Modern Energy Optimization Approach for Efficient Data Communication in IoT-Based Wireless Sensor Networks

Kumar, Lalit; Ahmad, Sultan; Routray, Sidheswar; Prabu, A.V.; Alharbi, Abdullah; Alouffi, Bader; Rajasoundaran, S.

doi:10.1155/2022/7901587

Cited by 17 publications

(8 citation statements)

References 46 publications

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“…SHS is made of various computing devices that act proactively on behalf of persistent users [ 18 ]. Hence, for making good decisions in SHS, we require essential features, for example, users' preferences need to be considered for finding their choice of interest in certain scenarios [ 19 – 24 ]. Here, user preferences deal with the information used for describing the situation of a person considering the physical medical status or requirements.…”

Section: Introductionmentioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

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Section: Introductionmentioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…They demonstrated their model using cloud infrastructure to reduce delay and cost in smart city energy distribution. [11][12][13][14] had developed a DRL-based Energy Management Model (EMM) for intelligent buildings. Their work focused on optimizing energy consumption using the DRL.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where, 𝐸 HVAC (𝑡) is the EC by the HVAC system at time 𝑡. ESS Cost: The cost associated with the ESS, 𝐶 ESS , includes charging costs during low-demand periods and savings from discharging during peak tariff periods. It is calculated as EQU (12).…”

Section: Cost Modelmentioning

confidence: 99%

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

Alsharafa,

Krishna

et al. 2024

JMC

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This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.

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“…It is the focus of Ref. [27] to select the cluster head in a non-uniform sensor node distribution. Each band includes a base station, which increases the likelihood of a node becoming a cluster head and the network's overall energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

Meenakshi,

Ahmad,

Prabu

et al. 2024

Tsinghua Sci. Technol.

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The Wireless Sensor Network (WSN) is a network that is constructed in regions that are inaccessible to human beings. The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments. They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area. The data have to be picked up by the sensor, and then sent to the sink node where they may be processed. The nodes of the WSNs are powered by batteries, therefore they eventually run out of power. This energy restriction has an effect on the network life span and environmental sustainability. The objective of this study is to further improve the Engroove Leach (EL) protocol's energy efficiency so that the network can operate for a very long time while consuming the least amount of energy. The lifespan of WSNs is being extended often using clustering and routing strategies. The Meta Inspired Hawks Fragment Optimization (MIHFO) system, which is based on passive clustering, is used in this study to do clustering. The cluster head is chosen based on the nodes' residual energy, distance to neighbors, distance to base station, node degree, and node centrality. Based on distance, residual energy, and node degree, an algorithm known as Heuristic Wing Antfly Optimization (HWAFO) selects the optimum path between the cluster head and Base Station (BS).They examine the number of nodes that are active, their energy consumption, and the number of data packets that the BS receives. The overall experimentation is carried out under the MATLAB environment. From the analysis, it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput, packet delivery and drop ratio, and average energy consumption.

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Modern Energy Optimization Approach for Efficient Data Communication in IoT-Based Wireless Sensor Networks

Cited by 17 publications

References 46 publications

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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