Siddeswara Mayura Guru

A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that PSO can achieve: a) as much as 3 times cost savings as compared to BRS, and b) good distribution of workload onto resources.

Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks

We describe the results of a performance evaluation of four extensions of Particle Swarm Optimisation (PSO) to reduce energy consumption in wireless sensor networks. Communication distances are an important factor to be reduced in sensor networks. By using clustering in a sensor network we can reduce the total communication distance, thus increasing the life of a network. We adopt a distance based clustering criterion for sensor network optimisation. From PSO perspective, we study the suitability of four different PSO algorithms for our application and propose modifications. An important modification proposed is to use a boundary checking routine for re-initialisation of a particle which moves outside the set boundary.

Energy efficient cluster formation in wireless sensor networks

Energy optimized cluster formation for a set of randomly scattered wireless sensors is presented. Sensors within a cluster are expected to be communicating with a cluster head only. The cluster heads summarize and process sensor data from the clusters and maintain the link with the base station. The clustering is driven by the minimization of energy for all the sensors. Recent developments in clustering are used to support the work, and a cluster visualization interface is used to observe the simulation results.

Optimized sink node path using particle swarm optimization

A wireless sensor network (WSN) is comprised of large number of sensors distributed in a monitoring field and a sink node to gather process and control data. The performance of the network depends on the behavior of the sink node and its location. An optimized sink node path will be efficient and economical for operation of the network. In this paper, we propose a novel method to derive the optimum path of a sink node in a fixed network of sensor nodes considering practical difficulties such as the limitation in the sink movement. The proposed evolutionary computing technique based simulator PSO-SIMSENS is an integrated system of particle swarm optimization and a sensor network simulator with an appropriate fitness function. This system can be configured for numerous applications such as manufacturing, bush fire monitoring etc. The simulation results show that our approach achieves efficient performance of WSN with maximum field coverage while sink node is mobile.

Hydrological Sensor Web for the South Esk Catchment in the Tasmanian state of Australia

The sensor Web is a distributed sensing system in which information is shared globally. The emergence of this technology will enable the integration of different sensing platforms with temporal and spatial variability. This has a potential to revolutionise hydrological monitoring and forecasting. Our project will establish a sensor Web test bed in the South Esk river catchment, which is located in the North East of Tasmania. The test bed will allow us to evaluate the emerging open geospatial consortium standards and specifications for sensor Web enablement (SWE) and provide a research platform for developing next-generation hydrological and water resource management tools. We intend to use short-term river flow forecasting as our use case for the SWE test bed.

An Extended Growing Self-Organizing Map for Selection of Clusters in Sensor Networks

Sensor networks consist of wireless enabled sensor nodes with limited energy. As sensors could be deployed in a large area, data transmitting and receiving are energy consuming operations. One of the methods to save energy is to reduce the communication distance of each node by grouping them in to clusters. Each cluster will have a cluster-head (CH), which will communicate with all the other nodes of that cluster and transmit the data to the remote base station. In this paper, we propose an extension to Growing Self-Organizing Map (GSOM) and describe the use of evolutionary computing technique to cluster wireless sensor nodes and to identify the cluster-heads. We compare the proposed method with clustering solutions based on Genetic Algorithm (GA), an extended version of Particle Swarm Optimisation (PSO) and four general purpose clustering algorithms. This could help to discover the clusters to reduce the communication energy used to transmit data when exact locations of all sensors are known and computational resources are centrally available. This method is useful in the applications where sensors are deployed in a controlled environment and are not moving. We have derived an energy minimisation model that is used as a criterion for clustering. The proposed method can also be used as a design tool to study and analyze the cluster formation for a given node placement.

Intelligent fastening with A‐BOLTTM technology and sensor networks

Threaded fasteners appear to be the best low cost option for applying a desired clamp load to assemble a joint, which can be disassembled, if necessary, at a low cost. A large number of joint failures are due to inadequate tension on the joint. Most conventional tightening methods only provide a vague indication of the bolt tension. In this paper we will discuss the development of the A‐BOLT™ system, which can measure the tension in the mechanical bolt to ±1 per cent accuracy of the proof load. The key element of the system is a specially designed mechanical bolt with a top‐mounted sensor to measure the bolt elongation. We will also describe the development of different generations of the A‐BOLT™ system and its industrial applications. Finally we will present an overview of research on the cluster based networking of wireless sensors to minimize energy consumption in a network of sensors.

Clustering sensor networks using growing self-organising map

Sensor networks consist of wireless enabled sensor nodes with limited energy. As sensors could be deployed in a large area, data transmitting and receiving are energy consuming operations. One of the methods to save energy is to reduce the transmission distance of each node by grouping nodes into clusters. Each cluster has a cluster-head (CH), which communicates with all the other nodes of that cluster and transmits the data to the remote base station. We describe the adaptation of a growing self-organising map (GSOM) to cluster the wireless sensor nodes and to identify the cluster-heads. We compare the results with a well-known clustering algorithm. We also describe the energy minimization criterion for clustering.

A Multi-Agent View of the Sensor Web

The rapid growth in sensor and ubiquitous device deployments has resulted in an explosion in the availability of data. The concept of the Sensor Web has provided a web-based information sharing platform to allow different organisations to share their sensor offerings. We compare the Open Geospatial Consortium - Sensor Web Enablement (OGC-SWE) with Multi-Agent System (MAS), and identify the similarities between the concepts. These similarities motivate the adoption of MAS based techniques to address related problems in OGC-SWE. Brokerage facilitators commonly used in MAS, the Yellow Pages Agent and Blackboard Agent, are considered to address service interaction issues identified within OGC-SWE. Furthermore, the use of MAS based reputation mechanisms are explored to address potential trust issues between service providers and consumers in OGC-SWE.

Performance evaluation of the impact of mobile base station on clustered wireless sensor networks

Base station mobility can be exploited to minimise the energy consumption in a wireless sensor network. This paper investigates the impact that base station movement has upon the performance of cluster-based wireless sensor networks. Three types of base station movement are considered: movement influenced by the position of cluster-heads, random movement and movement partially influenced by the position of cluster-heads. In spite of the overhead associated with finding the base station, the wireless sensor network was shown to gather at least 15% more data packets per Joule of energy with a mobile base station compared to a static base station.