Object Tracking Over Distributed WSNs With Consensus on Estimates and Missing Data

Vazquez-Olguin, Miguel; Shmaliy, Yuriy S.; Ibarra-Manzano, Oscar; Muñoz‐Minjares, Jorge; Lastre-Dominguez, Carlos

doi:10.1109/access.2019.2905514

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…The authors of [15] and [16] proposed games-based scheme and a distributed strategy with consensus on estimates and missing data in terms of multi-target tracking, respectively. The objective of maximizing information about the target state and of estimating real-time position is designed to service the total mobile sensor network better.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

et al. 2020

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Mobile target tracking-oriented sensor networks are a special kind of Mission-critical Sensor Networks (MCSN), in which the various missions with the diverse priorities exist. However, it is challenging to achieve real time tracking while keeping the MCSN a long life time with limited energy provision in a complicated environment. In this paper, we develop a collaborative perception and intelligent scheduling scheme, which jointly optimizes the system responding latency and tracking accuracy with the constraint of the available energy. A new hierarchical architecture is proposed to realize the coupled function of perception and computation. In particular, the multi-node collaborative perception scheme is applied to obtain the excellent sensing capacity, and the Unmanned Aerial Vehicles (UAVs) play as the edge nodes to provide the computing service for those resource-constrained sensor nodes. To reach the sustained target tracking, we propose an intelligent tracking policy by exploiting the deep deterministic policy gradient (DDPG) method. Simulation results demonstrate that the proposed intelligent collaboration scheme can improve the tracking accuracy by 45.5% compared with the random selection scheme. The system cost is also reduced approximately by 17.3% while guaranteeing the tracking accuracy. INDEX TERMS Multi-target tracking, energy consumption, collaborative perception, deep reinforcement learning.

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Section: Related Workmentioning

confidence: 99%

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

et al. 2020

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“…Wireless indoor localization has gained increasing research attention in past decades, as it plays an important role in many wireless applications such as personal surveillance, object tracking and vehicle navigation [9,18,26]. Various wireless indoor localization techniques have been proposed, including WiFi [35] [24], radio-frequency identification (RFID) [16] [4], Bluetooth [3] [12], Ultra Wide Bandwidth (UWB) [28] [10], etc.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of indoor localization based on channel state information ranging model

Gui

Xiao

Zhou

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Due to robustness against multi-path effect, channel state information (CSI) of Orthogonal Frequency Division Multiplexing (OFDM) systems is supposed to provide accurate distance measurement for indoor localization. However, we find that the original CSI ranging model is biased, so the model cannot be used to directly derive Cramer-Rao lower bound (CRLB) of positioning error for CSI-ranging based localization scheme. In this paper we first analyze the estimation bias of the original CSI ranging model according to indoor wireless channel model. Then we propose a negative power summation ranging model which can be used as an unbiased ranging model for both Line-Of-Sight (LOS) and Non-LOS scenarios. Subsequently, based on the proposed model, we derive both the CRLB of ranging error and the CRLB of positioning error for CSI-ranging localization scheme. Through simulation we validate the bias of the original ranging model and the approximately zero bias of our proposed ranging model. Through comprehensive experiments in different indoor scenarios, localization errors by different ranging models are compared to the CRLB, meanwhile our proposed ranging model is demonstrated to have better ranging and localization accuracy than the original ranging model.

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“…One fundamental behavior of a network of agents such as sensor or robotic networks is the consensus among agents on a variable [13], [15]. In wireless sensor networks, consensus is achieved by an iterative algorithm performed by each node, and all the sensors in the network may achieve global agreement using only local transmissions [16], [17]. For a sensor network, it constitutes the basic block for designing decentralized tracking and estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Supervisory Algorithm Against Intermittent and Temporary Faults in Consensus-Based Networks

Parlangeli

2020

IEEE Access

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In this paper, the consensus problem is addressed for multi-agent networks in which some nodes are expected to possibly misbehave as a consequence of temporary faults or errors. The solution proposed is based on the counteraction of a prescribed subset of nodes through the injection of additional corrective signals. Such corrections are the output of a fault compensation algorithm based on a simple elaboration of local data, whose structure is based on some input-output theoretical results achieved in this paper. A thorough discussion is reported on how to retrieve the algorithm coefficients using local data, so that the algorithm can be fully performed without any network topology knowledge in advance. Simulation results show the behavior of the adopted strategy in several different faulty conditions, and robustness of the compensation algorithm with respect to errors in the computation of the coefficients is also investigated. The results show that the proposed algorithm performs well in several scenarios of networked systems, and it can be suited to easily restore the consensus value to the original one, in spite of the drift initially caused by the fault. INDEX TERMS Collaborative sensor networks, consensus networks, fault compensation algorithms, robotic networks. GIANFRANCO PARLANGELI (Member, IEEE) received the M.Sc. degree (Hons.

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Object Tracking Over Distributed WSNs With Consensus on Estimates and Missing Data

Cited by 13 publications

References 31 publications

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

Performance analysis of indoor localization based on channel state information ranging model

A Supervisory Algorithm Against Intermittent and Temporary Faults in Consensus-Based Networks

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