On systems generating context triggers through energy harvesting

Rao, Vijay S.; Uttama, Surapong; Prasad, R. Venkatesha; Niemegeers, Ignas

doi:10.1109/mcom.2014.6829947

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…Recent efforts in the literature are applying kinetic energy harvester as a power-free vibration sensor for sensing. An interesting fact for energy harvesting powered IoT devices is that the physical contexts the device is monitoring are usually related to the energy harvesting source of the harvester [10]. For instance, for kinetic-powered wearable IoTs, Khalifa et al [11] have investigated the use of the AC voltage signal generated by KEH for wearable sensing.…”

Section: B Kinetic Energy Harvesting Based Context Sensingmentioning

confidence: 99%

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Lan

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 hours of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.

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Section: B Kinetic Energy Harvesting Based Context Sensingmentioning

confidence: 99%

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Lan

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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“…Two of those studies are on energy optimization ( [23], [24]), while resources optimization is the main strength of [25] and [26]. Two other studies are about energy saving ( [27], [28]) even if their main focus is not on optimization. Performance improvement and optimization (in combination with other QoS) is claimed to be the strength of 4 studies.…”

Section: Rq4 What Are the Strengths And Limitations Of Proposed Apprmentioning

confidence: 99%

Self-adaptation for cyber-physical systems

Muccini

Sharaf

Weyns

2016

Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

107

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Context: Cyber-physical systems (CPS) seamlessly integrate computational and physical components. Adaptability, realized through feedback loops, is a key requirement to deal with uncertain operating conditions in CPS.Objective: We aim at assessing state-of-art approaches to handle self-adaptation in CPS at the architectural level.Method: We conducted a systematic literature review by searching four major scientific data bases, resulting in 1103 candidate studies and eventually retaining 42 primary studies included for data collection after applying inclusion and exclusion criteria.Results: The primary concerns of adaptation in CPS are performance, flexibility, and reliability. 64% of the studies apply adaptation at the application layer and 24% at the middleware layer. MAPE (Monitor-Analyze-Plan-Execute) is the dominant adaptation mechanism (60%), followed by agents and self-organization (both 29%). Remarkably, 36% of the studies combine different mechanisms to realize adaptation; 17% combine MAPE with agents. The dominating application domain is energy (24%).Conclusions: Our findings show that adaptation in CPS is a cross-layer concern, where solutions combine different adaptation mechanisms within and across layers. This raises challenges for future research both in the field of CPS and self-adaptation, including: how to map concerns to layers and adaptation mechanisms, how to coordinate adaptation mechanisms within and across layers, and how to ensure system-wide consistency of adaptation.

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“…Each independent Bernoulli trial forms a memoryless Bernoulli process in (1). The average arrival energy per slot time is in (2).…”

Section: Energy-harvesting Modelmentioning

confidence: 99%

“…Energy‐harvesting technology, by which devices harvest energy from the ambient sources such as the sun, vibration, and heat and then convert the energy to usable electricity, has attracted attention recently [1, 2]. These energy‐harvesting devices can rely on intermittent energy to power themselves without manual battery replacement, thereby reducing the costs of deployment and maintenance.…”

Section: Introductionmentioning

confidence: 99%

Two paradigms in cellular Internet‐of‐Things access for energy‐harvesting machine‐to‐machine devices: push‐based versus pull‐based

Shih

Lin

Wei

2016

IET wirel. sens. syst.

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Equipping communication apparatuses with energy-harvesting technology could achieve system sustainability without human intervention. Though the harvested energy of existing techniques is limited and intermittent, it is sufficient to power devices in low-power-consuming machine-to-machine (M2M) communications. However, due to the large number of devices with time-varying energy arrival, the medium access control protocol should be redesigned. This study focuses on studying energy-harvesting M2M uplink cellular communications from the protocol design perspective, considering the properties of M2M and energy harvesting. The authors first explore the performance of two fundamental schemes: push-based and pull-based random access channel (RACH) procedures in terms of preamble collision probability, throughput, energy efficiency, and packet delay. In the push-based scheme, devices are self-energy-aware and there is no schedule signalling cost. However, the performance degrades as the device number increases. The pull-based scheme is an alternative to have stable throughput and energy efficiency, with sacrifice of the extra scheduling cost and increasing latency. As a result, a hybrid scheme is proposed to adaptively select the preamble transmission schemes based on the estimated device number. The hybrid scheme guarantees adequate packet delay under different traffic loads and varying energy levels.

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On systems generating context triggers through energy harvesting

Cited by 10 publications

References 11 publications

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Self-adaptation for cyber-physical systems

Two paradigms in cellular Internet‐of‐Things access for energy‐harvesting machine‐to‐machine devices: push‐based versus pull‐based

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