Self-optimisation using runtime code generation for wireless sensor networks

International audienceThis paper addresses the use of runtime code specialisation in resource-constrained embedded systems such as nodes of a Wireless Sensor Network (WSN), in order to improve software efficiency, hence the lifetime of WSN nodes. In our approach, runtime code specialisation is achieved with in-place runtime code generation. We present a self-optimising system using runtime code generation. Our system is able to automatically make the decision to generate specialised code and use it each time an improvement is observed in application performance. In the Internet of Things (IoT), devices usually have limited precision; our system adapts to theses devices decreasing precision in order to increase performance. We evaluate our system on floating point multiplication using the WisMote platform, where the specialised code executes more than 7 times faster than generic code, all overheads included. To the best of our knowledge, it is the first time that a runtime code generation system is used to automatically optimise code in such constrained devices as WSN nodes

Self-optimisation using runtime code generation for Wireless Sensor Networks Internet-of-Things

International audienceSelf-optimisation using runtime code generation for Wireless Sensor Network

From missions to systems : generating transparently distributable programs for sensor-oriented systems

Early Wireless Sensor Networks aimed simply to collect as much data as possible for as long as possible. While this remains true in selected cases, the majority of future sensor network applications will demand much more intelligent use of their resources as networks increase in scale and support multiple applications and users. Specifically, we argue that a computational model is needed in which the ways that data flows through networks, and the ways in which decisions are made based on that data, is transparently distributable and relocatable as requirements evolve. In this paper we present an approach to achieving this using high-level mission specifications from which we can automatically derive transparently distributable programs.Postprin

Modular Remote Reprogramming of Sensor Nodes

Wireless sensor networks are envisioned to be deployed in the absence of permanent network infrastructure and in environments with limited or no human accessibility. Hence, such deployments demand mechanisms to remotely (i.e., over the air) reconfigure and update the software on the nodes. In this paper we introduce DyTOS, a TinyOS based remote reprogramming approach that enables the dynamic exchange of software components and thus incrementally update the operating system and its applications. The core idea is to preserve the modularity of TinyOS, i.e., its componentisation, which is lost during the normal compilation process, and enable runtime composition of TinyOS components on the sensor node. The proposed solution integrates seamlessly into the system architecture of TinyOS: It does not require any changes to the programming model of TinyOS and all existing components can be reused transparently. Our evaluation shows that DyTOS incurs a low performance overhead while keeping a smaller – up to one third – memory footprint than other comparable solutions

Holons:Towards a systematic approach to composing systems of systems

The world's computing infrastructure is increasingly differentiating into self-contained distributed systems with various purposes and capabilities (e.g. IoT installations, clouds, VANETs, WSNs, CDNs, ...). Furthermore, such systems are increasingly being composed to generate systems of systems that offer value-added functionality. Today, however, system of systems composition is typically ad-hoc and fragile. It requires developers to possess an intimate knowledge of system internals and low-level interactions between their components. In this paper, we outline a vision and set up a research agenda towards the generalised programmatic construction of distributed systems as compositions of other distributed systems. Our vision, in which we refer uniformly to systems and to compositions of systems as holons, employs code generation techniques and uses common abstractions, operations and mechanisms at all system levels to support uniform system of systems composition. We believe our holon approach could facilitate a step change in the convenience and correctness with which systems of systems can be built, and open unprecedented opportunities for the emergence of new and previously-unenvisaged distributed system deployments, analogous perhaps to the impact the mashup culture has had on the way we now build web applications

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Distributed Online Machine Learning for Mobile Care Systems

Appendix D: Wavecomm Tech Docs removed for copyright reasonsTelecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis