Energy Harvesting and Information Transmission Mode Design for Cooperative EH-Abled IoT Applications in beyond 5G Networks

Gao, Hongyuan; Zhang, Shibo; Su, Yumeng; Diao, Ming

doi:10.1155/2020/6136298

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…Although, the integration of edge and 5G introduces issues of heterogeneous communication, and gaps in privacy. 192,[207][208][209][210][211][212][213][214] The future directions are discussed on the basis of emerging applications which are concluded based on the aforementioned paradigms. The results and drawbacks discussed for each paradigm would help in generalizing the type of algorithms that could be worked upon and used for future directions.…”

Section: Future Research Directionsmentioning

confidence: 99%

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Journey from cloud of things to fog of things: Survey, new trends, and research directions

et al. 2022

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With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency-sensitive and real-time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client-server model to edge computing along with their objectives and limitations. A state-of-the-art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping-stone in the field of emerging computing paradigms. K E Y W O R D Scloud computing, internet of things, cloud of things, fog computing, latency INTRODUCTIONDifferent computing paradigms have evolved over the decades as per the needs in various situations. The concept of the Cloud computing paradigm has evolved from distributed systems and it has gained attention by offering a plethora of services to end-users anywhere and anytime on pay per use basis. It has also become the business model in the last decade due to well-known characteristics like scalability, multi-tenant environment, virtualization, and resource pooling. It is observed that a large number of industries, government, as well as private organization, have shifted their applications and data to the cloud platform. Further, the Cloud is also playing a significant role in the advancement of Internet of Things (IoT) applications. As per the transformation and movement in ubiquitous computing, we need the integration

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Section: Future Research Directionsmentioning

confidence: 99%

“…Implemented at the edge level, local data is processed faster with higher availability in presence of 5G. Although, the integration of edge and 5G introduces issues of heterogeneous communication, and gaps in privacy 192,207‐214 …”

Section: Future Research Directionsmentioning

confidence: 99%

Journey from cloud of things to fog of things: Survey, new trends, and research directions

et al. 2022

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“…Based on the value L i ðtÞ at time t at the base station, MCS is selected, so that the probability of error in data transmission is a rejected small value. The selected MCS determines the 6 Wireless Communications and Mobile Computing maximum bandwidth of the channel C i ðtÞ at a point in time t.…”

Section: Radio Resource Allocationmentioning

confidence: 99%

“…Global trends in the telecommunication market show that wireless communication networks are becoming one of the key elements in the implementation of the Internet of Things (IoT) paradigm [2]. Due to the rapid growth of mobile data traffic, the popularity of IoT, and M2M, mobile operators are constantly focused on improving the quality of service (QoS) provision, developing 4G networks into the future software-defined heterogeneous 5G/6G networks based on Long-Term Evolution (LTE) technology [3][4][5][6]. LTE is now seen as the best technology to support MTC due to its Internet compatibility, high capacity, flexibility in radio resource management, and scalability.…”

Section: Introductionmentioning

confidence: 99%

QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

Beshley

Medvetskyi

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, we consider the saturation problem in the 3GPP LTE cellular system caused by the expected huge number of machine-type communication (MTC) devices, leading to a significant impact on both machine-to-machine (M2M) and human-to-machine H2H traffic. M2M communications are expected to dominate traffic in LTE and beyond cellular networks. In order to address this problem, we proposed an advanced architecture designed for 5G LTE networks to enable the coexistence of H2H/M2M traffic, supported by different priority strategies to meet QoS for each traffic. The queuing strategy is implemented with an M2M gateway that manages four queues allocated to different types of MTC traffic. The optimal radio resource allocation method in LTE and beyond cellular networks was developed. This method is based on adaptive selection of channel bandwidth depending on the QoS requirements and priority traffic aggregation in the M2M gateway. Additionally, a new simulation model is proposed which can help in studying and analyzing the mutual impact between M2M and H2H traffic coexistence in 5G networks while considering high and low priority traffics for both M2M and H2H devices. This simulator automates the proposed method of optimal radio resource allocation between the M2M and H2H traffic to ensure the required QoS. Our simulation results proved that the proposed method improved the efficiency of radio resource utilization to 13% by optimizing the LTE frame formation process.

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“…The corporative information transmission mode is essential for the IoT and the shared spectrum. In most of studies time slot mechanism is used for sensing channel, data communication and energy harvesting [14]. Considering the challenges of the existing spectrum sharing networks, it is necessary that, IoT nodes adept CR advances for spectrum sensing and selecting spectrum for energy harvesting without any external assistance along with region based multi-channel sensing approach to enable the IoT nodes to sense multiple regions simultaneously and to take the efficient decision.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Mahmood¹,

Khan²

2022

Computers, Materials &Amp; Continua

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Wireless Sensor Networks (WSNs) can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure and motion etc. WSNs may comprise thousands of Internet of Things (IoT) devices to sense and collect data from its surrounding, process the data and take an automated and mechanized decision. On the other side the proliferation of these devices will soon cause radio spectrum shortage. So, to facilitate these networks, we integrate Cognitive Radio (CR) functionality in these networks. CR can sense the unutilized spectrum of licensed users and then use these empty bands when required. In order to keep the IoT nodes functional all time, continuous energy is required. For this reason the energy harvested techniques are preferred in IoT networks. Mainly it is preferred to harvest Radio Frequency (RF) energy in the network. In this paper a region based multi-channel architecture is proposed. In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region. The Secondary User (SU) that are the licensed user is IoT enabled with Cognitive Radio (CR) techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy. To harvest energy efficiently and to reduce the energy consumption during sensing, the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel. From the experimental analysis, it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel. We also show that channel load can be highly reduced and channel utilization is proved to be more proficient. Thus, this proves the proposed architecture cost-effective and energy-efficient.

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Energy Harvesting and Information Transmission Mode Design for Cooperative EH-Abled IoT Applications in beyond 5G Networks

Cited by 6 publications

References 49 publications

Journey from cloud of things to fog of things: Survey, new trends, and research directions

Journey from cloud of things to fog of things: Survey, new trends, and research directions

QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

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