IEEE Access Special section Editorial: Mobile edge computing and mobile cloud computing: Addressing heterogeneity and energy issues of compute and network resources

[No abstract available

Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached$8. 8 trillion (95% uncertainty interval UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this,$13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and$1.4 billion was repurposed from existing health projects. $3.1 billion (22.4$2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7$1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Integrated vector instruction translator and offloading framework for mobile cloud computing / Junaid Shuja

Mobile Cloud Computing (MCC) facilitates energy efficient operations of mobile devices through computational offload. The mobile devices offload computations to nearby cloud servers while limiting energy consumption in the low-power wait mode. The MCC offloading frameworks are enabled by system virtualization, application virtualization, and native code migration techniques to address the heterogeneous computing architectures. The existing MCC offloading techniques suffer from either computational or communicational overheads leading to higher execution time and energy consumption on the cloud server. This research work addresses the overhead of conventional MCC offloading frameworks while focusing on vectorized applications based on Single Instruction Multiple Data (SIMD). We propose SIMDOM, a framework for SIMD instruction translation and offloading in heterogeneous MCC architectures. The SIMD translator utilizes re-compilation of SIMD instructions of the mobile device (ARM architecture) that are translated to corresponding cloud server instructions (x86 architecture). Based on inputs from the application, network, and mobile device energy profilers, the offloader module decides upon the feasibility of code offload. The SIMD translator is analyzed for its accuracy and translation overhead. The impact of code offload size, application partition, and device sleep time is investigated on the energy and time efficiency of the mobile applications. The lower feasibility bounds for server speed and application partition are derived from the system model. The SIMDOM framework prototype is implemented on a cloudlet and a cloud server. Results show that SIMDOM framework provides 85.66% energy and 3.93% time efficiency compared to MCC-disabled execution. Comparison with state-ofthe- art code offloading framework reveals that SIMDOM provides 55.99% energy and 57.30% time efficiency. The SIMDOM framework provides 31.10% higher energy efficiency while translating SIMD instructions as compared to existing MCC offloading frameworks. The improvement in energy and time efficiency increases the usability of MCC offloading frameworks for vectorized applications

Dependent task offloading with deadline-aware scheduling in mobile edge networks

In the field of the Internet of Things (IoT), Edge computing has emerged as a revolutionary paradigm that offers unprecedented benefits by serving the IoT at the network edge. One of the primary advantages of edge computing is that it reduces the job completion time by offloading tasks at the edge server from the IoT. Typically, a job is made up of dependent tasks in which the output of one task is required as the input to the other. This work proposes a directed cyclic graph model that represents the dependencies among these tasks focusing on jointly optimizing task dependencies with deadline constraints for tasks that are delay-sensitive. Thus, dependent tasks are scheduled while considering their deadlines using priority-aware scheduling. For tasks with no deadlines, the processing is done with First-Come-First-Serve (FCFS) scheduling. The tasks with a priority are offloaded to the suitable edge server for processing by using a priority queue to enhance the task satisfaction rate under deadline constraints. To model the suitable edge server decision, we use the Markov decision process (MDP) that minimizes the total completion time. Additionally, we model the mobility of users while offloading tasks to the edge servers. The throughput results demonstrate that the proposed strategy outperforms random offloading, the highest data rate offloading (HDR), the highest computing device (HCD), and delay-dependent priority-aware offloading (DPTO), by 66.67%, 43.75%, 27.78%, and 4.55%, respectively. Furthermore, the proposed strategy surpasses random, HDR, and HCD offloading in terms of task satisfaction rate by 20.48%, 16.28%, and 12.36%, respectively

Recent advances and challenges in mobile big data

The unabated flurry of research activities dedicated to gaining business insights from a flood of data generated by heterogeneous mobile sources, such as the Internet of Vehicles, sensors, and smartphones, has instigated a new research domain called MBD. At the core of this mobile environment, scalability, cost effectiveness, reliability, analytics, and security are important concerns. Coping with these issues in handling MBD requires understanding the challenges associated with it. Mobile computing and big data have been widely studied separately; however, very few studies have explored the convergence of these two domains. In this article, we critically review recent research efforts directed at MBD. We also classify the MBD by devising a thematic taxonomy that is based on source, analytics, applications, characteristics, security, and data type. Furthermore, we discuss the opportunities offered by MBD in terms of analytics. Some potential uses of MBD in healthcare, telecommunication, digital advertising, and transportation are also presented. Several open research challenges are discussed as future research directions. © 1979-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record*

Bringing Computation Closer toward the User Network: Is Edge Computing the Solution?

The virtually unlimited available resources and wide range of services provided by the cloud have resulted in the emergence of new cloud-based applications, such as smart grids, smart building control, and virtual reality. These developments, however, have also been accompanied by a problem for delay-sensitive applications that have stringent delay requirements. The current cloud computing paradigm cannot realize the requirements of mobility support, location awareness, and low latency. Hence, to address the problem, an edge computing paradigm that aims to extend the cloud resources and services and enable them to be nearer the edge of an enterprise's network has been introduced. In this article, we highlight the significance of edge computing by providing real-life scenarios that have strict constraint requirements on application response time. From the previous literature, we devise a taxonomy to classify the current research efforts in the domain of edge computing. We also discuss the key requirements that enable edge computing. Finally, current challenges in realizing the vision of edge computing are discussed. © 1979-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “ Muhammad Imran” is provided in this record*

Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution

The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer’s Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network’s contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission

Intelligent Target Coverage in Wireless Sensor Networks with Adaptive Sensors

Day by day innovation in wireless communications and micro-technology has evolved in the development of wireless sensor networks. This technology has applications such as healthcare supervision, home security, battlefield surveillance and many more. However, due to the use of small batteries with low power this technology faces the issue of power and target monitoring. There is much research done to overcome these issues with the development of different architecture and algorithms. In this paper, a scheduling machine learning algorithm called adaptive learning automata algorithm(ALAA) is used. It provides an efficient scheduling technique. Such that each sensor node in the network has been equipped with learning automata, and with this, they can select their proper state at any given time. The state of the sensor is either active or sleep. For the experiment, different parameters are used to check the consistency of the algorithm to schedule the sensor node such that it can cover all the targets with the use of less power. The results obtained from the experiments show that the proposed algorithm is an efficient way to schedule the sensor nodes to monitor all the targets with use of less power. On the whole, this paper manages to achieve its goal by contributing to the related research on wireless sensor networks with a new design of a learning automata scheduling algorithm. The ability of this proposed algorithm to use the minimum number of sensors to be in active state verified to reduce the use of power in the network. Thus, achieving the goal by enhancing the lifetime of wireless sensor networks

SELWAK: A Secure and Efficient Lightweight and Anonymous Authentication and Key Establishment Scheme for IoT Based Vehicular Ad hoc Networks

In recent decades, Vehicular Ad Hoc Networks (VANET) have emerged as a promising field that provides real-time communication between vehicles for comfortable driving and human safety. However, the Internet of Vehicles (IoV) platform faces some serious problems in the deployment of robust authentication mechanisms in resource-constrained environments and directly affects the efficiency of existing VANET schemes. Moreover, the security of the information becomes a critical issue over an open wireless access medium. In this paper, an efficient and secure lightweight anonymous mutual authentication and key establishment (SELWAK) for IoT-based VANETs is proposed. The proposed scheme requires two types of mutual authentication: V2V and V2R. In addition, SELWAK maintains secret keys for secure communication between Roadside Units (RSUs). The performance evaluation of SELWAK affirms that it is lightweight in terms of computational cost and communication overhead because SELWAK uses a bitwise Exclusive-OR operation and one-way hash functions. The formal and informal security analysis of SELWAK shows that it is robust against man-in-the-middle attacks, replay attacks, stolen verifier attacks, stolen OBU attacks, untraceability, impersonation attacks, and anonymity. Moreover, a formal security analysis is presented using the Real-or-Random (RoR) model