ANALYZING THE IMPACT OF EAVES ON ENERGY CONSUMPTION OF AODV ROUTING PROTOCOL FOR MANETS
Dr. K.Soumya , S. P. Setty , Andhra University, India
In this dynamic world, communication is a sine qua non for development. Communication represents sharing of information which can be local or remote. Though local communications may occur face to face between individuals remote communications take place among people over long distances. Mobile ad hoc networks (MANETs) are becoming an interesting part of research due to the increasing growth of wireless devices (laptops, tablets, mobiles etc.) and as well as wireless internet facilities like 4G/Wi-Fi. A MANET is any infrastructure-less network formed by independent and self-configuring nodes. Each node acts as router. In order to send data, the source node initiates a routing process by using a routing protocol. The nature of the wireless medium is always insecure. So, during routing many attacks can take place. The main objective of an eavesdropper is to grab the confidential information in the network. This secret information is used by a malicious node to perform further attacks. Here, the entire problem lies in identifying the eavesdropper because the eavesdropper acts a normal node in the network. In this paper, we analyzed the impact of eavesdropper while executing an Ad hoc On Demand routing (AODV) protocol in MANETs. All the simulations are done using QualNet 5.1 network simulator. From the results, it is found that the network performance degrades in presence of an eavesdropper
MANETs, AODV, eavesdropper, energy consumption, QualNet
For More Details :
http://aircconline.com/ijwmn/V11N1/11119ijwmn01.pdf
ENERGY AND PERFORMANCE EVALUATION OF REACTIVE, PROACTIVE, AND HYBRID ROUTING PROTOCOLS IN WIRELESS MESH NETWORK
Jean Louis Ebongue Kedieng Fendji1 and Sidoine Djuissi Samo2 , 1University Institute of Technology, Ngaoundéré , 2Apple College,United Arab Emirates
This paper evaluates the energy consumption of well-known routing protocols, along with other metrics such as throughput, packet delivery ratio (PDR), and delay in different scenarios. We consider two other metrics in order to capture the efficiency of the energy consumption: e-throughput which is the ratio between the consumed energy and the throughput; and the e-PDR which is the ratio between the consumed energy and the PDR. We compare four routing protocols: AODV, OLSR, and HWMP in Reactive and Proactive modes. The number of nodes is varying between 25 and 81 nodes, with different mobility models. Simulations are conducted using NS3 and the parameters of a real network interface card. From the results, AODV presents the lowest energy consumption and a better e-Throughput. OLSR provides a better e-PDR in mobile scenarios. With a smaller e-PDR and e-Throughput, the proactive mode of HWMP is more energy efficient than the reactive mode.
Energy consumption; AODV; OLSR; HWMP-R; HWMP-P; MANET; Wireless Mesh Networks; e-Throughput; e-PDR
For More Details :
http://aircconline.com/ijwmn/V11N1/11119ijwmn02.pdf
MACHINE LEARNING FOR QOE PREDICTION AND ANOMALY DETECTION IN SELF-ORGANIZING MOBILE NETWORKING SYSTEMS
Chetana V. Murudkar* and Richard D. Gitlin
University of South Florida,USA
Existing mobile networking systems lack the level of intelligence, scalability, and autonomous adaptability required to optimally enable next-generation networks like 5G and beyond, which are expected to be Self -Organizing Networks (SONs). It is anticipated that machine learning (ML) will be instrumental in designing future “x”G SON networks with their demanding Quality of Experience (QoE) requirements. This paper evaluates a methodology that uses supervised machine learning to predict the QoE level of the end user experiences and uses this information to detect anomalous behavior of dysfunctional network nodes (eNodeBs/base stations) in self-organizing mobile networks. An end-to-end network scenario is created using the network simulator ns-3, where end users interact with a remote host that is accessed over the Internet to run the most commonly used applications like file downloads and uploads and the resulting output is used as a dataset to implement ML algorithms for QoE prediction and eNodeB (eNB) anomaly detection. Three ML algorithms were implemented and compared to study their effectiveness and the scalability of the methodology. In the test network, an accuracy score greater than 99% is achieved using the ML algorithms. As suggested by the ns-3 simulation the use of ML for QoE prediction will help network operators understand end-user needs and identify network elements that are failing and need attention and recovery.
Machine learning, ns-3, QoE, SON
For More Details :
http://aircconline.com/ijwmn/V11N2/11219ijwmn01.pdf
POSITION ESTIMATION OF AUTONOMOUS UNDERWATER SENSORS USING THE VIRTUAL LONG BASELINE METHOD
Alexander Dikarev, Stanislav Dmitriev, Vitaliy Kubkin and Andrey Vasilenko , Underwater communication & navigation laboratory, Russia
This article contains a description of a mathematical model of an acoustic system for positioning autonomous underwater sensors using the virtual long base method, which can be used during the vessel’s collection of information over the deployed underwater network of autonomous sensors (underwater wireless sensors network), during the initial determination of the geographical position of the bottom long baseline elements or search, including cooperative, with the use of a swarm of autonomous surface vehicles (UASV) of emergency submerged objects equipped with an emergency beacon (for example, aircraft and ships); The article provides a scheme of an experimental set of equipment, as well as a description of the conducted field experiments and their results.
Underwater positioning system, VLBL, underwater wireless sensor network, emergency beacon positioning
For More Details :
http://aircconline.com/ijwmn/V11N2/11219ijwmn02.pdf
BIO-INSPIRED SEAMLESS VERTICAL HANDOVER ALGORITHM FOR VEHICULAR AD HOC NETWORKS
Mohanad M. Abdulwahhab1* and Mohd Fadlee A. Rasid1,2and Fazirulhisyam Hashim 1,2 , 1 Universiti Putra Malaysia, Malaysia , 2 Universiti Putra Malaysia, Malaysia
One of the most important factors to implement VANET is by considering the variety of wireless networks available around the city as well as the vehicles traffic scenarios. However, by providing a diverse range of wireless access technologies, it is necessary to provide continuous network connectivity as well as selecting the most suitable network technology and performance. Many researchers have worked on building algorithms for selecting the best network to improve the handover process. However, with high-speed vehicles mobility, the vertical handover process became the most challenging task in order to achieve real-time network selection. This paper proposes a bio-inspired network selection algorithm influenced by insect's behaviour which combines Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The proposed algorithm is applied to process multi-criteria parameters to evaluate the best available network and then execute the handover process seamlessly. The results demonstrate the benefits of the proposed Multi-Criteria ABC-PSO method by reducing the handover decision delays by 25%. It gives the optimum performance in terms of network selections and reduces the handover latency by 14.5%. The proposed algorithm also reduces the number of unnecessary handovers by 48% for three different mobility scenarios based on traffic environments (highway, urban and traffic jam).
Vehicular Ad Hoc Networks (VANETs), Vertical Handover, PSO, ABC, Seamless, Heterogeneous
For More Details :
http://aircconline.com/ijwmn/V11N5/11519ijwmn01.pdf
STATE-OF-THE-ART OF THE PHYSICAL LAYER IN UNDERWATER WIRELESS SENSOR NETWORKS
Faiza Al-Salti1 and N. Alzeidi2 , 1Muscat College, Oman , 2Sultan Qaboos University, Oman
With the current technology revolution, underwater wireless sensor networks (UWSNs) find several applications such as disaster prevention, water quality monitoring, military surveillance and fish farming. Nevertheless, this kind of networks faces a number of challenges induced by the nature of the underwater environment and its influence on the network physical media. Therefore, the ultimate objective of this paper is to lay down the key aspects of the physical layer of the underwater sensor networks (UWSNs). It discusses issues related to the characteristics and challenges of the underwater communication channel, differences between terrestrial wireless sensor networks and UWSNs, and acoustic propagation models in underwater. The paper also surveys some of the underwater acoustic modems. This study is essential to better understand the challenges of designing UWSNs and alleviate their effects.
Underwater wireless sensor networks, physical layer, acoustic, communication, channel models & acoustic modem
For More Details :
http://aircconline.com/ijwmn/V11N6/11619ijwmn02.pdf