A language for self-adaptive system requirements

Self-adaptive systems have the capability to autonomously modify their behaviour at run-time in response to changes in their environment. Such systems are now commonly built in domains as diverse as enterprise computing, automotive control systems, and environmental monitoring systems. To date, however, there has been limited attention paid to how to engineer requirements for such systems. As a result, selfadaptivity is often constructed in an ad-hoc manner. In this paper, we argue that a more rigorous treatment of requirements relating to self-adaptivity is needed and that, in particular, requirements languages for self-adaptive systems should include explicit constructs for specifying and dealing with the uncertainty inherent in self-adaptive systems. We present some initial thoughts on a new requirements language for selfadaptive systems and illustrate it using examples from the services domain. © 2008 IEEE.

A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty

Dynamically adaptive systems (DASs) are intended to monitor the execution environment and then dynamically adapt their behavior in response to changing environmental conditions. The uncertainty of the execution environment is a major motivation for dynamic adaptation; it is impossible to know at development time all of the possible combinations of environmental conditions that will be encountered. To date, the work performed in requirements engineering for a DAS includes requirements monitoring and reasoning about the correctness of adaptations, where the DAS requirements are assumed to exist. This paper introduces a goal-based modeling approach to develop the requirements for a DAS, while explicitly factoring uncertainty into the process and resulting requirements. We introduce a variation of threat modeling to identify sources of uncertainty and demonstrate how the RELAX specification language can be used to specify more flexible requirements within a goal model to handle the uncertainty. © 2009 Springer Berlin Heidelberg.

Adaptive message rate control of infrastructured DSRC vehicle networks for coexisting road safety and non-safety applications

Intelligent transport system (ITS) has large potentials on road safety applications as well as nonsafety applications. One of the big challenges for ITS is on the reliable and cost-effective vehicle communications due to the large quantity of vehicles, high mobility, and bursty traffic from the safety and non-safety applications. In this paper, we investigate the use of dedicated short-range communications (DSRC) for coexisting safety and non-safety applications over infrastructured vehicle networks. The main objective of this work is to improve the scalability of communications for vehicles networks, ensure QoS for safety applications, and leave as much as possible bandwidth for non-safety applications. A two-level adaptive control scheme is proposed to find appropriate message rate and control channel interval for safety applications. Simulation results demonstrated that this adaptive method outperforms the fixed control method under varying number of vehicles. © 2012 Wenyang Guan et al.

Full-field feed-forward equalizer with adaptive system optimization

We numerically investigate the combination of full-field detection and feed-forward equalizer (FFE) for adaptive chromatic dispersion compensation up to 2160 km in a 10 Gbit/s on-off keyed optical transmission system. The technique, with respect to earlier reports, incorporates several important implementation modules, including the algorithm for adaptive equalization of the gain imbalance between the two receiver chains, compensation of phase misalignment of the asymmetric Mach-Zehnder interferometer, and simplified implementation of field calculation. We also show that in addition to enabling fast adaptation and simplification of field calculation, full-field FFE exhibits enhanced tolerance to the sampling phase misalignment and reduced sampling rate when compared to the full-field implementation using a dispersive transmission line.

Facilitating intuitive-guided learning in a serious game through integration with a learning content management system

Increased global uptake of entertainment gaming has the potential to lead to high expectations of engagement and interactivity from users of technology-enhanced learning environments. Blended approaches to implementing game-based learning as part of distance or technology-enhanced education have led to demonstrations of the benefits they might bring, allowing learners to interact with immersive technologies as part of a broader, structured learning experience. In this article, we explore how the integration of a serious game can be extended to a learning content management system (LCMS) to support a blended and holistic approach, described as an 'intuitive-guided' method. Through a case study within the EU-Funded Adaptive Learning via Intuitive/Interactive, Collaborative and Emotional Systems (ALICE) project, a technical integration of a gaming engine with a proprietary LCMS is demonstrated, building upon earlier work and demonstrating how this approach might be realized. In particular, how this method can support an intuitive-guided approach to learning is considered, whereby the learner is given the potential to explore a non-linear environment whilst scaffolding and blending provide guidance ensuring targeted learning objectives are met. Through an evaluation of the developed prototype with 32 students aged 14-16 across two Italian schools, a varied response from learners is observed, coupled with a positive reception from tutors. The study demonstrates that challenges remain in providing high-fidelity content in a classroom environment, particularly as an increasing gap in technology availability between leisure and school times emerges.

Self-adaptive probabilistic roadmap generation for intelligent virtual agents

Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.