Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing

Pandey, Om Jee; Yuvaraj, Tankala; Paul, Joseph K.; Nguyen, Ha H.; Gundepudi, Karthikay; Shukla, Mahendra K.

doi:10.1109/tccn.2021.3114147

Cited by 24 publications

(12 citation statements)

References 50 publications

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“…A comprehensive proportional examination of the IMD-EACBR model compared to current models in terms of numbers of dead sensor nodes (NODSN) is shown in Table 4 and Figure 5. The outcomes exhibit that the IMD-EACBR approach resulted in effective outcomes with negligible values of NODSNs [18,19,[68][69][70]. For example, with 2000 rounds, the IMD-EACBR model achieved a decreased NODSN of 215 nodes, whereas the SFO, GWO, GA, ALO, and PSO models resulted in increased NODSNs of 367, 478, 500, 500, and 500 nodes, respectively.…”

Section: Experimental Validationmentioning

confidence: 98%

“…The following Table 1 describes the existing approach methodologies. O. J. Pandey et al [18] presented LPWANs, a multi-hop data directing approach. Since the multi-hop data program encounters a number of obstacles, including augmented data dormancy, increased meddling, and decreased data quantity (i.e., inefficient utilisation of bandwidth), we suggest a reinforcement learning strategy to handle these obstacles.…”

Section: Literature Reviewmentioning

confidence: 99%

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Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Neelakandan²,

et al. 2022

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The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

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Section: Experimental Validationmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Neelakandan²,

et al. 2022

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“…The Marcum function is presented to estimate the performance of LoRa in terms of Bit Error Rate (BER) in a timely and precise manner to improve the QoS [49]. The authors in [50], present a unique multi-hop data routing mechanism for IoT applications over LPWAN. The technology uses Quality-learning (Q-learning) to increase network performance in the context of QoS and…”

Section: F Quality Of Servicementioning

confidence: 99%

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

et al. 2022

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Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topologies such as star and mesh. We examine many current and emerging LPWAN technologies, as well as their system architectures and standards, and evaluate their ability to meet each design objective. In addition, the possible coexistence of LPWAN with other technologies, combining the best attributes to provide an optimum solution is also explored and reported in the current overview. Following that, a comparison of various LPWAN technologies is performed and their market opportunities are also investigated. Furthermore, an analysis of various LPWAN use cases is performed, highlighting their benefits and drawbacks. This aids in the selection of the best LPWAN technology for various applications. Before concluding the work, the open research issues, and challenges in designing LPWAN are presented.INDEX TERMS Low-power wide-area networks, wireless networks, Internet of Things, design objectives, network topology, architecture, and applications. I. INTRODUCTIONThe Internet of Things (IoT) has the potential to change the way we live and work by providing its essential services to all the smart devices which are connected to it. IoT alleviates various challenges of wireless networks such as robust internet connectivity among a large population of smart devices, energy constraints in mobile edge devices, fast data transfer, efficient bandwidth utilization, and increased data gathering capability/reliability at the receiver. To achieve these goals, the devices must be optimally connected to the network, resulting in a fast exchange of sensed data facilitating intelligent decision-making to control the physical world phenomenon yielding a smart ecosystem. Several independent studies have predicted that in coming years IoT plays a crucial role in various social and commercial applications such as smart healthcare, intelligent transporta-tion, climate-smart agriculture, rescue operations, logistics, smart cities, industries, utilities, smart buildings, consumer electronics, security, asset tracking, smart waste management systems, cognitive manufacturing, and Machine-to-Machine (M2M) communications [1]-[5]. Furthermore, it has been estimated that by 2025, the adoption of smart M2M gadgets and electronic goods can overtake the number of human subscribers utilizing smartphones, desktops, notebooks, and similar objects. According to the analys...

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“…In our elaborated RA scenario, the device learns which are the best resource blocks it should transmit based on its

Q

‐table storage of the rewards sent by the central node. The low‐complexity of QL makes it suitable to operate in crowded RA scenarios with many devices randomly transmitting short packets 17,18 . In Reference 19, an independent QL technique with a binary reward and a collaborative technique in which the device receives information on the congestion level of each time‐slot are proposed.…”

Section: Introductionmentioning

confidence: 99%

Multipower‐level Q‐learning algorithm for random access in nonorthogonal multiple access massive machine‐type communications systems

Silva

Abrão

2022

Trans Emerging Tel Tech

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The massive machine-type communications (mMTC) service will be part of new services planned to integrate the beyond fifth generation of wireless communication. In mMTC, thousands of devices sporadically access available resource blocks on the network. In this scenario, the massive random access problem arises when two or more devices collide when selecting the same resource block.There are several techniques to deal with this problem. One of them deploys Q-learning (QL), in which devices store in their Q-table the rewards sent by the central node that indicate the quality of the transmission performed. The device learns which are the best resource blocks to select and transmit in order to avoid collisions. We propose a multipower-level QL (MPL-QL) algorithm that uses nonorthogonal multiple access (NOMA) transmit scheme to generate transmission power diversity and allow accommodate more than one device in the same time-slot as long as the signal-to-interference-plus-noise ratio (SINR) exceeds a threshold value. The numerical results reveal that the best performance-complexity trade-off is obtained by using a higher number of power levels, typically eight levels. The proposed MPL-QL can deliver better throughput and lower latency when compared to other recent QL-based algorithms found in the literature. INTRODUCTIONMachine-type wireless communication will be more widely used in applications such as internet of things (IoT), smart house, virtual reality, etc. 1,2 The goal of the fifth generation (5G) of wireless communications involves achieve ubiquitous communication in networks with ultra-dense devices allocation. [3][4][5] A data consumption of nearly 5 zettabytes per month is estimated across 17 billion devices. 6 In addition, due to the outbreak of the COVID-19 pandemic, there has been an remarkable increase in remote activities in work, health and education areas, which will be much more frequent in the post-pandemic environment. 7 Devices connected to the wireless network use different types of service. In the 5G of wireless communications systems, a clear division into three main use modes was defined: 8 enhanced mobile broadband (eMBB) for devices that require high data rates as an augmented reality user; ultra-reliable low-latency communications (URLLC) for applications that require 99.999% communication reliability such as remote surgery, while holding end-to-end latency below 1

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Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing

Cited by 24 publications

References 50 publications

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

Multipower‐level Q‐learning algorithm for random access in nonorthogonal multiple access massive machine‐type communications systems

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