Static Wireless Networks Research Articles

Fairness-aware placement of multiple aerial base stations in wireless networks

Fairness-aware placement of multiple aerial base stations in wireless networks

HA2RS: HTTP Adaptive Augmented Reality Streaming System

This paper presents a novel HTTP adaptive augmented reality (AR) streaming system. The goal of the proposed system is to provide high quality AR streaming services with low latency through a time-varying wireless network. In the proposed system, we employ a progressive mesh technique to enhance the scalability of AR streaming services, and adopt the metafile structure to support various attributes of progressive meshes necessary for rendering, displaying, and scheduling according to wireless network conditions. Based on this metafile, the proposed system requests the next chunk among AR contents to improve human visual perceptual quality considering the displaying scale values of AR contents on the screen. The proposed system is fully implemented by using well-known open sources (e.g., Android, OpenCV, OpenGL, and Nexus) and C/C++. Finally, the system is examined in real wireless network environments.

Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems

Swarms of mobile robots are being widely applied for complex tasks in various practical scenarios toward modern smart industry. Federated learning (FL) has been developed as a promising privacy-preserving paradigm to tackle distributed machine learning tasks for mobile robotic systems in 5G and beyond networks. However, unstable wireless network conditions of the complex and harsh working environment may lead to poor communication quality and bring big challenges to traditional centralized global training in FL models. In this article, a Peer-to-Peer (P2P) based Privacy-Perceiving Asynchronous Federated Learning (PPAFL) framework is introduced to realize the decentralized model training for secure and resilient modern mobile robotic systems in 5G and beyond networks. Specifically, a reputation-aware coordination mechanism is designed and addressed to coordinate a group of smart devices dynamically into a virtual cluster, in which the asynchronous model aggregation is conducted in a decentralized P2P manner. A secret sharing based communication mechanism is developed to ensure an encrypted P2P FL process, while a Secure Stochastic Gradient Descent (SSGD) scheme is integrated with an Autoencoder and a Gaussian mechanism is developed to ensure an anonymized local model update, communicating within a few neighboring clients. The case study based experiment and evaluation in three different application scenarios demonstrate that the PPAFL can effectively improve the security and resilience issues compared with the traditional centralized approaches for smart mobile robotic applications in 5G and beyond networks.

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload.

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

Guest Editorial Special Issue on Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications

It is our pleasure to share with you this Special Issue, which brings together a diverse set of articles dealing with various aspects of semantic and goal-oriented communications, providing a snapshot of research activities in this highly active research area. Wireless communications and networking research has traditionally focused on improving the capacity and throughput of the underlying wireless network. However, recent explosion in data-driven machine learning applications and their reliance on huge datasets collected by edge devices have raised legitimate concerns that the increasing data traffic might soon overwhelm the capacity of current networks despite ongoing efforts to increase their capacity and efficiency. Also, most of the edge intelligence applications impose stringent delay constraints, which cannot be met by naive forwarding of data samples for processing at the receiver end. This made it obvious to researchers in both academia and industry that it is essential to analyze the “value” or “relevance” of collected data, and filter and prioritize the delivery of data based on its value/relevance as well as the wireless channel and network conditions. In this context, data value will be closely connected to the underlying signals and processes that generate the data, e.g., text, image, video, or sensor data, and what the receiver intends to do with the received data. This subjectivity of data value makes semantic and goal-oriented communication a rather elusive research topic, which has led to both an increasingly rich and active area of investigation, but also a controversial one, mainly due to the lack of clear and widely agreed-upon definitions of some of the core concepts and formulations. Despite these disagreements, there is almost unanimous consensus on the importance and potential impact of this line of investigation for the design of future communication systems and networks.

Design of wireless in-pipe inspection robot for image acquisition

PurposePipeline robots are often used in pipeline non-destructive testing. Given the need for long-range in-pipe inspections, this study aims to develop a wireless in-pipe inspection robot for image acquisition.Design/methodology/approachIn this paper, an in-pipe robot with a new mechanical system is proposed. This system combines a three-arm load-bearing structure with spring sleeves and a half-umbrella diametric change structure, which can ensure the stability of the camera when acquiring images while maintaining the robot’s flexibility. In addition, data were transmitted wirelessly via a system that uses a 433 MHz ultra-high frequency and wireless local-area network–based image transmission system. Software and practical tests were conducted to verify the robot’s design. A preliminary examination of the robot’s cruising range was also conducted.FindingsThe feasibility of the robot was demonstrated using CATIA V5 and MSC ADAMS software. The simulation results showed that the centre of mass of the robot remained in a stable position and that it could function in a simulated pipeline network. In the practical test, the prototype functioned stably, correctly executed remote instructions and transmitted in near real-time its location, battery voltage and the captured images. Additionally, the tests demonstrated that the robot could successfully pass through the bends in a 200-mm-wide pipe at any angle between 0° and 90°. In actual wireless network conditions, the electrical system functioned for 44.7 consecutive minutes.Originality/valueA wheeled wireless robot adopts a new mechanical system. For inspections of plastic pipelines, the robot can adapt to pipes with diameters of 150–210 mm and has the potential for practical applications.

The trade-offs between Fog Processing and Communications in latency-sensitive Vehicular Fog Computing

In a vehicular fog computing paradigm, connected autonomous vehicles are envisioned as processing nodes (i.e. fog nodes) so that end-devices may offload processing tasks to them. As such, both local and distributed processing on fog nodes will depend heavily on wireless network conditions and the current traffic demand. In this work, we investigate the trade-offs on the operation of fog nodes under different vehicle densities and network conditions and formalize a Time Constrained One-Shot Open First Price Auction for resource allocation in vehicular fog computing. Through a large-scale simulation study, we assess important aspects of the performance of fog nodes in Vehicular Fog Computing. We show that current wireless network standards may dictate the limits of processing despite the availability of processing power of fog nodes. Our results indicate the existence of trade-offs on the operation of fog nodes regarding message overhead and processing redundancy to achieve high task completion ratio. Finally, we evaluate the social welfare distribution of the task allocation achieved using the auction where higher message rates lead to higher costs.

Advanced Co-Simulation Platform for UAV Simulations Under Virtual Wireless Network Environments

In this paper, we propose an advanced co-simulation platform that performs the unmanned aerial vehicle (UAV) simulation and the wireless network simulation concurrently. As UAV-based applications become more advanced and complicated and the commands and data transmitted between UAV and ground control station significantly increase, it is vital to safely and robustly transmit the packets carrying commands and data in a wireless network environment. However, the conventional UAV simulation platforms do not take into account the wireless network conditions encountered in the real-world flight so they cannot sufficiently simulate packet transmission. To address this issue, co-simulation platforms have been developed to verify UAV-based applications under a virtual wireless network environment. However, they still cannot effectively support the co-simulation needed to develop the UAV-based applications. In this paper, we have derived a list of improvements to the conventional co-simulation platform based on analysis of the conventional ones. Based on these improvements, we have developed an advanced co-simulation platform consisting of a UAV simulation part, a wireless network simulation part, and an integration part. In this platform, a two-channel approach was introduced to parallelize the synchronization required for performing the UAV simulation and the wireless network simulation simultaneously. The experimental results carried out on three test scenarios demonstrate that the proposed platform accurately performs co-simulation by efficient interoperation of its three parts and by remarkable reduction of the processing time delay, and that it is extendible due to its modularized software architecture.

New Algorithm to Handle Routing with Load Balancing in Wireless Networks Using EERNN

In this paper, static wireless network load balance algorithm is proposed, that use only optimal paths from point-to-point to achieve good load balance. This algorithm based on Enhanced version of Elman Recurrent Neural Network (EERNN) to make load balance decision depended on two metrics (traffic load on node and probability of link failure). This algorithm make good work in terms of both metrics simultaneously. Also, this algorithm use only local information. The execution of the proposed algorithm is compared with ERNN based on (traffic load on node and probability of link failure). © 201x JASET, International Scholars and Researchers Association

Deep Learning (DL)-based adaptive transport layer control in UAV Swarm Networks

This paper focuses on the congestion control issues in Unmanned Aerial Vehicle (UAV) Swarm Networks (USNs). In a USN, many network factors can cause segment loss, including dynamic swarming, high mobility, and link fading loss. With traditional transport layer protocols such as Transmission Control Protocol (TCP), these losses are interpreted as congestion events and will cause the data sending rate being decreased dramatically, therefore impacting throughput. In this paper, a learning-based adaptive network coding scheme is proposed to handle segment loss. In this scheme, a certain amount of redundancy is attached to the original data. If the segment loss is caused by random factors (such as radio interference), the lost segments are retrieved by decoding. However, if the loss is caused by congestion, the sender will retransmit the lost segments and decrease the sending rate. The coding rate is a critical factor, which should guarantee that the random loss can be retrieved by decoding while the congestion loss triggers retransmission and sending rate deduction. To achieve this goal, a Deep Learning (DL) algorithm is proposed, which comprehensively considers the wireless network conditions and dynamically optimizes the coding rate. Our experimental results show that the DL-based network coding scheme provides improved throughput and end-to-end delay compared to the TCP and general network coding schemes.

Integration of Multi-Criteria Decision Support with Infrastructure of Smart Services for Sustainable Multi-Modal Transportation of Freights

The problems of developing online decision support systems concern the complexity of the transportation management processes, the multi-dimensionality of data warehouses (DWs), and the multi-criteria of decision making. Such issues arise in the management processes by supporting them with adaptable information and communication infrastructure for the management of the freight’s transportation processes. Our research domain involves the assurance of infrastructure for the provision of smart services for the operative management of multi-modal transportation of freights under the conditions of the heterogeneity of communication channels and multi-criteria decision support. The complexity of managing multi-modal transportation processes raises some problems for ensuring the infrastructure for finding more optimal management solutions by the requirements of green development. This research aims to develop the approach for providing the infrastructure of intelligent smart services for adaptable implementation in management processes of online multi-modal freight transportation. The objectives are forwarded to construct the multi-criteria decision-making components and integrate them into the multi-layered, computer-based information management of intelligent systems. We present the steps of extension of the structure of the knowledge-based system with a multi-criteria decision support method, and the possibilities to work under the conditions of wireless networks and the recognition of context-aware situations. The results are demonstrated for the analysis of alternatives by assessing the multi-modal transportation corridors between the East and West regions of Europe, and the opportunities to evaluate them for more effective managerial perspectives under sustainable development requirements.

An intelligent collaborative inference approach of service partitioning and task offloading for deep learning based service in mobile edge computing networks

AbstractAs the rapid evolution of smart devices and real‐time applications, many new kinds of computation‐intensive services have been emerged successively and the corresponding requirements have been growing dramatically. Extended from cloud computing, mobile edge computing (MEC) is a novel technology which can provide powerful computing resource at the proximity of resource‐restrained mobile devices. Thus, it enables collaboration between edge server and mobile device, which can improve the quality of experience for users. In this article, we propose an intelligent collaborative inference (ICI) approach for real‐time computation‐intensive services in MEC network, which can achieve intelligent service partitioning and partial task offloading. Since machine learning algorithms have been applied in many applications with the advancement of big data and computing power, we focus on the services based on deep‐learning. Particularly, we research a service based on Pose‐Net model to achieve human pose estimation in the field of computer vision. And we design relevant ICI algorithm to achieve fine‐grained video stream processing in consideration of video service requirement, deep neural network (DNN) model structure, mobile device capability, wireless network condition, and cooperative server workload. Based on Python programming language and TensorFlow library, we test the ICI approach with some practical simulation parameters on real hardware platforms. The experiment results show that the presented ICI approach have superior performance in terms of service frame rate and client energy consumption than other benchmark approaches.

Constraints Based Heuristic Approach for Task Offloading In Mobile Cloud Computing

Mobile devices are supporting a wide range of applications irrespective of their configuration. There is a need to make the mobile applications executable on mobile devices without concern of battery life. For optimizing mobile applications computational offloading is highly preferred. It helps to overcome the severity of scarce resources constraint mobile devices. In offloading, which part of the application to be offloaded, on which processor and what is available bandwidth rate are the main crucial issues. As subtasks of mobile applications are interdependent, efficient execution of application requires research of favorable wireless network conditions before to take the offloading decision. Broadly in mobile cloud computing the applications is either delay sensitive or delay tolerant. For delay sensitive applications completion time has the highest priority whereas for delay tolerant type of applications depending on the network conditions decision of offloading can be taken. Sometimes, computation time on a cloud server is less but it consumes high communication time which ultimately gives inefficient offloading results. To address this issue, we have proposed a heuristic based level wise task offloading (HTLO). It includes computation time, communication time and maximum energy available on the mobile device to take the decision of offloading. For simulation study, a mobile application is considered as a directed graph and all the tasks are executed on the basis of their levels. The overall results of the proposed heuristic approach are compared with state-of-the-art K-M LARAC algorithm and results show the improvement in execution time, communication time, mobile device energy consumption and total energy consumption.

Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory

In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the downlink traffic demand. Next, to guarantee a truthful information exchange between the BS and UAVs, using the framework of contract theory, an offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utility of the overloaded BS is maximized. Simulation results show that the proposed WEM approach yields a prediction error of around 10%. Compared with the expectation maximization and k-mean approaches, the WEM method shows a significant advantage on the prediction accuracy, as the traffic load in the cellular system becomes spatially uneven. Furthermore, compared with two event-driven deployment schemes based on the closest-distance and maximal-energy metrics, the proposed predictive approach enables UAV operators to provide efficient communication service for hotspot users in terms of the downlink capacity, energy consumption and service delay. Simulation results also show that the proposed method significantly improves the revenues of both the BS and UAV networks, compared with two baseline schemes.

IEEE Access Special Section Editorial: Green Communications on Wireless Networks

Green communications, focusing on energy efficiency, is a hot topic in both academic and industry communities since they can significantly improve sustainability concerning power resources and environmental conditions. Later, with much focus on this area, several green communication methods started enrolling into a more thoughtful phase, with compelling applications in several networks. For instance, a simple and effective green communication solution is to arrange a sleep mode device incorporated by several MAC protocols with broad wireless network applications. However, it is required to examine the trade-off between green communications’ energy efficiency and network requirements. In addition, it is crucial to appraise the performance concerning the energy consumption, the throughput, and the response time, regarding green communications under different wireless network conditions (e.g., different traffic and different channels).

A Load Balancing Solution for Improving Video Quality in Loaded Wireless Network Conditions

There is an increasing user demand for high-quality content-rich multimedia services. Despite their advantages, current wireless networks in general and wireless mesh networks in particular have limitations in terms of quality of service (QoS) provisioning, especially when dealing with increased amounts of time sensitive traffic such as video. This paper presents video load balancing solution (ViLBaS), a load balancing-based mechanism which enhances delivery performance of video services over multi-hop wireless mesh networks and improves user quality of experience (QoE) levels. ViLBaS involves performance monitoring at the level of the nodes and load balancing by off-loading traffic from loaded nodes to less loaded neighboring nodes. Simulation-based results show how the proposed ViLBaS improves video delivery performance in terms of both QoS and QoE metrics [delay, throughput, packet loss, and peak signal-to-noise-ratio (PSNR)]. The comparison is performed against three other traditional approaches in different network topologies, for diverse video flow distributions, and different sizes for the video queue.

Video loss prediction model in wireless networks.

This work discusses video communications over wireless networks (IEEE 802.11ac standard). The videos are in three different resolutions: 720p, 1080p, and 2160p. It is essential to study the performance of these media in access technologies to enhance the current coding and communications techniques. This study sets out a video quality prediction model that includes the different resolutions that are based on wireless network terms and conditions, an approach that has not previously been adopted in the literature. The model involves obtaining Service and Experience Quality Metrics, such as PSNR (Peak Signal-to-Noise Ratio) and packet loss. This article outlines a methodology and mathematical model for video quality loss in the wireless network from simulated data and its accuracy is ensured through the use of performance metrics (RMSE and Standard Deviation). The methodology is based on two mathematical functions, (logarithmic and exponential), and their parameters are defined by linear regression. The model obtained RMSE values and standard deviation of 2.32 dB and 2.2 dB for the predicted values, respectively. The results should lead to a CODEC (Coder-Decoder) improvement and contribute to a better wireless networks design.

Online learning offloading framework for heterogeneous mobile edge computing system

Cloud of Things (CoT) is a significant paradigm for bridging cloud resource and mobile terminals. Mobile edge computing (MEC) is a supporting architecture for CoT. The objectives of this paper are to describe and evaluate a method to handle the computation offloading problem during user mobility which minimizes the offloading failure rate in heterogeneous network. Furthermore, users’ mobility and their choices for offloading lead to the everchanging condition of wireless network and opportunistic resource available. By modeling such dynamic mobile edge environment, quantizing the user cost, failure penalty and diversified QoS requirements, computation offloading problem is converted into an online decision-making problem in a stochastic process. We divide the decision-making into two phases: offloading planning phase and offloading running phase. In both phases the learning agent can continuously improve the control policy. We also conduct a failure recovery policy to tackle different types of failure and is included in the decision-making process. The numerical results show that the proposed online learning offloading method for mobile users can derive the optimal offloading scheme compared with the baseline algorithms.

Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.

Device-to-Device (D2D) Communication as a Bootstrapping System in a Wireless Cellular Network

Device-to-Device (D2D) communication is considered to be a promising form of technology to distribute video contents to nearby users and devices at a high data rate and with few delays. In this paper, we propose utilizing the D2D services and cache of mobile devices as a bootstrapping system in a cellular network. The main objective of a bootstrapping-D2D (BD2D) system is to improve the quality of service in terms of achieving the minimum startup-time of popular video files in a cellular network. To achieve our objective, we propose a new caching concept for D2D communication in which each mobile device can cache and share the initial video portion or initial segments of the popular video files to other proximate users via a D2D communication link. We formulate an optimization problem that maximizes the cache-hit ratio of initial segments of the most popular video contents. The basic aim is to move the initial segments as close as possible to the wireless users. We also propose a network-assisted D2D architecture called mobile content delivery network-assisted-BD2D and algorithms that assist the BD2D system in the discovery of devices that have cached the initial segments of the requested video files, in cache placement decisions, and also in the registration of potential D2D devices at the core network. We evaluate the performance of the BD2D system and propose algorithms through extensive simulations under real wireless network conditions, such as interference, fading, and shadowing. Our concluding simulation results demonstrate that bootstrapping the initial segments using the short range BD2D system can significantly reduce the average startup-time of video files in comparison with bootstrapping the initial segments from the traditional cellular system.