An empirical assessment of the use of an algorithm factory for video delivery operations

Introduction

Video service providers are moving from focusing on Quality of Service (QoS) to Quality of Experience (QoE) in their video networks since the users' demand for high-quality video content is continually growing. By focusing on QoE, video service providers can provide their subscribers with a more personalized and engaging experience, which can help increase viewer satisfaction and retention. This focus shift requires not only a more sophisticated approach to network management and new tools and technologies to measure and optimize QoE in their networks but also a novel approach to video delivery operations.

Methods

This paper describes the components, interactions, and relationships of an algorithm factory for video delivery operation that assures high QoE for video streaming services. The paper also showcases the results of gradually implementing an algorithm factory in the video industry. Using a dataset from 2016 to 2022, we present the case of a European PayTV service provider that achieved improved performance measured by both objective and subjective metrics.

Results

The use of an algorithm factory significantly improved the PayTV service provider's performance. The study found a fivefold increase in the speed of critical incident resolution and a 59% reduction in the number of critical incidents, all while expanding the customer base and maintaining the same level of labor resources. The case also demonstrates a strong positive relation between the productivity measures of the PayTV operator and their survey-based quality ratings. These results underscore the importance of flawless QoS and operational excellence in delivering QoE to meet the evolving demands of viewers.

Discussion

The paper adds to the existing literature on relationships between operational efficiency, innovation, and subjective quality. The paper further offers empirical evidence from the PayTV industry. The insights provided are expected to benefit both traditional and over-the-top (OTT) video service providers in their quest to stay ahead in the rapidly evolving video industry. It may also translate to other service providers in similar industries committed to supporting high-quality service delivery.

Effective Energy Efficiency under Delay-Outage Probability Constraints and F-Composite Fading

The paradigm of the Next Generation cellular network (6G) and beyond is machine-type communications (MTCs), where numerous Internet of Things (IoT) devices operate autonomously without human intervention over wireless channels. IoT's autonomous and energy-intensive characteristics highlight effective energy efficiency (EEE) as a crucial key performance indicator (KPI) of 6G. However, there is a lack of investigation on the EEE of random arrival traffic, which is the underlying platform for MTCs. In this work, we explore the distinct characteristics of F-composite fading channels, which specify the combined impact of multipath fading and shadowing. Furthermore, we evaluate the EEE over such fading under a finite blocklength regime and QoS constraints where IoT applications generate constant and sporadic traffic. We consider a point-to-point buffer-aided communication system model, where (1) an uplink transmission under a finite blocklength regime is examined; (2) we make realistic assumptions regarding the perfect channel state information (CSI) available at the receiver, and the channel is characterized by the F-composite fading model; and (3) due to its effectiveness and tractability, application data are found to have an average arrival rate calculated using Markovian sources models. To this end, we derive an exact closed-form expression for outage probability and the effective rate, which provides an accurate approximation for our analysis. Moreover, we determine the arrival and required service rates that satisfy the QoS constraints by applying effective bandwidth and capacity theories. The EEE is shown to be quasiconcave, with a trade-off between the transmit power and the rate for maximising the EEE. Measuring the impact of transmission power or rate individually is quite complex, but this complexity is further intensified when both variables are considered simultaneously. Thus, we formulate power allocation (PA) and rate allocation (RA) optimisation problems individually and jointly to maximise the EEE under a QoS constraint and solve such a problem numerically through a particle swarm optimization (PSO) algorithm. Finally, we examine the EEE performance in the context of line-of-sight and shadowing parameters.

Energy-Efficient BWP Configuration for Multi-Slice Users

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users' battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users' energy efficiency and quality of service.

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar scenarios. This performance was observed to be consistent even under large-scale attacks.

A wormhole attack detection method for tactical wireless sensor networks

Wireless sensor networks (WSNs) are networks formed by organizing and combining tens of thousands of sensor nodes freely through wireless communication technology. WSNs are commonly affected by various attacks, such as identity theft, black holes, wormholes, protocol spoofing, etc. As one of the more severe threats, wormholes create passive attacks that are hard to detect and eliminate. Since WSN is often used in the tactical network field, a planned secure network is essential for military applications with high security. Guard nodes are traffic monitoring nodes used to supervise neighbors' data communication around the tactical networks. Therefore, this work proposes a Quality of Service (QoS) security mechanism to select multiple dual-layer guard nodes at different paths of the WSN based on the path qualities to detect wormholes. The entire network's links are categorized into high, normal, and low priority levels. As such, this study aimed to confirm the security of high priority nodes and links in the tactical network, avoid excessive overhead, and provide random security facilities to all nodes. The proposed measures of the QoS-based security provision, including link cluster formation, guard node selection, authenticated guard node identification, and intrusion detection, ensure economic and efficient network communication with different quality levels.

A Novel 6G Conversational Orchestration Framework for Enhancing Performance and Resource Utilization in Autonomous Vehicle Networks

A vision of 6G aims to automate versatile services by eliminating the complexity of human effort for Industry 5.0 applications. This results in an intelligent environment with cognitive and collaborative capabilities of AI conversational orchestration that enable a variety of applications across smart Autonomous Vehicle (AV) networks. In this article, an innovative framework for AI conversational orchestration is proposed by enabling on-the-fly virtual infrastructure service orchestration for Anything-as-a-Service (XaaS) to automate a network service paradigm. The proposed framework will potentially contribute to the growth of 6G conversational orchestration by enabling on-the-fly automation of cloud and network services. The orchestration aspect of the 6G vision is not limited to cognitive collaborative communications, but also extends to context-aware personalized infrastructure for 6G automation. The experimental results of the implemented proof-of-concept framework are presented. These experiments not only affirm the technical capabilities of this framework, but also push into several Industry 5.0 applications.

Intelligent Traffic Model for Unmanned Ground Vehicles Based on DSDV-AODV Protocol

Traffic systems have been built as a result of recent technological advancements. In application, dependable communication technology is essential to link any system needs. VANET technology is used to communicate data about intelligent traffic lights, which are focused on infrastructure during traffic accidents and mechanisms to reduce traffic congestion. To ensure reliable data transfer in VANET, appropriate routing protocols must be used. This research aims to improve data transmission in VANETs implemented in intelligent traffic lights. This study investigates the capability of combining the DSDV routing protocol with the routing protocol AODV to boost AODV on an OMNET++ simulator utilizing the 802.11p wireless standard. According to the simulation results obtained by analyzing the delay parameters, network QoS, and throughput on each protocol, the DSDV-AODV routing protocol performs better in three scenarios compared to QoS, delay, and throughput parameters in every scenario that uses network topology adapted to the conditions on the road intersections. The topology with 50 fixed + 50 mobile nodes yields the best results, with 0.00062 s delay parameters, a network QoS of 640 bits/s, and a throughput of 629.437 bits/s. Aside from the poor results on the network QoS parameters, the addition of mobile nodes to the topology influences both the results of delay and throughput metrics.

Modelling, simulation and performance evaluation of the IEEE 802.11e protocol with station mobility

In this article, we present a parameterized Colored Petri Net (CPN) model of the IEEE 802.11e protocol for wireless communications with mobile stations. CPNs provide a graphical model for the modeling and analysis of concurrent systems, which can be parameterized by the use of constants, and thus they allow us to create more flexible models. Our CPN model captures the protocol's behavior, and the specific parameters used for the 802.11e protocol and the scenarios to be evaluated are captured by the CPN parameters. The model presented is flexible enough to cover full customization of traffic types, user mobility and collision avoidance protocols. In this model, there is an access point (AP) which is visible to all the stations, and we assume that due to physical restrictions, there are two range groups. All the stations in the same range group are visible to each other. The impact of mobility is then analyzed by studying a situation in which the stations move in a controlled way to the same range group. The simulation results demonstrate the impact on network performance for sensitive and insensitive traffic types, as well as the role of the RTS/CTS protocol in collision avoidance, especially when users are located in different regions. Specifically, we show how the performance improves in the different scenarios when the stations move to the same area, where they can see each other, and we also study the impact on the performance for each type of traffic.

Cooperative-Aware Radio Resource Allocation Scheme for 5G Network Slicing in Cloud Radio Access Networks

The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model's performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.

User QoS-Based Optimized Handover Algorithm for Wireless Networks

Due to the development of wireless network technology, various applications relying on good network quality are widely used on mobile devices. Taking the commonly used video streaming service as an example, a network with high throughput and low packet loss rate can meet the service requirements. When the moving distance of the mobile device is greater than the signal coverage of the AP, it will trigger the handover process to connect to another AP, and cause the network to disconnect and reconnect instantaneously. However, frequently triggering the handover procedure will cause a significant drop in network performance and affect the operation of application services. In order to solve this problem, this paper proposes the OHA and OHAQR. The OHA considers whether the signal quality is good or bad, and uses the corresponding HM method to solve the problem of frequent handover procedures. The OHAQR integrates the QoS requirements of the throughput and packet loss rate into the OHA with the Q-handover score, to provide high-performance handover services with QoS. Our experimental results show that the OHA and OHAQR have 13 and 15 handovers in a high-density scenario, respectively, and are better than the other two methods. The actual throughput and packet loss rate of the OHAQR are 123 Mbps and 5%, respectively, and the network performance is better than that of other methods. The proposed method shows excellent performance in ensuring the network QoS requirements and reducing the number of handover procedures.

Network QoS Impact on Spatial Perception through Sensory Substitution in Navigation Systems for Blind and Visually Impaired People

A navigation system for individuals suffering from blindness or visual impairment provides information useful to reach a destination. Although there are different approaches, traditional designs are evolving into distributed systems with low-cost, front-end devices. These devices act as a medium between the user and the environment, encoding the information gathered on the surroundings according to theories on human perceptual and cognitive processes. Ultimately, they are rooted in sensorimotor coupling. The present work searches for temporal constraints due to such human-machine interfaces, which in turn constitute a key design factor for networked solutions. To that end, three tests were conveyed to a group of 25 participants under different delay conditions between motor actions and triggered stimuli. The results show a trade-off between spatial information acquisition and delay degradation, and a learning curve even under impaired sensorimotor coupling.

Impact of Packet Loss Rate on Quality of Compressed High Resolution Videos

Video delivered over IP networks in real-time applications, which utilize RTP protocol over unreliable UDP such as videotelephony or live-streaming, is often prone to degradation caused by multiple sources. The most significant is the combined effect of video compression and its transmission over the communication channel. This paper analyzes the adverse impact of packet loss on video quality encoded with various combinations of compression parameters and resolutions. For the purposes of the research, a dataset containing 11,200 full HD and ultra HD video sequences encoded to H.264 and H.265 formats at five bit rates was compiled with a simulated packet loss rate (PLR) ranging from 0 to 1%. Objective assessment was conducted by using peak signal to noise ratio (PSNR) and Structural Similarity Index (SSIM) metrics, whereas the well-known absolute category rating (ACR) was used for subjective evaluation. Analysis of the results confirmed the presumption that video quality decreases along with the rise of packet loss rate, regardless of compression parameters. The experiments further led to a finding that the quality of sequences affected by PLR declines with increasing bit rate. Additionally, the paper includes recommendations of compression parameters for use under various network conditions.

Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach

Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad-hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad-hoc vehicle networks, vehicles of everything, and the internet of things.

A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks

The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network's transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved.

Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking

Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock's power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA's High Rate Delay Tolerant Networking (HDTN) project.

Mathematical Modeling and Validation of Retransmission-Based Mutant MQTT for Improving Quality of Service in Developing Smart Cities

Unreliable networks often use excess bandwidth for data integration in smart cities. For this purpose, Messaging Queuing Telemetry Transport (MQTT) with a certain quality of service (QoS) is employed. Data integrity and data security are frequently compromised for reducing bandwidth usage while designing integrated applications. Thus, for a reliable and secure integrated Internet of Everything (IoE) service, a range of network parameters are conditioned to achieve the required quality of a deliverable service. In this work, a QoS-0-based MQTT is developed in such a manner that the transparent MQTT protocol uses Transmission Control Protocol (TCP)-based connectivity with various rules for the retransmission of contents if the requests are not entertained for a fixed duration. The work explores the ways to improve the overall content delivery probability. The parameters are examined over a transparent gateway-based TCP network after developing a mathematical model for the proposed retransmission-based mutant QoS-0. The probability model is then verified by an actual physical network where the repeated content delivery is explored at VM-based MQTT, local network-based broker and a remote server. The results show that the repeated transmission of contents from the sender improves the content delivery probability over the unreliable MQTT-based Internet of Things (IoT) for developing smart cities' applications.

Edge Based Priority-Aware Dynamic Resource Allocation for Internet of Things Networks

The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.

A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security

Addressing the recent trend of the massive demand for resources and ubiquitous use for all citizens has led to the conceptualization of technologies such as the Internet of Things (IoT) and smart cities. Ubiquitous IoT connectivity can be achieved to serve both urban and underserved remote areas such as rural communities by deploying 5G mobile networks with Low Power Wide Area Network (LPWAN). The current architectures will not offer flexible connectivity to many IoT applications due to high service demand, data exchange, emerging technologies, and security challenges. Hence, this paper explores various architectures that consider a hybrid 5G-LPWAN-IoT and Smart Cities. This includes security challenges as well as endogenous security and solutions in 5G and LPWAN-IoT. The slicing of virtual networks using software-defined network (SDN)/network function virtualization (NFV) based on the different quality of service (QoS) to satisfy different services and quality of experience (QoE) is presented. Also, a strategy that considers the implementation of 5G jointly with Weightless-N (TVWS) technologies to reduce the cell edge interference is considered. Discussions on the need for ubiquity connectivity leveraging 5G and LPWAN-IoT are presented. In addition, future research directions are presented, including a unified 5G network and LPWAN-IoT architecture that will holistically support integration with emerging technologies and endogenous security for improved/secured smart cities and remote areas IoT applications. Finally, the use of LPWAN jointly with low earth orbit (LEO) satellites for ubiquitous IoT connectivity is advocated in this paper.

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world's population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.

Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms.

Mobility-Aware Proactive Edge Caching Optimization Scheme in Information-Centric IoV Networks

Edge caching is a promising approach to alleviate the burden on the backhaul of network links. It has a significant role in the Internet of Vehicle (IoV) networks performance by providing cached data at the edge and reduce the burden of the core network caused by the number of participating vehicles and data volume. However, due to the limited computing and storage capabilities of edge devices, it is hard to guarantee that all contents are cached and every requirement of the device are satisfied for all users. In this paper, we design an Information-Centric Network (ICN) with mobility-aware proactive caching scheme to provide delay-sensitive services on IoV networks. The real-time status and interaction of vehicles with other vehicles and Roadside Units (RSU) is modeled using a Markov process. Mobility aware proactive edge caching decision that maximize network performance while minimizing transmission delay is applied. Our numerical simulation results show that the proposed scheme outperforms related caching schemes in terms of latency by 20-25% in terms of latency and by 15-23% in cache hits.

Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario.

Ensuring the QoE-Related Fairness to Reduce the User Abandonment Ratio

Nowadays, it is quite a challenge for app owners to keep users engaged with an app. Currently, the level of user abandonment is one of the key parameters that application owners are interested in. To meet these challenges, we conduct an extended study of a previously proposed solution that significantly reduces the abandonment rate of a given application. The investigated solution is based on the methods of fairness using the QoE and QoS approach. This paper shows that application abandonment ratios can be reduced by using an appropriate approach to fair bandwidth allocation. Adjusting the bandwidth allocation to users, taking into account the quality of the user experience, has a more effective effect on reducing app abandonment ratios than if quality of service is taken into account. This is because the users make the decision to abandon the application based on their feelings rather than technical parameters. In order to effectively reduce application abandonment ratios, a suitable bandwidth allocation algorithm must be used. This paper presents the impact of using different algorithms on the abandonment ratio and compares the popularly used algorithms and the previously proposed bandwidth allocation algorithm.

6G Networks for Next Generation of Digital TV Beyond 2030

This paper prosed a novel 6G QoS over the future 6G wireless architecture to offer excellent Quality of Service (QoS) for the next generation of digital TV beyond 2030. During the last 20 years, the way society used to watch and consume TV and Cinema has changed radically. The creation of the Over The Top content platforms based on Cloud Services followed by its commercial video consumption model, offering flexibility for subscribers such as n Video on Demand. Besides the new business model created, the network infrastructure and wireless technologies also permitted the streaming of high-quality TV and film formats such as High Definition, followed by the latest widespread TV standardization Ultra-High- Definition TV. Mobile Broadband services onset the possibility for consumers to watch TV or Video content anywhere at any time. However, the network infrastructure needs continuous improvement, primarily when crises, like the coronavirus disease (COVID-19) and the worldwide pandemic, creates immense network traffic congestions. The outcome of that congestion was the decrease of QoS for such multimedia services, impacting the user's experience. More power-hungry video applications are commencing to test the networks' resilience and future roadmap of 5G and Beyond 5G (B5G). For this, 6G architecture planning must be focused on offering the ultimate QoS for prosumers beyond 2030.

RPL Enhancement to Support Video Traffic for IoMT Applications

Internet of Multimedia Things (IoMT), a special subset of Internet of Things (IoT), is a novel paradigm which is progressively increasing and gaining in popularity, allowing many multimedia content based applications such as Wireless Multimedia Sensor Networks (WMSNs). The standard IPv6 Routing Protocol for Low power and Lossy Networks (RPL), originally designed for transmitting scalar data traffic of IoT applications, should be customized in order to deal with IoMT applications which pose new challenges and requirements in terms of the Quality of Service (QoS) and the user's Quality of Experience (QoE). For this purpose and thanks to the flexibility and adaptability of RPL, we propose, in this paper, a multipath version of RPL, not defined in the RPL specification RFC 6550, named MP-RPL. MP-RPL leverages the multiparent feature offered by RPL in order to construct multiples end-to-end paths of different qualities based on radio link quality measurements. It is intended to improve the video traffic delivery for IoMT based WMSN applications by simultaneously using the built paths while taking into account the video traffic differentiation as per priority levels. Compared to the traditional single-path RPL, the results obtained from simulations when considering the influence of video characteristics, show that our proposal provides feasible and acceptable QoS and QoE performance metrics for multimedia applications, while maintaining RPL compliance.

Multiple Access-Enabled Relaying with Piece-Wise and Forward NOMA: Rate Optimization under Reliability Constraints

The increasing proliferation of Internet-of-things (IoT) networks in a given space requires exploring various communication solutions (e.g., cooperative relaying, non-orthogonal multiple access, spectrum sharing) jointly to increase the performance of coexisting IoT systems. However, the design complexity of such a system increases, especially under the constraints of performance targets. In this respect, this paper studies multiple-access enabled relaying by a lower-priority secondary system, which cooperatively relays the incoming information to the primary users and simultaneously transmits its own data. We consider that the direct link between the primary transmitter-receiver pair uses orthogonal multiple access in the first phase. In the second phase, a secondary transmitter adopts a relaying strategy to support the direct link while it uses non-orthogonal multiple access (NOMA) to serve the secondary receiver. As a relaying scheme, we propose a piece-wise and forward (PF) relay protocol, which, depending on the absolute value of the received primary signal, acts similar to decode-and-forward (DF) and amplify-and-forward (AF) schemes in high and low signal-to-noise ratio (SNR), respectively. By doing so, PF achieves the best of these two relaying protocols using the adaptive threshold according to the transmitter-relay channel condition. Under PF-NOMA, first, we find the achievable rate region for primary and secondary receivers, and then we formulate an optimization problem to derive the optimal PF-NOMA time and power fraction that maximize the secondary rate subject to reliability constraints on both the primary and the secondary links. Our simulation results and analysis show that the PF-NOMA outperforms DF-NOMA and AF-NOMA-based relaying techniques in terms of achievable rate regions and rate-guaranteed relay locations.

Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network

Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately, for a non-expert user, understanding of these parameters can be too complex. Hence, the problem of translation of the parameters describing the transmission quality appears understandable to the user. This article presents the concept of using Machine Learning (ML) to solve the above-mentioned problem, i.e., a dynamic classification of the measured parameters describing the transmission quality in a certain scale. Thanks to this approach, describing the quality will become less complex and more understandable for the user. To date, some studies have been conducted. Therefore, it was decided to use different approaches, i.e., fusion of a neural network (NN) and a genetic algorithm (GA). GA’s were choosen for the selection of weights replacing the classic gradient descent algorithm. For learning purposes, 100 samples were obtained, each of which was described by four features and the label, which describes the quality. In the reasearch carried out so far, single classifiers and ensemble learning have been used. The current result compared to the previous ones is better. A relatively high quality of the classification was obtained when we have used 10-fold stratified cross-validation, i.e., SEN = 95% (overall accuracy). The incorrect classification was 5/100, which is a better result compared to previous studies.

5G connected and automated driving: use cases, technologies and trials in cross-border environments

Cooperative, connected and automated mobility (CCAM) across Europe requires harmonized solutions to support cross-border seamless operation. The possibility of providing CCAM services across European countries has an enormous innovative business potential. However, the seamless provision of connectivity and the uninterrupted delivery of real-time services pose technical challenges which 5G technologies aim to solve. The situation is particularly challenging given the multi-country, multi-operator, multi-telco-vendor, multi-car-manufacturer and cross-network-generation scenario of any cross-border scenario. Motivated by this, the 5GCroCo project, with a total budget of 17 million Euro and partially funded by the European Commission, aims at validating 5G technologies in the Metz-Merzig-Luxembourg cross-border 5G corridor considering the borders between France, Germany and Luxembourg. The activities of 5GCroCo are organized around three use cases: (1) Tele-operated Driving, (2) high-definition map generation and distribution for automated vehicles and (3) Anticipated Cooperative Collision Avoidance (ACCA). The results of the project help contribute to a true European transnational CCAM. This paper describes the overall objectives of the project, motivated by the discussed challenges of cross-border operation, the use cases along with their requirements, the technical 5G features that will be validated and provides a description of the planned trials within 5GCroCo together with some initial results.

On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

Enhancing SDN WISE with Slicing Over TSCH

IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic sources with different characteristics. Due to the great diversity of application scenarios, there is a need to implement additional capabilities that can guarantee an adequate level of reliability and that can adapt to the dynamic behavior of the applications in use. The use of SDNs (Software Defined Networks) extends the possibilities of control over the network and enables its deployment at an industrial level. The signaling traffic exchanged between nodes and controller is heavy and must occupy the same channel as the data traffic. This difficulty can be overcome with the segmentation of the traffic into flows, and correct scheduling at the MAC (Medium Access Control) level, known as slices. This article proposes the integration in the SDN controller of a traffic manager, a routing process in charge of assigning different routes according to the different flows, as well as the introduction of the Time Slotted Channel Hopping (TSCH) Scheduler. In addition, the TSCH (Time Slotted Channel Hopping) is incorporated in the SDN-WISE framework (Software Defined Networking solution for Wireless Sensor Networks), and this protocol has been modified to send the TSCH schedule. These elements are jointly responsible for scheduling and segmenting the traffic that will be sent to the nodes through a single packet from the controller and its performance has been evaluated through simulation and a testbed. The results obtained show how flexibility, adaptability, and determinism increase thanks to the joint use of the routing process and the TSCH Scheduler, which makes it possible to create a slicing by flows, which have different quality of service requirements. This in turn helps guarantee their QoS characteristics, increase the PDR (Packet Delivery Ratio) for the flow with the highest priority, maintain the DMR (Deadline Miss Ratio), and increase the network lifetime.

Semantic-Aware Security Orchestration in SDN/NFV-Enabled IoT Systems

IoT systems can be leveraged by Network Function Virtualization (NFV) and Software-Defined Networking (SDN) technologies, thereby strengthening their overall flexibility, security and resilience. In this sense, adaptive and policy-based security frameworks for SDN/NFV-aware IoT systems can provide a remarkable added value for self-protection and self-healing, by orchestrating and enforcing dynamically security policies and associated Virtual Network Functions (VNF) or Virtual network Security Functions (VSF) according to the actual context. However, this security orchestration is subject to multiple possible inconsistencies between the policies to enforce, the already enforced management policies and the evolving status of the managed IoT system. In this regard, this paper presents a semantic-aware, zero-touch and policy-driven security orchestration framework for autonomic and conflict-less security orchestration in SDN/NFV-aware IoT scenarios while ensuring optimal allocation and Service Function Chaining (SFC) of VSF. The framework relies on Semantic technologies and considers the security policies and the evolving IoT system model to dynamically and formally detect any semantic conflict during the orchestration. In addition, our optimized SFC algorithm maximizes the QoS, security aspects and resources usage during VSF allocation. The orchestration security framework has been implemented and validated showing its feasibility and performance to detect the conflicts and optimally enforce the VSFs.

Integrating IoT and Blockchain for Ensuring Road Safety: An Unconventional Approach

The Internet of things (IoT), the Internet of vehicles, and blockchain technology have become very popular these days because of their versatility. Road traffic, which is increasing day by day, is causing more and more deaths worldwide. The world needs a product that would reduce the number of road accidents. This paper suggests combining IoT and blockchain technology to mitigate road hazards. The new intelligent transportation system technologies and the subsequent emergence of 5G technologies will be a blessing, delivering the necessary speed to ensure both safety and quality of service (QoS). Hashgraph technology, a distributed ledger technology is used to create communication networks between the different vehicles and other relevant parameters. Scheduling the requests according to the priorities for ensuring better QoS quotient can be effectively done using hashgraph. We demonstrated how the hashgraph outstrips other equivalents platforms. The proposed model was simulated using OMNeT++ with proper design and network description files. A hardware implementation of the proposed model was also done. Messages were transferred between the vehicles and prioritized using a hashgraph. This paper proposes an effective model in reducing the accidents in terms of parameters like speed, security, stability, and fairness.

Quality of Service (QoS) improving schemes in optical networks

In optical networks, such as OPS/OBS, the network results into significant loss in the network layer. When the loss significantly deteriorates the QoS by increasing the Bit Error Rate (BER), a viable approach can be used to increase the performance. This paper presents state of the art of Quality of Service (QoS) schemes used for improving the performance of optical networks. Furthermore, some possible applications and performance data are summarized based on Packet Loss Rate (PLR), secrecy, survivability and other parameters. The different states of art methods proposed by several authors are compared with Coded Packet Transport (CPT) scheme. We believe that this study is valuable to researchers envisaging a novel approach to enhance the performance of optical networks for telecommunications networks of the future.

Performance Evaluation and Validation of QCM (Query Control Mechanism) for QoS-Enabled Layered-Based Clustering for Reactive Flooding in the Internet of Things

Internet of Things (IoT) facilitates a wide range of applications through sensor-based connected devices that require bandwidth and other network resources. Enhancement of efficient utilization of a heterogeneous IoT network is an open optimization problem that is mostly suffered by network flooding. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. Several query control mechanisms in the literature claimed to cater to the issues related to bandwidth, cost, and Quality of Service (QoS). This research article presented a statistical performance evaluation of different query control mechanisms that addressed minimization of energy consumption, energy cost and network flooding. Specifically, it evaluated the performance measure of Query Control Mechanism (QCM) for QoS-enabled layered-based clustering for reactive flooding in the Internet of Things. By statistical means, this study inferred the significant achievement of the QCM algorithm that outperformed the prevailing algorithms, i.e., Divide-and-Conquer (DnC), Service Level Agreements (SLA), and Hybrid Energy-aware Clustering Protocol for IoT (Hy-IoT) for identification and elimination of redundant flooding queries. The inferential analysis for performance evaluation of algorithms was measured in terms of three scenarios, i.e., energy consumption, delays and throughput with different intervals of traffic, malicious mote and malicious mote with realistic condition. It is evident from the results that the QCM algorithm outperforms the existing algorithms and the statistical probability value "P" < 0.05 indicates the performance of QCM is significant at the 95% confidence interval. Hence, it could be inferred from findings that the performance of the QCM algorithm was substantial as compared to that of other algorithms.

Design a prototype for automated patient diagnosis in wireless sensor networks

It is indeed necessary to design of an elderly support mobile healthcare and monitoring system on wireless sensor network (WSN) for dynamic monitoring. It comes from the need for maintenance of healthcare among patients and elderly people that leads to the demand on change in traditional monitoring approaches among chronic disease patients and alert on acute events. In this paper, we propose a new automated patient diagnosis called automated patient diagnosis (AUPA) using ATmega microcontrollers over environmental sensors. AUPA monitors and aggregates data from patients through network connected over web server and mobile network. The scheme supports variable data management and route establishment. Data transfer is established using adaptive route discovery and management approaches. AUPA supports minimizing packet loss and delay, handling erroneous data, and providing optimized decision-making for healthcare support. The performance of AUPA's QoS approach is tested using a set of health-related sensors which gather the patient's data over variable period of time and send from a source to destination AUPA node. Experimental results show that AUPA outperforms the existing schemes, namely SPIN and LEACH, with minimal signal loss rate and a better neighborhood node selection and link selection. It diminishes the jitter compared to the related algorithms. Graphical abstract Stack architecture of AUPA.

An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks

The imminent wireless technologies are demanding fusion of several networks with diverse technologies. A convenient user device for such an environment is the multihomed host, which is capable of making use of simultaneous multiple connections of heterogeneous networks and smartly distributing/receiving data among them. The aim of this work is to develop a mechanism for assessing the multihoming concept and to propose a system model for increasing its applicability. The proposed model provided a novel user-centric scheme for multihoming for multi-radio access technologies (multi-RAT). It is considered an evolution of traffic offloading for gaining more capacity, higher data rates, and real-time services. This work assumes data classifications, evaluates and ranks the available connections, and utilizes the application data in an unequal load balance scheme. This is achieved by analyzing the performance of three of the most widely used alternative-choice for multiple attributes decision making (MADM) methods. The analytical hierarchy process (AHP), along with a utility equation, is applied to the system model for unequal load balance solution. The proposed model is targeting an energy efficient mechanism which satisfies application needs. Moreover, it reveals an efficient transmission mechanism for a better quality of service, traffic management, and availability solutions. The simulation results show that the proposed system surpassed its former counterparts.

A QoS Based Adaptive Backoff Scheme for Vehicular Ad Hoc Networks

The media access control (MAC) protocol is a key element in the design of vehicular ad hoc networks (VANETs) that directly affects the network performance. The backoff schemes of existing MAC protocols apply the single backoff process and therefore are not suitable for multi-class data transmission. Additionally, they cannot satisfy the delay requirements of emergency data in the case of varying number of vehicles, causing an adverse effect to the intelligent transportation system (ITS). This paper presents a priority-based adaptive backoff scheme that can enhance the binary exponential backoff (BEB) algorithm as well as the polynomial backoff (QB) algorithm. This system distinguishes priority data with different delay requirements first and designs different backoff schemes for each type of data later. The two-dimensional Markov Chain is used to analyze the backoff scheme and determine the expressions for throughput and delay. The simulation results show that the backoff scheme provided by this paper can reduce the average data delay and regulate each kind of data delay adaptively, according to the varying number of vehicles and different delay requirements.

Performance Evaluation of a Quality of Service Control Scheme in Multi-Hop WBAN Based on IEEE 802.15.6

The performance of a quality of service (QoS) control scheme in a multi-hop wireless body area network (WBAN) based on the IEEE Std. 802.15.6 is evaluated. In medical Internet of Things systems, WBANs are an important technology. In a previous study, an optimal quality of service control scheme that employs a multiplexing layer for priority scheduling and a decomposable error control coding scheme for WBANs were proposed. However, the two-hop extension supported by IEEE Std.802.15.6 has not been considered. Here, the two-hop extension is applied. Then, the packet error ratio, number of transmissions, and energy efficiency of our previously proposed system are compared to a standard scheme under several conditions. Also, novel evaluations based on communication distance are conducted. Numerical results demonstrate that our proposed scheme, in which coding rates change relative to channel conditions, outperforms standard schemes in many aspects. In addition, those systems show the best performance when the communication distance of the first hop equals that of the second hop. In addition, the above result is theoretically clarified.

Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay

Both economic and environmental costs are driving much research in the area of the energy efficiency of networking equipment. This research has produced a great amount of proposals. However, the majority of them remain unimplemented due to the lack of flexibility of current hardware devices and a certain lack of enthusiasm from commercial vendors. At the same time, Software-Defined Networking (SDN) has allowed customers to control switching decisions with a flexibility and precision previously unheard of. This paper explores the potential convergence between the two aforementioned trends and presents a promising power saving algorithm that can be implemented using standard SDN capabilities of current switches, reducing operation costs on both data centers and wired access networks. In particular, we focus on minimizing the energy consumption in bundles of energy-efficient Ethernet links leveraging SDN. For this, we build on an existing theoretical algorithm and adapt it for implementing with an SDN solution. We study several approaches and compare the resulting algorithms not only according to their energy efficiency, but also taking into account additional QoS metrics. The results show that the resulting algorithm is able to closely match the theoretical results, even when taking into account the requirements of delay-sensitive traffic.

Value of Information Based Data Retrieval in UWSNs

Sensor nodes in underwater sensor networks may acquire data at a higher rate than their ability to communicate over underwater acoustic channels. Autonomous underwater vehicles may mitigate this mismatch by offloading high volumes of data from the sensor nodes and ferrying them to the sink. Such a mode of data transfer results in high latency. Occasionally, these networks need to report high priority events such as catastrophes or intrusions. In such a scenario the expectation is to have a minimal end-to-end delay for event reporting. Considering this, underwater vehicles should schedule their visits to the sensor nodes in a manner that aids efficient reporting of high-priority events. We propose the use of the Value of Information metric in order to improve the reporting of events in an underwater sensor network. The proposed approach classifies the recorded data in terms of its value and priority. The classified data is transmitted using a combination of acoustic and optical channels. We perform experiments with a binary event model, i.e., we classify the events into high-priority and low-priority events. We explore a couple of different path planning strategies for the autonomous underwater vehicle. Our results show that scheduling visits to sensor nodes, based on algorithms that address the value of information, improves the timely reporting of high priority data and enables the accumulation of larger value of information.

Source Coding Options to Improve HEVC Video Streaming in Vehicular Networks

Video delivery in Vehicular Ad-hoc NETworks has a great number of applications. However, multimedia streaming over this kind of networks is a very challenging issue because (a) it is one of the most resource-demanding applications; (b) it requires high bandwidth communication channels; (c) it shows moderate to high node mobility patterns and (d) it is common to find high communication interference levels that derive in moderate to high loss rates. In this work, we present a simulation framework based on OMNeT++ network simulator, Veins framework, and the SUMO mobility traffic simulator that aims to study, evaluate, and also design new techniques to improve video delivery over Vehicular Ad-hoc NETworks. Using the proposed simulation framework we will study different coding options, available at the HEVC video encoder, that will help to improve the perceived video quality in this kind of networks. The experimental results show that packet losses significantly reduce video quality when low interference levels are found in an urban scenario. By using different INTRA refresh options combined with appropriate tile coding, we will improve the resilience of HEVC video delivery services in VANET urban scenarios.

Systematic Review about QoS and QoE in Telemedicine and eHealth Services and Applications

The provision of Quality of Service (QoS) and Quality of Experience (QoE) is a mandatory requirement when transmitting telemedicine traffic, due to information relevance to maintain the patient's health. The main objective of this paper is to present a review of existing research works in the literature, referring to QoS and QoE in telemedicine and eHealth applications. The academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct and Web of Science, taking into account the date of publication from 2008 to the present. These databases cover the most information of scientific texts in multidisciplinary fields, engineering and medicine. Several search criteria were established such as 'QoS' AND 'eHealth' OR 'Telemedicine', 'QoE' AND 'eHealth' AND 'Telemedicine' etc. selecting the items of greatest interest. A total of 248 papers related to QoS and QoE in telemedicine and eHealth have been found, of which 39 papers have been identified as relevant works. The results show that the percentage of studies related to QoS in literature is higher with 74.36% to QoE with 25.64%. From the review of the research articles analyzed, it can be said that QoS and QoE in telemedicine and eHealth are important and necessary factors to guarantee the privacy, reliability, quality and security of data in health care systems.

Multiple Instances QoS Routing in RPL: Application to Smart Grids

The Smart Grid (SG) aims to transform the current electric grid into a "smarter" network where the integration of renewable energy resources, energy efficiency and fault tolerance are the main benefits. This is done by interconnecting every energy source, storage point or central control point with connected devices, where heterogeneous SG applications and signalling messages will have different requirements in terms of reliability, latency and priority. Hence, data routing and prioritization are the main challenges in such networks. So far, RPL (Routing Protocol for Low-Power and Lossy networks) protocol is widely used on Smart Grids for distributing commands over the grid. RPL assures traffic differentiation at the network layer in wireless sensor networks through the logical subdivision of the network in multiple instances, each one relying on a specific Objective Function. However, RPL is not optimized for Smart Grids, as its main objective functions and their associated metric does not allow Quality of Service differentiation. To overcome this, we propose OFQS an objective function with a multi-objective metric that considers the delay and the remaining energy in the battery nodes alongside with the dynamic quality of the communication links. Our function automatically adapts to the number of instances (traffic classes) providing a Quality of Service differentiation based on the different Smart Grid applications requirements. We tested our approach on a real sensor testbed. The experimental results show that our proposal provides a lower packet delivery latency and a higher packet delivery ratio while extending the lifetime of the network compared to solutions in the literature.

Narrowband Characterization of Near-Ground Radio Channel for Wireless Sensors Networks at 5G-IoT Bands

In this contribution, a narrowband radio channel model is proposed for rural scenarios in which the radio link operates under near-ground conditions for application in wireless sensor networks dedicated to smart agriculture. The received power attenuation was measured for both transmitter and receiver antennas placed at two different heights above ground: 0.2 and 0.4 m. Three frequency ranges, proposed for future 5G-IoT use case in agriculture, were chosen: 868 MHz, 2.4 GHz and 5.8 GHz. Three ground coverings were tested in a rural scenario: soil, short and tall grass fields. The path loss was then estimated as dependent of the radio link range and a three-slope log-normal path loss model was tailored. Results are explained in terms of the first Fresnel zone obstruction. Commercial Zigbee sensor nodes operating at 2.4 GHz were used in a second experiment to estimate the link quality from the experimental Radio Signal Strength Indicator (RSSI) received values. Two sensor nodes were placed at the same elevation above ground as in the previous experiment, only for short grass field case. The Quality of Service performance was determined in terms of theoretical bit error rate achieved for different digital modulations-BPSK, 8PSK and 16QAM-concluding remarkable results for an obstructed radio link.

A Distributed and Context-Aware Task Assignment Mechanism for Collaborative Mobile Edge Computing

Mobile edge computing (MEC) is an emerging technology that leverages computing, storage, and network resources deployed at the proximity of users to offload their delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since task offloading incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in such an environment are (i) should the workload be offloaded to the edge or computed in terminals? (ii) Which edge, among the available ones, should the task be offloaded to? In this paper, we formulate the task assignment problem as a one-to-many matching game which is a powerful tool for studying the formation of a mutual beneficial relationship between two sets of agents. The main goal of our task assignment mechanism design is to reduce overall energy consumption, while satisfying task owners’ heterogeneous delay requirements and supporting good scalability. An intensive simulation is conducted to evaluate the efficiency of our proposed mechanism.

A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks

In this paper, the concept of SDN (Software Defined Networking) is extended to be applied to wireless networks. Traditionally, in a wired SDN environment, the OpenFlow protocol is the communication protocol used to configure the flow table of forwarding elements (i.e., switches and Access Points). However, although in IEEE 802.11 networks there is no concept of forwarding, the SDN paradigm could also be applied to set up the wireless network dynamically, in order to improve the performance. In this case, not only the network elements, that is the Access Points, but also the mobile elements should configure their link and physical layers parameters following the guidelines of a centralized SDN controller. In particular, we propose a mechanism called DEDCA (Dynamic Enhanced Distributed Channel Access) to manage the channel access in wireless networks, and a framework that enables its implementation in 802.11-based wireless networks using SDN technology. The key aspect of this alternative solution is the control over the contention window size of the wireless terminals. Thus, an adequate response to dynamic and short-term Quality of Service (QoS) requirements can be offered to services running on these networks. DEDCA mechanism relies upon the use of a scalar parameter called gain. The mathematical model which has allowed us to obtain this parameter is presented and evaluated in this paper. Finally, the usefulness of the proposed solutions have been evaluated by means of their implementation in an example case.

Dynamic Cost-Aware Routing of Web Requests

Work within next generation networks considers additional network convergence possibilities and the integration of new services to the web. This trend responds to the ongoing growth of end-user demand for services that can be delivered anytime, anywhere, on any web-capable device, and of traffic generated by new applications, e.g., the Internet of Things. To support the massive traffic generated by the enormous user base and number of devices with reliability and high quality, web services run from redundant servers. As new servers need to be regularly deployed at different geographical locations, energy costs have become a source of major concern for operators. We propose a cost aware method for routing web requests across replicated and distributed servers that can exploit the spatial and temporal variations of both electricity prices and the server network. The method relies on a learning automaton that makes per-request decisions, which can be computed much faster than regular global optimization methods. Using simulation and testbed measurements, we show the cost reductions that are achievable with minimal impact on performance compared to standard web routing algorithms.

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems

Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.

IEEE 802.15.4 Frame Aggregation Enhancement to Provide High Performance in Life-Critical Patient Monitoring Systems

In wireless body area sensor networks (WBASNs), Quality of Service (QoS) provision for patient monitoring systems in terms of time-critical deadlines, high throughput and energy efficiency is a challenging task. The periodic data from these systems generates a large number of small packets in a short time period which needs an efficient channel access mechanism. The IEEE 802.15.4 standard is recommended for low power devices and widely used for many wireless sensor networks applications. It provides a hybrid channel access mechanism at the Media Access Control (MAC) layer which plays a key role in overall successful transmission in WBASNs. There are many WBASN's MAC protocols that use this hybrid channel access mechanism in variety of sensor applications. However, these protocols are less efficient for patient monitoring systems where life critical data requires limited delay, high throughput and energy efficient communication simultaneously. To address these issues, this paper proposes a frame aggregation scheme by using the aggregated-MAC protocol data unit (A-MPDU) which works with the IEEE 802.15.4 MAC layer. To implement the scheme accurately, we develop a traffic patterns analysis mechanism to understand the requirements of the sensor nodes in patient monitoring systems, then model the channel access to find the performance gap on the basis of obtained requirements, finally propose the design based on the needs of patient monitoring systems. The mechanism is initially verified using numerical modelling and then simulation is conducted using NS2.29, Castalia 3.2 and OMNeT++. The proposed scheme provides the optimal performance considering the required QoS.