http://hdl.handle.net/10453/222 2013-05-23T19:47:24Z 2013-05-23T19:47:24Z Lingarchani Amit http://hdl.handle.net/10453/17759 2012-10-12T03:31:40Z 2011-01-01T00:00:00Z

Environmental Challenges in Mobile Services Lingarchani Amit B. Unhelkar Pervasive mobile services are part of almost all business processes. These services are provided irrespective of location, time and place using devices such as mobile phones, smartphones and laptops. This boost in mobile services has also resulted in numerous environmental challenges ranging from design and manufacturing of the mobile device through to mobile service providers and corresponding network infrastructure. This chapter outlines the use of mobile services to increase customer base. In addition, it also provides a better view on opting mobile wireless services over wired services. Environmental challenges around the use of mobile services are described as part of the chapter. Finally some suggestions to reduce carbon emissions and to be energy efficient are provided. In short the chapter goes in line with sentence 'Going green is no longer optional from business vantage point'

2011-01-01T00:00:00Z Lowe David Murray Stephen Kong Xiaoying http://hdl.handle.net/10453/17760 2012-10-12T03:31:40Z 2011-01-01T00:00:00Z

Wireless Sensor Resource Usage Optimisation Using Embedded State Predictors Lowe David; Murray Stephen; Kong Xiaoying Mohammad S. Obaidat and Joaquim Filipe The increasing prevalence and sophistication of wireless sensors is creating an opportunity for improving, or in many cases enabling, the real-time monitoring and control of distributed physical systems. However, whilst a major issue in the use of these sensors is their resource utilisation, there has only been limited consideration given to the interplay between the data sampling requirements of the control and monitoring systems and the design characteristics of the wireless sensors. In this paper we describe an approach to the optimization of the resources utilized by these devices based on the use of synchronized state predictors. By embedding state predictors into the sensors themselves it becomes possible for the sensors to predict their optimal sampling rate consistent with maintaining monitoring or control performance, and hence minimize the utilization of limited sensor resources such as power and bandwidth.

2011-01-01T00:00:00Z Chaczko Zenon http://hdl.handle.net/10453/17762 2012-10-12T03:31:40Z 2012-01-01T00:00:00Z

WSN Clustering Using IC-SVD Algorithms Chaczko Zenon Moreno DÃ az, Roberto; Pichler, Franz; Quesada Arencibia, Alexis This chapter presents a new biomimetic approach for sensor placement, clustering and data routing in Wireless Sensor Networks that can be deployed and managed in ubiquitous applications such as: security, business, automation, home and healthcare, precision agriculture, ecosystem monitoring and many more. Since hierarchical clustering can reduce the resource usage in sensor networks, we investigate ImmunoComputing and SVD-based algorithms for sensor clustering, routing and management of sensornet resources. The simulation results show that the proposed approach can improve robustness and extend the life-span of network infrastructures.

2012-01-01T00:00:00Z Braun Robin Chaczko Zenon http://hdl.handle.net/10453/17761 2012-10-12T03:31:40Z 2012-01-01T00:00:00Z

Multi-dimensional Information Space View of Wireless Sensor Networks with Optimization applications Braun Robin; Chaczko Zenon Moreno DÃ az, Roberto; Pichler, Franz; Quesada Arencibia, Alexis This paper presents an optimization example using a new paradigm for viewing the work of Wireless Sensor Networks. In our earlier paper the Observed Field (OF) is described as a multi-dimensional Information Space (ISp). The Wireless Sensor Network is described as a Transformation Space (TS), while the information collector is a single point consumer of information, described as an Information Sink (ISi). Formal mathematical descriptions were suggested for the OF and the ISp. We showed how the TS can be formally thought of as a multi-dimensional transform function between ISp and ISi. It can be aggregated into a notional multi-dimensional value between {0, 1}. In this paper, this formal mathematical description is used to create a genetic algorithm based optimization strategy for creating routes through the TS, using a cost function based on mutual information. The example uses a connectivity array, a mutual information array and the PBIL algorithm.

2012-01-01T00:00:00Z