Intelligent Home Monitoring Using RSSI in Wireless Sensor Networks

Kausar, Firdous

doi:10.5121/ijcnc.2012.4603

Cited by 28 publications

(19 citation statements)

References 10 publications

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“…Such a system is based on big data analytics, which enables, for example, the informing of vehicles on a given road, the distances between them, events in front of them, weather conditions, and so on. The drivers of vehicles with the help of sensors and connectivity between vehicles receive notice of current dangers on the road, for instance (Ho & Spence, 2012;Kausar, Eisa, & Bakhsh, 2012).…”

Section: Iot In the Framework Of Kmmentioning

confidence: 99%

A Complex View of Industry 4.0

Roblek¹,

2016

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This article is focused on the importance and influence of Industry 4.0 and consequently the Internet-connected technologies for the creation of value added for organizations and society. The contribution of the article is mainly conceptual. With the development of the Internet, the Internet of things that is central to the new industrial revolution has led to "Industry 4.0." The aim of this article is to synthesize the known theory and practices of Industry 4.0, and to investigate the changes that will result from Industry 4.0 and with the development of the Internet of things.

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Section: Iot In the Framework Of Kmmentioning

confidence: 99%

A Complex View of Industry 4.0

Roblek¹,

2016

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“…In [9], the authors present a framework for inference of forest fire events based on vision-enabled nodes. The authors in [18] present a mechanism for home monitoring based on the received signal strength of nodes. In [30], a WSN application for the surveillance of critical areas is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Such events are related to e.g., security issues or violations of pre-defined constraints. 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

Kolomvatsos

Anagnostopoulos

Hadjiefthymiades

2017

IEEE Internet Things J.

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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.

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“…Such system collects contextual data from temperature, humidity, light, and air quality sensors and then centrally infer events. Moreover, the centralized context reasoning systems in [6,12], and [23] provide early inference of forest fire events based on visionenabled sensors, home monitoring based on the received signal strength of sensors, and surveillance of critical areas, respectively. In wireless sensors network deployments, e.g., [2,5,11,16], the back-end systems centrally provide event inference for specific areas by minimizing false alerts.…”

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

Anagnostopoulos

Kolomvatsos

2017

Appl Intell

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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

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Intelligent Home Monitoring Using RSSI in Wireless Sensor Networks

Abstract: ABSTRACT

Cited by 28 publications

References 10 publications

A Complex View of Industry 4.0

A Complex View of Industry 4.0

Distributed Localized Contextual Event Reasoning Under Uncertainty

Predictive intelligence to the edge through approximate collaborative context reasoning

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