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

Shih, Mei‐Ju; Lin, Guan-Yu; Wei, Hung‐Yu

doi:10.1049/iet-wss.2015.0040

Cited by 15 publications

(5 citation statements)

References 20 publications

(34 reference statements)

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“…For such cases, the energy efficiency of a UE can be increased with a lower data rate. In practice, the data-rate of radio-link between the UE and eNB is controlled by Modulation and Coding rate [106], [120], [121]. Fig.…”

Section: ) Energy Efficient Techniquesmentioning

confidence: 99%

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

et al. 2021

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The Machine-to-Machine (M2M) communication refers to an autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication.Motivated from these facts, in this paper, we present a survey on classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.

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Section: ) Energy Efficient Techniquesmentioning

confidence: 99%

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

et al. 2021

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“…An EH-assisted and social-aware transmission protocol for M2M communication is proposed in [20]. The authors of [21] proposed three different spectrum access schemes for EH-M2M communication with the goal of improving the performance in terms of throughput, delay and energy efficiency. However, this work did not consider the co-channel interference caused by spectrum sharing.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks

Tian

Searyoh

et al. 2020

Computer Communications

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Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the escalating spectrum deficient to guarantee the Quality of Service (QoS) meanwhile decreasing the energy consumption to achieve Green Communication (GC) became an important research topic. In this paper, we investigate the resource allocation problem for EH-CMNs underlaying cellular uplinks. We aim to maximize the energy efficiency of EH-CMNs with consideration of the QoS of Human-to-Human (H2H) networks and the available energy in EHdevices. In view of the characteristic of EH-CMNs, we formulate the problem to be a decentralized Discrete-time and Finite-state Markov Decision Process (DFMDP), in which each device acts as agent and effectively learns from the environment to make allocation decision without the complete and global network information. Owing to the complexity of the problem, we propose a Deep Reinforcement Learning (DRL)based algorithm to solve the problem. Numerical results validate that the proposed scheme outperforms other schemes in terms of average energy efficiency with an acceptable convergence speed.

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“…Internet of Things (IoT) network [1][2][3][4] aims to connect anything, from everywhere and at anytime. It is currently deployed in licensed and unlicensed industrial, scientific and medical (ISM) bands.…”

Section: Introductionmentioning

confidence: 99%

Packet collision analysis when heterogeneous unlicensed IoT technologies coexist

Ferré

Simon

2018

IET Networks

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This study deals with the impact of unlicensed Internet of Things (IoT) network coexistence in terms of packet collision. The classical packet collision studies based on the Poisson distributed process generally assume homogeneous networks with packets of equal lengths. The authors propose a new approach which can be applied to a more general context including heterogeneous networks and different packet lengths. The proposed approach is based on modelling the vulnerable period and the packet length as uniform random variables. Then, the two main IoT networks considered in unlicensed spectrum, LoRa and Sigfox, are being investigated in more detail. For these two networks, closed-form analytical formulas for the intra-and inter-network probability of packet collisions between nodes are provided and confirmed by simulations results. For this context, it is also shown that the ultra-narrow band feature of Sigfox is rather a drawback than an advantage from the collision point of view. To enhance the probability of collision, some possible evolutions of the Sigfox physical layer are finally suggested.

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

Cited by 15 publications

References 20 publications

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks

Packet collision analysis when heterogeneous unlicensed IoT technologies coexist

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