Nicola Bicocchi

On Self-Adaptation, Self-Expression, and Self-Awareness in Autonomic Service Component Ensembles

Software systems operating in open-ended and unpredictable environments have to become autonomic, i.e., capable of dynamically adapting their behavior in response to changing situations. To this end, key research issues include: (i) framing the schemes that can facilitate components (or ensembles of) to exhibit self-adaptive behaviors, (ii) identifying mechanisms to enable components or ensembles to self-express the most suitable adaptation scheme, and (iii) acquiring the proper degree of self-awareness to enable putting in action self-adaptation and self-expression schemes. In this position paper, with the help of a representative case study, we frame and discuss the above issues, survey the state of the art in the area, and sketch the main research challenges that will be faced in the ASCENS project towards the definition of a fully-fledged framework for autonomic services.

SOTA: Towards a General Model for Self-Adaptive Systems

The increasing complexity and dynamics in which software systems are deployed call for solutions to make such systems autonomic, i.e., capable of dynamically self-adapting their behavior in response to changing situations. To this end, proper models and software engineering tools are required to be available to support the design and development of autonomic systems. In this paper, we introduce a new general model, SOTA, for modeling the adaptation requirements. SOTA, by bringing together the lessons of goal-oriented modeling and of context-aware system modeling, has the potentials for tackling some key issues in the design and development of complex self-adaptive software systems. In particular, SOTA enables: early verification of requirements, identification of knowledge requirements for self-adaptation, and identification of the most suitable self-adaptive patterns.

Detecting activities from body-worn accelerometers via instance-based algorithms

The automatic and unobtrusive identification of user activities is one of the most challenging goals of context-aware computing. This paper discusses and experimentally evaluates instance-based algorithms to infer user activities on the basis of data acquired from body-worn accelerometer sensors. We show that instance-based algorithms can classify simple and specific activities with high accuracy. In addition, due to their low requirements, we show how they can be implemented on severely resource-constrained devices. Finally, we propose mechanisms to take advantage of the temporal dimension of the signal, and to identify novel activities at run time.

Supporting location-aware services for mobile users with the whereabouts diary

Modern handheld devices provided with localization capabilities could be used to automatically create a diary of users whereabouts, and use it as a complement of the user profile in many applications. In this paper we present the Whereabouts diary, an application/service to log the places visited by the user and to label them, in an automatic way, with descriptive semantic information. In particular, Web-retrieved data and the temporal patterns in which places are visited can be used to define such meaningful semantic labels. In this paper, we describe the general idea at the basis of our service and discuss our implementation and the associated experimental results. In addition, we illustrate an application that can fruitfully exploit the whereabouts diary as a supporting service, and discuss areas for future work.

Investigating ride sharing opportunities through mobility data analysis

Smart phones and social networking tools allow to collect large-scale data about mobility habits of people. These data can support advanced forms of sharing, coordination and cooperation possibly able to reduce the overall demand for mobility. Our goal is to develop a recommender system-to be integrated in smart phones, tablets, and in-vehicle platforms-capable of identifying opportunities for sharing cars and rides. We present a methodology, based on the extraction of suitable information from mobility traces, to identify rides along the same trajectories that are amenable for ride sharing. We provide experimental results showing the impact of this technology and we illustrate a Web-based platform implementing the key concepts presented.

Self-organizing virtual macro sensors

The future large-scale deployment of pervasive sensor network infrastructures calls for mechanisms enabling the extraction of general-purpose data at limited energy costs. The approach presented in this article relies on a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence to spatial regions characterized by similar sensing patterns, and to let distributed aggregation of sensorial data take place on a per-region basis. The result of this process is that a sensor network can be modeled as a collection of virtual macro sensors, each associated to a well-characterized region of the physical environment. Within each region, each physical sensor has the local availability of aggregated data about its region and is able to act as an access point to such data. This feature promises to be very suitable for a number of emerging usage scenarios. Our approach is described and evaluated in both a simulation environment and a real test bed, and quantitatively compared with related works in the area. Current limitations and areas of future development are also discussed.

Autonomic communication learns from nature

Autonomic communication focuses on distributed systems and management of network resources at both the infrastructure level and the user level. It is distinct from autonomic computing, which is more oriented toward application software and management of computing resources, although both share the same goals. Autonomic communication research probes into fundamental rethinking of communication, networking, and distributed computing paradigms, to deal with the complexities and dynamics of modern networks. Many researchers, including the authors, are looking to self-organization in nature - in colonies of insects, for example - for lessons they can apply to self-organizing autonomic communication networks.

Self-Organized Data Ecologies for Pervasive Situation-Aware Services: The Knowledge Networks Approach

Pervasive computing services exploit information about the physical world both to adapt their own behavior in a context-aware way and to deliver to users enhanced means of interaction with their surrounding environment. The technology to acquire digital information about the physical world is becoming more available, making services at risk of being overwhelmed by such growing amounts of data. This calls for novel approaches to represent and automatically organize, aggregate, and prune such data before delivering them to services. In particular, individual data items should form a sort of self-organized ecology in which, by linking and combining with each other into sorts of “knowledge networks” (KNs), they are able to provide compact and easy-to-be-managed higher level knowledge about situations occurring in the environment. In this context, the contribution of this paper is twofold. First, with the help of a simple case study, we motivate the need to evolve from models of “context awareness” toward models of “situation awareness” via proper self-organized “KN” tools, and we introduce a general reference architecture for KNs. Second, we describe the design and implementation of a KN toolkit that we have developed, and we exemplify and evaluate algorithms for knowledge self-organization integrated within it. Open issues and future research directions are also discussed.

Bridging vision and commonsense for multimodal situation recognition in pervasive systems

Pervasive services may have to rely on multimodal classification to implement situation-recognition. However, the effectiveness of current multimodal classifiers is often not satisfactory. In this paper, we describe a novel approach to multimodal classification based on integrating a vision sensor with a commonsense knowledge base. Specifically, our approach is based on extracting the individual objects perceived by a camera and classifying them individually with non-parametric algorithms; then, using a commonsense knowledge base, classifying the overall scene with high effectiveness. Such classification results can then be fused together with other sensors, again on a commonsense basis, for both improving classification accuracy and dealing with missing labels. Experimental results are presented to assess, under different configurations, the effectiveness of our vision sensor and its integration with other kinds of sensors, proving that the approach is effective and able to correctly recognize a number of situations in open-ended environments.

Self-Organizing Spatial Regions for Sensor Network Infrastructures

This paper focuses on sensor networks as shared environmental infrastructures, and presents an approach to enable a sensor network to self-partition itself at pre-defined energy costs, into spatial regions of nodes characterized by similar patterns of sensed data. Such regions can then be used to aggregate data on a per-region basis and to enable multiple mobile users to extract information at limited and pre-defined costs.