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59
ExScal: Elements of an Extreme Scale Wireless Sensor Network
, 2005
"... Project ExScal (for Extreme Scale) fielded a 1000+ node wireless sensor network and a 200+ node peertopeer ad hoc network of 802.11 devices in a 1.3km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and ..."
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Cited by 109 (14 self)
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Project ExScal (for Extreme Scale) fielded a 1000+ node wireless sensor network and a 200+ node peertopeer ad hoc network of 802.11 devices in a 1.3km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.
Multirobot area patrol under frequency constraints
 Annals of Math and Artificial Intelligence
, 2009
"... Abstract — This paper discusses the problem of generating patrol paths for a team of mobile robots inside a designated target area. Patrolling requires an area to be visited repeatedly by the robot(s) in order to monitor its current state. First, we present frequency optimization criteria used for e ..."
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Cited by 36 (16 self)
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Abstract — This paper discusses the problem of generating patrol paths for a team of mobile robots inside a designated target area. Patrolling requires an area to be visited repeatedly by the robot(s) in order to monitor its current state. First, we present frequency optimization criteria used for evaluation of patrol algorithms. We then present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly. I.
Designing localized algorithms for barrier coverage
 Proc. ACM MobiCom
, 2007
"... Global barrier coverage that requires much fewer sensors than full coverage, is known to be an appropriate model of coverage for movement detection applications such as intrusion detection. However, it has been proved that given a sensor deployment, sensors can not locally determine whether the depl ..."
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Cited by 35 (3 self)
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Global barrier coverage that requires much fewer sensors than full coverage, is known to be an appropriate model of coverage for movement detection applications such as intrusion detection. However, it has been proved that given a sensor deployment, sensors can not locally determine whether the deployment provides global barrier coverage, making it impossible to develop localized algorithms, thus limiting its use in practice. In this paper, we introduce the concept of local barrier coverage to address this limitation. Motivated by the observation that movements are likely to follow a shorter path in crossing a belt region, local barrier coverage guarantees the detection of all movements whose trajectory is confined to a slice of the belt region of deployment. We prove that it is possible for individual sensors to locally determine the existence of local barrier coverage, even when the region of deployment is arbitrarily curved. Although local barrier coverage does not always guarantee global barrier coverage, we show that for thin belt regions, local barrier coverage almost always provides global barrier coverage. To demonstrate that local barrier coverage can be used to design localized algorithms, we develop a novel sleepwakeup algorithm for maximizing the network lifetime, called Localized Barrier Coverage Protocol (LBCP). We show that LBCP provides close to optimal enhancement in network lifetime, while providing global barrier coverage most of the time. It outperforms an existing algorithm called Randomized Independent Sleeping (RIS) by up to 6 times.
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 34 (8 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
Avoiding energy holes in wireless sensor networks with nonuniform node distribution
 Parallel and Distributed Systems, IEEE Tran.s on
, 2008
"... Abstract—In this paper, we investigate the theoretical aspects of the nonuniform node distribution strategy used to mitigate the energy hole problem in wireless sensor networks (WSNs). We conclude that in a circular multihop sensor network (modeled as concentric coronas) with nonuniform node distrib ..."
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Cited by 23 (1 self)
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Abstract—In this paper, we investigate the theoretical aspects of the nonuniform node distribution strategy used to mitigate the energy hole problem in wireless sensor networks (WSNs). We conclude that in a circular multihop sensor network (modeled as concentric coronas) with nonuniform node distribution and constant data reporting, the unbalanced energy depletion among all the nodes in the network is unavoidable. Even if the nodes in the inner coronas of the network have used up their energy simultaneously, the ones in the outermost corona may still have unused energy. This is due to the intrinsic manytoone traffic pattern of WSNs. Nevertheless, nearly balanced energy depletion in the network is possible if the number of nodes increases in geometric progression from the outer coronas to the inner ones except the outermost one. Based on the analysis, we propose a novel nonuniform node distribution strategy to achieve nearly balanced energy depletion in the network. We regulate the number of nodes in each corona and derive the ratio between the node densities in the adjacent ði þ 1Þth and ith coronas by the strategy. Finally, we propose qSwitch Routing, a distributed shortest path routing algorithm tailored for the proposed nonuniform node distribution strategy. Extensive simulations have been performed to validate the analysis. Index Terms—Wireless sensor networks, nonuniform node distribution, energy hole problem, energyefficient routing. 1
Complete Optimal Deployment Patterns for FullCoverage and k−Connectiviy (k ≤ 6) Wireless Sensor Networks
 In Proc. of ACM MobiHoc
, 2008
"... Abstract—In this paper, we study deployment patterns to achieve full coverage and kconnectivity (k ≤ 6) under different ratios of the sensor communication range (denoted by Rc) to the sensing range (denoted by Rs) for homogeneous wireless sensor networks (WSNs). In particular, we propose new patter ..."
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Cited by 22 (7 self)
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Abstract—In this paper, we study deployment patterns to achieve full coverage and kconnectivity (k ≤ 6) under different ratios of the sensor communication range (denoted by Rc) to the sensing range (denoted by Rs) for homogeneous wireless sensor networks (WSNs). In particular, we propose new patterns for 3 and 5connectivity. We also discover that there exists a hexagonbased universally elemental pattern that can generate all known optimal patterns. The previously proposed Voronoibased approach can not be applied to prove the optimality of the new patterns due to their special features. We propose a new deploymentpolygon based methodology. We prove the optimality of deployment patterns to achieve threeconnectivity, fourconnectivity and fiveconnectivity for certain ranges of Rc/Rs, respectively, and prove the optimality of deployment patterns to achieve sixconnectivity under all ranges of Rc/Rs.
Reliable Density Estimates for Coverage and Connectivity in Thin Strips of Finite Length
, 2007
"... Deriving the critical density (which is equivalent to deriving the critical radius or power) to achieve coverage and/or connectivity for random deployments is a fundamental problem in the area of wireless networks. The probabilistic conditions normally derived, however, have limited appeal among pra ..."
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Cited by 19 (3 self)
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Deriving the critical density (which is equivalent to deriving the critical radius or power) to achieve coverage and/or connectivity for random deployments is a fundamental problem in the area of wireless networks. The probabilistic conditions normally derived, however, have limited appeal among practitioners because they are often asymptotic, i.e., they only make high probability guarantees in the limit of large system sizes. Such conditions are not very useful in practice since deployment regions are always finite. Another major limitation of most existing work on coverage and connectivity is their focus on thick deployment regions (such as a square or a disk). There is no existing work (including traditional percolation theory) that derives critical densities for thin strips (or annuli). In this paper, we address both of these shortcomings by introducing new techniques for deriving reliable density estimates for finite regions (including thin strips). We apply our techniques to solve the open problem of deriving reliable density estimates for achieving barrier coverage and connectivity in thin strips, where sensors are deployed as a barrier to detect moving objects and phenomena. We use simulations to show that our estimates are accurate even for small deployment regions. Our techniques bridge the gap between theory and practice in the area of coverage and connectivity, since the results can now be readily used in reallife deployments.
Strong barrier coverage of wireless sensor networks
 in Proc. of The ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc
, 2008
"... Constructing sensor barriers to detect intruders crossing a randomlydeployed sensor network is an important problem. Early results have shown how to construct sensor barriers to detect intruders moving along restricted crossing paths in rectangular areas. We present a complete solution to this prob ..."
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Cited by 18 (7 self)
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Constructing sensor barriers to detect intruders crossing a randomlydeployed sensor network is an important problem. Early results have shown how to construct sensor barriers to detect intruders moving along restricted crossing paths in rectangular areas. We present a complete solution to this problem for sensors that are distributed according to a Poisson point process. In particular, we present an efficient distributed algorithm to construct sensor barriers on long strip areas of irregular shape without any constraint on crossing paths. Our approach is as follows: We first show that in a rectangular area of width w and length ℓ with w = Ω(log ℓ), if the sensor density reaches a certain value, then there exist, with high probability, multiple disjoint sensor barriers across the entire length of the area such that intruders cannot cross the area undetected. On the other hand, if w = o(log ℓ), then with high probability there is a crossing path not covered by any sensor regardless of the sensor density. We then devise, based on this result, an efficient distributed algorithm to construct multiple disjoint barriers in a large sensor network to cover a long boundary area of an irregular shape. Our algorithm approximates the area by dividing it into horizontal rectangular segments interleaved by vertical thin strips. Each segment and vertical strip independently computes the barriers in its own area. Constructing “horizontal ” barriers in each segment connected by“vertical ” barriers in neighboring vertical strips, we achieve continuous barrier coverage for the whole region. Our approach significantly reduces delay, communication overhead, and computation costs compared to centralized approaches. Finally, we implement our algorithm and carry out a number of experiments to demonstrate the effectiveness of constructing barrier coverage.
Exploring insitu sensing irregularity in wireless sensor networks
 In SenSys ’07: Proceedings of the 5th international
, 2007
"... The circular sensing model has been widely used to estimate performance of sensing applications in existing analysis and simulations. While this model provides valuable highlevel guidelines, the quantitative results obtained may not reflect the true performance of these applications, due to the exi ..."
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Cited by 15 (3 self)
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The circular sensing model has been widely used to estimate performance of sensing applications in existing analysis and simulations. While this model provides valuable highlevel guidelines, the quantitative results obtained may not reflect the true performance of these applications, due to the existence of obstacles and sensing irregularity introduced by insufficient hardware calibration. In this project, we design and implement two Sensing Area Modeling (SAM) techniques useful in the real world. They complement each other in the design space. PSAM provides accurate sensing area models for individual nodes using controlled or monitored events, while VSAM provides continuous sensing similarity models using natural events in an environment. With these two models, we pioneer an investigation of the impact of sensing irregularity on application performance, such as coverage scheduling. We evaluate SAM extensively in realworld settings, using three testbeds consisting of 40 MICAz motes and 14 XSM motes. To study the performance at scale, we also provide an extensive 1,400node simulation. Evaluation results reveal several serious issues concerning circular models, and demonstrate significant improvements in several applications when SAM is used instead.
Data Fusion Improves the Coverage of Wireless Sensor Networks
, 2009
"... Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Alth ..."
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Cited by 15 (5 self)
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Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signaltonoise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. In particular, for signal path loss exponent of k (typically between 2.0 and 5.0), ρf = O(ρ 1−1/k d), where ρf and ρd are the densities of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.