Wireless Network Resources Research Articles

ESRA: Energy soaring-based routing algorithm for IoT applications in software-defined wireless sensor networks

Software-defined wireless sensor networking is an emerging networking architecture envisioned to play a critical role in the looming internet of things paradigm. Since energy is a scarce resource in wireless sensor networks, many energy-efficient routing algorithms were proposed to enhance the network lifetime. However, most of these algorithms lack network stability and reliability in the presence of dead nodes. This paper presents ESRA: Energy Soaring-based Routing Algorithm for IoT Applications in Software-Defined Wireless Sensor Networks, specifically for monitoring environment to address this shortcoming. The proposed ESRA algorithm efficiently selects the network cluster heads to be considered for solving the controller placement problem, intending to achieve network reliability and stability and enhance the network lifetime. The selection of controllers among the cluster heads is formulated as an NP-hard problem, considering the residual energy of the cluster heads, their spatial distance to the sink, and their load or density. To tackle this NP-hard problem, genetic algorithm is adopted to optimize the network lifetime, throughput, latency, and network reliability in the presence of different percentages of dead nodes. Simulation results showed that ESRA outperforms other three state-of-the art algorithms in terms of network lifetime and throughput by 15%, 20%, and 25%, in terms of energy savings by 10%, 20%, and 25%, and in terms of delay by 10%, 15%, and 20%. We also applied the proposed scheme on real networks adopted from the internet topology zoo, which showed promising results compared to other existing works.

Certificateless Multisignature Scheme Suitable for Network Coding

Network coding can save the wireless network resources and improve the network throughput by combining the routing with coding. Traditional multisignature from certificateless cryptosystem is not suitable for the network coding environment. In this paper, we propose a certificateless multisignature scheme suitable for network coding (NC-CLMSS) by using the sequential multisignature and homomorphic hash function. NC-CLMSS is based on the CDH and ECDL problems, and its security is detailedly proved in the random oracle (RO) model. In NC-CLMSS, the source node generates a multisignature for the message, and the intermediate node linearly combines the receiving message. NC-CLMSS can resist the pollution and forgery attacks, and it has the fixed signature length and relatively high computation efficiency.

Certificateless anti-quantum ring signcryption for network coding

Network coding is a kind of information interaction technology which combines the routing with coding, and it can improve the system robustness and save the wireless network resources. However, the traditional ring signcryption is not suitable for network coding and quantum computing settings. In view of this, we propose a certificateless anti-quantum ring signcryption for network coding (NC-CLAQRSC), which is devised based on the small integer solution and learning with errors problems. NC-CLAQRSC satisfies the confidentiality and existential unforgeability. NC-CLAQRSC has high communication efficiency and low computation cost.

Transfer Learning-powered Resource Optimization for Green Computing in 5G-Aided Industrial Internet of Things

Objective: Green computing meets the needs of a low-carbon society and it is an important aspect of promoting social sustainable development and technological progress. In the investigation, green computing for resource management and allocation issues is only discussed. Therefore, in the context of the 5G communication network, the investigation of the data classification and resource optimization of the Internet of Things are conducted. Method: The virtualization architecture of the heterogeneous wireless network resource based on 5G technology is designed. The related investigation is conducted based on 5G network and Internet of Things technology. Under the traditional method, the transfer learning is introduced to improve the AdaBoost (Adaptive Boosting) algorithm to classify the data. The investigated complete resource reuse method is used to optimize resources. A method that a sub-channel can be reused by a cellular link and any number of D2D links at the same time is proposed to conduct resource optimization investigation. Results: The investigation indicates that the classification accuracy of the algorithm is excellent for the data classification of the Internet of Things and has different advantages in various aspects compared with other algorithms. The designed algorithm can find a larger set of resource reuse and have a significant increase in spectrum utilization efficiency. Conclusion: The investigation can contribute to the boom in the Internet of Things in terms of data classification and resource optimization based on 5G.

Certificateless Network Coding Ring Signature Scheme

Traditional certificateless ring signature is not suitable for the network coding environment. In this view, a new certificateless network coding ring signature (CL-NCRS) is devised by using the techniques of multisource network coding and certificateless ring signature. Network coding is a kind of information interaction technology and can improve system robustness and save wireless network resources. CL-NCRS satisfies the unforgeability and unconditional anonymity; moreover, it has relatively higher communication and computational efficiency.

A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics

With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an urgent issue, as the growth of its volume is becoming a challenge for today’s mobile infrastructure. Thus, we have formalized the problem of access network selection to improve the quality of mobile services through the efficient use of heterogeneous wireless network resources and optimal horizontal–vertical handover procedures. We proposed a method for adaptive selection of a wireless access node in a heterogeneous environment. A structural diagram of the optimization stages for wireless heterogeneous networks was developed, making it possible to improve the efficiency of their functioning. A model for studying the processes of functioning of a heterogeneous network environment is proposed. This model uses the methodology of big data evaluation to perform data transmission monitoring, analysis of tasks generated by network users, and statistical output of vertical handover initiation in (2G/3G/4G/5G/Wi-Fi) mobile communication infrastructure. The model allows studying the issues of optimization of operators’ networks by implementing the algorithm of redistribution of its network resources and providing flexible load balancing with QoS users in mind. The effectiveness of the proposed solutions is evaluated, and the performance of the heterogeneous network is increased by 16% when using the method of static reservation of network resources, compared to homogeneous networks, and another 13% when using a uniform distribution of resources and a dynamic process of their reservation, as well as compared to the previous method. An appropriate self-optimizing technique based on vertical handover for load balancing in heterogeneous wireless networks, using big data analytics, improves the QoS for users.

Driving forces for Multi-Access Edge Computing (MEC) IoT integration in 5G

The emergence of Multi-Access Edge Computing (MEC) technology aims to extend cloud computing capabilities to the edge of the wireless access networks, i.e., closer to the end-users. Thus, MEC-enabled 5G wireless systems are envisaged to offer real-time, low-latency, and high-bandwidth access to the radio network resources. Thus, MEC allows network operators to open up their networks to a wide range of innovative services, thereby giving rise to a brand-new ecosystem and a value chain. Furthermore, MEC as an enabling technology will provide new insights into coherent integration of Internet of Things (IoT) in 5G wireless systems. In this context, this paper expounds the four key technologies, including Network Function Virtualization (NFV), Software Defined Networking (SDN), Network Slicing and Information Centric Networking (ICN), that will propel and intensify the integration of MEC IoT in 5G networks. Moreover, our goal is to provide the close alliance between MEC and these four driving technologies in the 5G IoT context and to identify the open challenges, future directions, and concrete integration paths.

Research on Application of wireless Communication System in Middleware of Internet of Things

With the popularization of mobile communication technology and Internet of things, wireless data transmission operation occupies an important position. Massive data generated by the Internet of things system is applied to the Internet of things system by wireless communication network and wireless resource management. Wireless network communication technology is the use of radio waves for data transmission, is a new type of communication information system, for the future development of communication system has established the direction. The wireless communication system is introduced into the middleware of the Internet of things system to improve the security and reliability of the Internet of things and ensure the timely and effective dissemination of information and data in the Internet of things system.

Priority Pricing for Efficient Resource Usage of Mobile Internet Access

Radio-frequency spectrum resources are finite and scarce, but their demand is increasing exponentially every year. Therefore, wireless network resources are too expensive to be wasted. To avoid waste, pricing techniques can efficiently control resource usage and manage user needs in networks. This study focuses on QoS-aware pricing for usage-based mobile Internet access charging. Specifically, I propose a heuristic algorithm for priority pricing with multiple service levels. The proposed algorithm is built on top of the existing equilibrium analysis methods. While being extensively studied for optimal price selection, the equilibrium methods make a few unrealistic assumptions, and so my methods adjust the solutions of the equilibrium methods to account for distortions that the real world creates. The evaluation results indicate that multiple equilibrium prices may exist, and the proposed scheme produces a pricing plan that is substantially more effective than existing equilibrium methods.

Communication Network for Sports Activity Monitoring Systems

In this paper, through research and analysis of the communication network of the physical activity monitoring system, we combine wearable technology and identification technology and design a physical health monitoring bracelet that integrates multifaceted physical data collection and effective identity matching function. We match the identity through the chip and collect the physical fitness data generated in the process of exercise and centralized test by the sensor in real-time. Finally, the data transmission is realized through the WIFI communication function to achieve the purpose of monitoring physical exercise and improving physical quality. To ensure the continuity and stability of information transmission, the joint transmission method of direct transmission and indirect transmission is essential. Besides, considering the energy causality limitation of sensor nodes and relay nodes, a collaborative transmission model of wireless body area network based on wireless cognitive network is constructed. And, a power allocation algorithm based on maximum ratio merging and wireless cognitive network is proposed, which puts forward a new idea for the future research of wireless body area network resource allocation.

A Proposed Solution for Route Reply Storm Problem to Improve DSR Protocol Performance in Wireless Sensor Networks

Due to the limited transmission range of the wireless sensor network interface, a node may need multiple network hops to exchange data with another node over networks and other issues cause an overhead and bad utilization on the network. As a consequently, if some nodes reply route request at the same time to the same destination, then these simultaneous replies from different nodes will cause collision at the destination which it can cause a problem named route reply storm. The dynamic source routing (DSR) protocol is a reactive source routing protocol for mobile IP network where nodes should sense and discover with nearby nodes. Each node acts as a router for sending and receiving the packets to/from other node. In this work, we present a new proposal as a solution for storm route reply problem in DSR; the objective of the new proposed solution is to present a way to reduce unnecessary packet flooding between source and destination nodes in order to enhance DSR protocol. Afterthought, this work proposed some case studies to verify our proposed solution for solving the problem route reply storm, our verifications proves our robustness and correctness of the proposed solution against two main WSN problems that may face any wireless sensor network including coverage problem and link failure problem. The simulation results achieved here for two routing protocols DSR protocol and proposed improved DSR protocol for energy efficiency parameters. Simulation results showed that the energy consumption of proposed approach is minimized by 25 % approximately as compared to previous standard DSR protocol while the network lifetime performance is increased by approximately 35 % as compared to standard DSR protocol. In summary, the proposed solution of DSR protocol can be energy efficient for wireless sensor networks resource management.

Joint Resource Allocation at Edge Cloud Based on Ant Colony Optimization and Genetic Algorithm

Both the radio resources in wireless networks and the computational resources in cloud have big impact on the performance of the mobile cloud computing system. In this paper, we study the joint radio and computational resource allocation in a mobile edge cloud system with a heterogeneous radio access network and a close-by edge cloud. The objective of the proposed resource allocation scheme is to maximize the system utility as well as satisfy the diverse quality requirements for the delay-sensitive and computation-intensive applications of mobile users. The requirements for economic cost reduction and energy conservation are considered in the proposed scheme to achieve the balance between the user-centric and network-centric resource allocation. The proposed scheme takes advantage of both ant colony optimization (ACO) and genetic algorithm (GA) to explore and exploit the search space to obtain the near optimal solution at the lower computational complexity. ACO is applied for generating the initial population, and GA operations such as mapping, crossover, and repair are proposed to improve the search ability and avoid premature convergence through the search of solution in a broader search space. Simulation results show that our proposed scheme outperforms the existing schemes in terms of convergence performance and the accuracy of final results. Moreover, the results demonstrate that it can not only achieve significant system utility improvement, but also achieve higher resource utilization as well as remarkably lower average latency.

IRS-Aided Physical Layer Network Slicing for URLLC and eMBB

5G and beyond 5G (B5G) wireless systems promise to support services with different requirements in the same network, as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communication (mMTC). One alternative is to consider the network slicing paradigm, where the wireless network resources are shared (or sliced) among active services with different requirements. In addition, another emerging technology, that is considered as a key enabler for B5G wireless systems, is the intelligent reflecting surfaces (IRS). From the deployment of an IRS, it is possible to improve the received signal quality and consequently increase the overall network capacity. Therefore, in this paper, we investigate the use of IRS to support simultaneous eMBB and URLLC services. We evaluate the achievable rate of an IRS-aided radio access network, where the uplink resources are shared between eMBB and URLLC users either under heterogeneous orthogonal multiple access (H-OMA) or heterogeneous non-orthogonal multiple access (H-NOMA) techniques. Results show that exploiting an IRS can considerably increase the eMBB rate and the URLLC reliability simultaneously, regardless of whether operating under H-OMA or H-NOMA. Moreover, we also provide some insights on the best user pairing strategy, showing that higher rates are achieved by matching many eMBB users near to the IRS with a URLLC user close to the base station.

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

The vehicular network is taking great attention from both academia and industry to enable the intelligent transportation system (ITS), autonomous driving, and smart cities. The system provides extremely dynamic features due to the fast mobile characteristics. While the number of different applications in the vehicular network is growing fast, the quality of service (QoS) in the 5G vehicular network becomes diverse. One of the most stringent requirements in the vehicular network is a safety‐critical real‐time system. To guarantee low‐latency and other diverse QoS requirements, wireless network resources should be effectively utilized and allocated among vehicles, such as computation power in cloud, fog, and edge servers; spectrum at roadside units (RSUs); and base stations (BSs). Historically, optimization problems have mostly been investigated to formulate resource allocation and are solved by mathematical computation methods. However, the optimization problems are usually nonconvex and hard to be solved. Recently, machine learning (ML) is a powerful technique to cope with the complexity in computation and has capability to cope with big data and data analysis in the heterogeneous vehicular network. In this paper, an overview of resource allocation in the 5G vehicular network is represented with the support of traditional optimization and advanced ML approaches, especially a deep reinforcement learning (DRL) method. In addition, a federated deep reinforcement learning‐ (FDRL‐) based vehicular communication is proposed. The challenges, open issues, and future research directions for 5G and toward 6G vehicular networks, are discussed. A multiaccess edge computing assisted by network slicing and a distributed federated learning (FL) technique is analyzed. A FDRL‐based UAV‐assisted vehicular communication is discussed to point out the future research directions for the networks.

An Indoor Positioning and Prewarning System Based on Wireless Sensor Network Routing Algorithm

One of the most important means to position abnormal devices is to efficiently utilize the resources of wireless sensor network (WSN) and make proper analysis of the relevant data. Therefore, this paper constructs an indoor positioning and prewarning system that utilizes energy efficiently and achieves a long lifecycle. Firstly, the adjacent round iteration load balancing (ARILB) routing algorithm was proposed, which elects the cluster heads (CHs) by the adjacent round strategy. In this way, the random components were eliminated in CH election. Next, a short‐distance multifrequency routing strategy was constructed between CHs to transmit the information to the sink, and a positioning algorithm was designed called ARILB‐received signal strength (RSS). The ARILB‐RSS positioning algorithm traverses the triangles formed by anchor nodes, forming multiple sets of ranging points; then, the optimal anchor node is recorded, and the path loss factor is iterated to reduce the positioning error. Simulation shows that the network survives 54.5% longer using ARILB than using the distributed energy‐efficient clustering (DEEC) algorithm; the packet delivery rate using ARILB was about 139% higher than that of low energy adaptive clustering hierarchy (LEACH) algorithm and 35% higher than that of uneven clustering routing algorithm based on chain‐cluster type (URCC) algorithm; ARILB‐RSS reduced the ranging error by 14.31% and then the positioning error by 26.79%.

Passive Wireless Sensor Network‐Based In‐Transit Health Status Monitoring for Railway Transportation

In this paper, passive wireless sensor network technology is used to conduct in‐depth research and analysis on the monitoring of the in‐transit health status of railway transportation. Safety detection sensors, train communication networks, and other onboard devices organically constitute a train sensor network with comprehensive state sensing, information aggregation, and business collaboration. First, this paper analyzes the three‐layer architecture of the sensing layer, network layer, and application layer of the train operation status safety detection sensor network. Based on analyzing the feasibility of Ethernet application in an onboard communication network, ring, multiring, and ladder‐type sensor network structure schemes are designed. Next, the real‐time performance and reliability of various sensor network structures are analyzed. First, the characteristics of bandwidth demand, priority, and importance of information transmission of each monitoring object are analyzed; then, the system utility function is established according to the bandwidth usage efficiency and communication demand of information transmission of each monitoring object; finally, the bandwidth allocation optimization model is solved by using a particle swarm optimization algorithm, and the remaining bandwidth resources are dynamically rationed on demand while ensuring the complete transmission of basic information of each monitoring object. Rail transit technology has profoundly affected and changed the travel and lifestyle of residents in the new era and will provide strong infrastructure support for the smooth advancement of new urbanization construction and development strategies. The remaining bandwidth resources are dynamically allocated on the premise of ensuring the complete transmission of basic information of each monitoring object. In summary, this paper presents a complete study on the optimal allocation of resources for wireless sensor networks dedicated to rail transit condition monitoring, starting from the perspectives of routing protocol optimization, data fusion rate optimization, and bandwidth dynamic allocation optimization to achieve the efficient use of limited resources. The achieved research results enrich the research content in the field of wireless monitoring of rail transit system conditions at home and abroad in a certain sense and will also provide theoretical and empirical support for practical practitioners, managers, and scholars of service condition monitoring of rail transit systems.

Research on resource allocation algorithm of wireless network based on game feedback

In order to solve the problems of poor de-noising low-resource utilisation, low fairness and serious interruption in resource allocation algorithm of wireless network based on chaotic immune algorithm, a wireless network resource allocation algorithm based on game feedback is proposed in this paper, which combines wavelet transform domain interpolation with low-pass filter combination. Effective de-noising of transmission resources; classification of users based on game theory, establishment of optimisation utility function, analysis of channel distribution. By comparing the two existing algorithms, the simulation environment is constructed, and the performance comparison experiments are carried out from four aspects: de-noising effect, resource utilisation, fairness and interruption. The results show that the algorithm is highly de-noising, improve the efficiency of resource utilisation and fairness of allocation, and allocates 0 interruption rate. Therefore, the method can flexibly configure and dynamically adjust the available resources of wireless transmission part to maximise transmission efficiency.

Research on resource allocation algorithm of wireless network based on game feedback

In order to solve the problems of poor de-noising low-resource utilisation, low fairness and serious interruption in resource allocation algorithm of wireless network based on chaotic immune algorithm, a wireless network resource allocation algorithm based on game feedback is proposed in this paper, which combines wavelet transform domain interpolation with low-pass filter combination. Effective de-noising of transmission resources; classification of users based on game theory, establishment of optimisation utility function, analysis of channel distribution. By comparing the two existing algorithms, the simulation environment is constructed, and the performance comparison experiments are carried out from four aspects: de-noising effect, resource utilisation, fairness and interruption. The results show that the algorithm is highly de-noising, improve the efficiency of resource utilisation and fairness of allocation, and allocates 0 interruption rate. Therefore, the method can flexibly configure and dynamically adjust the available resources of wireless transmission part to maximise transmission efficiency.

THE IMPACT OF MOBILE NETWORK SETTINGS ON THE POWER CONSUMPTION OF VEHICLE REMOTE CONTROL UNITS

The article is devoted to the study of the influence of mobile network settings on the energy consumption of electronic car control units. Signal quality and mobile network coverage are also important for both users and automotive devices that use real-time information from the network using cloud technologies to transmit, process and store data. For the quality of service measurements, Communications Regulatory Authority uses just HTTP data transmission (receipt) speed rate values in the operators’ UMTS (3G) networks (as CRA stated in annual report). But speed is not everything as many consumers in the industry are experiencing recently. In addition, improvements now also revolve about reducing the signaling load incurred when a radio bearer is established, modified or released. Another area for improvements is the signaling overhead on the air interface when little data is transferred as it consumes resources and causes battery drain from the mobile phone's point of view. The authors focused on the UMTS (the Universal Mobile Telecommunications System) 3G network, which is among the most popular 3G mobile communication technologies. We explore the RRC state machine of large cellular networks which operate in Lithuania. Note that in UMTS network, the centralized core network (CN) is responsible for ensuring switching and routing for the end users traffic. All Network management functions and required databases are also implemented in core network. The core network domain is further divided into sub categories: serving network domain, home network domain and the transit network domain. It consists of two kinds of General Packet Radio Service (GPRS) Support Nodes (GSNs), in 3GPP named as Gateway GSN (GGSN) and Serving GSN (SGSN) (Figure 1). SGSN is the central node of the packet switched (PS) domain. It provides for routing functionality and interacts with databases (like Home Location Register (HLR)) and manages many Radio Network Controllers (RNCs). So the authors were researching the RRC machines of Lithuanian cellular operators’ networks. The purpose of those experiments was to identify which network could provide a best user internet browsing experience, best user equipment battery consumption feature and best radio network resource utilization scheme. We can state, that comparing all these partitions, Operator2 has the optimal configuration for user experience, UE device battery consumption and network resource utilization of RRC state machine point of view.

Guest Editorial: Communication Technologies for Efficient Edge Learning

Traditional machine learning is centralized in the cloud (data centers). Recently, the security concern and the availability of abundant data and computation resources in wireless networks are pushing the deployment of learning algorithms toward the network edge. This has led to the emergence of a fast growing area, called edge learning, which integrates two originally decoupled areas: wireless communication and machine learning. It is widely expected that the advancements in edge learning would provide a platform for implementing edge artificial intelligence (AI) in 5G-and-Beyond systems and solving large-scale problems in our society ranging from autonomous driving to personalized healthcare. A typical edge learning framework (e.g., federated learning) features distributed learning over many wireless devices as coordinated by edge servers to cooperatively train a large-scale AI model using local data and CPUs/GPUs. The iterative learning process involves repeated downloading and uploading of high-dimensional (millions to billions) model parameters or their updates by tens to hundreds of devices. This will generate enormous data traffic, placing a heavy burden on the already congested radio access networks. The training problem cannot be efficiently solved using traditional wireless techniques targeting rate maximization and decoupled from learning. Achieving the goal of edge learning with high communication efficiencies calls for the design of new wireless techniques based on a communication-and-learning integration approach.