2-D coverage optimization in obstacle-based FOI in WSN using modified PSO

Priyadarshi, Rahul; Gupta, Bharat

doi:10.1007/s11227-022-04832-6

Cited by 18 publications

(4 citation statements)

References 22 publications

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“…DACOR minimises energy consumption by dynamically adjusting CHs to FNs based on distance, ensuring a distributed unequal clustering to several FNs [30]. A probabilistic formula for data routing is also developed to prioritise reliability and energy efficiency [31]. Different Fog Nodes (FNs) and uneven clustering-based network models can enhance the network's performance [32].…”

Section: Related Workmentioning

confidence: 99%

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Aalsalem

2024

PLoS ONE

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Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network’s status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs’ capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Aalsalem

2024

PLoS ONE

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“…Because of the interconnectivity of these diverse systems, the data from medical devices, wearable sensors, environmental monitors, and healthcare infrastructure can be seamlessly integrated. This enables a holistic approach to healthcare and provides healthcare providers with a comprehensive view of a patient's health and well-being [28]. These innovations have the potential to not only result in improved patient care but also in enhanced operational efficiency and a considerable reduction in the costs associated with healthcare, which may be accomplished via the use of data-driven decision-making.…”

Section: Introductionmentioning

confidence: 99%

Security and Privacy in Machine Learning for IoHT and IoMT: A Review

Priyadarshi,

Gheisari

2024

Preprint

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The emergence of Internet of Things (IoT) devices is revolutionizing healthcare. This dynamic healthcare environment relies on Internet of Healthcare Things (IoHT) and Internet of Medical Things (IoMT) technologies. Economic, social, and technological opportunities from the IoHT are transforming healthcare systems. This article covers the history, present condition, and future prospects of IoHT, as well as its topologies, platforms, services, applications, market trends, and solution types. It examines security issues, requirements, and a threat taxonomy, studying smart healthcare privacy and security. It explores future research and the challenges IoHT-based solutions face. International health rules and regulations based on the IoT and their social and economic effects on long-term development are also examined. Integrating IoMT into healthcare infrastructures enhances scalability, efficiency, reliability, and accuracy. Through communication networks and data-processing algorithms, IoMT creates smart hardware and software platforms that enhance decision-making. IoMT devices' low resources raise privacy and security concerns. Security and privacy issues are organised by healthcare IoMT adoption in this study. We give an extensive attack taxonomy against the IoMT infrastructure, encompassing devices, security mechanisms, and their pros and cons. Furthermore, it describes the privacy and security requirements for any unique security solutions against IoMT's multiple attack vectors. This article explains the complicated difficulties, security concerns, and future research pathways needed to safeguard and ensure the long-term expansion of these game-changing technologies in the healthcare ecosystem.

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“…SIAs are meta-heuristic algorithms that simulate the behavior of animal groups such as fish, birds, bees, and wolves, optimizing outcomes through simple, limited interactions between individuals and information exchange and cooperation within groups. Priyadarshi, R and Gupta, B [ 11 ] introduced an improved particle swarm optimization (PSO) algorithm to optimize coverage with minimal nodes. Zhu, WB et al [ 12 ] suggested a dual-tuned simplified group optimization (SSO) algorithm to maximize coverage areas and improve WSN performance.…”

Section: Introductionmentioning

confidence: 99%

Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor

Zhang

2023

Biomimetics

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The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed.

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2-D coverage optimization in obstacle-based FOI in WSN using modified PSO

Cited by 18 publications

References 22 publications

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Security and Privacy in Machine Learning for IoHT and IoMT: A Review

Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor

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