Providing Fault Tolerance via Complex Event Processing and Machine Learning for IoT Systems

Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms in IoT are static, tightly coupled, inflexible implementations that struggle to adapt in dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing reactive and proactive FT using Complex Event Processing (CEP) and Machine Learning (ML). Error-detection strategies are defined as nondeterministic finite automata (NFA) and implemented via CEP systems. Reactive-FT support is monitored and learned from to train ML models that proactively handle imminent future occurrences of known errors. We evaluated CPoF on an indoor agriculture system with experiments that used time and error correlations to preempt battery-depletion failures. We trained predictive models to learn from reactive-FT support and provide preemptive error recovery

Complex Patterns of Failure:Fault Tolerance via Complex Event Processing for IoT Systems

Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms for IoT are static, tightly coupled, and inflexible, and so they struggle to provide effective support for dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing FT support for IoT systems using Complex Event Processing (CEP) that promotes modularity and reusability in FT-support design. System defects are defined using our Vulnerabilities, Faults, and Failures (VFF) framework, and error-detection strategies are defined as nondeterministic finite automata (NFA) implemented via CEP systems. We evaluated CPoF on an automated agriculture system and demonstrated its effectiveness against three types of error-detection checks: reasonableness, timing, and reversal. Using CPoF, we identified unreasonable environmental conditions and performance degradation via sensor data analysis

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions