Article

Cognition in Wireless Sensor Networks: A Perspective

Dept. of Electr. & Comput. Eng., Queen's Univ., Kingston, ON, Canada
IEEE Sensors Journal (Impact Factor: 1.76). 04/2011; 11(3):582 - 592. DOI: 10.1109/JSEN.2010.2052033
Source: IEEE Xplore

ABSTRACT

Wireless Sensor Networks are believed to be the enabling technology for Ambient Intelligence. They hold the promise of delivering to a smart communication paradigm which enables setting up an intelligent network capable of handling applications that evolve from user requirements. Cognitive agents capable of making proactive decisions based on learning, reasoning and information sharing when interspersed in sensor networks, may help achieve end-to-end goals of the network even in the presence of multiple constraints and optimization objectives. Cognitive radio at the physical layer of such agents may enable the opportunistic use of the heterogeneous wireless environment. However, research efforts have been discrete and cognitive techniques have focused on improving specific aspects of the network or benefiting specific applications. The main contribution of this paper is providing the vision and advantage of a holistic approach to cognition in sensor networks, which can be achieved by incorporating learning and reasoning in the upper layers, and opportunistic spectrum access at the physical layer. Rather than providing an ostensive survey of cognitive architectures applicable to sensor networks, this paper provides the reader with a framework based on knowledge and cognition that can help achieve end-to-end goals of application-specific sensor networks.

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    • "I NDUSTRIAL Internet of Things (IoT) [1], [2] can be applied for gathering real-time data in production and monitoring the environmental metrics such as temperature, humidity, fire alarm, and toxical gas. For effective signal coverage and accurate target positioning, there are three basic regular deployment patterns: square lattice (SL), triangle lattice (TL), and hexagon lattice (HL) [3], [4]. "
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    ABSTRACT: Square lattice (SL), triangle lattice (TL), and hexagon lattice (HL) are widely used regular deployment patterns for industrial Internet of Things (IoT). However, the performance of cognitive-radio-based access with these three deployment patterns has not been explored yet. This paper first designs a transmission scheduling method named cognitive access for regular topology (CART) in cognitive radio sensor networks (CRSNs) with minimal occupation of channels and high transmission efficiency. CART consists of a timeslot-and-channel allocation scheme, a cooperative spectrum-sensing scheme, and a scheme for reporting spectrum-sensing results (SSRs) in SL, TL, and HL. This paper also analyzes CART’s cooperative spectrum-sensing performance, reception bandwidth, and transmission delay for different transmission interference range and coverage patterns. Based on numerical analysis and simulation, this paper makes comparison between CART and optimal transmission scheduling method for single channel, and that among these three deployment patterns. Results show the following insights for efficient deployment of CRSN: 1) TL yields the optimal cooperative spectrum-sensing performance among three patterns by data fusion rule of 3-out-of-7; 2) under the same conditions and area for deployment, SL provides the optimal reception bandwidth; 3) SL and TL lead to low transmission delay for critical complete coverage; 4) SL leads to the lowest transmission delay for critical multiple coverage; 5) the false alarm (FA) probability of individual node has little influence on transmission delay, while the probability of primary user’s (PU) occurrence has noticeably influence on it; and 6) increasing the ratio of transmission timeslot to spectrum-sensing timeslot may decrease transmission delay.
    Full-text · Article · Dec 2015 · IEEE Systems Journal
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    • "PU can communicate SU through spectrum sensing. A CRSN-based network has unique capability to monitor an appropriate environmental parameter in a cooperation with other nodes [2]. Although there exist many solutions in this direction, but still CR-based WSNs are in their early stages. "

    Full-text · Article · Sep 2015 · Pervasive and Mobile Computing
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    • "PU can communicate SU through spectrum sensing. A CRSN-based network has unique capability to monitor an appropriate environmental parameter in a cooperation with other nodes [2]. Although there exist many solutions in this direction, but still CR-based WSNs are in their early stages. "

    Full-text · Article · Sep 2015 · Pervasive and Mobile Computing
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