Minimum energy target tracking with coverage guarantee in wireless sensor networks

Lersteau, Charly; Rossi, André; Sevaux, Marc

doi:10.1016/j.ejor.2017.08.045

Cited by 29 publications

(26 citation statements)

References 27 publications

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“…• (CCP) We deploy four type-1 sensors (circles), i.e., (1, 1), (2, 1), (10, 1) and (16,1). There are four type-2 sensors (diamonds), which are (3, 2), (4, 2), (11,2) and (15,2).…”

Section: Problem Definitionmentioning

confidence: 99%

“…• (ASP) In period t = 1, the sensors (1, 1), (10, 1), (3,2) and (15,2) are active (black). The sensors (2, 1), (16, 1), (4, 2) and (11,2) are in standby mode (white) at t = 1.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The sensors (2, 1), (16, 1), (4, 2) and (11,2) are in standby mode (white) at t = 1. In period t = 2, the sensors (2, 1), (10, 1), (16,1), (4,2), and (11, 2) are active, whereas (1, 1), (3,2) and (15,2) are in standby mode.…”

Section: Problem Definitionmentioning

confidence: 99%

“…• (SAP) The active sensors (1, 1) and (3,2) are assigned to the sink (8, 0) at t = 1. Besides, (10,1) and (15,2) are assigned to the sink (14, 0) in the same period. At t = 2, the active sensors (2, 1) and (4, 2) transmit to the sink (8, 0), and the sink (14, 0) collects data from the sensors (10, 1), (16,1) and (11,2).…”

Section: Problem Definitionmentioning

confidence: 99%

“…, (11) -(13),(14),(15),(10) as the constraints (44b), (44d), (44e), (44f) -(44h), (44j), (44k), (44m), respectively, with y jkil and g jki variables and given (x,z,ū) values in RP (t).…”

mentioning

confidence: 99%

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Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks

Kabakulak

2019

Ad Hoc Networks

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A sensor is a small electronic device which has the ability to sense, compute and communicate either with other sensors or directly with a base station (sink). In a wireless sensor network (WSN), the sensors monitor a region and transmit the collected data packets through routes to the sinks. In this study, we propose a mixed-integer linear programming (MILP) model to maximize the number of time periods that a WSN carries out the desired tasks with limited energy and budget. Our sink and sensor placement, scheduling, routing with connected coverage (SP SRC) model is the first in the literature that combines the decisions for the locations of sinks and sensors, activity schedules of the deployed sensors, and data flow routes from each active sensor to its assigned sink for connected coverage of the network over a finite planning horizon. The problem is NP-hard and difficult to solve even for small instances. Assuming that the sink locations are known, we develop heuristics which construct a feasible solution of the problem by gradually satisfying the constraints.Then, we introduce search heuristics to determine the locations of the sinks to maximize the network lifetime. Computational experiments reveal that our heuristic methods can find near optimal solutions in an acceptable amount of time compared to the commercial solver CPLEX 12.7.0. Wireless sensor networks (WSNs) are composed of a large number of wireless devices, called sensors, equipped with communication and computing capabilities to monitor a region. A homogeneous WSN consists of identical sensors, whereas the communication and computing capability of the sensors are different in a heterogeneous network. WSNs are applied to various fields of technology thanks to their easy and cheap deployment features. They are used to gather information about human activities in health care, battlefield surveillance in military, monitor wildlife or pollution in environmental sciences, and so on [30].A sensor can collect data within its sensing range, process data as packets and transmit to a base station (sink) either directly or through other sensors which are within its communication range. Sensors consume energy for sensing, receiving data from other sensors and transmitting data to other sensors or a sink. Energy-aware operating is important for a sensor since it has limited battery energy. A sensor can carry out sensing and communicating tasks when it is active and consumes negligible energy in standby mode [13]. A sensor is no more a member of the WSN, when its battery energy depletes.The number of time periods that a WSN operates as desired is its lif etime and depends highly on the limited energy of the sensors. Hence, energy-aware usage of the sensors helps to prolong the network lifetime. The key factors that affect the energy consumption can be listed as follows: locations of the sensors and sinks in the network, schedule of the active or standby periods of the sensors, sink assignments of the sensors and data transmission routes from the sensors to their ...

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“…• (CCP) We deploy four type-1 sensors (circles), i.e., (1, 1), (2, 1), (10, 1) and (16,1). There are four type-2 sensors (diamonds), which are (3, 2), (4, 2), (11,2) and (15,2).…”

Section: Problem Definitionmentioning

confidence: 99%

“…• (ASP) In period t = 1, the sensors (1, 1), (10, 1), (3,2) and (15,2) are active (black). The sensors (2, 1), (16, 1), (4, 2) and (11,2) are in standby mode (white) at t = 1.…”

Section: Problem Definitionmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

“…, (11) -(13),(14),(15),(10) as the constraints (44b), (44d), (44e), (44f) -(44h), (44j), (44k), (44m), respectively, with y jkil and g jki variables and given (x,z,ū) values in RP (t).…”

mentioning

confidence: 99%

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Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks

Kabakulak

2019

Ad Hoc Networks

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Adaptive pursuit learning for energy‐efficient target coverage in wireless sensor networks

Upreti

Rauniyar

Kunwar

et al. 2020

Concurrency and Computation

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With the proliferation of technologies such as wireless sensor networks (WSNs) and the Internet of things (IoT), we are moving towards the era of automation without any human intervention. Sensors are the principal components of the WSNs that bring the idea of IoT into reality. Over the last decade, WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care monitoring, and so on. However, the energy efficiency of the sensor nodes in WSN remains a challenging issue due to the use of a small battery. Moreover, replacing the batteries of the sensor nodes deployed in a hostile environment frequently is not a feasible option.Therefore, intelligent scheduling of the sensor nodes for optimizing its energy-efficient operation and thereby extending the life-time of WSN has received a lot of research attention lately. In particular, this article investigates extending the lifetime of the WSN in the context of target coverage problems. To tackle this problem, we propose a scheduling technique for WSN based on a novel concept within the theory of learning automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, that is, either sleep or active, with an aim to cover all targets with the lowest energy cost possible. Our comprehensive experimental testing of the proposed algorithm not only verifies the efficiency of our algorithm, but it also demonstrates its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

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Nonfragile H_∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models

Shen

Wang

Shen

et al. 2020

Intl J Robust & Nonlinear

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In this article, the nonfragile H ∞ filtering problem is investigated for a class of discrete multirate time-delayed systems over sensor networks. The probabilistic packet dropout occurs during the information transmissions among the sensor nodes in the sensor network characterized by the Gilbert-Elliott model. In order to take the multirate sampling into account, the state updating period of the system and the sampling period of the sensors are allowed to be different. The variation of the filter gain is considered to reflect the physical errors with the filter implementation. The aim of this article is to design a set of nonfragile filters such that, in the presence of multirate sampling, time-delays, and packet dropouts, the filtering error dynamics is exponentially mean-square stable and also satisfies the H ∞ performance requirement. By using the Lyapunov-Krasovskii functional approach, a sufficient condition is derived, which ensures the exponential mean-square stability and the H ∞ performance requirement of the filtering error dynamics. Then, the filter gains are characterized in terms of the solution to a set of matrix inequalities. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme. K E Y W O R D SGilbert-Elliott model, multirate sampling, nonfragile H ∞ filtering, packet dropouts, sensor networks, time-delays

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Minimum energy target tracking with coverage guarantee in wireless sensor networks

Cited by 29 publications

References 27 publications

Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks

Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks

Adaptive pursuit learning for energy‐efficient target coverage in wireless sensor networks

Nonfragile H_∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models

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Minimum energy target tracking with coverage guarantee in wireless sensor networks

Cited by 29 publications

References 27 publications

Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks

Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks

Adaptive pursuit learning for energy‐efficient target coverage in wireless sensor networks

Nonfragile H∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models

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Product

Resources

About

Nonfragile H_∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models