Wireless Communications and Mobile Computing
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Acceptance rate11%
Submission to final decision151 days
Acceptance to publication66 days
CiteScore2.300
Journal Citation Indicator-
Impact Factor-

Analysis of Filtered Multicarrier Modulation Techniques Using Different Windows for 5G and Beyond Wireless Systems

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 Journal profile

Wireless Communications and Mobile Computing provides the R&D communities working in academia and the telecommunications and networking industries with a forum for sharing research and ideas in this fast moving field.

 Editor spotlight

Chief Editor Dr Cai is an Associate Professor in the Department of Computer Science at Georgia State University, USA and an Associate Director at INSPIRE Center.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

An Elliptic Curve Menezes–Qu–Vanston-Based Authentication and Encryption Protocol for IoT

The exponential growth of the Internet of Things (IoT) has led to a surge in data generation, critical for business decisions. Ensuring data authenticity and integrity over unsecured channels is vital, especially due to potential catastrophic consequences of tampered data. However, IoT’s resource constraints and heterogeneous ecosystem present unique security challenges. Traditional public key infrastructure offers strong security but is resource intensive, while existing cloud-based solutions lack comprehensive security and rise to latency and unwanted wastage of energy. In this paper, we propose a universal authentication scheme using edge computing, incorporating fully hashed Elliptic Curve Menezes–Qu–Vanstone (ECMQV) and PUF. This approach provides a scalable and reliable solution. It also provides security against active attacks, addressing man-in-the-middle and impersonation threats. Experimental validation on a Zybo board confirms its effectiveness, offering a robust security solution for the IoT landscape.

Research Article

An Intelligent Energy-Efficient Data Routing Scheme for Wireless Sensor Networks Utilizing Mobile Sink

Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs’ lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis.

Research Article

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

Research Article

Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.

Research Article

Reliability-Constrained Task Scheduling for DAG Applications in Mobile Edge Computing

The development of the internet of things (IoT) and 6G has given rise to numerous computation-intensive and latency-sensitive applications, which can be represented as directed acyclic graphs (DAGs). However, achieving these applications poses a huge challenge for user equipment (UE) that are constrained in computational power and battery capacity. In this paper, considering different requirements in various task scenarios, we aim to optimize the execution latency and energy consumption of the entire mobile edge computing (MEC) system. The system consists of single UE and multiple heterogeneous MEC servers to improve the execution efficiency of a DAG application. In addition, the execution reliability of a DAG application is viewed as a constraint. Based on the strong search capability and Pareto optimality theory of the cuckoo search (CS) algorithm and our previously proposed improved multiobjective cuckoo search (IMOCS) algorithm, we improve the initialization process and the update strategy of the external archive, and propose a reliability-constrained multiobjective cuckoo search (RCMOCS) algorithm. According to the simulation results, our proposed RCMOCS algorithm is able to obtain better Pareto frontiers and achieve satisfactory performance while ensuring execution reliability.

Research Article

Low Latency 5G IP Transmission Backhaul Network Architecture: A Techno-Economic Analysis

The steeply rising demand for mobile data drives the investigation of the transmission backhaul network architecture and cost for the fifth generation (5G) of mobile technologies. The proposed backhaul architecture will facilitate high throughput, low latency, scalability, low cost of ownership, and high capacity backhaul for 5G mobile technologies. This paper presents a transmission backhaul network architecture for 5G technology; the proposed internet protocol (IP) transmission backhauling architecture includes the data center, core network, distribution network, and access or IP random access network. A mathematical model for the data center IP core network, IP distributed network, and the IP access network for capital expenditure (Capex), operational expenditure (Opex), and the total cost of ownership (TCO) are presented, as well as a mathematical model for the entire backhauling architecture. The result shows that the increase in IP sites is positively proportional to the Capex and negatively proportional to the Opex. The selectivity analysis shows that the increase in bandwidth is directly proportional to the Capex, Opex, and TCO in the IP core network. The increase in data centers is directly proportional to the Capex, Opex, and TCO of the entire backhauling architecture.

Wireless Communications and Mobile Computing
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate11%
Submission to final decision151 days
Acceptance to publication66 days
CiteScore2.300
Journal Citation Indicator-
Impact Factor-
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