ABSTRACT This paper presents a new data clustering technique aimed at enhancing the performance of the trainable path‐cost algorithm and reducing the computational complexity of data clustering models. The proposed method facilitates the discovery of natural groupings and behaviours, which is crucial for effective coordination in complex environments. It identifies natural groupings within a set of features and detects the best clusters with similar behaviour in the data, overcoming the limitations of traditional state‐of‐the‐art methods. The algorithm utilises a density peak clustering method to determine cluster centers and then extracts features from paths passing through these peak points (centers). These features are used to train the support vector machine (SVM) to predict the labels of other points. The proposed algorithm is enhanced using two key concepts: first, it employs Q‐Generalised Extreme Value (Q‐GEV) under power normalisation instead of traditional generalised extreme value distributions, thereby increasing modelling flexibility; second, it utilises the random vector functional link (RVFL) network rather than the SVM, which helps avoid overfitting and improves label prediction accuracy. The effectiveness of the proposed clustering algorithm is evaluated through various experiments, including those on UCI benchmark datasets and real‐world data, demonstrating significant improvements across multiple performance metrics, including F1 measure, Jaccard index, purity, and accuracy, highlighting its capability in accurately identifying paths between similar clusters. Its average F1 measure, Jaccard index, purity, and accuracy is measured 76.87%, 56.29%, 80.29%, and 79.64%, respectively.

Recently, unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) networks are considered to provide effective and efficient solutions for disaster management. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption, and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. This article considers a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed-integer nonlinear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power-splitting architecture splits the source power for communication and EH. We jointly optimize user association, transmission power of user equipment (UE), task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt$k$-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of the interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading-EH, and no-offloading-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumption.

Drone-enabled vehicular ad-hoc network (VANET) is a promising solution for safe driving as it improves traffic efficiency and reliability by timely sharing road events and traffic information. However, there is an urgent need to tackle security, privacy, and computational delay related issues. In this paper, we propose a blockchain-based privacy preservation scheme using steganography to overcome the aforementioned issues in drone-enabled VANETs. The proposed scheme is based on a decentralized key management mechanism that combines lightweight authentication and key agreement. Data redundancy is avoided with the help of interplanetary file system (IPFS) which stores traffic event related data in blockchain through the smart contract. We further modified consensus algorithm using the practical byzantine fault tolerates algorithm and steganography to achieve better efficiency. Moreover, trust management is achieved by combining blockchain, IPFS, and steganography, which also enables the distributed storage and quick access to data for drone-enabled VANETs. We evaluate the performance of the proposed scheme in terms of response time, and computational time against incentive-based scheme.

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium, and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drains the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently.

Abstract Due to recent advancements in smart city traffic and transport monitoring industry 4.0 applications. Flying Ad‐Hoc Networks (FANETs) ability to cover geographically large areas, makes it a suitable technology to address the challenges faced during remote areas traffic monitoring. The implementation of drone based FANETs have several advantages in remote traffic monitoring, including free air‐to‐air drone assisted communication zone and smart surveillance and security. The drone‐based FANETs can be deployed within minutes without requiring physical infrastructure, making it suitable for mission critical applications in several areas of interests. Here a drone‐based FANETs application for smart city remote traffic monitoring is presented while addressing several challenges including coverage of larger geographical area and data communication links between FANETs nodes. A FANET‐inspired enhanced ACO algorithm that easily coped with drone assisted technology of FANETs is proposed to cover the large areas. Simulation results are presented to compare the proposed technique against different network lifetime and number of received packets. The presented results show that the proposed technique perform better compared to other state‐of‐the‐art techniques.

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.

Gamification is the use of game elements in domains other than games. Gamification use is often suggested for difficult activities because it enhances users’ engagement and motivation level. Due to such benefits, the use of gamification is also proposed in education environments to improve students’ performance, engagement, and satisfaction. Computer science in higher education is a tough area of study and thus needs to utilize various already explored benefits of gamification. This research develops an empirical study to evaluate the effectiveness of gamification in teaching computer science in higher education. Along with the learning outcomes, the effect of group size on students’ satisfaction level is also measured. Furthermore, the impact of gamification over time is analyzed throughout a semester to observe its effectiveness as a long-term learning technique. The analysis, covering both learning outcome and students’ satisfaction, suggests that gamification is an effective tool to teach tough courses at higher education level; however, group size should be taken into account for optimal classroom size and better learning experience.

In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs' licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.

A Tactile Internet-based nanonetwork is an emerging field that promises a new range of e-health applications, in which human operators can efficiently operate and control devices at the nanoscale for remote-patient treatment. A haptic feedback is inevitable for establishing a link between the operator and unknown in-body environment. However, haptic communications over the terahertz band may incur significant path loss due to molecular absorption. In this paper, we propose an optimization framework for haptic communications over nanonetworks, in which in-body nanodevices transmit haptic information to an operator via the terahertz band. By considering the properties of the terahertz band, we employ Brownian motion to describe the mobility of the nanodevices and develop a time-variant terahertz channel model. Furthermore, based on the developed channel model, we construct a stochastic optimization problem for improving haptic communications under the constraints of system stability, energy consumption, and latency. To solve the formulated nonconvex stochastic problem, an improved time-varying particle swarm optimization algorithm is presented, which can deal with the constraints of the problem efficiently by reducing the convergence time significantly. The simulation results validate the theoretical analysis of the proposed system.

Growing popularity of multimedia applications, pervasive connectivity, higher bandwidth, and euphoric technology penetration among bulk of the human race that happens to be cellular technology users, has fueled the adaptation to long-term evolution (LTE)/system architecture evolution. The LTE fulfills the resource demands of the next generation applications for now. We identify security issues in authentication mechanism used in LTE that without countermeasures might give super user rights to unauthorized users. The LTE uses static LTE key to derive the entire key hierarchy, i.e., LTE follows Evolved Packet System-Authentication and Key Agreement based authentication, which discloses user identity, location, and other personally identifiable information. To counter this, we propose a public key cryptosystem named “International mobile subscriber identity Protected Console Grid based Authentication and Key Agreement (IPG-AKA) protocol” to address the vulnerabilities related to weak key management. From the data obtained from threat modeling and simulation results, we claim that the IPG-AKA scheme not only improves security of authentication procedures, but also shows improvements in authentication loads and reduction in key generation time. The empirical results and qualitative analysis presented in this paper prove that IPG-AKA improves security in authentication procedure and performance in the LTE.

Recently, nodes cooperation has emerged as a popular means for improving the quality of multimedia delivery over fifth‐generation cellular networks. However, in the conventional relaying scheme such as amplify‐and‐forward (AaF), there is a higher probability of duplicate packets at the receiver node which affect the decoding probability and consequently deteriorate the quality of multimedia transmission. In this study, the authors propose a cooperative multimedia transmission protocol based on a novel merge‐and‐forward relaying and the best relay selection (RS) schemes. Their best RS scheme is based on two important parameters: (i) two‐hop link distances and (ii) minimum block error rate value. Moreover, to combat the packet loss for enhanced and reliable video delivery, they adopt application layer forward error correction scheme which is based on the most improved and advanced version of fountain codes (i.e. RaptorQ codes). They evaluate the performance of the proposed scheme under different time‐sharing scenarios between the direct and best indirect transmission links in terms of decoding failure probability, decoding overhead, peak signal‐to‐noise ratio, and mean opinion score. Simulation results show that the proposed scheme outperforms the conventional AaF relaying scheme.