Ad hoc wireless networks perform the difficult task of multi-hop communication in an environment without a dedicated infrastructure, with mobile nodes and changing network topology. Different deployments exhibit various constraints, such as energy limitations, opportunities, such as the knowledge of the physical location of the nodes in certain scenarios, and requirements, such as real-time or multi-cast communication. In the last 15 years, the wireless networking community designed hundreds of new routing protocols targeting the various scenarios of this design space. The objective of this paper is to create a taxonomy of the ad hoc routing protocols, and to survey and compare representative examples for each class of protocols. We strive to uncover the requirements considered by the different protocols, the resource limitations under which they operate, and the design decisions made by the authors.
A b s t r a b l n this paper, we show how genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. In particular, we optimize our recently proposed weightcd clustering algorithm (WCA). The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique. Encoding the individual chromosomes is an essential part of the mapping process; each chromosome contains information about the clusterheads and the members thereof, as obtained from the original WCA. The genetic algorithm then uses this information to obtain the best solution (chromosome) defined by the fitness function. The proposed technique is such that each clusterhead haudles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the medium access control WAC) protocol. Consequently, it results in the minimum number ofclusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA. Moreover, the loads among clusters are more evenly balanced by a factor of ten.
In this paper, we demonstrate how simulated annealing algorithm can be applied to clustering algorithms used in ad hoc networks; specifically our recently proposed weighted clustering algorithm(WCA) is optimized by simulated annealing. As the simulated annealing stands to be a powerful stochastic search method, its usage for combinatorial optimization problems was found to be applicable in our problem domain. The problem formulation along with the parameters is mapped to be an individual solution as an input to the simulated annealing algorithm. Input consists of a random set of clusterhead set along with its members and the set of all possible dominant sets chosen from a given network of N nodes as obtained from the original WCA. Simulated annealing uses this information to find the best solution defined by computing the objective function and obtaining the best fitness value. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the MAC protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA.
Mobile ad hoc network consists of freely moving nodes communicating with each other through wireless links. In this paper, we propose a load balancing algorithm for these networks with nodes having different processing powers and thus can perform extensive computations apart from forwarding packets for other nodes. These nodes will also have various degrees of battery powers as well. Due to the he& erogeneity of the systems in terms of processing and battery powers, naturally, there will be load imbalance. If the workload is distributed among the nodes in the system based on the resources of individual nodes, the average execution time can be minimized and the lifetime of the nodes can be maximized. Our proposed load balancing algorithm takes into consideration several realistic parameters such as processing and batter powers of each node, and communication cost for the loads being transfered between the overloaded and underloaded nodes. Simulation experiments demonstrate that our proposed algorithm aehieves performance improvements in terms of processor utilization, execution time, and balance factor.
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