Power management in energy harvesting sensor networks

Abstract

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.

GRAPMAN: Gradual power manager for consistent throughput of energy harvesting wireless sensor nodes

“…The first dynamic duty-cycling scheme was proposed by Kansal et al in their seminal work on energy harvesting theory [1]. In their approach, the energy source is assumed to be periodic and a single period is discretized into slots of equal duration.…”

“…The predictor must be able to estimate the harvested energy over the next source period. The predictor from [1] is used, which works by assuming that the energy input at a given slot will be similar to the energy inputs at the same slot on previous periods. The expected energy generation is learned using an EWMA filter:…”

“…For many long-term applications, replacing the batteries of each node is not a feasible solution if they are deployed in a harsh environment or if the network size is large. A promising approach to tackle this problem is to enable the nodes to be entirely powered by the energy harvested in their environments [1]- [5]. In an EH-WSN, each node is equipped with at least one energy harvester and one or more energy storage devices.…”

“…In the latter case, the purpose of the power management policies is to attain long-term sustainability, which is achieved when over long time periods the harvested energy is greater than or equal to the consumed energy, an operating mode called Energy Neutral Operation (ENO) [1].…”

Learning in Unknown Reward Games: Application to Sensor Networks

“…Specifically, acoustic sensors, which can be used to detect foot or vehicle traffic, are deployed in an urban setting to monitor nearby traffic. The sensors run on energy harvested from the environment; That is, they operate in an "energyneutral" mode, such that their energy use is equal to their generation [1,17]. Since the signal-processing required to detect events is typically the most energy intensive activity, the sensors cannot be permanently powered.…”