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A survey of energy-efficient scheduling mechanisms in sensor networks

Published on Oct 1, 2006in Mobile Networks and Applications 2.50
· DOI :10.1007/s11036-006-7798-5
Lan Wang28
Estimated H-index: 28
(University of Memphis),
Yang Xiao56
Estimated H-index: 56
(University of Memphis)
Abstract
Sensor networks have a wide range of potential, practical and useful applications. However, there are issues that need to be addressed for efficient operation of sensor network systems in real applications. Energy saving is one critical issue for sensor networks since most sensors are equipped with nonrechargeable batteries that have limited lifetime. To extend the lifetime of a sensor network, one common approach is to dynamically schedule sensors' work/ sleep cycles (or duty cycles). Moreover, in cluster-based networks, cluster heads are usually selected in a way that minimizes the total energy consumption and they may rotate among the sensors to balance energy consumption. In general, these energy-efficient scheduling mechanisms (also called topology configuration mechanisms) need to satisfy certain application requirements while saving energy. In this paper, we provide a survey on energy-efficient scheduling mechanisms in sensor networks that have different design requirements than those in traditional wireless networks. We classify these mechanisms based on their design assumptions and design objectives. Different mechanisms may make different assumptions about their sensors including detection model, sensing area, transmission range, failure model, time synchronization, and the ability to obtain location and distance information. They may also have different assumptions about network structure and sensor deployment strategy. Furthermore, while all the mechanisms have a common design objective to maximize network lifetime, they may also have different objectives determined by their target applications.
  • References (39)
  • Citations (308)
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References39
Newest
Published on Dec 1, 2005in Mobile Networks and Applications 2.50
Kui Wu27
Estimated H-index: 27
(University of Victoria),
Yong Gao13
Estimated H-index: 13
(University of British Columbia)
+ 1 AuthorsYang Xiao56
Estimated H-index: 56
(University of Memphis)
Wireless sensor networks consist of a large number of tiny sensors that have only limited energy supply. One of the major challenges in constructing such networks is to maintain long network lifetime as well as sufficient sensing areas. To achieve this goal, a broadly-used method is to turn off redundant sensors. In this paper, the problem of estimating redundant sensing areas among neighbouring wireless sensors is analysed. We present simple methods to estimate the degree of redundancy without ...
181 Citations Source Cite
Published on Dec 1, 2005in Mobile Networks and Applications 2.50
Jing Deng47
Estimated H-index: 47
(Syracuse University),
Yunghsiang S. Han24
Estimated H-index: 24
(National Taipei University)
+ 1 AuthorsPramod K. Varshney61
Estimated H-index: 61
(Syracuse University)
In order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. In this paper, we study the node sleep scheduling problem in the context of clustered sensor networks. We propose and analyze the Linear Distance-based Scheduling (LDS) technique for sleeping in each cluster. The LDS scheme selects a sensor node to sleep with higher probability when it is farther away f...
132 Citations Source Cite
Published on Sep 1, 2005in Computer Communications 2.61
Jing Deng47
Estimated H-index: 47
(Syracuse University),
Yunghsiang S. Han24
Estimated H-index: 24
(National Chi Nan University)
+ 1 AuthorsPramod K. Varshney61
Estimated H-index: 61
(Syracuse University)
In order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. However, determining which of the sensor nodes should be put into the sleep state is non-trivial. As the goal of allowing nodes to sleep is to extend network lifetime, we propose and analyze a Balanced-energy Scheduling (BS) scheme in the context of cluster-based sensor networks. The BS scheme aims to e...
105 Citations Source Cite
Published on Aug 1, 2005in Mobile Networks and Applications 2.50
Chi-Fu Huang17
Estimated H-index: 17
(National Chiao Tung University),
Yu-Chee Tseng58
Estimated H-index: 58
(National Chiao Tung University)
One of the fundamental issues in sensor networks is the coverage problem, which reflects howwell a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. The sensing ranges of sensors can be unit disks or non-unit disks. We present polynomial-time algorithms, in terms of the number of s...
1,001 Citations Source Cite
Published on May 1, 2005in IEEE Transactions on Mobile Computing 4.10
Matthew Miller17
Estimated H-index: 17
(University of Illinois at Urbana–Champaign),
Nitin H. Vaidya65
Estimated H-index: 65
(University of Illinois at Urbana–Champaign)
For increasing the life of sensor networks, each node must conserve energy as much as possible. In this paper, we propose a protocol in which energy is conserved by amortizing the energy cost of communication over multiple packets. In addition, we allow sensors to control the amount of buffered packets since storage space is limited. To achieve this, a two-radio architecture is used which allows a sensor to "wakeup" a neighbor with a busy tone and send its packets for that destination. However, ...
283 Citations Source Cite
Published on Mar 1, 2005in IEEE Communications Magazine 9.27
Edoardo Biagioni10
Estimated H-index: 10
,
Silvia Giordano31
Estimated H-index: 31
,
Ciprian Dobre20
Estimated H-index: 20
The articles in this special section focus on ad hoc and sensor networks. Today we are witnessing an interesting paradigm shift from the traditional client-server computing model, and the Internet of Things (IoT) is driving it. The reality is that the cost of Internet connectivity for wearable and mobile technology is decreasing, and our daily routines depend more on the use of devices that come equipped with more sensors and much more interesting capabilities at much lower costs. As a result, s...
64 Citations Source Cite
Published on Jan 1, 2005in Mobile Networks and Applications 2.50
Bo Li49
Estimated H-index: 49
,
Y. Thomas Hou2
Estimated H-index: 2
+ 2 AuthorsTaieb Znati2
Estimated H-index: 2
6 Citations
Published on Jan 1, 2005in Ad Hoc & Sensor Wireless Networks 0.75
Honghai Zhang11
Estimated H-index: 11
(University of Illinois at Urbana–Champaign),
Jennifer C. Hou40
Estimated H-index: 40
(University of Illinois at Urbana–Champaign)
In this paper, we address the issues of maintaining sensing coverage and connectivity by keeping a minimum number of sensor nodes in the active mode in wireless sensor networks. We investigate the relationship between coverage and connectivity by solving the following two sub-problems. First, we prove that if the radio range is at least twice the sensing range, complete coverage of a convex area implies connectivity among the working set of nodes. Second, we derive, under the ideal case in which...
1,219 Citations
Published on Jan 1, 2005
Lan Wang28
Estimated H-index: 28
(University of Memphis),
Yang Xiao56
Estimated H-index: 56
(University of Memphis)
In this paper, we provide a survey on energy-efficient scheduling mechanisms in sensor networks that have different design requirements than those in traditional wireless networks. We classify these mechanisms based on their design assumptions and design objectives. Different mechanisms may make different assumptions about their sensors including detection model, sensing area, transmission range, failure model, time synchronization, and the ability to obtain location and distance information. Th...
20 Citations Source Cite
Published on Sep 26, 2004 in ACM/IEEE International Conference on Mobile Computing and Networking
Santosh Kumar39
Estimated H-index: 39
(Ohio State University),
Ten-Hwang Lai30
Estimated H-index: 30
(Ohio State University),
József Balogh23
Estimated H-index: 23
(Ohio State University)
Sensor networks are often desired to last many times longer than the active lifetime of individual sensors. This is usually achieved by putting sensors to sleep for most of their lifetime. On the other hand, surveillance kind of applications require guaranteed k-coverage of the protected region at all times. As a result, determining the appropriate number of sensors to deploy that achieves both goals simultaneously becomes a challenging problem. In this paper, we consider three kinds of deployme...
576 Citations Source Cite
Cited By308
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Published on Jun 1, 2019in Journal of Intelligent Manufacturing 3.67
Ahmad Farhat1
Estimated H-index: 1
(University of Burgundy),
Christophe Guyeux14
Estimated H-index: 14
(University of Burgundy)
+ 3 AuthorsAbbas Hijazi4
Estimated H-index: 4
(Lebanese University)
In this article, we used wireless sensor network (WSN) techniques for monitoring an area under consideration, in order to diagnose its state in real time. What differentiates this type of network from the traditional computer ones is that it is composed by a large number of sensor nodes having very limited and almost nonrenewable energy. A key issue in designing such networks is energy conservation because once a sensor depletes its resources, it will be dropped from the network. This will lead ...
3 Citations Source Cite
Published on Apr 1, 2019
Banu Kabakulak1
Estimated H-index: 1
(Boğaziçi University)
Abstract 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 an...
1 Citations Source Cite
Published on Jan 1, 2019in Computer Standards & Interfaces 1.47
Pablo Adasme4
Estimated H-index: 4
(University of Santiago, Chile)
Abstract In this paper, we consider the problem of scheduling optimal sub-trees at different time intervals for wireless sensor network (WSN) communications with partial coverage. More precisely, we minimize the total power consumption of the network while taking into account time dimension and multichannel diversity where different disjoint subsets of nodes are required to be active and connected under a tree topology configuration. Optimization problems of these types may arise when designing ...
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Published on Dec 1, 2018 in Global Communications Conference
Delaram Amiri1
Estimated H-index: 1
,
Arman Anzanpour6
Estimated H-index: 6
(University of Turku)
+ 4 AuthorsDutt Nikil51
Estimated H-index: 51
(University of California, Irvine)
The Internet of Things is a key enabler of mobile health-care applications. However, the inherent constraints of mobile devices, such as limited availability of energy, can impair their ability to produce accurate data and, in turn, degrade the output of algorithms processing them in real-time to evaluate the patient's state. This paper presents an edge-assisted framework, where models and control generated by an edge server inform the sensing parameters of mobile sensors. The objective is to ma...
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Published on Sep 1, 2018 in Ad Hoc Networks
Xiao-Chen Hao4
Estimated H-index: 4
(Yanshan University),
Liyuan Wang1
Estimated H-index: 1
(Yanshan University)
+ 2 AuthorsBai Chen1
Estimated H-index: 1
(Yanshan University)
Abstract Since the wireless sensor network (WSN) consists of large number of sensors with limited energy resource, how to prolong the network lifetime is an inherent problem in wireless sensor network topology control. Motivated with this problem, we present a novel Markov lifetime prediction model (MLPM) for each single node to forecast their lifetime from a mode transition perspective. MLPM realizes the real-time prediction of node lifetime until the node died. Besides, on the basis of this mo...
1 Citations Source Cite
Published on Aug 6, 2018
Pablo Adasme4
Estimated H-index: 4
(University of Santiago, Chile),
Ismael Soto7
Estimated H-index: 7
(University of Santiago, Chile),
Fabian Seguel2
Estimated H-index: 2
(University of Santiago, Chile)
In this paper, we consider the degree constrained k-cardinality minimum spanning tree network problem (k-DCMST). This problem arises as a combination of two classical optimization problems, namely the degree constrained and k-minimum spanning tree problems (Resp. DCMST and k-MST). Let G(V, E) be a connected undirected graph formed with vertex and edge sets V and E, respectively. The DCMST problem asks for a minimum spanning tree where each maximum vertex degree is limited to a certain constant d...
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Published on Jun 1, 2018in IEEE Internet of Things Journal 5.87
Antonio Caruso11
Estimated H-index: 11
(University of Salento),
Stefano Chessa22
Estimated H-index: 22
(University of Pisa)
+ 2 AuthorsJuan Carlos López13
Estimated H-index: 13
(University of Castilla–La Mancha)
Outdoor Internet of Things (IoT) applications usually exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices. In this context, we propose a new dynamic programming algorithm for the optimization of the scheduling of the tasks in IoT devices that harvest energy by means of a solar panel. We show that the pr...
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Published on Apr 18, 2018in Sensors 2.48
Jie Zhou1
Estimated H-index: 1
,
Yan Liang20
Estimated H-index: 20
+ 2 AuthorsQuan Pan26
Estimated H-index: 26
A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell prod...
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Published on Mar 1, 2018in European Journal of Operational Research 3.43
Charly Lersteau2
Estimated H-index: 2
(Centre national de la recherche scientifique),
André Rossi16
Estimated H-index: 16
,
Marc Sevaux20
Estimated H-index: 20
(Centre national de la recherche scientifique)
Abstract Wireless Sensor Networks (WSN) are composed of low-cost sensors designed to monitor targets inside their sensing range. The sensors are randomly dispatched in a region and have a limited battery capacity. The targets are moving and their trajectory are subject to uncertainty. A way to save energy of the WSN is to activate subsets of sensors covering all the targets. The challenge of this paper is to preserve and balance the residual capacities of the sensors in order to perform further ...
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