Multisource wireless energy harvesting-based medium access control for rechargeable sensors

Shao, Chenglong; Roh, Heejun; Kim, Tae‐Kyung; Lee, Wonjun

doi:10.1109/tce.2016.7514670

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…One solution to this issue is presented in [ 165 ], where an interference cancellation technique is applied to make sure that all the nodes have sufficient energy to operate, and utilizes an adaptive sleep duration management to provide node synchronization and reduce the collision rate. Another method to prevent the energy and data frame interference is the clustering approach, which is presented in [ 166 ].…”

Section: Energy Harvesting Mac Protocolsmentioning

confidence: 99%

A Comprehensive Review on Energy Harvesting Integration in IoT Systems from MAC Layer Perspective: Challenges and Opportunities

Famitafreshi

Afaqui

Melià-Seguí

2021

Sensors

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The Internet of Things (IoT) is revolutionizing technology in a wide variety of areas, from smart healthcare to smart transportation. Due to the increasing trend in the number of IoT devices and their different levels of energy requirements, one of the significant concerns in IoT implementations is powering up the IoT devices with conventional limited lifetime batteries. One efficient solution to prolong the lifespan of these implementations is to integrate energy harvesting technologies into IoT systems. However, due to the characteristics of the energy harvesting technologies and the different energy requirements of the IoT systems, this integration is a challenging issue. Since Medium Access Control (MAC) layer operations are the most energy-consuming processes in wireless communications, they have undergone different modifications and enhancements in the literature to address this issue. Despite the essential role of the MAC layer to efficiently optimize the energy consumption in IoT systems, there is a gap in the literature to systematically understand the possible MAC layer improvements allowing energy harvesting integration. In this survey paper, we provide a unified framework for different wireless technologies to measure their energy consumption from a MAC operation-based perspective, returning the essential information to select the suitable energy harvesters for different communication technologies within IoT systems. Our analyses show that only 23% of the presented protocols in the literature fulfill Energy Neutral Operation (ENO) condition. Moreover, 48% of them are based on the hybrid approaches, which shows its capability to be adapted to energy harvesting. We expect this survey paper to lead researchers in academia and industry to understand the current state-of-the-art of energy harvesting MAC protocols for IoT and improve the early adoption of these protocols in IoT systems.

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Section: Energy Harvesting Mac Protocolsmentioning

confidence: 99%

A Comprehensive Review on Energy Harvesting Integration in IoT Systems from MAC Layer Perspective: Challenges and Opportunities

Famitafreshi

Afaqui

Melià-Seguí

2021

Sensors

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“…Lastly, we study the case where Equ. (8) can be solved exactly as opposed to being approximated; see Equ. (9).…”

Section: A Markov Decision Process Modelmentioning

confidence: 99%

“…This is important as APs are responsible for delivering data. In a different example, the authors of [8] design a signaling protocol that pairs sinks and sensor nodes, either for charging or data collection; both of which are carried out simultaneously. They, however, did not consider data delivery from APs.…”

Section: Introductionmentioning

confidence: 99%

On energy and data delivery in wireless local area networks with RF charging nodes

Chin

2017

2017 27th International Telecommunication Networks and Applications Conference (ITNAC)

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Devices can now be powered wirelessly by Access Points (APs). However, an AP cannot transmit frequently to charge devices as it may starve other nearby APs operating on the same channel. Consequently, there is a need to schedule the transmissions of APs to ensure their data queues remain short whilst charging energy-harvesting devices. We present a finite-horizon Markov Decision Process (MDP) to capture the queue states at APs and also channel conditions to nodes. We then use the MDP to investigate the following transmission policies: max weight, max queue, best channel state and random. Our results show that the max weight policy has the best performance in terms of queue length and delivered energy.

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“…As such, the prolongation of battery lifetime of mobile devices, i.e., sensors and actuators, has been identified as an important priority in the next-generation IoT (NGIoT) [ 2 , 3 ], being of paramount importance in improving autonomy, scalability, and intelligence of smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare applications. To achieve the aforementioned objectives, energy harvesting has been recognized as a promising approach for the NGIoT, which creates several non-trivial challenges at the design and optimization of appropriate communication protocols [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Ultra-Dense Wireless Powered Networks

Diamantoulakis

Papanikolaou

Karagiannidis

2021

Sensors

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The internet-of-things (IoT) is expected to have a transformative impact in several different domains, including energy management in smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare. To this direction, the maintenance cost of IoT deployments has been identified as one of the main challenges, which is directly related to energy efficiency and autonomy of IoT solutions. In order to increase the energy sustainability of next-generation IoT, wireless power transfer (WPT) emerged as a promising technology; however, its effectiveness is hindered as the distance between the base station and the wireless powered IoT devices increases. To counter this effect, decentralized approaches based on the use of distributed densely deployed remote radio heads (RRHs) can be utilized to diminish the distance between the transmitting and the receiving nodes. A trade-off ensues from the use of RRHs as power beacons (PBs) or access points (APs) that enable either energy transfer during downlink or information reception during uplink, respectively. To balance this trade-off, in this work, the maximization of the ergodic rate in ultra-dense wireless powered networks is investigated. In more detail, three different protocols are introduced, optimized, and compared to each other: density splitting, time splitting, and hybrid time and density splitting, which are based on the optimization of the portion of the number of RRHs that are employed as PBs or APs at a specific time instance. Additionally, two different policies are taken into account regarding the PBs’ power constraint. The formulated problems that correspond to the combination of the proposed protocols with each of the two considered power constraint policies are optimally solved by using convex optimization tools and closed-form solutions are derived that result to useful insights. Finally, numerical results are provided, which illustrate the ergodic rate achieved by each of the proposed protocols and offer interesting conclusions regarding their comparison, which are directly linked to design guidelines and the required capital and operational expenses.

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Multisource wireless energy harvesting-based medium access control for rechargeable sensors

Cited by 18 publications

References 20 publications

A Comprehensive Review on Energy Harvesting Integration in IoT Systems from MAC Layer Perspective: Challenges and Opportunities

A Comprehensive Review on Energy Harvesting Integration in IoT Systems from MAC Layer Perspective: Challenges and Opportunities

On energy and data delivery in wireless local area networks with RF charging nodes

Optimization of Ultra-Dense Wireless Powered Networks

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