Energy-Harvesting Active Networked Tags (EnHANTs)

Margolies, Robert; Gorlatova, Maria; Sarik, John; Stanje, Gerald; Zhu, Jian‐Gang; Miller, Paul Chamness; Szczodrak, Marcin; Vigraham, Baradwaj; Carloni, Luca P.; Kinget, Peter R.; Kymissis, Ioannis; Zussman, Gil

doi:10.1145/2831236

Cited by 43 publications

(20 citation statements)

References 43 publications

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“…In particular, future research will focus on extending the analysis to non-clique toplogies. Moreover, evaluation with custom designed ultra-low-power nodes (e.g., [41]), that have improved energy awareness compared to the TI eZ430-RF2500-SEH nodes, would enable to better assess the tradeoffs related to the protocol design. Finally, considering unique application characteristics and their relation to groupput and anyput is an open problem.…”

Section: Resultsmentioning

confidence: 99%

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Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints

Chen

Ghaderi

Rubenstein

et al. 2016

Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies

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Wireless object tracking applications are gaining popularity and will soon utilize emerging ultra-low-power deviceto-device communication. However, severe energy constraints require much more careful accounting of energy usage than what prior art provides. In particular, the available energy, the differing power consumption levels for listening, receiving, and transmitting, as well as the limited control bandwidth must all be considered. Therefore, we formulate the problem of maximizing the throughput among a set of heterogeneous broadcasting nodes with differing power consumption levels, each subject to a strict ultra-lowpower budget. We obtain the oracle throughput (i.e., maximum throughput achieved by an oracle) and use Lagrangian methods to design EconCast -a simple asynchronous distributed protocol in which nodes transition between sleep, listen, and transmit states, and dynamically change the transition rates. We also show that EconCast approaches the oracle throughput. The performance is evaluated numerically and via extensive simulations and it is shown that EconCast outperforms prior art by 6x -17x under realistic assumptions. Finally, we implement EconCast using the TI eZ430-RF2500-SEH energy harvesting nodes and experimentally show that in realistic environments it obtains 57% -77% of the achievable throughput.

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Section: Resultsmentioning

confidence: 99%

“…Furthermore, a node does not need to know its power budget ρ explicitly (e.g., in the case of energy harvesting [41]), although this knowledge can be incorporated, if available.…”

Section: Protocol Descriptionmentioning

confidence: 99%

Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints

Chen

Ghaderi

Rubenstein

et al. 2016

Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies

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“…Due to the inherently heterogenous nature of fog systems, we expect them to include a wider range of service execution options than the options provided in traditional cloud computing systems. In particular, we expect services in fog systems to be offered at a range of reliability options, starting from expensive high-availability services with "five nines" uptime guarantees (i.e., 99.999% availability, or the downtime of no more than 5.2 minutes per year) [36], to cheaper limited or frequently interrupted services provided by low-end nodes, including nodes with long sleep cycles and energy-harvestingbased intermittently powered nodes [37], [38].…”

Section: A Fog Services: Heterogeneity and The Need For Auto-tuningmentioning

confidence: 99%

Virtualized Control Over Fog: Interplay Between Reliability and Latency

İnaltekin

Gorlatova

Chiang

2018

IEEE Internet Things J.

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This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum. The main application scenarios include low-latency cyber-physical systems in which real-time control actions are required in response to the changes in states of an IoT node. In such cases, deploying controller software on a cloud server is often not tolerable due to delay from the network edge to the cloud. Hence, it is desirable to trade reliability with latency by moving controller logic closer to the network edge. Modeling the IoT node as a dynamical system that evolves linearly in time with quadratic penalty for state deviations, recursive expressions for the optimum control policy and the resulting minimum cost value are obtained by taking virtual fog controller reliability and response time latency into account. Our results indicate that latency is more critical than reliability in provisoning virtualized control services over fog endpoints, as it determines the swiftness of the fog control system as well as the timeliness of state measurements. Based on a realistic drone trajectory tracking model, an extensive simulation study is also performed to illustrate the influence of reliability and latency on the control of autonomous vehicles over fog.

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“…In our experiments, we use hardware from [7]. There are also numerous other hardware options for EH nodes [4], [18], [19], computational RFIDs [20], and mm 3 -scale wireless devices [21].…”

Section: Power Consumptionmentioning

confidence: 99%

“…Often, these nodes are meant to interact with a reader, but architectures are emerging that handle scenarios where no reader may be present, or where the number of nodes overwhelms the readers' availability. These scenarios can be supported by Energy Harvesting (EH) tags (e.g., [3], [4] and references therein) that are able to communicate peer-to-peer and are powered by an ambient energy source (e.g., light).…”

Section: Introductionmentioning

confidence: 99%

Panda: Neighbor Discovery on a Power Harvesting Budget

Margolies

Grebla

Chen

et al. 2016

IEEE J. Select. Areas Commun.

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Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze Panda and select its parameters to maximize the ND rate subject to a homogenous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs.We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting. Index TermsNeighbor discovery, energy harvesting, wireless A partial and preliminary version of this paper will appear in IEEE INFOCOM'16 [1], April 2016.

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Energy-Harvesting Active Networked Tags (EnHANTs)

Cited by 43 publications

References 43 publications

Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints

Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints

Virtualized Control Over Fog: Interplay Between Reliability and Latency

Panda: Neighbor Discovery on a Power Harvesting Budget

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