Performance evaluation of resource allocation policies for energy harvesting devices

We focus on resource allocation for energy harvesting devices. We analytically and numerically evaluate the performance of algorithms that determine time fair energy allocation in systems with predictable and stochastic energy inputs. To gain insight into the performance of networks of devices, we obtain results for the simple cases of a single node and a link. Due to the need for low complexity algorithms, we focus on simple policies (some of which proposed in the past as heuristics) and analytically derive performance guarantees. We also evaluate the performance via simulation, using real-world energy traces that we collected for over a year, and in a testbed of energy harvesting devices developed within the EnHANTs project.

[1]  Chows Chee-Seng Multigrid algorithms and complexity results for discrete-time stochastic control and related fixed-point problems , 1989 .

[2]  Dong Kun Noh,et al.  Efficient flow-control algorithm cooperating with energy allocation scheme for solar-powered WSNs , 2012, Wirel. Commun. Mob. Comput..

[3]  Ananth Krishnamurthy,et al.  Dynamic node activation in networks of rechargeable sensors , 2005, INFOCOM 2005.

[4]  B SrivastavaMani,et al.  Power management in energy harvesting sensor networks , 2007 .

[5]  Dusit Niyato,et al.  Sleep and Wakeup Strategies in Solar-Powered Wireless Sensor/Mesh Networks: Performance Analysis and Optimization , 2007, IEEE Transactions on Mobile Computing.

[6]  Kevin Fu,et al.  On the limits of effective hybrid micro-energy harvesting on mobile CRFID sensors , 2010, MobiSys '10.

[7]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[8]  Mani B. Srivastava,et al.  Power management in energy harvesting sensor networks , 2007, TECS.

[9]  Leandros Tassiulas,et al.  Control of wireless networks with rechargeable batteries [transactions papers] , 2010, IEEE Transactions on Wireless Communications.

[10]  Chee-Seng Chow,et al.  Multigrid algorithms and complexity results for discrete-time stochastic control and related fixed-point problems , 1989 .

[11]  Dan Rubenstein,et al.  Challenge: ultra-low-power energy-harvesting active networked tags (EnHANTs) , 2009, MobiCom '09.

[12]  Benjamin Van Roy,et al.  Approximate Linear Programming for Average-Cost Dynamic Programming , 2002, NIPS.

[13]  Prasun Sinha,et al.  Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks , 2008, SenSys '08.

[14]  Benjamin Van Roy,et al.  A Cost-Shaping Linear Program for Average-Cost Approximate Dynamic Programming with Performance Guarantees , 2006, Math. Oper. Res..

[15]  Gil Zussman,et al.  Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms , 2011, IEEE Transactions on Mobile Computing.

[16]  Prasun Sinha,et al.  Joint Energy Management and Resource Allocation in Rechargeable Sensor Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[17]  Dan Rubenstein,et al.  Prototyping Energy Harvesting Active Networked Tags (EnHANTs) with MICA2 Motes , 2010, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[18]  R. Srikant,et al.  Asymptotically Optimal Energy-Aware Routing for Multihop Wireless Networks With Renewable Energy Sources , 2007, IEEE/ACM Transactions on Networking.

[19]  Andrew G. Barto,et al.  Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks , 2007, 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[20]  Shie Mannor,et al.  Online learning in Markov decision processes with arbitrarily changing rewards and transitions , 2009, 2009 International Conference on Game Theory for Networks.