Network Service Providers Research Articles

“Just trying to adjust to the new reality that seems to be changing every hour”: Lessons learned from nation-wide peer meetings on COVID-19 with rural disability service providers

ABSTRACT At the onset of the COVID-19 pandemic, providers of independent living services for rural disabled people were forced to adapt how they conducted their operations. This study is a primary analysis of data based on transcripts from eight meetings of a nationwide network of service providers (n = 40-150 participants per meeting), who met virtually to provide peer support during the unfolding pandemic. We used qualitative thematic analysis to understand the ways these service providers adapted to address the needs of rural disabled people during the pandemic. We identified four main themes describing organizational adaptations. Although this was a predominantly challenging time, service providers identified ways their adaptations were beneficial. These included creating new ways to connect, reaching more people with disabilities, and reducing commuting time to provide services. Service providers intended to continue using their adapted strategies and platforms for providing services, and thus projected these benefits would be long-lasting.

Reliable Dynamic Service Chain Scheduling in 5G Networks

As a key enabler of future 5G network, Service Function Chain (SFC) forwards the traffic flow along a chain of Virtual Network Functions (VNFs) to provide network services flexibility. One of the most important problems in SFC is to deploy the VNFs and schedule arriving requests among computing nodes to achieve low latency and high reliability. Existing works consider a static network and assume that all SFC requests are known in advance, which is impractical. In this paper, we focus on the dynamic 5G network environment where the SFC requests arrive randomly and the computing nodes can redeploy all types of VNF with a time cost. We formulate the problem of SFC scheduling in NFV-enabled 5G network as a mixed integer non-linear programing. The objective is to maximize the number of requests satisfying the latency and reliability constraints. To solve the problem, we propose an efficient algorithm to decide the redundancy of the VNFs while minimizing delay. Then we present a state-of-art Reinforcement Learning (RL) to learn SFC scheduling policy to increase the success rate of SFC requests. The evaluation result shows that our proposed RL solution can increase the success rate by 18.7% over the benchmark algorithms.

The constitutionality of the new Indian CERT-In VPN rules

Abstract This article examines the constitutionality of the Cyber Security Directions released by Computer Emergency Response Team India (CERT-In). The new guidelines issued by CERT-In, the nodal agency of the Ministry of Electronics and Information Technology, have been in the news in India due to concerns being raised by various companies and privacy watchdogs like the Internet Freedom Foundation that the guidelines were affecting the fundamental right to privacy and personal autonomy of the individuals. The guidelines promulgated give CERT-In the authority to demand and retain various kinds of personally identifiable information for more than 5 years. The mandates related to virtual private network service providers are unreasonable and violative of user privacy, while the domain of information that is to be collected is ambiguous and unspecified for the purpose, thus increasing the chances of surveillance and potential censorship. The authors also give suggestions on how to overcome anomalies which are present in the guidelines issued by CERT-In.

Hosting capacity improvement for solar systems based on model predictive controller of volt-watt-var smart inverter functions

ABSTRACT Photovoltaic (PV) system adoption is accelerating quickly, raising worries among electricity network service providers about how to accommodate more PV while keeping system operating indices within acceptable bounds. This idea, which is typically about Hosting Capacity (HC), has recently gotten more interest. Modern smart inverters provide control possibilities in Volt-Var and Volt-Watt based on system voltage level management. In this paper, the impact of various connection standards that encompass these tactics on the capacity of solar PV systems to store energy and their usability in the delivery of low-voltage systems is thoroughly examined. Modeling of smart inverters has been validated using MATLAB SIMULINK with various Volt-Var and Volt-Watt control functions. Volt-Watt-Var control has been designed based on Model Predictive Control (MPC) and comparing the results with a PID controller. The proposed MPC control has been applied on the IEEE-9 bus with combined controls. The impact of various connection requirements on photovoltaic Hosting Capacity (PVHC) is investigated to determine the best connection strategy(s) to handle the problem of voltage encroachment. In addition, the paper gives a deeper knowledge of HC enhancement using sophisticated inverter control features within a low-voltage distribution network of converters, besides it shows the effects of over-voltage and under-voltage on the system parameters.

Near-Optimal and Collaborative Service Caching in Mobile Edge Clouds

In this paper, we study the problem of service caching in an MEC network under a service market with multiple network service providers competing for both computation and bandwidth resources in terms of Virtual Machines (VMs) in the MEC network. We first propose an Integer Linear Program (ILP) solution and a randomized rounding algorithm, for the problem without VM sharing among different network service providers. We then devise a distributed and stable game-theoretical mechanism for the problem with VM sharing among network service providers, with the aim to minimize the social cost of all network service providers, through introducing a novel cost sharing model and a coalition formation game. We also analyze the performance guarantee of the proposed mechanism, Strong Price of Anarchy (SPoA). We thirdly consider the cost- and delay-sensitive service caching problem with temporal VM sharing, and propose a mechanism with provable SPoA. We finally evaluate the performance through extensive simulations and a real world test-bed implementation. Experimental results demonstrate that the proposed algorithms outperform existing approaches by achieving at least 40% lower social cost via service caching and resource sharing among different network service providers.

Stable Service Caching in MECs of Hierarchical Service Markets With Uncertain Request Rates

A 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing the computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network of a hierarchical service market. We first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with an approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed game to measure its efficiency loss due to selfishness of players. When the request rates of services are not given in advance, we study the service caching problem with request rate uncertainty, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.

Psychiatrist Networks In Medicare Advantage Plans Are Substantially Narrower Than In Medicaid And ACA Markets.

Medicare Advantage now covers twenty-eight million older adults, many of whom have mental health needs. Enrollees are often restricted to providers who participate in a health plan's network, which may present a barrier to care. We used a novel data set linking network service areas, plans, and providers to compare psychiatrist network breadth-the percentage of providers in a given area that are considered "in network" for a plan-across Medicare Advantage, Medicaid managed care, and Affordable Care Act plan markets. We found that nearly two-thirds of psychiatrist networks in Medicare Advantage were narrow (that is, they contained fewer than 25percent of providers in a network's service area) compared with approximately 40percent in Medicaid managed care and Affordable Care Act plan markets. We did not observe similar differences in network breadth for primary care physicians or other physician specialists across markets. Amid efforts to strengthen network adequacy, our findings suggest that psychiatrist networks in Medicare Advantage are particularly narrow, which may disadvantage enrollees as they attempt to obtain mental health services.

Technologies and Business Practices of Mobile Network Service Providers in Ghana

Technologies and Business Practices of Mobile Network Service Providers in Ghana

Explore Benefits of Distributed Fiber Optic Sensing for Optical Network Service Providers

We review various applications of distributed fiber optic sensing (DFOS) and machine learning (ML) technologies that particularly benefit telecom operators' fiber networks and businesses. By leveraging relative phase shift of the reflectance of coherent Rayleigh, Brillouin and Raman scattering of light wave, the ambient environmental vibration, acoustic effects, temperature and fiber/cable strain can be detected. Fiber optic sensing technology allows optical fiber to support sensing features in addition to its conventional role to transmit data in telecommunications. DFOS has recently helped telecom operators by adding multiple sensing features and proved feasibility of co-existence of sensing and communication systems on same fiber. We review the architecture of DFOS technique, and show examples where optical fiber sensing helps enhance network operation efficiency and create new services for customers on deployed fiber infrastructures, such as determination of cable locations, cable cut prevention, perimeter intrusion detection and networked sensing applications. In addition, edge AI platform allows data processing to be conducted on-the-fly with low latency. Based on discriminative spatial-temporal signatures of different events of interest, real-time processing of the sensing data from the DFOS system provides results of the detection, classification and localization immediately.

A Hierarchical Multi-Stage Coordination of Inverters for Voltage Control in Active Distribution Networks with a µPMU-PDC

This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface.

Legal regulation and functioning problems of state-guaranteed legal aid model in the Republic of Moldova

Legal aid is one of the conditions for a fair trial. In some cases, states guarantee its provision by establishing publicly funded systems for providing such assistance. It is obvious that the existence of these systems is impossible without the involvement of professionals capable of providing quality legal services. Moreover, the organization of the work of these specialists and the procedure for providing state-guaranteed legal assistance by them can be completely different. Each of the models used may have its own advantages and disadvantages. The state-guaranteed legal aid model that exists in the Republic of Moldova is mixed. It combines both the disadvantages and advantages of the individual models included in it, which are explored in this article. Following the study, the author joins the experts’ opinion that the state-guaranteed legal aid system existing in the Republic of Moldova is one of the most harmonious and open, with a wide network of quality legal service providers.

The Governance of Big Data and Artificial Intelligence in Network Industries

An important precondition for the development of artificial intelligence (AI) in network industries is the access to big data and the attendant necessities of data sharing and data portability. The goal of this paper is to analyze the changing needs of entrepreneurial decision-making to exhaust the innovation potential of AI-driven big data value chains taking into account AI-specific ethical, security and privacy regulations. The analytical concept of AI-powered big data virtual networks is investigated with a focus on the governance of 5G-based big data value chains required for Internet of Things (IoT) applications in particular smart networks. Although several actors may be involved—such as broadband providers, cloud service providers, geopositioning service providers, or sensor network service providers—the final responsibility for bundling these different service components lies in the hands of the AI-powered big data virtual network providers. In addition to the required data privacy and security regulations, the exploration of new liability rules for AI interacting with traditional technologies is becoming relevant, taking into account AI-specific ethical and transparency obligations. Firstly, the complementary roles of the EU data regulatory framework and European AI regulatory framework are examined. Secondly, the network economic concept of AI-powered big data virtual networks is elaborated taking into account the required regulations. Thirdly, the heterogeneity of AI systems—required for a variety of IoT applications—is considered, with a particular focus on the application of AI within the transportation sector.

Determination of Accomplice of Network Pyramid Selling Crime

Due to the particularity of the network itself, the form of the accomplice of the network pyramid selling crime is more complex, showing new characteristics of stronger concealment, faster spreading speed and lower crime cost. The initial organizer and leader of the establishment of the network pyramid selling organization and the expanded organizer and leader of the new level after joining the network pyramid selling organization should bear the corresponding criminal responsibility according to their position and role in the network pyramid selling organization; As for the behavior of network service providers and network technology supporters providing network services and technical support for network pyramid selling crimes, the joint Crime of Organizing or Leading the Pyramid Selling Activities or the Crime of Illegally Using Information Networks and other information network crimes shall be distinguished according to the specific circumstances.

A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks.

This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.

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.

Centralized Intake Models and Recommendations for Their Use in Non-Acute Mental Health Services: A Scoping Review.

Centralized intake [CI] or single-entry models are utilized in health systems to facilitate service access by reducing waiting times. This scoping review aims to consolidate the Literature on CI service models to identify their characteristics and rationales for their use, as well as contexts in which they are used and challenges and benefits in implementing them. The review also aims to offer some lessons learned from the Literature and to make recommendations for its implementation in non-acute mental health services. The findings show that CI is mostly considered when there is increased demand for services and clients are required to navigate multiple services that operate individually. Successful models have meaningfully engaged all stakeholders from the outset and the telephone is the most common mode of intake. Recommendations are made for planning and preparation, for elements of the model, and for setting up the service network. When successfully implemented, CI has been shown to improve access and increase demand for services. However, if CI is not supported by a network of service providers who offer care that is acceptable to clients, the purpose of its implementation could be lost.

Pre-Owned ICE car market: A boon to Middle income groups in times of transition to EVs & Sustainability

The current study aims to evaluate if middle-income households could make use of the used car resale market as a viable option during the switch to EVs and sustainability. Convenience sampling is used to gather data from 112 respondents, and techniques like SPSS and regression analysis are then used to better understand the results. The findings indicate a positive relationship between used cars and the transitional period. The availability of replacement parts, after- sales support, model, shape, engine capacity, and other criteria are positively correlated with the transition phase, with cost and regulatory restrictions having a significant impact. There are several challenges and unknowns in the used ICE vehicle market, including as inconsistent pricing, a lack of trustworthy models, and concerns about the environmental impact of operating older ICE cars. To keep older cars working efficiently and safely for a longer period of time, a network of specialists and service providers that specialize in maintaining and repairing them is used.

Security Resource Scheduling Algorithm for Intelligent UAV Communication Network Based on Optimized Subgraph Isomorphism and Link Partition

The resource scheduling problem of UAV communication networks has always been a hot research issue in the current environment. However, most of the current research focuses on meeting the user’s time cost requirements and rarely considers the user’s security requirements in the scheduling process. To solve the problems of low efficiency, more resource fragments, low network reliability, and unbalanced load, this paper improves the virtual network mapping model into a three-layer structure consisting of a security service request layer, virtual network provider layer, and security resource layer, so that it can be applied to the special scenario of security resource scheduling in an unmanned aerial communication network. An intelligent UAV communication network security resource scheduling algorithm based on optimized subgraph isomorphism and link segmentation is proposed. Intelligent UAVs can predict all possible access nodes and intelligently provide network services. The algorithm first improves and enriches the node constraint conditions and evaluation indicators, integrates the node resource attributes and topology attributes, optimizes the sorting process, then calculates the mapping domain in advance, considers the regional comprehensive resource capacity to balance the load, and finally combines the subgraph isomorphism and link segmentation technology to carry out a one-stage mapping scheduling to achieve the security service request in the network and the efficient allocation of network resources required to provide security services. The simulation results of the OMNeT++ show that the proposed algorithm improves the mapping success rate and the long-term average cost-benefit ratio of the UAVs.

Strategic Pricing of Electric Vehicle Charging Service Providers in Coupled Power-Transportation Networks

Electric vehicles (EVs) are developing at a rapid pace. The EV charging station (EVCS) plays an indispensable role in supplying EV charging demand and promoting the transportation electrification. This work proposes a pricing method to maximize the profit of EVCS owners, charging service providers. First, the tri-level pricing framework is established where EVCSs set charging prices at the upper level, EVs make route and charging options at the middle level, and the electricity price is cleared in power distribution network at the lower level. Second, the singlelevel optimal pricing model is established with traffic flow assigned, power generation scheduled and electricity price explicitly contained via KKT conditions. The proposed model considers the impact of EVCS configuration on traffic assignment and pricing strategy. The operation risk brought by uncertainties is managed via Conditional value at risk. Third, a decomposition solution algorithm is proposed, which tackles the computational burdens and overcomes the infeasibility of existing algorithms. Finally, the real-world urban network-based case studies verify the effectiveness and merits of the proposed method.

Connected and Automated Mobility Services in 5G Cross-Border Environments: Challenges and Prospects

The next generation of mobile networks, namely 5G, promises significant qualitative and quantitative advances for multiple vertical domains. However, most studies and investigations assess these advances under the implicit assumption of a single network service provider, with typical national coverage. In this article, we take a close look at the automotive sector and highlight a series of challenges emerging in the context of its inherent (inter)national mobility and the corresponding importance of cross-border and/or multioperator environments. Our target is to pinpoint the key influential factors affecting the transition toward seamless (cooperative) connected and automated mobility services within and across national borders. To this end, we identify and analyze a series of challenges in the areas of, networking, application, security, and regulation. We further present and discuss a series of corresponding solutions investigated in the pragmatic context of our experimental activities.