Reasoning over knowledge-based generation of situations in context spaces to reduce food waste

With the ever-growing amount of connected sensors (IoT), making sense of sensed data becomes even more important. Pervasive computing is a key enabler for sustainable solutions, prominent examples are smart energy systems and decision support systems. A key feature of pervasive systems is situation awareness which allows a system to thoroughly understand its environment. It is based on external interpretation of data and thus relies on expert knowledge. Due to the distinct nature of situations in different domains and applications, the development of situation aware applications remains a complex process. This thesis is concerned with a general framework for situation awareness which simplifies the development of applications. It is based on the Situation Theory Ontology to provide a foundation for situation modelling which allows knowledge reuse. Concepts of the Situation Theory are mapped to the Context Space Theory which is used for situation reasoning. Situation Spaces in the Context Space are automatically generated with the defined knowledge. For the acquisition of sensor data, the IoT standards O-MI/O-DF are integrated into the framework. These allow a peer-to-peer data exchange between data publisher and the proposed framework and thus a platform independent subscription to sensed data. The framework is then applied for a use case to reduce food waste. The use case validates the applicability of the framework and furthermore serves as a showcase for a pervasive system contributing to the sustainability goals. Leading institutions, e.g. the United Nations, stress the need for a more resource efficient society and acknowledge the capability of ICT systems. The use case scenario is based on a smart neighbourhood in which the system recommends the most efficient use of food items through situation awareness to reduce food waste at consumption stage.

Offshore Sourcing in Software Development: Case Studies of Finnish-Russian Cooperation

The study examines international cooperation in product development in software development organisations. The software industry is known for its global nature and knowledge-intensity, which makes it an interesting setting to examine international cooperation in. Software development processes are increasingly distributed worldwide, but for small or even medium-sized enterprises, typical for the software industry, such distribution of operations is often possible only in association with crossing the company’s boundaries. The strategic decision-making of companies is likely to be affected by the characteristics of the industry, and this includes decisions about cooperation or sourcing. The objective of this thesis is to provide a holistic view on factors affecting decisions about offshore sourcing in software development. Offshore sourcing refers to a cooperative mode of offshoring, where a firm does not establish its own presence in a foreign country, but utilises a local supplier. The study examines product development activities that are distributed across organisational and geographical boundaries. The objective can be divided into two subtopics: general reasons for international cooperation in product development and particular reasons for cooperation between Finnish and Russian companies. The focus is on the strategic rationale at the company level, in particular in small and medium-sized enterprises. The theoretical discourse of the study builds upon the literature on international cooperation and networking, with particular focus on cooperation with foreign suppliers and within product development activities. The resource-based view is also discussed, as heterogeneity and interdependency of the resources possessed by different firms are seen as factors motivating international cooperation. Strategically, sourcing can be used to access resources possessed by an industrial network, to enhance the product development of a firm, or to optimise its cost structure. In order to investigate the issues raised by the theoretical review, two empirical studies on international cooperation in software product development have been conducted. The emphasis of the empirical part of the study is on cooperation between Finnish and Russian companies. The data has been gathered through four case studies on Finnish software development organisations and four case studies on Russian offshore suppliers. Based on the material from the case studies, a framework clarifying and grouping the factors that influence offshore sourcing decisions has been built. The findings indicate that decisions regarding offshore sourcing in software development are far more complex than generally assumed. The framework provides a holistic view on factors affecting decisions about offshore sourcing in software development, capturing the multidimensionality of motives for entering offshore cooperation. Four groups of factors emerged from the data: A) strategy-related aspects, B) aspects related to resources and capabilities, C) organisation-related aspects, and D) aspects related to the entrepreneur or management. By developing a holistic framework of decision factors, the research offers in-depth theoreticalunderstanding of offshore sourcing rationale in product development. From the managerial point of view, the proposed framework sums up the issues that a firm should pay attention to when contemplating product development cooperation with foreign suppliers. Understanding different components of sourcing decisions can lead to improved preconditions for strategising and engaging in offshore cooperation. A thorough decisionmaking process should consider all the possible benefits and risks of product development cooperation carefully.

A Belief rule-based environmental responsibility assessment system for small and medium-sized enterprises

Various environmental management systems, standards and tools are being created to assist companies to become more environmental friendly. However, not all the enterprises have adopted environmental policies in the same scale and range. Additionally, there is no existing guide to help them determine their level of environmental responsibility and subsequently, provide support to enable them to move forward towards environmental responsibility excellence. This research proposes the use of a Belief Rule-Based approach to assess an enterprise’s level commitment to environmental issues. The Environmental Responsibility BRB assessment system has been developed for this research. Participating companies will have to complete a structured questionnaire. An automated analysis of their responses (using the Belief Rule-Based approach) will determine their environmental responsibility level. This is followed by a recommendation on how to progress to the next level. The recommended best practices will help promote understanding, increase awareness, and make the organization greener. BRB systems consist of two parts: Knowledge Base and Inference Engine. The knowledge base in this research is constructed after an in-depth literature review, critical analyses of existing environmental performance assessment models and primarily guided by the EU Draft Background Report on "Best Environmental Management Practice in the Telecommunications and ICT Services Sector". The reasoning algorithm of a selected Drools JBoss BRB inference engine is forward chaining, where an inference starts iteratively searching for a pattern-match of the input and if-then clause. However, the forward chaining mechanism is not equipped with uncertainty handling. Therefore, a decision is made to deploy an evidential reasoning and forward chaining with a hybrid knowledge representation inference scheme to accommodate imprecision, ambiguity and fuzzy types of uncertainties. It is believed that such a system generates well balanced, sensible and Green ICT readiness adapted results, to help enterprises focus on making improvements on more sustainable business operations.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.