Unequal clustering and scheduling in Wireless Sensor Network using Advance Genetic Algorithm

Yas, Ruwaida M; Hashem, Soukaena H.

doi:10.1088/1742-6596/1530/1/012076

Cited by 3 publications

(7 citation statements)

References 9 publications

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“…E_(i,j) is ensure to select the CH with higher residual energy, and can be found using equation (22): E_(i,j)=(e_j (t))/(∑_(l N_i)▒〖e_l (t)〗) (22) Where where e_j (t) is residual energy of 〖CH〗_j, and e_l (t) is the residual energy of the ith CH neighbors.…”

Section: Discussionmentioning

confidence: 99%

“…In this method, the cluster centers are selected randomly from the deployed sensor nodes, then the cluster formation is performed using the following steps [22].…”

Section: Ga-based Clusteringmentioning

confidence: 99%

“…In this case, a new set of the remaining sensor nodes in each cluster has to be rescheduled in order to complete the task. According to this mechanism, GA-based scheduling can be explained in the following steps [22].…”

Section: Multi Objective Ga Nodes Scheduling (Moga-scheduling)mentioning

confidence: 99%

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Intelligent Approaches for Enhancing Networked Routing Protocol

Yas

Hashim

2021

eijs

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The rapid evolution of wireless networking technologies opens the door to the evolution of the Wireless Sensor Networks (WSNs) and their applications in different fields. The WSN consists of small energy sensor nodes used in a harsh environment. The energy needed to communicate between the sensors networks can be identified as one of the major challenges. It is essential to avoid massive loss, or loss of packets, as well as rapid energy depletion and grid injustice, which lead to lower node efficiency and higher packet delivery delays. For this purpose, it was very important to track the usage of energy by nodes in order to improve general network efficiency by the use of intelligent methods to reduce the energy used to extend the life of the WSN and take successful routing decisions. For these reasons, designing an energy-efficient system that utilizes intelligent approaches is considered as the most powerful way to prolong the lifetime of the WSN. The proposed system is divided into four phases (sensor deployment phase, clustering phase, intra-cluster phase, and inter-cluster phase). Each of these phases uses a different intelligent algorithm with some enhancements. The performance of the proposed system was analyzed and evaluations were elaborated with well-known existing routing protocols. To assess the proficiency of the proposed system and evaluate the endurance of the network, efficiency parameters such as network lifetime, energy consumption, and packet delivery to the Sink (Base station) were exploited. The experimental outcomes justify that the proposed system surpasses the existing mechanisms by 50%.

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Section: Discussionmentioning

confidence: 99%

“…In this method, the cluster centers are selected randomly from the deployed sensor nodes, then the cluster formation is performed using the following steps [22].…”

Section: Ga-based Clusteringmentioning

confidence: 99%

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Intelligent Approaches for Enhancing Networked Routing Protocol

Yas

Hashim

2021

eijs

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“…An algorithm called Ant Colony Optimization (ACO) was established, derived from the actual ant's behavior. Pheromone was used as a mediator to communicate path within each other [5][6][7]. As known, the pheromone is alchemical unsteady substance, which emitted by the ant; then, it can keep track of previous paths and then make decision for the next movement.…”

Section: Related Workmentioning

confidence: 99%

Selecting the Best Routing Traffic for Packets in LAN via Machine Learning to Achieve the Best Strategy

Zhang

Liao

2021

Complexity

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The application of machine learning touches all activities of human behavior such as computer network and routing packets in LAN. In the field of our research here, emphasis was placed on extracting weights that would affect the speed of the network's response and finding the best path, such as the number of nodes in the path and the congestion on each path, in addition to the cache used for each node. Therefore, the use of these elements in building the neural network is worthy, as is the exploitation of the feed forwarding and the backpropagation in the neural network in order to reach the best prediction for the best path. The goal of the proposed neural network is to minimize the network time delay within the optimization of the packet paths being addressed in this study. The shortest path is considered as the key issue in routing algorithm that can be carried out with real time of path computations. Exploiting the gaps in previous studies, which are represented in the lack of training of the system and the inaccurate prediction as a result of not taking into consideration the hidden layers' feedback, leads to great performance. This study aims to suggest an efficient algorithm that could help in selecting the shortest path to improve the existing methods using weights derived from packet ID and to change neural network iteration simultaneously. In this study, the design of the efficient neural network of appropriate output is discussed in detail including the principles of the network. The findings of the study revealed that exploiting the power of computational system to demonstrate computer simulation is really effective. It is also shown that the system achieved good results when training the neural network system to get 2.4% time delay with 5 nodes in local LAN. Besides, the results showed that the major features of the proposed model will be able to run in real time and are also adaptive to change with path topology.

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“…For many years, the TSP has played the role of an initial proving ground for the new ideas that are associated with those two alternatives. Those new ideas make the TSP an optimal subject for the case study [23][24][25].…”

Section: Case Study: Travelling Salesman Problem (Tsp)mentioning

confidence: 99%

Development of an enhanced scatter search algorithm using discrete chaotic Arnold’s cat map

Rafash

Saeed

Talib

2021

EEJET

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Solving optimization problems is an ever-growing subject with an enormous number of algorithms. Examples of such algorithms are Scatter Search (SS) and genetic algorithms. Modifying and improving of algorithms can be done by adding diversity and guidance to them. Chaotic maps are quite sensitive to the initial point, which means even a very slight change in the value of the initial point would result in a dramatic change of the sequence produced by the chaotic map Arnold's Cat Map. Arnold's Cat Map is a chaotic map technique that provides long non-repetitive random-like sequences. Chaotic maps play an important role in improving evolutionary optimization algorithms and meta-heuristics by avoiding local optima and speeding up the convergence. This paper proposes an implementation of the scatter search algorithm with travelling salesman as a case study, then implements and compares the developed hyper Scatter Arnold's Cat Map Search (SACMS) method against the traditional Scatter Search Algorithm. SACMS is a hyper Scatter Search Algorithm with Arnold's Cat Map Chaotic Algorithm. Scatter Arnold's Cat Map Search shows promising results by decreasing the number of iterations required by the Scatter Search Algorithm to get an optimal solution(s). Travelling Salesman Problem, which is a popular and well-known optimization example, is implemented in this paper to demonstrate the results of the modified algorithm Scatter Arnold's Cat Map Search (SACMS). Implementation of both algorithms is done with the same parameters: population size, number of cities, maximum number of iterations, reference set size, etc. The results show improvement by the modified algorithm in terms of the number of iterations required by SS with an iteration reduction of 10–46 % and improvements in time to obtain solutions with 65 % time reduction

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Unequal clustering and scheduling in Wireless Sensor Network using Advance Genetic Algorithm

Cited by 3 publications

References 9 publications

Intelligent Approaches for Enhancing Networked Routing Protocol

Intelligent Approaches for Enhancing Networked Routing Protocol

Selecting the Best Routing Traffic for Packets in LAN via Machine Learning to Achieve the Best Strategy

Development of an enhanced scatter search algorithm using discrete chaotic Arnold’s cat map

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