Abstract:

Contemporary ICT infrastructures are trending towards a pervasive substrate of internet-connected sensors, actuators and human interfaces. Effective use of this pervasive infrastructure is key to solving 21st century challenges such as: mass transportation, energy conservation and environmental monitoring. Building effective applications that execute on this infrastructure requires advanced middleware support that respects the resource constraints of embedded devices. In this talk, I will present recent work from KU Leuven in the area of middleware support for sensing applications, with a focus on the Loosely-coupled Component Infrastructure (LooCI). I will then discuss emerging opportunities for ‘social sensing’ wherein online social networks are used to source and configure participatory sensor networks.

Bio:

Dr. Danny Hughes is an Assistant Professor with the iMinds-DistriNet group of the department of Computer Science at KU Leuven, Belgium. He received his PhD in Computer Science from Lancaster University in 2007. He has since worked as a Visiting Scholar at UC Berkeley (USA) and Universidade de São Paulo (Brazil) and as a lecturer at Xi’an Jiaotong-Liverpool University (China). His research interests are in distributed systems, with a focus on middleware support for dynamic systems such as wireless sensor networks, peer-to-peer networks and online social networks. You can find out more about his research at: https://distrinet.cs.kuleuven.be/people/showMember.do?memberID=u0061846

Abstract
Wireless sensor networks are attracting increasing interest but suffering from severe challenges such as power constraints and low data reliability. Sensors are often energy-hungry and cannot operate over a long period, and the data they collect are frequently erroneous due to complex causes. Thus a challenging research question is how to optimise energy consumptions on sensors while keeping the collected sensor data accurate. The current literature often treat these two problems separately, however, in this talk we will present an integrated self-organising solution for model-based data collection that can preserve sensors’ energy by reducing the amount of communications and as well as deal with sensor errors.

Bio

Lei Fang is a PhD student supervised by Prof. Simon Dobson and Dr Dharini Balasubramaniam. He received his first degree in Information and Computing Science from the University of Liverpool. His research interests reside in sensor data modelling, fault detection and inference.

Abstract:

Remote monitoring is considered an essential part of future eHealth systems to enable the delivery of healthcare outside clinical sites at reduced cost, while improving quality of patient care. We examine the use of online social networks for re- mote health monitoring. By exploiting the existing infrastructure, initial costs can be reduced and fast application development is possible. Facebook is used as an example platform: as a platform allowing user-defined applications, development is flexible and can be arranged quickly to suit different requirements of patients and health professionals. We analyse the general requirements of a remote monitoring scenario and the process of building and using a Facebook application to meet these requirements. Four different access viewpoints are implemented to suit the requirements of each user in our example scenario to form a carer network: the patient, the doctor in charge, professional carers, and family members of the patient. The suitability of the application is analysed including security and privacy issues. We conclude that online social media systems could offer a suitable platform for developing certain types of remote monitoring capability.

Bio:

Chonlatee Khorakhun is a second year PhD student, supervised by Prof. Saleem Bhatti. Before coming to St. Andrews, Chonlatee had completed an M.Sc. in Information and Communication Systems at University of Technology Hamburg-Harburg and worked in industry in Germany.

Abstract:

Centralised orchestration of service-oriented workflows presents significant scalability challenges, these include: the consumption of network bandwidth, degradation of performance, and single points of failure. These challenges are particularly prominent when dealing with highly distributed data-intensive workflows, which involve large quantities of intermediate data that need to be routed through a centralised engine. In this talk we present a dataflow specification language and a distributed architecture that attempt to address these scalability challenges. Our language provides simple abstractions for orchestrating large-scale web service workflows and separates between the workflow logic and its execution. It is based on a data-driven execution model that permits parallelism to improve the workflow performance. Unlike classical approaches of distributed computing, our architecture allows the computation to be moved “closer” to services in the workflow; this is achieved by partitioning the workflow specification into smaller fragments which may be sent to remote locations for execution.

Bio:

Ward is a research student supervised by Dr. Adam Barker and Prof. Alan Dearle. He completed an M.Sc. in Software Engineering at the University of St Andrews. His research interests span the areas of software engineering, distributed computing, and service-oriented architecture, with a focus on building practical solutions for improving the scalability and performance of software systems.