Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks

By Christopher M. Vigorito, Deepak Ganesan, Andrew Barto. Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, 2007

445 citations recorded by the source corpus.

Increasingly many wireless sensor network deployments are using harvested environmental energy to extend system lifetime. Because the temporal profiles of such energy sources exhibit great variability due to dynamic weather patterns, an important problem is designing an adaptive duty-cycling mechanism that allows sensor nodes to maintain their power supply at sufficient levels (energy neutral operation) by adapting to changing environmental conditions. Existing techniques to address this problem are minimally adaptive and assume a priori knowledge of the energy profile. While such approaches are reasonable in environments that exhibit low variance, we find that it is highly inefficient in more variable scenarios. We introduce a new technique for solving this problem based on results from adaptive control theory and show that we achieve better performance than previous approaches on a broader class of energy source data sets. Additionally, we include a tunable mechanism for reducing the variance of the node's duty cycle over time, which is an important feature in tasks such as event monitoring. We obtain reductions in variance as great as two-thirds without compromising task performance or ability to maintain energy neutral operation.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3581d4865a62254a9e9e388ace23628783405c15

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Citations in the corpus

Energy Harvesting Sensor Nodes: Survey and Implications (2011, background): “Similar to [64], that maximized node duty cycle while ensuring energy neutral operation, the MESW metric aims to maximize the data rate at each node with energy neutrality.”
FarmBeats: An IoT Platform for Data-Driven Agriculture (2017, background): “duty-cycling sensors backed by energy harvesting sensor systems [22,59], FarmBeats aims to achieve the objective of energy neutrality.”
Piezoelectric Energy Harvesting Devices: An Alternative Energy Source for Wireless Sensors (2012, background): “This direct implementation of the duty cycling technique, though popular in energy harvesting for wireless sensor networks, is not always the best choice [51–54].”
Energy-Harvesting Wireless Sensor Networks (EH-WSNs) (2018, methodology): “Existing energy-neutral systems are designed to use analytical models and protocols to schedule tasks in accordance with the amount of energy harvested from the environment (for both predictive and reactive approaches) (Kansal and Srivastava 2005; Vigorito et al. 2007).”
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms (2011, methodology): “The MDP formulations can be easily extended to consider other parameters, such as the cost to change the energy spending rate s(i).”
Joint mobile energy replenishment and data gathering in wireless rechargeable sensor networks (2011, background): “In this section, we briefly review some related work in the literature, which includes the work on energy replenishment in wireless networks, and the work on mobile data gathering in wireless sensor networks.”
Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection (2008, background): “Energy harvesting techniques may also complement battery power [15], although the unpredictability of energy supply typical of harvesting makes it diﬃcult to manage duty cycling schedules [16].”
Energy harvesting by means of flow-induced vibrations on aerospace vehicles (2016, background): “Similarly, reference [175] focused on making WSNs adaptive to the changing environment.”