Abstract
A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of $p$ -degrees of connectivity concerning objective functions’ evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for $p$ -disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway.
Highlights
With the advent of technologies for the Internet of Things (IoT), machine-to-machine communication, and the related ecosystem, a new paradigm of Industrial IoT (IIoT) has recently emerged
We propose an enhanced Particle Swarm Optimization (PSO) (EQPSO) algorithm for IIoT based on quantum PSO, differential evolution operator and crossover operator
We have considered a scenario in which sensor nodes and fog nodes are deployed in IIoT with faulttolerant network topology in a heterogeneous layer framework [42]
Summary
With the advent of technologies for the Internet of Things (IoT), machine-to-machine communication, and the related ecosystem, a new paradigm of Industrial IoT (IIoT) has recently emerged. The IIoT contains intelligent machines, robots, equipment, and tools with multiple IoT sensors to monitor and control the required parameters. The IIoT may comprise anything related to industrial sectors such as factories, factory floors, warehouses, shipyards, locomotives, trailers, cargo planes, and similar. It can be deployed in diverse applications of manufacturing, production, supply chain, quality assurance, predictive maintenance and control, optimization of resources, and others. The emerging fields of artificial intelligence, Big data, and Blockchain, there are huge prospects for IIoT deployments to achieve the emerging paradigms of Factory as a Service (FaaS), Machine as a Service (MaaS), Equipment as a Service (EaaS), and others. IIoT solutions require energy efficient and resilient operations, enhanced connectivity, co-existence, interoperability, and data security. Edge and fog computing are used for VOLUME XX, 2017
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