Survey on Data-Centric based Routing Protocols for Wireless Sensor Networks

The great concern for energy that grew with the technological advances in the field of networks and especially in sensor network has triggered various approaches and protocols that relate to sensor networks. In this context, the routing protocols were of great interest. The aim of the present paper is to discuss routing protocols for sensor networks. This paper will focus mainly on the discussion of the data-centric approach (COUGAR, rumor, SPIN, flooding and Gossiping), while shedding light on the other approaches occasionally. The functions of the nodes will be discussed as well. The methodology selected for this paper is based on a close description and discussion of the protocol. As a conclusion, open research questions and limitations are proposed to the reader at the end of this paper

Fuzzy Logic-based Trusted and Power-aware Routing Protocol in Mobile Ad-hoc Networks

Mobile ad-hoc networks (MANETs) have attracted much attention from researchers lately because MANETs are able to provide networks in areas with unavailable fixed network infrastructure. However, some mobile nodes may misbehave by dropping packets to conserve power usage because mobile ad-hoc networks nodes are usually battery operated. In this paper, a fuzzy logic-based routing protocol that considers the battery level of nodes, hop count, and trust among the nodes is proposed. The proposed routing protocol adaptively selects routes that use minimum hop count with the highest level of trust and a sufficient battery level to enhance the reliability of route selection while maintaining the percentage of successfully delivered packets. The result of the simulation shows that the proposed protocol can achieve a high ratio of successfully delivered packets, a lower average end-to-end delay, and a normalized routing load

Performance analysis of black hole and worm hole attacks in MANETs

A Mobile Ad Hoc Network MANET is composed of a freely and mobility set of mobile nodes. They form a temporary dynamic wireless network without any infrastructure. Since the nodes act as both host and router in their communication, they act as a router provide connectivity by forwarding data packets among intermediate nodes to the destination. The routing protocol is used to grove their communication and connectivity as example, the Ad On-demand distance vector (AODV) routing protocol. However, due to the lack of security vulnerabilities of routing protocols and the absence of infrastructure, MANET is vulnerable to various security threats and attacks. This paper examines the impact of two types of attacks on AODV routing protocol using Network Simulator version 2 (NS2) environment. These attacks are Blackhole and Wormhole Attacks. The aim of both of them is to prevent data packets to reach the destination node and dropping all the traffic.

Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection

With the whirlwind evolution of technology, the quantity of stored data within datasets is rapidly expanding. As a result, extracting crucial and relevant information from said datasets is a gruelling task. Feature selection is a critical preprocessing task for machine learning to reduce the excess data in a set. This research presents a novel quasi-reflection learning arithmetic optimization algorithm - firefly search, an enhanced version of the original arithmetic optimization algorithm. Quasi-reflection learning mechanism was implemented for enhancement of population diversity, while firefly algorithm metaheuristics were used to improve the exploitation abilities of the original arithmetic optimization algorithm. The aim of this wrapper-based method is to tackle a specific classification problem by selecting an optimal feature subset. The proposed algorithm is tested and compared with various well-known methods on ten unconstrained benchmark functions, then on twenty-one standard datasets gathered from the University of California, Irvine Repository and Arizona State University. Additionally, the proposed approach is applied to the Corona disease dataset. The experimental results verify the improvements of the presented method and their statistical significance