nan Zaheeruddin

Maximization the lifetime of wireless sensor network by minimizing energy hole problem with exponential node distribution and hybrid routing

In wireless sensor network (WSN), there is a problem of energy hole, which means nodes near the sink region will die sooner because these nodes send their own data to sink as well as forward data for other nodes. After energy hole comes, data cannot be transmitted to sink even though energy is still remained in outer region nodes which affects the lifetime of the networks. In this paper we analyze jointly effect of exponential distribution of nodes toward the sink and hybrid routing so that the lifetime of wireless sensor network will be improved. Simulation experiments shows the effectiveness of our approach.

Minimizing the Energy Hole Problem in Wireless Sensor Networks by Normal Distribution of Nodes and Relaying Range Regulation

Energy hole problem is a crucial issue in wireless sensor network (WSN). Nodes near the sink region will die sooner from outer sub-regions because these nodes send their own data as well as forward outer sub-regions data to the sink. So after very short time energy hole comes near the sink region. After that, data cannot be transmitted to sink even though energy is still remained in outer region nodes which affects the lifetime of the network. In this paper we analyze jointly effect of normal distribution of nodes and relaying range regulation for data transmission so that the problem of energy hole will be minimized and lifetime of Wireless sensor network will be prolonged. Simulation experiments show the effectiveness of our approach.

Improvement of Lifetime of Wireless Sensor Network by Jointly Effort of Exponential Node Distribution and Mixed Routing

In wireless sensor network, there is a problem of energy hole, which means nodes near the sink region will die sooner because these nodes send their own data to sink as well as forward data for other nodes. After energy hole comes, data cannot be transmitted to sink even though energy is still remained in outer region nodes which affects the lifetime of the networks. In this paper we analyze jointly effect of exponential distribution of nodes toward the sink and mixed routing so that the lifetime of wireless sensor network will be improved. Simulation experiments show the effectiveness of our approach.

An elitist nondominated sorting genetic algorithm for QoS multicast routing in wireless networks

Abstract Due to the increasing popularity of real time multimedia applications, Quality-of-Service (QoS) based multicast routing has emerged as an active area of research. The fundamental requirements of many multimedia applications are cost minimization and bounded end-to-end delay. In addition, video data traffic is sensitive to packet loss and delay variance. Hence, multiobjective optimization seems to be the most appropriate method for such complex problems. We, therefore, formulate QoS based multicast routing as a multiobjective optimization problem using Elitist Nondominated Sorting Genetic Algorithm (NSGA-II). To enhance the performance of NSGA-II, we propose a new encoding scheme that aims to achieve a diversified solution set and faster convergence of search towards optimal Pareto front. It has also been observed that identical solutions cause loss of diversity which degrades the performance of NSGA-II algorithm. To overcome this drawback, the second enhancement based on replacement strategy is used. In this approach, one copy of identical solution is retained and new random solutions are introduced in the population to obtain a well distributed Pareto front. The results of new encoding scheme and replacement strategy are compared with other existing evolutionary multiobjective algorithms to demonstrate the effectiveness of the proposed approach. To further strengthen the usefulness of modified algorithm, the experimental results are validated using statistical significance tests.

Prolonging the Lifetime of Wireless Sensor Network by Exponential Node Distribution and Ant-colony Optimization Routing

Wireless Sensor Networks (WSN) consists of small nodes to collect useful information from environment, but nodes have limited power. Nodes in WSN transmit data to the sink which is fully equipped with energy source and from sink data is transmitted to base station which is capable of connecting the WSN to an existing communications infrastructure or to Internet where user can access to reported information. Sensor nodes near to sink is transmitted their own information as well as forward information from the outer nodes. After some time, sensor nodes near to the sink will lose their energy and then die although outer region nodes are still alive, but information cannot be transmitted to sink because inner region sensor nodes are dead means energy hole comes near to sink, so lifetime of WSN is affected. To minimize energy-hole problem near sink and prolong lifetime of WSN, different approaches are used. In our approach, nodes according to exponential distribution are deployed then ant colony optimization technique is used to route the information.

Energy-aware bee colony approach to extend lifespan of wireless sensor network

Abstract Wireless sensor networks (WSNs) consist of an immense number of tiny wireless sensor nodes deployed in a limited areas. The sensor nodes collect information from the areas and send to a central base station. The sensor nodes in such networks bring limited energy sources and in many situations, it is so difficult to recharge or replace the limited energy source of these tiny sensor nodes. For that reason, it is essential that these networks function under energy-efficient protocols and arrangements. This paper presents a novel Energy-Aware Bee Colony Approach to extend lifespan of WSN. The paper proposes an efficient clustering approach which chooses the best cluster-head with the help of bee colony approach. Through simulations, we demonstrate that our proposed solution uses the smallest amount of energy during the clustering procedure and chooses the best cluster-heads in contrast to advanced clustering approaches in WSN.

A Multiobjective Model for Energy Efficient QoS Provisioning Using Genetic Algorithm

In this paper, we investigate the problem of finding optimal path in a multicast tree that minimizes the energy consumption and also satisfies the quality-of-service (QoS) requirement of the application. Delay and packet loss probability are important QoS requirement for audio and video transmission. One approach for minimizing the energy consumption is power control. Transmission power control uses variable power to minimize the total transmitted power at the leaf nodes in the multicast tree. It has been established that that reducing the transmission power results in energy savings but increases the number of forwarding nodes in the tree. This may negatively impact the QoS requirement of the application. We model the issue as a multiobjective optimization problem to minimize the total energy consumption, delay and the packet loss in a multicast tree. We adapt the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA - II) to find the Pareto front for the problem. The experimental results on the sample test network are presented to demonstrate the effectiveness of the proposed model.

Energy efficient QoS provisioning: A multi-colony ant algorithm approach.

With the advancement of technology, there is a growing desire to provide support for real time multimedia services over wireless ad hoc networks. These applications demand strict Quality-of-Service (QoS) guarantees by the underlying network in terms of delay, delay variance and packet loss. However, limited battery life of the nodes in a wireless network poses significant challenges in finding QoS optimized multicast routes. The depletion of energy resources of the node may lead to a disjoint network that can severely affect the QoS requirement of the application. In this paper, we investigate the problem of building a multicast tree that aims energy conservation while meeting the QoS demands of the application. We formulate the issue as a multiobjective optimization problem and apply a multi-colony ant approach to find the Pareto front for the new problem. The experimental results are presented to demonstrate the effectiveness of the algorithm. The results are compared with Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II).