A Security Model for Perceptive 5G-Powered BC IoT Associated Deep Learning

Davuluri, Sandeep Kumar; Alvi, Syed Ahad Murtaza; Aeri, Manisha; Agarwal, Abhishek; Serajuddin, Mohammad; Hasan, Zafarul

doi:10.1109/icict57646.2023.10134487

Cited by 3 publications

(1 citation statement)

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“…IoT is a word that is frequently used, although its meaning is still unclear. The IoT is a technological advance that will https://doi.org/10.56578/jse020403 shape how we communicate and compute in the future [2]. Through combining every item for communication via embedded devices, it intends to extend the Internet's accessibility and create a highly decentralized system of gadgets that could interface with both people and other things [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Energy Efficiency in IoT-WSN Systems via a Hybrid Crow Search and Firefly Algorithm

Dlamini,

Mwashita

2023

JSE

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In the realm of enterprise technology, Internet of Things (IoT)-based wireless devices have witnessed significant advancements, enabling seamless interactions among machines, sensors, and physical objects. A critical component of IoT, Wireless Sensor Networks (WSN), have proliferated across various real-time applications, influencing daily life in both critical and non-critical domains. These WSN nodes, typically small and battery-operated, necessitate efficient energy management. This study focuses on the integration of crow search optimization and firefly algorithms to optimize energy efficiency in IoT-WSN systems. It has been observed that the energy reserve (RE) of a node and its communication costs with the base station are pivotal in determining its likelihood of becoming a Cluster Head (CH). Consequently, energy-saving data aggregation techniques are paramount to prolonging network longevity. To this end, a hybrid approach combining crow search and firefly optimization has been proposed. The crow search algorithm plays a significant role in enhancing data transfer efficiency, while the firefly algorithm is instrumental in selecting optimal cluster heads. This integrated methodology not only promises to extend the network's lifespan but also ensures a balance between energy conservation and data transmission efficacy.

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Section: Introductionmentioning

confidence: 99%