The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.

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The Internet of Things (IoT) is susceptible to several identities, primarily based on attacks. However, these attacks are controlling for IoT due to extraordinary growth in consumers’ density and slight analysis with low power access nodes. In this work, we explore the possible flaws associated with security for IoT environment insensitively meant for transfer conditions. We proposed a novel design aimed at detecting a spoofing attack that inspects the probability distributions of received power founded for the regions designed for mobile (moving) users. Additionally, we examine the influence on the Confidentiality Scope of targeted consumers in the absence and presence of observer. Our approaches were done through simulation results used for three diverse regions. Grounded on outcomes, we suggest an algorithm called MTFLA, which will guarantee detection and protection techniques intended to protect vastly sensitive areas, i.e., wherever the chance of an attack is maximized. We provide a comparison among various security algorithms prepared for the energy consumption of different patterns. Simulation results revealed that the proposed algorithm for protection (MTFL) is verified to be energy-proficient (secure garnering). It decreases the energy prerequisite for encrypting the data. We evaluated our techniques over simulation results for sensitive region information built on fuzzy logic.

In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In Initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.

Mobile ad hoc networks are the “spontaneous networks” which create a temporary network in any place and any time without using any extra fixed radio device of a full infrastructure network. Each device in this network works as a router to develop end-to-end communication connections and move independently in any direction. Mostly, mobile ad hoc networks use the IEEE 802.11b protocol with carrier-sense multiple access with collision avoidance medium access control layer protocol for sharing a common medium among the nodes simultaneously. Due to this distributed medium, the routing and medium access control layer of the mobile ad hoc network are prone to attacks. Among several attackers, blackhole attacker is the dangerous one which causes the loss of all data packets of devices in the network. Efficient medium access control protocol designs in this respect play a key role in determining channel utilization, network delay, and, more importantly, network security. In the proposed work, preamble information is used with time-division multiple access medium access control. The preamble time-division multiple access uses time synchronization for each time slot and does not assign much time to the blackhole attacker due to a fixed time slot. As a result, blackhole is not stable in all communications and such an attack is effectively defended. Simulation results show that, in the presence of the blackhole attacker, carrier-sense multiple access with collision avoidance has a high packet loss ratio and low network throughput as compared to the proposed preamble time-division multiple access.

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profits or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, the spam review detection problem has gained much attention from communities and researchers, but still there is a need to perform experiments on real-world large-scale review datasets. This can help to analyze the impact of widespread opinion spam in online reviews. In this work, two different spam review detection methods have been proposed: (1) Spam Review Detection using Behavioral Method (SRD-BM) utilizes thirteen different spammer's behavioral features to calculate the review spam score which is then used to identify spammers and spam reviews, and (2) Spam Review Detection using Linguistic Method (SRD-LM) works on the content of the reviews and utilizes transformation, feature selection and classification to identify the spam reviews. Experimental evaluations are conducted on a real-world Amazon review dataset which analyze 26.7 million reviews and 15.4 million reviewers. The evaluations show that both proposed models have significantly improved the detection process of spam reviews. Specifically, SRD-BM achieved 93.1% accuracy whereas SRD-LM achieved 88.5% accuracy in spam review detection. Comparatively, SRD-BM achieved better accuracy because it works on utilizing rich set of spammers behavioral features of review dataset which provides in-depth analysis of spammer behaviour. Moreover, both proposed models outperformed existing approaches when compared in terms of accurate identification of spam reviews. To the best of our knowledge, this is the first study of its kind which uses large-scale review dataset to analyze different spammers' behavioral features and linguistic method utilizing different available classifiers. INDEX TERMS Online product reviews, spam reviews, spam review detection, linguistic features, spammer behavioral features.

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A substitution box is a core component of the popular symmetric-key algorithms. However, the major problem of the conventional substitution boxes is the statistic behavior, which is employed as a fixed-size lookup table. To solve the fixed-size lookup table problem, various substitution box construction methods were proposed with key control, but it is hard to enhance all cryptographic properties, for example, linear and differential probabilities. Thus, chaos is applied for key control in designing robust substitution boxes due to unpredictable and random-like behavior. Moreover, the confusion and diffusion properties of cryptography can be achieved by chaos. This article introduces an efficient construction of a key-dependent substitution box based on the mixing property of the chaotic sine map. The substitution box so constructed has very low differential and linear approximation probabilities. The experimental results confirmed that the proposed method to construct substitution box has acceptable cryptographic properties to resist against various cryptanalysis.