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This journal publishes papers on a number of topics ranging from design to practical experiences with operational high performance/speed networks.
The topics covered will include but not be limited to:
- Communication network architectures
- Evolutionary networking protocols, services and architectures
- Network Security
Authors: Ding, Hangchao | Tang, Huayun | Jia, Chen | Wang, Yanzhao
Article Type: Research Article
Abstract: Bi-deniable Encryption scheme means that when the sender and the receiver are coerced, the coercer can obtain fake plaintext, random numbers, and secret keys. It’s a solution strategy in the case of information leakage. Compared with traditional encryption, deniable encryption can provide secret communications in situations of coercion in the Post-Quantum era. Compared with sender-deniable encryption and receiver-deniable encryption, bi-deniable encryption can achieve secret communications in the situation that both sender and receiver are coerced by the coercer. So, we propose to design a bi-deniable encryption scheme under the multi-distribution model. In our bi-deniable encryption scheme, we construct a …bi-deniable encryption scheme based on the assumption of Decision-Learning With Errors (DLWE) under the multi-distribution model. Firstly, the principle of Inner Product Predicate Encryption (IPPE) is applied in our scheme. Secondly, we apply the framework of Bi-Translucent Set (BTS), combined with inner product predicate encryption. Thirdly, we construct a series of probabilistic polynomial time algorithms, which apply linear transformation between different lattice structures, and Regev dual encryption. Fourthly, the statistical indistinguishability between the sampling algorithm with discrete Gaussian sampling algorithm, and the computational indistinguishability between LWE’s ciphertext samples with uniform samples, the property of indistinguishability is applied in theorem proving, which obtain Indistinguishability under Chosen Plaintext Attacks (IND-CPA) security and the property of bi-deniability. Given the value range of the Gaussian parameter and security parameter in our scheme, the correctness of the bi-deniable encryption scheme is guaranteed. We also give the security proof of IND-CPA and bi-deniability’s property by a series of games. The ‘Inner-Product Bi-Translucent Set’ Bi-Deniable Encryption scheme under the multi-distribution model is based on Decision-LWE assumption, and can avoid quantum-resistant attacks. The bi-deniable encryption scheme is firstly constructed by the inner product with the Decision-LWE assumption, which can provide better properties of security and deniability. Show more
Keywords: Multi-distribution model, bi-deniable encryption, Decision-LWE, inner product, Bi-Translucent Set, post-quantum cryptography
DOI: 10.3233/JHS-230181
Citation: Journal of High Speed Networks, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Singh, Charanjeet | Singh, Pawan Kumar
Article Type: Research Article
Abstract: Massive MIMO (M-MIMO) devices are the key tool to meet the performance stards established for 5G-wireless communication. However, more Radio Frequency (RF) chains needed in base station (BS) with a huge count of transmitting antennas, involve expensive hardware and computing complexities. In order to decrease the RF chains needed in BS, this work intended to use the optimal transmit antenna selection (TAS) strategy. This strategy is gaining a lot of interest since the optimization algorithm aids in the ability to enhance the system performance considerably the efficiency and secrecy rate. This work proposes a novel Coati Adopted Pelican Optimization (CA-PO) …for choosing the optimal TA by considering efficiency as well as secrecy rate. In addition, the CA-PO algorithm makes the decision on which antenna to be elected. At last, the supremacy of CA-PO-based TAS is proven from the analysis regarding secrecy rate and EE analysis. Accordingly, the proposed CA-PO method for MF for set up 2 has attained a higher EE of 0.976; whereas, the DMOA, COA, MRFO, POA and BEA techniques have got a relatively lower EE of 0.968. Show more
Keywords: M-MIMO, Optimal TAS, efficiency, secrecy rate, CA-PO Algorithm
DOI: 10.3233/JHS-230087
Citation: Journal of High Speed Networks, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Neelakantan, Puligundla | Gangappa, Malige | Rajasekar, Mummalaneni | Sunil Kumar, Talluri | Suresh Reddy, Gali
Article Type: Research Article
Abstract: This study presents a novel approach to optimize resource allocation, aiming to boost the efficiency of content distribution in Internet of Things (IoT) edge cloud computing environments. The proposed method termed the Caching-based Deep Q-Network (CbDQN) framework, dynamically allocates computational and storage resources across edge devices and cloud servers. Despite its need for increased storage capacity, the high cost of edge computing, and the inherent limitations of wireless networks connecting edge devices, the CbDQN strategy addresses these challenges. By considering constraints such as limited bandwidth and potential latency issues, it ensures efficient data transfer without compromising performance. The method focuses …on mitigating inefficient resource usage, particularly crucial in cloud-based edge computing environments where resource costs are usage-based. To overcome these issues, the CbDQN method efficiently distributes limited resources, optimizing efficiency, minimizing costs, and enhancing overall performance. The approach improves content delivery, reduces latency, and minimizes network congestion. The simulation results substantiate the efficacy of the suggested method in optimizing resource utilization and enhancing system performance, showcasing its potential to address challenges associated with content spreading in IoT edge cloud calculating situations. Our proposed approach evaluated metrics achieves high values of Accuracy is 99.85%, Precision at 99.85%, specificity is 99.82%, sensitivity is 99.82%, F-score is 99.82% and AUC is 99.82%. Show more
Keywords: Cloud computing, resource allocation, Internet of things, deep Q network, reinforcement Learning
DOI: 10.3233/JHS-230165
Citation: Journal of High Speed Networks, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Muthukumar, S. | Ashfauk Ahamed, A.K.
Article Type: Research Article
Abstract: The “Distributed Denial of Service (DDoS)” threats have become a tool for the hackers, cyber swindlers, and cyber terrorists. Despite the high amount of conventional mitigation mechanisms that are present nowadays, the DDoS threats continue to enhance in severity, volume, and frequency. The DDoS attack has highly affected the availability of the networks for the previous years and still, there is no efficient defense technique against it. Moreover, the new and complex DDoS attacks are increasing on a daily basis but the traditional DDoS attack detection techniques cannot react to these threats. On the other hand, the hackers are employing …very innovative strategies to initiate the threats. But, the traditional methods can become effective and reliable when combined with the deep learning-aided approaches. To solve these certain issues, a framework detection mechanism for DDoS attacks utilizes an attention-aided deep learning methodology. The primary thing is the acquisition of data from standard data online sources. Further, from the garnered data, the significant features are drawn out from the “Deep Weighted Restricted Boltzmann Machine (RBM)” using a “Deep Belief Network (DBN)”, in which the parameters are tuned by employing the recommended Enhanced Gannet Optimization Algorithm (EGOA). This feature extraction operation increases the network performance rate and also diminishes the dimensionality issues. Lastly, the acquired features are transferred to the model of “Attention and Cascaded Recurrent Neural Network (RNN) with Residual Long Short Term Memory (LSTM) (ACRNN-RLSTM)” blocks for the DDoS threat detection purpose. This designed network precisely identifies the complex and new attacks, thus it increases the trustworthiness of the network. In the end, the performance of the approach is contrasted with other traditional algorithms. Hence, the simulation outcomes are obtained that prove the system’s efficiency. Also, the outcomes displayed that the designed system overcame the conventional threat detection techniques. Show more
Keywords: DDoS Attack Detection, deep learning, features extraction, restricted Boltzmann machine, hyper-parameters optimization, enhanced gannet optimization algorithm, attention and cascaded recurrent neural network with residual long short term memory
DOI: 10.3233/JHS-230142
Citation: Journal of High Speed Networks, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Sawant, Surabhi Sushant | Helonde, Jagdish B. | Burade, Prakash G.
Article Type: Research Article
Abstract: Localization of WSN sensor nodes gains significance in order to grasp the idea for dead node replacement and re-establishing the network from data loss. In this research, the node localization and the position estimation of the dead nodes during routing are more effectively identified by the Felis bee localization protocol. The node localization is significant for effective communication and data transfer throughout the network with high signal strength. The developed approach assists in identifying the dead nodes and replaces them with less localization error thus, the signal strength of the receiver side is maximum. By utilizing the information on the …exact position of the anchor nodes, the unknown location of the dead nodes is estimated by the Felis bee localization protocol. The Felis bee localization protocol enhances the potential of node localization concerning the energy, localization error, and multi-objective function, and attains the minimum RMSE as 0.566 at the 25th round, maximum RSSI as −47.925 dBm as well as the Energy at round 25 as 0.6517 J and the Throughput as 0.2952bps over the simulation area of 10 × 100. Further the established localization reaches the minimum RMSE as 0.673, the maximum Energy as 0.5141 J, maximum throughput as 0.5682,and the maximum RSSI as −53.911 dBm, at the 50th round over simulation area 200 × 200 and surpasses other conventional localization techniques. Show more
Keywords: Wireless Sensor Networks, node localization, Felis bee optimization, signal strength, position estimation
DOI: 10.3233/JHS-230131
Citation: Journal of High Speed Networks, vol. Pre-press, no. Pre-press, pp. 1-34, 2024
Authors: Elmahi, M.Y. | Osman, N.I.M.
Article Type: Research Article
Abstract: Routing protocols for Internet of Things (IoT) play a major role in the performance of the network. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) suffers from a number of limitations including congestion of higher-level nodes and unbalanced topology. This paper proposes a novel Objective Function called Load Balanced Minimum Rank with Hysteresis Objective Function (LB_MRHOF), which assigns child nodes to the most suitable parent in the topology. The Objective Function utilizes a weight of the Expected Transmission Count (ETX) and number of children to calculate the Composite ETX and Number of Children (CENOC) which estimates the load …on each node. The attained CENOC is used to select the optimum parent for each node in the topology, where nodes with high CENOC are avoided in the parent selection process. The proposed Objective Function has been evaluated under random and hierarchical network topologies. In addition, the evaluation has investigated the influence of the number of nodes by testing for small, medium and large-scale networks. Results have shown that the proposed Objective Function outperforms MRHOF, OF_FUZZY and OF-EC in terms of Packet Delivery Ratio (PDR) and reduces nodal hop-count under all tested scenarios, with no compromise in energy consumption. They have also revealed that the best performance achieved by LB_MRHOF is attained under large-scale networks. The resulting network topology which is formed by the proposed Objective Function has shown improved balance and more depth. Show more
Keywords: Internet of Things, RPL, Objective Function, Load Balance, ETX
DOI: 10.3233/JHS-230026
Citation: Journal of High Speed Networks, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Wang, Junchi | Xiao, Hong | Jiang, Wenchao | Li, Ping | Li, Zelin | Wang, Tao
Article Type: Research Article
Abstract: In the actual industrial application of robots, the characteristics of robot malfunctions change accordingly as the working environment becomes increasingly diverse and complex. Utilizing the original fault diagnosis models in new working environments correspondingly leads to a decline in the performance and the generalization capability of the model. Moreover, the monitoring data collected in new working processes often has limited or no labels, making the diagnosis models trained with this data unable to identify faults accurately. In this paper, we propose a Domain adaptive Cross-process Fault Diagnosis method (DCFD) to leverage knowledge from existing working processes for diagnosing faults in …new working processes. DCFD uses Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to measure the difference between the current working processes and the previous working processes, enhancing the fault diagnosis capability of the robotic system in cross-process scenarios. DCFD achieves an average fault classification accuracy of 98% on 12 types of migration tasks, which demonstrates the effectiveness of DCFD on cross-process fault diagnosis classification tasks in real-time industrial application scenarios. Show more
Keywords: Industrial robots, fault diagnosis, transfer learning, domain adaptation
DOI: 10.3233/JHS-230235
Citation: Journal of High Speed Networks, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
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