SQPR: Stream Query Planning with Reuse

When users submit new queries to a distributed stream processing system (DSPS), a query planner must allocate physical resources, such as CPU cores, memory and network bandwidth, from a set of hosts to queries. Allocation decisions must provide the correct mix of resources required by queries, while achieving an efficient overall allocation to scale in the number of admitted queries. By exploiting overlap between queries and reusing partial results, a query planner can conserve resources but has to carry out more complex planning decisions. In this paper, we describe SQPR, a query planner that targets DSPSs in data centre environments with heterogeneous resources. SQPR models query admission, allocation and reuse as a single constrained optimisation problem and solves an approximate version to achieve scalability. It prevents individual resources from becoming bottlenecks by re-planning past allocation decisions and supports different allocation objectives. As our experimental evaluation in comparison with a state-of-the-art planner shows SQPR makes efficient resource allocation decisions, even with a high utilisation of resources, with acceptable overheads

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

A Model of Emotion as Patterned Metacontrol

Adaptive agents use feedback as a key strategy to cope with un- certainty and change in their environments. The information fed back from the sensorimotor loop into the control subsystem can be used to change four different elements of the controller: parameters associated to the control model, the control model itself, the functional organization of the agent and the functional realization of the agent. There are many change alternatives and hence the complexity of the agent’s space of potential configurations is daunting. The only viable alternative for space- and time-constrained agents —in practical, economical, evolutionary terms— is to achieve a reduction of the dimensionality of this configuration space. Emotions play a critical role in this reduction. The reduction is achieved by func- tionalization, interface minimization and by patterning, i.e. by selection among a predefined set of organizational configurations. This analysis lets us state how autonomy emerges from the integration of cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. Emotion-based morphofunctional systems are able to exhibit complex adaptation patterns at a reduced cognitive cost. In this article we show a general model of how emotion supports functional adaptation and how the emotional biological systems operate following this theoretical model. We will also show how this model is also of applicability to the construction of a wide spectrum of artificial systems1

The Software Continuum Concept: Towards a Biologically Inspired Model for Robust E-Business Software Automation

This paper introduces a new concept, the software continuum concept based on the observation that exists a general parallelism between the software continuum from bits to business/Internet ecosystems and the natural continuum from particles to ecosystems. The general parallelism suggests that homeomorphisms may be identified and therefore some concepts, processes, and/or mechanisms in one continuum can be investigated for application in the other continuum. We argue that the homeomorphisms give rise to a biologically-inspired architectural framework for addressing robust control, robust intelligence, and robust autonomy issues in e-business software and other business-IT integration challenges. As application, we examine the mapping of a major enterprise-level architecture framework to the biologically-inspired framework. Design considerations for robust intelligence and autonomy in large-scale software automation and some major systemic features for flexible business-IT integration are also discussed

A survey of secure middleware for the Internet of Things

The rapid growth of small Internet connected devices, known as the Internet of Things (IoT), is creating a new set of challenges to create secure, private infrastructures. This paper reviews the current literature on the challenges and approaches to security and privacy in the Internet of Things, with a strong focus on how these aspects are handled in IoT middleware. We focus on IoT middleware because many systems are built from existing middleware and these inherit the underlying security properties of the middleware framework. The paper is composed of three main sections. Firstly, we propose a matrix of security and privacy threats for IoT. This matrix is used as the basis of a widespread literature review aimed at identifying requirements on IoT platforms and middleware. Secondly, we present a structured literature review of the available middleware and how security is handled in these middleware approaches. We utilise the requirements from the first phase to evaluate. Finally, we draw a set of conclusions and identify further work in this area

A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE

L’Ambient Intelligence (AmI) è caratterizzata dall’uso di sistemi pervasivi per monitorare l’ambiente e modificarlo secondo le esigenze degli utenti e rispettando vincoli definiti globalmente. Questi sistemi non possono prescindere da requisiti come la scalabilità e la trasparenza per l’utente. Una tecnologia che consente di raggiungere questi obiettivi è rappresentata dalle reti di sensori wireless (WSN), caratterizzate da bassi costi e bassa intrusività. Tuttavia, sebbene in grado di effettuare elaborazioni a bordo dei singoli nodi, le WSN non hanno da sole le capacità di elaborazione necessarie a supportare un sistema intelligente; d’altra parte senza questa attività di pre-elaborazione la mole di dati sensoriali può facilmente sopraffare un sistema centralizzato con un’eccessiva quantità di dettagli superflui. Questo lavoro presenta un’architettura cognitiva in grado di percepire e controllare l’ambiente di cui fa parte, basata su un nuovo approccio per l’estrazione di conoscenza a partire dai dati grezzi, attraverso livelli crescenti di astrazione. Le WSN sono utilizzate come strumento sensoriale pervasivo, le cui capacità computazionali vengono utilizzate per pre-elaborare i dati rilevati, in modo da consentire ad un sistema centralizzato intelligente di effettuare ragionamenti di alto livello. L’architettura proposta è stata utilizzata per sviluppare un testbed dotato degli strumenti hardware e software necessari allo sviluppo e alla gestione di applicazioni di AmI basate su WSN, il cui obiettivo principale sia il risparmio energetico. Per fare in modo che le applicazioni di AmI siano in grado di comunicare con il mondo esterno in maniera affidabile, per richiedere servizi ad agenti esterni, l’architettura è stata arricchita con un protocollo di gestione distribuita della reputazione. È stata inoltre sviluppata un’applicazione di esempio che sfrutta le caratteristiche del testbed, con l’obiettivo di controllare la temperatura in un ambiente lavorativo. Quest’applicazione rileva la presenza dell’utente attraverso un modulo per la fusione di dati multi-sensoriali basato su reti bayesiane, e sfrutta questa informazione in un controllore fuzzy multi-obiettivo che controlla gli attuatori sulla base delle preferenze dell’utente e del risparmio energetico.Ambient Intelligence (AmI) systems are characterized by the use of pervasive equipments for monitoring and modifying the environment according to users’ needs, and to globally defined constraints. Furthermore, such systems cannot ignore requirements about ubiquity, scalability, and transparency to the user. An enabling technology capable of accomplishing these goals is represented by Wireless Sensor Networks (WSNs), characterized by low-costs and unintrusiveness. However, although provided of in-network processing capabilities, WSNs do not exhibit processing features able to support comprehensive intelligent systems; on the other hand, without this pre-processing activities the wealth of sensory data may easily overwhelm a centralized AmI system, clogging it with superfluous details. This work proposes a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part, based on a new approach to knowledge extraction from raw data, that addresses this issue at different abstraction levels. WSNs are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts in order to carry on symbolic reasoning. The aim of the reasoning is to plan a sequence of actions that will lead the environment to a state as close as possible to the users’ desires, taking into account both implicit and explicit feedbacks from the users, while considering global system-driven goals, such as energy saving. The proposed conceptual architecture was exploited to develop a testbed providing the hardware and software tools for the development and management of AmI applications based on WSNs, whose main goal is energy saving for global sustainability. In order to make the AmI system able to communicate with the external world in a reliable way, when some services are required to external agents, the architecture was enriched with a distributed reputation management protocol. A sample application exploiting the testbed features was implemented for addressing temperature control in a work environment. Knowledge about the user’s presence is obtained through a multi-sensor data fusion module based on Bayesian networks, and this information is exploited by a multi-objective fuzzy controller that operates on actuators taking into account users’ preference and energy consumption constraints

Many-core and heterogeneous architectures: programming models and compilation toolchains

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInopartially_openembargoed_20211002Barchi, Francesc