User Priorities Research Articles

A Multichannel Allocation Strategy Based on Preemption Threshold and Preemption Probability in Cognitive Radio Networks

In traditional multichannel cognitive radio networks (CRNs), users are split into two different priorities. Because of the low priority of secondary users (SUs), SU packets’ transmissions are easily interrupted by primary users (PUs). In this paper, two control parameters, called preemption threshold H and preemption probability q, are used to regulate the preemption behavior of PU packets to improve the transmission performance of SU packets. When all channels in the system are occupied, the preemption behavior of PU packets will be adjusted according to the amount of SU packets that are transmitting in the system. If the amount is larger than H, the recently arrived PU packet either preempts a channel with probability q or leaves the system with probability 1 − q . The central controller manages the system’s channel usage right and determines a series of access behaviors of user packets. Considering the possible imperfect sensing, a discrete-time queueing model is developed with the proposed preemption control mechanism. Then we obtain some performance index expressions of PU and SU packets founded on the system’s state transition matrix and make the corresponding performance figures through numerical experiment. Finally, we construct a system utility function and determine the optimal preemption threshold and preemption probability through the seagull optimization algorithm (SOA). Experimental data show that the proposed mechanism by setting preemption threshold and preemption probability can significantly reduce SU packets’ outage rate and improve SU packets’ throughput rate.

Effects of consumers and health providers working in partnership on health services planning, delivery and evaluation.

Effects of consumers and health providers working in partnership on health services planning, delivery and evaluation.

A Game-Theoretical Learning Approach for Spectrum Trading in Cognitive Satellite-Terrestrial Networks

This letter investigates the bandwidth allocation problem for discontinuous frequency bands in cognitive satellite-terrestrial networks (CSTNs). Different from existing studies, we take the realistic assumption that the available idle spectrum bands are disconnected. Most of the existing works focused on the continuous bandwidth allocation problem and proposed corresponding algorithms optimizing continuous variables, which cannot directly solve the bandwidth allocation problem for discontinuous frequency bands. Besides, we also take the individual bandwidth requirement, the priority of terrestrial cognitive users, and the limitation on the total available bandwidth into consideration. The bandwidth allocation for discontinuous frequency bands is formulated as a bandwidth auction game, which is proven to have at least one pure strategy Nash Equalization (NE) solution. Furthermore, an improved best response learning algorithm based on exploration and exploitation (BREE) is proposed to achieve satisfying performance. Finally, simulations are conducted to verify the convergence and superiority of the proposed BREE algorithm.

Load Management System with Integration of Renewable Energy Resources

This article presents, a load management system is designed and implemented to integrate renewable energy resources (RES) (solar and wind), which manage the load according to the supply/demand and the user's priorities. The system is implemented on a hybrid system integrating wind energy, solar energy, utility supply, and battery energy storage system. Load management is carried out via switching of the loads. The sources can also be turned ON and OFF. During excess power, the battery module works as an energy storage unit or backup energy supply unit during demand. Loads can be turned ON and OFF wirelessly via GSM. The grid operator can switch the loads by simply sending a command via a short service message (SMS). In the end, the system is tested, and the results are presented. The hybrid system is simulated in MATLAB/Simulink first and then hardware implementation is carried out, which involves integrating renewable resources via converters and load management by switching using a microcontroller (Arduino).

A User-Oriented Adaptive-Optimal Car Parking Management System Towards Smart Livings

Existing parking management approaches do not consider specific requirements, priorities, user comfort, or modes of use when allocating a parking spot in a large park. As a result, vehicles carrying multiple passengers but staying for a limited period often have to drive further, searching for a parking spot, which increases fuel consumption, emissions, waste of time, and discomfort of users due to extra walking distance. In this paper, we consider the need for both sustainability and comfortable livings in a future smart city and propose an adaptive-optimal scheme that takes advantage of parking efficiency based on the passenger information and flexibly provides the optimal parking spot to the individual. We presume that the management system has information about the number of users, user priority, and expected stay time when a car arrives or a parking request is made. The best parking slot is assigned based on the available parking slots and the given objectives, such as the shortest travel distance inside the parking zone for a low pollution, the shortest walking distance per user, or a combination of both with some trade-off. The decision process is fine-tuned using parking data obtained from a model of a large car park of a shopping complex, and the results of the proposed scheme are compared with other schemes. The findings indicate that overall time spent in the parking lot, as well as individual walking and travel distances, have significantly improved.

Throughput analysis of 2-D OCDMA/pure ALOHA networks with access control and two user classes of variable length for multimedia traffic

In a previous research, the throughput of a 2-D optical code-division multiple-access (OCDMA)/unslotted ALOHA (U-ALOHA)/channel load sensing protocol network using an optical hard limiter and channel code was analyzed. This scheme assumed a fixed message length and one user class. However, the current and future themes of networks is multimedia traffic with variable message length and two different user classes of real-time and non-real-time. In this paper, we propose a 2-D OCDMA/U-ALOHA network with access control and two user classes of variable message length. We assume that the number of fixed-length packets in a message is geometrically distributed and perform access control by assigning two user classes with different access permission probabilities. The numerical results show the high priority user class (e.g. real-time data traffic) can maintain maximum system throughput under the heavy load at the expense of low priority user class (e.g. non-real-time data traffic). The proposed network protocol could obtain 100 Gbps and be a promising alternative for local area networks (LANs) and broadband optical access networks for larger capacity in response to fast growing of multimedia data traffic.

A Proficient Fair Resource Allocation in the Channel of Multiuser Orthogonal Frequency Division Multiplexing using a Novel Hybrid Bat-Krill Herd Optimization

The wireless communication system in the present generation address more challenges because of priority based Resource Allocation (RA) to Real-Time (RT), Best Effort (BE), as well as Non-Real Time (NRT) users in the Wi-Max (Worldwide Inter-operability for Microwave Access) network. Furthermore, the fall of packet and data overflow became an excessive problem in the network. To address this problem, Multi-Input and Multi-Output based Orthogonal Frequency Division Multiplexing (MIMO-OFDM) channel is introduced but in some cases, it also met some challenges in resource allocation strategy. Therefore, this research is to emphasize the Quality of Service (QoS) and prevents data flooding in nodes by the proposed Detain Permissive Network (DPN) model. Furthermore, a novel Hybrid Bat and Krill Herd Optimization (HB-KHO) is proposed for the DPN to allocate the resources based on the priority of users in the MIMO-OFDM channel based Wi-Max network. The execution of the proposed method is carried out in NS-2 platform. The simulation outcome has been attained the finest priority-based resource allocation in terms of better fairness, throughput, and Packet Delivery Ratio (PDR). Moreover, the outcome from the novel proposed technique is compared with existing resource allocation techniques and the comparison shows that the proposed technique is effectively allocated resources to all users in the network.

A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

Mobile application (app) reviews are feedback about experiences, requirements, and issues raised after users have used the app. The iteration of an app is driven by bug reports and user requirements analyzed and extracted from app reviews, which is a problem that app designers and developers are committed to solving. However, a great number of app reviews vary in quality and reliability. It is a difficult and time-consuming challenge to analyze app reviews using manual methods. To address this, a novel approach is proposed as an automated method to predict high priority user requests with fourteen extracted features. A semi-automated approach is applied to annotate requirements with high or low priority with the help of app changelogs. Reviews from six apps were retrieved from the Apple App Store to evaluate the feasibility of the approach and interpret the principles. The performance comparison results of the approach greatly exceed the IDEA method, with an average precision of 75.4% and recall of 70.4%. Our approach can be applied to specific app development to assist app developers in quickly locating user requirements and implement app maintenance and evolution.

A multi-stage graph based algorithm for survivable Service Function Chain orchestration with backup resource sharing

Network softwarisation introduces flexibility in network management by enabling the deployment of network functions as software modules running on virtual machines. However, this creates new concerns for service availability and reliability due to multiple sources of failures at both software and hardware levels, potentially resulting in service degradations and penalties due to Service Level Agreement (SLA) violations. The survivability of critical services can best be guaranteed by pro-actively provisioning dedicated backup resources for these services, but at the cost of a high resource consumption. Aware of the divergent requirements of future services, a promising alternative is envisaged, allowing non-critical users to use the unused backup resources of high priority users. However, this approach poses a stringent challenge if a critical service disruption occurs, requiring the computation of a traffic rerouting solution for the non-critical requests when preempted from their borrowed resources. In this paper we first propose a generic multi-stage graph based algorithm as an alternative algorithm for Service Function Chain (SFC) deployments. Simulation results demonstrate that the proposed algorithm is optimized in terms of resource utilization, resulting in a 10% improvement in terms of acceptance ratio compared to a given state of the art algorithm, and within a 4% margin of the optimal solution. Based on the mentioned algorithm, we propose a new migration-aware algorithm for the mapping of non-critical services, enabling the non-critical services to borrow the unused backup resources from the critical services while minimizing the probability of preemption they could experience. The migration-aware algorithm results in more than an 8% resource saving, in most scenarios, compared to a dedicated backup strategy, and more than a 70% performance improvement in terms of the number of service preemptions, compared to a cost based algorithm. Additionally, whenever low priority users are preempted from their borrowed resources, we propose a new QoS-aware global-rerouting algorithm for remapping those users, reducing the impact of the service interruption thanks to avoiding the migration of surviving VNFs and virtual links when feasible. The proposed algorithm is shown to outperform a service restoration strategy based on local rerouting, in terms of successful service restoration and resource consumption.

Considerations for Evidence Frameworks in Education Research

In this chapter, we describe and compare the standards for evidence used by three entities that review studies of education interventions: Blueprints for Healthy Youth Development, Social Programs that Work, and the What Works Clearinghouse. Based on direct comparisons of the evidence frameworks, we identify key differences in the level at which effectiveness ratings are granted (i.e., intervention vs. outcome domain), as well as in how each entity prioritizes intervention documentation, researcher independence, and sustained versus immediate effects. Because such differences in priorities may result in contradictory intervention ratings between entities, we offer a number of recommendations for a common set of standards that would harmonize effectiveness ratings across the three entities while preserving differences that allow for variation in user priorities. These include disentangling study rigor from intervention effectiveness, ceasing vote counting procedures, adding replication criteria, adding fidelity criteria, assessing baseline equivalence for randomized studies, making quasi-experiments eligible for review, adding criteria for researcher independence, and providing effectiveness ratings at the level of the outcome domain rather than the intervention.

Implementation of ELECTRE Method for Decision Support System

This study aims to design a decision support system to determine users’ priority of office inventory items at PT. INL. Deciding the priority of users of office inventory items aims to determine the most appropriate employees to use office inventory items in their daily work so that each employee’s performance can be more optimal. Deciding office inventory users’ priority is done using the ELECTRE (Elimination Et Choix Traduisant La Realite) method. The result of this research is a decision scavenger system that can help and facilitate companies in selecting and prioritizing users of office inventory items at the company. Employees who are prioritized as inventory users in the Electre method are the alternative that has the most value of 1, which outperforms other alternatives.

An Intelligent Method for Bandwidth Utilization in IEEE 802.11e Wireless LAN

IEEE 802.11 Wireless LAN, popularly known as WiFi, has become the admired source of internet connectivity for most of the offices as well as organizations. Due to the rapid growth of multimedia data and VoIP and also to provide better quality of service (QoS), bandwidth and management of bandwidth have become important factors in 802.11 wireless LAN. In 802.11 wireless LAN, Enhanced Distributed Coordination Access (EDCA) mechanism provides QoS with service differentiation by setting different priority to different traffic types. Some of the available methods provide priority based on user category also. In this paper we have proposed an intelligent bandwidth management technique based on the user priority value, traffic priority value and the bandwidth requirement value. By combining these three values, a new metric called request weight value is calculated. Based on this request weight value, dynamically bandwidth is allocated to the users. From the simulation results, we have seen that the proposed method performs well in proper utilization of bandwidth among the available users.

Distributed Competitive Decision Making Using Multi-Armed Bandit Algorithms

This paper tackles the problem of Opportunistic Spectrum Access (OSA) in the Cognitive Radio (CR). The main challenge of a Secondary User (SU) in OSA is to learn the availability of existing channels in order to select and access the one with the highest vacancy probability. To reach this goal, we propose a novel Multi-Armed Bandit (MAB) algorithm called \(\epsilon\)-UCB in order to enhance the spectrum learning of a SU and decrease the regret, i.e. the loss of reward by the selection of worst channels. We corroborate with simulations that the regret of the proposed algorithm has a logarithmic behavior. The last statement means that within a finite number of time slots, the SU can estimate the vacancy probability of targeted channels in order to select the best one for transmitting. Hereinafter, we extend \(\epsilon\)-UCB to consider multiple priority users, where a SU can selfishly estimate and access the channels according to his prior rank. The simulation results show the superiority of the proposed algorithms for a single or multi-user cases compared to the existing MAB algorithms.

Balancing user requirements and implementation difficulties in the requirements engineering of production tools for user-generated content: a case study of an animation application

Some content forms, such as animation, are superior in many aspects but rarely created in UGC systems partially due to the lack of available content-producing tools. Requirements engineering of User-Generated Content project can be difficult because of the diversities of user types, user abilities and client performance resulted from heterogeneous stakeholders. Based on the joint perspective of requirement analysis and quality control, this paper aims to propose an Exploratory Factor Analysis (EFA) integrated Quality Function Deployment (QFD) method. It divides users into expert and regular users, and allows the former to directly participate in design decisions and the latter to participate in prioritization. A case study of the requirements engineering of a Web-based animation application was carried out. The results showed the applicability of the proposed method to develop and manage requirements from all stakeholders. The integration of EFA contributed to the identification of unstated requirements to some extent. Also, the House of Quality was constructed and the quality requirements were obtained during the analysis. This approach helps analysts to sort out the priorities of user and technical requirements and achieve balance between them.

Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

Retracted] Finder‐MCTS: A Cognitive Spectrum Allocation Based on Traveling State Priority and Scenario Simulation in IoV

With the increasing number of intelligent connected vehicles, the problem of scarcity of communication resources has become increasingly obvious. It is a practical issue with important significance to explore a real‐time and reliable dynamic spectrum allocation scheme for the vehicle users, while improving the utilization of the available spectrum. However, previous studies have problems such as local optimum, complex parameter setting, learning speed, and poor convergence. Thus, in this paper, we propose a cognitive spectrum allocation method based on traveling state priority and scenario simulation in IoV, named Finder‐MCTS. The proposed method integrates offline learning with online search. This method mainly consists of two stages. Initially, Finder‐MCTS gives the allocation priority of different vehicle users based on the vehicle’s local driving status and global communication status. Furthermore, Finder‐MCTS can search for the approximate optimal allocation solutions quickly online according to the priority and the scenario simulation, while with the offline deep neural network‐based environmental state predictor. In the experiment, we use SUMO to simulate the real traffic flows. Numerical results show that our proposed Finder‐MCTS has 36.47%, 18.24%, and 9.00% improvement on average than other popular methods in convergence time, link capacity, and channel utilization, respectively. In addition, we verified the effectiveness and advantages of Finder‐MCTS compared with two MCTS algorithms’ variations.

The 5G network slicing using SDN based technology for managing network traffic

Applications of fifth-generation (5G) slices enhance the services according to availability, which depends on the priority of urgent situations and users’ preferences. Managing network traffic is still in significant demand during peak times because many users try to access the available services. This demand increases network traffic, reduces the services’ speed, and prevents the accessing facilities from the service providers. In network management, the basic requirements of three slice types standardized by the Enhanced Mobile Broadband (eMBB), Internet of Things (IoT), and Ultra-Reliable, and Low Latency Communications (URLLC) 5G slice committees allow us to determine the availability of 5G slices. Network slice algorithms based on rate, latency, and connectivity simplify these problems through system modeling. According to the demand for accessing problems, we can enhance the availability in the following three ways. Firstly, the rate levels are prioritized for improving the availability of service, which may not depend on the latency and connectivity. Secondly, latency levels are prioritized for improving the availability of service, which may not depend on the rate and connectivity. Finally, the levels of connectivity are prioritized to improve the availability of service, which may not depend on the latency and rate. To resolve the problems, we can employ the software-defined network (SDN) technology such as SDN based multiple access, which provides flexible configurations when users deploy the different services and applications. This strategic research paper aims to investigate novel slice algorithms of the network traffic system based on software-defined multiple access (SoDeMa) for enhancing network traffic performance. Using this system modeling, we can improve 5G slices’ availability depended on the average response time against the services.

QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks

In the fifth-generation (5G) wireless-network system, the convergence of multiple networks of different standards as well as that of high- and low-frequency networks exists since a long time. Owing to the inability of 5G networks to predict the user quality of service (QoS) accurately, these networks are prone to issues such as access congestion, low QoS, and frequent congestion in one network while other network resources remain idle. Therefore, 5G networks fail to meet the QoS requirements and also prevent effective resource utilization. The deployment of multi-connectivity technologies can facilitate the optimization of the multinetwork convergence-system architecture. However, such technologies are faced with several challenges. Existing literature mainly focuses on the development of a multi-connectivity flow-control scheme to determine the secondary nodes (SNs) to which the master node (MN) should distribute data. This paper presents a three-step, QoS-forecasting, intelligent flow-control scheme, wherein the user equipment (UE) determines the data-flow direction based on the network characteristics and load handled by each node. Subsequently, the MN determines the initial user priority based on load balancing, user characteristics, and fairness. Finally, the MN allocates data to each SN in accordance with the QoS and average transmission capability of UE. The simulation results reveal that the proposed algorithm improves the system throughput significantly compared to the single connectivity and traditional fixed-data-split methods. Furthermore, the proposed method facilitates transmission-delay reduction, radio-link failure-probability control, and improved system robustness.

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Buildings are responsible for 33% of final energy consumption, and 40% of direct and indirect CO2 emissions globally. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding to this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method analyzes several factors including electricity price, uncertainty in climatic factors, availability of renewable energy sources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management to minimize the reliance on the grid and electricity cost, especially during the peak hours. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid by 84%, which offers electricity cost savings by 87%. Finally, two smart active buildings configurations are financially analyzed for the next thirty years. Based on the results, the proposed smart building with solar Photo-Voltaic (PV), wind turbine, inverter, and 40.5 kWh energy storage has a financial breakeven point after 9 years with wind turbine and 8 years without it. This implies that implementing wind turbines in the proposed building is not financially beneficial.

Security Improvement With QoS Provisioning Using Service Priority and Power Allocation for NOMA-IoT Networks

Non-orthogonal multiple access (NOMA) has gained attention as a promising multiple access scheme for the Internet of Things (IoT). A typical setting of user ordering in NOMA networks with user priority difference allows a service priority for solely low-rate high-priority users. In contrast, the diverse quality of service (QoS) requirements and service priorities are prerequisite features of users in the IoT. In this paper, we consider a downlink transmission scenario for NOMA-IoT networks in which the base station (BS) simultaneously serves the two users with a priority difference. To tackle the requirements of the IoT, we consider two schemes: a service priority scheme for high-priority user (SP-HP), and a service priority scheme for low-priority user (SP-LP). Meanwhile, the BS adopts a power allocation strategy to realize the desirable QoS provision for high-priority user and optimize the outage experience of low-priority user in an opportunistic manner. It is novel and interesting to extend the NOMA-IoT framework for a malicious attempt of a passive eavesdropper. To investigate the efficiency and security performances of both schemes, the connection and secrecy outage probabilities of both users are characterized, and their closed-form expressions are derived over Rayleigh fading channels. An effective secrecy throughput (EST) is presented to holistically characterize the performance of the system. Numerical results validate the accuracy of the theoretical results. The results suggest that the transmit power of both users in each scheme can be optimized for the maximum EST, and a selection of an optimal scheme for the reliable and secure transmissions of both users is possible under certain channel conditions.