Conditions for unique graph realizations
SIAM Journal on Computing
The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
The bits and flops of the n-hop multilateration primitive for node localization problems
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Contour estimation using collaborating mobile sensors
DIWANS '06 Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks
Probabilistic localization for outdoor wireless sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Sequence-Based Localization in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Connectivity-based localization of large-scale sensor networks with complex shape
ACM Transactions on Sensor Networks (TOSN)
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Contour approximation in sensor networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Energy-based sensor network source localization via projection onto convex sets
IEEE Transactions on Signal Processing
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
Understanding Node Localizability of Wireless Ad Hoc and Sensor Networks
IEEE Transactions on Mobile Computing
Inner-Distance-Based Shape Recognition of Target Object Using Binary Sensors
ICPADS '12 Proceedings of the 2012 IEEE 18th International Conference on Parallel and Distributed Systems
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Cooperative localization, where sensors exchange the information with each other to determine their locations, has received considerable attention. In this work, we study the cooperative localization in order to investigate several fundamental properties that have not been well addressed so far. We formulate the cooperative localization in a general setting, where a relative or absolute location map is obtained, depending on the number of anchors. The (relative or absolute) location map is the output of an optimization problem, where the objective function is given as a norm of a space where a vector composed of distances between sensors is defined. We show that several error bounds and the estimation bias of the cooperative localization can be obtained by simple arguments (e.g. by using triangle inequality) without specifying the detail of the objective function. Next, we theoretically and numerically verify that the cooperative localization has a preferable scaling property such that the estimation becomes more accurate as sensors are more densely deployed. Finally, we consider the problem that the objective functions used in the cooperative localization are usually multimodal and have a number of local optima and saddle points. We show that the gradient descent algorithm starting from a random prior (initial estimates) often fails to find the optimal solution when the distance measurements between some pair of sensors are not available. We propose a new prior, called shortest-path-distance-based prior, which is very powerful for obtaining accurate estimates even when the distances between some sensor pairs are not measurable.