Thesis Presentations Group 2

Title: A Framework and a Web Application for self-assessment of Sustainable Green ICT practices in SMEs
Author: Khan, Farniba
Supervisor(s): Professor Jari Porras
Hosting Institution: Lappeenranta University of Technology
Abstract: Green ICT has received significant attention in organizations to reduce the adverse effect of global warming since the last decade. Several maturity models have been proposed for tracking Green ICT practices in organizations. However, due to the lack of Green ICT knowledge, limited financial resources associated with the adaptation of those models, organizations specially SMEs often tend to ignore them. On the other hand, the reason behind the increasing interest for Green ICT practices in organizations not only depends on the desire to attain environment-friendly atmosphere, but also facilitating and sustaining business goal such as cost reduction, competitive advantages and stakeholders’ pressure. Current literature defines that SMEs around the world try to follow some common strategies such as virtualization and consolidation of devices, energy efficiency and disposal of ICT equipment for greening ICT and sustaining their businesses. Therefore, this research proposes a framework which provides combination of existing Green ICT and sustainable ICT maturity models by mapping with the strategies that businesses are already following. Out of this framework, a web application has been developed that provides questionnaire to identify the present situation of Green ICT practices of SMEs and guidelines for improvement. The application has been tested in the SMEs of both Finland and Bangladesh. The results have been analyzed in the context of Green ICT practices of SMEs of developed and developing countries. Finally, a survey has been conducted to analyze SMEs’ perception about the sustainability aspects of businesses of this application.
Paper: Insert link to thesis paper.

Title: A Belief Rule-Based Flood Risk Assessment Expert System using Real-Time Sensor Data Streaming
Author: Monrat, Ahmed Afif
Supervisor(s): Professor Mohammad Shahadat Hossain; Professor Karl Andersson
Hosting Institution: Luleå University of Technology, University of Chittagong
Abstract: Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. Integrated BRBES produces reliable results comparing from the other data-driven approaches. Data for the expert system has been collected targeting different case study areas from Bangladesh to validate the integrated system.
Paper: Insert link to thesis paper.

Title: Performance Evaluation of IoT Platform in Green ICT Applications
Author: Qureshi, Daniyal Akhtar
Supervisor(s): Prof. Arkady Zaslavsky; Dr. Karan Mitra; Dr. Saguna Saguna
Hosting Institution: Commonwealth Scientific and Industrial Research Organisation; Lulea University of Technology
Abstract: With the advent of Internet of Things (IoT), its deployments and applications has grown in an exponential rate during the past few years. This growth has led scientists, stakeholders, and industrial companies to the prediction that about 50 billion of things (IoT) will be installed by 2020 in diverse applications such as transport, healthcare, utility, education and home automation. Large data streams generated by sensors; be it data acquisition, storage, or processing, this expansion in physical deployments and connecting things drive the development of cloud-based middleware for management. To date, hundreds of IoT platforms fluxing the market (both open-source and commercial) with various complexities, pricing and services. This paper, we will develop a methodology based on TPC benchmarking to evaluate the performance of cloud-based framework or otherwise known as IoT Platform. The main objective of this research is to provide insight into key parameters in each layer of the platform affecting the overall performance of the framework. Air quality monitoring using OpenIoT (an open source IoT Platform) as a use-case will be used for the deployment of methodology.
Paper: Insert link to thesis paper.

Title: Architecting and Designing Sustainable Smart City Services in Living Lab Environment
Author: Alam, Md Tawseef
Supervisor(s): Professor Jari Porras; Professor Ahmed Seffah
Hosting Institution: Lappeenranta University of Technology
Abstract: Insert thesis abstract(200 words)
Paper: Insert link to thesis paper.

Title: Energy - Aware Cloud Infrastructure for IoT Big Data Processing
Author: Ganesan, Madhubala
Supervisor(s): Prof. Colin Pattinson; Dr. Ah-Lian Kor
Hosting Institution: Leeds Beckett University
Abstract: Internet of Things (IoT) along with Big Data Analytics is poised to become the backbone of Smart and Sustainable Systems which bolster economic, environmental and social sustainability. Cloud-based data centers provide computing power to churn out valuable information from voluminous IoT data. Multifarious servers in the data centers turn out to be the black hole of superfluous energy burn contributing to 23% of the global Carbon dioxide (CO2) emissions in ICT industry. IoT energy concerns are addressed by researches carried out on low-power sensors and improved Machine-to-Machine communications. However, cloud-based data centers still face energy–related challenges. Virtual Machine (VM) consolidation is an approach towards energy efficient cloud infrastructure. Although several works show convincing results of the potential of VM consolidation in simulated environments, there is inadequacy in terms of investigations on real, physical cloud infrastructure for big data workloads. This work intends to evaluate dynamic VM consolidation approaches by combining algorithms from literature. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as Big data platform. This work studies the performance based on Service Level Agreement (SLA) metrics and energy usage of compute hosts. The corresponding results are presented based on which the best combination of algorithms is recommended.
Paper: Insert link to thesis paper.