Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

Secure store and forward proxy for dynamic IoT applications over M2M networks

Internet of Things (IoT) applications are expected to generate a huge unforeseen amount of traffic flowing from Consumer Electronics devices to the network. In order to overcome existing interoperability problems, several standardization bodies have joined to bring a new generation of Machine to Machine (M2M) networks as a result of the evolution of wireless sensor/actor networks and mobile cellular networks to converged networks. M2M is expected to enable IoT paradigms and related concepts into a reality at a reasonable cost. As part of the convergence, several technologies preventing new IoT services to interfere with existing Internet services are flourishing. Responsive, message-driven, resilient and elastic architectures are becoming essential parts of the system. These architectures will control the entire data flow for an IoT system requiring sometimes to store, shape and forward data among nodes of a M2M network to improve network performance. However, IoT generated data have an important personal component since it is generated in personal devices or are the result of the observation of the physical world, so rises significant security concerns. This article proposes a novel opportunistic flexible secure store and forward proxy for M2M networks and its mapping to asynchronous protocols that guarantees data confidentiality

From FPGA to ASIC: A RISC-V processor experience

This work document a correct design flow using these tools in the Lagarto RISC- V Processor and the RTL design considerations that must be taken into account, to move from a design for FPGA to design for ASIC