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38
A minimum cost heterogeneous sensor network with a lifetime constraint
 IEEE Transactions on Mobile Computing
, 2005
"... Abstract—We consider a heterogeneous sensor network in which nodes are to be deployed over a unit area for the purpose of surveillance. An aircraft visits the area periodically and gathers data about the activity in the area from the sensor nodes. There are two types of nodes that are distributed ov ..."
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Cited by 68 (1 self)
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Abstract—We consider a heterogeneous sensor network in which nodes are to be deployed over a unit area for the purpose of surveillance. An aircraft visits the area periodically and gathers data about the activity in the area from the sensor nodes. There are two types of nodes that are distributed over the area using twodimensional homogeneous Poisson point processes; type 0 nodes with intensity (average number per unit area) 0 and battery energy E0; and type 1 nodes with intensity 1 and battery energy E1. Type 0 nodes do the sensing while type 1 nodes act as the cluster heads besides doing the sensing. Nodes use multihopping to communicate with their closest cluster heads. We determine the optimum node intensities ( 0, 1) and node energies (E0, E1) that guarantee a lifetime of at least T units, while ensuring connectivity and coverage of the surveillance area with a high probability. We minimize the overall cost of the network under these constraints. Lifetime is defined as the number of successful data gathering trips (or cycles) that are possible until connectivity and/or coverage are lost. Conditions for a sharp cutoff are also taken into account, i.e., we ensure that almost all the nodes run out of energy at about the same time so that there is very little energy waste due to residual energy. We compare the results for random deployment with those of a grid deployment in which nodes are placed deterministically along grid p ffiffiffiffiffi points. We observe that in both cases 1 scales approximately as 0. Our results can be directly extended to take into account unreliable nodes.
Energyaware Management for Clusterbased Sensor Networks
 Computer Networks
, 2003
"... Networking unattended sensors is expected to have a significant impact on the efficiency of many military and civil applications. Sensors in such systems are typically disposable and expected to last until their energy drains. Therefore, energy is a very scarce resource for such sensor systems and h ..."
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Cited by 31 (1 self)
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Networking unattended sensors is expected to have a significant impact on the efficiency of many military and civil applications. Sensors in such systems are typically disposable and expected to last until their energy drains. Therefore, energy is a very scarce resource for such sensor systems and has to be managed wisely in order to extend the life of the sensors for the duration of a particular mission. In this paper, we present a novel approach for energyaware management of sensor networks that maximizes the lifetime of the sensors while achieving acceptable performance for sensed data delivery. The approach is to dynamically set routes and arbitrate medium access in order to minimize energy consumption and maximize sensor life. The approach calls for network clustering and assigns a lessenergyconstrained gateway node that acts as a cluster manager. Based on energy usage at every sensor node and changes in the mission and the environment, the gateway sets routes for sensor data, monitors latency throughout the cluster, and arbitrates medium access among sensors. We also describe a timebased Medium Access Control (MAC) protocol and discuss algorithms for assigning time slots for the communicating sensor nodes. Simulation results show an order of magnitude enhancement in the time to network partitioning, 11 % enhancement in network lifetime predictability, and 14 % enhancement in average energy consumed per packet. Keywords: Sensor networks Energyaware network management, PowerAware Communication, Energyefficient design.
NAWMS: Nonintrusive Autonomous Water Monitoring System
"... Water is nature’s most precious resource and growing demand is pushing fresh water supplies to the brink of nonrenewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with finegrained realtime information has yielded benefit ..."
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Cited by 25 (2 self)
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Water is nature’s most precious resource and growing demand is pushing fresh water supplies to the brink of nonrenewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with finegrained realtime information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users. The goal of our work is to furnish users with an easytoinstall selfcalibrating system that provides information on when, where, and how much water they are using. The system uses wireless vibration sensors attached to pipes and, thus, neither plumbing nor special expertise is necessary
Relay Placement for Higher Order Connectivity in Wireless Sensor Networks
"... Sensors typically use wireless transmitters to communicate with each other. However, sensors may be located in a way that they cannot even form a connected network (e.g, due to failures of some sensors, or loss of battery power). In this paper we consider the problem of adding the smallest number o ..."
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Cited by 22 (1 self)
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Sensors typically use wireless transmitters to communicate with each other. However, sensors may be located in a way that they cannot even form a connected network (e.g, due to failures of some sensors, or loss of battery power). In this paper we consider the problem of adding the smallest number of additional (relay) nodes so that the induced communication graph is 2connected 1. The problem is NPhard. In this paper we develop O(1)approximation algorithms that find close to optimal solutions in time O((kn) 2) for achieving kedge connectivity of n nodes. The worst case approximation guarantee is 10, but the algorithm produces solutions that are far better than this bound suggests. We also consider extensions to higher dimensions, and the scheme that we develop for points in the plane, yields a bound of 2dMST where dMST is the maximum degree of a minimumdegree Minimum Spanning Tree in d dimensions using Euclidean metrics. In addition, our methods extend with the same approximation guarantees to a generalization when the locations of relays are required to avoid certain polygonal regions (obstacles). We also prove that if the sensors are uniformly and identically distributed in a unit square, the expected number of relay nodes required goes to zero as the number of sensors goes to infinity.
A cellular learning automata based clustering algorithm for wireless sensor networks
 Sensor Letters
, 2008
"... In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. In the second part of the paper, based on the proposed model a new clust ..."
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Cited by 22 (11 self)
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In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. In the second part of the paper, based on the proposed model a new clustering algorithm for sensor networks is designed. The proposed clustering algorithm is fully distributed and the nodes in the network don't need to be fully synchronized with each other. The proposed clustering algorithm consists of two phases; initial clustering and reclustering. Unlike existing methods in which the reclustering phase is performed periodically on the entire network, reclustering phase in the proposed method is performed locally whenever it is needed. This results in a reduction in the consumed energy for reclustering phase and also allows reclustering phase to be performed as the network operates. The proposed clustering method in comparison to existing methods produces a clustering in which each cluster has higher number of nodes and higher residual energy for the cluster head. Local reclustering, higher residual energy in cluster heads and higher number of nodes in each cluster results in a network with longer lifetime. To evaluate the performance of the proposed algorithm several experiments have been conducted. The results of experiments have shown that the proposed clustering algorithm outperforms existing clustering methods in terms of quality of clustering measured by the total number of clusters, the number of sparse clusters and the remaining energy level of the cluster heads. Experiments have also shown that the proposed clustering algorithm in comparison to other existing methods prolongs the network lifetime.
Modeling and worstcase dimensioning of clustertree wireless sensor networks
 In Proceedings of the 27th IEEE RealTime Systems Symposium (RTSS’06
, 2006
"... Timesensitive Wireless Sensor Network (WSN) applications require finite delay bounds in critical situations. This paper provides a methodology for the modeling and the worstcase dimensioning of clustertree WSNs. We provide a fine model of the worstcase clustertree topology characterized by its ..."
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Cited by 21 (2 self)
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Timesensitive Wireless Sensor Network (WSN) applications require finite delay bounds in critical situations. This paper provides a methodology for the modeling and the worstcase dimensioning of clustertree WSNs. We provide a fine model of the worstcase clustertree topology characterized by its depth, the maximum number of child routers and the maximum number of child nodes for each parent router. Using Network Calculus, we derive “plugandplay ” expressions for the endtoend delay bounds, buffering and bandwidth requirements as a function of the WSN clustertree characteristics and traffic specifications. The clustertree topology has been adopted by many clusterbased solutions for WSNs. We demonstrate how to apply our general results for dimensioning IEEE 802.15.4/Zigbee clustertree WSNs. We believe that this paper shows the fundamental performance limits of clustertree wireless sensor networks by the provision of a simple and effective methodology for the design of such WSNs. 1.
Constrained relay node placement in wireless sensor networks to meet connectivity and survivability requirements
 in Proc. IEEE INFOCOM
"... Abstract—One approach to prolong the lifetime of a wireless sensor network (WSN) is to deploy some relay nodes to communicate with the sensor nodes, other relay nodes, and the base stations. The relay node placement problem for wireless sensor networks is concerned with placing a minimum number of r ..."
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Cited by 21 (1 self)
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Abstract—One approach to prolong the lifetime of a wireless sensor network (WSN) is to deploy some relay nodes to communicate with the sensor nodes, other relay nodes, and the base stations. The relay node placement problem for wireless sensor networks is concerned with placing a minimum number of relay nodes into a wireless sensor network to meet certain connectivity or survivability requirements. Previous studies have concentrated on the unconstrained version of the problem in the sense that relay nodes can be placed anywhere. In practice, there may be some physical constraints on the placement of relay nodes. To address this issue, we study constrained versions of the relay node placement problem, where relay nodes can only be placed at a set of candidate locations. In the connected relay node placement problem, we want to place a minimum number of relay nodes to ensure that each sensor node is connected with a base station through a bidirectional path. In the survivable relay node placement problem, we want to place a minimum number of relay nodes to ensure that each sensor node is connected with two base stations (or the only base station in case there is only one base station) through two nodedisjoint bidirectional paths. For each of the two problems, we discuss its computational complexity and present a framework of polynomial time (1)approximation algorithms with small approximation ratios. Extensive numerical results show that our approximation algorithms can produce solutions very close to optimal solutions. Index Terms—Approximation algorithms, connectivity and survivability, relay node placement, wireless sensor networks (WSNs). I.
Localized PowerAware Routing in Linear Wireless Sensor Networks
"... Energyefficency is a key concern when designing protocols for wireless sensor networks (WSN). This is of particular importance in commercial applications where demonstrable return on investment is a crucial factor. One such commercial application that motivated this work is telemetry and control fo ..."
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Cited by 8 (6 self)
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Energyefficency is a key concern when designing protocols for wireless sensor networks (WSN). This is of particular importance in commercial applications where demonstrable return on investment is a crucial factor. One such commercial application that motivated this work is telemetry and control for freight railroad trains. Since a railroad train has a global linear structure by nature, we consider in this paper linear WSNs as sensor networks having, roughly, a linear topology. Aiming at such networks, we introduce two routing schemes that efficiently utilize energy: Minimum Energy Relay Routing (MERR) and Adaptive MERR (AMERR). We derive a theoretical lower bound on the optimal power consumption of routing in a linear WSN, where we assume a Poisson model for the distribution of nodes along a linear path. We evaluate the efficiency of our protocols with respect to the theoretical optimal lower bound and with respect to other wellknown protocols. AMERR achieves optimal performance for practical deployment settings, while MERR rapidly approaches optimal performance as sensors are more densely deployed. Compared to other protocols, we show that MERR and AMERR are less complex and have better scalability. We also postulate how both protocols might be generalized to a twodimensional WSN.
Distributed Formation of Overlapping Multihop Clusters in Wireless Sensor Networks
"... Abstract – Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Most of the published clustering algorithms strive to generate the minimum number of disjoint clusters. However, we argue that guaranteeing some degree of overlap among clusters ..."
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Cited by 7 (0 self)
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Abstract – Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Most of the published clustering algorithms strive to generate the minimum number of disjoint clusters. However, we argue that guaranteeing some degree of overlap among clusters can facilitate many applications, like intercluster routing, topology discovery and node localization, recovery from cluster head failure, etc. We formulate the overlapping multihop clustering problem as an extension to the kdominating set problem. Then we propose MOCA; a randomized distributed multihop clustering algorithm for organizing the sensors into overlapping clusters. We validate MOCA in a simulated environment and analyze the effect of different parameters, e.g. node density and network connectivity, on its performance. The simulation results demonstrate that MOCA is scalable, introduces low overhead and produces approximately equalsized clusters. I.
Faulttolerant relay node placement in wireless sensor networks
 LNCS
"... Abstract. The paper addresses the relay node placement problem in twotiered wireless sensor networks. Given a set of sensor nodes in an Euclidean plane, our objective is to place minimum number of relay nodes to forward data packets from sensor nodes to the sink, such that: 1) the network is connec ..."
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Cited by 7 (0 self)
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Abstract. The paper addresses the relay node placement problem in twotiered wireless sensor networks. Given a set of sensor nodes in an Euclidean plane, our objective is to place minimum number of relay nodes to forward data packets from sensor nodes to the sink, such that: 1) the network is connected, 2) the network is 2connected. For case one, we propose a (6 + ε)approximation algorithm for any ε>0with polynomial running time when ε is fixed. For case two, we propose two approximation algorithms with (24 + ε) and(6/T +12+ε), respectively, where T is the ratio of the number of relay nodes placed in case one to the number of sensors.