Non-real Time Research Articles

PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

SummaryFog computing, an emerging technology, extends Cloud computing services to the network's edge in the proximity of the application request. This extension yields improvement in Bandwidth (BW) utilization, faster responses to Real‐Time (RT) and Internet of Things (IoT) requests, and the provision of the heterogeneous resource services. While extensive work has been conducted on resource allocation for RT and Non‐Real‐Time (NRT) requests separately in Fog as well as Cloud computing, there is limited focus on resource provisioning for mixed RT and NRT requests in the Fog‐integrated Cloud (FiC) environment. Moreover, the majority of the existing provisioning methods primarily consider parameters from the system's perspective, overlooking crucial user aspects such as deadline and request size. To address the gap, this work introduces a resource provisioning method named “Parameter Matching of Realtime and Non‐Realtime Applications (PMRNA),” which considers user parameters and resource information, gathered by the broker. The performance evaluation of the proposed model is done in CloudSim, using various combinations of RT and NRT requests along with diverse Fog and Cloud resource configurations. Evaluation metric includes average Execution Time (ET), average Waiting Time (WT), average Turn Around Time (TAT), and resource utilization. The experimental results demonstrate a significant reduction in both average WT and average TAT for the diverse pool of RT and NRT requests in the FiC compared to the Cloud‐only environment.

Digital twin enabled cellular network management and prediction

Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.

An Energy Efficient Tree-Based Routing Protocol for Wireless Multimedia Sensor Networks

The recent technological advances in micro electromechanical systems have promoted the development of a powerful class of sensor-based distributed intelligent systems capable of retrieving multimedia information, namely Wireless Multimedia Sensor Networks (WMSNs). WMSNs are gaining more popularity day by day as they are envisioned to support a large number of both non-real time and real-time multimedia applications. Routing in WMSN is very challenging task. Multipath establishes several path for data transmission rather than single path. In this paper we proposed An Energy Efficient Tree Based Multipath Routing for WMSN’s (EETMRP) technique that will intend to provide a reliable transmission of data for data synchronization at the destination node with low energy consumption. This is done by efficiently utilizing the energy availability and the received signal strength of the nodes to identify multiple routes to the destination. EETMRP discusses that all of the packets of audio, image and scalar data are spread over the nodes lying on different possible paths between the source and the sink, in proportion to their residual energy and received signal strength. The objective of EETMRP is to assign more loads to highest energetic paths and fewer loads to lower energetic paths so that uniform resource utilization of all available paths can be ensured. It uses multiple paths between source and the sink which is intended to provide a reliable transmission environment with low energy consumption, by efficiently utilizing the energy availability of the nodes to identify multiple routes to the destination and efficiently balanced the loads of all sensor nodes. We also focused on node failure and it recovery process during packet transmission and during the start of packet transmission. Finally we proposed an algorithm based on our proposal to simulate and analysis our proposed routing protocol.

Optimizing User-Level Packet Scheduling Performance through Optimal CQI-Based Resource Allocation in LTE

Our research investigates the Dynamic Resource Allocation in the Downlink of OFDMA-based LTE-A networks. It addresses both user-level and system-level packet scheduling performance. At the user-level, a Traffic Differentiator stage segregates packet queues from active users into different service queues based on service types. Users are prioritized within each service queue based on their QoS requirements and wireless channel conditions, utilizing SPSSA. At the system-level, fairness among users is a key consideration. We propose the PITDSA in the TD Scheduler stage, which aims to allocate just enough radio resource to real-time and non-real time services and assign the remaining available resource to background service. In the FD Scheduler stage, we propose an optimal CQI selection algorithm for resource allocation to exploit frequency domain multi-user diversity. We present our simulation results and analyses of all novel algorithms, and the performance of the Optimal CQI selection algorithm is compared with other algorithms. Our proposed scheduling algorithm demonstrates improved QoS for real-time and non-real time services while maintaining a good traded user-level and system-level performance. Future work can focus on optimizing the throughput or the fairness or both, and more advanced and complex techniques can be designed with the same goal.

Performability analysis of multi-service call admission control schemes in LTE networks

Two important Quality-of-Service (QoS) measures for cellular networks are the fractions of New Calls (NCs) and Handoff Calls (HCs). These types of calls are usually blocked or dropped for two reasons: radio channels are occupied (performance reason) or failure of radio channels (availability reason). Call Admission Control (CAC) is a technique that accepts or rejects user requests in a network. This paper proposes two new CAC schemes for Long Term Evolution (LTE) networks supporting two classes of services (Real-Time (RT) and Non-Real-Time (NRT)). The proposed CAC schemes are composites, meaning that they consider performance changes associated with the failure and recovery of radio channels. We use the Probabilistic Model Checking (PMC) to perform a comparative analysis between the performability measures (combination of performance and availability measures) of the proposed composite CAC schemes and an existing pure performance model. First, we model the composite CAC schemes with multidimensional Continuous Time Markov Chain (CTMC). Then, we specify QoS requirements through the CTMC using the Continuous-time Stochastic Logic (CSL). Finally, we quantify the performability measures of the composite CAC schemes by checking CSL steady-state, transient, and path formulas using the PRISM model checker.

Cloud-Based Dynamic Task Scheduling and Load Balancing Using a Variety of Intelligent Schemes

One of the main problems with cloud computing is load balancing, which divides tasks into virtual machines (VMs) with varying lengths and completion times. As a result, keeping VMs loaded up during job scheduling and allocation is difficult. In this research, we suggested secure load balancing in a cloud context and resource-aware dynamic job scheduling utilizing intelligent algorithms. The process for the planned work is as follows: (1). The Human Mental Search (HMS) method is used to cluster Real-Time and Non-Real Time jobs into four categories: Real-Time and Short, Real-Time and Long, Non-Real Time and Short, and Non-Real Time and Long. (2). Knowledge of Energy and Delay Combined Tasks Tasks are planned based on task clustering, and the fuzzy VIKOR method is used to schedule them in the order of priority. (3). Security Risks aware VM Clustering, which employs the Hierarchical Agglomerative Clustering algorithm, consists of three distinct entities: a VM's information collector, which compiles data about VMs; a load monitor, which oversees the entire datacenter; and a decision maker, which assigns tasks by choosing the best VM. (4). Co-Resident assaults were stopped by the continuous-Actor-Critical-Algorithm (C- ACAL), which is used to pick the best VM. Using CloudSim, which measures performance in terms of response time, latency, resource utilization, throughput, energy consumption, and task execution success rate, the simulation is carried out.

LungEcho - Resource Constrained Lung Ultrasound Video Analysis Tool for Faster Triaging and Active Learning

Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of the severity of lung involvement into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a significantly large clinically annotated dataset and are computationally heavy and are most of the time non-real time. In this work, a real-time light weight active learning-based approach is presented for faster triaging in COVID-19 subjects in resource constrained settings. The tool, based on the you look only once (YOLO) network, has the capability of providing the quality of images based on the identification of various LUS landmarks, artefacts and manifestations. This tool also predict the severity of lung infection and make use of the possibility of active learning based on the feedback from clinicians or on the image quality. The capability of this tool to summarize the significant frames which are having high severity of infection and high image quality will be helpful for clinicians to discern things more easily. The results show that the proposed object detection tool has a mean average precision (mAP) of 66% at an Intersection over Union (IoU) threshold of 0.5 for the prediction of LUS landmarks with initial training on less than 1000 images. The 14MB lightweight YOLOv5s network achieves 123 FPS while running on a Quadro P4000 GPU. The tool is available for usage and analysis upon request from the authors and details can be found online.

AN ENHANCED RESOURCE ALLOCATION SCHEME FOR LONG TERM EVOLUTION ADVANCED NETWORK

The emerging Long-Term Evolution Advanced (LTE-A) is an extended version of Long-Term Evolution (LTE) fourth-generation (4G) wireless broadband technologies that supports high speed transmission of multimedia applications, like video streaming, multimedia online game, Voice over IP (VoIP), browsing over the web etc. The Third Generation Partnership Project (3GPP) does not define a standard packet scheduling strategy in LTE-A. Therefore, it is expected for researchers and network service providers to design efficient schemes. This paper proposed an Enhanced Resource Allocation Scheme (E-RAS) that improves the packet scheduling strategy of LTE-A by addressing issues related to delay and fairness of users with both high and low channel conditions, for both Real Time (RT) and Non-Real Time (NRT) flows. The aim was achieved by using a fair spectrum allocation mechanism that consider the priority of RT flows of cell-centre users, without neglecting the throughput requirements of edge-centre users. Moreover, the proposed algorithm introduces an efficient traffic load balancing mechanism to efficiently regulate traffic load in LTE-A network. To evaluate the proposed scheme, simulation was conducted using the MATLAB programming with LTE-A system level simulator. The result obtained shows that the proposed scheme achieves better delay for RT and NRT traffics by 7% and 11% respectively as against the Resource Allocation Scheme to Optimize the Throughput (RASOT) scheme.

O-RAN architecture, interfaces, and standardization: Study and application to user intelligent admission control

Open radio access network (O-RAN), driven by O-RAN Alliance is based on the disaggregation of the traditional RAN systems into radio unit (RU), distributed unit (DU) and central unit (CU) components. It provides a unique opportunity to reduce the cost of wireless network deployment by using open-source software, serving as a foundation for O-RAN compliant functions, and by utilizing low-cost, generic white-box hardware for radio components. Relying on the two core pillars of openness and intelligence, there has been a coordinated global effort from operators and equipment providers to enhance the RAN architecture and improve its performance through virtualized network elements and open interfaces that incorporate intelligence in RAN. With the increased complexity of 5G networks and the demand to fulfill requirements, intelligence is becoming a key factor for automated deployment, operation, and optimization of open wireless networks. The first thrust of this paper surveys the AI/ML architecture in O-RAN specifications, key discussion points and future standardization directions, respectively. In the second part, we introduce a proof-of-concept use case on AI-driven network optimization within the near real-time RAN intelligent controller (near-RT RIC) and non-real time RIC (non-RT RIC). In particular, we investigate the user admission control problem, led by a deep learning-based algorithm, implemented as an xApp for network performance enhancement. Extensive system-level simulations are performed with NS-3 LTE to assess the proposed admission control algorithm. Accordingly, the proposed dynamic algorithm shows a significant admission control performance improvement and flexibility, compared to existing admission control static techniques, while satisfying the stringent quality of service targets of admitted devices. Finally, the paper offers insightful conclusions and findings on the AI-based modeling, model inference performance, key performance challenges and future research directions, respectively.

Dipartite principle in the mechanism of the real right’s alteration in China

The discrepancy of real right transfer mode decides the registered effects of real estate register (Confrontation or Confirmation Doctrine) and its examinational scope, further influencing the credibility and applied scope of the real estate register. The defined basis of the transfer mode is the attitude toward the juristic act of liability and juristic act of disposition, the causality and nonature of the juristic act of disposition. According to the research on the history, axiology and systematology, the transfer mode with the idealism has been cancelled in Chinese legislation, but there are still some exceptions. The enacting of the article 595 of the Civil Code is to distinguish the relationship between sales contracts and other contracts, which is unable to combine it with the article 598 and then deem the double effects of the sales contracts. The distinguish between juristic act of liability and juristic act of disposition in Chinese civil law has been confirmed by the juridical practices, which contained in the article 215 of the Civil Code. This distinguish is a summary of social and life experiences, particularly outstanding in the presale of house, the real estate register and the non-real time sales. In the real estate register, the consensus of both parties includes in the registration application, their signature means the agreement for the change of real right. The article 597 of the Civil Code stipulates the juristic act of liability’s effect when the seller has no right to dispose it. The distinguish principle in the theory of juristic act of real right, is not only beneficial to protect the third party, but can also clearthe legal relation in the complicated disputes. In this condition, some scholars still deny that the separation of juristic act of liability and juristic act of disposition is induced by the preconceived notions. Although the principle of abstraction has some advantages to protect the transaction safety and simplify the trading programs, no identified basis can be found in the examinational scope and related law when the authority handles the registration.

Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network

The heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From a communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy-aided networks offer to curtailed fossil fuel consumption, so to reduce the environmental pollution. This paper proposes a renewable energy based power supply architecture for the off-grid HetNet using a novel energy sharing model. Solar photovoltaics (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, a biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel-weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication delays. Furthermore, the proposed algorithm with an extended discontinuous reception (eDRX) and power saving mode (PSM) for narrowband internet of things (IoT) applications extends the battery lifetime for IoT devices. HOMER optimization software is used to perform optimal system architecture, economic, and carbon footprint analyses while the Monte-Carlo simulation tool is used for evaluating the throughput and energy efficiency performances. The proposed algorithms are validated through the practical data of the rural areas of Bangladesh from which it is evident that the proposed power supply architecture is energy-efficient, cost-effective, reliable, and eco-friendly.

PV, or not PV: using backcasting to explore policy, market and governance implications of local decarbonisation pathways such as urban PV

Since 2018, over three quarters of UK local governments have signed climate emergency declarations signalling a new phase in local climate action. Many areas are developing climate action plans and considering what policies could be adopted to increase the use of distributed energy resources, such as building-integrated photo-voltaic (PV) generation. In this paper we demonstrate how city-scale backcasting can be used as a tool to go beyond technical feasibility and explore the policy, market and governance implications of decarbonisation pathways across the local and nation scales. Using urban PV as an example we model three scenarios for increasing installed capacity in the UK city of Bristol and demonstrate how, in each case, PV switches from being a minor ‘negative load’ on the energy system to being the dominant factor determining the volatility and predictability of the energy demand during daylight hours. Current approaches to integrating distributed energy resources into the wider system tend to focus on predictive modelling and half-hourly pricing to drive demand-side response. Our analysis indicates the risks of relying on these non-real time methods and suggests that more granular techniques such as triggering local DSR based on changes in electricity system frequency could better manage the variability of high levels of PV deployment at the local scale. We also demonstrate the importance of developing new local approaches to system integration that bring together network operators, local authorities and other local actors to deploy renewable generation and flexibility assets in coordination.

VC-PPQ: Privacy-Preserving Q-Learning Based Video Caching Optimization in Mobile Edge Networks

Mobile edge caching has great advantages in alleviating video traffic pressure and reducing transmission delay, which is considered as a hopeful solution to improve video resource utilization and achieve faster service response. However, due to insufficient and non-real time acquisition of the status between edge servers and mobile users, it is still difficult for existing solutions to improve the video quality of experience (VQoE) for mobile users. Besides, due to the mobility of users and malicious third-party attacks, users may suffer the risk of personal privacy (e.g. location, trajectory, preference, etc.) leakage when requesting video services that are scattered in different areas. In order to efficiently deal with these problems, this paper proposes a privacy-preserving Q-learning based video caching optimization framework in mobile edge networks (VC-PPQ), including a privacy scheme for user data and a dynamic video caching algorithm. Specifically, in the first task, the local differential privacy model is leveraged to protect the locations and preferences of users. Then, a data aggregation model is provided to make trade-off between aggregation accuracy and privacy protection. In the second task, we further obtain the video popularity in user's area by a computation service model, then formulate a caching objective function combined with transcoding technologies. Afterwards, Q-learning is adopted to obtain the goal of caching optimization, which can achieve low delay and high hit ratio. Finally, extensive simulations prove that our VC-PPQ framework delivers prominent performance advantages in terms of user privacy and caching services, which greatly improves the VQoE of users.

APF-IRRT*: An Improved Informed Rapidly-Exploring Random Trees-Star Algorithm by Introducing Artificial Potential Field Method for Mobile Robot Path Planning

An Informed RRT* (IRRT*) algorithm is one of the optimized versions of a Rapidly-exploring Random Trees (RRT) algorithm which finds near-optimal solutions faster than RRT and RRT* algorithms by restricting the search area to an ellipsoidal subset of the state space. However, IRRT* algorithm has the disadvantage of randomness of sampling and a non-real time process, which has a negative impact on the convergence rate and search efficiency in path planning applications. In this paper, we report a hybrid algorithm by combining the Artificial Potential Field Method (APF) with an IRRT* algorithm for mobile robot path planning. By introducing the virtual force field of APF into the search tree expansion stage of the IRRT* algorithm, the guidance of the algorithm increases, which greatly improves the convergence rate and search efficiency of the IRRT* algorithm. The proposed algorithm was validated in simulations and proven to be superior to some other RRT-based algorithms in search time and path length. It also was performed in a real robotic platform, which shows that the proposed algorithm can be well executed in real scenarios.

High spatial resolution of topographic imaging and Raman mapping by differential correlation-confocal Raman microscopy.

Confocal Raman microscopy (CRM) has found applications in many fields as a consequence of being able to measure molecular fingerprints and characterize samples without the need to employ labelling methods. However, limited spatial resolution has limited its application when identification of sub-micron features in materials is important. Here, we propose a differential correlation-confocal Raman microscopy (DCCRM) method to address this. This new method is based on the correlation product method of Raman scattering intensities acquired when the confocal Raman pinhole is placed at different (defocused) positions either side of the focal plane of the Raman collection lens. By using this correlation product, a significant enhancement in the spatial resolution of Raman mapping can be obtained. Compared with conventional CRM, these are 23.1% and 33.1% in the lateral and axial directions, respectively. We illustrate these improvements using in situ topographic imaging and Raman mapping of graphene, carbon nanotube, and silicon carbide samples. This work can potentially contribute to a better understanding of complex nanostructures in non-real time spectroscopic imaging fields.

Optimal resource scheduling algorithm for cell boundaries users in heterogenous 5G networks

Deployment of small cells over the existing cellular network is an effective solution to improve the system coverage and throughput of fifth generation (5G) mobile communication networks. The arrival of the 5G mobile networks have demonstrated the importance of advanced scheduling techniques to manage the limited frequency spectrum available while achieving 5G transmission requirements. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that mitigate the interference between the different cells. In this research work, we formulate an optimization problem for heterogenous networks (HetNets) for resource allocation to maximize the system throughput among the cell center users (CCUs) and cell edge users (CEUs). We solve the optimization problem by effective utilization of the weight factors distribution for resource allocation. A novel Utility-based Resource Scheduling Algorithm (URSA) optimizes the resource sharing among the users with better delay budget of each application. The designed URSA ameliorates fairness along with reduced cross layer interference for real and non-real time applications. Performance of the URSA has been evaluated and compared most relevant state of art algorithms using the matlab based simulators. Furthermore, simulation results validate the superiority of the proposed scheduling scheme against conventional techniques in terms of throughput, fairness, and spectral efficiency.

Using “Google trends” for dengue surveillance and epidemiological research

Millions of people worldwide search online for health-related information and search engines have become an increasingly popular resource for accessing health-related information and provides valuable source. Key words used as well as the number and geographic location of searches can provide trend data, available by Google trends. In this study exploring this resource using dengue disease as an example. Objectives were to use Google trends data for comparison across different locations in India for the past 5 years, and to assess the specific search terms used in Google trends data and to correlate the real time dengue outbreak of Tamil Nadu with Google trend search. It was a cross sectional study. Data collection was done via Google search queries and record was included. Weekly trends were accessed from Google Trends. Data is a randomly collected sample of real time and non-real time Google search queries. Search traffic for the string “dengue fever” reflected increased likelihood of exposure and the string “dengue symptoms and treatment” had higher relative traffic during rainy season. Cities and states with the highest amount of search traffic for “dengue disease” overlapped where dengue is endemic. Found that search trend data produced by Google to approximate the seasonality, spikes at September to November every year and geographic distribution also identified in dengue disease. Web search query data were found to be capable of tracking dengue activity and predict periods of large incidence of dengue with high accuracy and may prove useful.

Modified algorithm of unimodal thresholding for FPGA implementation

Thresholding is one of the basic digital image processing procedures that is commonly used for segmentation or edge detection. The final result of thresholding is significantly affected by the used threshold value. The several methods for determining the optimal threshold have been developed. Otsu's and Rosin's methods are the most used algorithms for computing the threshold value in non-real time image processing on a PC. These methods have not been used in FPGA circuits so far because of complicated calculation algorithm. In this paper we propose modification of Rosin's algorithm based on the geometric transformation of the histogram. Our approach simplifies calculations and allows FPGA implementation of Rosin's method. The simulations with three different images confirmed, that the proposed algorithm gives the same results as the original.

An Enhanced Industrial Wireless Communication Network for Hard Real Time Performance Substation Automation Purposes

Wireless communication network (WCN) technologies are charming solutions to bolster the conventional electrical substations with the intention of take the fashion of smart substation such as reduction in equipment, minimize the maintenance costs, flexibility, and expansion. However, the harsher challenge facing WCN employing in the electrical substation is the real time protection of substation automation system (SAS) for the high voltage devices in terms of the latency and the reliability in particular the substations of old topologies. This work suggests WCN including special types of the intermediate devices (Switched-Access Points (S-AP) and Multi Wireless Domain- Access Point (MWD-AP)) to address the expected packets congestion by creating independent wireless channels domains offering wireless channels reliability in such network topology that deals with real time data traffic (RT) and the non-real time data traffic (NRT). Riverbed modeler is adopted to simulate the model of the electrical substation network due to the rich tools of communication networks in term of industry environment. The results indicate, the suggested WCN can handle the hard real time requirements of protection from latency and data reliability points of view in case of basic capacity of 802.11a/n standards at the level of ≤ 4 msec and high data reliability.

Performance Evaluation of Call Admission Control Schemes in 3GPP Long Term Evolution Networks

Radio resource management (RRM) techniques are employed for resource utilization in wireless broadband networks. One of the major RRM techniques that are aimed at controlling the admission and rejection of new or handoff calls/connections in a network is known as Call Admission Control (CAC). CAC accepts or rejects a connection request based on a certain pre-determined criterion(s). Performance Evaluation of some existing CAC schemes was presented in this paper. The schemes evaluated are; Adaptive Call Admission Control Scheme with Bandwidth reservation termed as (ACAC), QoS-Aware Call Admission Control Scheme termed as (QA-CAC) and Enhanced Adaptive Call Admission Control Scheme with bandwidth reservation termed (EA-CAC). Vienna LTE system level simulator was used for the simulation experiments conducted. After several experiments, the results show that the QA-CAC and EA-CAC schemes perform better in terms of increasing the throughput of real-time (RT) traffic while all the three schemes almost have the same performance in terms of admitting the non-real-time (NRT) traffic.