numerical experiments and compare them under different experiment settings.

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“…Such information includes the distances between itself and its neighbors, the number of its active neighbors, etc. For example, in [13,14], a sensor decides to inactive if it finds at least one active neighbor. Neighbor distance-based scheduling and neighbor number-based scheduling are two approaches for sensor nodes making activity decision at each decision stage.…”

Section: Related Workmentioning

confidence: 99%

Coverage Aware Scheduling in Wireless Sensor Networks: An Optimal Placement Approach

Jamali

Hatami

2015

Wireless Pers Commun

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Area coverage is an important issue in wireless sensor networks, which reflects how well an area is monitored or tracked by sensors. But, since a sensor network has restricted energy resources, energy efficiency is vital for this area coverage. One of the most efficient solutions to provide energy-aware area coverage is scheduling. That is, without any assumption about sensors' locations, only a distributed and parallel scheduling method determines which sensors should be on and which ones should be off in each decision period. The ultimate objective is to maximize network lifetime and keeping a target level of area coverage. A major part of the algorithms proposed in this field, schedule a sensor node activity based on its neighbors' information. Such information includes the distances of a node from its neighbors, the number of its active neighbors, etc. Indeed, message exchange is essential in the implementation of these algorithms which causes to increased energy consumption. In this paper, we propose a distributed scheduling algorithm, at which, each node itself decides to make its sensor on or off based on its location information and the node density over the target area. For this purpose, we first compute the minimum number of nodes that are enough to cover the target area. Then we obtain the best locations for theses nodes. Based on these computed location the area is partitioned into some sub-area, each one coverable by only one sensor. Then in each sub-area, a local scheduling procedure schedules the activation order of sensor. Simulation results show that the proposed algorithm, called CAOP, can maximize the network lifetime while maintaining complete area coverage.

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A Location-free Algorithm of Energy-Efficient Connected Coverage for High Density Wireless Sensor Networks

Cited by 23 publications

References 19 publications

Energy-Efficient Sensing Coverage and Communication for Wireless Sensor Networks

Energy-Efficient Sensing Coverage and Communication for Wireless Sensor Networks

Minimizing mobile sensor movements to form a line K-coverage

Coverage Aware Scheduling in Wireless Sensor Networks: An Optimal Placement Approach

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