Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

Towards Self-Healing in Wireless Sensor Networks.

Faults in WSN are very common and appear in different levels of the system. For pervasive applications to be adopted by end-users there is a need for autonomic selfhealing. This paper discusses our initial approach to selfhealing in WSN and describes experiments with two case studies of body sensor deployment. We evaluate the impact of sensor faults on activity and gesture classification accuracy respectively and develop mechanisms that will allow detection of those faults during systems operation. © 2009 IEEE

Fault tolerant radiation monitoring system using wireless sensor and actor network in a nuclear facility

In nuclear facilities, the reading of the sensors is very important in the assessments of the system state. The existence of an abnormal state could be caused by a failure in the sensor itself instead of a failure in the system. So, being unable to identify the main cause of the “abnormal state” and take proper actions may end in unnecessary shutdown for the nuclear facility that may have expensive economic consequences. That is why, it is extremely important for a supervision and control system to identify the case where the failure in the sensor is the main cause for the existence of an abnormal state. In this paper, a system based on a wireless sensor network is proposed to monitor the radiation levels around and inside a nuclear facility. A new approach for validating the sensor readings is proposed and investigated using the Castalia simulator

Fault tolerant radiation monitoring system using wireless sensor and actor network in a nuclear facility

In nuclear facilities, the reading of the sensors is very important in the assessments of the system state. The existence of an abnormal state could be caused by a failure in the sensor itself instead of a failure in the system. So, being unable to identify the main cause of the “abnormal state” and take proper actions may end in unnecessary shutdown for the nuclear facility that may have expensive economic consequences. That is why, it is extremely important for a supervision and control system to identify the case where the failure in the sensor is the main cause for the existence of an abnormal state. In this paper, a system based on a wireless sensor network is proposed to monitor the radiation levels around and inside a nuclear facility. A new approach for validating the sensor readings is proposed and investigated using the Castalia simulator

Micro Sensor Node for Air Pollutant Monitoring: Hardware and Software Issues

Wireless sensor networks equipped with various gas sensors have been actively used for air quality monitoring. Previous studies have typically explored system issues that include middleware or networking performance, but most research has barely considered the details of the hardware and software of the sensor node itself. In this paper, we focus on the design and implementation of a sensor board for air pollutant monitoring applications. Several hardware and software issues are discussed to explore the possibilities of a practical WSN-based air pollution monitoring system. Through extensive experiments and evaluation, we have determined the various characteristics of the gas sensors and their practical implications for air pollutant monitoring systems

AMBROSia: An Autonomous Model-Based Reactive Observing System

Observing systems facilitate scientific studies by instrumenting the real world and collecting corresponding measurements, with the aim of detecting and tracking phenomena of interest. Our AMBROSia project focuses on a class of observing systems which are embedded into the environment, consist of stationary and mobile sensors, and react to collected observations by reconfiguring the system and adapting which observations are collected next. In this paper, we report on recent research directions and corresponding results in the context of AMBROSia

Dynamic Probabilistic Model Checking for Sensor Validation in Industry 4.0 Applications

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical Systems and Enterprise Planning into manufacturing operations. This kind of integration consists of a combination of connected sensors and run-time control algorithms. Consequential control decisions are driven by sensor-generated data. Hence, the trustworthiness of the sensor network readings is increasingly crucial to guarantee the performance and the quality of a manufacturing task. However, existing methodologies to test such systems often do not scale to the complexity and dynamic nature of today’s sensor networks. This paper proposes a novel run-time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework can rigorously quantify the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor’s trustworthiness enhances the accuracy of the system failure prediction

Data fusion and type-2 fuzzy inference in contextual data stream monitoring

Data stream monitoring provides the basis for building intelligent context-aware applications over contextual data streams. A number of wireless sensors could be spread in a specific area and monitor contextual parameters for identifying phenomena e.g., fire or flood. A back-end system receives measurements and derives decisions for possible abnormalities related to negative effects. We propose a mechanism, which based on multivariate sensors data streams, provides real-time identification of phenomena. The proposed framework performs contextual information fusion over consensus theory for the efficient measurements aggregation while time-series prediction is adopted to result future insights on the aggregated values. The unanimous fused and predicted pieces of context are fed into a Type-2 fuzzy inference system to derive highly accurate identification of events. The Type-2 inference process offers reasoning capabilities under the uncertainty of the phenomena identification. We provide comprehensive experimental evaluation over real contextual data and report on the advantages and disadvantages of the proposed mechanism. Our mechanism is further compared with Type-1 fuzzy inference and other mechanisms to demonstrate its false alarms minimization capability

Assinalamentos de testes para um algoritmo de diagnóstico em nível de sistema para redes de sensores sem fio

Resumo: Este trabalho se propõe a comparar três abordagens de construção de assinalamentos de testes para um algoritmo de diagnóstico em nível de sistema. As abordagens apresentadas visam o problema da detecção de alarmes falsos (falsos positivos) em uma rede de sensores sem ó onde os sensores monitoram o ambiente com o objetivo de gerar alarmes sobre a ocorrência de determinados eventos. Considere uma rede de sensores onde um conjunto de t sensores próximos geograficamente enviam sinais de alarme a uma unidade central da rede, com maior capacidade de processamento, chamada sink, informando a detecção de determinado fenômeno. Para garantir que os alarmes gerados não são falsos, o sink solicita a execução de testes mútuos entre os sensores presentes na região que contém os nodos que reportaram os alarmes. O resultado dos testes é enviado ao sink que, então, utiliza um algoritmo de diagnóstico em nível de sistema para identificar os sensores falhos. O algoritmo de diagnóstico é bem sucedido na execução desta tarefa se os testes executados pelos sensores são suficientes para alcançar determinada diagnosticabilidade do sistema, a qual depende de propriedades topológicas da rede de sensores e de certas condições presentes na literatura para formar assinalamentos de teste t-diagnosticáveis. Este trabalho apresenta três estratégias de testes que asseguram que a iagnosticabilidade desejada para o sistema seja alcançada com um consumo minimizado de energia. Resultados experimentais avaliam o comportamento das estratégias e comparam o consumo de energia apresentado entre elas em redes com diferentes topologias e densidades, com diferentes valores de t e com variações na distância entre os sensores que geram alarmes