Journal of High Speed Networks - Volume 30, issue 4

Authors: Fellah, Soumaya | Fellah, Khadidja

Article Type: Research Article

Abstract: Software Defined Network (SDN) is an emerging technology that centralizes network control and automates network management by separating the data plane from the control plane. The control plane is a set of controllers that manage the network switches. The crucial problem of positioning these controllers is known as the Controller Placement Problem (CPP). The optimal controllers positioning is very constrained and affects the different network performance parameters, such as, controller-to-switch latency, inter-controller latency, number of hops between controller and switch and inter-controller. In this paper, we study the impact of optimizing some parameters on the others, we have developed different …approaches, and each considers one or two parameters. Moreover, we propose a balanced optimization approach that considers the various network performance parameters. We formulate the problem as an Integer Linear Programming model that considers all parameters using the Lexicographic Multi-objective optimization method. By ensuring a balance between all parameters, network performance is significantly improved. The obtained results reveal that our approach is more inclusive and provides very interesting results. Show more

Keywords: Controller placement, SDN, lexicographic optimization, latency, Integer Linear Programming

DOI: 10.3233/JHS-230136

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 497-516, 2024

Authors: Khandait, Pratibha | Hubballi, Neminath

Article Type: Research Article

Abstract: Deep Packet Inspection (DPI) methods are extensively used in traffic classification. These methods extract unique application content either at byte or bit level granularity and represent them as signatures. DPI involves string or regular expression matching, which is computationally expensive, and evaluating signatures at bit-level granularity makes it even more inefficient. With the ever-increasing bandwidth and the high-speed internet traffic, the software implementations of DPI have become a performance bottleneck. In this paper, we propose HClass , a DPI-based network traffic classifier completely implemented in software to speed up signature matching. Our contributions with HClass are three-fold. First, we …propose a hybrid signature matching technique with a combination of bit and byte-level signatures. Second, we propose methods to perform bit-level signature matching with byte/word level operations to cope with software implementations and be compatible with general-purpose CPU operations. Third, it uses a two-phase signature matching where first-phase signatures are short and quickly identify the potential application(s), and the second-phase signatures verify the potential application(s) to reduce false positives. We perform experiments with HClass on three datasets and report classification performance and execution time improvement of HClass with our implementations in C language. Show more

Keywords: Accelerating traffic classification, Deep Packet Inspection, computational efficiency, bit-level signatures, byte-level signature

DOI: 10.3233/JHS-230145

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 517-533, 2024

Authors: Kumar, G. Harish | Rao, P. Trinatha

Article Type: Research Article

Abstract: A potential way to handle the future requirements of wireless data traffic is the Massive Multiple Input Multiple Output (MIMO) antenna systems. The most effective method to satisfy the demand for wireless data traffic is to enhance the Spectral Efficiency of the existing spectrum since the wireless spectrum is a limited resource. In the MIMO network, cell-free, energy-efficient, and user-centric are considered as most important parameters to achieve effective communication. Therefore, a new energy efficiency optimization scheme is developed in a massive MIMO system to improve the system’s capacity and spectral efficiency. The multi-channel optimization problem is effectively rectified with …the help of this newly designed energy efficiency optimization scheme. Here, the “Singular Value Decomposition (SVD)” method is utilized for the implementation of a sub-channel grouping scheme, where the sub-channels are arranged in descending order based on the results attained from SVD. After arranging the sub-channels, the sub-channel grouping is carried out, and then the energy efficiency optimization is provided with the help of Integrated Fruit Fly with Salp Swarm Optimization (IFFSSO). This energy-efficient algorithm improves the system capacity and spectral efficiency. The experimental outcome is revealed through various conventional models to ensure the energy efficiency of the recommended model. Show more

Keywords: Massive Multiple Input Multiple Output, Wireless Powered Communication Networks, multi-channel optimization problem, Integrated Fruit Fly with Salp Swarm Optimization

DOI: 10.3233/JHS-230173

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 535-555, 2024

Authors: Wang, Rui | Liu, Xin | Chang, Yingxian | Ma, Lei | Liu, Donglan | Zhang, Hao | Zhang, Fangzhe | Sun, Lili | Yao, Honglei | Yu, Hao

Article Type: Research Article

Abstract: In recent years, there has been a noticeable surge in electric power load due to economic development and improved living standards. The growing need for smart power solutions, such as leveraging user electricity data to forecast power peaks and utilizing power data statistics to enhance end-user services, has been on the rise. However, the misuse and unauthorized access of data have prompted stringent regulations to safeguard data integrity. This paper presents a novel decentralized collaborative machine learning framework aimed at predicting peak power loads while protecting the privacy of users’ power data. In this scheme, multiple users engage in collaborative …machine learning training within a peer-to-peer network free from a centralized server, with the objective of predicting peak power loads without compromising users’ local data privacy. The proposed approach leverages blockchain technology and advanced cryptographic techniques, including multi-key homomorphic encryption and consistent hashing. Key contributions of this framework include the development of a secure dual-aggregate node aggregation algorithm and the establishment of a verifiable process within a decentralized architecture. Experimental validation has been conducted to assess the feasibility and effectiveness of the proposed scheme, demonstrating its potential to address the challenges associated with predicting peak power loads securely and preserving user data privacy. Show more

Keywords: Electricity data, privacy protection, decentralized machine learning

DOI: 10.3233/JHS-230198

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 557-567, 2024

Authors: Regilan, S. | Hema, L.K.

Article Type: Research Article

Abstract: Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy …efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design. Show more

Keywords: Wireless sensor networks, cluster head selection, routing, genetic algorithm, energy efficiency, grid-based topology

DOI: 10.3233/JHS-230209

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 569-582, 2024

Authors: Vhatkar, Kapil | Kathole, Atul B. | Kshirsagar, Aniruddha P | Katti, Jayashree

Article Type: Research Article

Abstract: The machine learning technique has been used to increase cloud management’s intelligence. Effective resource provisioning also preserves the environment. Manual cloud management has some difficult problems, such as complexity in cloud systems and scale issues. Hence, this paper introduces a new task for managing the resources in the cloud using deep learning. The aim is to predict the overall workload and server status prediction to the cloud resource management. Initially, performance monitoring is performed to keep aware of the performance of the application and guarantee the cloud application’s performance. In the suggested work, the required data is collected for the …resource utilization on multiple Virtual Machine (VM) metrics. The VM provisioning is performed next to rectify the issues of resource provisioning. After that, the workload and server status prediction is conducted, where the Weighted Recurrent Neural Network (W-RNN) is adopted. After attaining the predicted workload, the VM placement module is carried out. Here, the virtual resource’s quantity is attained. Moreover, the multi-objective functions like resource utilization; cost, energy, time, and Quality of Service (QoS) are derived in this phase with the help of the Improved Rain Optimization Algorithm (IROA). Subsequently, the VM recycling is performed in the suggested work. Here, a resource collector is given for the virtual resources recycling task. It scans the applications of the cloud in the data centre and processes the VM recycling for every application. While considering the statistical analysis of the IROA-W-RNN-based resource management system achieved a mean of 56.27% than JAYA-W-RNN, 21.09% than SCO-W-RNN, 60.2% than MFOA-W-RNN, and 16.74% than DA-W-RNN for configuration 4. Finally, the numerical analysis is conducted to validate the presented resource management task with the aid of various conventional tasks. Show more

Keywords: Resource management in cloud applications, virtual machine provisioning, Weighted Recurrent Neural Network, placement and recycling, workload and server status prediction

DOI: 10.3233/JHS-230212

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 583-606, 2024

Authors: Hasan, Raqibul | Souri, Alireza

Article Type: Research Article

Abstract: This paper proposes a low power consuming system for monitoring elderly people’s activities and their health conditions. The proposed system has two activity recognition modules: smartphone sensor-based wearable module; infrared grid sensor-based remote module. The two activity recognition modules work in a coordinated way. The fraction of the time the person is detected by the infrared sensor, the smartphone remains idle. As a result, energy consumption in the smartphone is reduced significantly, and hence the battery lifetime is increased. In the smartphone, a Feed-forward Neural Network (FNN) based activity recognition algorithm is implemented using fixed-point computation to further reduce energy …consumption. A Convolutional Neural Network is used in the infrared sensor-based activity recognition module. The proposed system also has real-time health monitoring capability, which is based on ECG signal classification. A FNN leveraging fixed-point operation is used for ECG signal classification on an embedded ARM processor. Proposed fixed-point implementations of the FNNs are faster than floating-point implementation and require 50% less memory to store the neural network model parameters without loss of classification accuracy. Show more

Keywords: IoT system, elderly monitoring, ECG signal classification, activity recognition, fixed-point operation

DOI: 10.3233/JHS-240001

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 607-618, 2024

Authors: Lachure, Jaykumar | Doriya, Rajesh

Article Type: Research Article

Abstract: Smart agriculture has shifted the paradigm by integrating advanced technologies, particularly weed management. This paper introduces an innovative approach to weed control by applying a Wavelet-based Convolution Neural Network (WCNN). In the era of precision agriculture, our study explores the integration of WCNN into real-world scenarios, emphasizing its adaptability to diverse environmental conditions. Utilizing the spatial-frequency analysis features of wavelets and convolutional neural networks, the WCNN model is the most effective at finding weeds, classifying them, and managing them specifically in agricultural fields in real-time. This research contributes to the scientific discourse on smart agriculture and addresses the challenges of …invasive weeds, presenting a sustainable solution for optimizing resource utilization. Our investigation includes a detailed exploration of WCNN’s adaptive learning mechanisms and dynamic adjustment to changing agricultural landscapes. The model seamlessly integrates with existing smart farming infrastructure, showcasing a substantial reduction in manual intervention and a simultaneous increase in agricultural productivity. We incorporate fog computing and resource optimization into our framework, enhancing the efficiency of onboard data processing. To evaluate the real-world efficacy of WCNN, we conducted comprehensive experiments in texture classification and image labelling using two distinct datasets: the plant seedling and soybean weed datasets. Results demonstrate the superior performance of WCNN, achieving higher accuracy in training and test scenarios with significantly fewer parameters than traditional CNNs. For the soybean weed dataset, WCNN achieved remarkable accuracy in the training (0.9970) and testing (0.9987) phases, with correspondingly low losses of 0.0109 and 0.0048. The WCNN model demonstrated high accuracy during training (0.9739) and testing (0.9902), with minimal losses of 0.0898 and 0.0239 in the plant seedling dataset. Show more

Keywords: Convolutional neural network, fog computing, weed seedling, spatial & spectral decomposition, wavelet

DOI: 10.3233/JHS-240019

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 619-638, 2024

Authors: Yang, Jinsong | Hu, Yuanchao | Xiao, Xing | Meng, Chenxu | Zeng, Lingcheng | Li, Xinhai

Article Type: Research Article

Abstract: The Internet of Things (IoT) necessitates secure communication and high availability among objects at the network edge to ensure reliable object-to-object transactions. In the IoT networks, despite resource limitations, especially at the edge of the network, the potential for error is high. Therefore, a mechanism to increase the reliability, lifetime, and stability of the network is necessary. In this paper, we introduce a trust evaluation framework based on a reliability-based friendly relationship method in IoT networks. We present a conceptual trust model that captures the overall performance of the IoT social network based on parameters such as nodes’ communication history …experiences. Trust in the IoT network is built upon a harmonious communication environment that aligns with the trustworthiness of each object and its ability to maintain continuous interactions. We propose an empirical Trust Indicator (TI) that captures individual agents’ experiences in IoT groups, considering the results of system executions, current experience values, and timestamps of interactions. Mathematical models are developed to analyze the dynamics of trust, including trust increase through increased reliability and collaborative interactions and trust decay due to non-cooperative interactions and lack of communication. The model parameters in IoT groups through simulation show that in this system based on the level of reliability and its increase or decrease, its direct effect can be evaluated by quantitative measurement of mean time to failure (MTTF), which is a measure of devices trust and the network itself. Show more

Keywords: Internet of Things (IoT), trust communication, edge computing, reliability, friendly relationship

DOI: 10.3233/JHS-240037

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 639-655, 2024

Authors: Singh, Jagdeep | Dhurandher, Sanjay Kumar | Kumar, Vinesh | Woungang, Isaac | Barolli, Leonard

Article Type: Review Article

Abstract: In the contemporary era, blockchain technology has brought about a significant transformation in the realm of digital currency through innovations like Bitcoin. A blockchain serves as a decentralized ledger, ensuring an immutable record of transactions across a network. Recent observations indicate the pivotal role of blockchain technology not only in the financial sector but also in networking. This study considers blockchain as the essential link in establishing a genuinely decentralized, trustless, and secure environment for network nodes. The objective is to provide a systematic and comprehensive overview of futuristic endeavours in this domain. The exploration begins with an examination of …the fundamental operational concepts of blockchains and how these systems achieve decentralization, security, and suitability. The focus then shifts towards addressing open research challenges within blockchain technologies, particularly in securing diverse communication networks such as Distributed Computing, Vehicular Ad-hoc Networks, Opportunistic Networks, and Delay Tolerant Networks. Simulation results underscore the superior security performance of blockchain, especially under conditions of attack. Show more

Keywords: Wireless networks, blockchain-based framework, security, blockchain technology, cryptography, distributed ledger, consensus mechanisms, attacks, authentication, integrity, confidentiality, scalability

DOI: 10.3233/JHS-240075

Citation: Journal of High Speed Networks, vol. 30, no. 4, pp. 657-677, 2024