Energy harvesting active networked tags (EnHANTs) for ubiquitous object networking

Gorlatova, Maria; Kinget, Peter R.; Kymissis, Ioannis; Rubenstein, Dan; Wang, Xiaodong; Zussman, Gil

doi:10.1109/mwc.2010.5675774

Cited by 93 publications

(50 citation statements)

References 15 publications

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“…Many applications for energy harvesting sensor networks, such as structural health monitoring [3], disaster recovery [4], and health monitoring [5], require real-time reliable network protocols and efficient task scheduling. In such networks, it is important to dynamically schedule node and network tasks based on the remaining energy and current energy intake, as well as predictions about future energy availability.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware task allocation for energy harvesting sensor networks

Edalat

Motani

2016

J Wireless Com Network

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Ambient energy harvesting is a solution to mitigate the typical finite energy supply of sensor nodes in wireless sensor networks (WSNs). On the one hand, the uncertainty of energy availability in energy harvesting systems makes network protocol design challenging. On the other hand, the fact that energy is continuously replenished opens up avenues for protocol design based on prediction of future energy arrivals. One of the key application scenarios for sensor networks is task allocation, in which a central entity allocates tasks to a set of geographically distributed sensor nodes to accomplish an overall objective. In this work, we consider a scenario in which the sensor nodes are equipped with devices capable of harvesting ambient energy, e.g., solar panels to harvest the Sun's energy, and focus on energy-aware strategies for adaptive task allocation. We decompose the static task allocation problem into two phases: scheduling of the task graph and task mapping to the appropriate sensor nodes. The solution objectives are to minimize the makespan and maximize the fairness in energy-driven task mapping (i.e., energy-balancing), while satisfying the task precedence constraints and energy harvesting causality constraints. We employ a novel energy prediction model which incorporates seasonal changes in solar energy harvesting as well as sudden weather changes. In case of an error in available energy prediction, a dynamic adaptation phase runs to avoid violation of the task allocation objectives. The performance of our proposed algorithms, in terms of energy-balancing and scheduling length, is evaluated through simulation and compared with other approaches, including a genetic algorithm as a baseline. We achieve more balanced residual energy levels across the network while attaining a near optimum scheduling length. By utilizing the dynamic adaptation phase, for certain runs of simulation, the missing ratio, which is the percentage of times in which the task allocation fails due to a temporal shortage of energy availability, is dramatically decreased.

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Section: Related Workmentioning

confidence: 99%

Energy-aware task allocation for energy harvesting sensor networks

Edalat

Motani

2016

J Wireless Com Network

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“…EnHANTs: [6] The EnHANTS project explores the challenges for a new tier of energy-neutral, self-reliant nodes. Their research goals are very similar to ours: build and network indoor solar-based energy-harvesting sensor nodes with severe energy, size, and cost constraints.…”

Section: Architecturesmentioning

confidence: 99%

“…The average power the battery could source is P = ρL 3 /T . A conservative estimate of the average solar irradiance on an indoor surface, H d , is 10 µW/cm 2 , and the average power is P = H d L 2 [6]. Setting these two expressions equal to each other and solving for L gives 1 cm as the crossover point where solar (1 cm 2 ) beats batteries (1 cm 3 ) over a seven year horizon, as Figure 1 shows.…”

Section: Introductionmentioning

confidence: 99%

Grafting energy-harvesting leaves onto the sensornet tree

Yerva¹,

Campbell²,

Bansal³

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

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We study the problem of augmenting battery-powered sensornet trees with energy-harvesting leaf nodes. Our results show that leaf nodes that are smaller in size than today's typical battery-powered sensors can harvest enough energy from ambient sources to acquire and transmit sensor readings every minute, even under poor lighting conditions. However, achieving this functionality, especially as leaf nodes scale in size, requires new platforms, protocols, and programming. Platforms must be designed around low-leakage operation, offer a richer power supply control interface for system software, and employ an unconventional energy storage hierarchy. Protocols must not only be low-power, but they must also become low-energy, which affects initial and ongoing synchronization, and periodic communications. Systems programming, and especially bootup and communications, must become low-latency, by eliminating conservative timeouts and startup dependencies, and embracing high-concurrency. Applying these principles, we show that robust, indoor, perpetual sensing is viable using off-the-shelf technology.

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“…It is worth mentioning that our methodological approach is devoted to system-level implications rather than being focused on circuit-level challenges. As recent research shows, we believe that one of the next steps for PHY IR-UWB systems research has to regard both decreasing energy consumption and solving problems from a more general and wide-sense system-level view Gorlatova et al (2010).…”

Section: A Case Study: the Energy Detection Receivermentioning

confidence: 99%