Transactions on Emerging Telecommunications Technologies最新文献

Vehicular ad hoc networks (VANET) are one of the advanced technologies for distributing dynamic vehicular information across the globe. The VANET is extensively used in many applications, especially road safety applications and intelligent transport systems (ITS). However, direct communication causes high bandwidth (BW) requirement and power consumption. Hence, this article introduces a clustering-based mechanism to communicate vehicles with infrastructures. The cluster head (CH) is formed based on certain rules, and nodes or vehicles are combined. But, maintaining stability remains challenging for the traditional clustering mechanism. Moreover, the developed technique must examine the malicious and reduce the risk of fake information sharing. This research emphasizes a trust-based clustering mechanism to select the CH based on a vehicle's knowledge, reputation, and experience. In addition to this, the backup head is also analyzed to promote trust in each vehicle. After clustering, secure routing is undertaken. For this, a bionic remora optimization algorithm (BROA) is proposed, and it considers the hop Count Field as well as the transmission range of vehicles to select the best routes. The performance measures such as end-to-end delay ratio, packet delivery ratio (PDR), throughput, trust values, energy consumption are analyzed and compared with existing techniques. In an experimental scenario, the proposed technique has an end-to-end delay of 3.8 ms, PDR of 98%, trust value of .4 and .06 for wormhole and Sybil attack, energy of and throughput of 78.7 kbps are attained. The outcome results prove the efficacy of a proposed method.

Sheena BG, Snehalatha N. Multi-objective metaheuristic optimization-based clustering with network slicing technique for internet of things-enabled wireless sensor networks in 5G systems. Trans Emerg Telecommun Technol. 2022;1:e4626.

The institution location name “KATTANKULATHUR” was missing in the correspondence section.

The correct correspondence address is below.

B. Gracelin Sheena, Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur-603203, Chennai, Tamil Nadu, India. Email: [email protected], Email: [email protected]

We apologize for this error.

Mobile Ad hoc Networks (MANETs) is a self-organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET-based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi-objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO-based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state-of-the-art models such as PCA-FELM, CTAA-MPSO, FLS-RE, LEACH, DCAIDS, WOA-GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity-based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET-based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO-based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

The Named Data Networking (NDN) architecture, known for its caching strategies and name-based routing, is an exemplary paradigm for content distribution across Internet of Things (IoT) devices. In the environment of NDN-IoT, there is an urgent demand for a lightweight signature authentication scheme suitable for terminal devices to ensure the integrity of Data packets and the legitimacy of their sources. Many researchers opt for employing certificateless public key cryptography measures to enhance the security of communication among terminal devices in NDN-IoT. However, among the array of proposed solutions, issues such as lack of resistance against signer identity exposure, susceptibility to man-in-the-middle attacks, and replay attacks persist. Some researchers advocate for partitioning the devices in NDN-IoT into different zones, yet there remains a deficiency in the design of packet exchange mechanisms across distinct zones. To address these issues, this paper proposes a novel blockchain-based certificate-less signature scheme in the NDN-IoT environment that integrates key features such as distributed legitimate producer management, inter-domain interaction mechanisms, anonymous identity protection, and blockchain storage optimization. The overarching goal is to provide robust security services for resource-constrained devices within the NDN infrastructure while ensuring authenticity and integrity of data packets while alleviating the burden of certificate management on end devices. Compared to similar existing solutions, our proposed method incurs only 34% of the computational overhead required for Data packet signature verification, while maintaining equivalent cache occupancy and achieving higher security performance.

Of late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real-time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT-enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault-detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy-Aware Hierarchical Rule-based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state-of-the-art models.

Recently developed trends in wireless communication encounter extremely high surge in data traffic, which makes it inevitable to employ energy efficient techniques to reduce detrimental consequence of carbon emission over society. This concern has motivated us to upgrade our previous work on index and mode modulated orthogonal frequency division multiplexing (IMM-OFDM) for achieving dual benefits in terms of spectral and energy efficiency. In this paper, we have proposed a generalized index and mode modulated OFDM scheme with variable number of subcarrier activation. Novelty of this generalized scheme is its capability to act as a unified model for classic OFDM and three other benchmark index modulated OFDM schemes along with our previously proposed IMM-OFDM scheme. This new scheme outperforms those five descendent schemes in terms of energy efficiency and error performance as indicated by simulation results. Spectral efficiency improvement in this scheme is achieved through optimum sets of active subcarrier number, which are determined to gain most optimized trade-off between spectral and energy efficiency with least detector complexity. This generalized parent scheme can replace individual models of its five descendent schemes and consequently can be considered to be one of the most promising candidates for next generation mobile communication system.

In response to the increasing demand for efficient resource utilization in data center networks (DCNs), the development of intelligent load-balancing algorithms has become crucial. This article introduces the dual double deep Q network (DDQN) algorithm, designed for software-defined networking (SDN) environments within data centers. By leveraging deep reinforcement learning, DDQN addresses the challenges posed by dynamic traffic patterns, diverse flow requirements, and the coexistence of elephant and mice flows. Our algorithm adopts a comprehensive SDN approach, evaluating the network's status by analyzing switch load and bandwidth utilization. Using convolutional neural networks for elephant and mice flows in DCN, our algorithm enables adaptive learning and training tailored to the specific demands of elephant flows. Employing a double deep Q network architecture (DDQN), DDQN optimizes paths for both elephant and mice flows independently. Real-time adaptation mechanisms make routing decisions based on the robust learning capabilities of DDQN, enhancing network utilization and reducing packet loss by generating optimal forwarding paths according to the current network state and traffic patterns. Simulations conducted in a Mininet environment with RYU as the controller, utilizing a fat-tree data center topology, validate the efficacy of DDQN. The results demonstrate its effectiveness in achieving higher throughput, lower latency, and superior load balancing compared to traditional algorithms like equal-cost multipath and Hedera.

Unmanned aerial vehicles (UAVs) are becoming a popular research topic in applications that do not require human intervention. A variety of applications and devices coexist in the environment where UAVs operate, resulting in a serious spectrum shortage. Therefore, cognitive radio (CR) is a promising solution for opportunistic access to underutilized spectrum bands by the primary user (PU) through cooperative spectrum sensing (CSS) technique. However, the flexible location of UAVs makes CSS inefficient and even difficult to be implemented. In view of this, a cognitive UAV network model consisting of a pair of UAVs which follows a circular flight trajectory to participate in CSS is proposed in a spectrum sensing frame structure. According to the local energy detection, we further propose an optimization problem about the stopping time in a quickest detection paradigm and conduct out Bayesian detection method with feedback to minimize the sensing delay and the false alarm probability by optimizing the stopping time. Moreover, we theoretically derive the optimal threshold pair and prove the optimal stopping time by means of Markov process. At last, a series of numerical simulations are shown to corroborate the proposed Bayesian detection method with feedback, in terms of the false alarm probability, the sensing delay, and achievable throughput. In contrast to the classic Neyman-Pearson and Bayesian detection methods, the advantage of Bayesian detection method with feedback sensing is presented.

In this paper, we investigate the performance of an energy harvesting (EH)-enabled multiple relay network operating over generalized - fading channels. Our approach involves utilizing a partial relay selection strategy and employing an amplify and forward relay protocol for efficient EH from RF signals. Additionally, we integrate a selection combining scheme at the receiver to combine both direct and relaying path signals. In our research, we first obtain the exact closed-form expression of the cumulative distribution function (CDF) for the system under investigation. Subsequently, we derive expressions for the outage probability (OP) and the moment generating function (MGF) using the derived CDF expression. Furthermore, with the assistance of a CDF-based approach, we derive closed-form expressions for the average symbol error rate (ASER) for coherent quadrature amplitude modulation (QAM) schemes, including rectangular QAM (RQAM) and hexagonal QAM (HQAM). We also analyze the ASER expression for non-coherent frequency shift keying (NCFSK), leveraging the derived MGF expression. To gain valuable insights into the performance of EH-enabled multiple relay networks, we assess the asymptotic expression of the OP. The outcomes indicate how the behavior of the system is affected by various factors such as time switching ratio, channel fading components, number of relays, and the distance between nodes. To corroborate the accuracy of the analytical findings, Monte Carlo simulations are executed.

Side-channel analysis (SCA) is a type of cryptanalytic attack that uses unintended ‘side-channel’ leakage through the real-world execution of the cryptographic algorithm to crack a secret key of an embedded system. These side-channel errors can be discovered through tracking the energy usage of the device performing the technique, electromagnetic radiations while the encryption process, execution time, cache hits/misses, and others. Nowadays, deep learning-based detection techniques are considered as emerging techniques that have been proposed for attack detection. Deep learning architectures have the ability to learn autonomously and concentrate on difficult features, in contrast to machine learning models. In light of these factors, the work's motive is thought to be the proposal of a deep learning-based attack detection method. Many methods are used to decrease these assaults, however, the majority of them are inefficient and time-demanding. In order to address these challenges, this study employs a novel deep learning-based methodology. Pre-processing, feature extraction, and SCA classification are the three stages of the approach proposed in this work. First, pre-processing is used to remove unnecessary information and improve the quality of the input using data cleaning and min-max normalization. The previously processed data are then fed as input into the proposed hybrid deep learning architecture. A Deep Residual Capsule Auto-Encoder (DR_CAE) model is introduced in the proposed study. The deep residual neural network-50 (DRNN-50) is utilized to extract relevant features in this case, while the side channel analysis is done by using capsule auto-encoder (CAE). The parameters of the proposed model are adjusted using the modified white shark optimization (MWSO) technique to improve its performance. In the results section, the proposed model is compared to various existing models in terms of accuracy, precision, recall, F-measures, time, and so on. The proposed framework has an accuracy of 98.802%, F-measures of 98.801%, kappa coefficient of 97.6%, the precision value of 98.81%, and recall value of 98.80%.