Energy Conservation Scheme for IEEE 802.15.4 Based Battery-Free Wireless Sensor Networks

Zhu, Yibo; Lv, Hangyu; Li, Yanyan; Li, Ertao; Chi, Kaikai

doi:10.1109/nana.2016.30

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…There is also a lot of research which not only considers the issue of energy consumption but also how to make the devices maintain the normal operation by energy harvesting 25‐28 . Hu et al 25 make a comprehensive survey to such problems.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, it is crucial to coordinate and balance WET and WIT for simultaneous wireless information and power transfer, which evolves to the integrated data and energy communication networks (IDENs). After coming up with a new power supply for the battery‐free devices, the paper 27 designs an energy conservation scheme for the nodes to save energy. Considering contemporary wireless sensor network (WSN) adopts IEEE 802.15.4 standard in its medium access control (MAC) layer and physical (PHY) layer, the 802.15.4 based battery‐free (BF)‐WSN is modeled using Markov chain.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies

Wang

Yang

Wan

et al. 2020

Trans Emerging Tel Tech

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By taking into account not only the node specifics but also the benefits of mobile edge computing, an integrated strategy for energy saving of Internet of Things (IoT) devices is proposed in this article. This strategy consists of two algorithms at both the end and the edge. Considering the changeable battery level and downlink communication traffic of the battery‐powered wireless nodes, an energy efficient automatic mode switching algorithm is designed at the end. Three different kinds of working modes are designed based on the features of the end nodes and the various application requirements. This algorithm tends to enable the end nodes to automatically select and switch to the proper working mode according to the actual conditions. At the edge server with much stronger processing and computing capabilities which belongs to a higher layer, the dynamic sampling rate adjustment algorithm is designed. It can adaptively adjust the sampling frequencies of the end nodes and thus reduce their working durations. The proposed integrated solution aims to decrease the energy consumption of IoT devices as much as possible and thus prolong their battery lives. The simulation results have shown both the effectiveness and the efficiency of the proposed end and edge integrated strategy in terms of energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies

Wang

Yang

Wan

et al. 2020

Trans Emerging Tel Tech

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“…In the authors focus FEC applied to minimize latency and similarly PDR simulations are performed. In a FEC mechanism embedded into the mesh routing of 6LoWPAN is presented. In the authors define a mechanism for intermediate nodes to forward fragments without having to fully reassemble and fragment each datagram at each hop.…”

Section: Introductionmentioning

confidence: 99%

6LoWPAN fragmentation in the context of IoT based media real time communication

Herrero

2020

Internet Technology Letters

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Many Internet of Things (IoT) applications, like presence detection, rely on wireless sensors transmitting Real Time Communication (RTC) media that is processed at remote servers. The use of IEEE 802.15.4 physical and data‐link layers is pretty widespread as they provide enough throughput to transmit compressed media. In this context, however, the most important issue is that IEEE 802.15.4 limits the Maximum Transmission Unit (MTU) size to 127 bytes. The Low power Wireless Personal Area Networks (6LoWPAN) IPv6 adaptation protocol provides a mechanism that adds fragmentation capabilities to traditional IPv6 and allows the transmission of typically large media frames. Because it is not always possible to configure codec based fragmentation, alternatively relying on 6LoWPAN fragmentation can severely affect Quality of Service (QoS) when the wireless channel exhibits high packet loss. In this paper we focus on analyzing this problem including its implications and proposing possible solutions.

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“…According to IEEE 802.15.4 MAC layer, the maximum number of re-transmissions is set to 3, which is equivalent to z = 4. 27 Furthermore, RPs buffers are enough large to cache their sensing data and the forwarded ones from neighborhood until the MDCs pass to collect them. 16 However, smaller groups with fewer sensors near the BS (unequal clustering approach) solve hotspot and energy problems but create dropped packets.…”

mentioning

confidence: 99%

Optimized data gathering in a heterogeneous Internet of Things network

Hamidouche

Aliouat

Ari

et al. 2021

Int J Communication

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Recent research introduces data gathering using mobile data collectors to conserve energy and elevate the hotspot problem. However, many of them suffer from buffer overflow due to the limited memory capacity of objects, even more, when dealing with heterogeneous Internet of Things (IoT) devices. Emerging bio-inspiration technologies provide a novel direction for data collection and make it more intelligent and available. In this paper, we present an intelligent data gathering schema by taking into consideration buffer overflow called Center Gravity Mobile data collector based on Salp Swarm Algorithm (CGMSSA).To conduct data collection to improve the network performance, we adopt a hybridized distributed bio-inspired technique to elect chefs and salp swarm intelligence to control data collector movement. First, we select chefs using grey wolf optimization. Then, several groups are formed. Then, a mobile data collector is adopted to access chefs, following the buffer overflow value and gather information. Considerable experiments are conducted to demonstrate that our solution can efficiently enhance the lifetime of the network and increase data throughput.

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Energy Conservation Scheme for IEEE 802.15.4 Based Battery-Free Wireless Sensor Networks

Cited by 6 publications

References 20 publications

Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies

Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies

6LoWPAN fragmentation in the context of IoT based media real time communication

Optimized data gathering in a heterogeneous Internet of Things network

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