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112
Wireless Sensor Network Localization Techniques
"... Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localizat ..."
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Cited by 209 (5 self)
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Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the onehop localization algorithms based on these measurements. A detailed investigation on multihop connectivitybased and distancebased localization algorithms are presented. A list of open research problems in the area of distancebased sensor network localization is provided with discussion on possible approaches to them.
Semidefinite programming based algorithms for sensor network localization
 ACM Transactions on Sensor Networks
, 2006
"... An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate distance information. The problem is set up to find a set of sensor positions such that given distance constraints are satisfied. The nonconvex constraints in the formulat ..."
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Cited by 113 (7 self)
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An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate distance information. The problem is set up to find a set of sensor positions such that given distance constraints are satisfied. The nonconvex constraints in the formulation are then relaxed in order to yield a semidefinite program which can be solved efficiently. The basic model is extended in order to account for noisy distance information. In particular, a maximum likelihood based formulation and an interval based formulation are discussed. The SDP solution can then also be used as a starting point for steepest descent based local optimization techniques that can further refine the SDP solution. We also describe the extension of the basic method to develop an iterative distributed SDP method for solving very large scale semidefinite programs that arise out of localization problems for large dense networks and are intractable using centralized methods. The performance evaluation of the technique with regard to estimation accuracy and computation time is also presented by the means of extensive simulations. Our SDP scheme also seems to be applicable to solving other Euclidean geometry problems where points are locally connected.
Semidefinite programming approaches for sensor network localization with noisy distance measurements
 IEEE Transactions on Automation Science and Engineering
, 2006
"... Abstract — A sensor network localization problem is to determine the positions of the sensor nodes in a network given incomplete and inaccurate pairwise distance measurements. Such distance data may be acquired by a sensor node by communicating with its neighbors. We describe a general semidefinite ..."
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Cited by 94 (8 self)
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Abstract — A sensor network localization problem is to determine the positions of the sensor nodes in a network given incomplete and inaccurate pairwise distance measurements. Such distance data may be acquired by a sensor node by communicating with its neighbors. We describe a general semidefinite programming (SDP) based approach for solving the graph realization problem, of which the sensor network localization problems is a special case. We investigate the performance of this method on problems with noisy distance data. Error bounds are derived from the SDP formulation. The sources of estimation error in the SDP formulation are identified. The SDP solution usually has a rank higher than the underlying physical space, which when projected onto the lower dimensional space generally results in high estimation error. We describe two improvements to ameliorate such a difficulty. First, we propose a regularization term in the objective function that can help to reduce the rank of the SDP solution. Second, we use the points estimated from the SDP solution as the initial iterate for a gradientdescent method to further refine the estimated points. A lower bound obtained from the optimal SDP objective value can be used to check the solution quality. Experimental results are presented to validate our methods and show that they outperform existing SDP methods.
Robust distributed node localization with error management
 In Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’06). ACM
, 2006
"... Location knowledge of nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks. This paper introduces a novel iterative method ILS for node localization starting with a relatively small number of anchor nodes in a ..."
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Cited by 69 (4 self)
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Location knowledge of nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks. This paper introduces a novel iterative method ILS for node localization starting with a relatively small number of anchor nodes in a large network. At each iteration, nodes are localized using a leastsquares based algorithm. The computation is lightweight, fast, and anytime. To prevent error from propagating and accumulating during the iteration, the error control mechanism of the algorithm uses an error registry to select nodes that participate in the localization, based on their relative contribution to the localization accuracy. Simulation results have shown that the active selection strategy significantly mitigates the effect of error propagation. The algorithm has been tested on a network of Berkeley Mica2 motes with ultrasound TOA ranging devices. We have compared the algorithm with more global methods such as MDSMAP and SDPbased algorithm both in simulation and on real hardware. The iterative localization achieves comparable location accuracy in both cases, compared to the more global methods, and has the advantage of being fully decentralized.
Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays
 In Percom
, 2007
"... Activity monitoring, a crucial task in many applications, is often conducted expensively using video cameras. Also, effectively monitoring a large field by analyzing images from multiple cameras remains a challenging problem. In this paper, we introduce a novel application of the recently developed ..."
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Cited by 47 (9 self)
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Activity monitoring, a crucial task in many applications, is often conducted expensively using video cameras. Also, effectively monitoring a large field by analyzing images from multiple cameras remains a challenging problem. In this paper, we introduce a novel application of the recently developed RFID technology: using RF tag arrays for activity monitoring, where data mining techniques play a critical role. The RFID technology provides an economically attractive solution due to the low cost of RF tags and readers. Another novelty of this design is that the tracking objects do not need to attach any transmitters or receivers, such as tags or readers. By developing a practical faulttolerant method, we offset the noise of RF tag data and mine frequent trajectory patterns as models of regular activities. Our empirical study using real RFID systems and data sets verifies the feasibility and the effectiveness of our design. 1.
Localization for largescale underwater sensor networks
, 2006
"... Abstract. In this paper, we study the localization problem in largescale underwater sensor networks. The adverse aqueous environments, the node mobility, and the large network scale all pose new challenges, and most current localization schemes are not applicable. We propose a hierarchical approach ..."
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Cited by 47 (6 self)
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Abstract. In this paper, we study the localization problem in largescale underwater sensor networks. The adverse aqueous environments, the node mobility, and the large network scale all pose new challenges, and most current localization schemes are not applicable. We propose a hierarchical approach which divides the whole localization process into two subprocesses: anchor node localization and ordinary node localization. Many existing techniques can be used in the former. For the ordinary node localization process, we propose a distributed localization scheme which novelly integrates a 3dimensional Euclidean distance estimation method with a recursive location estimation method. Simulation results show that our proposed solution can achieve high localization coverage with relatively small localization error and low communication overhead in largescale 3dimensional underwater sensor networks. 1
Distributed and collaborative estimation over wireless sensor networks
 in IEEE Conference on Decision and Control
, 2006
"... Abstract — A new distributed algorithm for cooperative estimation of a slowly timevarying signal using a wireless sensor network is presented. The estimate in each node is based on a so called consensus algorithm, which weights measurements and estimates of neighboring nodes. The algorithm is there ..."
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Cited by 46 (13 self)
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Abstract — A new distributed algorithm for cooperative estimation of a slowly timevarying signal using a wireless sensor network is presented. The estimate in each node is based on a so called consensus algorithm, which weights measurements and estimates of neighboring nodes. The algorithm is therefore scalable with the number of network nodes. It requires only limited information exchange between nodes and computations in each node. The weights are locally optimized based on a minimum variance criterion. Numerical results show that the proposed algorithm exhibits good performance compared to other distributed algorithms proposed in the literature. I.
Beyond Trilateration: On the Localizability of Wireless Adhoc Networks
"... Abstract — The proliferation of wireless and mobile devices has fostered the demand of context aware applications, in which location is often viewed as one of the most significant contexts. Classically, trilateration is widely employed for testing network localizability; even in many cases it wrongl ..."
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Cited by 42 (12 self)
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Abstract — The proliferation of wireless and mobile devices has fostered the demand of context aware applications, in which location is often viewed as one of the most significant contexts. Classically, trilateration is widely employed for testing network localizability; even in many cases it wrongly recognizes a localizable graph as nonlocalizable. In this study, we analyze the limitation of trilateration based approaches and propose a novel approach which inherits the simplicity and efficiency of trilateration, while at the same time improves the performance by identifying more localizable nodes. We prove the correctness and optimality of this design by showing that it is able to locally recognize all 1hop localizable nodes. To validate this approach, a prototype system with 19 wireless sensors is deployed. Intensive and largescale simulations are further conducted to evaluate the scalability and efficiency of our design. I.
A Survey on Localization for Mobile Wireless Sensor Networks
"... Abstract. Overthepastdecadewehavewitnessedtheevolutionof wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of smallprof ..."
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Cited by 29 (2 self)
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Abstract. Overthepastdecadewehavewitnessedtheevolutionof wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of smallprofile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of realworld mobile sensor applications that require position estimation. 1
A Distributed Minimum Variance Estimator for Sensor Networks
"... Abstract—A distributed estimation algorithm for sensor networks is proposed. A noisy timevarying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors’ measurements and estimates. The weights are adaptively up ..."
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Cited by 25 (4 self)
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Abstract—A distributed estimation algorithm for sensor networks is proposed. A noisy timevarying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors’ measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This bound decreases with the number of neighboring nodes. The estimation properties of the algorithm are illustrated via computer simulations, which are intended to compare our estimator performance with distributed schemes that were proposed previously in the literature. The results of the paper allow to tradingoff communication constraints, computing efforts and estimation quality for a class of distributed filtering problems.