Environmental Monitoring Using Wireless Sensors: A Simulation Approach

IEEE Trans. Syst. Man Cybern, Syst.

Hadjiefthymiades

2017

Abstract-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.

Section: A Motivationmentioning

confidence: 99%

Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring

IEEE Trans. Syst. Man Cybern, Syst.

Hadjiefthymiades

2017

“…An analysis on architectural solutions and a case study on distributed context inference for environmental monitoring is discussed in [5]. IoT devices are utilized to (i) monitor a specific area and (ii) deliver the captured contextual data to a central system [15]. The authors in [24] present a context monitoring framework involving computing aggregate methodologies.…”

Section: Related Workmentioning

confidence: 99%

“…As an example, in security applications, a monitoring infrastructure is imperative by adopting a centralized architecture which applies an efficient mechanism to derive alerts when specific criteria are satisfied [18], [30]. Another application domain is environmental monitoring [11], [15].…”

Section: Introductionmentioning

confidence: 99%

Distributed Localized Contextual Event Reasoning Under Uncertainty

IEEE Internet Things J.

Hadjiefthymiades

2017

Abstract-We focus on Internet of Things (IoT) environments where sensing and computing devices (nodes) are responsible to observe, reason, report and react to a specific phenomenon. Each node (e.g., an unmanned vehicle or an autonomous device) captures context from data streams and reasons on the presence of an event. We propose a distributed predictive analytics scheme for localized context reasoning under uncertainty. Such reasoning is achieved through a contextualized, knowledge-driven clustering process, where the clusters of nodes are formed according to their belief on the presence of the phenomenon. Each cluster enhances its localized opinion about the presence of an event through consensus realized under the principles of Fuzzy Logic (FL). The proposed FLdriven consensus process is further enhanced with semantics adopting Type-2 Fuzzy Sets to handle the uncertainty related to the identification of an event. We provide a comprehensive experimental evaluation and comparison assessment with other schemes over real data and report on the benefits stemmed from its adoption in IoT environments.

“…From the sensing and processing perspective, normally in the literature, the sensing devices monitor a specific area and deliver the captured data to a back-end system for processing, event inference, and alerts/decision making [10,24]. Analysis on architectural solutions and case studies on event inference mechanisms is discussed in [3].…”

Section: Related Workmentioning

confidence: 99%

“…Events are related to critical aspects, e.g., security issues or violations of predefined constraints. For instance, in security and environmental monitoring applications, a monitoring infrastructure is imperative to apply an efficient mechanism to derive alerts when specific criteria are satisfied [1,8,10,12,23]. We can identify two main orientations in terms of data acquisition, transfer and contextual reasoning: -Orientation 1: Centralized Context Reasoning.…”

Section: Introductionmentioning

confidence: 99%

Predictive intelligence to the edge through approximate collaborative context reasoning

2017

Appl Intell

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.Keywords Collaborative event inference · Federated reasoning · Edge predictive intelligence · Optimal stopping theory · Adaptive vector quantization · Type-2 fuzzy logic inference