Scaling self-timed systems powered by mechanical vibration energy harvesting

Wenck, Justin; Collier, Jamie; Siebert, Jeff; Amirtharajah, Rajeevan

doi:10.1145/1773814.1773816

Cited by 9 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One common approach is to harvest energy from the environment sources, such as solar energy [1], kinetic energy [2], and wind [3]. However, harvested energy supply is not stable and controllable.…”

Section: Related Workmentioning

confidence: 99%

Sustaining a perpetual wireless sensor network by multiple on-demand mobile wireless chargers

Wang

et al. 2015

2015 IEEE 12th International Conference on Networking, Sensing and Control

View full text Add to dashboard Cite

Wireless sensor networks are traditionally constrained by finite energy supply. Recent wireless charging technology enables possibility for timely replenishing consumed energy of dispersed sensors. This paper investigates on-demand wireless charging problem with multiple mobile chargers, which can sustain a large-scale wireless sensor network operation perpetually without knowing energy consumption information of sensors a priori. We evaluate the nearest-first and recent-rarestfirst strategies, which schedule multiple chargers for incoming charging requests. Also four synthesized occurrence processes of charging requests are presented and then used to compare scheduling strategies in terms of concurrent charging load and serving latency. Simulation results show that the nearest-first scheduling strategy suits for uniform charging request occurrence processes, while the recent-rarest-first scheduling strategy is better for localized charging request occurrence processes. I. INTRODUCTIONWireless sensor networks have been intensively investigated and prototyped in the past decade. Conventionally, the sensors are powered by batteries or super-capacitors. As the sensors monitor the surrounding environment, relevant data would be generated. Afterwards, such data is forwarded in a hop-by-hop manner towards the sink that would make the application-specific processing. Energy constraint imposed by finite-capacity batteries and super-capacitors are increasingly hindering the practical deployment of sensor networks. It is desirable for wireless sensor networks to operate perpetually.Energy replenishment is a promising approach. For instance, harvesting energy from the environment, such as solar energy, wind, and vibration, has been studied in the past. Yet the harvested energy is almost always in a very low rate and normally cannot sustain continuous operation of the individual sensors. Furthermore, the harvested energy rate often varies considerably and unpredictably, which further limits the workload of individual sensors. Recently, wireless charging technology opens new opportunities for mitigating the energy constraint of sensors. In particular, there are three kinds of concrete implementation: inductive coupling, electromagnetic radiation, and magnetic resonant coupling. Inductive coupling can only function in several centimeters, and hence is not suitable for wireless charging individual sensors. Electromagnetic radiation and magnetic resonant coupling have a maximum charging range of 1-3 meters, and usually the wireless chargers are mounted on mobile vehicles in order to charge large-scale geographically distributed sensors practically.Nearly all mobile charger based sensor charging studies focus on offline programming, where the charger moves to

show abstract

Section: Related Workmentioning

confidence: 99%

Sustaining a perpetual wireless sensor network by multiple on-demand mobile wireless chargers

Wang

et al. 2015

2015 IEEE 12th International Conference on Networking, Sensing and Control

View full text Add to dashboard Cite

show abstract

“…Figs. 4(a) and 4(b) depict the fraction of events captured by the three policies for the two different event types W (40, 3) and P (2, 10), respectively. For the periodic policy, which activates the sensor for θ 1 time slots every θ 2 slots, we fix θ 1 = 3 and calculate θ 2 (e) = θ1δ1 e + θ1δ2 eμ , where μ is the expectation of the event inter-arrival times to achieve energy balance.…”

Section: Simulationsmentioning

confidence: 99%

Dynamic Activation Policies for Event Capture with Rechargeable Sensors

Zhu

Cheng

Chen

et al. 2012

2012 IEEE 32nd International Conference on Distributed Computing Systems

View full text Add to dashboard Cite

We consider the problem of event capture by a rechargeable sensor network. We assume that the events of interest follow a renewal process whose event inter-arrival times are drawn from a general probability distribution, and that a stochastic recharge process is used to provide energy for the sensors' operation. Dynamics of the event and recharge processes make the optimal sensor activation problem highly challenging. In this paper we first consider the single-sensor problem. Using dynamic control theory, we consider a fullinformation model in which, independent of its activation schedule, the sensor will know whether an event has occurred in the last time slot or not. In this case, the problem is framed as a Markov decision process (MDP), and we develop a simple and optimal greedy policy for the solution. We then further consider a partial-information model where the sensor knows about the occurrence of an event only when it is active. This problem falls into the class of partially observable Markov decision processes (POMDP). Since the POMDP's optimal policy has exponential computational complexity and is intrinsically hard to solve, we propose an efficient heuristic clustering policy and evaluate its performance. Finally, our solutions are extended to handle a network setting in which multiple sensors collaborate to capture the events. We provide extensive simulation results to evaluate the performance of our solutions.

show abstract

“…Nevertheless, replenishing the consumed energy is a promising way for achieving perpetual operation of sensor networks. Hence, harvesting energy from the environment such as solar energy [3], kinetic energy [4], and radio energy [5], are received increasingly more attention.…”

Section: Related Workmentioning

confidence: 99%

Event inter-arrival time weighted activation policies for rechargeable wireless sensors

Wang

et al. 2012

Proceedings of 2012 2nd International Conference on Computer Science and Network Technology

View full text Add to dashboard Cite

Rechargeable wireless sensors can harvest energy from the environment and monitor interesting events perpetually. Yet the energy harvesting rate on average is much smaller than the energy consumption rate at which the sensor uninterruptedly runs. Hence, the sensor should activate itself only at time slots where the interesting event would occur highly probabilistically. This paper considers the renewal process for event occurrence, and formulates event inter-arrival time weighted activation problem. This problem is resolved by linear programming technique. Finally, we present numerical evaluation results of the derived weighted activation policy under a series of concrete event occurrence distributions as well as weight distributions.

show abstract

Scaling self-timed systems powered by mechanical vibration energy harvesting

Cited by 9 publications

References 35 publications

Sustaining a perpetual wireless sensor network by multiple on-demand mobile wireless chargers

Sustaining a perpetual wireless sensor network by multiple on-demand mobile wireless chargers

Dynamic Activation Policies for Event Capture with Rechargeable Sensors

Event inter-arrival time weighted activation policies for rechargeable wireless sensors

Contact Info

Product

Resources

About