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Cognitive Sensors and IoT: Architecture, Deployment, and Data Delivery


Author: Fadi Al-Turjman

Publisher: CRC Press


Publish Date: September 19, 2017

ISBN-10: 1138102296

Pages: 280

File Type: PDF

Language: English

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Book Preface

Smart environments, enabled by Wireless Sensor Networks (WSNs), have emerged as one of the most promising applications in the Internet of Things (IoT) era. These smart environments require that the underlying sensor network infrastructure be enriched with smart devices, so that the network can understand and respond to requests from multiple users with diverse information requirements. Now, the use of artificial intelligence has enabled some amount of userrequirement awareness in sensor networks. However, there is no architectural framework about how cognition is to be incorporated in the network, or where the smart decision making shall be implemented.

In addition, WSN implementations are mostly addresscentric, where users must specify the location from where data must be gathered. But this is counterintuitive to how users would like to access information in a smart environment. Moreover, managing the large IP address space becomes problematic as the network size expands to vast counts of sensor nodes.

In this book, I propose a novel framework called cognitive informationcentric sensor network (ICSN) for the IoT. This framework is built on top of cognitive nodes, capable of knowledge representation, learning, and reasoning, along with an information-centric approach for data delivery, which are central to the idea of the ICSN. Moreover, we discuss the most appropriate deployment strategy for these cognitive nodes under realistic assumptions that cares about the quality of information (QoI). In addition, a quality of information (QoI) aware data delivery strategy, with the analytic hierarchy process (AHP) as the reasoning technique, is used to identify data delivery paths that dynamically adapt to changing network conditions and user requirements. Latency, reliability, and throughput are the attributes used to identify the QoI along the delivery path. Furthermore, heuristic learning techniques are explored to improve the success rate of data delivery and hit ratio which applying in-network caching.

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