Sleep Schedule for Fast and Efficient Control of Parameters in Wireless Sensor-Actor Networks

Vasanthi, N. A.; Annadurai, S.

doi:10.1109/comswa.2006.1665220

Cited by 12 publications

(14 citation statements)

References 7 publications

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“…. , n, the deterministic scheduling performance µ d in (5) is almost surely upper bounded by the stochastic scheduling performance µ s in (7).…”

Section: Stochastic Scheduling Problemmentioning

confidence: 99%

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Stochastic sensor scheduling via distributed convex optimization

Elia

2015

Automatica

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a b s t r a c tIn this paper, we propose a stochastic scheduling strategy for estimating the states of N discrete-time linear time invariant (DTLTI) dynamic systems, where only one system can be observed by the sensor at each time instant due to practical resource constraints. The idea of our stochastic strategy is that a system is randomly selected for observation at each time instant according to a pre-assigned probability distribution. We aim to find the optimal pre-assigned probability in order to minimize the maximal estimate error covariance among dynamic systems. We first show that under mild conditions, the stochastic scheduling problem gives an upper bound on the performance of the optimal sensor selection problem, notoriously difficult to solve. We next relax the stochastic scheduling problem into a tractable suboptimal quasi-convex form. We then show that the new problem can be decomposed into coupled small convex optimization problems, and it can be solved in a distributed fashion. Finally, for scheduling implementation, we propose centralized and distributed deterministic scheduling strategies based on the optimal stochastic solution and provide simulation examples.

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“…. , n, the deterministic scheduling performance µ d in (5) is almost surely upper bounded by the stochastic scheduling performance µ s in (7).…”

Section: Stochastic Scheduling Problemmentioning

confidence: 99%

“…See Appendix. ✷ Remark 2 While Problem (5) provides an important motivation, Problem (7) is relevant on its own right and can be applied without the restriction on A i being invertible.…”

Section: Stochastic Scheduling Problemmentioning

confidence: 99%

Stochastic sensor scheduling via distributed convex optimization

Elia

2015

Automatica

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“…WSN is widely used in various applications such as environmental monitoring, battlefield surveillance, industrial process control, and home applications [3][4][5]. These networks are distinguished from traditional wireless networks because of its unique characteristics namely, the dense node deployment, unreliability of sensor nodes, severe energy consumption, and storage constraints [6][7][8][9]. WSN is a passive network, where sensors gather and forward environmental information to the sink.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Routing Protocols in Wireless Sensor–Actor Networks: A Review

Kakarla

Majhi

Battula

2015

Int J Wireless Inf Networks

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Wireless sensor-actor networks (WSANs) are applicable in versatile domains ranging from very common to those which demand reliable actions in the event area. The unique characteristics of WSANs and the resourceconstrained nature of the constituent sensor nodes give rise to design energy and delay efficient routing protocols. Many researchers have made considerable efforts to meet these challenges by designing energy and delay efficient protocols. In this paper, a comprehensive survey is presented on existing routing protocols for WSAN to analyze and list out their merits and demerits. The study broadly segregates the existing routing protocols into two categories i.e., cluster based and non-cluster based protocols. The cluster based protocols structure the physical network into virtual groups, whereas the non-cluster based protocols either use flooding or broadcasting mechanisms for communication. All the routing protocols under consideration have been simulated in a common simulation platform. Their performances are analyzed with respect to average end-to-end delay, packet delivery ratio, and average energy dissipation in the network individually. In addition, the best protocols among both the categories are compared to derive an overall conclusion.

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“…It has attracted much research effort in recent years [4], [5], [6]. As energy limitations is one of the major obstacles to the extensive adoption of such a technology [7], [8], many efficient WSN scheduling methods have been proposed to manage the energy consumption [9], [10], [11]. In [4], an adaptive energyefficient multisensor scheduling scheme was proposed for collaborative target tracking in WSNs.…”

Section: Introductionmentioning

confidence: 99%

Self-learning sensor scheduling for target tracking in wireless sensor networks based on adaptive dynamic programming

Xiao

Song

2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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This paper proposes a novel self-learning sensor scheduling scheme, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for target tracking in wireless sensor networks (WSNs). It employs Kalman filter estimation technique to predict the tracking accuracy. A performance index function is established based on the predicted energy consumption and tracking error. A self-learning scheduling method is proposed based on the adaptive dynamic programming algorithm. Numerical example shows the effectiveness of the proposed approach.Index Terms-Wireless sensor networks, sensor scheduling, Kalman filter, adaptive dynamic programming algorithm, selflearning.

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Sleep Schedule for Fast and Efficient Control of Parameters in Wireless Sensor-Actor Networks

Cited by 12 publications

References 7 publications

Stochastic sensor scheduling via distributed convex optimization

Stochastic sensor scheduling via distributed convex optimization

Comparative Analysis of Routing Protocols in Wireless Sensor–Actor Networks: A Review

Self-learning sensor scheduling for target tracking in wireless sensor networks based on adaptive dynamic programming

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