Earticle

This With the expansion of information and communication technology, the term ubiquitous computing is a known concept used to describe the current era of interconnected digital systems, which is silently posing a new threat to private information leakage through encryption applications. This work aims to acquaint individuals with the privacy information forms of expression in networking technology and then proposes secure multi-party computation to achieve personal information protection. This work emphasizes that, for the time being, homomorphic computing research approaches privacy in ubiquitous environments without considering the various aspects and fundamental principles of privacy. Finally, this work highlights the need for multidisciplinary research in the area and the importance that homomorphic computing algorithm research receives input from other related disciplines, such as law and psychology. This research aims to contribute to the ongoing discourse on the nature of privacy and its role in ubiquitous environments, providing insights for future research. Although research on privacy in the area of ubiquitous computing expands in many different directions and covers various topics, privacy issues are still open, and it appears that feasible and effective solutions are still quite far from being realized. A critical analysis of this research on privacy in ubiquitous environments reveals a significant shift: up to now, it has been the government's role to provide the framework for privacy protection. Homomorphic encryption is an encryption scheme that allows operations on encrypted data, and it can be applied in any system using various public key algorithms. When data is transferred to the public area, various encryption algorithms are used to secure both the operations and storage of the data. However, to process data located on a remote proxy server while preserving privacy, homomorphic encryption is crucial, especially for network-based operations.

Vision Transformer (ViT) 기반 모델들은 이미지 분류 등 다양한 시각적 인식 작업에서 강력한 성능 을 보이지만, 모든 토큰을 동일하게 활용하는 방식은 불필요한 연산 증가와 중요한 정보 희석 문제를 초래할 수 있다. 특히, Global Average Pooling (GAP)은 모든 토큰을 평균화하여 특징을 요약하는데, 이 과정에서 핵심 정보 손실 위험이 존재한다. 본 논문에서는 GAP 과정에서 중요한 토큰만을 선별적으로 샘플링하여 활용하는 새로운 방법을 제안한다. 이 방법은 추가적인 학습 가능한 파라미터 없이 동작하 며, 연산량을 줄이면서도 효과적인 특징 요약이 가능함을 실험을 통해 확인하였다.

컴퓨터 게임은 컴퓨터 기술을 통해 놀이를 새로운 형태의 텍스트로 전환시킨다. 놀이가 가지는 인간학적 층위가 대상에 대한 상상적 전유 라면, 컴퓨터 기술은 이 상상적 전유의 계기를 생산하고, 동시에 상상 적 전유를 지탱할 수 있는 요소를 생산한다. 컴퓨터 게임에서 발생하 는‘게임 행위’는 그런 의미에서 인간의 상상적 전유와 이 전유를 가능 케 하는 인공적 경험들 사이에서 이뤄진다. 그리고 이를 통해 우리는 ‘놀이-이미지’라는 새로운 이미지 형태를 생산, 경험하게 된다.

Computer game transforms play into the new text form using computer technology. Humanistic layer of play means the imaginative possession of the object. Computer technology in computer game produces the motive of the imaginative possession and the elements to support the imaginative possession.‘ Game-action’ in computer game occurs between the imaginative possession and the artificial experience. With‘game-acition’, we produce and experience the‘play-image’as new image form.

Aerial Computing, utilizing unmanned aerial vehicles (UAVs), has emerged as a promising solution to enhance mobile cloud computing (MCC) infrastructure for the Internet of Things (IoT). The continuous generation of vast amounts of data by IoT devices requires efficient processing and monitoring for timely decision-making. However, wireless connections between IoT devices and remote servers can be unreliable, resulting in data loss. UAVs, with their increasing processing power and autonomy, can act as bridges between IoT devices and remote servers such as edge or cloud computing. In that context, this paper proposes a continuous time Markov chain (CTMC) models for an aerial computing system to evaluate system dependability metrics including availability and reliability. Sensitivity analysis is conducted to provide extended CTMC models with improved system availability. The proposed advanced model reduces downtime by 62 h compared to the baseline model, showcasing the potential of UAVs in enhancing the availability and reliability of MCC infrastructures. The use of UAVs and MCC in aerial computing is believed to be a win?win solution for cost-effective and energy-saving communication and computation services in various environments.

Future internet aims to function as a neutral in-network storage and computation platform, essential for enabling 6G and beyond wireless use cases. Information-Centric Networking and Edge Computing are key paradigms driving this vision by offering diversified services with fast response times across heterogeneous networks. This approach requires effective coordination to dynamically utilize resources like links, storage, and computation in near real-time within a non-homogenous and distributed computing environment. Additionally, networks must be aware of resource availability and reputational information to manage unknown and partially observed dynamic systems, ensuring the desired Quality of Experience (QoE). This paper provides a comprehensive evaluation of edge computing technologies, starting with an introduction to its architectural frameworks. We examine contemporary research on essential aspects such as resource allocation, computation delegation, data administration, and network management, highlighting existing research gaps. Furthermore, we explore the synergy between edge computing and 5G, and discuss advancements in 6G that enhance solutions through edge computing. Our study emphasizes the importance of integrating edge computing in future considerations, particularly regarding sustainable energy and standards.

Closed Circuit Television (CCTV) based Surveillance has become the fundamental part of the security Systems. In most cases, surveillance feeds are only used as evidence. The emergence of Edge Computing gives hope for enabling real time surveillance systems that focuses on prevention of crimes. The proposed architecture consists of a Convolutional Neural Network (CNN) enabled in an edge device, with reduced computational complexity, which classifies various actions like Pulling, pushing and other hand movements and locates the identified activities in the image frame using bounding boxes. The proposed architecture gives an alert whenever a suspicious activity is detected. The system was found efficient when validated against the Dataset taken from the SRM IST Campus.

In this work, we study how to minimize the average system delay in a cloud computing center with heterogeneous servers, where each server may have a different average service rate. We consider M?Mi?C?K with a single server threshold and double congestion thresholds. The analytical models and performance measures are derived for the systems considered. The effect of the average arrival rate on performance measures is studied. It is shown that M?Mi?C?K with a single server threshold and double congestion thresholds outperforms M?Mi?C?K in terms of the average system delay. Finally, a computer simulation is written to verify the accuracy of the analytical results.

This paper proposes high-speed computing decoders for polar codes based on a neural network. To compensate for the performance gap with the successive cancellation decoder, we propose applying the recurrent neural network to the BP decoder with a modified factor graph to reduce the computational complexity of the decoder without any performance degradation. The results of the performance simulation conducted in this paper reveal that the proposed decoder requires substantially less computational complexity than the conventional decoders to achieve the same bit error rate performance.

Due to the resource-constraint nature and lack of lightweight computing solutions for diagnostic devices in healthcare IoT, provisioning time-critical responses is still challenging. In this paper, we propose DC-Health, a Dew Computing enabled IoT healthcare solution for offline and ultra-low latency decisions. The proposed solution connects a large number of healthcare devices and provisions user-specific services even when Internet connectivity is not available. The computation module is placed at the extreme edge rather than the cloud to reduce the complexity and to improve the user-specific services. In addition to the other computing facilities provided by the cloud, fog, and edge, our solution performs with a negligible dependency on the Internet.We develop a prototype of DC-Health, which monitors the heart condition using the ECG sensors with end-mile services, flexibility in terms of user-control, and mobility feature. The experimental implementations show that the proposed architecture minimizes the network response time by at least 92% and 98%, compared to the fog and cloud-based approaches, respectively. Along with this, the proposed technique also reduces the CPU and memory usages, and response time by around 30% compared to the conventional method.

Conventional split computing approaches for AI models that generate large outputs suffer from long transmission and inference times. Due to the limited resources of the edge server and selfish MDs, some MDs cannot offload their tasks and sacrifice their performance. To address these issues, we formulate an optimization problem to determine one or two split points that minimize inference latency while ensuring fair offloading among MDs. Additionally, we devise a low-complexity heuristic algorithm called fast and fair split computing (F2SC). Evaluation results demonstrate that F2SC reduces inference time by 3.8%~20.1% compared to the conventional approaches while maintaining fairness.

Internet of Things (IoT) enables different smart environment objects to communicate without involving humans. Recently, IoT has started a new challenge within the healthcare sector called the Internet of Medical Things (IoMT). The huge amounts of data generated by IoMT entities need to be analyzed in real-time to improve the performance and quality of service of the IoMT applications. Mobile Edge Computing-enabled 5G system is shown as a successful paradigm to address such an obstacle. Numerous frameworks are introduced in literature based on this idea. This paper presents a thorough discussion of MEC-based IoMT healthcare systems.

With the increasing advancement in the applications of the Internet of Things (IoT), the integrated Cloud Computing (CC) faces numerous threats such as performance, security, latency, and network breakdown. With the discovery of Fog Computing these issues are addressed by taking CC nearer to the Internet of Things (IoT). The key functionality of the fog is to provide the data generated by the IoT devices near the edge. Processing of the data and data storage is done locally at the fog node rather than moving the information to the cloud server. In comparison with the cloud, Fog Computing delivers services with high quality and quick response time. Hence, Fog Computing might be the optimal option to allow the Internet of Things to deliver an efficient and highly secured service to numerous IoT clients. It allows the administration of the services and resource provisioning outside CC, nearer to devices, at the network edge, or ultimately at places specified by Service Level Agreements (SLA’s). Fog Computing is not a replacement to CC, but a prevailing component. It allows the processing of the information at the edge though still delivering the option to connect with the data center of the cloud. In this paper, we put forward various computing paradigms, features of fog computing, an in-depth reference architecture of fog with its various levels, a detailed analysis of fog with IoT, various fog system algorithms and also systematically examine the challenges in Fog Computing which acts as a middle layer between IoT sensors or devices and data centers of the cloud.

This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods.

Cloud computing services are becoming more popular for various reasons which include ‘having no need for capital expenditure’ and ‘the ability to quickly meet business demands’. However, what seems to be an attractive option may become a substantial expenditure as more projects are moved into the cloud. Cloud service companies provide different pricing options to their customers that can potentially lower the customers’ spending on the cloud. Choosing the right combination of pricing options can be formulated as a linear mixed integer programming problem, which can be solved using optimization.

In many of 5G applications, end devices with lack of computing power often need to carry out heavy computations involving multimedia data. Edge computing has emerged as a promising solution to circumvent scarce resources at end devices, with moderate delays compared to cloud computing. In this work, we study the problem of offloading video processing tasks to edge servers. To this end, we develop a deep reinforcement learning based method for selecting either local or edge server to process video frames. We demonstrate the performance of our method through experiments with video frame transform tasks.

Numerous consensus mechanisms have been suggested to cater to the specific characteristics of fog computing. To comprehensively understand their unique features, performance, and applications in fog computing, it is crucial to conduct a systematic analysis of these mechanisms. For this study, 79 relevant articles were carefully selected based on predefined criteria. Among these articles, 35 employed work-proof-based consensus mechanisms, 24 utilized voting-based mechanisms, and 22 adopted capability-based mechanisms. Among the 26 identified consensus mechanisms, proof of work remains the most prevalent one. It is important to note that the scope of this paper is limited to the research available in the predominant databases at the time of writing. Future research may expand to include additional databases and more recent literature in this domain.

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.

The emergence of Multi-Access Edge Computing (MEC) technology aims to extend cloud computing capabilities to the edge of the wireless access networks, i.e., closer to the end-users. Thus, MEC-enabled 5G wireless systems are envisaged to offer real-time, low-latency, and high-bandwidth access to the radio network resources. Thus, MEC allows network operators to open up their networks to a wide range of innovative services, thereby giving rise to a brand-new ecosystem and a value chain. Furthermore, MEC as an enabling technology will provide new insights into coherent integration of Internet of Things (IoT) in 5G wireless systems. In this context, this paper expounds the four key technologies, including Network Function Virtualization (NFV), Software Defined Networking (SDN), Network Slicing and Information Centric Networking (ICN), that will propel and intensify the integration of MEC IoT in 5G networks. Moreover, our goal is to provide the close alliance between MEC and these four driving technologies in the 5G IoT context and to identify the open challenges, future directions, and concrete integration paths.

This paper presents a test bed demonstration of NeuroEdge computing for face recognition using a novel neuromorphic chip- NM500. First, a general description and important specifications of the NM500 are presented. Second, a face recognition test-bed case study is used to demonstrate the efficacy and efficiency of the chip. Neuromorphic technology offers scalability and consistent recognition time, which is required by real-time networked systems, and presents a considerable advantage for real-time computations, making them virtually independent of the dataset size. In this study, intelligent edge computing technology was introduced using NeuroEdge. The performance was verified using a face recognition test. The results demonstrated that using neuromorphic technology, such as the NM500 chip, saves the time needed for training systems and does not impose the burden of requiring many datasets for effective training.

As the number of low Earth orbit satellites is increasing, new algorithms and high-performance computing resources are needed to handle the enormous real-time arrival data generated by satellites. Based on the requirements, this paper proposes a novel real-time adaptive speckle filtering selection algorithm for satellite synthetic aperture radar (SAR) images. The use of high-performance filters generates high-quality images whereas it introduces delays. Thus, a real-time adaptive algorithm which achieves time-average SAR image quality maximization subject to delays using Lyapunov optimization, where the delay is formulated with a queuing model. Evaluation results show that the proposed algorithm guarantees desired performance improvements.