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Improving the impact of power efficiency in mobile cloud applications using cloudlet model

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Improving the impact of power efficiency in mobile cloud app...

作者： Tawalbeh, Lo'ai Tawalbeh, Mohammad A. Aldwairi, Monther Department of Computing and Cyber Security Texas A&M University-San Antonio San AntonioTX United States Faculty of Computing Engineering and Science Staffordshire University Stafford United Kingdom College of Technological Innovation Zayed University Dubai United Arab Emirates

The applications and services of Information and Communication Technologies are becoming a very essential part of our daily life. In addition, the spread of advanced technologies including the cloud and mobile cloud computing (MCC), wireless communication, and smart devices made it easy to access the internet and utilize unlimited number of services. For example, we use mobile applications to carry out critical tasks in hospitals, education, finance, and many others. This wide useful usage makes the smart devices an essential component of our daily life. The limited processing capacity and battery lifetime of mobile devices are considered main challenges. This challenge is increased when executing intensive applications. The MCC is believed to overcome these limitations. There are many models in MCC and one efficient model is the cloudlet-based computing. In this model, the mobile devices users communicate with the cloudlets using cheaper efficient technologies, and offload the job requests to be executed on the cloudlet rather than on the enterprise cloud or on the device itself. In this article, we investigated the cloudlet-based MCC architecture, and more specifically, the cooperative cloudlets model. In this model, the applications that require intensive computations such as image processing are offloaded from the mobile device to the nearest cloudlet. If the task cannot be accomplished at this cloudlet, the cloudlets cooperate with each other to accomplish the user request and send the results back to the user. To demonstrate the efficiency of this cooperative cloudlet-based MCC model, we conducted real experiments that execute selected applications such as: object code recognition, and array sorting to measure the delay and power consumption of the cloudlet-based system. Moreover, suitable cloud/mobile cloud simulators such as CloudSim and MCCSim will be used to perform simulation experiments and obtain time and power results. © 2020 John Wiley & Sons, Ltd.

关键词： Electric power utilization

Node to node secure data communication for IoT devices using diffie-hellman, AES, and MD5 cryptographic schemes

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Node to node secure data communication for IoT devices using...

2019 International Conference on cyber security and Internet of Things, ICSIoT 2019

作者： Quist-Aphetsi, Kester Xenya, Michael Christopher Computer Science Department Ghana Technology University College Ghana Computer Engineering Department Ghana Technology University College Ghana Cyber Security Division CRITAC Ghana Information Assurance and Intelligent Research CRITAC Ghana

ISBN: (纸本)9781728174174

Identifying the right source and target nodes in IoT as well as secure data communication is very vital in ensuring security, validity and reliability of IoT communication. Since more devices are being programmed to exchange data and commands autonomously in IoT and wireless sensor networks, security and authenticity of such transmitted data is very crucial. This calls for strong security approaches to prevent both passive and active attackers against data communication. This work proposed a scheme for securing communication among IoT devices by applying Diffie-Hellman encryption algorithm to authenticate the communicating nodes while using AES and MD5 to encrypt and validate the data transfer among the nodes. © 2019 IEEE.

关键词： Data transfer

On MOS-enabled differentiated VoIP provisioning in campus software defined networking 15

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On MOS-enabled differentiated VoIP provisioning in campus so...

15th International Conference on Emerging Technologies, ICET 2019

作者： Bakhshi, Taimur Nadir, Ibrahim Center for Information Management and Cyber Security National University of Computer and Emerging Sciences Lahore Pakistan National University of Computer Emerging Sciences Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781728154039

Software defined networking (SDN) promises a paradigm shift in real-time traffic management by offering centralized network control and programmability. The present work leverages SDN technology for dynamic resource allocation in campus environments with a view to improve Voice over Internet Protocol (VoIP) communication between end-systems. To correctly record user perceived voice quality the mathematical E-Model and Mean Opinion Score (MOS) mapping is used. Using administrator defined minimum MOS threshold for VoIP calls, the SDN control plane is used to compute real-time network load, and dynamically allocate network resources between calling agents. During validation, the proposed design is tested over a six hundred node simulated campus SDN environment. The quantitative and qualitative QoE results for the present approach, depict an MOS improvement of between 17-33% over conventional route selection and traffic management schemes. Furthermore, the relative consistency in improved MOS values, translating for constantly high call quality motivate further adoption of the present design in practical campus SDN setting. © 2019 IEEE.

关键词： Software defined networking

FastER: Fast On-Demand Entity Resolution in Property Graphs

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arXiv 2025年

作者： Wang, Shujing Kwashie, Selasi Bewong, Michael Hu, Junwei Nofong, Vincent M. Miao, Shiqi Feng, Zaiwen Huazhong Agricultural University Wuhan China AI & Cyber Futures Institute Charles Sturt University Australia School of Computing Mathematics and Engineering Charles Sturt University Australia Department of Computer Science and Engineering University of Mines and Technology Ghana

Entity resolution (ER) is the problem of identifying and linking database records that refer to the same real-world entity. Traditional ER methods use batch processing, which becomes impractical with growing data volumes due to high computational costs and lack of real-time capabilities. In many applications, users need to resolve entities for only a small portion of their data, making full data processing unnecessary—a scenario known as "ER-on-demand". This paper proposes FastER, an efficient ER-on-demand framework for property graphs. Our approach uses graph differential dependencies (GDDs) as a knowledge encoding language to design effective filtering mechanisms that leverage both structural and attribute semantics of graphs. We construct a blocking graph from filtered subgraphs to reduce the number of candidate entity pairs requiring comparison. Additionally, FastER incorporates Progressive Profile Scheduling (PPS), allowing the system to incrementally produce results throughout the resolution process. Extensive evaluations on multiple benchmark datasets demonstrate that FastER significantly outperforms state-of-the-art ER methods in computational efficiency and real-time processing for on-demand tasks while ensuring reliability. We make FastER publicly available at the Github link here. © 2025, CC BY-NC-SA.

关键词： Batch data processing

Aggregate Authentication for Massive Internet of Things in 5G Networks 4th

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Aggregate Authentication for Massive Internet of Things in 5...

4th International Symposium on Mobile Internet security, MobiSec 2019

作者： Syalim, Amril Anggorojati, Bayu Baek, Joonsang Gerbi, Daniel You, Ilsun Faculty of Computer Science University of Indonesia Depok Indonesia School of Computing and Information Technology University of Wollongong WollongongNSW Australia Department of Information Security Engineering Soonchunhyang University Asan-si Korea Republic of

ISBN: (纸本)9789811596087

Massive Internet of Things (IoT) applications are characterized by a massive number of low-powered devices with small data storage. In those applications, authentication is very important to guarantee the origin of data produced by each device. With such characteristics, the authentication scheme needs to have small footprint and low complexity computation. Likewise, the number of authentication signaling needs to be significantly reduced to accommodate massive amount of IoT devices trying to connect to the network simultaneously, which is similar to Denial of Service (DoS) attacks. In 3GPP TR 33.899 V1.3.0, an architecture of the authentication system that consists of the aggregation nodes and the authenticators is proposed. The aggregation nodes receive the authenticated data from the devices and send the aggregated authenticated data to the authenticators. In this article, we discuss the possibility to implement more efficient authentication scheme for a massive Internet of Things (IoT) applications enabled in 5G networks. In particular, we analyze the performance of the system using some signature aggregation methods and identify the main research challenges in this area. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Authentication

Data Deletion and Recovery of Androd Plaforms

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Data Deletion and Recovery of Androd Plaforms

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Hyungchan Kim Yeonghun Shin Sungbum Kim Minju Kim Wooyeon Jo Taeshik Shon Platform Tech Team WINS CO. Ltd Seongnam Republic of Korea Department of Artificial Intelligence Convergence Network Ajou University Suwon Republic of Korea Department of Computer Engineering Ajou University Suwon Republic of Korea Department of Cyber Security Ajou University Suwon Republic of Korea

ISBN: (纸本)9781665458429

The Android platform accounts for 83% of the global smartphone operating system market share. Android smartphones store personal information and smartphone usage data. In order to prevent cyber security threats, it is important to manage data stored in Android smartphones. In this paper, we analyze the deleted data and recovery possibilities remaining in smartphones using Android 9 and 10. The deleted data recoverability analysis is performed based on the scenario of deleting data using the app's own features. We confirmed that deleted data can be recovered from the android messenger application. This paper result is expected to be applicable to IoT devices based on Android platform and we found that sustained research on Android platform should be conducted from the perspective of forensics.

关键词： Performance evaluation Social networking (online) Scientific computing Forensics Operating systems Data acquisition Internet of Things

Differential advising in multi-agent reinforcement learning

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arXiv 2020年

作者： Ye, Dayong Zhu, Tianqing Cheng, Zishuo Zhou, Wanlei Yu, Philip S. School of Computer Science China University of Geosciences Wuhan China Centre for Cyber Security and Privacy School of Computer Science University of Technology Sydney Australia Department of Computer Science University of Illinois at Chicago United States

Agent advising is one of the main approaches to improve agent learning performance by enabling agents to share advice. Existing advising methods have a common limitation that an adviser agent can offer advice to an advisee agent only if the advice is created in the same state as the advisee’s concerned state. However, in complex environments, it is a very strong requirement that two states are the same, because a state may consist of multiple dimensions and two states being the same means that all these dimensions in the two states are correspondingly identical. Therefore, this requirement may limit the applicability of existing advising methods to complex environments. In this paper, inspired by the differential privacy scheme, we propose a differential advising method which relaxes this requirement by enabling agents to use advice in a state even if the advice is created in a slightly different state. Compared with existing methods, agents using the proposed method have more opportunity to take advice from others. This paper is the first to adopt the concept of differential privacy on advising to improve agent learning performance instead of addressing security issues. The experimental results demonstrate that the proposed method is more efficient in complex environments than existing methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

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arXiv 2022年

作者： Wehbi, Osama Arisdakessian, Sarhad Wahab, Omar Abdel Otrok, Hadi Otoum, Safa Mourad, Azzam Guizani, Mohsen Cyber Security Systems and Applied AI Research Center Department of CSM Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal MontrealQC Canada Khalifa University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Dubai United Arab Emirates Division of Science New York University Abu Dhabi United Arab Emirates Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model. © 2022, CC BY-NC-SA.

关键词： Game theory

Detection of Deepfake videos using long distance attention

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arXiv 2021年

作者： Lu, Wei Liu, Lingyi Luo, Junwei Zhao, Xianfeng Zhou, Yicong Huang, Jiwu School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100195 China School of Cyber Security University of Chinese Academy of Sciences Beijing100195 China Department of Computer and Information Science University of Macau 999078 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video contents and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and inter-frame inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively. The two components are designed using a novel long distance attention mechanism. The one component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves the state-of-the-art performance, and the proposed long distance attention method can effectively capture pivotal parts for face forgery. Copyright © 2021, The Authors. All rights reserved.

关键词： Time domain analysis