Fast downstream to many (computational) RFIDs

Aantjes, Henko; Majid, Amjad Yousef; Pawełczak, Przemysław; Tan, Jethro; Parks, Aaron; Smith, Joshua R.

doi:10.1109/infocom.2017.8056987

Cited by 18 publications

(25 citation statements)

References 12 publications

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“…Although backscattering enables small form factors, low-cost and lowpower solutions, backscattering-backed communications suffer from frequent power interrupts, collisions, low data rates, and short communication ranges. The majority of existing research efforts on this field is on uplink communications; however, there are some recent studies aiming at speeding up the downlink data transfer [62,63].…”

Section: (B) Wireless Communication Under Intermittent Operationmentioning

confidence: 99%

Energy-driven computing

Sliper

Cetinkaya

Weddell

et al. 2019

Phil. Trans. R. Soc. A.

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For decades, the design of untethered devices has been focused on delivering a fixed quality of service with minimum power consumption, to enable battery-powered devices with reasonably long deployment lifetime. However, to realize the promised tens of billions of connected devices in the Internet of Things, computers must operate autonomously and harvest ambient energy to avoid the cost and maintenance requirements imposed by mains- or battery-powered operation. But harvested power typically fluctuates, often unpredictably, and with large temporal and spatial variability. Energy-driven computers are designed to treat energy-availability as a first-class citizen, in order to gracefully adapt to the dynamics of energy harvesting. They may sleep through periods of no energy, endure periods of scarce energy, and capitalize on periods of ample energy. In this paper, we describe the promise and limitations of energy-driven computing, with an emphasis on intermittent operation. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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Section: (B) Wireless Communication Under Intermittent Operationmentioning

confidence: 99%

Energy-driven computing

Sliper

Cetinkaya

Weddell

et al. 2019

Phil. Trans. R. Soc. A.

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“…In the access phase, the reader has successfully inventoried the tags and prepared to execute READ, WRITE and other commands. The transmission of down‐link mainly refers to a large amount of data is written to the CRFID tag that has been researched deeply in [7, 10]. Uplink aims to read the sensor data of tag quickly, the commercial reader does not support bulk uplink transmission and is unable to send large amounts of real‐time data rapidly from the sensor to the reader, which degrades the performance of the CRFID.…”

Section: Protocol Designmentioning

confidence: 99%

“…EPC C1G2 Standard [7] enhances the down‐link throughput effectively by using an optional Block‐Write command to achieve continuous WRITE operation on a tag. Similar works include [8–10]. Zheng [11] replace bitwise calculations cyclic redundancy check (CRC) with a look‐up table method for data validation.…”

Section: Introductionmentioning

confidence: 99%

LILAC: computable capabilities based high performance protocol for CRFID

Zhao

et al. 2019

IET Communications

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“…For instance, a new wave of embedded systems powered by radio waves is emerging [19, 30, 39, 45, 49-51, 53, 59, 70, 76, 77]. The emergence of such hardware platforms has led to the development of instrumentation, debugging, and prototyping tools for such systems [1,11,14,[25][26][27]66].…”

Section: Related Workmentioning

confidence: 99%

Untitled

2019

ACM Trans. Sen. Netw.

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Energy-neutral Internet of Things requires freeing embedded devices from batteries and powering them from ambient energy. Ambient energy is, however, unpredictable and can only power a device intermittently. Therefore, the paradigm of intermittent execution is to save the program state into non-volatile memory frequently to preserve the execution progress. In task-based intermittent programming, the state is saved at task transition. Tasks are fixed at compile time and agnostic to energy conditions. Thus, the state may be saved either more often than necessary or not often enough for the program to progress and terminate. To address these challenges, we propose Coala, an adaptive and efficient task-based execution model. Coala progresses on a multi-task scale when energy permits and preserves the computation progress on a sub-task scale if necessary. Coala's specialized memory virtualization mechanism ensures that power failures do not leave the program state in non-volatile memory inconsistent. Our evaluation on a real energy-harvesting platform not only shows that Coala reduces runtime by up to 54% as compared to a state-of-the-art system, but also it is able to progress where static systems fail. CCS Concepts: • Hardware → Power and energy; • Software and its engineering → Embedded software; Virtual memory;

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Fast downstream to many (computational) RFIDs

Cited by 18 publications

References 12 publications

Energy-driven computing

Energy-driven computing

LILAC: computable capabilities based high performance protocol for CRFID

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