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Development of Spectrum Sharing Protocol for Cognitive Radio Internet of Things

Authors
  • Tarek mohamed ibrahim hafez, Dina
Publication Date
Dec 18, 2020
Source
HAL
Keywords
Language
English
License
Unknown
External links

Abstract

Internet of Things (IoT) presents a new life style by developing smart homes, smart grids, smart city, smart transportation, etc., so IoT is developing rapidly. However recent researches focus on developing the IoT applications disregarding the IoT spectrum scarcity problem facing it. Integrating Internet of Things (IoT) technology and Cognitive Radio Networks (CRNs), forming Cognitive Radio Internet of Things (CRIoTs), is an economical solution for overcoming the IoT spectrum scarcity. The aim of this thesis is to solve the problem of spectrum sharing for CRIoT; the work in thesis is presented in three parts, each represents a contribution. Our first contribution is to propose two new protocols to solve the problem of channel status prediction for interweave CRNs. Both protocols use Hidden Markov Model (HMM). In the training stage of both protocols, the available data are trained to produce two HMM models, an idle HMM model and a busy one. Both models are used together to produce the 2-model HMM. In the prediction stage the first protocol uses Bayes theorem and the 2-model HMM, while the second protocol uses Support Vector Machine (SVM) employing the parameters produced from applying the 2-model HMM, named 2-model HMM-SVM. The 2-model HMM-SVM outperforms the classical HMM and 2-model HMM in terms of the true percentage, the inaccuracy and the probability of primary users’ collision (false negative prediction). In our second contribution, we proposed a centralized time slotted packet scheduling protocol for CRIoTs. It uses Discrete Permutation Particle Swarm Optimization (DP-PSO) for scheduling the IoT device packets among the free slots obtained from applying cognitive radio networks' channel estimation technique proposed in the first part. Our proposed protocol is applied to smart healthcare facility. Configuring three main building blocks for the used application architecture; the IoT devices block, the first layer fog nodes block and the central fog server. Each group of IoT devices is connected to a fog node, the entire fog nodes in the system are connected to the central fog node. The proposed protocol is named Scheduling based-on Discrete Permutation Particle Swarm Optimization (SDP-PSO). An objective fitness function is formulated with three parameters; maximizing the fairness index among fog nodes, minimizing the packets' queuing delay and minimizing the number of dropped packets that exceeded their allowed time in the network without being sent. The performance of the proposed SDP-PSO protocol overcomes an old protocol named spectrum auction in terms of the fairness between fog nodes, the average queuing delay, the number of dropped packets and the time and the space complexity. Finally, in the third contribution, we proposed a distributed packets' scheduling protocol for CRIoTs. Our proposed protocol can be applied to an urban traffic control. The configured system in this part consists of three main building blocks; the IoT devices block, the first fog layer block (Road Side Units (RSUs)) and the second fog layer block. Each group of IoT devices is connected to a RSU, each group of RSU are connected to a fog node which acts as their cluster head. The fog nodes are connected together forming a partial mesh network. The proposed distributed packets' scheduling protocol for CRIoTs is applying three distributed access strategies together with the SDP-PSO proposed in the second part to schedule the packets on the estimated free slots resulted from applying the protocol proposed in the first part. The used access strategies are the classical round robin, in addition to two proposed ones named; the vertex cover and the enhanced round robin. An objective fitness function near similar to that used in the centralized protocol, was applied but with some differences to make it suitable for distributed scheduling.

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