Deriving Information Requirements from Responsibility Models

This paper describes research in understanding the requirements for complex information systems that are constructed from one or more generic COTS systems. We argue that, in these cases, behavioural requirements are largely defined by the underlying system and that the goal of the requirements engineering process is to understand the information requirements of system stakeholders. We discuss this notion of information requirements and propose that an understanding of how a socio-technical system is structured in terms of responsibilities is an effective way of discovering this type of requirement. We introduce the idea of responsibility modelling and show, using an example drawn from the domain of emergency planning, how a responsibility model can be used to derive information requirements for a system that coordinates the multiple agencies dealing with an emergency

Privacy, security, and trust issues in smart environments

Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning

Specifying and analysing reputation systems with coordination languages

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools. First, we define a parametric Klaim specification of a reputation system that can be instantiated with different reputation models. Then, we consider stochastic specification obtained by considering actions with random (exponentially distributed) duration. The resulting specification enables quantitative analysis of properties of the considered system. Feasibility and effectiveness of our proposal is demonstrated by reporting on the analysis of two reputation models

Network-aware Evaluation Environment for Reputation Systems

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run. We present a software tool (NEVER) for network-aware evaluation of reputation systems and their rapid prototyping through experiments performed according to user-specified parameters. To demonstrate effectiveness of NEVER, we analyse reputation models based on the beta distribution and the maximum likelihood estimation

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

A socio-cognitive and computational model for decision making and user modelling in social phishing

Systems software quality, and system security in particular, is often compromised by phishing attacks. The latter were relatively easy to detect through phishing content filters, in the past. However, it has been increasingly difficult to stop more recent and sophisticated social phishing attacks. To protect the citizens from new types of phishing attacks, software quality engineers need to provide equally sophisticating preventive technology that models people’s reactions. The authors considered the behaviour of people on the Internet from a socio-cognitive perspective and deduced who could be more prone to be spoofed by social phishing techniques. The authors herein propose a computational and interdisciplinary metamodelling methodology, which can assist in capturing and understanding people’s interactive behaviour when they are online. Online behaviour can reveal Internet users’ knowledge, information, and beliefs in a given social context; these could also constitute significant factors for trust in social phishing circumstances which, in turn, can provide valuable insights and decision making meta-knowledge for recognition of potential victims of phishers. The proposed modelling approach is illustrated and explained using real-life phishing cases. This meta-model can i) help social computing and phishing researchers to understand users’ trust decisions from a socio-cognitive perspective, and ii) open ways to integrate artificial intelligence design techniques within software quality management practices in order to protect citizens from being spoofed by social phishing attacks. Thus, this software design quality approach will increase system security as a proactive maintenance strategy