scholarly journals TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mingzhi Wang ◽  
Tao Wu ◽  
Xiaochen Fan ◽  
Penghao Sun ◽  
Yuben Qu ◽  
...  
Keyword(s):  
Critical Path ◽  
Rapid Development ◽  
Dynamic Networks ◽  
Communication Technologies ◽  
Computation Offloading ◽  
Limited Bandwidth ◽  
Computing Services ◽  
Iot Devices ◽  
Dependent Tasks ◽  
Edge Networks

With the rapid development of wireless communication technologies and the proliferation of the urban Internet of Things (IoT), the paradigm of mobile computing has been shifting from centralized clouds to edge networks. As an enabling paradigm for computation-intensive and latency-sensitive computation tasks, mobile edge computing (MEC) can provide in-proximity computing services for resource-constrained IoT devices. Nevertheless, it remains challenging to optimize computation offloading from IoT devices to heterogeneous edge servers, considering complex intertask dependency, limited bandwidth, and dynamic networks. In this paper, we address the above challenges in MEC with TPD, that is, temporal and positional computation offloading with dynamic-dependent tasks. In particular, we investigate channel interference and intertask dependency by considering the position and moment of computation offloading simultaneously. We define a novel criterion for assessing the criticality of each task, and we identify the critical path based on a directed acyclic graph of all tasks. Furthermore, we propose an online algorithm for finding the optimal computation offloading strategy with intertask dependency and adjusting the strategy in real-time when facing dynamic tasks. Extensive simulation results show that our algorithm reduces significantly the time to complete all tasks by 30–60% in different scenarios and takes less time to adjust the offloading strategy in dynamic MEC systems.

scholarly journals Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 167
Author(s):  
Ivan Kholod ◽  
Evgeny Yanaki ◽  
Dmitry Fomichev ◽  
Evgeniy Shalugin ◽  
Evgenia Novikova ◽  
...  
Keyword(s):  
Open Source ◽  
Rapid Development ◽  
Image Data ◽  
Ease Of Use ◽  
Security And Privacy ◽  
Data Sets ◽  
Data Set ◽  
Limited Bandwidth ◽  
Comparative Review ◽  
Iot Devices

The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use.

scholarly journals Towards Secure Network Computing Services for Lightweight Clients Using Blockchain

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Xu ◽  
Guojun Wang ◽  
Jidian Yang ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Service Providers ◽  
Security Analysis ◽  
Service Provisioning ◽  
Network Computing ◽  
Security Risks ◽  
Blockchain Technology ◽  
Computing Services ◽  
Viable Solution ◽  
Secure Network ◽  
Iot Devices

The emerging network computing technologies have significantly extended the abilities of the resource-constrained IoT devices through the network-based service sharing techniques. However, such a flexible and scalable service provisioning paradigm brings increased security risks to terminals due to the untrustworthy exogenous service codes loading from the open network. Many existing security approaches are unsuitable for IoT environments due to the high difficulty of maintenance or the dependencies upon extra resources like specific hardware. Fortunately, the rise of blockchain technology has facilitated the development of service sharing methods and, at the same time, it appears a viable solution to numerous security problems. In this paper, we propose a novel blockchain-based secure service provisioning mechanism for protecting lightweight clients from insecure services in network computing scenarios. We introduce the blockchain to maintain all the validity states of the off-chain services and edge service providers for the IoT terminals to help them get rid of untrusted or discarded services through provider identification and service verification. In addition, we take advantage of smart contracts which can be triggered by the lightweight clients to help them check the validities of service providers and service codes according to the on-chain transactions, thereby reducing the direct overhead on the IoT devices. Moreover, the adoptions of the consortium blockchain and the proof of authority consensus mechanism also help to achieve a high throughput. The theoretical security analysis and evaluation results show that our approach helps the lightweight clients get rid of untrusted edge service providers and insecure services effectively with acceptable latency and affordable costs.

scholarly journals DESTRUCTIVE OF CURRENT INFORMATION: CONTENT ANALYSIS OF THE HEADLINES OF NEWS AGGREGATORS IN UKRAINE, USA AND RUSSIA

2021 ◽  
Author(s):  
Yaryna Pryshliak ◽  
Keyword(s):  
Quantitative Data ◽  
Rapid Development ◽  
Communication Technologies ◽  
The United States ◽  
Human History ◽  
Media Space ◽  
Negative News ◽  
The Media ◽  
The Impact ◽  
News Headlines

The article outlines the impact of negative news on the minds of recipients, describes the reasons for the audience’s demand for negative information and represents the quantitative data of destructive information in the media space of Ukraine, USA and Russia. The rapid development of communication technologies, which contributes to the creation and dissemination of the largest volumes of information in human history, and therefore negative news, explains the relevance of the chosen topic. The main objectives of the study are news headlines that appear in the feed of the Google News aggregator (regional versions of the United States, Ukraine and Russia).

scholarly journals Integrated computerization of education in the context of the intellectual growth

2021 ◽  
Vol 7 (3A) ◽  
pp. 504-511
Author(s):  
Volodymyr Bekh ◽  
Valerii Akopian ◽  
Sergiy Yashanov ◽  
Ilya Devterov ◽  
Bogdan Kalinichenko
Keyword(s):  
Information And Communication Technologies ◽  
Information Society ◽  
General Trend ◽  
Rapid Development ◽  
Communication Technologies ◽  
Digital Information ◽  
Initial Information ◽  
Information And Communication ◽  
The Individual ◽  
World Development

The rapid development in the world of information and communication technologies makes it possible to say that now they are one of the most common ways of teaching. These technologies influence the formation of methods and methods of pedagogical activity, open up new opportunities for communication and obtaining information. Informatization and computerization of education acts as a component of the general trend of global processes of world development, as an initial information and communication basis for the harmonious development of the individual and social systemic information. Preparing a student for an active and fruitful life in a modern digital information society is one of the main tasks of the modern stage of modernization of the education system.

scholarly journals The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

2021 ◽  
Vol 2021 (1) ◽  
pp. 209-228
Author(s):  
Yuantian Miao ◽  
Minhui Xue ◽  
Chao Chen ◽  
Lei Pan ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Rapid Development ◽  
Black Box ◽  
Problem Space ◽  
Specific Data ◽  
Learning Techniques ◽  
Audio Data ◽  
Iot Devices ◽  
Asr System

AbstractWith the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy.

scholarly journals An intelligence optimization method based on crowd intelligence for IoT devices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ke Wang ◽  
Zheming Yang ◽  
Bing Liang ◽  
Wen Ji
Keyword(s):  
Genetic Algorithm ◽  
Design Methodology ◽  
Rapid Development ◽  
Optimization Method ◽  
The Internet ◽  
Content Type ◽  
Communication Capacity ◽  
Iot Devices ◽  
Crowd Intelligence ◽  
The Internet Of Things

Purpose The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently. Design/methodology/approach In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices. Findings Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level. Originality/value This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

An enhanced algorithm for scheduling dependent tasks in cloud computing environment

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
H. Hamza ◽  
A.F.D Kana ◽  
M.Y. Tanko ◽  
S. Aliyu
Keyword(s):  
Cloud Computing ◽  
Turnaround Time ◽  
Cloud Services ◽  
Computing Environment ◽  
Computing Services ◽  
Average Improvement ◽  
Cloud Computing Services ◽  
User Tasks ◽  
Search Approach ◽  
Dependent Tasks

Cloud computing is a model that aims to deliver a reliable, customizable and scalable computing environment for end-users. Cloud computing is one of the most widely used technologies embraced by sectors and academia, offering a versatile and effective way to store and retrieve documents. The performance and efficiency of cloud computing services always depend upon the performance of the execution of user tasks submitted to the cloud system. Scheduling of user tasks plays a significant role in improving the performance of cloud services. Accordingly, many dependent task scheduling algorithms have been proposed to improve the performance of cloud services and resource utilization; however, most of the techniques for determining which task should be scheduled next are inefficient. This research provided an enhanced algorithm for scheduling dependent tasks in cloud that aims at improving the overall performance of the system. The Dependent tasks were represented as a directed acyclic graph (DAG) and the number of dependent tasks and their total running time were used as a heuristic for determining which path should be explored first. Best first search approach based on the defined heuristic was used to traverse the graph to determine which task should be scheduled next. The results of the simulation using WorkflowSim toolkit showed an average improvement of 18% and 19% on waiting time and turnaround time were achieved respectively.

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