Towards a Formal Model of Recursive Self-Reflection

Self-awareness holds the promise of better decision making based on a comprehensive assessment of a system\u27s own situation. Therefore it has been studied for more than ten years in a range of settings and applications. However, in the literature the term has been used in a variety of meanings and today there is no consensus on what features and properties it should include. In fact, researchers disagree on the relative benefits of a self-aware system compared to one that is very similar but lacks self-awareness. We sketch a formal model, and thus a formal definition, of self-awareness. The model is based on dynamic dataflow semantics and includes self-assessment, a simulation and an abstraction as facilitating techniques, which are modeled by spawning new dataflow actors in the system. Most importantly, it has a method to focus on any of its parts to make it a subject of analysis by applying abstraction, self-assessment and simulation. In particular, it can apply this process to itself, which we call recursive self-reflection. There is no arbitrary limit to this self-scrutiny except resource constraints

Building Programmable Wireless Networks: An Architectural Survey

In recent times, there have been a lot of efforts for improving the ossified Internet architecture in a bid to sustain unstinted growth and innovation. A major reason for the perceived architectural ossification is the lack of ability to program the network as a system. This situation has resulted partly from historical decisions in the original Internet design which emphasized decentralized network operations through co-located data and control planes on each network device. The situation for wireless networks is no different resulting in a lot of complexity and a plethora of largely incompatible wireless technologies. The emergence of "programmable wireless networks", that allow greater flexibility, ease of management and configurability, is a step in the right direction to overcome the aforementioned shortcomings of the wireless networks. In this paper, we provide a broad overview of the architectures proposed in literature for building programmable wireless networks focusing primarily on three popular techniques, i.e., software defined networks, cognitive radio networks, and virtualized networks. This survey is a self-contained tutorial on these techniques and its applications. We also discuss the opportunities and challenges in building next-generation programmable wireless networks and identify open research issues and future research directions.Comment: 19 page

Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

Synthesis of Embedded Software using Dataflow Schedule Graphs

In the design and implementation of digital signal processing (DSP) systems, dataflow is recognized as a natural model for specifying applications, and dataflow enables useful model-based methodologies for analysis, synthesis, and optimization of implementations. A wide range of embedded signal processing applications can be designed efficiently using the high level abstractions that are provided by dataflow programming models. In addition to their use in parallelizing computations for faster execution, dataflow graphs have additional advantages that stem from their modularity and formal foundation. An important problem in the development of dataflow-based design tools is the automated synthesis of software from dataflow representations. In this thesis, we develop new software synthesis techniques for dataflow based design and implementation of signal processing systems. An important task in software synthesis from dataflow graphs is that of {\em scheduling}. Scheduling refers to the assignment of actors to processing resources and the ordering of actors that share the same resource. Scheduling typically involves very complex design spaces, and has a significant impact on most relevant implementation metrics, including latency, throughput, energy consumption, and memory requirements. In this thesis, we integrate a model-based representation, called the {\em dataflow schedule graph} ({\em DSG}), into the software synthesis process. The DSG approach allows designers to model a schedule for a dataflow graph as a separate dataflow graph, thereby providing a formal, abstract (platform- and language-independent) representation for the schedule. While we demonstrate this DSG-integrated software synthesis capability by translating DSGs into OpenCL implementations, the use of a model-based schedule representation makes the approach readily retargetable to other implementation languages. We also investigate a number of optimization techniques to improve the efficiency of software that is synthesized from DSGs. Through experimental evaluation of the generated software, we demonstrate the correctness and efficiency of our new techniques for dataflow-based software synthesis and optimization