Reconfigurable Wireless Networks Research Articles

Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.

Guest Editorial Special Issue on Energy-Efficient Reconfigurable Wireless Communication and Networks

The next-generation wireless communication and networks are envisioned to become controllable, programmable, and intelligent by leveraging emerging technologies such as Intelligent Reflecting Surface (IRS) and Cognitive Radio. Reconfigurable wireless networks aim to enable “intelligence” into the existing system to perceive and assess the available resources, autonomously learn to adapt to the wireless environment’s dynamism and reconfigure its operating mode to maximize the utility of the available resources. Moreover, it needs to be energy efficient to support the technological limitations on energy supply and mitigate the effect of the information and communication technologies. Apart from that, the newly designed solutions need to be energy efficient to accommodate the power consumption of the numerous devices in wireless communication and Networks. This special issue aims to motivate the researchers to design novel schemes and architectures for the energy-efficient reconfigurable wireless communication and networks. This issue has 47 high-quality papers covering several aspects of reconfigurable wireless communication and networks. Among them, 17 papers with significant original contributions were eventually selected for publications in this special issue.

Energy-Efficient Federated Learning With Intelligent Reflecting Surface

Federated learning is a new paradigm to support resource-intensive and privacy-aware learning applications. It enables the Internet-of-Things (IoT) devices to collaboratively train a global model to accomplish a machine learning task without sharing private data. In practice, the IoT devices powered by batteries finish the local training and interact with the central server via wireless links. However, the repeated interaction between IoT devices and the central server would consume considerable resources. Motivated by the emerging technology of intelligent reflecting surface (IRS), we propose to leverage the IRS to reconfigure the wireless propagation environment to maximize the utilization of the available resources. Specifically, we consider the critical energy efficiency issue in the reconfigurable wireless communication network. We formulate an energy consumption minimization problem in an IRS-assisted federated learning system subject to the completion training time constraint. An iterative resource allocation algorithm is proposed to jointly configure the parameters with proven fast convergence. Simulation results validate that the proposed algorithm converges fast and can achieve significant energy savings, especially when the number of reflecting elements is large and when the IRS is properly configured.

RDRL: A Recurrent Deep Reinforcement Learning Scheme for Dynamic Spectrum Access in Reconfigurable Wireless Networks

Reconfigurable wireless network can flexibly provide efficient spectrum access service and keep stable operation in highly dynamic environment. In this paper, a primary-prioritized recurrent deep reinforcement learning algorithm for dynamic spectrum access based on cognitive radio (CR) technology is proposed. The spectrum Markov state is modeled to capture the evolution behavior to achieve the priority queuing of the primary users and the secondary users. According to the spectrum access strategies of the secondary users under different optimal criteria, we can obtain the best tradeoff benefits of spectrum access fairness and throughput. Furthermore, we proposed a learning-based algorithm for dynamic spectrum access, which allows the secondary users to modify their parameters to select the optimal access policy to maximize network throughput utilization. The Dueling Deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Network (Dueling DQN) with prioritized experience replay combined with recurrent neural network is used to improve the convergence speed. Extensive experimental results demonstrate that the proposed RDRL scheme outperforms the existing Dueling DQN and DQN schemes in terms of convergence speed and channel throughput.

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

AI-Empowered Propagation Prediction and Optimization for Reconfigurable Wireless Networks

Vehicular ad-hoc network (VANET) is one of the most important components to realizing intelligent connected vehicles, which is a high-commercial-value vertical application of the fifth-generation (5G) mobile communication system and beyond communications. VANET requires both ultrareliable low latency and high-data rate communications. In order to evolve towards the reconfigurable wireless networks (RWNs), the 5G mobile communication system is expected to adapt the key parameters of its radio nodes rapidly. However, the current propagation prediction approaches are difficult to balance accuracy and efficiency, which makes the current network unable to perform autonomous optimization agilely. In order to break through this bottleneck, an accurate and efficient propagation prediction and optimization method empowered by artificial intelligence (AI) is proposed in this paper. Initially, a path loss model based on a multilayer perception neural network is established at 2.6 GHz for three base stations in an urban environment. Not like empirical models using environment types or deterministic models employing three-dimensional environment models, this AI-empowered model explores the environment feature by introducing interference clutters. This critical innovation makes the proposed model so accurate as ray tracing but much more efficient. Then, this validated model is utilized to realize a coverage prediction for 20 base stations only within 1 minute. Afterward, key parameters of these base stations, such as transmission power, elevation, and azimuth angles of antennas, are optimized using simulated annealing. This whole methodology paves the way for evolving the current 5G network to RWNs.

A Deep Learning Based Explainable Control System for Reconfigurable Networks of Edge Devices

Edge devices that operate in real-world environments are subjected to unpredictable conditions caused by environmental forces such as wind and uneven surfaces. Since most edge systems such as autonomous vehicles exhibit dynamic properties, it is clear that reinforcement learning can be a powerful tool for improving system accuracy. Successful maintenance of the position of a vehicle in such environments has been recently achieved with the aid of Deep Reinforcement Learning (DRL) that dynamically adjusts the Reconfigurable Wireless Network (RWN) response. Deep Neural Networks (DNNs) are often seen as black boxes, as neither the acquired knowledge nor the decision rationale can be explained. This raises issues with the transparency and trustworthiness of the system because the underlying AI models are not governed by any mathematical or physics laws. In this paper, we explain the process of a DNN utilisation in an autonomous dynamic positioning system by gauging reactions of the DNN to predefined constraints and capturing the associated conditions that influence the DNN in a time series. We introduce a novel digitisation technique that reduces interesting patterns of time series data into single-digits to obtain a cross comparable view of the conditions. By analysing the clusters formed on this cross comparable view, we discovered multiple intensities of environmental conditions spanning across 44% of moderate conditions and 33% and 23% of harsh and mild conditions respectively. Our analysis showed that the proposed system can provide stable responses to uncertain conditions by predicting randomness.

LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

Detection accuracy of current machine-learning approaches to intrusion detection depends heavily on feature engineering and dimensionality-reduction techniques (e.g., variational autoencoder) applied to large datasets. For many use cases, a tradeoff between detection performance and resource requirements must be considered. In this paper, we propose Loci-Constellation-based Intrusion Detection System (LC-IDS), a general framework for network intrusion detection (detection of already known and previously unknown routing attacks) for reconfigurable wireless networks (e.g., vehicular ad hoc networks, unmanned aerial vehicle networks). We introduce the concept of ‘attack-constellation’, which allows us to represent all the relevant information for intrusion detection (misuse detection and anomaly detection) on a latent 2-dimensional space that arises naturally by considering the temporal structure of the input data. The attack/anomaly-detection performance of LC-IDS is analyzed through simulations in a wide range of network conditions. We show that for all the analyzed network scenarios, we can detect known attacks, with a good detection accuracy, and anomalies with low false positive rates. We show the flexibility and scalability of LC-IDS that allow us to consider a dynamic number of neighboring nodes and routing attacks in the ‘attack-constellation’ in a distributed fashion and with low computational requirements.

AI-Assisted Energy-Efficient and Intelligent Routing for Reconfigurable Wireless Networks

Intelligent network management for reconfigurable wireless networks such as 5G and beyond applications is crucial for many industrial applications, and has been the subject of ongoing research. This paper proposes an Artificial Intelligence(AI)-Assisted energy-efficient and intelligent routing, based on both energy efficiency prioritization and AI theory, in order to meet the exacting demands particularly in a real-world scenario. Specifically, to achieve network intelligence and quality of service (QoS), we use the AI theory to enhance routing adaptivity for intelligent network management in reconfigurable wireless networks. The software-defined networking idea is used to achieve this goal from a network-level perspective. To facilitate self-awareness, self-study, self-decision making, and self-configuration, we construct a mathematical model to convert the energy-efficient and intelligent routing problem into a multi-constraint optimal problem. Then an AI-assisted intelligent routing algorithm is designed to dynamically and adaptively change link weighs, which allows us to achieve optimal energy efficiency. Findings from our simulation suggest the potential of our proposed approach.

Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks

The flexibility offered by reconfigurable wireless networks, provide new opportunities for various applications such as online AR/VR gaming, high-quality video streaming and autonomous vehicles, that desire high-bandwidth, reliable and low-latency communications. These applications come with very stringent Quality of Service (QoS) requirements and increase the burden over mobile networks. Currently, there is a huge spectrum scarcity due to the massive data explosion and this problem can be solved by helps of Reconfigurable Wireless Networks (RWNs) where nodes have reconfiguration and perception capabilities. Therefore, a necessity of AI-assisted algorithms for resource block allocation is observed. To tackle this challenge, in this paper, we propose an actor-critic learning-based scheduler for allocating resource blocks in a RWN. Various traffic types with different QoS levels are assigned to our agents to provide more realistic results. We also include mobility in our simulations to increase the dynamicity of networks. The proposed model is compared with another actor-critic model and with other traditional schedulers; proportional fair (PF) and Channel and QoS Aware (CQA) techniques. The proposed models are evaluated by considering the delay experienced by user equipment (UEs), successful transmissions and head-of-the-line delays. The results show that the proposed model noticeably outperforms other techniques in different aspects.

Genetic Algorithm Routing Protocol for Mobile Ad Hoc Network

Mobile ad hoc network (MANET) is a dynamically reconfigurable wireless network with time-variable infrastructure. Given that nodes are highly mobile, MANET’s topology often changes. These changes increase the difficulty in finding the routes that the packets use when they are routed. This study proposes an algorithm called genetic algorithm-based location-aided routing (GALAR) to enhance the MANET routing protocol efficiency. The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination. The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization. Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99% with less network overhead. Moreover, GALAR was compared to other algorithms in terms of different network evaluation measures. The GALAR algorithm significantly outperformed the other algorithms that were used in the study.

A Linear Systems Perspective on Intrusion Detection for Routing in Reconfigurable Wireless Networks

Reconfigurable wireless networks, such as ad hoc or wireless sensor networks, do not rely on fixed infrastructure. Nodes must cooperate in the multi-hop routing process. This dynamic and open nature make reconfigurable networks vulnerable to routing attacks that could degrade significantly network performance. Intrusion detection systems consist of a set of techniques designed to identify hostile behavior. In this paper, there are several approaches for intrusion detection in reconfigurable network routing such as collaborative, statistical, or machine learning-based techniques. In this paper, we introduce a new approach to intrusion detection for reconfigurable network routing based on linear systems theory. Using this approach, we can discriminate routing attacks by considering the system's z-plane poles. The z-plane can be thought of as a two dimensional feature space that arises naturally. It is independent of the number of network attack detection metrics and does not require extra dimensionality reduction. Two different host-based intrusion detection techniques, inspired by this new linear systems perspective, are presented and analyzed through a case study. The case study considers the effects of attack severity and node mobility to the attack detection performance. High attack detection accuracy was obtained without increasing packet overhead for both techniques by analyzing locally available information.

New Power-Oriented Methodology for Dynamic Resizing and Mobility of Reconfigurable Wireless Sensor Networks

This paper deals with reconfigurable wireless sensor networks (RWSNs) that should be adapted to their environment in order to minimize the energy consumption during the communication among nodes and consequently to maximize the lifetime of the network as much as possible. An RWSN is assumed to be composed of a set of nodes located in distributed zones such that each node executes reconfigurable software tasks to control local sensors. We propose in a previous work a zone-based multiagent architecture for an RWSN where a communication protocol is well-defined to optimize distributed reconfigurations. This architecture combines all possible reconfiguration forms of the network to be adapted to its environment under energy constraints. With the multiagent architecture we gain in term of energy to be consumed by each node in the network. After applying a set of reconfiguration scenarios, the total charge of the network decreases. In order to preserve this charge as much as possible before the next recharging operation, in this particular study, we tend to resize geographically the zones in an RWSN. We propose also to move the mobile nodes in order to increase the network lifetime. A new run-time power oriented methodology is reported, which manages the zones and their mobile nodes in order to control the energy consumption. Two dynamic solutions are applied: 1) the resizing of zones and 2) the mobility of nodes. For the new proposed methodology, we present a set of equation systems to model the resizing of zones and the mobility of nodes. The solution of these systems allows us to conclude that we can conserve more energy during the communication among network elements (nodes, stations, and agents). The contributions are applied to a case study that we simulate with the reconfigurable wireless network environment 1 to analyze the research originality. 1 http://lisi-lab.wix.com/rwinproject

Distributed algorithms for a robust topology using reconfigurable radios wireless sensor networks

Distributed algorithms for a robust topology using reconfigurable radios wireless sensor networks

Reconfigurable Wireless Networks

Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

Effective Energy Constraint Routing with On-Demand Routing Protocols in MANET

MANET is a dynamically reconfigurable wireless network with no fixed infrastructure. Each node acts as a router and host and it moves in an arbitrary manner in many ad hoc networks. It is significant that an energy conscious routing strategy should be adopted to minimize the energy required for communication. In such an environment, each host acts as a router and forwards packets to the next hop in order to reach, hop by hop, the final target. The major requirement in the MANET is which are usually characterized by mobile nodes with limited battery, is to limit the energy consumption.So, in this paper we are going to do the effective routing with the on demand protocols. The proposed method will try to prove that RAODV is better protocol than AODV and DSR using Network Simulator. To meet the new challenges, innovative protocols are needed to achieve energy efficiency, flexible scalability and adaptability and good network performance. This paper seeks to study and analyze the on demand routing protocols with identical loads and environment conditions and also evaluate their relative performance with respect to performance metrics.

A user centred design evaluation of the potential benefits of advanced wireless sensor networks for fire-in-tunnel emergency response

This study aimed to assess, from an end-user perspective, the potential role of reconfigurable wireless networks in responding to a fire-in-tunnel incident. The study was based on a multi-media, scenario-based simulation of an incident, and assessment of the benefits and drawbacks of the new technology by subject matter experts in relation to their operational goals, with particular emphasis on support for situation awareness. Advanced wireless networks were shown to have considerable potential for improving the effectiveness, efficiency and confidence of emergency responders at various phases in a fire-in-tunnel incident, due to access to more accurate, complete and reliable information. A key requirement was to ensure that new technologies provided the right information, not just more information, at the point of need.

Design and implementation of a cognitive engine functional architecture

Intelligence is the most important characteristic for cognitive wireless networks. A cognitive engine built on reconfigurable wireless networks is the key to implementing this characteristic. The design and implementation of a cognitive engine is important in research on the theory of initiative cognition for cognitive wireless networks. This paper first discusses research on cognitive loops, then investigates cognitive functions in the loop through the design of a universal cognitive engine functional architecture, and finally verifies the architecture on the platform of a cognitive engine prototype system.

Stable Route Link in on-Demand Multicast Routing Protocol for Ad Hoc Networks

Mobile ad hoc networks (MANET) are provisionally connected networks with no permanent infrastructure. A mobile ad hoc network is a self-organized and energetically reconfigurable wireless network without wired infrastructure and central administration. Nodes in the mobile ad hoc network can immediately create a communication structure while each node moves in a random way. Multicasting is capable of performing required services for ad hoc applications. The dynamic nature of the network topology and limited resources, maintaining and finding the path for multicasting data is still further challenging. Several protocols have been designed for multicasting in mobile ad hoc networks. On demand multicast routing protocol is one such protocol. ODMRP is mesh based and on-demand protocol that uses forwarding group to communicate a mesh for each multicasting group. The aim of the proposed algorithm is to find the stable path selection in ODMRP for forwarding packets. The basic on demand multicast routing protocol path selection uses minimum delay principle. The proposed algorithm considers node energy in path selection from source to destination. This article discusses the studies on output parameters such as control overhead and end to end delay by varying the input parameters viz., multicast groups size and mobility in the developed algorithm. Experimental results confirm that this approach can improve stability of path due to node energy consumption.

An improved location-aided routing protocol for mobile ad hoc networks with greedy approach

A mobile ad hoc network (MANET) is a dynamically reconfigurable wireless network that does not have a fixed infrastructure. Due to the high mobility of nodes, the network topology of MANETs always ...