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On the lifetime of wireless sensor networks
 TOSN
"... Network lifetime has become the key characteristic for evaluating sensor networks in an applicationspecific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to ..."
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Cited by 59 (10 self)
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Network lifetime has become the key characteristic for evaluating sensor networks in an applicationspecific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to lifetime considerations. A great number of algorithms and methods were proposed to increase the lifetime of a sensor network—while their evaluations were always based on a particular definition of network lifetime. Motivated by the great differences in existing definitions of sensor network lifetime that are used in relevant publications, we reviewed the state of the art in lifetime definitions, their differences, advantages, and limitations. This survey was the starting point for our work towards a generic definition of sensor network lifetime for use in analytic evaluations as well as in simulation models—focusing on a formal and concise definition of accumulated network lifetime and total network lifetime. Our definition incorporates the components of existing lifetime definitions, and introduces some additional measures. One new concept is the ability to express the service disruption tolerance of a network. Another new concept is the notion of timeintegration: in many cases, it is sufficient if a requirement is fulfilled over a certain period of time, instead of at every point in time. In addition, we combine coverage and connectivity to
On fullview coverage in camera sensor networks
 In INFOCOM 2011
"... Abstract—Camera sensors are different from traditional scalar sensors as different cameras from different positions can form distinct views of the object. However, traditional disk sensing model does not consider this intrinsic property of camera sensors. To this end, we propose a novel model called ..."
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Abstract—Camera sensors are different from traditional scalar sensors as different cameras from different positions can form distinct views of the object. However, traditional disk sensing model does not consider this intrinsic property of camera sensors. To this end, we propose a novel model called fullview coverage. An object is considered to be fullview covered if for any direction from 0 to 2π (object’s facing direction), there is always a sensor such that the object is within the sensor’s range and more importantly the sensor’s viewing direction is sufficiently close to the object’s facing direction. With this model, we propose an efficient method for fullview coverage detection in any given camera sensor networks. We also derive a sufficient condition on the sensor density needed for fullview coverage in a random uniform deployment. Finally, we show a necessary and sufficient condition on the sensor density for fullview coverage in a triangular lattice based deployment. I.
ObstacleResistant Deployment Algorithms for Wireless Sensor Networks
"... Abstract—Node deployment is an important issue in wireless sensor networks (WSNs). Sensor nodes should be efficiently deployed in a predetermined region in a lowcost and highcoveragequality manner. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and ..."
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Cited by 8 (0 self)
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Abstract—Node deployment is an important issue in wireless sensor networks (WSNs). Sensor nodes should be efficiently deployed in a predetermined region in a lowcost and highcoveragequality manner. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and, therefore, increase hardware costs and create coverage holes. This paper presents the efficient obstacleresistant robot deployment (ORRD) algorithm, which involves the design of a node placement policy, a serpentine movement policy, obstaclehandling rules, and boundary rules. By applying the proposed ORRD, the robot rapidly deploys a nearminimal number of sensor nodes to achieve full sensing coverage, even though there exist unpredicted obstacles with regular or irregular shapes. Performance results reveal that ORRD outperforms the existing robot deployment mechanism in terms of power conservation and obstacle resistance and, therefore, achieves better deployment performance. Index Terms—Deployment, obstacles, wireless sensor network (WSNs). I.
Solving the Wakeup Scattering Problem Optimally
"... In their EWSN’07 paper [1], Giusti et al. proposed a decentralized wakeup scattering algorithm for temporally spreading the intervals in which the nodes of a wireless sensor network (WSN) are active, and showed that the resulting schedules significantly improve over the commonlyused random ones, e ..."
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In their EWSN’07 paper [1], Giusti et al. proposed a decentralized wakeup scattering algorithm for temporally spreading the intervals in which the nodes of a wireless sensor network (WSN) are active, and showed that the resulting schedules significantly improve over the commonlyused random ones, e.g., by providing greater area coverage at less energy costs. However, an open question remained about whether further improvements are possible. Here, we complete the work in [1] by providing a (centralized) optimal solution that constitutes a theoretical upper bound for wakeup scattering protocols. Simulation results shows that the decentralized algorithm proposed in [1] comes within 4 % to 11% of the optimum. Moreover, we show that the modeling framework we use to derive the solution, based on integer programming techniques, allows for a particularly efficient solution. The latter result discloses important opportunities for the practical utilization of the model. The model is also general enough to encompass alternative formulations of the problem.
Convergence of Distributed WSN algorithms: the wakeup scattering problem
"... Abstract. In this paper, we analyze the problem of finding a periodic schedule for the wakeup times of a set of nodes in a Wireless Sensor Network that optimizes the coverage of the region the nodes are deployed on. An exact solution of the problem entails the solution of an Integer Linear Program ..."
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Abstract. In this paper, we analyze the problem of finding a periodic schedule for the wakeup times of a set of nodes in a Wireless Sensor Network that optimizes the coverage of the region the nodes are deployed on. An exact solution of the problem entails the solution of an Integer Linear Program and is hardly viable on low power nodes. Giusti et. al. [6] have recently proposed an efficient decentralized approach that produces a generally good suboptimal solution. In this paper, we study the convergence of this algorithm by casting the problem into one of asymptotic stability for a particular class of linear switching systems. For general topologies of the WSN, we offer local stability results. In some specific special cases, we are also able to prove global stability properties. 1
Optimal Placement of Readers in an RFID Network Using Particle Swarm Optimization
"... Abstract. An RFID network consists of a set of tags and readers. The cost and the number of tags covered directly depend on the number of readers. So, finding optimal number of readers and their positions to cover all tags is one of the most important issues in an RFID network. In this paper, we hav ..."
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Abstract. An RFID network consists of a set of tags and readers. The cost and the number of tags covered directly depend on the number of readers. So, finding optimal number of readers and their positions to cover all tags is one of the most important issues in an RFID network. In this paper, we have proposed a reader placement technique in a departmental store equipped with RFID network using Particle Swarm Optimization (PSO). The proposed algorithm finds minimal number of readers along with their position with 100 % coverage of tagged items. Simulated results also show the algorithms effectiveness in achieving the optimal solution.
An Integrated Protocol for Maintaining Connectivity and Coverage under Probabilistic Models for Wireless Sensor Networks
 AD HOC & SENSOR WIRELESS NETWORKS VOL. 7, PP. 295–323
, 2009
"... ..."
Minimum Cost Sensor Coverage of Planar Regions
"... Abstract—We consider the placement of sensors with circular sensing regions for qcoverage of planar regions. We first consider the placement of sensors of multiple types and costs over a specified set of locations to minimize the total sensors ’ cost. We present two approximate solutions to this pr ..."
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Abstract—We consider the placement of sensors with circular sensing regions for qcoverage of planar regions. We first consider the placement of sensors of multiple types and costs over a specified set of locations to minimize the total sensors ’ cost. We present two approximate solutions to this problem with multiplicative factors of 3 and 1 + 1/l of the optimal cost, where l is a tunable parameter. We then present a method to transform a region coverage instance into an equivalent point coverage instance and show a relationship between the cost of the optimal coverage of the two instances. This transformation enables us to use better studied approximation algorithms for point coverage to derive good sensor deployments for region coverage.
A New Distributed Algorithm for Even Coverage and Improved Lifetime in a Sensor Network
"... Abstract—In “areasensing ” applications of sensor networks, such as surveillance or target tracking, each sensor node has a sensing radius within which it can monitor events. Coverage problems in sensor networks have largely focused on such applications, where the goal of good coverage is one of en ..."
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Cited by 1 (1 self)
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Abstract—In “areasensing ” applications of sensor networks, such as surveillance or target tracking, each sensor node has a sensing radius within which it can monitor events. Coverage problems in sensor networks have largely focused on such applications, where the goal of good coverage is one of ensuring that each point in the region of interest is within the sensing radius of at least one node. On the other hand, in “spotsensing” applications, each node makes a measurement (such as temperature or humidity) at the precise location of the node and there is no concept of a sensing radius. In this paper, we introduce a new coverage problem that is more meaningful to spotsensing applications. In such cases, good coverage usually implies even coverage across points in the region. We borrow from the field of economics and adapt a wellaccepted measure of inequality, the Gini index, to develop a metric for the evenness of coverage by a sensor network. Based on mathematical results on the expected distances between neighboring nodes, we present a new distributed algorithm, called EvenCover, for each node to determine if and when it should sleep or sense. We prove that the expected Gini index is 1 − 1 / √ 2 ≈ 0.293 when the spatial distribution of sensing nodes is given by a Poisson random process. On the other hand, when the sensing nodes are perfectly evenly distributed, we show that the Gini index has a lower bound of 0.2. These two results serve as points of reference to evaluate the coverage achieved by the EvenCover algorithm. We present a thorough simulationbased comparison of EvenCover against other distributed algorithms showing that it achieves better evenness and significantly increased lifetime. In addition, we discover that evenness of coverage permits a graceful degradation of the network as nodes exhaust their energy resources. I.
Distributed Sleep Scheduling in Wireless Sensor Networks via Fractional Domatic Partitioning
"... Abstract. We consider setting up sleep scheduling in sensor networks. We formulate the problem as an instance of the fractional domatic partition problem and obtain a distributed approximation algorithm by applying linear programming approximation techniques. Our algorithm is an application of the G ..."
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Abstract. We consider setting up sleep scheduling in sensor networks. We formulate the problem as an instance of the fractional domatic partition problem and obtain a distributed approximation algorithm by applying linear programming approximation techniques. Our algorithm is an application of the GargKönemann (GK) scheme that requires solving an instance of the minimum weight dominating set (MWDS) problem as a subroutine. Our two main contributions are a distributed implementation of the GK scheme for the sleepscheduling problem and a novel asynchronous distributed algorithm for approximating MWDS based on a primaldual analysis of Chvátal’s setcover algorithm. We evaluate our algorithm with ns2 simulations. 1