Multiple Parameters Optimization for Cognitive Radio Environment Employing Cuckoo Search Algorithm

2018 ◽  
Author(s):  
May Kamil Al-Azzawi ◽  
Haider S. Hatem ◽  
Mohammad Ibrahim Shujaa
Keyword(s):  
Cognitive Radio ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parameters Optimization ◽  
Multiple Parameters

scholarly journals A Model to Improve the Effectiveness and Energy Consumption to Address the Routing Problem for Cognitive Radio Ad Hoc Networks by Utilizing an Optimized Cuckoo Search Algorithm

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3464
Author(s):  
Ramahlapane Lerato Moila ◽  
Mthulisi Velempini
Keyword(s):  
Energy Consumption ◽  
Cognitive Radio ◽  
Ad Hoc ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Delivery Ratio ◽  
Routing Problem ◽  
Free Spectrum ◽  
Spectrum Mobility

A cognitive radio ad hoc network (CRAHN) is a mobile network that can communicate without any form of centralized infrastructure. The nodes can learn about the environment and make routing decisions. Furthermore, distributed computing, spectrum mobility, and the Internet of Things have created large data sets, which require more spectrum for data transmission. Unfortunately, the spectrum is a scarce resource that underutilized by licensed users, while unlicensed users are overcrowding the free spectrum. The CRAHNs technology has emerged as a promising solution to the underutilization of the spectrum. The focus of this study is to improve the effectiveness and energy consumption of routing in order to address the routing problem of CRAHNs through the implementation of the optimized cuckoo search algorithm. In CRAHNs, the node and spectrum mobility cause some frequent link breakages within the network, which degrades the performance of the routing protocols. This requires a routing solution to this routing problem. The proposed scheme was implemented in NS2 installed in Linux operating system, with a cognitive radio cognitive network (CRCN) patch. From the experimental results, we observed that the proposed OCS-AODV scheme outperformed CS-DSDV and ACO-AODV schemes. It obtained at least 3.87% packet delivery ratio and 2.56% and lower packets lost. The scheme enabled the mobile nodes to adjust accordingly to minimize energy consumption. If not busy, they switch to an idle state to save battery power.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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