Energy efficiency maximization in a wireless powered IoT sensor network for water quality monitoring

Olatinwo, Segun O.; Joubert, Trudi-Heleen

doi:10.1016/j.comnet.2020.107237

Cited by 26 publications

(22 citation statements)

References 27 publications

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“…The results provided managed to minimize the total energy consumption compared to conventional OFDMA based offloading schemes. Olatinwo and Joubert [127] present a novel approach to allocating resources in IoT water quality monitoring using a sensor network (IoTSN) for a more sustainable application. A meta-heuristic method is used to define an EE optimization question and the result was compared with a non-meta-heuristic algorithm.…”

Section: Internet Of Thingsmentioning

confidence: 99%

Energy-efficient non-orthogonal multiple access for wireless communication system

Darus

Idris

Hashim

2023

IJECE

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<p>Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed.</p>

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Section: Internet Of Thingsmentioning

confidence: 99%

Energy-efficient non-orthogonal multiple access for wireless communication system

Darus

Idris

Hashim

2023

IJECE

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“…To address the power and time management issues of the formulated optimization problem, a PSO algorithm was adapted. In [92], we describe an adapted PSO algorithm to solve both time and power resource management problems of an IoT network to improve energy efficiency. In [93], a PSO algorithm was applied to a cognitive wireless sensor network to address the spectrum sensing problem and determine which of the devices that may use the channel in order to improve the network energy efficiency and throughput.…”

Section: Meta-heuristic Optimization Approachesmentioning

confidence: 99%

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Olatinwo

Joubert

2022

IEEE Access

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In this study, we conducted a bibliometric analysis and comprehensive review of the studies published between the period of 2012 and 2022 on resource management in the internet of things (IoT) networks using the Scopus database to determine the current state of research and gain insight into the research challenges and opportunities in the field. The bibliometric analysis technique was employed to bibliometrically analyze the published studies that were collected from the Scopus database and this helped to discover the majority of research subjects in the field of resource management in IoT networks. Following this, we conducted a comprehensive review of the relevant studies to provide an insight into the recent progress and the research gaps in the field. According to the results of our bibliometric analysis and the comprehensive review, we discovered that resource management problems in IoT networks is still a growing challenge as a result of the limited available resources for operating IoT networks. Consequently, the resource management problem is a critical research area due to the advantages of IoT in terms of collecting vital data that could be used in analyzing and predicting human behavior as well as environmental conditions. Also, the results of our bibliometric analysis and comprehensive review further revealed that research on the use of artificial techniques, such as optimization approaches and game theory approaches, for resource management are common while research on the use of the modern artificial intelligence technique, like deep learning approaches, is less common. Therefore, this study aims to fill the research gap in the area of resource management in IoT networks by introducing the use of deep learning approaches. Deep learning is a powerful artificial intelligence method that is advantageous for obtaining low-complexity resource allocation solutions in a near real-time. Also, various open research issues that are associated with the use of deep learning approaches are highlighted as future research directions to enable the development of novel deep learning models for IoT networks.

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“…Samanta and Tang 21 presented a dynamic micro-service scheduling (DYME) for the mobile edge computing in IoT platform and discussed the computational complexity of the scheduling algorithm in IoT. Olatinwo and Joubert 22 presented novel approaches to the allocation of resources in Internet of things sensor network (IoTSN) systems applied to water-quality monitoring for optimization and more sustainable utilization of resources. IoT devices usually need to communicate with each other and some remote gateways through multi-hop communications.…”

Section: Related Workmentioning

confidence: 99%

Resource scheduling based on routing tree and detection matrix for Internet of things

Bai

Zhang

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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Effective scheduling of limited communication resources is one of the critical methods for data transmission in the Internet of things. However, the time slot utilization rate of many existing resource scheduling methods of Internet of things is not high. This article proposes a new efficient resource scheduling based on routing tree and detection matrix for Internet of things. In heterogeneous Internet of things, according to the different working modes and functions, the nodes are divided into Internet of things devices, routing nodes, and base station. We use time slot multiplexing to improve the time slot utilization of continuous transmission in Internet of things. First, the time slot allocation table in a round is obtained by the time slot scheduling based on the routing tree. Then, the collision matrix and the transmission matrix are established based on the time slot allocation table in a round. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix. The experimental results show that the resource scheduling based on routing tree and detection matrix effectively improves the utilization of time slots and improves the throughput of the Internet of things.

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Energy efficiency maximization in a wireless powered IoT sensor network for water quality monitoring

Cited by 26 publications

References 27 publications

Energy-efficient non-orthogonal multiple access for wireless communication system

Energy-efficient non-orthogonal multiple access for wireless communication system

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Resource scheduling based on routing tree and detection matrix for Internet of things

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