Experiments on localization of wireless sensors using airborne mobile anchors

2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

et al. 2016

Self Cite

Localization is fundamental for many wireless sensor network applications. Localizing ground-based fixed nodes through an airborne mobile anchor node is particularly useful for sensors deployed from the air, yet its dynamics are not well-understood. In this work-in-progress paper, we propose a new formulation for probabilistic localization using gradient descent, and compare it with a deterministic multilateration algorithm. We perform simulations to evaluate the localization accuracy using designated airborne mobile anchor's position. We also study the impact in both favorable and poor geometrical position of the mobile anchor node during localization. Results to date show that probabilistic gradient descent algorithm outperforms deterministic multilateration in all scenarios, with localization error reduced by up to 75%.

Section: B Deterministic Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards probabilistic localization using airborne mobile anchors

Ahmad

Bergmann

2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

et al. 2016

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“…The values of the path-loss model parameters used in our experiments are d 0 = 1m, p 0(dBm) = −33.44 and n p = 3.567. These values are based on the results reported in [16].…”

Section: Proposed Algorithmmentioning

confidence: 99%

RSSI-based self-localization with perturbed anchor positions

Kumar

Arablouei

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

et al. 2017

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We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.

“…The values of the path-loss model parameters used in our experiments are d 0 = 1 m, p 0 = −33.44 dBm, and η = 3.567. These values are based on the results reported in [19].…”

Section: A Problem and Assumptionsmentioning

confidence: 99%

Multi-mode tracking of a group of mobile agents

Kumar

Arablouei

2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC)

et al. 2017

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We consider the problem of tracking a group of mobile nodes, which have limited computational and energy resources, using noisy RSSI measurements and position estimates available within the group. Existing solutions such as clusterbased GPS duty-cycling, individual tracking, and multilaterationbased localization and tracking can only partially deal with the challenges of dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. To efficiently cope with these challenges, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the group size and resource availability into consideration and determines the best solution at any particular time instance. We consider a clustering approach where a cluster head assigns the task of GPS activation and coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes simulated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to 20% in comparison with the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using around 50% less energy.