Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service

The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptability and scalability in complex, dynamically-changing environments. This paper introduces the Dynamic Resource Allocator using RL-CNN (DRARLCNN), a novel machine learning model addressing these shortcomings. By merging Convolutional Neural Networks (CNN) for feature extraction and Reinforcement Learning (RL) for decision-making, DRARLCNN optimizes resource allocation, minimizing latency and maximizing Quality of Service (QoS). Utilizing a state-of-the-art "5G Resource Allocation Dataset", the research employs Python, TensorFlow, and OpenAI Gym to implement and test the model in a simulated 5 G environment. Results demonstrate the effectiveness of DRARLCNN, showcasing an impressive R2 score of 0.517, MSE of 0.035, and RMSE of 0.188, surpassing existing methods in allocation efficiency and latency. The DRARLCNN model not only outperforms existing methods in allocation efficiency and latency but also sets a new benchmark for future research in dynamic 5G resource allocation. Through its innovative approach and promising results, DRARLCNN opens avenues for further advancements in optimizing resource allocation within dynamic 5G networks.

Toward QoS Monitoring in IoT Edge Devices Driven Healthcare-A Systematic Literature Review

Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the "quality of service" (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

The Age of Information in Wireless Cellular Systems: Gaps, Open Problems, and Research Challenges

One of the critical use cases for prospective fifth generation (5G) cellular systems is the delivery of the state of the remote systems to the control center. Such services are relevant for both massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) services that need to be supported by 5G systems. The recently introduced the age of information (AoI) metric representing the timeliness of the reception of the update at the receiver is nowadays commonly utilized to quantify the performance of such services. However, the metric itself is closely related to the queueing theory, which conventionally requires strict assumptions for analytical tractability. This review paper aims to: (i) identify the gaps between technical wireless systems and queueing models utilized for analysis of the AoI metric; (ii) provide a detailed review of studies that have addressed the AoI metric; and (iii) establish future research challenges in this area. Our major outcome is that the models proposed to date for the AoI performance evaluation and optimization deviate drastically from the technical specifics of modern and future wireless cellular systems, including those proposed for URLLC and mMTC services. Specifically, we identify that the majority of the models considered to date: (i) do not account for service processes of wireless channel that utilize orthogonal frequency division multiple access (OFDMA) technology and are able to serve more than a single packet in a time slot; (ii) neglect the specifics of the multiple access schemes utilized for mMTC communications, specifically, multi-channel random access followed by data transmission; (iii) do not consider special and temporal correlation properties in the set of end systems that may arise naturally in state monitoring applications; and finally, (iv) only few studies have assessed those practical use cases where queuing may happen at more than a single node along the route. Each of these areas requires further advances for performance optimization and integration of modern and future wireless provisioning technologies with mMTC and URLLC services.

Application-Aware Scheduling for IEEE 802.15.4e Time-Slotted Channel Hopping Using Software-Defined Wireless Sensor Network Slicing

Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application's QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application's QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.

A Connected World: System-Level Support Through Biosensors

The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.

IoT based smart waste management system in aspect of COVID-19

The rapid evolution of the IoT has led to various research challenges for improving smart city applications. Owing to the characteristics and virtues of IoT services, waste management has emerged as a prominent issue in today's society. An undiscerning illegal eviction of waste, lack of waste disposal and management systems, and inept waste management policies have resulted in severe health and environmental challenges. Based on an integrative review, the proposed technique provides insight into the potential of smart cities and associated communities in assisting waste management initiatives. This study has referred to the existing waste management issues in urban areas and proposed an IoT-based smart waste management system of India in aspects of COVID-19 afflicted houses. Our system intends to improve waste management by making regular environmental sterility and making COVID situations more convenient. The proposed framework ensures a solution for efficiently handling waste generated in urban areas, focusing on the interaction among concessioners and waste generators to monitor the unfilled level of bins. This proposal offers dynamic waste collection scheduling and route optimization while achieving quality of service.

[Technology «Care assistant»: organization of care assistant' activities and quality of service (based on materials of the Volgograd region).]

The organization of the activity of сare assistant using the «Care Assistant» technology in the Volgograd region is the subject of consideration in this study. The provisions of E.Giddens' structural theory help to determine the specifics of the organization of the care assistant's activities. The analysis of the activities of care professionals and the assessment of the quality of service is based on the results of an expert survey of сare assistant and a questionnaire survey of service recipients in the region. The formal rules governing the activities of the subjects of the long-term care system constitute the macro-level of the «Care Assistant» technology. The meso-level includes the forms and methods of care professionals' activities within a separate social service organization. The micro-level is the interaction of сare assistant with various specialists at all stages of providing social services in long-term care. The survey results showed that care assistants provide free services included in the social package of long-term care. Defining the specifics of the organization of the care assistants, the author identified problems related to working conditions, the provision of additional services, paperwork, education. The results of the questionnaire survey of service recipients indicate that respondents are satisfied with the quality of service, availability and timeliness of social services.

[Assessment of patient satisfaction with orthodontic services provided in medical organizations of various forms of ownership]

OBJECTIVE

The study of expectations and satisfaction with the quality of orthodontic care provided to children in public and private dental organizations.

MATERIAL AND METHODS

The study was conducted at the clinical bases of the Borovsky Institute of Dentistry of the Sechenov First Moscow State Medical University, Vladimirsky Moscow Regional Research Clinical Institute, Videntis LLC in the period from January to April 2022. An anonymous questionnaire was developed for the study: "Questionnaire for patients to assess the quality and conditions of orthodontic medical services in a medical organization". All data are processed using the statistical software SPSS v. 20.

RESULTS AND DISCUSSION

According to respondents, the quality of service in both public and private dental organizations depends on the material and technical equipment of the medical organization, the attitude of medical personnel, the duration of treatment and the qualifications of orthodontists. Satisfaction of orthodontic care in public dental organizations corresponded to a high level in 73.4% of cases, an average level of 15.6% of cases, a low level in 11.0% of cases; in private dental organizations, a high level was noted in 98.8% of cases, an average level in 1.2% of cases, a low level in 0% of cases (in private dental organizations, not a single respondent noted the quality of services provided as low). Among the main reasons for dissatisfaction with the service of patients, the lack of diagnostic equipment, the unfriendly attitude of the secondary medical and administrative staff, as well as the duration of treatment should be highlighted.

CONCLUSION

A sociological survey to assess patient satisfaction is a tool for determining the effectiveness of any medical organization, while the assessment of the quality of service of respondents depends on the material and technical equipment of the dental organization, the attitude of medical personnel, the duration of treatment and the qualifications of orthodontists. In this regard, it is very important to apply this method of satisfaction assessment when providing high-quality orthodontic care to children both in public and private dental organizations in order to improve the quality of service in a dental medical organization.

AQMDRL: Automatic Quality of Service Architecture Based on Multistep Deep Reinforcement Learning in Software-Defined Networking

Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence algorithms. Due to the strong decision-making and generalization ability, deep reinforcement learning (DRL) has been used in SDN intelligent routing and scheduling mechanisms. However, traditional deep reinforcement learning algorithms present the problems of slow convergence rate and instability, resulting in poor network quality of service (QoS) for an extended period before convergence. Aiming at the above problems, we propose an automatic QoS architecture based on multistep DRL (AQMDRL) to optimize the QoS performance of SDN. AQMDRL uses a multistep approach to solve the overestimation and underestimation problems of the deep deterministic policy gradient (DDPG) algorithm. The multistep approach uses the maximum value of the n-step action currently estimated by the neural network instead of the one-step Q-value function, as it reduces the possibility of positive error generated by the Q-value function and can effectively improve convergence stability. In addition, we adapt a prioritized experience sampling based on SumTree binary trees to improve the convergence rate of the multistep DDPG algorithm. Our experiments show that the AQMDRL we proposed significantly improves the convergence performance and effectively reduces the network transmission delay of SDN over existing DRL algorithms.

A Crisis in the Health System and Quality of Healthcare in Economically Developed Countries

A health crisis caused by a pandemic tested the effectiveness of national healthcare systems by testing both financing and organizational and technical performance of patient care. At that time, the structural flaws in healthcare systems and inequalities in the level of healthcare in its different dimensions and countries due to resource constraints were highlighted. Therefore, the paper concentrates on investigating how the crisis in the health system affects the quality of healthcare services as a result of changes in the availability of financial, material, and human resources belonging to this system. The quantitative data, in terms of healthcare characterizing the OECD countries and selected non-member economies, treated as an example of economically developed regions, were chosen for the analysis. The study included five areas of resources, i.e., demographic, financial, human, technical, and the delivery of basic services in healthcare. T-test method for dependent samples, supplemented with Hedge's g statistics, was applied to test the differences between the mean values of individual indicators. The results indicate the occurrence of changes in some areas of the healthcare system due to a crisis. Identifying areas that are particularly vulnerable to sudden changes in the healthcare system helps to understand which resource areas need to be strategically managed first, as shifts in levels respond to deteriorating healthcare quality outcomes.

Resilience of Multi-Layer Communication Networks

Advances in the future communication technologies and capabilities of new services in heterogeneous network systems have increased the need for modelling and analysing various aspects of both the resilience of networked systems and usability from the user's point of view. We extend the traditional network reliability theory to cover a wider scope of quality requirements and applications. The proposed method can be used to model the resilience of different structured networks, and the quality of information services. We use the term resilience to cover both the technical and quality-of-service aspects of user requirements. The modelling method is demonstrated with a use case of a multilayer communication network system. However, the method can be used to model any kind of technological network, such as wireless, sensor, and backbone networks.

Multi-Connectivity for 5G Networks and Beyond: A Survey

To manage a growing number of users and an ever-increasing demand for bandwidth, future 5th Generation (5G) cellular networks will combine different radio access technologies (cellular, satellite, and WiFi, among others) and different types of equipment (pico-cells, femto-cells, small-cells, macro-cells, etc.). Multi-connectivity is an emerging paradigm aiming to leverage this heterogeneous architecture. To achieve this, multi-connectivity proposes to enable UE to simultaneously use component carriers from different and heterogeneous network nodes: base stations, WiFi access points, etc. This could offer many benefits in terms of quality of service, energy efficiency, fairness, mobility, and spectrum and interference management. Therefore, this survey aims to present an overview of multi-connectivity in 5G networks and beyond. To do so, a comprehensive review of existing standards and enabling technologies is proposed. Then, a taxonomy is defined to classify the different elements characterizing multi-connectivity in 5G and future networks. Thereafter, existing research works using multi-connectivity to improve the quality of service, energy efficiency, fairness, mobility management, and spectrum and interference management are analyzed and compared. In addition, lessons common to these different contexts are presented. Finally, open challenges for multi-connectivity in 5G networks and beyond are discussed.

Development of an Efficiency Platform Based on MQTT for UAV Controlling and DoS Attack Detection

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.

Quality-of-Service-Centric Design and Analysis of Unmanned Aerial Vehicles

Recent years have witnessed rapid development and great indignation burgeoning in the unmanned aerial vehicles (UAV) field. This growth of UAV-related research contributes to several challenges, including inter-communication from vehicle to vehicle, transportation coverage, network information gathering, network interworking effectiveness, etc. Due to ease of usage, UAVs have found novel applications in various areas such as agriculture, defence, security, medicine, and observation for traffic-monitoring applications. This paper presents an innovative drone system by designing and developing a blended-wing-body (BWB)-based configuration for next-generation drone use cases. The proposed method has several benefits, including a very low interference drag, evenly distributed load inside the body, and less radar signature compared to the state-of-the-art configurations. During the entire procedure, a standard design approach was followed to optimise the BWB framework for next-generation use cases by considering the typically associated parameters such as vertical take-off and landing and drag and stability of the BWB. Extensive simulation experiments were performed to carry out a performance analysis of the proposed model in a software-based environment. To further confirm that the model design of the BWB-UAV is fit to execute the targeted missions, the real-time working environments were tested through advanced numerical simulation and focused on avoiding cost and unwanted wastages. To enhance the trustworthiness of this said computational fluid dynamics (CFD) analysis, grid convergence test-based validation was also conducted. Two different grid convergence tests were conducted on the induced velocity of the Version I UAV and equivalent stress of the Version II UAV. Finite element analysis-based computations were involved in estimating structural outcomes. Finally, the mesh quality was obtained as 0.984 out of 1. The proposed model is very cost-effective for performing a different kind of manoeuvring activities with the help of its unique design at reasonable mobility speed and hence can be modelled for high-speed-based complex next-generation use cases.

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.

Quality of Service Aware Orchestration for Cloud-Edge Continuum Applications

The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.

QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing

Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%.

CovaDel: a blockchain-enabled secure and QoS-aware drone delivery framework for COVID-like pandemics

With increase in the use of contactless deliveries during the times such as COVID-19 pandemic, the emphasis was to minimize the human presence to reduce the spread of virus. In this regard, drones are one promising alternative to be used as delivery agents. However, security and Quality of Service (QoS) are major concerns while making use of drones for deliveries. In order to secure the drone communication system, we propose, CovaDel: a blockchain-based scheme to secure data transactions for the drone delivery use case that works in a phased manner. The proposed scheme make use of decoupled blockchain architecture to overcome the limited resources capabilities of drones to perform blockchain-based computations. Further, to ensure the QoS adherence, we also propose a QoS-aware communication approach that handles collisions and congestion on the basis of firefly algorithm’s attractiveness parameter (light intensity) received by the drones. Results obtained using a simulated environment verify the efficacy of the proposed scheme on the basis of gas consumed, transaction time, average network throughput, and delay.

Enhanced Dynamic Spectrum Access in UAV Wireless Networks for Post-Disaster Area Surveillance System: A Multi-Player Multi-Armed Bandit Approach

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.

[Hemodialysis and nursing: a pilot study on patient-perceived quality]

Introduction: Dialysis is a form of renal replacement therapy that requires several changes in the relational, emotional, work, and family sphere. It can be a cause of stress due to various factors. Objective: The purpose of our study has been to evaluate the perception of the nursing care received by dialyzed patients. Method: In 2021, a cross sectional study was conducted in Perugia hospital by administering to dialyzed patients a questionnaire built on the Newcastle satisfaction with nursing scale. Results: 30 patients participated in the study: the mean age was 68.9 ±15.1, 66.7% were male, 50% had a high school diploma, 86.7% were retired, and 50% were dialyzed for less than 5 years. Negative perceptions of the assistance received were mainly reported by women, younger patients, and patients who had been in therapy for only a few years. Discussion: Our study highlights several aspects that are fundamental to improving the quality of nursing care. There also needs to be a greater attention to certain types of patients, to improve their experience and consequently their quality of life.

Application Layer ARQ Algorithm for Real-Time Multi-Source Data Streaming in UAV Networks

Because of the specific characteristics of Unmanned Aerial Vehicle (UAV) networks and real-time applications, the trade-off between delay and reliability imposes problems for streaming video. Buffer management and drop packets policies play a critical role in the final quality of the video received by the end station. In this paper, we present a reactive buffer management algorithm, called Multi-Source Application Layer Automatic Repeat Request (MS-AL-ARQ), for a real-time non-interactive video streaming system installed on a standalone UAV network. This algorithm implements a selective-repeat ARQ model for a multi-source download scenario using a shared buffer for packet reordering, packet recovery, and measurement of Quality of Service (QoS) metrics (packet loss rate, delay and, delay jitter). The proposed algorithm MS-AL-ARQ will be injected on the application layer to alleviate packet loss due to wireless interference and collision while the destination node (base station) receives video data in real-time from different transmitters at the same time. Moreover, it will identify and detect packet loss events for each data flow and send Negative-Acknowledgments (NACKs) if packets were lost. Additionally, the one-way packet delay, jitter, and packet loss ratio will be calculated for each data flow to investigate the performances of the algorithm for different numbers of nodes under different network conditions. We show that the presented algorithm improves the QoS of the video data received under the worst network connection conditions. Furthermore, some congestion issues during deep analyses of the algorithm's performances have been identified and explained.

ESCOVE: Energy-SLA-Aware Edge-Cloud Computation Offloading in Vehicular Networks

The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach.

DAMAC: A Delay-Aware MAC Protocol for Ad Hoc Underwater Acoustic Sensor Networks

In a channel shared by several nodes, the scheduling algorithm is a key factor to avoiding collisions in the random access-based approach. Commonly, scheduling algorithms can be used to enhance network performance to meet certain requirements. Therefore, in this paper we propose a Delay-Aware Media Access Control (DAMAC) protocol for monitoring time-sensitive applications over multi-hop in Underwater Acoustic Sensor Networks (UASNs), which relies on the random access-based approach where each node uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) to determine channel status, switches nodes on and off to conserve energy, and allows concurrent transmissions to improve the underwater communication in the UASNs. In addition, DAMAC does not require any handshaking packets prior to data transmission, which helps to improve network performance in several metrics. The proposed protocol considers the long propagation delay to allow concurrent transmissions, meaning nodes are scheduled to transmit their data packets concurrently to exploit the long propagation delay between underwater nodes. The simulation results show that DAMAC protocol outperforms Aloha, BroadcastMAC, RMAC, Tu-MAC, and OPMAC protocols under varying network loads in terms of energy efficiency, communication overhead, and fairness of the network by up to 65%, 45%, and 726%, respectively.

Tele-Monitoring System for Chronic Diseases Management: Requirements and Architecture

In the last years a large variety of eHealth services and Apps for professional medical users have been developed for different scenarios. The increasing elderly population (+100% in 2050) makes urgent to implement tele-medicine paradigm in the healthcare structures. The need of monitoring large number of patients distributed over the territory, together with the lack of medical resources, makes the adoption of Information Communication Technologies (ICT) crucial for the future healthcare services. This paper presents an ICT architecture model for the provision of tele-monitoring services within a novel proposed remote monitoring concept for healthcare, considering the new Family and Community Nurse (FCN). An integrated and personalized tele-monitoring solution is presented, through a detailed description of the reference network architecture and service platform. Moreover, the preliminary results of the experimental activities carried out for the evaluation of the system in terms of usability in operational scenarios are provided.

A Comparative Study of Traffic Classification Techniques for Smart City Networks

Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS requirements. However, conventional traffic classification methods, such as the port-based method, are inefficient because of their inability to handle dynamic port allocation and encryption. Traffic classification using machine learning has gained research interest as an alternative method to achieve high performance. In fact, machine learning embeds intelligence into network functions, thus improving network management. In this study, we apply machine learning algorithms to predict network traffic classification. We apply four supervised learning algorithms: support vector machine, random forest, k-nearest neighbors, and decision tree. We also apply a port-based method of traffic classification based on applications' popular assigned port numbers. Then, we compare the results of this method to those obtained from the machine learning algorithms. The evaluation results indicate that the decision tree algorithm provides the highest average accuracy among the evaluated algorithms, at 99.18%. Moreover, network traffic classification using machine learning provides more accurate results and higher performance than the port-based method.

Quality of Service Provision in Fog Computing: Network-Aware Scheduling of Containers

State-of-the-art scenarios, such as Internet of Things (IoT) and Smart Cities, have recently arisen. They involve the processing of huge data sets under strict time requirements, rendering the use of cloud resources unfeasible. For this reason, Fog computing has been proposed as a solution; however, there remains a need for intelligent allocation decisions, in order to make it a fully usable solution in such contexts. In this paper, a network-aware scheduling algorithm is presented, which aims to select the fog node most suitable for the execution of an application within a given deadline. This decision is made taking the status of the network into account. This scheduling algorithm was implemented as an extension to the Kubernetes default scheduler, and compared with existing proposals in the literature. The comparison shows that our proposal is the only one that can execute all the submitted jobs within their deadlines (i.e., no job is rejected or executed exceeding its deadline) with certain configurations in some of the scenarios tested, thus obtaining an optimal solution in such scenarios.

FASDQ: Fault-Tolerant Adaptive Scheduling with Dynamic QoS-Awareness in Edge Containers for Delay-Sensitive Tasks

As the requirement for real-time data analysis increases, edge computing is being implemented to leverage the resources of edge devices to reduce system response times and decrease the latency. However, due to the resource constraints of edge clouds, edge servers are more prone to failures than other systems. Therefore, guaranteeing the reliability of services in edge clouds is critical. In this paper, we propose a fault-tolerant adaptive scheduling mechanism with dynamic quality of service (QoS) awareness (FASDQ), which extends the primary/backup (PB) model by applying QoS on demand to task copies. The aim of the method is to reduce the latency and achieve reliable service for tasks by changing the execution time of task copies. This paper also proposes a container resource-adaptive adjustment mechanism, which adjusts the timing of resources when the available resources cannot meet the task copy requirements. Finally, this paper reports the results of simulation experiments on the EdgeCloudSim platform to evaluate the difference in performance between FASDQ and other methods. The results show that the mechanism effectively reduces the execution time of task copies and outperforms other methods in terms of reliability and general resource utilization.

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

Pervasive sensing is increasing our ability to monitor the status of patients not only when they are hospitalized but also during home recovery. As a result, lots of data are collected and are available for multiple purposes. If operations can take advantage of timely and detailed data, the huge amount of data collected can also be useful for analytics. However, these data may be unusable for two reasons: data quality and performance problems. First, if the quality of the collected values is low, the processing activities could produce insignificant results. Second, if the system does not guarantee adequate performance, the results may not be delivered at the right time. The goal of this document is to propose a data utility model that considers the impact of the quality of the data sources (e.g., collected data, biographical data, and clinical history) on the expected results and allows for improvement of the performance through utility-driven data management in a Fog environment. Regarding data quality, our approach aims to consider it as a context-dependent problem: a given dataset can be considered useful for one application and inadequate for another application. For this reason, we suggest a context-dependent quality assessment considering dimensions such as accuracy, completeness, consistency, and timeliness, and we argue that different applications have different quality requirements to consider. The management of data in Fog computing also requires particular attention to quality of service requirements. For this reason, we include QoS aspects in the data utility model, such as availability, response time, and latency. Based on the proposed data utility model, we present an approach based on a goal model capable of identifying when one or more dimensions of quality of service or data quality are violated and of suggesting which is the best action to be taken to address this violation. The proposed approach is evaluated with a real and appropriately anonymized dataset, obtained as part of the experimental procedure of a research project in which a device with a set of sensors (inertial, temperature, humidity, and light sensors) is used to collect motion and environmental data associated with the daily physical activities of healthy young volunteers.

A Bandwidth Control Arbitration for SoC Interconnections Performing Applications with Task Dependencies

Current System-on-Chips (SoCs) execute applications with task dependency that compete for shared resources such as buses, memories, and accelerators. In such a structure, the arbitration policy becomes a critical part of the system to guarantee access and bandwidth suitable for the competing applications. Some strategies proposed in the literature to cope with these issues are Round-Robin, Weighted Round-Robin, Lottery, Time Division Access Multiplexing (TDMA), and combinations. However, a fine-grained bandwidth control arbitration policy is missing from the literature. We propose an innovative arbitration policy based on opportunistic access and a supervised utilization of the bus in terms of transmitted flits (transmission units) that settle the access and fine-grained control. In our proposal, every competing element has a budget. Opportunistic access grants the bus to request even if the component has spent all its flits. Supervised debt accounts a record for every transmitted flit when it has no flits to spend. Our proposal applies to interconnection systems such as buses, switches, and routers. The presented approach achieves deadlock-free behavior even with task dependency applications in the scenarios analyzed through cycle-accurate simulation models. The synergy between opportunistic and supervised debt techniques outperforms Lottery, TDMA, and Weighted Round-Robin in terms of bandwidth control in the experimental studies performed.

A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.

Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service

This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m² as well as in a large area of 10,000 m². Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.

A Systematic Analysis of Narrowband IoT Quality of Service

Narrowband-IoT (NB-IoT) is part of a novel group of access technologies referred to as Low-Power Wide Area Networks (LPWANs), which provide energy-efficient and long-range network access to IoT devices. Although NB-IoT Release 13 has been deployed by Mobile Network Operators (MNO), detailed Quality of Service (QoS) evaluations in public networks are still rare. In this paper, systematic physical layer measurements are conducted, and the application layer performance is verified. Special consideration is given to the influence of the radio parameters on the application layer QoS. Additionally, NB-IoT is discussed in the context of typical smart metering use cases. The results indicate that NB-IoT meets most theoretical Third Generation Partnership Project (3GPP) design goals in a commercial deployment. NB-IoT provides a wide coverage by using signal repetitions, which improve the receiver sensitivity, but simultaneously increase the system latency. The maximum data rates are consistent over a wide range of coverage situations. Overall, NB-IoT is a reliable and flexible LPWAN technology for sensor applications even under challenging radio conditions. Four smart metering transmission categories are analyzed, and NB-IoT is verified to be appropriate for applications that are not latency sensitive.

A Study to Evaluate the Efficacy of Different Interventions for Improving Quality of Maternal Health Care Service in China

Abstract Background/Introduction: The quality of maternal health care service is a crucial determinant of maternal morbidities and mortalities. This study aimed to explore feasibility and relative efficacy of WeChat (WC), of specialist team (ST) service, and of combined of both interventions (WC-ST) for improving the quality of maternal health care in China. Materials and Methods: A four-arm randomized controlled trial of 1,400 pregnant women was conducted in three hospitals in Chengdu, Southwest China, from December 2016 to October 2017. Eligible women were randomly assigned to either of three intervention groups or the control group (service as usual; SAU). Main outcome measures were satisfaction rate and uptakes of maternal health care service at 49 days postpartum based on questionnaire survey. Results: No significant differences in satisfaction rate were found among four groups at baseline (p = 0.981), and significant group differences were noted at 49 days postpartum (p < 0.001), with the highest rate from WC-ST group (98.6%), followed by that of ST (95.2%) and WC (91.6%) groups, and SAU group being the lowest (85.2%). The same pattern of group difference was observed in measures of health care uptake behaviors. Most health care uptake measures from the baseline to post-trial were significantly improved within each of the intervention groups, while most such measures in the control group were not different from baseline to post-trial. Discussion and Conclusions: The WC and ST service is feasible and potentially effective in improving the quality of maternal health care service in China. The study has revealed limitations and options for improvement in future main trial.

Analysis of Efficient Spectrum Handoff in a Multi-Class Hybrid Spectrum Access Cognitive Radio Network Using Markov Modelling

Cognitive radio networks (CRNs) rely on sensing of the licensed spectrum of a primary network to dynamically ascertain underutilized portion of the spectrum, thus affording additional communication opportunities. In a CRN, a single homogeneous spectrum access, such as interweave only deprives the secondary users (SUs) of channel access during handoff, particularly at high primary network traffic. Therefore, providing quality-of-service (QoS) to multi-class SUs with diverse delay requirements during handoff becomes a challenging task. In this paper, we have evolved a Markov-based analytical model to ascertain the gain in non-switching spectrum handoff scheme for multi-class SUs employing hybrid interweave-underlay spectrum access strategy. To satisfy the QoS requirements of the delay-sensitive traffic, we have analyzed the impact of hybrid spectrum access scheme for prioritized multi-class SUs traffic. The results show substantial improvement in spectrum utilization, average system throughput and extended data delivery time compared to conventional CRN using interweave only spectrum access. This demonstrates the suitability of the proposed scheme towards meeting QoS requirements of the delay-sensitive SU traffic while improving the overall performance for delay-tolerant SU traffic as well.

CATSWoTS: Context Aware Trustworthy Social Web of Things System

The inevitable revolution of the Internet of Things (IoT) and its benefits can be witnessed everywhere. Two major issues related to IoT are the interoperability and the identification of trustworthy things. The proposed Context-Aware Trustworthy Social Web of Things System (CATSWoTS) addresses the interoperability issue by incorporating web technologies including Service Oriented Architecture where each thing plays the role of a service provider as well as a role of service consumer. The aspect of social web helps in getting recommendations from social relations. It was identified that the context dependency of trust along with Quality of Service (QoS) criteria, for identifying and recommending trustworthy Web of Things (WoT), require more attention. For this purpose, the parameters of context awareness and the constraints of QoS are considered. The research focuses on the idea of a user-centric system where the profiles of each thing (level of trustworthiness) are being maintained at a centralized level and at a distributed level as well. The CATSWoTS evaluates service providers based on the mentioned parameters and the constraints and then identifies a suitable service provider. For this, a rule-based collaborative filtering approach is used. The efficacy of CATSWoTS is evaluated with a specifically designed environment using a real QoS data set. The results showed that the proposed novel technique fills the gap present in the state of the art. It performed well by dynamically identifying and recommending trustworthy services as per the requirements of a service seeker.

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization

Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.

On Providing Multi-Level Quality of Service for Operating Rooms of the Future

The Operating Room (OR) plays an important role in delivering vital medical services to patients in hospitals. Such environments contain several medical devices, equipment, and systems producing valuable information which might be combined for biomedical and surgical workflow analysis. Considering the sensibility of data from sensors in the OR, independently of processing and network loads, the middleware that provides data from these sensors have to respect applications quality of service (QoS) demands. In an OR middleware, there are two main bottlenecks that might suffer QoS problems and, consequently, impact directly in user experience: (i) simultaneous user applications connecting the middleware; and (ii) a high number of sensors generating information from the environment. Currently, many middlewares that support QoS have been proposed by many fields; however, to the best of our knowledge, there is no research on this topic or the OR environment. OR environments are characterized by being crowded by persons and equipment, some of them of specific use in such environments, as mobile x-ray machines. Therefore, this article proposes QualiCare, an adaptable middleware model to provide multi-level QoS, improve user experience, and increase hardware utilization to middlewares in OR environments. Our main contributions are a middleware model and an orchestration engine in charge of changing the middleware behavior to guarantee performance. Results demonstrate that adapting middleware parameters on demand reduces network usage and improves resource consumption maintaining data provisioning.

Mothers' experiences while admitted to a residential parenting unit: a qualitative study

Background: In Australia, most states have residential parenting units that provide parenting support to parents (usually mothers) who are experiencing significant parenting difficulties with their infants or toddlers. The three most common reasons for admission to a residential service are: sleep and settling issues, adjustment to parenting, and feeding issues. Aim: The overall study aim was to explore mothers' experience of a residential admission, as one tool to increase the "patient (mother) voice" within the residential parenting service and provide a mechanism for staff to understand the impact of their interactions with mothers on the care delivery process. Design: A qualitative descriptive approach and thematic analysis were used. One hundred mothers provided responses to a routinely collected questionnaire that asked about their experience while admitted to one of three residential parenting units. All mothers were eligible to participate. Results: Three major themes were identified: not knowing what to expect; working collaboratively with parents; and facilitating maternal learning. Mothers identified that they had increased parenting confidence levels, and gained new parenting knowledge and skills as an outcome of the residential stay. Conclusions: The value of a residential stay is clearly articulated by the mothers in the stories collected. These themes have affirmed that the residential units are parent-focused. Some mothers were surprised by the nurses' willingness to listen to their preferences about their child's care and to work with them adapting interventions to their cultural and home context.

Provider-Related Predictors of Utilization of University Health Services in Nigeria

Background

The utilization of health services is an important policy concern in most developing countries. Many staff and students do not utilize the health services within the university system despite the availability of good quality services. This study investigated the provider-related factors related to utilization of university health service by staff and students in a privately owneduniversity in Nigeria.

Methods

The perception of the quality of a university health service was investigated among a cross-section of 600 university staff and students who were selected by a stratified random sampling scheme. A self-administered questionnaire-based study was conducted. The structure, process and output predictors of utilization of the university health facility were assessed. Data analysis was carried out using Stata I/C 15.0.

Results

The average age of the participants was 22.93±7.58 years. About two-thirds of them did not have opinion about the mortality and morbidity rates at the university health center. Significant proportions of the participants reported good perceptions about the structure and process quality of service indicators. Utilization of the university health center was predicted by some structure and process indicators namely; the availability/experience of staff (AOR 2.44; CI 1.67–3.58), the organization of healthcare (AOR 1.64; CI 1.11–2.41), the continuity of treatment (AOR 1.74; CI 1.12–2.70) and the waiting time (AOR 0.41; CI 0.28–0.61).

Conclusion

The utilization of university health services was predicted by availability/experience of staff, the organization of healthcare, the waiting time and the continuity of care. The structure-process-outcome approach discriminates between the students and staff who utilize the university health center and those who donot. It also suggests a complex interplay of factors in the prediction of choice of a health facility.

Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

At present, most publish/subscribe middlewares suppose that there are equal Quality of Service (QoS) requirements for all users. However, in many real-world Internet of Things (IoT) service scenarios, different users may have different delay requirements. How to provide reliable differentiated services has become an urgent problem. The rise of Software-Defined Networking (SDN) provides endless possibilities to improve the QoS of publish/subscribe middlewares due to its greater programmability. We can encode event topics and priorities into flow entries of SDN switches directly to meet customized requirements. In this paper, we first propose an SDN-like publish/subscribe middleware architecture and describe how to use this architecture and priority queues supported by OpenFlow switches to realize differentiated services. Then we present a machine learning method using the eXtreme Gradient Boosting (XGBoost) model to solve the difficult issue of getting the queuing delay of switches accurately. Finally, we propose a reliable differentiated services guarantee mechanism according to the queuing delay and the programmability of SDN to improve QoS, namely, a two-layer queue management mechanism. Experimental evaluations show that the delay predicted by the XGBoost method is closer to the real value; our mechanism can save end-to-end delay, reduce packet loss rate, and allocate bandwidth more reasonably.

Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System

Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of C O 2 , humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City.

Energy-Efficient IoT Service Brokering with Quality of Service Support

The Internet of Things (IoT) is becoming real, and recent studies highlight that the number of IoT devices will significantly grow in the next decade. Such massive IoT deployments are typically made available to applications as a service by means of IoT platforms, which are aware of the characteristics of the connected IoT devices⁻usually constrained in terms of computation, storage and energy capabilities⁻and dispatch application's service requests to appropriate devices based on their capabilities. In this work, we develop an energy-aware allocation policy that aims at maximizing the lifetime of all the connected IoT devices, whilst guaranteeing that applications' Quality of Service (QoS) requirements are met. To this aim, we formally define an IoT service allocation problem as a non-linear Generalized Assignment Problem (GAP). We then develop a time-efficient heuristic algorithm to solve the problem, which is shown to find near-optimal solutions by exploiting the availability of equivalent IoT services provided by multiple IoT devices, as expected especially in the case of massive IoT deployments.

Optimal Resource Management and Binary Power Control in Network-Assisted D2D Communications for Higher Frequency Reuse Factor

Device-to-device (D2D) communications can be adopted as a promising solution to attain high quality of service (QoS) for a network. However, D2D communications generates harmful interference when available resources are shared with traditional cellular users (CUs). In this paper, network architecture for the uplink resource management issue for D2D communications underlaying uplink cellular networks is proposed. We develop a fractional frequency reuse (FFR) technique to mitigate interference induced by D2D pairs (DPs) to CUs and mutual interference among DPs in a cell. Then, we formulate a sum throughput optimization problem to achieve the QoS requirements of the system. However, the computational complexity of the optimization problem is very high due to the exhaustive search for a global optimal solution. In order to reduce the complexity, we propose a greedy heuristic search algorithm for D2D communications so as to find a sub-optimal solution. Moreover, a binary power control scheme is proposed to enhance the system throughput by reducing overall interference. The performance of our proposed scheme is analyzed through extensive numerical analysis using Monte Carlo simulation. The results demonstrate that our proposed scheme provides significant improvement in system throughput with the lowest computational complexity.

A Survey of Energy-Efficient Communication Protocols with QoS Guarantees in Wireless Multimedia Sensor Networks

In recent years, wireless multimedia sensor networks (WMSNs) have emerged as a prominent technique for delivering multimedia information such as still images and videos. Being under the great spotlight of research communities, however, multimedia delivery over resourceconstraint WMSNs poses great challenges, especially in terms of energy efficiency and quality-ofservice (QoS) guarantees. In this paper, recent developments in techniques for designing highly energy-efficient and QoS-capable WMSNs are surveyed. We first study the unique characteristicsand the relevantly imposed requirements of WMSNs. For each requirement we also summarize their existing solutions. Then we review recent research efforts on energy-efficient and QoS-awarecommunication protocols, including MAC protocols, with a focus on their prioritization and service differentiation mechanisms and disjoint multipath routing protocols.

A Joint Multi-Path and Multi-Channel Protocol for Traffic Routing in Smart Grid Neighborhood Area Networks

In order to improve the management mechanisms of the electric energy transport infrastructures, the smart grid networks have associated data networks that are responsible for transporting the necessary information between the different elements of the electricity network and the control center. Besides, they make possible a more efficient use of this type of energy. Part of these data networks is comprised of the Neighborhood Area Networks (NANs), which are responsible for interconnecting the different smart meters and other possible devices present at the consumers’ premises with the control center. Among the proposed network technologies for NANs, wireless technologies are becoming more relevant due to their flexibility and increasing available bandwidth. In this paper, some general modifications are proposed for the routing protocol of the wireless multi-hop mesh networks standardized by the IEEE. In particular, the possibility of using multiple paths and transmission channels at the same time, depending on the quality of service needs of the different network traffic, is added. The proposed modifications have been implemented in the ns-3 simulator and evaluated in situations of high traffic load. Simulation results show improvements in the network performance in terms of packet delivery ratio, throughput and network transit time.

Deep Learning Cluster Structures for Management Decisions: The Digital CEO

This paper presents a Deep Learning (DL) Cluster Structure for Management Decisions that emulates the way the brain learns and makes choices by combining different learning algorithms. The proposed model is based on the Random Neural Network (RNN) Reinforcement Learning for fast local decisions and Deep Learning for long-term memory. The Deep Learning Cluster Structure has been applied in the Cognitive Packet Network (CPN) for routing decisions based on Quality of Service (QoS) metrics (Delay, Loss and Bandwidth) and Cyber Security keys (User, Packet and Node) which includes a layer of DL management clusters (QoS, Cyber and CEO) that take the final routing decision based on the inputs from the DL QoS clusters and RNN Reinforcement Learning algorithm. The model has been validated under different network sizes and scenarios. The simulation results are promising; the presented DL Cluster management structure as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.

A Clustering WSN Routing Protocol Based on k-d Tree Algorithm

Clustering in wireless sensor networks has been widely discussed in the literature as a strategy to reduce power consumption. However, aspects such as cluster formation and cluster head (CH) node assignment strategies have a significant impact on quality of service, as energy savings imply restrictions in application usage and data traffic within the network. Regarding the first aspect, this article proposes a hierarchical routing protocol based on the k-d tree algorithm, taking a partition data structure of the space to organize nodes into clusters. For the second aspect, we propose a reactive mechanism for the formation of CH nodes, with the purpose of improving delay, jitter, and throughput, in contrast with the low-energy adaptive clustering hierarchy/hierarchy-centralized protocol and validating the results through simulation.

Unified Channel Management for Cognitive Radio Sensor Networks Aided Internet of Things

Cognitive capabilities are indispensable for the Internet of Things (IoT) not only to equip them with learning, thinking, and decision-making capabilities but also to cater to their unprecedented huge spectrum requirements due to their gigantic numbers and heterogeneity. Therefore, in this paper, a novel unified channel management framework (CMF) is introduced for cognitive radio sensor networks (CRSNs), which comprises an (1) opportunity detector (ODR), (2) opportunity scheduler (OSR), and (3) opportunity ranker (ORR) to specifically address the immense and diverse spectrum requirements of CRSN-aided IoT. The unified CMF is unique for its type as it covers all three angles of spectrum management. The ODR is a double threshold based multichannel spectrum sensor that allows an IoT device to concurrently sense multiple channels to maximize spectrum opportunities. OSR is an integer linear programming (ILP) based channel allocation mechanism that assigns channels to heterogeneous IoT devices based on their minimal quality of service (QoS) requirements. ORR collects feedback from IoT devices about their transmission experience and generates special channel-sensing order (CSO) for each IoT device based on the data rate and idle-time probabilities. The simulation results demonstrate that the proposed CMF outperforms the existing ones in terms of collision probability, detection probability, blocking probability, idle-time probability, and data rate.