Instrumenting wireless sensor networks for real-time surveillance

Chen, P.; Oh, Songhwai; Manzo, Michael; Sinopoli, Bruno; Sharp, Cory; Whitehouse, Kamin; Tolle, O.; Jeong, Jaein; Dutta, Prabal; Hui, J.Y.; Schaffert, S.; Kim, Sukun; Taneja, Jay; Zhu, Bin; Roosta, Tanya; Howard, Micah; Culler, David; Sastry, Shivakumar

doi:10.1109/robot.2006.1642177

Cited by 33 publications

(6 citation statements)

References 15 publications

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“…The extension presented in this paper lies in an analysis of GMH for estimating also other aggregate functions, i.e., distributed summing and distributed graph order estimation. In our analyses, the algorithm with a different mixing parameter is bounded by a distributed stopping criterion with varied initial configurations (i.e., counter threshold takes the values (10 −2 , 10 −4 , 10 −5 , 10 −6 ), and accuracy takes the values (3,5,7,10,20,40,60, 80, 100)). The experimental part consists of four scenarios (i.e., these functionalities are analyzed: distributed averaging, distributed summing -the initial inner states are multiplied by the graph order n, distributed summing -the final estimates are multiplied by the graph order n, a distributed graph order estimation).…”

Section: Resultsmentioning

confidence: 99%

“…The authors of [39] propose a novel consensus-based algorithm for data aggregation over WSNs by combining MH with the convex optimized weights algorithm, thereby optimizing both the transient and the steady-state algorithm phase. In [40], MH is applied to target tracking in WSNs for surveillance tasks. The proposed approach is to convert binary detections into finer position reports by applying the spatial correlation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Kenyeres¹,

Kenyeres²

2020

Adv. sci. technol. eng. syst. j.

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Over the last decades, numerous distributed consensus-based algorithms have found a wide application as a complementary mechanism for data aggregation in wireless sensor networks. In this paper, we provide an analysis of the generalized Metropolis-Hastings algorithm for data aggregation with a fully-distributed stopping criterion. The goal of the implemented stopping criterion is to effectively bound the algorithm execution over wireless sensor networks. In this paper, we analyze and compare the performance of the mentioned algorithm with various mixing parameters for distributed averaging, for distributed summing, and for distributed graph order estimation. The algorithm is examined under different configurations of the implemented stopping criterion over random geometric graphs by applying two metrics, namely the mean square error and the number of the iterations for the consensus. The goal of this paper is to examine the applicability of the analyzed algorithm with the stopping criterion to estimating the investigated aggregate functions in wireless sensor networks. In addition, the performance of the algorithm is compared to the average consensus algorithm bounded by the same stopping criterion.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Kenyeres¹,

Kenyeres²

2020

Adv. sci. technol. eng. syst. j.

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“…C ENTRALIZED detection of a binary event (e.g., the presence of an intruder) by monitoring a region of interest (ROI) is one of the most important applications of wireless sensor networks (WSN) [1], [2], [3]. The sensor node (SN) may be static (i.e., with no movement capabilities) or dynamic.…”

Section: Introductionmentioning

confidence: 99%

On Trajectory Design for Intruder Detection in Wireless Mobile Sensor Networks

Nurellari

Licea

Ghogho

et al. 2021

IEEE Trans. on Signal and Inf. Process. over Networks

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We address the problem of detecting the invasion of an intruder into a region of interest (ROI) which is monitored by a distributed bandwidth-constrained wireless mobile sensor network (WMSN). We design periodic trajectories for the mobile sensor nodes (MSNs) such that high detection probabilities are obtained while maintaining the MSNs' energy consumption low. To reduce the transmission and processing burden on the MSNs, we propose an operation algorithm based on two modes, surveying mode and conf irmation mode. In the former, to efficiently detect the intruder while using little mechanical energy, we optimize the surveying path such that the sensed area is maximized. During this mode, each MSN performs local detection and switches to the confirmation mode if and only if the intruder is suspected to be present. In the confirmation mode, each MSN collects further measurements over a predefined duration to reduce the detection uncertainly. A binary local hypothesis testing is performed at each MSN and only positive test statistics are transmitted to the FC where the ultimate decision is taken. Simulations results show the merits of the proposed two-mode operation algorithm in terms of detection performance and energy efficiency.

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“…C ENTRALIZED detection of a binary event is one of the most important applications of wireless sensor networks (WSNs) [1], [2]. Deployed over a field, multiple coordinated SNs report their processed observations to a fusion center (FC).…”

Section: Introductionmentioning

confidence: 99%

A Secure Optimum Distributed Detection Scheme in Under-Attack Wireless Sensor Networks

Nurellari

McLernon

Ghogho

2018

IEEE Trans. on Signal and Inf. Process. over Networks

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Abstract-We address the problem of centralized detection of a binary event in the presence of β fraction falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidthconstrained under − attack spatially uncorrelated distributed wireless sensor network (WSN). The SNs send their one-bit test statistics over orthogonal channels to the fusion center (FC), which linearly combines them to reach to a final decision. Adopting the modified deflection coefficient as an alternative function to be optimized, we first derive in a closed-form the FC optimal weights combining. But as these optimal weights require a − priori knowledge that cannot be attained in practice, this optimal weighted linear FC rule is not implementable. We also derive in a closed-form the expressions for the attacker "flipping probability" (defined in paper) and the minimum fraction of compromised SNs that makes the FC incapable of detecting. Next, based on the insights gained from these expressions, we propose a novel and non-complex reliability-based strategy to identify the compromised SNs and then adapt the weights combining proportional to their assigned reliability metric. In this way, the FC identifies the compromised SNs and decreases their weights in order to reduce their contributions towards its final decision. Finally, simulation results illustrate that the proposed strategy significantly outperforms (in terms of FC's detection capability) the existing compromised SNs identification and mitigation schemes.

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Instrumenting wireless sensor networks for real-time surveillance

Cited by 33 publications

References 15 publications

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

On Trajectory Design for Intruder Detection in Wireless Mobile Sensor Networks

A Secure Optimum Distributed Detection Scheme in Under-Attack Wireless Sensor Networks

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