Adaptive Duty Cycling for Energy Harvesting Systems

Hsü,; Zahedi,; Kansal,; Srivastava,; Raghunathan,

doi:10.1109/lpe.2006.4271832

Cited by 64 publications

(16 citation statements)

References 8 publications

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“…Then, let N sch be the number of scheduling periods during a day, let N sch · ∆t be 24 h and let t N sch be t 0 + (N sch − 1) · ∆t. Based on E act (n s i ) and previous work [1,2], the residual energy at time t N sch at node i for both time intervals, T D and T N , can then be modeled as…”

Section: A Measurement Of Residual Energymentioning

confidence: 99%

“…To demonstrate the efficiency of the BUCKET algorithm, we programmed two existing basic algorithms for a performance comparison: Maximization of the sum of the duty cycle (MSDC) [1,2] and equal duty cycle allocation (EDCA), as shown in Figure 9. Figure 9 shows the energy allocations using the MSDC scheme, the BUCKET algorithm and the EDCA scheme with the aforementioned parameters.…”

Section: B Performance Evaluationmentioning

confidence: 99%

“…In [1,2], performance enhancement, expressed in terms of duty cycle, is investigated using Heliomote sensor-mote devices. The authors sought to maximize the total duty cycle assuming uniform traffic and point-to-point communication; however, these assumptions are nontrivial when extended to more general sensor networks that are characterized by variable traffic patterns, and that allow inter-networking among the sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

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BUCKET: Scheduling of Solar-Powered Sensor Networks via Cross-Layer Optimization

et al. 2015

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Renewable solar energy harvesting systems have received considerable attention as a possible substitute for conventional chemical batteries in sensor networks. However, it is difficult to optimize the use of solar energy based only on empirical power acquisition patterns in sensor networks. We apply acquisition patterns from actual solar energy harvesting systems and build a framework to maximize the utilization of solar energy in general sensor networks. To achieve this goal, we develop a cross-layer optimization-based scheduling scheme called BUCKET (Binding optimization of dUty Cycling and net-worKing through Energy Tracking), which is formulated in fourstages: prediction of energy harvesting and arriving traffic, internode optimization at the transport and network layers, intranode optimization at the medium access control (MAC) layer, and flow control of generated communication task sets using a tokenbucket algorithm. Monitoring of the structural health of bridges is shown to be a potential application of an energy-harvesting sensor network. The example network deploys five sensor types: temperature, strain gauge, accelerometer, pressure, and humidity. In the simulations, the BUCKET algorithm displays performance enhancements of 12 ∼ 15% over those of conventional methods in terms of the average service rate.Index Terms-Binding optimization of duty cycling and networking through energy tracking (BUCKET), cross-layer optimization, equal duty cycle allocation (EDCA), energy-harvesting sensor network, inter-and intra-node optimization, maximization of the sum of the duty cycle (MSDC).1530-437X (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2014.2363900, IEEE Sensors Journal 2 harvesting sensor node networks. In Section III, the overall operation of our solar-powered cross-layer sensor network model is explained. Implementation of the BUCKET algorithm for real-time scheduling is discussed in Section IV. The core processes of the BUCKET algorithm: inter-and intra-node optimization are presented in Sections V and VI, respectively. Section VII demonstrates the performance enhancement attained through extensive simulation. Finally, we conclude the paper in Section VIII. II. RELATED WORK A. Optimization of Energy-Harvesting Sensor NodesPrevious studies have addressed the maximization of the efficiency of solar energy use via optimized hardware design, including the efforts in [5]-[7]. In each of these studies, a simple routing or scheduling algorithm for resource allocation is developed over a single layer, rather than over multiple layers [8,9]. The authors in [1,2] explore the design of an optimal duty cycle implemented at the MAC layer based on energyharvesting power m...

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Section: A Measurement Of Residual Energymentioning

confidence: 99%

Section: B Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BUCKET: Scheduling of Solar-Powered Sensor Networks via Cross-Layer Optimization

et al. 2015

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“…One of the major directions in exploiting energy harvesting is adaptive duty cycling in sensor networks. In particular, [8,11,12] describe adaptation of the duty cycle to the characteristics of a periodic energy source and [37] develops a "battery-centric" (not requiring an energy source model) duty-cycle adaptation algorithm. Taking energy harvesting into account when making routing decisions is studied in [18,38,39].…”

Section: Related Workmentioning

confidence: 99%

“…Although networking protocols for energy harvesting nodes recently started gaining attention [6,8,9,[11][12][13]18,22,23,27,35,37,38] (see Section 2), to the best of our knowledge, the cross layer interactions between circuit design, energy harvesting, communications, and networking have not been studied in depth. Current RF transceiver designs, communication and networking protocols, and energy harvesting and management techniques have been developed in isolation and are inadequate for the envisioned EnHANTs applications.…”

Section: Introductionmentioning

confidence: 99%

Challenge

Gorlatova

Kinget

Kymissis

et al. 2009

Proceedings of the 15th Annual International Conference on Mobile Computing and Networking

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This paper presents the design challenges posed by a new class of ultra-low-power devices referred to as Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and self-reliant (in terms of energy) devices that can be attached to objects that are traditionally not networked (e.g., books, clothing, and produce), thereby providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects (items in a store, boxes in transit), and determining locations of disaster survivors. Recent advances in ultra-low-power wireless communications, ultra-wideband (UWB) circuit design, and organic electronic harvesting techniques will enable the realization of EnHANTs in the near future. In order for EnHANTs to rely on harvested energy, they have to spend significantly less energy than Bluetooth, Zigbee, and IEEE 802.15.4a devices. Moreover, the harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using ultra-low-power circuits, the energy required to receive a bit is an order of magnitude higher than the energy required to transmit a bit). These special characteristics pose several new cross-layer research problems. In this paper, we describe the design challenges at the layers above the physical layer, point out relevant research directions, and outline possible starting points for solutions.

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An autonomous distributed control method based on tie‐set graph theory in future grid

Nakayama

Shinomiya

Watanabe

2012

Circuit Theory & Apps

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SUMMARY In future power grids where electricity flows bidirectionally, the essential problem is to maximize the total efficiency of distributed energy resources. In complicated and large‐scale systems such as modern power distribution networks, maximizing the efficiency of the entire system as a whole is extremely difficult. To solve the global optimization problem of such a complex network, this paper proposes an efficient distributed control method for future grid on the basis of tie‐set graph theory, where a tie‐set is a set of all the edges in a loop of a graph. On the basis of tie‐set graph theory, global optimization of an entire network can be realized as a result of local optimization in μ‐dimensional liner vector space, where μ is the nullity of the underlying graph of a power network. Although each tie‐set has its limited local information, an entire network is gradually optimized in an orderly manner because of the theoretical basis of a tie‐set graph. Simulation results of several thousand‐node networks demonstrate balanced allocation of dispersed energy resources and thus effectiveness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.

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Adaptive Duty Cycling for Energy Harvesting Systems

Cited by 64 publications

References 8 publications

BUCKET: Scheduling of Solar-Powered Sensor Networks via Cross-Layer Optimization

BUCKET: Scheduling of Solar-Powered Sensor Networks via Cross-Layer Optimization

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An autonomous distributed control method based on tie‐set graph theory in future grid

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