Cloud-edge computing is an emerging computing model based on Service Oriented Architecture that provides reliable and available cloud services as scalable resources by collaborating fog nodes on Internet of Things (IoT) environments. One of the important issues on service discovery is energy efficiency and security for existing cloud providers and fog nodes. An optimal service discovery and selection approach as an NP-Hard problem can effective on decreasing time and cost in cloud providers to achieve through maximum capacity of Quality of Service (QoS) factors. To address of the above challenges, this paper focuses on above-mentioned outcomes and presents a QoS-aware cloud-edge service discovery and selection model in IoT environment. This model is evaluated based on a hybrid multi-objective meta-heuristic algorithm based on a Grey Wolf Optimizer and a Genetic Algorithm (GWO-GA) for evaluating QoS factors as non-functional properties. The proposed model is meant to guarantee QoS factors such as the response time, energy consumption and cost factors for the service discovery and selection problem in the IoT environment. Experimental showed that the proposed method performs 30% better than the other algorithms for decreasing cost factor.

QoS-Aware Service Discovery and Selection Management for Cloud-Edge Computing Using a Hybrid Meta-Heuristic Algorithm in IoT

Holistic survey on energy aware routing techniques for IoT applications

Recent advances in multi-objective grey wolf optimizer, its versions and applications

An Approach to Optimize LoRa Network Performance for Efficient IoT Applications

The emerging needs of innovative services in different areas led to the development of advanced intelligent systems using the heterogeneous technologies, devised by Internet of Things (IoT). IoT focuses on integrating the networks to facilitate smooth services to the humans. The interface between mobility patterns and the routing protocols contributes significantly to alter the performance of network. This paper proposes routing protocol based on Sunflower based grey wolf optimization (SFG) algorithm for improving the network lifetime. The first step is the simulation of IoT and then, the multipath routing is initiated in the IoT network. The SFG algorithm selects the best path from the multipath available for routing, based on Context awareness, Network lifetime, Residual Energy, Trust, and Delay. Finally, the multipath routing takes place in the IoT network through optimal routing path selected using the proposed SFG algorithm. The proposed SFG algorithm is designed by integrating sun flower optimization (SFO) and the grey wolf optimizer (GWO) such that the optimal routes are selected. The proposed SFG outperformed other methods with minimal delay of 0.779 s, maximal energy of 0.203 J, maximal network lifetime of 98.039%, and maximal throughput of 47.368%, respectively.

Multi-objective Sunflower Based Grey Wolf Optimization Algorithm for Multipath Routing in IoT Network

A performance of network is evaluated by considering different parameters. The network lifetime depends on many factors Residual energy, Link lifetime and Delay. The Major Challenge in IoT is to the increased lifetime of low power and lossy network (RPL).The process considering input and output to evaluate Network performance by considering the above factors. The proposed system makes use of FIS (Fuzzy Inference System) for selecting the best path to maximize network lifetime. The outcome obtained by using MATLAB and Network performance is increased. The excellent route is selected if Residual Energy is 194, Link quality is 51.2 and Delay is 1.05 then excellent route quality is 73.4%.

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S. N. Shinde

Papers

Multi-objective Sunflower Based Grey Wolf Optimization Algorithm for Multipath Routing in IoT Network

Fuzzy Logic Approach for Routing in Internet of Things Network