Low Power Wake-up Signaling for Dense Sensor Networks

In wireless sensor networks, nodes are required to reduce their idle listening time in order to minimize energy consumption and increase their lifetime. The idle listening time is decreased by making the nodes remain in sleep mode and wake up only when they are required to sense or transmit information about an event. This strategy requires the source node to send a wake-up signal before it transmits data to the network. This wake-up signal requires energy and the objective of this work is to lower this signal\u27s energy consumption.;In order to achieve this, this work uses Near Field Magnetic Induction Communication technology (NFMIC) that can communicate wirelessly over short distances using low power. Using this technology, the wake-up signal will be magnetic field lines rather than an RF signal. When a source node wants to wake up its neighbors, it generates magnetic field lines disseminating as bubbles. These lines resonate across the receiver coils of neighboring nodes, thus interrupting the sleeping nodes and waking them. This idea was implemented on a node using three different NFMIC systems. Each system had different combinations of TX and RX coils and communicated using different transmitted power. The most efficient setup was determined based on the wakeup energy efficiency. Finally, this work presents the effect of obstacles on NFMIC systems by testing them on one of our NFMIC systems. As a result of this test, the various obstacles were classified into three categories based on their attenuation effect

AutoTaSC : Model driven development for autonomic software engineering

Whilst much research progress has been achieved towards the development of autonomic software engineering tools and techniques including: policy-based management, modelbased development, service-oriented architecture and model driven architecture. They have often focused on and started from chosen object-oriented models of required software behaviour, rather than domain model including user intentions and/or software goals. Such an approach is often reported to lead to "misalignment" between business process layer and their associated computational enabling systems. This is specifically noticeable in adaptive and evolving business systems and/or processes settings. To address this long-standing problem research has over the years investigated many avenues to close the gap between business process modelling and the generation of enactment (computation) layer, which is responsive to business changes. Within this problem domain, this research sets out to study the extension of the Model Driven Development (MOD) paradigm to business/domain model, that is, how to raise the abstraction level of model-driven software development to the domain level and provide model synchronisation to trace and analyse the impact of a given model change. The main contribution of this research is the development of a MOD-based design method for autonomic systems referred to as AutoTaSC. The latter consists of a series of related models, where each of which represents the system under development at a given stage. The first and highest level model represents the abstract model referred to as the Platform Independent Model (PIM). The next model encapsulates the PIM model for the autonomic system where the autonomic capabilities and required components (such as monitor, sensor, actuator, analyser, policy, etc.) are added via some appropriate transformation rules. Targeting a specific technology involves adding, also via transformation rules, specific information related to that platform from which the Platform Specific Model (PSM) for the autonomic system is extracted. In the last stage, code can be generated for the specific platform or technology targeted in the previous stage, web services for instance. In addition, the AutoTaSC method provides a situated model synchronisation mechanism, which is designed following the autonomic systems principles. For instance, to guarantee model synchronisation each model from each AutoTaSC stage has an associated policy-based feedback control loop, which regulates its reaction to detected model change. Thus, AutaTase method model transformation approach to drive model query, view and synchronisation. The Auto'Iast? method was evaluated using a number of benchmark case-studies to test this research hypothesis including the effectiveness and generality of AutaTaSe design method

AutoTaSC : Model driven development for autonomic software engineering

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WOF: Towards Behavior Analysis and Representation of Emotions in Adaptive Systems

International audienceWith the increasing use of new technologies such as Communicating Objects (COT) and the Internet of Things (IoT) in our daily life (connected objects, mobile devices, etc.), designing Intelligent Adaptive Distributed software Systems (DIASs) has become an important research issue. Human face the problem of mastering the complexity and sophistication of such systems as those require an important cognitive load for end-users who usually are not expert. Starting from the principle that it is to technology-based systems to adapt to end-users and not the reverse, we address the issue of how to help developers design and produce such systems. We then propose WOF, an object oriented Framework founded on the concept of Wise Object (WO), a metaphor to refer to human introspection and learning capabilities.To make systems able to learn by themselves, we designed introspection, monitoring and analysis software mechanisms such that WOs can learn and construct their own knowledge. We then define a WO as a software-based entity able to learn by itself on itself (i.e. on services it is intended to provide) and also on the others (i.e. the way others use its services). A WO is seen as an avatar of either a physical or a logical object (e.g. device/software component).In this paper, we introduce the main requirements for DIASs as well as the design principles of WOF. We detail the WOF conceptual architecture and the Java implementation we built for it. To provide application developers with relevant support, we designed WOF with the minimum intrusion in the application source code. Adaptation and distribution related mechanisms defined in WOF can be inherited by application classes. In our Java implementation of WOF, object classes produced by a developer inherit the behavior of Wise Object (WO) class. An instantiated system is a Wise Object System (WOS) composed of WOs that interact through an event bus. In the first version of WOF, a WO was able to use introspection and monitoring built-in mechanisms to construct knowledge on: (a) services it is intended to render; (b) the usage done of its services. In the current version, we integrated an event-based WO simulator and a set of Analyzer classes to provide a WO with the possibility to use different analysis models and methods on its data. Our major goal is that a WO can be able to identify common usage of its services and to detect unusual usage. We use the metaphor of emotions to refer to unusual behavior (stress, surprise, etc.). We show in the paper a first experiment based on a statistical analysis method founded on stationary processes to identify usual/unusual behavior