The Experts below are selected from a list of 2442624 Experts worldwide ranked by ideXlab platform
B Paramasivan - One of the best experts on this subject based on the ideXlab platform.
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enhancing energy efficiency and load balancing in mobile ad hoc network using dynamic genetic algorithms
Journal of Network and Computer Applications, 2016Co-Authors: M Kaliappan, Susan Augustine, B ParamasivanAbstract:Mobile Ad hoc Network (MANET) is a kind of self-configuring networks. 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 the 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 dynamic 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 energy efficiency of the entire 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 gives a better performance than EIGA in terms of load-balancing. Graphical abstractDisplay Omitted HighlightsWe formulated dynamic load balanced clustering problem with existing dynamic optimization problem.A genetic operation such as selection, fitness function, mutation and crossover is applied for cluster head selection. Distance of nodes and energy parameters are considered to select an optimal cluster head and to balance the load.We used Elitism-based Immigrants Genetic Algorithm and Memory Enhanced Genetic Algorithm to solve dynamic changing environment within a cluster.The proposed MEGA and EIGA ensure the fairness in terms of packet delivery ratio, energy consumption, network lifetime and delay.
M Kaliappan - One of the best experts on this subject based on the ideXlab platform.
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enhancing energy efficiency and load balancing in mobile ad hoc network using dynamic genetic algorithms
Journal of Network and Computer Applications, 2016Co-Authors: M Kaliappan, Susan Augustine, B ParamasivanAbstract:Mobile Ad hoc Network (MANET) is a kind of self-configuring networks. 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 the 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 dynamic 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 energy efficiency of the entire 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 gives a better performance than EIGA in terms of load-balancing. Graphical abstractDisplay Omitted HighlightsWe formulated dynamic load balanced clustering problem with existing dynamic optimization problem.A genetic operation such as selection, fitness function, mutation and crossover is applied for cluster head selection. Distance of nodes and energy parameters are considered to select an optimal cluster head and to balance the load.We used Elitism-based Immigrants Genetic Algorithm and Memory Enhanced Genetic Algorithm to solve dynamic changing environment within a cluster.The proposed MEGA and EIGA ensure the fairness in terms of packet delivery ratio, energy consumption, network lifetime and delay.
L M Patnaik - One of the best experts on this subject based on the ideXlab platform.
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dynamic hierarchical communication paradigm for wireless sensor networks a centralized energy efficient approach
International Conference on Conceptual Structures, 2008Co-Authors: Suraiya Tarannum, S Srividya, D S Asha, R Padmini, L Nalini, K R Venugopal, L M PatnaikAbstract:Effective utilization of limited power resources by the sensors is pre-eminent to wireless sensor networks. Organizing the network into balanced clusters based on assigning equal number of sensors to each cluster may have the consequence of unbalanced load on the cluster heads. By-product of this is unbalanced consumption of energy by the nodes which leads to minimization of network lifetime. We put forth a Sink administered Load balanced Dynamic Hierarchical Protocol (SLDHP) to balance the load on the principal nodes. Hierarchical layout of the sensors endows the network with increased lifespan. Outcome of this protocol also includes substantial saving of the energy consumed by the nodes. Simulation results indicate significant improvement of performance over Base station Controlled Dynamic Clustering Protocol (BCDCP).
Brian Towles - One of the best experts on this subject based on the ideXlab platform.
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adaptive channel queue routing on k ary n cubes
ACM Symposium on Parallel Algorithms and Architectures, 2004Co-Authors: Arjun Singh, William J Dally, Amit K Gupta, Brian TowlesAbstract:This paper introduces a new adaptive method, Channel Queue Routing (CQR), for Load-Balanced routing on k-ary n-cube interconnection networks. CQR estimates global congestion in the network from its channel queues while relying on the implicit network backpressure to transfer congestion information to these queues. It uses this estimate to decide the directions to route in each dimension. It further load balances the network by routing in the selected directions adaptively. The only other algorithm that uses global congestion in its routing decision is the Globally Adaptive Load-Balance (GAL) algorithm introduced in [13]. GAL performs better than any other known routing algorithm on a wide variety of throughput and latency metrics. However, there are four serious issues with GAL. First, it has very high latency once it starts routing traffic non-minimally. Second, it is slow to adapt to changes in traffic. Third, it requires a complex method to achieve stability. Finally, it is complex to implement. These issues are all related to GAL's use of injection queue length to infer global congestion. CQR uses channel queues rather than injection queues to estimate global congestion. In doing so, it overcomes the limitations of GAL described above while matching its high performance on all the performance metrics described in [13]. CQR gives much lower latency than GAL at loads where non-minimal routing is required. It adapts rapidly to changes in traffic, is unconditionally stable, and is simple to implement.
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globally adaptive load balanced routing on tori
IEEE Computer Architecture Letters, 2004Co-Authors: Arjun Singh, William J Dally, Brian Towles, Amit K GuptaAbstract:We introduce a new method of adaptive routing on k-ary n-cubes, Globally Adaptive Load-Balance (GAL). GAL makes global routing decisions using global information. In contrast, most previous adaptive routing algorithms make local routing decisions using local information (typically channel queue depth). GAL senses global congestion using segmented injection queues to decide the directions to route in each dimension. It further load balances the network by routing in the selected directions adaptively. Using global information, GAL achieves the performance (latency and throughput) of minimal adaptive routing on benign traffic patterns and performs as well as the best obliviously Load-Balanced routing algorithm (GOAL) on adversarial traffic.
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goal a load balanced adaptive routing algorithm for torus networks
International Symposium on Computer Architecture, 2003Co-Authors: Arjun Singh, William J Dally, Amit K Gupta, Brian TowlesAbstract:We introduce a Load-Balanced adaptive routing algorithm for torus networks, GOAL - Globally Oblivious Adaptive Locally - that provides high throughput on adversarial traffic patterns, matching or exceeding fully randomized routing and exceeding the worst-case performance of Chaos [2], RLB [14], and minimal routing [8] by more than 40%. GOAL also preserves locality to provide up to 4.6× the throughput of fully randomized routing [19] on local traffic. GOAL achieves global load balance by randomly choosing the direction to route in each dimension. Local load balance is then achieved by routing in the selected directions adaptively. We compare the throughput, latency, stability and hot-spot performance of GOAL to six previously published routing algorithms on six specific traffic patterns and 1,000 randomly generated permutations.
Seyed Naser Hashemi - One of the best experts on this subject based on the ideXlab platform.
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solving the load balanced clustering and routing problems in wsns with an fpt approximation algorithm and a grid structure
Pervasive and Mobile Computing, 2019Co-Authors: Ramin Yarinezhad, Seyed Naser HashemiAbstract:Abstract Clustering is an efficient technique in designing routing algorithms for Wireless Sensor Networks (WSNs), which prolongs the network lifetime and leads to scalability. However, in clustered WSNs, the cluster heads consume more energy than the other nodes and thus die sooner. Therefore, the load of sensor nodes must be balanced among the cluster heads in order to prolong the network lifetime. This problem is called Load-Balanced clustering problem, which is an NP-hard problem. In this paper, we propose an approximation algorithm to solve this problem with an approximation ratio of 1.1. This algorithm runs in fixed-parameter tractable time. We use a virtual grid infrastructure in the network, which makes the algorithm practical for large-scale WSNs. We also propose a routing algorithm based on this structure. The routing algorithm reduces and balances the energy consumption in the network by finding proper paths between each cluster head and the sink. The simulation results show that the proposed algorithm is practical for large-scale WSNs in addition to having a better performance compared with other similar algorithms.