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Unmanned Aerial Vehicles (UAVs) will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication (URLLC) in futuristic Sixth-Generation (6G) networks. However, several security vulnerabilities and attacks have plagued previous generations of communication systems; thus, physical layer security, especially against eavesdroppers, is vital, especially for upcoming 6G networks. In this regard, UAVs have appeared as a winning candidate to mitigate security risks. In this paper, we leverage UAVs to propose two methods. The first method utilizes a UAV as Decode-and-Forward (DF) relay, whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices. Moreover, we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception, i.e. increasing secrecy rate, and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay. Additionally, simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height, blocklength, decoding error probability and transmitter-receiver separation distance. Lastly, we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

The various data collected by urban sensor devices need a huge storage space. The cloud’s powerful calculation ability provides decent performance support and reduces the overhead on local storage. There are many kinds of data collected by urban sensor devices, and the same kind of sensors often need to upload large numbers of files together, which are usually private, so secure cloud storage in group members is essential. However, the cloud may try to modify or hide data for its own benefit, which requires the detection of a third-party auditor. This paper puts forward a new public audit scheme. Some changes have been made to the way of generating the private key, the parameters of key generation come from the feature information set of the same sensor. When the two information sets are close enough, the correctness of the authenticator generated by this file can be verified. When the sensor is revoked due to damage or loss, this scheme will effectively revoke and regenerate the private key with less overhead, thus preventing the leakage of data privacy. This also avoids the huge overhead caused by re-downloading data to generate authenticators in traditional schemes. In addition, our scheme is effective in tracking malicious members and reducing the computing overhead of the group. Finally, the experimental results show the effectiveness and practicality of the proposed scheme.

With the advancement in the Internet of Medical Things (IoMT) infrastructure, network security issues have become a serious concern for hospitals and medical facilities. For this, a variety of customized network security tools and frameworks are used to distract several generalized attacks such as botnet-based distributed denial of services attacks (DDoS) and zero-day network attacks. Thus, it becomes difficult to operate routine IoMT services and tasks in between the under-attack scenario. This paper discusses a novel approach named Duo-Secure IoMT framework that uses multi-modal sensory signals’ data to differentiate the attack pattern and routine IoMT devices’ data. The proposed model uses a combination of two techniques such as dynamic Fuzzy C-Means clustering along with customized Bi-LSTM technique that processes sensory medical data securely along with identifying attack patterns within the IoMT network. As a case study, we are using a dataset to evaluate heart disease which consists of 36 attributes and 18940 instances. The performance evaluation shows that the proposed model evaluates a) prediction of heart issues and b) identification of network malware with an individual accuracy of 92.95% and multi-modal joint accuracy of 89.67% in the IoMT-based distributed network environment.

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.

The Internet of Medical Things (IoMT) facilitates patients with all-time-connected medical devices through cost-effective solutions and a feeling of comfort with round-the-clock hospital support. The patients who cannot visit hospitals for routine checkups prefer the usage of IoMT devices. The healthcare facilities also rely on the real-time statistics of IoMT machines and diagnose problems and their solutions within a small interval of time. This could only be possible when a robust convergence of technologies is used with IoMT devices. This review study discusses how we may apply the convergence of IoMT devices with trending technologies and focuses on delivering a new dimension of novel IoMT usage through broad multi-homing dense networks. It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. The review study also discusses various IoMT convergence open challenges and opportunities through the labor industry healthcare system.

The increasing energy consumption is a legacy of the fast improvement of ICT (Information and Communication Technology). It is also contrary to the current energy conservation and emission reduction concept. There are several reasons for high energy consumption. Among them, we find that the increase in base station density of the 5G heterogeneous network (5G HetNets) is prominent. We present a micro base station deployment strategy in 5G HetNets for obtaining high energy efficiency. It optimizes target values as are trade-offs at different user distribution probabilities to improve adaptation to different user distribution scenarios. An energy deployment algorithm based on high efficiency for micro base stations is considered as jointly optimizing micro base station’s number, deployment location, and power configuration. Simulation results show that the proposed combinatorial optimization strategy effectively improves the system energy efficiency compared to the deployment method, regardless of the power factor. Moreover, it verifies that the micro base station power affects the energy efficiency in HetNets.

The continued growth of the industrial Internet of Things (IoT) paves the way for the fifth industrial revolution (Industry 5.0), which is expected to further enhance the wireless connectivity and human machine collaboration. Nonetheless, Industry 5.0 will inevitably face the great challenges such as large scale connectivity and explosive data traffic because of the proliferation of IoT devices. To meet these challenges, we propose a novel non-orthogonal multiple access (NOMA)-based overlay cognitive radio (OCR) network for Industry 5.0. Specifically, the hybrid full-duplex (FD)/ half-duplex (HD) mode at the secondary user (SR) is adopted and in-phase and quadrature phase imbalance (IQI) is introduced to better comply with the practical scenarios. We investigate the outage probability (OP) and ergodic sum rate (ESR) of the considered network with the Nakagami-m channels. Extensive simulation experiments are performed to verify the accuracy of the analysis. From the simulation results, we can conclude: i) Increasing the signal-to-noise ratio (SNR), OP decreases but ESR increases and tends to a fixed value at high SNR regimes, indicating that the existence of IQI leads to a ceiling for the ESR; ii) The primary system has higher reliable performance at low SNR regions when SR is in FD mode, and for ergodicity, the system with FD relay is superior to HD relay; iii) The reliable performance of OMA system for primary users will be better since there is no interference at the relay.

5G has been launched in a few countries of the world, so now all focus shifted towards the development of future 6G networks. 5G has connected all aspects of society. Ubiquitous connectivity has opened the doors for more data sharing. Although 5G is providing low latency, higher data rates, and high-speed yet there are some security-related vulnerabilities. Those security issues need to be mitigated for securing 6G networks from existing challenges. Classical cryptography will not remain enough for securing the 6G network. As all classical cryptography can be disabled with the help of quantum mechanics. Therefore, in the place of traditional security solutions, in this article, we have reviewed all the existing quantum solutions of 5G existing security issues to mitigate them and secure 6G in a Future Quantum World.

The tremendous growth in the internet of things (IoT) technology has provided great support for e-healthcare applications. The remote health monitoring of patients at home is gaining popularity with the increase in design of number of IoT enabled wearable devices. The secure and efficient health data retrieval during continuous uploading of huge data on network is a challenge. In addition, another challenge exists due to limited resources available with IoT device to proceed efficient data retrieval. On the other hand, the edge cloud in edge computing technology provide abundant resources to its devices for various computations, storage and processing. The integration of IoT with edge would solve the resource constraint issue. However, efficient retrieval of health data from large pool of data would still be an issue due to IP based solutions offered by IoT. The Named Data Networking (NDN) is a promising solution for efficient delivery of data based on its name based content searching. This paper proposed NDN communication based secure remote healthcare monitoring framework by integrating IoT with edge computing where security to the each individual content is provided through hashing and encryption mechanism. The performance of the proposed framework is evaluated against its companion in Icarus simulator. The results proved the efficiency of our framework for content retrieval delay and cost associated in accessing the required data.

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

In recent years, cyber-physical systems (CPSs) have gained a lot of attention from academia, industry and government agencies, considered to be the world’s third wave of information technology, following computers and the internet [...]

Employee churn analytics is the process of assessing employee turnover rate and predicting churners in a corporate company. Due to the rapid requirement of experts in the industries, an employee may switch workplaces, and the company then has to look for a substitute with the training to deal with the tasks. This has become a bottleneck and the corporate sector suffers with additional cost overheads to restore the work routine in the organization. In order to solve this issue in a timely manner, we identify several ML techniques that examine an employee’s record and assess factors in generalized ways to assess whether the resource will remain to continue working or may leave the workplace with the passage of time. However, sensor-based information processing is not much explored in the corporate sector. This paper presents an IoT-enabled predictive strategy to evaluate employee churn count and discusses the factors to decrease this issue in the organizations. For this, we use filter-based methods to analyze features and perform classification to identify firm future churners. The performance evaluation shows that the proposed technique efficiently identifies the future churners with 98% accuracy in the IoT-enabled corporate sector organizations.

A software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) applications. In the literature, the binary pattern for nested cardinality constraints (BPNCC) approach has been proposed to compute all possible combinations of development features for IoT applications without violating any relationship constraints. Relationship constraints are a predefined set of rules for the selection of features from an FM. Due to high probability of relationship constraints violations, obtaining optimum features combinations from large IoT-based FMs are a challenging task. Therefore, in order to obtain optimum solutions, in this paper, we have proposed multi-objective optimum-BPNCC that consists of three independent paths (first, second, and third). Furthermore, we applied heuristics on these paths and found that the first path is infeasible due to space and execution time complexity. The second path reduces the space complexity; however, time complexity increases due to the increasing group of features. Among these paths, the performance of the third path is best as it removes optional features that are not required for optimization. In experiments, we calculated the outcomes of all three paths that show the significant improvement of optimum solution without constraint violation occurrence. We theoretically prove that this paper is better than previously proposed optimization algorithms, such as a non-dominated sorting genetic algorithm and an indicator-based evolutionary algorithm.

Big data analytics has simplified the processing complexity of extremely large data sets through ecosystems, such as Hadoop, MapR, and Cloudera. Apache Hadoop is an open-source ecosystem that manages large data sets in a distributed environment. MapReduce is a programming model that processes massive amount of unstructured data sets over Hadoop cluster. Recently, Hadoop enhances its homogeneous storage function to heterogeneous storage and stores data sets into multiple storage media, i.e., SSD, RAM, and DISK. This development increases the performance of data block placement strategy and allows a client to store large data sets into multiple storage media efficiently than homogeneous storage. However, this evolution increases the consumption of computing capacity and memory usage over MapReduce job scheduling. The scheduler processes MapReduce job into homogeneous container having configuration of CPU, memory, DISK volume, and network I/O, and accesses, processes, and stores data sets over heterogeneous storage media. This produces a processing latency of locating and pairing source data set to MapReduce tasks and results an abnormal high consumption of computing capacity and memory usage in a Datanode. Similarly, when scheduler assigns MapReduce jobs to multiple Datanodes, the same processing latency can severely affect the performance of whole cluster. In this paper, we present Storage-Tag-Aware Scheduler (STAS) that reduces processing latency by scheduling MapReduce jobs into heterogeneous storage containers, i.e., SSD, DISK, and RAM container. STAS endorses job with a tag of storage media, such as JobSSD, JobDISK, and JobRAM and parses them into heterogeneous shared-queues, which assign processing configuration to enlist jobs. STAS manager then schedules shared-queue jobs into heterogeneous MapReduce containers and generates an output into storage media of the cluster. The experimental evaluation shows that STAS optimizes the consumption of computing capacity and memory usage efficiently than available schedulers in a Hadoop cluster.