Optimizing the collection of local decisions for time-constrained distributed detection in WSNs

Laitrakun, Seksan; Coyle, Eugene

doi:10.1109/infcom.2013.6566992

Cited by 13 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the proof shown in Appendix A of [43], by applying Gauss-Hermite quadrature integration, we can show that where J is the number of sample points, r j is the jth root of Hermite polynomial, and C j is the associated weight of the jth root. By using J = 1 , where r 1 = 0 and C 1 = √ π , we (36) m = nm log q m|1 q M+1|1…”

Section: Appendix C: Proof Of Propositionmentioning

confidence: 57%

“…The necessary condition for the optimal transmission probabilities ρ ⋆ in (19) is that the function ∂ ∂ρ m P 0 P F + P 1 P M is equal to zero at ρ m = ρ ⋆ m for all m. The derivative By substituting ( 42) into (41) and after some mathematical arrangement, we can show that By setting (43) equal to zero, the term inside the curly brackets must be equal to zero and, after some mathematical arrangement, we have the following equality:…”

Section: Appendix D: Proof Of Propositionmentioning

confidence: 99%

See 1 more Smart Citation

Collision-aware distributed detection with population-splitting algorithms

Laitrakun

2021

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

We consider the design of distributed detection algorithms for single-hop, single-channel wireless sensor networks in which sensor nodes send their local decisions to a fusion center (FC) by using a random access protocol. There is also limited time to collect local decisions before a final decision must be made. We thus propose and analyze a modified random access protocol in which the FC combines slotted ALOHA with a population-splitting algorithm called population-splitting-based random access (PSRA) and collision-aware distributed detection according to an estimate-then-fuse approach. Under the PSRA, only sensor nodes whose observations fall in a particular range of reliability will send their decisions in a specific frame by using slotted ALOHA. At the end of the collection time, the FC applies the collision-aware distributed detection to make a final decision. Here, the FC will first observe the state of each time slot—idle, successful, collision—in each frame, use this information to estimate the number of sensor nodes participating in each frame, and, then, compute a final decision using a population-based fusion rule. An approximation of the optimal transmission probability of the slotted ALOHA is determined to minimize the probability of error. Numerical results show that, unlike slotted-ALOHA-based data networks, the transmission probability maximizing the number of successful time slots does not optimize the performance of the proposed distributed detection. Instead, the proposed distributed detection performs best with a transmission probability that induces many collisions.

show abstract

Section: Appendix C: Proof Of Propositionmentioning

confidence: 57%

Section: Appendix D: Proof Of Propositionmentioning

confidence: 99%

Collision-aware distributed detection with population-splitting algorithms

Laitrakun

2021

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, this centralised data processing scheme suffers from energy-consuming multi-hop communication and is fragile to the failure of several critical relaying sensor nodes. To enable scalable data processing in a large-scale WSN, decentralised data processing has received extensive research interests recently (Predd et al, 2006;Wittenburg et al, 2012;Schmidt et al, 2012;Laitrakun and Coyle, 2013). In the decentralised data processing scheme, the sensor nodes no longer send their measurements to the fusion centre.…”

Section: Related Workmentioning

confidence: 99%

Joint event detection and environment perception in decentralised wireless sensor networks

Zhu

Xue

et al. 2016

IJSNET

View full text Add to dashboard Cite

In an event detection application, sensor nodes measure signals that are emitted from multiple events and attenuated by the environment, and then collaborate to estimate the locations and magnitudes of the events. Existing event detection algorithms often assume that the number of the events is known in advance and/or the attenuation coefficient of the environment is given. This paper considers the case that both the number of the events and the attenuation coefficient of the environment are unknown. Through exploiting the sparse nature of the events, we propose an ℓ-norm regularised least squares formulation that automatically estimates the number of the events as well as their locations and magnitudes; the attenuation coefficient of the environment also appears as an optimisation variable. We develop a decentralised algorithm and its accelerated variant to solve the joint event detection and environment perception problem using the alternating direction method of multipliers (ADMM).

show abstract

“…, clearly declare which class the target belongs to. The fusion center makes a decision according to some fusion rules, like counting rules [ 10 ], weighted sum [ 11 ], Neyman–Pearson criterion [ 12 ], or the max-log fusion [ 13 ]. The typical fusion HD scheme is the majority voting rule [ 14 ], though it has great advantage in easy implementation, the low fusion accuracy decreases it practicability.…”

Section: Introductionmentioning

confidence: 99%

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

Zhang¹,

Zhang²

2015

Sensors

View full text Add to dashboard Cite

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule.

show abstract

Optimizing the collection of local decisions for time-constrained distributed detection in WSNs

Cited by 13 publications

References 19 publications

Collision-aware distributed detection with population-splitting algorithms

Collision-aware distributed detection with population-splitting algorithms

Joint event detection and environment perception in decentralised wireless sensor networks

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

Contact Info

Product

Resources

About