Context Aware Data Perception in Cognitive Internet of Things - Cognitive Agent Approach

Context Aware Data Perception in Cognitive Internet of Things - Cognitive Agent Approach

Lokesh B. Bhajantri (Department of Information Science and Engineering, Basaveshwar Engineering College, Bagalkot, India) and Prashant M. Baluragi (Basaveshwar Engineering College, Bagalkot, India)
DOI: 10.4018/IJHIoT.2020070101
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In the past, the existing Internet of Things caused traffic congestion and receiver uncertainty problems due to insufficient data transfer between the nodes or devices for data perception. The authors have proposed the method for context-aware data perception in the cognitive internet of things environment. The proposed context-aware data perception is described in the following stages, initially nodes in Cognitive Internet of Things network are clustered effectively using adaptive pillar ‘K' means clustering algorithm. After the formation of effective clusters, the cognitive agent performs the effective context-aware data learning using support-based convolutional neural networks. Finally, adaptive fuzzy logic defines the effective decision for data perception. The experimental results show that the proposed method outperforms the cognitive agent approaches of data perception in terms of network lifetime, energy consumption, data perception accuracy, and throughput in the cognitive internet of things.
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The internet of things (IoT) world view gives connectivity to devices or nodes for data sharing between the nodes or devices which are connected by the high speed of the network. These nodes sense the data from the physical environment, collect and process the sensed data to its respective cluster head, and such data is transmitted to the sink node. Consequently, the IoT is utilized in a number of applications such as e-health monitoring, transportation, environment monitoring, logistics management, and so on (Mustafa et al., 2017). The sensing innovation under IoT has assumed a noteworthy job in the discovery and regulation of debacles in various controls (Fadi et al., 2019). In the following IoT presents one-of-a-kind difficulties to the radio system since the various number of IoT devices will want to get to the medium whenever. The cognitive radios (CRs) combined with arbitrary access procedures can help to ease occlusion in the system (Ting et al., 2019). A CR is a promising technology that let the possibility to not exist harmful interference by maintaining low outage probability (Raluca et al., 2018). The CR innovation can consequently distinguish the radio condition, composition the transmission parameters, and significantly improve range proficiency. In this manner, CR can be incorporated into the IoT world view. IoT joined with displaying is alluded to as CIoT (Yousaf et al., 2018) (Chung-Sheng et al., 2017). The Cognitive Internet of Things (CIoT) is a new system perspective, where (physical/virtual) nodes or items are hooked and bring on as administrators, with minimal human intercession (Qihui et al., 2018), which is helped by distributed computing, CIoT has some new abilities, for example, artificial intelligence (AI), data perception, resource management, and information mining. Since the immense measure of information produced by IoT is put away and handled by virtual machines (VMs) in the cloud (Dongyang et al., 2018). Currently, the data gathered by the sensors/objects are not utilized properly in-terms of bandwidth, security, and privacy constraints as well as enormous unstructured data from logs and social frameworks are abundant in the quantity of data. These data can be combined in a real time basis and valuable information can be extracted by CIoT capabilities. Research emerges that IoT in the social insurance industry can encourage better care with decreased costs, diminished direct patient-medicinal services staff association, and pervasive access to quality consideration (Shamim and Ghulam, et al., 2016).

As of late cognitive computing innovations have developed to make applications more intelligent and progressively shrewd. In spite of the fact that the blend of IoT and psychological processing has an incredible guarantee to reform our life exercises (Shamim et al., 2017). The Cognitive-Industrial IoT (IIoT) gives superior about imparting, registering, controlling, and even high level of machine insight for rising savvy mechanical IoT applications, for example, subjective assembling and Industry 4.0 and remote sensor systems dependent on secure route plot for smaller scale flying robots (Yousaf et al., 2018). With the on-going improvement of CIoT and the capability of cyber physical system (CPS), figures that everyday exercises become more brilliant, and wise. The blend of CIoT and CPS can enormously improve the quality of life (Feng et al., 2018). The quick and precise assignment preparing is significant in the present unique world. Thusly the IoT it is important to have a cognitive or intelligent model of task scheduling algorithm to handle the heterogeneous multiprocessor conditions like cloud computing (Shubhradeep et al., 2018). There exists few endeavours to use IoT-based framework for elderly observing and care, a large portion of which target just certain parts of old prerequisites from a restricted perspective (e.g., wellbeing checking, security observing, etc.) (Iman et al., 2016; Dohr et al., 2010). The list of issues and challenges in CIoT mentioned are standards for cross interoperability with heterogeneous networks, machine to machine standardization, self-aware, and self-organizing networks, open framework for the CIoT, fault tolerance, adhoc deployable and configurable networks, dynamic and adaptable in17perability, open platform for CIoT, distributed energy efficient data processing, security and routing, access control and accounting schemes for IoT, low cost, secure, and high performance authentication, cognitive computing for IoT, context-aware computing, resource management for CIoT, connectivity, power, adaptability, context aware data perception, and scalability. One of the significant issues in CIoT is a data perception.

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