Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing “ do-operations .” The obtained CBN could then be employed for causal inference. The main challenges of this approach included “non-doable variables” and limited scalability. To address these issues, we propose three extensions: (i) early pruning weakly correlated relations to reduce the number of required do-operations, (ii) introducing aggregate variables that summarize relations between weakly coupled sub-systems, and (iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. We illustrate and evaluate the efficiency of these contributions via examples from the smart home and power grid domain. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way toward applications in large CPS.

The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems . This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.

Due to the increasing number of car-centered connected services, making efficient use of limited radio resources is critical in vehicular communications. Hybrid vehicular networks dispose of multiple Radio Access Technologies (RATs) like cellular and vehicle-to-vehicle (V2V) networks, with complementary characteristics that allow for developing smarter network traffic distribution methods. This paper proposes a self-adaptive clustering system for ensuring a suitable trade-off between data aggregation (over the cellular network) and communication congestion due to cluster management (within the V2V network). The system's algorithms use a distributive justice approach for selecting cluster heads, to improve fairness among car drivers and hence help the social acceptability of self-adaptive clustering. Simulation results show that this approach significantly improves fairness over time without affecting network performance. This solution can thus optimize the usage of radio resources, reducing cellular access costs, without the need for uniformization among different mobile operators' access plans.

The international conference IEEE SASO (Self-Adapting and Self-Organizing Systems) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for sharing ideas on algorithms, technologies, tools, and applications across a wide range of scientific fields. In 2014, the conference was hosted by Imperial College in London, United Kingdom; its scientific program comprised full papers, short papers, poster presentations, demo sessions, workshops, and tutorials. This special issue of ACM TAAS champions some of the most solid research results of SASO 2014, presenting selected, revised, and extended best articles.

Designing the control infrastructure of future “smart” power grids is a challenging task. Future grids will integrate a wide variety of heterogeneous producers and consumers that are unpredictable and operate at various scales. Information and Communication Technology (ICT) solutions will have to control these in order to attain global objectives at the macrolevel, while also considering private interests at the microlevel. This article proposes a generic holonic architecture to help the development of ICT control systems that meet these requirements. We show how this architecture can integrate heterogeneous control designs, including state-of-the-art smart grid solutions. To illustrate the applicability and utility of this generic architecture, we exemplify its use via a concrete proof-of-concept implementation for a holonic controller, which integrates two types of control solutions and manages a multiscale, multiobjective grid simulator in several scenarios. We believe that the proposed contribution is essential for helping to understand, to reason about, and to develop the “smart” side of future power grids .

This article introduces the design of the AutoHome service-oriented framework to simplify the development and runtime adaptive support of autonomic pervasive applications. To this end, we describe our novel open infrastructure for building and executing home applications. This includes the amalgamation of the two computing areas of autonomics and service orientation, to produce a component-based platform providing facilities including monitoring, touchpoints, and other common autonomic services. This infrastructure uniquely blends the advantages of distributed autonomic control with global conflict management in a management hierarchy. We discuss this platform in terms of pervasive home systems and show how one would develop such a system for two examples of automated home applications: intruder detection and medical support, respectively. Both applications were built within our framework and evaluated showing that the use of the framework introduces minimal overheads but provides many benefits. We then conclude by highlighting the contributions of AutoHome and a discussion about the lessons learned, limitations, and future research directions.

The recent proliferation of ever smaller and smarter electronic devices, combined with the introduction of wireless communication and mobile software technologies enables the construction of a large variety of pervasive applications, such as home supervision and alarm systems. The inherent complexity of such applications along with their nonexpert clientele raises the necessity for autonomic management solutions. Nonetheless, such solutions remain difficult to conceive, as they must deal with the increased volatility, heterogeneity and distribution of the pervasive domain, while ensuring stringent performance and dependability requirements. This paper proposes that reusable support for autonomic management solutions be provided by middleware platforms, along with already existing middleware services, such as security and transactions. Following this approach, a service oriented component platform, iPOJO, was extended with elementary autonomic management capabilities. These include monitoring and effector touch points, as well as embedded autonomic management functions, such as service dependency management. IPOJO is an open source Apache project and has been successfully employed to implement several research projects in the pervasive domain. This paper presents iPOJOpsilas extension with reusable autonomic management middleware services.

Component technologies are increasingly being used for building enterprise systems, as they can address complex functionality and flexibility problems and reduce development and maintenance costs. Nonetheless, current component technologies provide little support for predicting and controlling the emerging performance of software systems that are assembled from distinct components.This paper presents a framework for automating the performance management of complex, component-based systems. The adopted approach is based on the alternate usage of multiple component variants with equivalent functional characteristics, each one optimized for a different running environment. A fully-automated framework prototype for J2EE is presented, along with results from managing a sample enterprise application on JBoss. A mechanism that uses monitoring data to learn and automatically improve the framework's management behaviour is proposed. The framework imposes no extra requirements on component providers, or on the component technologies.