SOSE: Smart Offloading Scheme using Computing Resources of Nearby Wireless Devices for Edge Computing Services

Offloading of all or part of any cloud service computation, when running processing-intensive Mobile Cloud Computing Services (MCCS), to servers in the cloud introduces time delay and communication overhead. Edge computing has emerged to resolve these issues, by shifting part of the service computation from the cloud to edge servers near the end-devices. An innovative Smart Cooperative Computation Offloading Framework (SCCOF), to leverage computation offloading to the cloud has been previously published by us [1]. This paper proposes SOSE; a solution to offload sub-tasks to nearby devices, on-the-go, that will form an “edge computing resource, we call SOSE_EDGE” so to enable the execution of the MCCS on any end-device. This is achieved by using short-range wireless connectivity to network between available cooperative end-devices. SOSE can partition the MCCS workload to execute among a pool of Offloadees (nearby end-devises; such as Smartphones, tablets, and PC’s), so to achieve minimum latency and improve performance while reducing battery power consumption of the Offloader (end-device that is running the MCCS). SOSE established the edge computing resource by: (1) profiling and partitioning the service workload to sub-tasks, based on a complexity relationship we developed. (2) Establishing peer2peer remote connection, with the available cooperative nearby Offloadees, based on SOSE assessment criteria. (3) Migrating the sub-tasks to the target edge devices in parallel and retrieve results. Scenarios and experiments to evaluate SOSE show that a significant improvement, in terms of processing time (>40%) and battery power consumption (>28%), has been achieved when compared with cloud offloading solutions

Multi-model running latency optimization in an edge computing paradigm

Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable multiple model inference tasks to be conducted concurrently on resource-constrained edge devices, allowing us to achieve one goal collaboratively rather than getting high quality in each standalone task. However, the high overall running latency for performing multi-model inferences always negatively affects the real-time applications. To combat latency, the algorithms should be optimized to minimize the latency for multi-model deployment without compromising the safety-critical situation. This work focuses on the real-time task scheduling strategy for multi-model deployment and investigating the model inference using an open neural network exchange (ONNX) runtime engine. Then, an application deployment strategy is proposed based on the container technology and inference tasks are scheduled to different containers based on the scheduling strategies. Experimental results show that the proposed solution is able to significantly reduce the overall running latency in real-time applications

Perspective Chapter: A View – Cloud-Edge Computing Technology

In the computing era, the users of Internet grow tremendously. Each individual is using a number of devices that access data indefinitely. For any desired task, we will have three sorts of basic work in terms of storing, processing, and computation. In distributed world, many applications depending upon individual or enterprise rely on cloud computing. Edge computing has also evolved after cloud computing. Both the technologies have many common characteristics. The advantage of edge computing technology is service provisioning and it is done at the device level closer to the user rather than cloud. The application that needs faster response time or that holds sensitive data almost relied on edge technology for its computation

DEO: A Smart Dynamic Edge Offloading Scheme using Processing Resources of Nearby Wireless Devices to Form an Edge Computing Engine

Edge computing reduces connectivity costs and network traffic congestion over cloud computing, by offering local resources (processing and storage) at one hop closer to the end-users. I.e. it reduces the Round-Trip Time (RTT) for offloading part of the processing workload from end-nodes/devices to servers at the edge. However, edge servers are normally pre-setup as part of the overall computing resource infrastructure, which is tough to predict for mobile/IoT deployments. This paper introduces a smart Dynamic Edge Offloading scheme, (we named it DEO), that forms the “edge computing resource” on-the-go, as needed from nearby available devices in a cooperative sharing environment. This is especially necessary for hosting mobile/IoT applications traffic at crowded/urban situations, and, for example, when executing a processing intensive Mobile Cloud Computing Service (MCCS) on a Smartphone (SP). DEO implementation is achieved by using a short-range wireless connectivity between available cooperative end-devices, that will form the edge computing resource. DEO includes an intelligent cloud-based engine, that will facilitate the engagement of the edge network devices. For example, if the end-device is a SP running an MCCS, DEO will partition the processing of the MCCS into sub-tasks, that will be run in parallel on the newly formed “edge resource network” of other nearby devices. Our experiments prove that DEO reduces the RTT and cost overhead by 62.8% and 75.5%, when compared to offloading to a local edge server or a cloud-based server

Cybersecurity: reducing the attack surface

Almost 60% of the world’s population has access to the internet and most organisations today rely on internet connectivity to conduct business and carry out daily operations. Further to this, it is estimated that concepts such as the Internet of Things (IoT) will facilitate the connections of over 125 billion ‘things’ by the year 2030. However, as people and devices are becoming more and more interconnected, and more data is being shared, the question that must be asked is – are we doing so securely? Each year, cybercriminals cost organisations and individuals millions of dollars, using techniques such as phishing, social engineering, malware and denial of service attacks. In particular, together with the Covid-19 pandemic, there has been a so-called ‘cybercrime pandemic’. Threat actors adapted their techniques to target people with Covid-19-themed cyberattacks and phishing campaigns to exploit their stress and anxiety during the pandemic. Cybersecurity and cybercrime exist in a symbiotic relationship in cyberspace, where, as cybersecurity gets stronger, so the cybercriminals need to become stronger to overcome those defenses. And, as the cybercriminals become stronger, so too must the defenses. Further, this symbiotic relationship plays out on what is called the attack surface. Attack surfaces are the exposed areas of an organisation that make systems more vulnerable to attacks and, essentially, is all the gaps in an organisation’s security that could be compromised by a threat actor. This attack surface is increased through organisations incorporating things such as IoT technologies, migrating to the cloud and decentralising its workforce, as happened during the pandemic with many people working from home. It is essential that organisations reduce the digital attack surface, and the vulnerabilities introduced through devices connected to the internet, with technical strategies and solutions. However, the focus of cybersecurity is often on the digital attack surface and technical solutions, with less of a focus on the human aspects of cybersecurity. The human attack surface encompasses all the vulnerabilities introduced through the actions and activities of employees. These employees should be given the necessary cybersecurity awareness, training and education to reduce the human attack surface of organisations. However, it is not only employees of organisations who are online. All individuals who interact online should be cybersecurity aware and know how to reduce their own digital and human attack surfaces, or digital footprints. This paper emphasises the importance of utilising people as part of the cybersecurity defense through the cultivation of cybersecurity cultures in organisations and a cybersecurity conscious society

Verify and trust: A multidimensional survey of zero-trust security in the age of IoT

The zero-trust (ZT) model assumes that all users, devices, and network traffic should not considered as trusted until proven. The Zero-trust model emphasizes the importance of verifying and authenticating every user and device, and limiting access to resources based on the principle of least privilege. Under the principle of the zero-trust model, devices are granted access after they have been successfully presented with their authentication credentials and access rights based on different factors, such as user identity, device health, location, and behaviour. Access controls are then continuously evaluated and updated as user properties, locations and behaviour change. The zero-trust model can be applied in various domains (healthcare, manufacturing, financial services, government etc.) to provide a comprehensive approach to cybersecurity that helps organizations to reduce risk and protect critical assets. This paper aims to provide a comprehensive and in-depth analysis of the zero-trust model, its principles, and its applications, as well as to propose recommendations for organizations looking to adopt this approach. We explore the major components of the zero-trust framework and their integration across different practical domains. Finally, we provide insightful discussions on open research issues within the zero-trust model in terms of the security and privacy of users and devices. This paper should help researchers and practitioners understand the importance of a zero-trust framework and adopt the zero-trust model for effective security, privacy, and resilience of their networks

A Smart Edge Computing Resource, formed by On-the-go Networking of Cooperative Nearby Devices using an AI-Offloading Engine, to Solve Computationally Intensive Sub-tasks for Mobile Cloud Services

The latest Mobile Smart Devices (MSDs) and IoT deployments have encouraged the running of “Computation Intensive Applications/Services” onboard MSDs to help us perform on-the-go sub-tasks required by these Apps/Services such as Analysis, Banking, Navigation, Social Media, Gaming, etc. Doing this requires that the MSD have powerful processing resources to reduce execution time, high connectivity throughput to minimise latency and high-capacity battery for power consumption so to not impact the MSD availability/usability in between charges. Offloading such Apps from the host-MSD to a Cloud server does help but introduces network traffic and connectivity overhead issues, even with 5G. Offloading to an Edge server does help, but Edge servers are part of a pre-planned overall computing resource infrastructure, that is tough to predict when demands/rollout is generated by a push from the MSDs/Apps makers and pull by users. To address this issue, this research work has developed a “Smart Edge Computing Resource”, formed on-the-go by the networking of cooperative MSDs/Servers in the vicinity of the host-MSD that is running the computing-intensive App. This solution is achieved by: Developing an intelligent engine, hosted in the Cloud, for profiling “computing-intensive Apps/Services” for appropriately partitioning the overall task into suitable sub-task-chunks so to be executed on the host-MSD together/in association with other available nearby computing resources. Nearby resources can include other MSDs, PCs, iPads and local servers. This is achieved by implementing an “Edge-side Computing Resource engine” that intelligently divides the processing of Apps/Services among several MSDs in parallel. Also, a second “Cloud-side AI-engine” to recruit any available cooperative MSDs and provide the host-MSD with decisions of the best scenario to partition and offload the overall App/Services. It uses a performance scoring algorithm to schedule the sub-tasks to execute on the assisting resource device that has a powerful processor and high-capacity battery power. We built a dataset of 600 scenarios to boost up the offloading decision for further executions, using a Deep Neural Network model. Dynamically forming the on-the-go resource network between the chosen assisting resource devices and the App/Service host-MSD based on the best wireless connectivity possible between them. This is achieved by developing an Importance Priority Weighting cost estimator to calculate the overhead cost and efficiency gain of processing the sub-tasks on the available assisting devices. A local peer-to-peer connectivity protocol is used to communicate, using “Nearby API and/or Post API”. Sub-tasks are offloaded and processed among the participating devices in parallel while results are retrieved upon completion. The results show that our solution has achieved, on average, 40.2% more efficient processing time, 28.8% less battery power consumption and 33% less latency than other methods of executing the same Apps/Services

An internet of things enabled system for real-time monitoring and predictive maintenance of railway infrastructure

The railway industry plays a pivotal role in the socioeconomic landscape of many countries. However, its operation poses considerable challenges in terms of safety, environmental impact, and the intricacies of intertwined technical and social structures. Addressing these challenges necessitates the adoption of innovative approaches and advanced technologies. This doctoral research delves into the potential of the Internet of Things (IoT) as an enabler for railway infrastructure monitoring and predictive maintenance, aiming to enhance reliability, efficiency, and safety within the industry. Rooted in a pragmatic modelist philosophical stance, this thesis employs an exploratory sequential mixed-method approach incorporating qualitative and quantitative methodologies. The research process involves engaging with key stakeholders to gain insights into the challenges faced in railway maintenance and the opportunities presented by IoT implementation. Following this, an IoT system is developed, and a comprehensive value-creation framework is proposed for its effective implementation within the railway sector. The findings of this investigation underscore the transformative potential of IoT integration in railway infrastructure monitoring, yielding significant improvements in maintenance processes, safety, and operational efficiency. Furthermore, this doctoral research provides a foundation for future innovation and adaptation in the railway industry, contributing to its ongoing evolution and resilience in an ever-changing technological landscape

Комп’ютерне моделювання в наукоємних технологіях: збірник наукових праць міжнародної науково-технічної конференції (23-25 листопада 2022 р., м. Харків, Україна)

Для викладачів, наукових працівників, аспірантів, студентів вишів. Робочі мови конференції: українська, англійська.VIII Міжнародна науково-технічна конференція «Комп’ютерне моделювання в наукоємних технологіях» (КМНТ-2022) відбулася на базі Харківського національного університету імені В.Н.Каразіна 23-25 листопада 2022 року. Співорганізаторами цього наукового заходу виступили: ННЦ Харківський фізико-технічний інститут, MAX PLANCK INSTITUTE OF MICROSTRUCTURE PHYSICS, Київський національний університет імені Тараса ШЕВЧЕНКА, INSTITUTE OF NUCLEAR CHEMISTRY AND TECHNOLOGY (Warsaw, Poland), Рівненський державний гуманітарний університет, Національний аерокосмічний університет імені М. Є. Жуковського (ХАРКІВ), ЗАТ « Інститут інформаційних технологій » (Харків), Херсонський національний технічний університет, TEAM INTERNATIONAL SERVICES, INC. (Lake Mary, USA). Конференція проходила в онлайн режимі за наступними основними напрямками роботи: 1. Математичне моделювання технологічних процесів та приладів. 2. Моделювання інформаційних процесів у складних і розподілених системах. 3. Системи автоматизованого збору та когнітивного представлення наукових даних. 4. Моделювання фізичних процесів в радіаційних, плазмових та інших сучасних технологіях. 5. Безпека інформаційних систем і технологій. 6. Моделі процесів розробки та оцінки якості програмного забезпечення. Також було організовано загальну об’єднану секцію за всіма науковими напрямами конференції для студентів, аспірантів та молодих вчених, на якій молоді науковці мали змогу додатково доповісти результати своїх робіт. Метою цієї конференції було представлення та обговорення нових, оригінальних результатів досліджень українських та закордонних вчених у галузі математичного моделювання та обчислювальних методів, інформаційних технологій та захисту інформації. Крім того, конференція мала на меті організувати співпрацю науковців та студентів України та наших закордонних колег задля розвитку науки та освіти в Україні.The VIII International Scientific and Technical Conference "Computer modelling in high tech" (CMHT-2022) was held at V. N. Karazin Kharkiv National University on 23-25 November 2022. The co-organisers of this scientific event were: NSC Kharkiv Institute of Physics and Technology, MAX PLANCK INSTITUTE FOR MICROSTRUCTURE PHYSICS, Taras Shevchenko National University of Kyiv, INSTITUTE OF NUCLEAR CHEMISTRY AND TECHNOLOGY (Warsaw, Poland), Rivne State University of Humanities, National Aerospace University named after M.E. Zhukovsky (Kharkiv), CJSC "Institute of Information Technologies" (Kharkiv), Kherson National Technical University, TEAM INTERNATIONAL SERVICES, INC. (Lake Mary, USA). The conference was held online, the main themes being: 1. Mathematical modelling of technological processes and devices. 2. Modelling of information processes in complex and distributed systems. 3. Systems of the computer-aided acquisition and cognitive representation of the scientific data. 4. Modelling of physical processes in radiation, plasma and other modern technologies. 5. Security of information systems and technologies. 6. Models of software development and quality assessment. The joint section on all covered themes was organized for students, postgraduates and young scientists, where young scientists presented the results of their work. The purpose of this conference was to present and discuss results of new, original research of Ukrainian and foreign scientists in the field of mathematical modelling and computational methods, information technology and information security. In addition, the aim of conference was organizing the cooperation between Ukrainian scientists and students and our foreign colleagues to promote development of science and education in Ukraine