Locating tiny sensors in time and space: a case study

Girod, Lewis; Bychkovskiy, V. A.; Elson, Jeremy; Estrin, Deborah

doi:10.1109/iccd.2002.1106773

Cited by 182 publications

(120 citation statements)

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“…In [8] numerical evaluation of various protocols is done in the context of optical sensor networks. In [13,14] the authors consider a particular technique for estimating the distances between sensors and proceed to real experiments based on Motes 1 sensor systems. From a complexity point of view, in [3,6] NP-hardness results are provided for the localization and connex problems.…”

Section: Related Workmentioning

confidence: 99%

Numerical Estimation of the Impact of Interferences on the Localization Problem in Sensor Networks

Bouget

Leone

Rolim

2006

Experimental Algorithms

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Abstract.In this paper we numerically analyze the impact of interferences in wireless networks. More particularly, the impact on the probability of success of a localization algorithm. This problem is particularly relevant in the context of sensor networks. Actually, our numerical experiments provide information about this algorithm, even when we do not consider interferences. Indeed, in this case, sharp threshold is observed on the probability of success of the algorithm. Moreover, our numerical computations show that the main harmful interferences are the ones occurring between sensors which get localized at the same time and send in turn their own location. This is demonstrated by varying the time span of the random waiting time before the emissions. We then observe that the longer the waiting time the closer the curves are to the ones obtained without considering interferences. Hence, this proves to be an efficient way of reducing the impact of interferences on the localization algorithm. Moreover, our numerical experiments demonstrate that among the sectors of disk with same area, the one with the smaller radius of emission and larger angle of emission is the more appropriate to the localization algorithm.

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Section: Related Workmentioning

confidence: 99%

Numerical Estimation of the Impact of Interferences on the Localization Problem in Sensor Networks

Bouget

Leone

Rolim

2006

Experimental Algorithms

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“…Other techniques using high-level processing to assist the background modeling also have been proposed [21], [12] Time Synchronization: Time synchronization in sensor networks has been studied extensively in the literature and several protocols have been proposed [7], [9], [16], [17], [13], [18]. Mote-PC synchronization was achieved in [10] by connecting the GPIO ports of a mote and IPAQ PDA.…”

Section: Related Workmentioning

confidence: 99%

Multi-Modal Target Tracking Using Heterogeneous Sensor Networks

Kushwaha

Amundson

Völgyesi

et al. 2008

2008 Proceedings of 17th International Conference on Computer Communications and Networks

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Abstract-The paper describes a target tracking system running on a Heterogeneous Sensor Network (HSN) and presents results gathered from a realistic deployment. The system fuses audio direction of arrival data from mote class devices and object detection measurements from embedded PCs equipped with cameras. The acoustic sensor nodes perform beamforming and measure the energy as a function of the angle. The camera nodes detect moving objects and estimate their angle. The sensor detections are sent to a centralized sensor fusion node via a combination of two wireless networks. The novelty of our system is the unique combination of target tracking methods customized for the application at hand and their implementation on an actual HSN platform.

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“…For the former one the location information is obtained with additional hardware and extra distance and angle information such as RSSI [1], TOA [2], TDOA [3] and AOA [4]. But for the latter one the connectivity information is only needed to get the sensor nodes position, such as convex programming algorithm [5], DV-Hop algorithm and MDS-MAP algorithm [6]- [7].…”

Section: Introductionmentioning

confidence: 99%