A randomized scheduling algorithm for energy harvesting wireless sensor networks achieving nearly 100% throughput

Gül, Ömer Melih; Uysal-Biyikoğlu, Elif

doi:10.1109/wcnc.2014.6952774

Cited by 17 publications

(12 citation statements)

References 8 publications

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“…In order to illustrate the performance of our policy, we assume the utility function f (x) = log(1 + x) for x ∈ Z + , and for noninteger x ∈ R + , f (x) is defined according to (16).…”

Section: B Online Resultsmentioning

confidence: 99%

“…[14] and [15] show the optimality of a Round-Robin based myopic policy that schedules the K nodes with the largest beliefs to maximize the immediate reward under different system models. In [16], [17], under the infinite battery assumption, a uniformizing random ordered policy that selects the sensors based on a predefined random priority list and the outcome of transmissions in the previous time slot is shown to be asymptotically optimal in infinite horizon for a broad class of energy harvesting process. The proposed queue-length based myopic policy is also very similar to the longest-connected-queue server allocation policy studied in [23], [24], etc.…”

Section: Related Workmentioning

confidence: 99%

“…where (18) follows from Fatou's Lemma, and (19) follows from the concavity and monotonicity of function f , (20) follows from Lemma 4, and the last equality follows from the extended definition of f (x) in (16).…”

Section: Lemma 5 the Objective Function In (12) Is Upper Bounded Asmentioning

confidence: 99%

“…where f N i=1 λ i is defined according to (16). The definition of C * t implies that C * t ⊆ C t , |C * t | ≤ |C t | = m t .…”

Section: B Proof Of Theoremmentioning

confidence: 99%

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Optimal Scheduling of Collaborative Sensing in Energy Harvesting Sensor Networks

Yang

2015

IEEE J. Select. Areas Commun.

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In this paper, we consider a collaborative sensing scenario where sensing nodes are powered by energy harvested from ambient environment. In each time slot, an active sensor consumes one unit amount of energy to take an observation and transmit it back to a fusion center (FC). After receiving observations from all of the active sensors in a time slot, the FC aims to extract information from them. We assume that the sensing utility generated by the observations is a concave function of the number of the active sensing nodes in that slot. Our objective is to develop a sensing scheduling policy so that the time average utility generated by the sensors is maximized. We first consider an offline setting, where the energy harvesting profile over duration [0, T − 1] for each sensor is known beforehand. Assuming infinite battery capacity at sensors, we show that the optimal scheduling structure has a "majorization" property, and propose a procedure to construct a collaborative sensing policy with the identified structure explicitly. We then consider an online setting, under which the energy harvesting profile is available causally. Assuming the energy harvesting processes at individual sensors are independent but not necessarily identical Bernoulli processes, we show that the expected long-term time average sensing utility has an upper bound under any feasible scheduling policy satisfying the energy causality constraints. We then propose a randomized myopic policy, which aims to select a number of sensors with the highest energy levels to perform the sensing task in each slot. We show that the time average utility generated under the proposed policy converges to the upper bound almost surely as time T approaches infinity, thus it is optimal. The corresponding convergence rate is also explicitly characterized.

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“…In order to illustrate the performance of our policy, we assume the utility function f (x) = log(1 + x) for x ∈ Z + , and for noninteger x ∈ R + , f (x) is defined according to (16).…”

Section: B Online Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Lemma 5 the Objective Function In (12) Is Upper Bounded Asmentioning

confidence: 99%

“…where f N i=1 λ i is defined according to (16). The definition of C * t implies that C * t ⊆ C t , |C * t | ≤ |C t | = m t .…”

Section: B Proof Of Theoremmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Scheduling of Collaborative Sensing in Energy Harvesting Sensor Networks

Yang

2015

IEEE J. Select. Areas Commun.

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“…Compared with the vast literature on single node scenario, the problem of analyzing and modeling the interaction among multiple EH nodes at the MAC layer in a network has received limited attention so far. [11] considers a single-hop TDMA wireless network where a FC collects data from multiple EHS nodes with infinite battery size, and find a low complexity scheduling policy that maximizes the total throughput of the data backlogged system under the assumption that unit energy is consumed for the transmission of one packet. The authors in [12] discuss the extension of their proposed energy management policies to multiple EHS nodes scenario.…”

Section: Related Workmentioning

confidence: 99%

Optimal Reliability in Energy Harvesting Industrial Wireless Sensor Networks

Lei

Kuang

Shen

et al. 2016

IEEE Trans. Wireless Commun.

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For Industrial Wireless Sensor Networks, it is essential to reliably sense and deliver the environmental data on time to avoid system malfunction. While energy harvesting is a promising technique to extend the lifetime of sensor nodes, it also brings new challenges for system reliability due to the stochastic nature of the harvested energy. In this paper, we investigate the optimal energy management policy to minimize the weighted packet loss rate under delay constraint, where the packet loss rate considers the lost packets both during the sensing and delivering processes. We show that the above energy management problem can be modeled as an infinite horizon average reward constraint Markov decision problem. In order to address the well-known curse of dimensionality problem and facilitate distributed implementation, we utilize the linear value approximation technique. Moreover, we apply stochastic online learning with post-decision state to deal with the lack of knowledge of the underlying stochastic processes. A distributed energy allocation algorithm with water-filling structure and a scheduling algorithm by auction mechanism are obtained. Experimental results show that the proposed algorithm achieves nearly the same performance as the optimal offline value iteration algorithm while requiring much less computation complexity and signaling overhead, and outperforms various existing baseline algorithms.

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Near-Optimal Data Communication Between Unmanned Aerial and Ground Vehicles

Gül

2021

Advances in Intelligent Systems and Computing

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A randomized scheduling algorithm for energy harvesting wireless sensor networks achieving nearly 100% throughput

Cited by 17 publications

References 8 publications

Optimal Scheduling of Collaborative Sensing in Energy Harvesting Sensor Networks

Optimal Scheduling of Collaborative Sensing in Energy Harvesting Sensor Networks

Optimal Reliability in Energy Harvesting Industrial Wireless Sensor Networks

Near-Optimal Data Communication Between Unmanned Aerial and Ground Vehicles

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