Energy efficient path reconstruction in wireless sensor network using iPath

Wireless sensor networks operate through commonly self-organized sensor nodes to transfer data in a multi-hop approach to a central sink. In order to support fine-grained diagnostic analysis and optimize the performance level of the networks, the reconstruction of per-packet routing path is essential. However, in large-scale networks, the performance levels of the current path reconstruction method decline rapidly, with loss of links. An efficient approach to fully comprehend the complex internal behavior of network is through the reconstruction of the routing path of each received packet at the sink side. This paper discussed the added of energy efficiency parameter to enhance the inference Path (iPath). Thus, the iPath by added the energy efficiency enables the reconstruction of the per-packet routing paths of large-scale networks, by providing a stable and efficient route to exchange messages between source and destination in a timely manner. This work uses iterative boosting algorithm to find an alternative path with less distance and energy consumption. To achieve energy efficiency, it compresses the packet information by using GZIP tools in JAVA. Energy efficient iPath (E-iPath) is evaluated with several variations of nodes in WSN deployments as well as large-scale simulations. The findings demonstrate that E-iPath surpasses other current approaches such as EEPMM. E-iPath has accomplished low transmission overhead which it has reduced 13% of the energy consumption and has gained significant reconstruction ratio compared with iPath

A hybrid heuristic of variable neighbourhood descent and great deluge algorithm for efficient task scheduling in grid computing

To achieve the ultimate success of global collaborative resource sharing in Grid computing, an effective and efficient Grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. Scheduling tasks to resources in Grid computing is a challenging task and known as a NP hard problem. In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in Grid computing environment with an objective of minimising the makespan. Simulation experiments have been conducted to examine the impact of hybridising GD and VND. In addition, the performance of the proposed algorithm has been evaluated and compared with some other recent meta-heuristics in the literature. The experimental simulation results show that our proposed algorithm outperforms the other algorithms in the literature and the performance improvement achieved by this hybrid strategy is effective and efficient with respect to makespan and computational time as it can obtain good quality (makespan) of solutions while obviating the drawback of requiring high computational cost from the VND

Performance analysis of MPI approaches and PThread in multi-core system

Comparison among the HPC techniques has been made in order to address the highest and lowest performance of each in terms of execution time, speedup and efficiency when it is used with the HPc multicore system. The matrix multiplication in a variant size is used as a common complex task to examine the performance of each approach. FSKTM server has been used as an HPC multicore system to perform the approaches and tasks. Based on the results, it shows that Hybrid MPI/OpenMP approach is the best in terms of execution time, speed up and efficiency than other approaches when the matrix size is very high (>1024×1024 size). Furthermore, the results show that the compiler version has a significant impact over the performance of Pthread. With a new compiler, the performance becomes much better due to the improvement in code translation

Performance evaluation of load balancing algorithm for virtual machine in data centre in cloud computing

Cloud computing has become biggest buzz in the computer era these days.It runs entire operating systems on the cloud and doeverything on cloud to store data off-site.Cloud computing is primarily based on grid computing, but it’s a new computational model.Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services.The primary substance of cloud computing is to deal the computing power,storage,different sort of stages and services which assigned tothe external users on demand through the internet.Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing.In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example,when cloud clients try to access and send request tothe same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers.Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded.A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time.The proposed hybrid algorithm has been experimented by using CloudSim simulator.The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm

Cluster optimization in VANET using MFO algorithm and K-Means clustering

Wireless Technology is developing very fast. Researchers are actively researching in wireless communication as the technology for wireless communication has been growing quickly. Vehicular Ad Hoc Networks (VANETs), a cutting-edge technology in this area, have the potential to make a significant contribution to smart transportation systems in the future. VANETs offer a framework for communication that enhances traffic services and aids in lowering accident rates. Maintaining stability in Vehicular Ad-Hoc Network (VANET) clustering is difficult tasks due to high node mobility. First issue in VANET clustering is the Cluster Head (CH) selection since the CH has critical role in data routing and responsible for coordinating both inter and intra cluster communication. Second issue is the high mobility of nodes that cause difficulty to retain clustering optimization and will lead to inefficiency in network communication. Introduce MFO algorithm for simulate the movement behavior of moths and update the position based upon movements. Proven to be an effective and efficient method for solving optimization problem. To design K-Means algorithm that portion nodes based on their proximities by optimize the distance between nodes within same cluster by assigning them to the closet cluster center. Improving clustering efficiency by sending frequent updates to the CH in term of improving scalability, coverage, and clustering result, while reducing communication and energy consumption. Overall, the MFO Algorithm and K-Means algorithm can be used in combination to optimize the clustering in VANET, leading to better network performance, more reliable communication, and improved efficiency

Incorporating the range-based method into GridSim for modeling task and resource heterogeneity

Over the years, many heuristic algorithms have been proposed for solving various Grid scheduling problems. GridSim simulator has become a very popular simulation tool and has been widely used by Grid researchers to test and evaluate the performance of their proposed scheduling algorithms. As heterogeneity is one of the unique characteristics of Grid computing, which induces additional challenges in designing heuristic-based scheduling algorithms, the main concern when performing simulation experiments for evaluating the performance of scheduling algorithms is how to model and simulate different Grid scheduling scenarios or cases that capture the inherent nature of heterogeneity of Grid computing environment. However, most simulation studies that based on GridSim have not considered the nature of heterogeneity. In this paper, we propose a new simulation model that incorporates the range-based method into GridSim for modeling and simulating heterogeneous tasks and resources in order to capture the inherent heterogeneity of Grid environments that later can be used by other researchers to test their algorithms

Effective black hole attacks in MANETs

Black hole or packet drop attack is a denial of service attack on routing protocols in which malicious nodes fabricate routing information, attract packets routed through them and then deliberately drop these packets. Most of the black hole attack simulations are performed by constantly fabricating routing information and thus consistently attracting packets to them, which can be easily detected by the intrusion detection system. In this study, a complicated and difficult to detect black hole attack is proposed. The malicious nodes only perform packet drop when they are in the advantageous positions or locations in the networks. This study investigates the impact of the proposed black hole attack performed by random as well as critical nodes, to the network performance. Critical nodes are nodes that reside along the most active traffic paths and results show that the attacks performed by these nodes cause significant damage to the networks or substantial reduction in packet delivery ratio in comparison to that of random nodes

An estimation-based dynamic load balancing algorithm for efficient load distribution and balancing in heterogeneous Grid computing environment

To realise the true potential of Grid computing, resource management is playing a crucial role. Nevertheless, due to the nature of dynamism and heterogeneity in Grid computing, Grid resource management with the capability of effective and efficient load distribution and balancing remains a challenge. In this study, a dynamic load balancing algorithm is proposed for efficient load distribution and balancing in heterogeneous Grid computing environment. Extensive simulation experiments are carried out to evaluate the effectiveness of the proposed algorithm using the most popular simulator namely GridSim. The comparative results of simulation experiments show that the proposed load balancing approach gives superior performance and outperforms contemporary load balancing approaches in the literature. The findings reveal that the proposed load balancing approach is able to effectively utilise the resources while ensuring a relatively low degree of imbalance of load when dealing with different levels of heterogeneity in a Grid computing environment