Enhanced AODV Routing Protocol Using Leader Election Algorithm

Failure of communication link in mobile ADHOC network is major issue. For the failure of link the performance of network is degraded. Due to mobility of mobile node brake the communication link and path of routing is failed. For the repairing of routing node used various algorithm such as leader election, distributed and selection algorithm. The failure of link decease the performance of routing protocol in mobile ad-hoc network, for the improvement of quality of service in mobile ad-hoc network various authors proposed a different model and method for prediction of link. The prediction of link decreases the failure rate of mobile node during communication. The leader election algorithm plays a major role in link failure prediction algorithm the process of link failure prediction implied in form of distributed node distribution. Proposed a new link stability prediction method based on current link-related or user-related information in shadowed environments. The modified protocol acquired the process of thresholds priority Oder on the basic of neighbor’s node. The selection of neighbor node deepens on the mode operation in three sections. According to order of state create cluster of priority of group. After creation of group calculate average threshold value and compare each group value with minimum threshold value and pass the control message for communication. Through this process mode of activation state of node is minimized the time of route establishment and maintenance. The selection of proper node in minimum time and other node in sleep mode the consumption of power is reduces. We modified SBRP protocol for selection of node during on demand request node according to sleep and activation mode of communication. Each node locally assigned priority value of node. For the evaluation of performance used network simulator NS-2.35. And simulate two protocol one is AODV-LE protocol, these protocol patch are available for the simulation purpose. And another protocol is AODV-LE-ME. AODV-LE-ME protocol is modified protocol of leader election protocol for the selection of mobile node during the communication. DOI: 10.17762/ijritcc2321-8169.15016

Load Balanced Clustering Technique in MANET using Genetic Algorithms

Mobile adhoc network (MANET) has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load-balanced clustering problem (DLBCP). Load-balancing and reliable data transfer between all the nodes are essential to prolong the lifetime of the network. MANET can also be partitioned into clusters for maintaining the network structure. Generally, Clustering is used to reduce the size of topology and to accumulate the topology information. It is necessary to have an effective clustering algorithm for adapting the topology change. In this, we used energy metric in genetic algorithm (GA) to solve the DLBCP. It is important to select the energy- efficient cluster head for maintaining the cluster structure and balance the load effectively. In this work, we used genetic algorithms such as elitism based immigrants genetic algorithm (EIGA) and memory enhanced genetic algorithm (MEGA) to solve DLBCP. These schemes select an optimal cluster head by considering the distance and energy parameters. We used EIGA to maintain the diversity level of the population and MEGA to store the old environments into the memory. It promises the load -balancing in cluster structure to increase the lifetime of the network. Experimental results show that the proposed schemes increases the network lifetime and reduces the total energy consumption. The simulation results show that MEGA and EIGA give a better performance in terms of load-balancing

Wireless Sensor Network Clustering with Machine Learning

Wireless sensor networks (WSNs) are useful in situations where a low-cost network needs to be set up quickly and no fixed network infrastructure exists. Typical applications are for military exercises and emergency rescue operations. Due to the nature of a wireless network, there is no fixed routing or intrusion detection and these tasks must be done by the individual network nodes. The nodes of a WSN are mobile devices and rely on battery power to function. Due the limited power resources available to the devices and the tasks each node must perform, methods to decrease the overall power consumption of WSN nodes are an active research area. This research investigated using genetic algorithms and graph algorithms to determine a clustering arrangement of wireless nodes that would reduce WSN power consumption and thereby prolong the lifetime of the network. The WSN nodes were partitioned into clusters and a node elected from each cluster to act as a cluster head. The cluster head managed routing tasks for the cluster, thereby reducing the overall WSN power usage. The clustering configuration was determined via genetic algorithm and graph algorithms. The fitness function for the genetic algorithm was based on the energy used by the nodes. It was found that the genetic algorithm was able to cluster the nodes in a near-optimal configuration for energy efficiency. Chromosome repair was also developed and implemented. Two different repair methods were found to be successful in producing near-optimal solutions and reducing the time to reach the solution versus a standard genetic algorithm. It was also found the repair methods were able to implement gateway nodes and energy balance to further reduce network energy consumption

Enhancing Cooperation in MANET Using the Backbone Group Model (An Application of Maximum Coverage Problem)

AbstractMANET is a cooperative network in which every node is responsible for routing and forwarding as a result consumes more battery power and bandwidth. In order to save itself in terms of battery power and bandwidth noncooperation is genuine. Cooperation can be enhanced on the basis of reduction in resource consumption by involving a limited number of nodes in routing activities rather than all. To get accurate selection of nodes to define a backbone several works have been proposed in the literature. These works define a backbone with impractical assumptions that is not feasible for MANET. In this paper we have presented the Backbone Group (BG) model, which involve the minimum number of nodes called BG in routing activities instead of all. A BG is a minimal set of nodes that efficiently connects the network. We have divided a MANET in terms of the single hop neighborhood called locality group (LG). In a LG we have a cluster head (CH), a set of regular nodes (RNs) and one or more border nodes (BNs). The CHs are responsible for the creation and management of LG and BG. The CHs use a BG for a threshold time then switches to another BG, to involve all nodes in network participation. The proposed model shows its effectiveness in terms of reduction in routing overhead up to a ratio (n2: n2/k) where k is the number of LGs

Design and Performance Analysis of Genetic Algorithms for Topology Control Problems

In this dissertation, we present a bio-inspired decentralized topology control mechanism, called force-based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each autonomous mobile node to achieve a uniform spread of mobile nodes and to provide a fully connected network over an unknown area. We present a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This dissertation emphasizes the use of mobile nodes to achieve a uniform distribution over an unknown terrain without a priori information and a central control unit. In contrast, each mobile node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. We have implemented simulation software in Java and developed four different testbeds to study the effectiveness of different GA-based topology control frameworks for network performance metrics including node density, speed, and the number of generations that GAs run. The stochastic behavior of FGA, like all GA-based approaches, makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for homogeneous and inhomogeneous Markov chain models of our FGA. Furthermore, convergence characteristic analysis helps us to choose the nearoptimal values for communication range, the number of mobile nodes, and the mean node degree before sending autonomous mobile nodes to any mission. Our analytical and experimental results show that our FGA delivers promising results for uniform mobile node distribution over unknown terrains. Since our FGA adapts to local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for commercial and military applications

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications