Search

As a branch of cloud computing, edge computing has received extensive attention. Edge computing offloads computing-intensive tasks to the computing-capable devices (i.e., edge servers) at the network edge of the cloud, and the edge servers assist in mobile devices to perform data analysis, which can significantly reduce the execution time of computing-intensive tasks on mobile devices. However, due to the poor quality of wireless channel communication, it may degrade the overall quality of the edge offloading when the mobile devices are linked to edge servers, that is, the communication link between the mobile device and the edge server may become a bottleneck. In this study, we propose a new edge computing scheme to support remote object detection by splitting the deep neural network (DNN), and we design a framework to reduce the amount of data transmitted over wireless links. Specially, the DNN is divided into a head modular and a tail modular, which are deployed on mobile devices and edge servers, respectively. Furthermore, the wireless link transmits the output of the last layer in the head modular to the edge server, instead of sending all the initial tensors of images to the edge server. And we employ three object detection models with complex structures, that is, Faster RCNN (regions with CNN features), Mask RCNN and Keypoint RCNN as the basic models, and modify their network architectures from the following aspects: 1) we introduce the information bottleneck principle and split the network architecture into a head modular and a tail modular, and we perform knowledge distillation to compress the head modular; 2) we embed a neural filter (NF) into the head modular to pre-filter images with no detection objects by using a simple convolutional neural (CNN) network, and the NF performs a binary classification task in the head modular. Lastly, we conduct experiments on the real COCO 2017 dataset and the PASCAL VOC 2012 dataset, and the results show that the proposed method outperforms other baselines. Especially, in person keypoint detection, the mAP (mean average precision) of the proposed method is at least 1.64% higher than other baselines on the COCO 2017 dataset. In addition, the prediction latency of the proposed splitting scheme at the data transmission rate of 1Mbps is at least 49.17% lower than that of local computing scheme. [ABSTRACT FROM AUTHOR]

In cloud storage, attribute-based keyword searchable encryption realizes multi-user fine-grained authorization access control and outsourcing data sharing. However, (1) resisting key regeneration is an urgent problem to be solved in practical applications of attribute-based keyword search; (2) the general attribute-based keyword search scheme ignores the application scenario where multiple data owners jointly manage files. In addition, the interaction of data between intelligent terminals and cloud servers has become more frequent, leading to issues such as central load, transmission efficiency, and data privacy protection in cloud computing. To address the above issues, we propose an attribute-based searchable encryption scheme with the multi-data owner based on edge computing. In this scheme, the Pedersen (k , n) secret sharing protocol and the Reciprocal protocol are used to realize the common management and sharing of data by the multi-data owner to enhance data security. Secondly, the attribute set and identity information of each user are embedded into the private key to prevent key regeneration and resist user collusion attacks. Thirdly, edge computing is introduced, and part of the storage and decryption work is outsourced to edge nodes to reduce the computing load of users and data transmission bandwidth. Security proof and performance analysis show that the proposed scheme has strong security and practicability in multi-user sharing scenarios such as Online Education with multi-data owners. [ABSTRACT FROM AUTHOR]

With the popularity of IoT (Internet of Things) applications, edge computing has received lots of attention. To meet data privacy protection requirements of edge nodes and cope with their unbalanced data distribution, federated learning (FL), a distributed learning framework, is widely used in intelligent edge computing applications. However, recent studies have shown that FL still suffers from privacy leakage problems, including membership inference, data reconstruction, etc. However, these studies mainly focus on the feature information of private data. In this paper, we concern the user-level label privacy in FL. We propose LDIA, a label distribution inference attack against FL in edge computing, exploring the possibility that an honest but curious cloud server can infer the proportions of samples per label in the edge user's private data. LDIA is inspired by the observation that parameter changes in the output layer of a model can reflect the label distribution of training data. We use a neural network to learn individual features of the output layer updates over different label distributions, and then perform inference from local models uploaded by users. Our comprehensive evaluation shows that LDIA is effective on various datasets in different settings, demonstrating the severe privacy leakage in FL-based edge computing. [ABSTRACT FROM AUTHOR]

The study systematically reviews the integration of Fog and Edge Computing within Learning Analytics to enhance data privacy and security in educational settings that use cloud computing. Employing the PRISMA methodology, we analyze current literature from Web of Science and Scopus databases to examine how these decentralized computing technologies can mitigate the risks associated with centralized cloud storage by processing data closer to its source. Our findings highlight the significant potential of Fog and Edge Computing to transform Learning Analytics by enabling real-time, context-aware data analysis that supports personalized learning while ensuring stringent data privacy. This approach challenges conventional data management practices, advocating for privacy by design in developing new strategies and frameworks. The research underscores the need for collaborative efforts in establishing standards and guidelines for secure and effective technology use in education, pointing towards the necessity of addressing technical, operational, and ethical challenges to maximize the benefits of fog and edge computing in Learning Analytics. • Fog and Edge computing improve data privacy for Learning Analytics and minimizes dependence on centralized cloud storage. • Nearer analytical technologies to users promotes a personalized and secure learning environment. • Future research should focus on creating secure architectures and policies to leverage Fog and Edge Computing in education. [ABSTRACT FROM AUTHOR]

In recent years, the Industrial Internet of Things (IIoT) has played an indispensable role in the age of intelligence. The data of IIoT is extremely private and valuable. Therefore, privacy protection and secure communication in the IIoT have been the focus of attention. Since terminal devices in the IIoT collect massive amounts of sensitive data in real time, traditional cloud computing architectures cannot satisfy the requirements of the IIoT. Edge computing is an emerging computing paradigm to solve the problems of delay and bandwidth in traditional cloud computing architectures. In edge computing architecture, one-to-many communication is a secure and highly efficient communication method. Due to the complex heterogeneity of the IIoT, it faces various potential threats. In order to solve the dilemma mentioned above, we propose an efficient heterogeneous signcryption scheme based on edge computing for IIoT to achieve multi-message and multi-receiver communication between edge servers and data users, where the edge server belongs to identity-based cryptography while data users belong to certificateless cryptography. In the random oracle model, our proposed scheme is proved to satisfy confidentiality and unforgeability based on the discrete logarithm assumption and the computational Diffie–Hellman assumption. Experimental results indicate that our proposal achieves better performance than existing schemes. Hence, our scheme has more advantages for IIoT. [ABSTRACT FROM AUTHOR]

In this paper, nearly 40 commonly used deep neural network(DNN) models are selected, and their cross-platform and cross-inference frameworks are deeply analysed. The main metrics of accuracy, the total number of model parameters, the computational complexity, the accuracy density, the inference time, the memory consumption and other related parameters are used to measure their performance. The heterogeneous computing experiment is implemented on both the Google Colab cloud computing platform and the Jetson Nano embedded edge computing platform. The obtained performance is compared with that of two previous computing platforms: a workstation equipped with an NVIDIA Titan X Pascal and an embedded system based on an NVIDIA Jetson TX1 board. In addition, on the Jetson Nano embedded edge computing platform, different inference frameworks are investigated to evaluate the inference efficiency of the DNN models. Regression models are established to characterize the variation in the computing performance of different DNN classification algorithms so that the inference results of unknown models can be estimated. ANOVA methods are proposed to quantify the differences between models. The experimental results have important guiding significance for the better selection, deployment and application of DNN models in practice. Codes are available at this https URL https://github.com/Foreverzfy/Model-Test. [ABSTRACT FROM AUTHOR]

• A method of cloud edge computing is proposed to solve the economic dispatch. • Multi-agent deep reinforcement learning is adopted to realize cloud edge computing. • The cloud server and each edge server respond to each agent. • The proposed method can protect data privacy. In view of the risks and challenges of privacy data leakage and the communication burden in the traditional economic dispatch for active distribution network with multi-microgrids, this paper proposes a cloud edge computing method for economic dispatch of active distribution network with multi-microgrids. In this method, the cloud server is responsible for the calculation of active distribution network, and each edge server is in charge of the calculation of its own microgrid. The multi-agent deep reinforcement learning is employed to realize cloud edge collaborative computing, where each edge server and cloud server corresponds to an agent. Through case analysis, the reliability of the cloud edge computing method is confirmed. The simulation results show that the proposed method can provide a high-quality solution for economic dispatch of active distribution network with multi-microgrids on the premise of protecting data privacy and reducing the communication burden. [ABSTRACT FROM AUTHOR]

• The multi-sensor fusion method has high accuracy for tillage depth and area detection. • The designed edge computing device could process subsoiling information in real-time. • The cloud-end data interactive mechanism ensuring safety and stability of the system. • The system has reliably ability for monitoring and management subsoiling on a large scale. An edge computing based monitoring system using Internet of Things architecture (IoT) was proposed, aiming at automatically monitoring subsoiling parameters, counting subsoiling quantity, evaluating subsoiling quality, analyzing subsoiling efficiency and managing subsoiling operation. In this study, tillage depth computing algorithm based on motion attitudes of tractor and subsoiler was studied, and an edge computing device was developed to collect and process subsoiling data in real-time. A cloud-end data interaction mechanism was designed to provide a safe, stable and reliable way for data transmission between the device and cloud. High-performance cloud computing was used to count indicators of subsoiling quantity such as total area, effective area and operation mileage, evaluate subsoiling quality indicators such as average tillage depth and area compliance rate, and analyze subsoiling efficiency indicators such as time utilization rate and effective mileage rate. Field tests showed that the error of tillage depth detection was less than 1.2 cm, and the error of subsoiling area detection was less than 1%. The subsoiling data in 2021 from a selected units in the system showed that the system could count the subsoiling quantity, analyze the subsoiling quality and efficiency, and meet the requirements of subsoiling monitoring and management by the friendly human-computer interaction interface. The subsoiling data collected for 7 years by the system was analyzed, and results showed that the total registered edge computing device number was 51,252, the peak value of annual cumulative on-line number was 20,560, the peak value of single-day on-line was 6,694, the peak data transmission rate was 0.034 GB/s, the peak value of single-day concurrent calculation subsoiling area was 43,900 ha, and the cumulative monitoring subsoiling area was 8.98 million hectares, which showed that the system has good concurrent calculation ability and stable and reliable operation. This paper provides an efficient monitoring, statistics, analysis and management tool for large-scale subsoiling, and has high practical value. [ABSTRACT FROM AUTHOR]

Edge computing is an emerging computing paradigm, which extends the functions of cloud center to the edge of the networks, and brings great convenience to solving problems of delay and bandwidth in the traditional cloud computing paradigm. In edge computing architecture, one-to-many communication is a important communication mode that supports the edge nodes to send one message to multiple terminal devices in one broadcast report. To build a secure one-to-many communication, several multi-receiver signcryption (MRSC) schemes have been brought forward to ensure the security of broadcast messages. However, the existing MRSC schemes need the heavy computation and communication cost, and is not suitable for IoT terminal devices with limited resources. Besides, most of the existing MRSC schemes are vulnerable to the security and privacy leakage, and fail in achieving the decryption fairness. To solve these problems, based on the implicit certificate (IC) cryptosystem and polynomial interpolation evaluation, this paper proposes an provably secure multi-receiver IC-based signcryption (MRICSC) scheme for one-to-many communication in edge computing. The security analysis evidences the proposed MRICSC scheme can ensure the security. With the experimental results indicating that the proposed MRICSC scheme achieves the better performance than existing schemes. [Display omitted] [ABSTRACT FROM AUTHOR]

In traditional cloud computing, tasks will be offloaded to the could, which often leads to high latency and low quality of service. To avoid this disadvantage, edge computing was introduced. The fundamental issue in edge computing is selecting some tasks to be computed in the edge, which is called the offloading problem. In most previous studies, the communication cost between any two tasks is often assumed to be symmetric in different sides and is often ignored within the same side. In this paper, we consider a heterogeneous offloading model, where the communication cost exists everywhere and is asymmetric. With the help of semidefinite relaxation, we give an algorithm for the offloading problem. If the Laplacian matrix with respect to the offloading problem is positive semidefinite, the theoretical guarantee can be proved to be 2 π. The performance of the proposed algorithm is also evaluated via numerical analysis. The experimental results show that the algorithm is very effective. [Display omitted] • Edge computing can avoid long delay between the cloud and users. • A deep theoretical analysis for the offloading problem. • Design a semidefinite relaxation based approximation algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a hierarchical and multilayered computing framework, specifically tailored to analyze the benefits of Named Data Networking (NDN) enabled microservice-centric in-network computations in autonomous vehicular networks. The proposed framework comprises three layers: an autonomous vehicular network layer, an Edge computing layer, and a centralized Cloud.Modifications were made to the vanilla NDN codebase to facilitate communication between the vehicular network layer, Edge computing and Cloud layer, utilizing the C++ Boost Asio library and IP tunneling mechanism. A microservices-based driver assistance application, consisting of various heterogeneous microservices, was emulated in.NET Core and deployed on different Edge computing terminals and the Cloud. Additionally, RESTful APIs have been developed to enable the vehicular network layer and the physical Edge layer to offload microservice-centric computation requests and retrieve the corresponding computational outcomes. The Entity framework is employed to ensure proper tracking and management of these requests. An HTML-based user interface has also been developed for a visual representation of the request pattern. Extensive testbed experiments reveal that the proposed system significantly optimizes bandwidth consumption, reduces latency, and increases the computed results delivery ratio when compared to conventional monolithic systems. [ABSTRACT FROM AUTHOR]

The ongoing realization of a transition towards increased reliance on edge computing is evident. This shift not only underscores the importance of interdependence in knowledge sharing but also contributes to a reduction in adaptation and transmission time through cloud computing directly, along with decreased computational demands compared to edge-to-edge computing. A persistent challenge in knowledge transfer stems from the shift in dependence from high-performance computing systems to compact models, making the adjustment cumbersome. Even with cross-edge knowledge transfer, pre-processing remains essential to facilitate seamless adaptation—a necessity that endures despite the escalating volume of data. Consequently, the system tends to become overburdened with instance filtering instead. To address this issue, we present an optimization for small-to-small inter-edge transfer learning of a comparable size. This is achieved through a self-supervised domain-adaptive protocol, utilizing self-target curriculum-based pseudo-labeling creation, new statistical alignment, and lightweight attention. In the multi-view verification using public visual DA datasets, we demonstrate a remarkable improvement of 73.6 (+4.2 %) in Top-1 performance. Our results consistently outperform other methods, particularly when using the latent feature input technique. This advantage is most noticeable at the 10-th layer for latent feature input. Crucially, our approach incurs the modest increase in computational demands, adding just 100 K-units of parameters and MFLOPS of computational power. Furthermore, through model enhancement via acceleration and compression, we achieve significant compression—up to 6.28 times faster at FP32 and 7.18 times faster at FP16 runtimes—resulting in an increased FPS to 124.9 and 142.9, respectively. This demonstration effectively illustrates the feasibility of advancing inter-edge transfer learning with our approach. [ABSTRACT FROM AUTHOR]

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing. [ABSTRACT FROM AUTHOR]

The advancements in positioning technologies have led to the emergence of various location-based services, resulting in a drastic increase in location-based data generation, producing big-data. Location data are often linked with user privacy, as they can reveal sensitive information such as the places visited by a person. Moreover, most location-based services involve resource-constrained devices, needing lightweight data processing approaches. Due to these reasons, privacy and efficiency have been two of the primary components of location-based data processing. The existing approaches do not study both issues in the same setting. Consequently, current methods fail to provide efficient privacy preservation solutions towards location-based data stream processing. To address these issues, we investigate the effective integration of edge computing, cloud computing and differential privacy for location-based data clustering, which is an essential area in service recommendations (e.g. recommending the closest hotels to a particular location). In the proposed setup, we use local differential privacy to ensure user privacy. Next, we apply edge-based clustering on the differentially private input data using mobile edge devices. Next, the centroids of the clusters are collected at a cloud server to generate final clustering in a privacy-preserving manner. Our experiments show that the proposed approach provides maximum accuracy of 90% on lower privacy budgets (e.g. ɛ = 0.45-0.5). • Location data stream clustering requires advanced computing and storage capabilities. • Edge–cloud interplay can provide a potential solution to this problem. • The privacy of location data is another major issue that limits the effective use of cloud resources. • It is essential to look at both efficiency and privacy of location data stream clustering. • An efficient distributed edge–cloud scenario for location data clustering with differential privacy is proposed. [ABSTRACT FROM AUTHOR]

Recently, great progress has been witnessed in the application of mobile cloud computing in the field of health care such as online medical inquiries. However, due to the limitation of cognitive intelligence, QoE (Quality of Experience) is hampered by two problems, the first of which is that the traffic pressure of the core network cannot well meet the requirements of delay-sensitive emotional services, especially for users with different emergencies, while the second is that current applications cannot provide personalized service for different users. Based on the two problems, we propose an emotion-aware mobile edge computing architecture based on emotional task priority to guide the allocation of edge resources and to provide intelligent and personalized emotional services with higher QoE. Specifically, we first introduce the entities involved in the proposed architecture of emotion-aware mobile edge computing system. Next, we describe our optimal computing resource allocation strategy, including important concepts and a detailed algorithm. Finally, we build a test platform and conduct experiments, which show that the proposed architecture obtains better performance in terms of system utility compared with baseline methods. [ABSTRACT FROM AUTHOR]

Experimental data can aid in gaining insights about a system operation, as well as determining critical aspects of a modeling or simulation process. In this paper, we analyze the data acquired from an extensive experimentation process in a serverless Function as a Service system (based on the open source Apache Openwhisk) that has been deployed across 3 available cloud/edge locations with different system setups. Thus, they can be used to model distribution of functions through multi-location aware scheduling mechanisms. The experiments include different traffic arrival rates, different setups for the FaaS system, as well as different configurations for the hardware and platform used. We analyze the acquired data for the three FaaS system setups and discuss their differences presenting interesting conclusions with relation to transient effects of the system, such as the effect on wait and execution time. We also demonstrate interesting trade-offs with relation to system setup and indicate a number of factors that can affect system performance and should be taken under consideration in modeling attempts of such systems. • Experimental data analysis of 3 different setups of a FaaS system. • Insights into the transient effects of the system. • Considerations in terms of modeling approaches. • Explore interesting conclusions regarding the system setup and its trade-offs • Examine hidden factors influencing performance. [ABSTRACT FROM AUTHOR]

Accurate and fast short-term traffic flow prediction plays a vital role in the construction of future smart cities. To address large bandwidth consumption and high delay in traditional cloud solutions, and improve the accuracy and timeliness of traffic prediction, this paper introduces a novel three-layer Cloud-Edge-IoT traffic flow edge computing architecture, and proposes a short-term Traffic Flow Prediction Method based on Spatial–Temporal Correlation (TFPM-STC), in which Principal Component Analysis (PCA) is adopted for analyzing the intersection correlation, and the Convolution-Gated Recurrent Unit (Conv-GRU) and Bidirectional GRU (Bi-GRU) are used for extracting the spatial–temporal and periodic features of the traffic flow. Experiment results show that compared with existing methods, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of TFPM-STC are reduced by about 16.5 points on average, and there is a significant reduction in training time and prediction time. • A novel traffic flow edge computing architecture is proposed. • A traffic flow prediction method based on spatial–temporal correlation is proposed. • Using principal component analysis method saves training time and prediction time. • Extracting spatial–temporal and periodic feature improves the accuracy of prediction. [ABSTRACT FROM AUTHOR]

With the development of Internet of things (IoT), billions of sensors, actuators and other intelligent terminal devices (ITDs) are connected to the Internet. In this situation, traditional cloud computing models are not fully suitable because these ITDs generate too much data which may cause severe network congestion. As a result, edge computing is proposed to solve this problem, which can provide computation and storage services for ITDs with a distributed model on the edge networks. While, this novel edge paradigm brings emerged problems of restricted computation, limited storage and unstable network, as the storage is distributed typically as a key role. So, in this paper, it proposes a distributed multi-level storage (DMLS) model with a multiple-factors least frequently used (mLFU) algorithm to solve the problem. In this model, storage levels are composed of ITDs on the edge, so when the storage space of a node is not enough, the mLFU is used to remove a part of data from the current nodes and upload the date to the upper storage levels. To reduce the impact of data loss caused by unstable edge networks, a factor of importance is introduced in the mLFU, which means that data with high importance is uploaded to the upper levels first. Experiments show that the hit rate of the mLFU is stabilized at 74% that is almost equal to the typical LFU, while the important data loss rate is about 35% lower than the LFU and the random replacement algorithm. [ABSTRACT FROM AUTHOR]

With the recent progress of science and the development of society, the development of artificial intelligence technology and robot theory have become increasingly mature. Compared to the development of Internet, robot industry is a branch of traditional industry, and the popularization of social robots has always been the bottleneck. Robot intelligence is an important part of artificial intelligence, which brings new challenges to human production and life for it usually needs cloud computing system for the systematic implementations. Compared to cloud computing, edge computing brings powerful computing resources and efficient services to the network edge, which has lower latency, lower bandwidth consumption, higher energy efficiency and better privacy protection. Data envelopment analysis (DEA) enriches theory of production function and its application technology in microeconomics. Meanwhile, it has advantages in avoiding subjective factors, simplifying algorithm and reducing errors. Based on these novel technologies, this paper analyzes development trend of intelligent social robot with specialzed analysis of the electric engineering and data analytic perspectives. The sample expeirment has shown rapid development. Compared with the other state-of-the-art methodologies, the proposed framework is efficient. [ABSTRACT FROM AUTHOR]

Sensor cloud provides a new way for the owners of different WSNs (Wireless Sensor Networks) to share their data to other parties (users/organizations). The edge-computing-assisted sensor-cloud systems (EC-assisted SCSs) can even supply real-time services for different parties. However, both the edge servers and the cloud servers in the systems are prone to be attacked in insecure network environments, since they are the bridges connecting both the end users and the WSNs in EC-assisted SCSs and attacking the servers can bring much more benefits for the attackers than just compromising some sensor nodes. In this paper, the problem of secure Top- k query in EC-assisted SCSs was studied, considering that non-trusted edge servers and non-trusted cloud servers exist in the systems, and a novel scheme was proposed to solve the problem. To achieve privacy preservation of the sensed data items and their scores, a secure key distribution protocol was designed, and some data-processing or integrity verification algorithms were also developed in the proposed scheme to preserve the data integrity of the query results. Thorough security analyses and extensive simulations show that the proposed scheme is efficient and secure, and it is perfectly fit for Top- k query in EC-assisted SCSs. [ABSTRACT FROM AUTHOR]

Edge computing has received wide attention due to the benefits it brings compared to the existing cloud computing model. With the emergence of this new paradigm, content service providers (CSPs) can extend their existing cloud-based CDN (content delivery network) to edge environments to achieve cost-effectiveness. Specifically, for those regions with many access requests, edge services can be rented for saving bandwidth costs. However, the cost of renting edge services is not negligible. If edge services are rented in places with few access requests, high renting costs will cause economic losses instead. Therefore, CSPs need to make rental decisions dynamically without any knowledge of the future. For solving this problem, we summarize it as an online edge service renting problem, and propose a randomized online edge-renting algorithm for CSPs to rent edge services cost-effectively. Through theoretical analysis, we prove that the cost of our algorithm would not exceed e / (e − 1 + α) times compared to the corresponding optimal algorithm, where α is the bandwidth discount of edge service compared to the cloud. Lastly, by verifying through experiments, the results show that our online algorithm can help CSPs save 11.9% of the total cost in edge service renting. [ABSTRACT FROM AUTHOR]

• Facilitating the Monitoring and Management of Structural Health in Civil Infrastructures. • An Edge/Fog/Cloud architecture for Structural Health Monitoring. • Unified interface to configure all the necessary steps in an SHM deployment. • Lightweight virtualization of components with containers for a portable distribution and scalability. • Performance analysis in a civil infrastructure with Fog and Cloud deployments. Structural Health Monitoring (SHM) is nowadays a requirement for civil infrastructures like tunnels, bridges and viaducts. With the advances provided by the Internet of Things (IoT) in recent years in areas such as wireless communications, miniaturisation and application protocols, this paradigm is spreading to the wider society to ensure appropriate protection of critical infrastructures. Both fog and edge computing have also strongly contributed to SHM and the IoT by bringing computing as close as possible to where data is produced, thereby reducing the latency response with respect to traditional cloud integrations. In this paper, an Edge/Fog/Cloud architecture for SHM in civil infrastructures is presented. The main goal of this architecture is to create and provide a flexible framework involving all the required components for the management, monitoring and deployment of SHM solutions, enabling high availability and easy distribution of the components over the architecture. The architecture has been evaluated as an alternative to the real deployment in a tunnel of a cloud architecture showing the benefits of adopting an Edge/Fog/Cloud hierarchy. [ABSTRACT FROM AUTHOR]

Vehicular networking allows vehicles with sensing capabilities to carry out communication from vehicle to vehicle or with accessible roadside units. To attain effective communication in vehicular networking, we have proposed a hybrid technological solution in terms of vehicular edge computing (VEC) utilizing resources, cloud and edge server environment. The proposed architecture supports a frequent communication between a vehicle and a legitimate edge server without any trusted party communication. Both security and privacy are two key challenges in edge based vehicular communications due to the adoption of a real-time environment that is required to respond to hundreds of moving vehicles. This paper presents an edge computing-based framework for vehicular communication to ensure security, message integrity, and privacy. The presented framework attains all security attributes in the presence of active as well as passive adversaries. The formal security proof ensures its correctness and robustness. The analysis indicates that it is secure and efficient, being based on new edge computing framework as compared to general cloud computing framework. [ABSTRACT FROM AUTHOR]

The cloud–edge–client hierarchical computing architecture takes into account the dual advantages of cloud and edge computing. However, in realistic scenarios, the explosive increase of distributed data promotes the widespread application of the cloud–edge–client architecture, but also brings a variety of security threats. Relying on the advantages of federated learning technology, this has led to a growing interest in federated learning as the intersection of machine learning and cloud–edge–client fields. However, recent studies found that federated learning is potentially vulnerable to data poisoning attacks. Existing explorations of federated learning poisoning attacks and defense techniques in the cloud–edge–client environment are largely attack capabilities-specific and privacy protection-based : they are designed but fail to consider the characteristics of federated learning vulnerability and the difference in attack intensity, and lack flexibility. Yet few different attack capabilities and general defense strategies have been developed. To address these concerns, we introduce federated cloud–edge–client learning collaborative framework. Based on this architecture, a client-launched multi-label flipping attack method is discussed, which exhibits different attack capabilities. Further, we propose SPAD, a Server-based Parameter Adaptive Defense method in response to different attack intensities, to identify malicious attackers and benign participants, thereby improving the security of the federated learning image classification task. The results on image classification tasks demonstrate that in cloud–edge–client scenario, the multi-label flipping attack method presents different degrees of poisoning effect, and SPAD method has strong feasibility and effectiveness against poisoning attacks, which verify the significant advantages of the proposed method. • We explore the different attack intensities caused by MLFA methods under different settings, which are superior to non-MLFA. • Based on the FCECL collaborative framework, we explore the threats brought by the vulnerability of distributed learning. • We further propose a defense method, SPAD, which can adapt to poisoning attacks of different intensities. • Extensive experimental results demonstrate that MLFA and SPAD methods show outstanding advantages. [ABSTRACT FROM AUTHOR]

A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network may affect the overall network performance and safety. These vehicles may behave intelligently to avoid detection. To effectively control and monitor such security threats, an efficient Trust Management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers which is the major focus of this work. We propose a hybrid solution, which integrates Edge Computing and Multi-agent modeling in a Trust Management system for vehicular networks. The proposed solution also aims to overcome the limitations of the two commonly utilized approaches in this context: cloud computing and Peer-to-Peer (P2P) networking. Our framework has a set of features that make it an efficient platform to address the major security challenges in vehicular networks including latency, scalability, uncertainty, data accessibility, and malicious behavior detection. Performance of the approach is evaluated by simulating a realistic environment. Experimental results show that the proposed approach outperforms similar approaches from literature for various performance indicators. [ABSTRACT FROM AUTHOR]

The osmotic computing paradigm sets out the principles and algorithms for simplifying the deployment of Internet of Things (IoT) applications in integrated edge-cloud environments. Various existing simulation frameworks can be used to support integration of cloud and edge computing environments. However, none of these can directly support an osmotic computing environment due to the complexity of IoT applications and heterogeneity of integrated edge-cloud environments. Osmotic computing suggests the migration of workload to/from a cloud data center to edge devices, based on performance and security trigger events. We propose 'IoTSim-Osmosis– a simulation framework to support the testing and validation of osmotic computing applications. In particular, our detailed related work analysis demonstrates that IoTSim-Osmosis is the first simulation framework to enable unified modeling and simulation of complex IoT applications over heterogeneous edge-cloud environments. IoTSim-Osmosis is demonstrated using an electricity management and billing application case study, for benchmarking various run-time QoS parameters, such as IoT battery use, execution time, network transmission time and consumed energy. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment. [ABSTRACT FROM AUTHOR]

Deep neural networks are the state of the art for various machine learning problems dealing with large amounts of rich sensor data. It is often desirable to evaluate these models on edge devices instead of relying on cloud computing. In this paper, we perform traffic counting using surveillance cameras. Edge computing is required as only aggregated counts should leave the device and not the privacy sensitive video frames. Unfortunately, only small object detection models are suited for edge devices which results in sub-optimal performance. We introduce location specific models that are each trained for one specific camera. The model does not need to generalize to other locations. We show that smaller specialized models can outperform large general purpose models. We propose an automated way to train these small models without human intervention. We experimentally show that we can achieve a similar counting accuracy with 5x fewer parameters than state-of-the-art techniques. • We obtain high traffic counting accuracy in the real world using lightweight models. • We introduce location specific models that process the data of a single camera. • Since the models only need to work well for a single camera, they can be very small. • The location specific models can be trained automatically without human intervention. [ABSTRACT FROM AUTHOR]

To supervise massive generated data by the Internet of Things (IoT) efficiently, we face two issues that should be addressed which are: (1) heterogeneity or satisfying diversity among IoT devices, and (2) privacy-preserving or preventing unintentional disclosure of sensitive data. Through observation, we found that existing solutions apply one common privacy-preserving rule for all devices while they address the heterogeneity issue separately that lead to unappealing performance. In this paper, we propose a framework for addressing the heterogeneity issue and privacy-preserving of IoT devices at the network edge using a novel proposed ontology data model. Besides, it leverages the proposed ontology to obtain a privacy-preserving method by frequently changing the privacy-preserving behaviors of IoT devices. Through simulation, we show that our solution overhead is less than 9 percent in the worst situation so that it is affordable to most IoT devices in one of its applications that is smart city. [ABSTRACT FROM AUTHOR]

The purposeful acts of bioterrorism and the emergence of new pathogens call for developing efficient biosurveillance systems that are capable of detecting health security threats and localizing their origins. For this sake, several biosurveillance systems have been developed and deployed in different regions around the world. The objectives of these systems are to efficiently detect threats and effectively manage responses. Although modern biosurveillance systems start to leverage recent advances in internet-of-things and communication technologies to detect natural biological outbreaks, it is not evident whether these systems can be generalized to detect threats related to purposeful biological attacks. As opposed to natural disease outbreaks, biological attacks can be triggered concurrently at different locations and in vital areas that are traversed by a large number of humans. In this paper, we propose an efficient framework to detect multiple concurrent threats and localize their origins based on real-time information and mobile-edge computing support. The proposed framework is organized into a hierarchical architecture. At the micro-level, monitored humans are equipped with wearable sensors that periodically capture the monitored human vital signs. To process the captured information efficiently, the monitored environment is partitioned into cells. Each cell is assigned a mobile-edge computing (MEC) component that is responsible for processing the monitoring information within its cell. The MEC components then send the processed information to a super component for final processing and to detect possible threats. The experimental results show that the proposed framework accurately detects multiple concurrent health security threats and localize their origins. [ABSTRACT FROM AUTHOR]

Due to technological advancement and deployment of handheld devices, mobile computing devices have gained the user attention to save resource usage, service access, and application use. In practice, it can be deployed widely to experience massive data storage and less computation cost. However, wireless channel access is vulnerable and insecure to prevent various potential attacks such as replay, redirection, etc. As a consequence, a seamless secure anonymous authentication scheme (S-SAAS) is proposed to create a secure session in cloud-based mobile edge computing. Since IoT, cloud computing, big-data and mobile technologies are converged to permit the group-based communication systems, it is highly demanded to strengthen the authentication process in order to reduce the security risk and vulnerabilities. Moreover, the experimental analysis proves that the proposed S-SAAS scheme consumes less signaling congestion and routing control overhead to achieve better service connection rate. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC) offers cloud computing capabilities at the edge of the network. Growing demand for low-latency services requires Service Function Chains (SFCs) to be scaled up beyond MEC network to core network. To adapt to network dynamics and provide low-latency services, being able to migrate SFCs when needed is of paramount importance. However, migration of SFCs among edge and core networks such that average latency is optimized as well as considering resource consumption is an intractable challenge because improper migration of Virtual Network Functions (VNFs) results in failure of meeting the requirements of network policies. In this paper, we investigate SFCs in edge-core networks and model the Latency-aware Edge-Core SFCs Migration problem based on open Jackson networks. Two SFC migration algorithms, i.e., Profit-driven Heuristic Search (PHS) and Average Utilization Based (AUB), are proposed to efficiently optimize average latency of all SFCs in edge-core networks. Extensive evaluation results show that PHS optimizes average latency by 19.5%, while AUB can further reduce average latency by up to 36.9% by allowing a marginally higher number of VNF migrations. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence techniques have been adopted for many real-time applications. The Internet of Things (IoT) network comprises many sensing devices and physical objects for information gathering and further transmission. In addition to being sent to the receiving nodes, the collected data also needs to be received promptly. Also, many solutions have been proposed for IoT-based embedded systems using edge computing but they are not fully protected against unidentified communication threats. In such circumstances, such systems decrease the trust ratio, and communication performance is compromised. In this research, we describe an optimization model based on IoT-edged technology that incorporates cloud computational intelligence. Furthermore, edge nodes employ artificial intelligence algorithms to provide the optimal outcome for selecting trustworthy forwarded data and lengthen the connected time for smart devices. Firstly, the edge devices extract useful information from the IoT nodes, and accordingly, it provides a decision module based on optimization computing. Secondly, utilizing cryptographic approaches, edge technology secures the multi-layers of the IoT system and ensures data privacy with integrity. Finally, the proposed model is tested and verified for its performance than other related studies in terms of energy consumption, packet delivery ratio, and data delay. [ABSTRACT FROM AUTHOR]

Abstract Cloud based architectures present many challenges, ranging from poorly dimensioned hardware to lengthy wait times and resource balancing issues. Most of these problems can be mitigated by using proper performance evaluation techniques, depending on the accuracy of the model abstracting the real system. The adoption of a formalism over another to describe the considered infrastructure plays a crucial role in achieving this goal. In this sense, multiformalism proves itself to be a powerful modeling approach describing each system component according to the most suitable representation. This paper presents a novel modeling method oriented to the prediction of cloud architectures performance, suitable for joining the advantages of high level modeling abstractions and of the detail of a specialized simulator. Generalized Stochastic Petri Nets are used to describe the workload and the behavior of users and applications, while Cloudsim, a well known cloud infrastructure simulator, is adopted for the cloud part. A case study of a simplified Edge Computing application is presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. However, due to the relatively long distance between remote cloud and end nodes, cloud computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this paper, we propose a novel trust evaluation model based on the trust transitivity on a chain assisted by mobile edge nodes, which is used to ensure the reliability of nodes in the Internet of Things and prevent malicious attacks. The mobile edge nodes offer a new solution to solve the above problems with relatively strong computing and storage abilities. Firstly, we design calculation approaches to different trust chains to measure their trust degrees. Secondly, we propose an improved Dijkstra's algorithm for collecting trust information of sensor nodes by mobile edge nodes. Finally, the experimental results show that our trust model based on mobile edge nodes can evaluate sensor nodes more precisely and enhance the security on the Internet of Things. • An efficient trust chain evaluation model assisted by mobile edge nodes is proposed. • The model provides calculation methods of the value of the different trust chains. • Trust information of sensor nodes are collected by an improved Dijkstra algorithm. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) enables smart mobile devices to handle computationally intensive tasks with limited resources. By offloading tasks to the edge of the network closer to mobile devices, mobile devices can provide users with intelligent services with shorter latency and lower energy consumption than cloud computing. while mobile edge computing is superior in terms of reliability and low latency, it is still not a complete replacement for cloud computing. In order to make full use of the large amount of network resources of cloud computing as well as to take advantage of parallel computing, this paper investigates the partial offloading of multiple mobile devices in the cloud-side collaboration scenario. Through ​​game theory, we first obtain the edge-to-terminal optimal offloading decision, then find the cloud-edge optimal offloading decisions for all terminal devices that choose to offload to the edge, and finally reach the goal of optimal offloading on the three sides of the cloud-edge-terminal. Based on this idea, we propose a two-stage computing offloading algorithm (TSGTA). The whole algorithm is divided into two stages, and both stages are designed to offload algorithms based on game theory. The simulation results show that under the time-delay constraint, this method has an average improvement of 32% compared with the traditional method. [ABSTRACT FROM AUTHOR]

A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous Parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost. [ABSTRACT FROM AUTHOR]

The user demand increment and resource restrictions in IoT can provide efficient services without considering the service cost and service quality. Nowadays various computing technologies like fog, cloud, and edge computing and their confluence of IoT are required. The standard IoT services can be transformed into composite services via the incorporation of cloud computing with IoT. These IoT composite services are not suitable for various QoS scenarios which results in performance degradation and exhibited real-time adaptive sensing issues. Therefore, a novel Deep Q-learning Network-based Hybrid Enhanced Chimp Artificial Hummingbird (DQN-Hybrid ECAH) approach is proposed to overcome the state-of-art approach problems. The DQN-Hybrid ECAH approach represents a significant novelty in the field of IoT, as it utilizes a combination of advanced techniques to overcome the challenges posed by composite services in IoT. In particular, the use of deep Q-learning networks allows for the optimization of composite service quality, while the enhanced chimp artificial hummingbird techniques ensure that the approach is both efficient and effective. From the experimental evaluation, it is evident that the proposed approach is superior to other state-of-the-art methods. Specifically, the packet delivery ratio (PDF) and throughput rate obtained were high, with minimum jitter and average end-to-end delay. This demonstrates the effectiveness of the DQN-Hybrid ECAH approach in enhancing composite service quality in IoT applications. Overall, the proposed DQN-Hybrid ECAH approach represents a significant contribution to the field of IoT, providing a novel solution to the challenges associated with composite services in IoT. The approach has the potential to significantly improve the performance of IoT applications, while also reducing the real-time adaptive sensing issues that are often encountered in this context. [ABSTRACT FROM AUTHOR]

Thanks to the explosive evolution of 6G, Artificial intelligence, Blockchain, Cloud computing, Data, and Edge computing (6G+ABCDE), it is possible to make Intelligent Transportation Systems (ITS) more autonomous, intelligent, and efficient. Conventional ITS technologies have been applied and deployed worldwide, and autonomous vehicles have also made a qualitative leap. The revolution of ITS magnitude is on the horizon, and the singularity may happen in the 6G Autonomous Intelligent Transportation Systems (6G-AITS). We indicate that the human-centric transportation system will still be the key to 6G-AITS. The 6G-AITS is a three-dimensional intelligent system, which is driven by data and intelligence, and characterized by sharing and collaboration, thereby achieving the service-oriented demand response. To support this vision, we explore the 6G-AITS changes in terms of system architecture, communication, and resource allocation, as well as its key features. We elaborate on the mechanism and three applications of 6G-AITS. Furthermore, we explore the challenges of 6G-AITS and consider some issues that go beyond technologies that could affect the development of 6G-AITS. Key objective of this work is to provide an exploration of the imperative requirements to develop 6G-AITS and guidance for the research and development of 6G-AITS. [ABSTRACT FROM AUTHOR]

Thermal error compensation is one of the most research-oriented topics in manufacturing with rising importance in the industry. This paper presents an innovative Industry 4.0 application of thermal error compensation for precision engineering. A federated learning-based thermal error compensation approach running in the cloud is applied to two machine tools, one located at ETH Zürich, and another one at TU Wien. Although environmental conditions and thermal error behaviour of both machines differ, the implemented knowledge transfer across machines is a viable compensation strategy, albeit with limited precision. A detailed comparison of the two machines of the same type under the same load conditions shows foreseeable similarities in behaviour, but also clear differences due to the different configurations and lifetime status. The cloud-based compensation reduced the crucial thermal errors in the best case of both machine tools by more than 80% under critical conditions. • A new cloud-based thermal error compensation approach is introduced. • Federated learning in terms of cloud and edge computing for reaching a high level of safety and security is presented. • Detailed analysis of the same type of machine tool with different wear and slightly different configurations. • The transferability of the model calibration period (learning phase) is reached by model transfer (transfer learning). • Compensation results already reduce the thermal error by up to 80 %. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices have gained more prevalence in ambient assisted living (AAL) systems. Reliability of AAL systems is critical especially in assuring the safety and well-being of elderly people. Runtime verification (RV) is described as checking whether the observed behavior of a system conforms to its expected behavior. RV techniques generally involve heavy formal methods; thus, it is poorly utilized in the industry. Therefore, we propose a democratization of RV for IoT systems by presenting a model-based testing (MBT) approach. To enable modeling expected behaviors of an IoT system, we first describe an extension to a UML profile. Then, we capture the expected behavior of an interaction that is modeled on a Sequence Diagram (SD). Later, the expected behaviors are translated into runtime monitor statements expressed in Event-Processing Language (EPL), which are executed at the edge of the IoT network. We further demonstrate our contributions on a sample AAL system. [ABSTRACT FROM AUTHOR]

The rapid development of cloud computing and the Internet of Things (IoT) have facilitated near real-time optimization of the group distributed manufacturing systems. Currently, the most common technique to accomplish near-real-time optimization is cloud–edge cooperation for offloading optimization tasks. The tasks are partially offloaded to the cloud to be completed, and the remaining are kept at the edge. Due to the complexity of task offloading, such as capacity restrictions of cloud and edge computing resources, or task deadlines, unbalanced or insufficient tasks are offloaded to cloud and edge, causing time delay. To address the imbalance and insufficiency in the task offloading process, a mixed-integer programming model was developed to reduce the latency of task calculation. The task offloading problem is decomposed into two sub-problems: 1) Defining priorities for the tasks in near real-time. 2) Determining if the task is offloaded to the cloud. A multi-agent deep reinforcement learning with attention mechanism (MaDRLAM) framework is proposed to solve the two-step decision problem. The MaDRLAM framework consists of two agents, and each agent corresponds to a sub-problem. Each agent comprises an encoder and a decoder, and the two agents cooperate in devising an offloading strategy for the tasks. The Encoder and Decoder built for each agent are based on the Transformer structure. Unlike the traditional Transformer, we added the Pointer networks to the Transformer to solve the proposed decision problem. Besides, an improved multi-actor and single-critic strategy based on the REINFORCE algorithm is designed to train the proposed MaDRLAM. Finally, Extensive computational experiments are conducted on instances with a varying number of tasks, different task data sizes, and different cloud computing capacities. Computational results show that the proposed framework can find a solution with a GAP value of less than 1% within 1 s for each instance. The proposed framework is competitive in both solution accuracy and solution time compared with other offloading strategies. [ABSTRACT FROM AUTHOR]

• First comprehensive scientific meta-analysis of the overlap of the attacks in the Cloud, Edge and IoT. • First review to construct an original catalogue of the identified attacks to all the three paradigms. • Concise threat catalogue as well as more universal solution which could contribute to ensuring the security of the whole ecosystem. • Suggestions of a number of possible solutions and countermeasures which, when applied, may contribute to safer Cloud, Edge, and IoT. Cloud computing, edge computing, and Internet-of-Things - these new Internet concepts have already irreversibly changed and influenced people's lives. The security of the three patterns must not be taken for granted, though. Similar to other emerging technologies, the cloud, edge and IoT are under constant attack. In the case of these paradigms, the potential breaches may paralyse transportation systems, business sectors or even turn out to be life-threatening. So far, there has not yet been a scientific meta-analysis of the overlap of the attacks in the cloud, edge and IoT; one that would result in constructing a concise threat catalogue as well as seeking a more universal solution which could contribute to ensuring the security of the whole ecosystem. In order to fill this substantial gap, this systematic review gathers, analyses and extracts data from a substantial number of quality scientific literature papers pertaining to the security issues of the cloud, edge and IoT, and compares the attacks in each concept in order to find the areas of overlap. Another contribution of this paper is that it constructs a catalogue of the identified attacks. In addition to this, it also suggests a number of possible solutions and countermeasures which, when applied, may contribute to safer cloud, edge and IoT alike. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become the forefront of bridging different technologies together. It brings rise to online computational services that make mundane tasks convenient. However, the volume of devices connecting to the network started to increase. In turn, services that thrived on centralized storage are being strained and overloaded. As applications and software advances, processing and computational power become a concern to technology companies. With data risks and large numbers of connected devices, cloud computing has become outdated. Devices are forced to commit unnecessary expenses to stay relevant in the market due to the increase in software complexity. This need for change resulted in the introduction of edge computing. Edge computing distributes the computational strain between the server and the devices. This contribution allows the cloud to accommodate more users and devices are no longer in need to make significant changes to their design every so often. Many real-time applications have evolved to require high amounts of processing power to execute. For example, sound classification comes with massive computational needs due to its affiliation with neural networks and deep learning. This paper aims to create a feasible and deployable real-time sound classification system. There were three configurations tested in this paper. The results of our experiments show that cloud computing and edge computing alone cannot cater to a technological market that is exponentially growing in size and complexity. However, the same results show promise in finding optimal configurations in terms of a combination of end device power consumption, application runtime and server latency to systems instead of focusing on a single model. Overall, it is better to take into consideration the strengths and weaknesses of each computing architecture. In finding a reasonable configuration balance, lower power consumption in end devices and lesser computational strain on cloud servers is a must. [ABSTRACT FROM AUTHOR]