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Wireless networks: The journal of mobile communication, computation and information
Kluwer Academic Publishers
Wireless networks: The journal of mobile communication, computation and information

Kluwer Academic Publishers

1022-0038

Wireless networks: The journal of mobile communication, computation and information/Journal Wireless networks: The journal of mobile communication, computation and informationSCIEIISTP
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    Improving energy efficiency via the use of IoT technologies with intelligent network clustering

    Radwan S. AbujassarOmar Al-Jarrah
    3533-3553页
    查看更多>>摘要：Abstract The transmission of large volumes of sensor node data is the biggest challenge for IoT networks. Communication power overuse threatens nodes’ survivability. Thus, network challenges including QoS, security, network heterogeneity, congestion avoidance, reliable routing, and energy savings must be addressed. Data transmission between companies requires routing mechanisms. Data aggregation is critical to reducing traffic congestion, operational costs, energy use, and network lifetime. When IoT data is consolidated, route planning for reliability, energy efficiency, and effectiveness is tough. This work presents Cluster-based energy-aware & nearest adjacent neighbour (CEAAN), a novel routing approach using NS2 simulation. This approach predicts delivery success using decision trees and neural networks. We consider CEAAN routing scheme predictability, node popularity, power consumption, speed, and location during model training. According to simulations, CEAAN outperforms NS2’s trustworthy routing scheme in terms of successful delivery, lost messages, overhead, and hop count. However, these changes only slightly enhance buffer length and occupancy. The hybrid routing technique entails cluster construction, as well as intra- and inter-cluster routing. CEAAN outperformed earlier studies in network resilience, packet transmission efficiency, end-to-end latency, and energy utilization.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Task offloading strategy based on improved double deep Q network in smart cities

    Bin WuLiwen MaJia CongJie Zhao...
    3555-3570页
    查看更多>>摘要：Abstract With the rapid development of smart cities, edge computing is confronted with the challenges of a sharp increase in the number of devices and computing tasks. How to efficiently perform task offloading to optimize the utilization of computing resources and reduce latency and energy consumption has become an urgent problem to be solved. This paper proposes a task offloading strategy based on an improved Double Deep Q-Network (DDQN), by designing a new reward function and optimizing the experience replay mechanism to enhance the success rate of task offloading and the learning efficiency of the agent. Additionally, considering that task execution may fail due to excessive load, this paper proposes a load balancing remedial strategy and improves the heuristic sub-algorithm in the greedy algorithm based on the characteristics of intensive tasks to increase the success rate of task offloading. Experimental results show that, compared with three other baseline algorithms in scenarios with different device densities, the proposed algorithm in this paper achieves significant improvements in important indicators such as task success rate, waiting time, and energy consumption.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    SqueezeNet-based key generation for secure device-to-device group authentication in 5G wireless networks

    K. Prabhu ChandranGangu DharmarajuAlok MisraAleem Ali...
    3571-3589页
    查看更多>>摘要：Abstract Device-to-device (D2D) communication is one of the prime components of 5th Generation (5G) networks because it improves the ability of the network and enables sustainability for communal applications. Similarly, authentication in D2D is an inbuilt concern due to the repetition of connection and departure of the network repeatedly. To bridge this concern, a secure D2D authentication module in 5G Wireless Networks is established. Here, the entities employed for this technique are User Equipment (UE), Access and Mobility Function (AMF), Session Management Function (SMF), and Next Generation Node B (gNB). The steps followed for D2D group authentication namely, setup, user registration, D2D Discover, Session request, Key agreement, Group activation and Key update. Initially, AMF chooses a prime and a system secret to determine a system elliptic curve by providing the security parameter and then the system parameters are obtained. After that, every D2D user must register with AMF to protect the member’s privacy. Moreover, a secure D2D group session is performed and then every group member should send a session request to SMF. The members initiate to discuss the session key after observing the user session identity and producing the group session key. The gNB compares the entire values from the requests whether it is similar or not. If it is equal, the gNB initiates the session by providing an activation request to the users. Finally, D2D users upgrade the key to offer backward secrecy. Here, the key is generated by employing SqueezeNet. The performance measures employed here are communication time, computational time, energy consumption and load gained a minimum of 1.490 s, 1.473 s, 0.275 J and 0.742.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    CNN-AC algorithm for hybrid precoding in millimeter-wave massive MIMO systems

    Ruiyan DuTiangui LiGuangyu MengFulai Liu...
    3591-3601页
    查看更多>>摘要：Abstract Hybrid precoding is one of the promising technologies for millimeter-wave communications. As a crucial structure of hybrid precoding, an adaptively-connected (AC) structure can achieve a trade-off between spectral efficiency (SE) and energy efficiency (EE). On account of a large quantity of phase shifters (PSs), the AC structure usually suffers from low spectral efficiency. To tackle this problem, this paper proposes a hybrid precoding algorithm based on a convolutional neural network (CNN) for the AC structure, named CNN-AC algorithm. The proposed CNN-AC algorithm translates the optimization problem from hybrid precoding to a predictive neural network problem. Firstly, a new CNN framework is constructed to predict the vectorized hybrid precoding matrix. Specifically, three unique network layers are constructed to meet the certain constraint. Then, using the fully digital precoder as a reference label, the CNN is trained to minimize the distance between the hybrid precoders and the digital precoders with the estimated channel gain as input. Finally, the estimated channel gain is input into the CNN, resulting in an output hybrid precoding matrix. Simulation results confirm that the CNN-AC algorithm offers satisfactory SE and high EE.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Heuristic intelligent optimal controller for adaptive frequency band selection in 6G optical-RF heterogeneous networks

    Mohammed Ahmed AbdulNabiBashar J. HamzaAhmad Taha Abdulsadda
    3603-3624页
    查看更多>>摘要：Abstract The development of 6G wireless networks brings very important challenges in choosing frequency bands on a highly diverse spectrum of communication technologies, especially those with various weather conditions. This paper presents a real-time dynamic mathematical model called the heuristic intelligent optimal controller, HIOC, which has been designed to choose optimally the frequency bands in real time over Sub-6 GHz, cmWave, mmWave, THz, and Optical Wireless bands in both Line-of-Sight, LOS, and Non-Line-of-Sight, NLOS, environments. Extensive simulation results were presented to show that Sub-6 GHz is stable for any kind of weather and, therefore, can be considered more reliable in harsh conditions with rain, fog, and dust. In contrast, higher frequency waves such as mmWave and THz are superior in clear condition performance but led to severe BER degradation under bad weather conditions, especially for the case of NLOS. It points out that the good performance of Optical Wireless is restricted to only LOS and clear weather conditions, while it is not suitable for NLOS or other bad environmental conditions. The proposed HIOC model dynamically adapts to fluctuating environmental and network conditions for the optimal data transmission rate, minimum errors, latency, and energy use. The key findings allow for scalable, efficient, and resilient communication for 6G heterogeneous networks in a wide range of environmental conditions.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Development of hybrid weighted networks of RNN and DBN for facilitating the secure information system in cyber security using meta-heuristic improvement

    R. Lakshman NaikSourabh JainManjula Bairam
    3625-3660页
    查看更多>>摘要：Abstract As communication and information technologies are integral to everyone’s daily activities, the significance of cybersecurity has become more pronounced due to the growing vulnerability of these technologies to cyber threats. Traditional cyber security systems use various preventive measures to secure the information and trust authentication methods are used to provide the essential security measure against cyber attacks. These methods are efficient and are also equipped to perform in real-world scenarios. However, the conventional cyber security system does not provide essential security against all types of cyber attacks as they are nature-distributed for controlling the systems. Securing these Distributed Control Systems is highly significant for providing a secure and risk-free operation of the connected systems from cyber attacks and other threats. Therefore, a novel method of risk prediction and risk mitigation is developed using the heuristic-based Hybrid Deep Weighted Networks for protecting the data in the information system. The recommended work is based on risk analysis and a cyber security framework built around the information technology security system. This model aimed to design the cyber security system by mitigating all the threats in that particular information system. The main aim is to predict the risk level and mitigate the security threats completely from the system. To achieve this, initially, the data are gathered from different sources and given to the HDWN. The HDWN is developed by combining the Deep Belief Network and a Recurrent Neural Network. These two networks help to predict the risk values of the threat. To attain the enhanced results, the parameters in this model are optimized by using a hybrid algorithm known as African Vultures with Water Wave Optimization, which is developed by combining the Water Wave Optimization algorithm with the African Vulture Optimization Algorithm. Another intention of this model is to mitigate the threat present in the system. Based on the predicted risk value, the system generates warning signals to alert the admin to block the communication. Thus, the threat and risk from the system are predicted and mitigated without interrupting the system’s performance. Finally, the performance validation is performed on the developed model by comparing it with diverse approaches, and the results demonstrate that the proposed model provides impressive outcomes in ensuring data security.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Optimized dynamic task scheduling in cloud computing for big data processing

    D. RadhikaM. Duraipandian
    3661-3672页
    查看更多>>摘要：Abstract In the current tech landscape, Big Data processing is necessary due to the heavy reliance on data-driven technologies. Traditional computing struggles with the sheer volume of data, prompting the integration of cloud computing with Big Data to unlock vast possibilities and mitigate data management challenges. Cloud services facilitate resource access, catering to the Big Data processing needs of diverse organizations, from small-scale to large-scale enterprises. In cloud computing, task scheduling plays a pivotal role in resource allocation. Addressing the shortcomings of static task scheduling, this study introduces dynamic task scheduling. This approach leverages a support vector machine (SVM) for VM classifications and an optimized moth flame optimizations (MFO) algorithm for efficient task allocation. The SVM classifies VMs into four categories based on workload status: unstable/high resource utilization, moderately stable/moderate utilization, and two categories of stable VMs with differing resource needs. Subsequently, the MFO algorithm allocates tasks to these VM categories, focusing on enhancing load balancing and system efficiency. Comparison with traditional particle swarm optimizations and min–max algorithms highlights the superiority of the suggested method. It achieves notable improvements: reducing task waiting time (TWT) by 20%, enhancing task finishing time (TFT) by 15%, and boosting resource utilization efficiency by 25%. Consistently outperforming conventional methods across diverse metrics ensures effective task allocation and system optimization for cloud-based Big Data processing. This research introduces an efficient dynamic task scheduling framework, significantly refining resource allocation and system efficiency within cloud environments. The proposed model signifies substantial advancements over traditional algorithms, catering to the demands of modern data-driven technologies.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    SecureID authenticated key pre-distribution scheme for IoT networks using elliptic curve cryptography

    Chandan GoswamiAvishek AdhikariPinaki Sarkar
    3673-3694页
    查看更多>>摘要：Abstract The Internet of Things (IoT) is a vibrant idea in information and communication technology. Secure communication within the networks for the Internet of Things under adversarial situations requires suitable key agreement among communicating parties towards performing encryption and authentication. IoT security requires distributed key management and lightweight authentication. One of the most popular strategies of key management is key pre-distribution. For lightweight authentication, elliptic curve cryptography (ECC) based protocols are appropriate for IoT devices with limited resources because they allow for less power consumption due to smaller key sizes and faster operation. In this regard, we propose a new secureID authenticated key pre-distribution scheme (KPS) using ECC and name it as Base scheme based on secureID encryption and authentication. In our proposed combinatorial design-based Base scheme, every IoT device has a number of keys in the order of square root of the total number of IoT devices in the network. One of the key aspects of our Base scheme is that any pair of IoT devices in the entire network can interact directly, which makes system communication more efficient. We enhance our Base scheme into Base Subset scheme which significantly improving its resilience and enabling the development of the hash chain Base Subset scheme (HC (Base Subset scheme)) and bidirectional hash chain on Base Subset scheme (2HC (Base Subset scheme)). These schemes encrypt and authenticate the secure id of each device and the individual device transmits it to other devices. On receiving these packages, every recipient device verifies and decrypts them to obtain the device id of the sender. Our schemes outperform state-of-the-art proposals when implemented in a practical deployment zone.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Analysis of delay distribution for downstream message deliveries in sparse VANETs

    Yao-Jen LiangDavid ShiungJeng-Ji Huang
    3695-3703页
    查看更多>>摘要：Abstract Vehicular ad hoc network (VANET) has widely been considered as a promising wireless networking technology to provide a variety of services or applications for the development of intelligent transportation systems (ITS). Messages are delivered between vehicles in a VANET for exchanges or updates of information. However, a sparse VANET may frequently suffer from network disconnections. In this paper, the routing of downstream messages in network disconnections is discussed, and the distribution of the delay incurred during the restoration of a network disconnection is calculated, where the Laplace transform and the inverse Laplace transform are utilized to calculate the distribution of the sum of random variables. Numerical results show that our proposed analysis provides better accuracy in predicting the end-to-end delay of a connection that may contain more than one network disconnection.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Design and analysis of quad port MIMO antenna for mm-wave and K-band applications using DGS

    Amit AbhishekPriyadarshi Suraj
    3705-3726页
    查看更多>>摘要：Abstract This research paper introduces a quad port MIMO antenna significant for K-band and millimeter-wave applications, specifically for short-range communications, spanning a frequency range from 17.4 to 29.4 GHz (with S11 ≤ − 10 dB). The frequency range of the K-band is 18–27 GHz and the 5G communication band lies between 24 and 28 GHz (mm-wave). The proposed frequency range validates these two applicable bands. The antenna's dimension is 10 × 10 × 0.8 mm3 (equivalent to 1.33λo × 1.33λo × 0.10λo) with a bandwidth of 51.06% and a remarkable peak radiation efficiency of 97%. Initially, a monopole antenna measuring 5 × 5 × 0.8 mm3, employing a staircase half-ring ground structure was designed to operate within the desired frequency band. Subsequently, a quad-element MIMO antenna was developed by orienting each radiator perpendicular to each other, ensuring the isolation greater than 35 dB. The enhancement in isolation is attributed to integrating a DGS structure at the ground plane. Various MIMO diversity performance metrics such as ECC, DG, TARC, MEG, CCL, and MIMO VSWR were also evaluated. The optimization process involved exploring different MIMO configurations and antenna sizes, culminating in a proposed MIMO antenna with a peak gain of 6.2 dBi. Simulation and optimization were carried out using the HFSS 19 platform.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature