This paper addresses Quality of Experience (QoE)-driven, self-optimizing storage for distributed file sharing—a field gaining increasing attention in cloud and edge systems research. We present a novel platform for secure file sharing, centered on QoE-driven optimization of the Zettabyte File System (ZFS). The proposed four-module architecture integrates ZFS with reinforcement learning (RL) to dynamically tune QoE metrics such as latency, throughput, and caching efficiency, adapting to evolving workloads and user expectations. By leveraging RL, the system continuously optimizes ZFS configurations for enhanced performance. The four-layer architecture provides a coherent end-to-end framework that links user-level QoE signals to low-level ZFS tunables, while incorporating blockchain-based traceability to ensure transparency and trust. Experimental evaluations demonstrate that the adaptive deep Q-learning strategy improves storage performance and QoE compared to static configurations, establishing a new benchmark for QoE-driven decentralized storage.

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX.

Software-Defined Networking (SDN) architecture was developed to address the shortcomings of traditional network architectures. It allows system administrators to easily manage and configure the network by separating and abstracting the control plane from the data plane. All the knowledge and intelligence of SDN is concentrated in a software entity called the SDN controller, making the network programmable. However, a large-scale SDN architecture, particularly in the IoT domain, requires the implementation of a physically distributed control mechanism. Such a mechanism, based on the East/West interface raises many challenges in terms of scalability, reliability, security, consistency, and traceability. The development of the Blockchain allows addressing some of these challenges. In this paper, we present a design using Blockchain technology to improve SDNs in terms of trackability and discuss the adaptations required for large-scale deployment. Experimental results clearly show that the use of a proof-of-authority consensus algorithm in combination with a Merkle tree approach reduces the impact in terms of latency as well as in terms of Gas consumption.

Towards the next generation networks, low earth orbit (LEO) satellites have been considered as a promising component for beyond 5G networks. In this paper, we study downlink LEO-5G communication systems in a practical scenario, where the integrated LEO-terrestrial system is over-loaded by serving a number of terminals with high-volume traffic requests. Our goal is to optimize resource scheduling such that the amount of undelivered data and the number of unserved terminals can be minimized. Due to the inherent hardness of the formulated quadratic integer programming problem, the optimal algorithm requires unaffordable complexity. To solve the problem, we propose a near-optimal algorithm based on alternating direction method of multipliers (ADMM-HEU), which saves computational time by taking advantage of the distributed ADMM structure, and a low-complexity heuristic algorithm (LC-HEU), which is based on estimation and greedy methods. The results demonstrate the near-optimality of ADMM-HEU and the computational efficiency of LC-HEU compared to the benchmarks.

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless power communication networks (WPCN-s), where the UAV provides energy for mobile user nodes (M-UNs) and receives information from M-UNs. The movement of M-UN complies with a Gauss-Markov random model. To ensure acceptable quality-of-service (QoS), we consider dynamically planning the flight path of the UAV according to the movements of M-UNs. Since the flight time of UAV is restricted by limited energy, nonorthogonal multiple access (NOMA) is adopted to access a large number of M-UNs for simultaneous information transmission. Based on the above considerations, we aim to maximize the throughput via path planning of the UAV, subject to the QoS requirements of M-UNs and the UAV's energy constraint. To handle the challenges brought by dynamically changing channels to solving the problem, we propose a QoS-based double deep Q-learning network (DDQN). Numerical simulation results show that, compared with the conventional algorithms, the proposed framework achieves higher throughput.

On behalf of the Program Committee of the 18th IEEE International Conference on Embedded and Ubiquitous Computing (IEEE EUC 2020), we would like to welcome you to join the conference in Guangzhou, China, December 29, 2020 - January 1, 2021.

Wireless devices have been used to investigate the environment and to understand our physical world. In this work, we undertake the challenging problem of identifying location of obstacles and objects by WiFi signals. Gathering wireless sensory data to form an image is difficult since wireless signals are susceptible to multipath. Moreover, reconstructing an image of unknown objects based on the measurements of sparse signals is an ill-posed problem. To tackle these problems, we first present a linear model using received signal strength indicator (RSSI) measurements. We define the sparse beamforming problem as an ℓ0-norm optimization problem, then use the iterative reweighted ℓ1heuristic algorithm to obtain an optimal solution as a multipath. Finally, the multipath fading is removed by using Machine Learning. More specifically, we use Support Vector Regression (SVR) to identify a clear image of the unknown object. Our results show that the proposed method can reconstruct signals as a 3D image with a satisfactory visual appearance, i.e. the generated data mesh is well defined and smooth compared to previous work.

Use of wireless signal for identification of unknown object, or technology to see-through a wall to form an image, is gaining growing interest from various fields including law enforcement and military sectors, disaster management, or even in civilian sectors such as construction sites. The great challenge in the implementation of such technology is the stochastic disturbances on wireless signal which will result in a signal with missing samples. Compressive Sensing (CS) is a powerful tool for estimating the missing samples since it can find accurate solution to largely underdetermined linear wireless signals. However, sparse models like CS can also suffer from information loss dues to stochastic lossy nature of wireless, making CS not to have accurate information for reconstruction of a signal. In this paper, we developed a theoretical and experimental framework for the mapping of obstacles by reconstructing the wireless signal based on a sparse signal. We apply tensor format to perform the computations along each mode by relaxing the tensor constraints to obtain accurate results. The proposed framework demonstrates how to take 2D signals, formulate estimate signals and produce a 3D image location in a completely unknown area inside of the obstacle (wall).

In this paper, we developed a theoretical and experimental framework for the mapping of obstacles using WiFi, based on a small number of wireless channel samples. This is very challenging due to the numerous channel coefficients to be estimated over the time-varying channel and the channel estimation of a wireless signal transmission to be considered for compressive sampling. In a typical communication system, the signal is sampled at least twice at the highest frequency contained in the signal. However, this limits efficient ways to compress the signal, as it places a huge burden on sampling the entire signal while only a small number of the transform coefficients are needed to represent the signal. To tackle this problem, we will focused on a mathematical optimization problem for the most efficient compressed sensing method called ℓ1-norm, known as Basis Pursuit. Before optimizing the problem, the noise was removed from the signal, namely, multipath fading. Our experimental results show the improved performance in the number of iterations for obtaining a framework for the mapping of obstacles.

We address an energy-efficient scheduling problem for practical multiple-input single-output (MISO) systems with stringent execution-time requirements. Optimal user-group scheduling is adopted to enable timely and energy-efficient data transmission, such that all the users' demand can be delivered within a limited time. The high computational complexity in optimal iterative algorithms limits their applications in real-time network operations. In this paper, we rethink the conventional optimization algorithms, and embed machine-learning based predictions in the optimization process, aiming at improving the computational efficiency and meeting the stringent execution-time limits in practice, while retaining competitive energy-saving performance for the MISO system. Numerical results demonstrate that the proposed method, i.e., optimization with machine- learning predictions (OMLP), is able to provide a time-efficient and high-quality solution for the considered scheduling problem. Towards online scheduling in real-time communications, OMLP is of high computational efficiency compared to conventional optimal iterative algorithms. OMLP guarantees the optimality as long as the machine- learning based predictions are accurate.

It is a critical requirement of the future 5G communication networks to provide high speed and significantly reduce the network energy consumption. Energy efficient networks along with an energy saving strategy in mobile devices play a vital role in the mobile revolution. The new strategies should not only focus on wireless base stations, which consumes most of the power, but also considers the other power consumption elements for future mobile communication networks, including User Equipment (UE). In this paper, we have proposed a method that calculates the power consumption of a 5G network by considering its main elements based on current vision of 5G network infrastructure. Our proposed model uses the component based methodology that simplifies the process by taking into account the different high power consuming elements. The proposed method is evaluated by considering the three UE's DRX models and Virtual Base Station (VBS) with respect to different DRX timer in terms of Power Saving (PS) and delay as performance parameters.

The combination of forward error correction (FEC) and interleaving can be used to improve free‐space optical communication systems. Recent research has optimised the codeword length and interleaving depth under the assumption of a fixed buffering size; however, how the buffering size influences the system performance remains unsolved. This study models the system performance as a function of buffering size and FEC recovery threshold, which allows system designers to determine optimum parameters in consideration of the overhead. The modelling is based on statistics of temporal features of correct data reception and burst error length through the measurement of the channel good time and outage time. The experimental results show good coherence with the theoretical values. This method can also be applied in other channels if a continuous‐time‐Markov‐chain model of the channel can be derived.

Wireless radio-frequency (RF) technologies has had universal wide-scale deployment. Despite this, the practical limitations of the RF spectrum being unable to meet the challenges (e.g. lack of security, high interference, limited bandwidth, scalability) of RF based communication networks have become increasingly apparent over the past decade. With the ever-growing of data heavy wireless communications, especially on the last mile (e.g. wireless mesh network) and the backbone, methods are required to help addresses the problem RF have been facing for the next generation network. Faced with such daunting obstacles in RF-only networks, the use of Free Space Optical (FSO) for wireless communications was proposed. FSO is a promising solution to security, limited spectrum bandwidth, the scalability problem of wireless mesh networks, but also the advantage of large transmission distance, free license, interference immunity, and high-bandwidth. Despite the major advantages of FSO technology, its widespread use has been hampered by atmospheric turbulence-induced fading. However, FSO is still consider to be a practical solution to RF challenges. To maximize the potential of FSO networks, we study the problem of maximum path resource flow for a route and minimum linear delay while the FSO topology under atmospheric turbulence. Such methods can be easy adopted to SDN to manage the data flow at the link-or network-layer.

The 4G standard Long Term Evolution-Advanced (LTE-A) has been deployed in many countries. Now, technology is evolving towards the 5G standard since it is expecting to start its service in 2020. The 5G cellular networks will mainly contain in cloud computing and primarily Quality of Service (QoS) parameters (e.g. delay, loss rate, etc.) influence the cloud network performance. The impact of user perceived Quality of Experience (QoE) using multimedia services, and application significantly relies on the QoS parameters. The key challenge of 5G technology is to reduce the delay less than one millisecond. In this paper, we have described a method that minimizes the overall network delay for multimedia services; which are constant bit rate (VoIP) and variable bit rate (video) traffic model. We also proposed a method that measures the user's QoE for video streaming traffic using the network QoS parameters, i.e. delay and packet loss rate. The performance of proposed QoE method is compared with QoV method, and our proposed QoE method performs best by carefully handle the impact of QoS parameters. The results show that our described method successfully reduces the overall network delays, which result to maximize the user's QoE.

The 3rd Generation Partnership Project (3GPP) introduced the new radio access technology, LTE (Long Term Evolution) and LTE-Advanced, which has the capability to provide larger bandwidth and low latencies on a wireless network in order to fulfill the demand of Users' Equipment (UEs) with acceptable Quality of Service (QoS). One of the data-heavy applications that has exploded on the market is Video. This calls for accurate modeling of video traffic. To ensure the quality and correctness of complex systems with Video traffic, it is important to evaluate the behavior of the traffic in the heterogeneous environment. This paper presents measurements on LTE Video traffic which were performed in a real environment. We derived a semi-Markov model which accurately reproduces the statistics of composite Video measurements over LTE network.

Abstract Vehicular ad hoc networks (VANETs) are witnessing in recent years a rapid development for road transmissions and are considered as one of the most important types of next generation networks, in which drivers can have access anywhere and anytime to information. However, vehicles have to deal with many challenges such as the links failures due to their frequent mobility as well as limited degrees of freedom in their mobility patterns. In this paper, we propose a new quality of service multicast and multipath routing protocol for VANETs, based on the paradigm of bee's communication, called multicast quality of service swarm bee routing for VANETs (MQBV). The MQBV finds and maintains robust routes between the source node and all multicast group members. Therefore, the average end‐to‐end delay and the normalized overhead load should be reduced, while at the same time increasing the average bandwidth and the packet delivery ratio. Extensive simulation results were obtained using ns‐2 simulator in a realistic VANET settings and demonstrated the efficiency of the proposed protocol. Copyright © 2013 John Wiley & Sons, Ltd.

The 4G standard Long Term Evolution (LTE) utilizes discontinuous reception (DRX) to extend the user equipments battery lifetime. DRX permits an idle UE to power off the radio receiver for two predefined sleep period and then wake up to receive the next paging message. Two major basic power saving models proposed to data are the 3GPP ETSI model and industrial DRX model proposed by Nokia. While previous studies have investigated power saving with the 3GPP ETSI models, the industrial DRX model has not been considered for analytical studies to date. Thus, there is a need to optimize the DRX parameters in the industrial model so as to maximize power saving without incurring network reentry and packet delays. In this paper, we take an overview of various static DRX cycles of the LTE/LTE-Advanced power saving mechanisms by modelling the system with bursty packet data traffic using a semi-Markov process. Using this analytical model, we will show the tradeoff relationship between the power saving and wake-up delay performance in the industrial model.

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications, consequently, a better experience for the end user. Since cellular communication is ready available, LTE communication has been designed to work at high speeds for vehicular communication. The challenge is that the protocols in LTE/LTE-Advanced should not only provide good packet delivery but also adapt to changes in the network topology due to vehicle volume and vehicular mobility. It is a critical requirement to ensure a seamless quality of experience ranging from safety to relieving congestion as deployment of LTE/LTE-Advanced become common. This requires learning how to improve the LTE/LTE-Advanced model to better appeal to a wider base and move toward additional solutions. In this paper we present a feasibility analysis for performing vehicular communication via a queueing theory approach based on a multi-server queue using real LTE traffic. A M/M/m model is employed to evaluate the probability that a vehicle finds all channels busy, as well as to derive the expected waiting times and the expected number of channel switches. Also, when a base station (eNB) becomes overloaded with a single-hop, a multi-hop rerouting optimization approach is presented.

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications. However, these data-heavy applications require lots of battery power on the user equipment. To extend the user equipment battery lifetime, plus further support various services and large amount of data transmissions, the 3GPP standards for LTE/LTE-Advanced has adopted discontinuous reception (DRX). In this paper, we take an overview of various static/fixed DRX cycles of the LTE/LTE-Advanced power saving mechanisms, by modelling the system with bursty packet data traffic using a semi-Markov process. Based on the analytical model, we will show the trade-off relationship between the power saving and wake-up delay performance. This work will help to select the best parameters when LTE/LTE-Advanced DRX is implemented depending on the protocols and desired outcome of the traffic.

Single carrier frequency division multiple access (SC-FDMA) has been adopted as the standard multiple access scheme for 3GPP LTE uplink. In comparison to orthogonal frequency division multiple access (OFDMA), the subcarriers assigned to each user are required to be consecutive in SC-FDMA localized scheme, which imposes more difficulties on resource allocation problem. Subject to this constraint, various optimization objectives, such as utility maximization and power minimization, have been studied for SC-FDMA resource allocation. In this paper, we focus on developing a general algorithm framework with near-optimal performance and polynomial-time complexity to maximize the total utility for SC-FDMA systems. The proposed algorithm is based on low-complexity estimation for the partial solution space. Compared with existing algorithms, simulation results show that our algorithm improves the system utility significantly and has less deviation to global optimum. In addition, the proposed algorithm framework allows a flexible trade-off between computational effort and solution performance by varying the complexity of estimation approaches.

The recent explosive growth of voice over IP (VoIP) solutions calls for accurate modelling of VoIP traffic. This study presents measurements of ON and OFF periods of VoIP activity from a significantly large database of VoIP call recordings consisting of native speakers speaking in some of the world's most widely spoken languages. The impact of the languages and the varying dynamics of caller interaction on the ON and OFF period statistics are assessed. It is observed that speaker interactions dominate over language dependence which makes monologue‐based data unreliable for traffic modelling. The authors derive a semi‐Markov model which accurately reproduces the statistics of composite dialogue measurements.

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications, consequently, a better experience for the end user. To extend the user equipment battery lifetime, plus further support various services and large amount of data transmissions, the 3GPP standards for LTE/LTE-Advanced has adopted discontinuous reception (DRX). However, there is a need to optimize the DRX parameters, so as to maximize power saving without incurring network re-entry and packet delays. In this paper, we provide an overview of the fixed frame DRX cycle and compare it against an adjustable DRX cycle of the LTE/LTE-Advanced power saving mechanism, by modelling the system with bursty packet data traffic using a semi-Markov process. Based on the analytical model, we will show the trade-off relationship between the power saving and wake-up delay performance.

Abstract The Internet has become a universal communication network tool. It has evolved from a platform that supports best‐effort traffic to one that now carries different traffic types including those involving continuous media with quality of service (QoS) requirements. As more services are delivered over the Internet, we face increasing risk to their availability given that malicious attacks on those Internet services continue to increase. Several networks have witnessed denial of service (DoS) and distributed denial of service (DDoS) attacks over the past few years which have disrupted QoS of network services, thereby violating the Service Level Agreement (SLA) between the client and the Internet Service Provider (ISP). Hence DoS or DDoS attacks are major threats to network QoS. In this paper we survey techniques and solutions that have been deployed to thwart DoS and DDoS attacks and we evaluate them in terms of their impact on network QoS for Internet services. We also present vulnerabilities that can be exploited for QoS protocols and also affect QoS if exploited. In addition, we also highlight challenges that still need to be addressed to achieve end‐to‐end QoS with recently proposed DoS/DDoS solutions. Copyright © 2010 John Wiley & Sons, Ltd.

Recently, there has been increasing interest in applying MPLS to IP-based wireless access networks [1]. During a handover on Mobile MPLS-based networks, a registration update message is sent from the Foreign Agent (FA) to the Home Agent (HA). This update message results in a handover delay because of the distance the registration update message must travel from the FA to the HA. To decrease frequent registration handover delays, a micro-mobility approach over MPLS-based networks (also known as Hierarchical Mobile MPLS (H-MPLS)) [2] method was proposed to overcome the limitations of the Mobile MPLS protocol. Studies have shown that with H-MPLS, the Mobile Node (MN) experiences considerably lower handover delays when compared with the delay incurred with conventional mobile MPLS [2], [3]. In this paper we propose to extend the use of H-MPLS by introducing Intermediate Label Information Base (I-LIB) on a Label Switching Router (LSR), which uses the MPLS forwarding and localized signaling. Our simulations demonstrate that with an I-LIB, the end-to-end delay is reduced as a result of a decrease in the handover latency during the establishment of a new Label Switched Path (LSP). With increasing usage of wireless devices supporting delay sensitive traffic (such as voice and video over IP), it is essential to provide lower end-to-end by minimizing handover delays since large delays cause Quality of Service (QoS) degradations.