This paper considers a wireless sensor powered by an energy harvesting device, which reports data of varying importance to its receiver. Modeling the ambient energy supply by a two-state Markov chain («GOOD» and «BAD»), assuming a finite battery capacity constraint, and associating data transmission with a given energy cost, we propose low-complexity transmission policies, that achieve near-optimal performance in terms of the average long-term importance of the reported data. In particular, we derive the performance of the Balanced Policy (BP), which adapts the transmission probability to the harvesting state, such that energy harvesting and consumption are balanced. Our analysis demonstrates that the performance of the BP largely depends on the power-to-depletion, defined as the power that a fully charged battery can supply on average over a BAD period. Numerical results show that the optimal BP achieves near-optimal performance and that a BP which avoids energy overflow further reduces the gap with respect to the globally optimal policy. A heuristic BP, based on the analysis of a system with a deterministic and periodic energy supply, is also proposed, and the parallels between the deterministic system and its stochastic counterpart are discussed.
- Energy harvesting
- Markov decision processes
- Wireless sensor networks
ASJC Scopus subject areas
- Electrical and Electronic Engineering