A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

Abstract

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

Cybersecurity Threats, Countermeasures and Mitigation Techniques on the IoT: Future Research Directions

“…The simplest and easiest methods are often used by attackers to gain access to a network or device. The device becomes the first point of entry for an attacker if it is vulnerable to spiteful scripts and misdirection caused by inadequate code testing [75,76]. Based on three layers of IoT architecture, Figure 9 illustrates the most common threats.…”

Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence

“…However, data latency and privacy are some major research objectives in the cloud-based paradigm. Many machine learning-based methods have been proposed to solve the process of data aggregation and routing issues for IoT-based constraint networks and drastically reduce the disturbance of wireless channels [9][10][11]. However, the collaboration of heterogeneous IoE demands for edge computing to bring the data processing closer towards the source network.…”

Retracted: A Neighborhood and Machine Learning‐Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”