Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues

Mobile Edge Computing (MEC) is a modern paradigm that involves moving computing and storage resources closer to the network edge, reducing latency, and enabling innovative, delay-sensitive applications. Within MEC, computation offloading refers to the process of transferring computationally intensive tasks or processes from mobile devices to edge servers, optimizing the performance of mobile applications. Traditional numerical optimization methods for computation offloading often necessitate numerous iterations to attain optimal solutions. In this paper, we provide a tutorial on how Deep Neural Networks (DNNs) resolve the challenges of computation offloading. The article explores various applications of DNNs in computation offloading, encompassing channel estimation, caching, AR and VR applications, resource allocation, mode selection, unmanned aerial vehicles (UAVs), and vehicle management. We present a comprehensive taxonomy that categorizes these applications, and offer an overview of existing schemes, comparing their effectiveness. Additionally, we outline the open research issues that can be addressed through the application of DNNs in MEC offloading. We also highlight specific challenges related to DNN utilization in computation offloading. In conclusion, we affirm that DNNs are widely acknowledged as invaluable tools for optimizing computation offloading in MEC

Intelligent IoT- and UAV-Assisted Architecture for Pipeline Monitoring in OGI

With the advent of the Internet of Things (IoT) and unmanned aerial vehicles (UAVs) in industrial application scenarios, oil and gas industry (OGI) automation is undergoing a remarkable transformation. Existing monitoring methods like IoT sensor-based surveillance offer accuracy but struggle with transmission inefficiency. Conversely, UAV-based surveillance enables seamless communication but limited sensing capabilities. This article addresses the challenges of latency, energy efficiency, and cost in state-of-the-art leakage detection technologies for OGI pipelines. A three-tier architecture is proposed, integrating IoT, UAVs, and artificial intelligence-empowered edge computing to enhance pipeline surveillance. We aim to propose specialized routing that addresses IoT energy and fault tolerance issues, while UAVs act as relays to transmit data efficiently to control centers, considering factors like UAV energy and data complexity. Intelligent edge services optimize data transmission, prolong UAV lifespan, and manage latency. Various use cases are explored, and open research challenges with potential solutions are presented

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

Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

This paper presents an in-depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-integer nonlinear optimization model for energy consumption minimization is developed. Then, based on the analysis of the concave-convex properties of this optimization model, this paper uses variable relaxation and nonconvex optimization theory to transform the problem into a convex optimization problem. Subsequently, two optimization algorithms are designed: for the relaxation optimization problem, an iterative optimization algorithm based on the Lagrange dual method is designed; based on the branch-and-bound integer programming method, the iterative optimization algorithm is used as the basic algorithm for each step of the operation, and a global optimization algorithm is designed for transmitting power allocation, computational offloading strategy, dynamic adjustment of local computing power, and receiving energy channel selection strategy. Finally, the simulation results verify that the scheduling strategy of the frequency division technique proposed in this paper has good energy consumption minimization performance in mobile edge computation offloading. Our model is highly efficient and has a high degree of accuracy. The anomaly detection method based on a decision tree combined with deep learning proposed in this paper, unlike traditional IoT attack detection methods, overcomes the drawbacks of rule-based security detection methods and enables them to adapt to both established and unknown hostile environments. Experimental results show that the attack detection system based on the model achieves good detection results in the detection of multiple attacks

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

A review of social background profiling of speakers from speech accents

Social background profiling of speakers is heavily used in areas, such as, speech forensics, and tuning speech recognition for accuracy improvement. This article provides a survey of recent research in speaker background profiling in terms of accent classification and analyses the datasets, speech features, and classification models used for the classification tasks. The aim is to provide a comprehensive overview of recent research related to speaker background profiling and to present a comparative analysis of the achieved performance measures. Comprehensive descriptions of the datasets, speech features, and classification models used in recent research for accent classification have been presented, with a comparative analysis made on the performance measures of the different methods. This analysis provides insights into the strengths and weaknesses of the different methods for accent classification. Subsequently, research gaps have been identified, which serve as a useful resource for researchers looking to advance the field

Blockchain-Enabled Framework for Transparent Land Lease and Mortgage Management

A land administration system (LAS) is a structured framework designed to govern the management of land resources in a specific region or country. However, LAS faces challenges like inefficiencies, a lack of transparency, and susceptibility to fraud. The digitization of land records improved efficiency but failed to address manipulation, centralized databases, and double-spending issues. Traditional lease and mortgage management systems also suffer from complexity, errors, and a lack of real-time validation. At present, a significant influx of land transactions produces substantial data, classifiable as big data due to constant minute-to-minute occurrences like land transfers, acquisitions, document verification, and leasing/mortgaging transactions. In this context, we present a Blockchain-driven system that not only tackles alteration and double-spending issues in traditional systems but also implements distributed data management. Current state-of-the-art solutions do not fully incorporate crucial features of Blockchain, such as transparency, prevention of double-spending, auditability, immutability, and user participation. To tackle this problem, this research introduces a comprehensive Blockchain-powered framework for lease and mortgage management, addressing transparency, user involvement, and double-spending prevention. Unlike existing solutions, our framework integrates key Blockchain characteristics for a holistic approach. Through practical use cases involving property owners, banks, and financial institutions, we establish a secure, distributed, and transparent method for property financing. We verify the system by employing smart contracts and assess the cost and security parameters while validating the blockchain-based mortgage and lease functions