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Abstract In the era of industry 4.0 and the widespread use of digital devices, the number of cyber attacks poses an escalating and diverse threat, jeopardizing users' online activities. Intrusion detection systems (IDS) emerge as pivotal solutions, playing a crucial role in detecting anomalous signals within network systems. To counter novel attack patterns, IDS systems require periodic rule updates for effective identification of unusual signals. Typically, these policies are updated based on rule‐based or deep learning algorithms to enhance detection performance. However, the insufficient number of labeled samples remains a challenge for real‐world deployment. In this article, an automated labeling method is presented that has shown high effectiveness, requiring minimal hardware resources, and applicable to IDS systems. Additionally, the approach utilizes transfer learning combined with attention mechanisms to boost the efficiency of abnormal signal detection. The results from the approach are compared with those of a reference model, illustrating an overall improvement of nearly 10% in our model's performance compared to the reference model. This underscores the effectiveness of automating rule adjustments for IDS, contributing significantly to reducing associated financial costs. The research addresses the challenges in deploying IDS in real‐world scenarios and provides a valuable contribution to enhancing cyber threat detection capabilities. A preprint has previously been published [11].

Cloud computing has become the cornerstone of modern technology, propelling industries to unprecedented heights with its remarkable and recent advances. However, the fundamental challenge for cloud service providers is real-time workload prediction and management for optimal resource allocation. Cloud workloads are characterized by their heterogeneous, unpredictable, and fluctuating nature, making this task even more challenging. As a result of the remarkable achievements of deep learning (DL) algorithms across diverse fields, scholars have begun to embrace this approach to addressing such challenges. It has become the defacto standard for cloud workload prediction. Unfortunately, DL algorithms have been widely recognized for their vulnerability to adversarial examples, which poses a significant challenge to DL-based forecasting models. In this study, we utilize established white-box adversarial attack generation methods from the field of computer vision to construct adversarial cloud workload examples for four cutting-edge deep learning regression models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Network (1D-CNN) and attention-based models. We evaluate our study with three widely recognized cloud benchmark datasets: Google trace, Alibaba trace, and Bitbrain. The findings of our analysis unequivocally indicate that DL-based cloud workload forecasting models are highly vulnerable to adversarial attacks. To the best of our knowledge, we are the first to conduct systematic research exploring the vulnerability of DL-based models for workload forecasting in the cloud data center, highlighting the inherent hazards to both security and cost-effectiveness in cloud data centers. By raising awareness of these vulnerabilities, we advocate the urgent development of robust defensive mechanisms to enhance the security of cloud workload forecasting in a constantly evolving technical landscape.

The identification of suitable feature subsets from High-Dimensional Low-Sample-Size (HDLSS) data is of paramount importance because this dataset often contains numerous redundant and irrelevant features, leading to poor classification performance. However, the selection of an optimal feature subset from a vast feature space creates a significant computational challenge. In the domain of HDLSS data, conventional feature selection methods often face challenges in achieving a balance between reducing the number of features and preserving high classification accuracy. Addressing these issues, the study introduces an effective framework that employs a filter and wrapper-based strategy specifically designed to address the classification challenges inherent in HDLSS data. The framework adopts a multi-step approach where ensemble feature selection integrates five filter ranking approaches: Chi-square ( $\chi ^{2}$ ), Gini index (GI), F-score, Mutual Information (MI), and Symmetric uncertainty (SU) to identify the top-ranking features. In the subsequent stage, a wrapper-based search method is utilized, which employs the Differential Evaluation (DE) metaheuristic algorithm as the search strategy. The fitness of feature subsets during this search is assessed based on a weighted combination of the error rate of the Support Vector Machine (SVM) classifier and the ratio of feature cardinality. The datasets, after undergoing dimensionality reduction, are then utilized to construct classification models using SVM, K-Nearest Neighbors (KNN), and Logistic Regression (LR). The approach was evaluated on 13 HDLSS datasets to assess its efficacy in selecting appropriate feature subsets and improving Classification Accuracy (ACC) analog with Area Under the Curve (AUC). Results show that the proposed ensemble with wrapper-based approach produces a smaller number of features (ranging between 2 and 9 for all datasets), while maintaining a commendable average AUC and ACC (between 98% and 100%). The comparative analysis reveals that the proposed method surpasses both ensemble feature selection and non-feature selection approaches in terms of feature reduction and ACC. Additionally, when compared to various other state-of-the-art methods, this approach demonstrates commendable performance.

One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this objective, we examine a hybrid multi-access scheme inside the finite blocklength (FBL) regime. This system combines the benefits of non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes with the aim of fulfilling the objectives of future wireless communication networks. In addition, a reconfigurable intelligent surface (RIS) is utilized to facilitate the establishment of the downlink transmission between the base station and mobile devices in situations when impediments impede their direct communication linkages. This paper aims to minimize the worst-case decoding-error probability for all mobile users by jointly optimizing transmission power, transmitting beamforming, blocklength, RIS reflection, and user pairing. To deal with the coupled variables in the formulated mixed-integer non-convex optimization problem, we decompose it into three sub-problems, namely, 1) decoding order determination problem, 2) joint transmission power, receiving beamforming, RIS reflection, and blocklength optimization problem, and 3) optimal user pairing problem. Then, we provide the sequential convex approximation (SCA) and semidefinite relaxation (SDR)-based algorithms as potential solutions for iteratively addressing the deconstructed first two sub-problems at a fixed random user pairing. In addition, the Hungarian matching approach is employed to address the challenge of optimizing user pairing. In conclusion, we undertake a comprehensive simulation, which reveals the advantageous qualities of the proposed algorithm and its superior performance compared to existing benchmark methods.

Edge computing has emerged as a promising computing paradigm that enables real-time data processing and analysis closer to the data source and boosts decision-making applications in a safe manner. On the other hand, the microservice is a new type of architecture that can be dynamically deployed, migrating across edge clouds on demand. Therefore, the combination of these two technologies can provide numerous benefits, including improved performance, reduced latency, and better resource utilization. In this paper, we present a thorough analysis of state-of-the-art research on the use of microservices in edge computing environments. We take into consideration several distinct microservice research directions, including coordination, orchestration, repositories, scheduling, autoscaling, deployment, resource management, and different security issues. Furthermore, we explore the potential applications of microservices in edge computing across various domains. Finally, the unsolved research issues and future directions of emerging trends in this area are also discussed.

Blockchain (BC) is a burgeoning technology that has emerged as a promising solution to peer-to-peer communication security and privacy challenges. As a revolutionary technology, blockchain has drawn the attention of academics and researchers. Cryptocurrencies have already effectively utilized BC technology. Many researchers have sought to implement this technique in different sectors, including the Internet of Things. To store and manage IoT data, we present in this paper a lightweight BC-based architecture with a modified raft algorithm-based consensus protocol. We designed a Device Agent that executes a novel registration procedure to connect IoT devices to the blockchain. We implemented the framework on Docker using the Go programming language. We have simulated the framework on a Linux environment hosted in the cloud. We have conducted a detailed performance analysis using a variety of measures. The results demonstrate that our suggested solution is suitable for facilitating the management of IoT data with increased security and privacy. In terms of throughput and block generation time, the results indicate that our solution might be 40% to 45% faster than the existing blockchain.

The development of salient object detection is crucial in ubiquitous applications. Existing state-of-the-art models tend to have complex designs and a significant number of parameters, prioritizing performance improvement over efficiency. Hence, there pose significant challenges to deploying them in edge devices. The intricacy in these models stems from the complicated encoder-decoder that aims to effectively generate and integrate coarse and semantic features. To address this problem, we introduced EC2Net, an efficient attention-based cross-context network for salient object detection. To start with, we introduce the shallow crossed-context aggregation (SCCA) mechanism to enhance and preserve object boundaries for shallow layers. We introduced a deep cross-context aggregation (DCCA) mechanism to enhance semantic features in deep layers. Subsequently, we introduced the dual cross-fusion module (DCFM) to efficiently merge shallow and deep features. The proposed modules complement each other, enabling EC2Net to accurately detect salient objects with reduced computational overhead. Through experiments on five standard datasets, the proposed method demonstrated competitive performance while utilizing fewer parameters, FLOPS, and memory storage than other resource-intensive models.

Due to increased reliance on technology and cloud-based services, cyber risks are more common. Advanced persistent threats make it difficult to detect attacks, hence Endpoint Detection and Response (EDR) was adopted in 2013. EDR uses a scanning application on each endpoint machine to monitor and capture events and logs. However, EDR is vulnerable to attacks by malware, so a lightweight malware detector is needed. Image-based malware classification is a technique for classifying malware based on its representative image, but previous studies have not been integrated with EDR. This research aims to integrate EDR with an image-based malware classifier. A basic EDR implementation named Deep Ocean Protection System (DOPS) has been developed with two pre-trained models (Mobilenet V2 and Inception V3) fine-tuned with MalImg and BODMAS datasets. The models were evaluated with the DikeDataset and Mobilenet V2 fine-tuned with BODMAS 4.0.0 performed best in terms of loading and prediction time with a high AUC score of 0.8615. Inception V3 fine-tuned with BODMAS 4.0.0 also achieved a remarkable AUC score of 0.9392. These results show the potential of integrating image-based malware detection with EDR.

The sheer unpredictability of content popularity, diversified user preferences and demands, and privacy concerns for data sharing all create hurdles to develop proactive content caching strategies in self-driving cars. Therefore, to address these concerns, we investigate in detail the role of proactive content caching methods in self-driving cars for improving quality-of-experience (QoE) and content retrieval cost in this work. We develop a low-complexity content popularity prediction mechanism in a hierarchical federated setting. In particular, we use a self-attention technique with an LSTM-based prediction mechanism to extract local content popularity patterns in self-driving cars. However, the local contents will not be sufficient to satisfy the passenger’s requirements. Hence, using the popular contents of other self-driving cars will solve the requirement constraint but poses some privacy issues. We use the privacy-preserving decentralized model training framework of Federated Learning (FL) to tackle this issue. Specifically, we deploy the hierarchical Federated Averaging (FedAvg) algorithm on local models obtained from self-driving cars to develop a regional and global content popularity prediction model at RSU and MBS, respectively. Extensive simulations on real-world datasets show the proposed approach improves cache space utilization by maximizing the local cache hit ratio, and further, minimizes the content retrieval cost for self-driving cars as compared with alternative methods.

In recent years, Knowledge Distillation has obtained a significant interest in mobile, edge, and IoT devices due to its ability to transfer knowledge from the large and complex teacher to the lightweight student network. Intuitively, Knowledge Distillation refers to forcing the student to mimic the teacher’s neuron responses to improve the generalization of the student by deploying the distillation losses as the regularization terms. However, the non-linearity of the hidden layers and the high dimensionality of the feature maps make the knowledge transfer a rigorous task. Though numerous methods have been proposed to transfer the teacher’s neuron responses in the form of diverse feature characteristics such as attention, contrastive representation, and so on, to the best of our knowledge, no prior works considered feature-level non-linearity during distillation. In this work, we ask, does feature-level non-linearity-based approaches can improve student performance? For investigating those concerns, we propose a novel knowledge distillation technique called the NeuRes (Neuron’s Responses) via distilling the Sparse Activation Maps (SAMs) to transfer the highly activated Neurons Responses to the student to enhance the representation capability. Proposed NeuRes selects the highly activated neuron responses that produce Sparse Activation Maps (SAMs) while transferring the knowledge based on activation normalization. Our proposed NeuRes also transfers the translation invariant features using auxiliary classifiers and augmented data to improve students’ generalization. The detailed ablation studies and extensive experiments on model compression, transferability, adversarial robustness, and few-shot learning verify that NeuRes outperforms state-of-the-art distillation techniques on the standard benchmark datasets.

Abstract Background subtraction used in object detection, tracking and action recognition is a typical method that separates foreground objects from the background. These applications require accuracy and a complexity reduction technique. Some approaches have been proposed to either increase accuracy or decrease complexity. However, the trade‐off between increasing accuracy and reducing the complexity of background subtraction is a big challenge. To address this issue, a background subtraction‐based real‐time moving object‐detection approach is proposed. The key contribution in authors' proposal is to use a colour image and a novel colour‐gradient blending fused image to achieve accurate background/foreground segmentation. The fused image is a combination of a gradient image and a colour image to correct illumination variations and preserve the edge information. Also, thresholds are adaptively selected based on the dynamic background behaviour to attain a more robust classification system. The proposed model based on real‐time and complex videos from the CD‐2012 and CD‐2014 change detection data sets, and the CMD data set is evaluated. Experimental results indicate that authors' method processes around 43 frames per second and requires six bytes of memory per pixel, which is noticeably more efficient and less complex than other background subtraction methods.

Caching popular content at the network edge, such as roadside units (RSUs), is a promising solution that enhances the user’s quality-of-experience (QoE) and reduces network traffic. In this regard, the most challenging issue is to correctly predict the future popularity of contents and effectively store them in the cache of edge nodes. Thus, in this paper, we propose a distributed proactive caching scheme at the edge to optimize the content retrieval cost and improve the QoE of the mobile users. This proactive content caching scheme, namely Distributed Collaborative Learning (DCoL), is a non-parametric content popularity prediction mechanism in a distributed setting. Next, we show the advantage of DCoL as two folds: (i) it leverages distributed content popularity information to develop local content caching strategy, and (ii) it exploits the regional database using the long short-term memory (LSTM)-based prediction model to capture the dependency between requested contents. Simulation results using real datasets demonstrate that our scheme yields 8.9% and 18% gains, respectively, in terms of the cache hit efficiency and content retrieval cost, compared with a competitive centralized baseline, and outperforms other traditional caching strategies.

Multi-access edge computing (MEC) can improve the users' computational capacity and battery life by moving computing services to the network edge. In addition, data-content caching on a MEC server improves the user quality of experience and decreases the backhaul network congestion. Moreover, non-orthogonal multiple access (NOMA) has recently been implemented to increase network throughput and capacity. Combining these techniques can improve the user performance and benefit the network. This paper investigates a combined computational offloading and data-content caching problem for NOMA-MEC systems. The aim was to achieve the minimum total completion latency of all users by jointly optimizing the offloading decision, caching strategy, computational resource, and power allocation. This satisfies the constraints within the scope of the potential violation for energy consumption, offloading decision, and the computation and storage capacity of the MEC server. The formulated problem is a mixed-integer non-linear programming and a non-convex problem. To solve this challenging problem, a block successive upper-bound minimization method was implemented to obtain efficient solutions. Numerous simulation results were presented to demonstrate the convergence and efficacy of the proposed algorithm. Compared with other schemes of all-offloading, local-only, and equal resources, our proposed algorithm can approximately reduce the total completion latency by 17.68%, 26.02%, and 70.98%.

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.

The moving object detection refers to the detection of physical moving objects from a video, which is applied in video surveillance, object recognition, object counting, human-computer interaction, and so on. Moreover, nowadays, real-time moving object detection is used as services in the cloud, edge, and fog computing. However, the existing methods do not meet the trade-off between accuracy and complexity. To address these issues, we present a background subtraction-based moving object detection method, called Fast-D. In this paper, we look at the `non-smoothing color feature' to make the moving object detection more robust in real-time. Each color feature is given equal significance during the classification of a pixel. Background model and threshold are initialized for each pixel. And then, the background model and threshold are updated dynamically when there are changes in the background of the video. Adaptive post-processing is considered to discard salt and pepper noise and fill holes in the detected moving object silhouettes. The evaluation of our proposed method on four complex datasets exhibits the significance.

Deep learning-based action recognition in videos has obtained much attention because of achieving remarkable performance in diverse applications. However, due to the heterogeneous background and noisy spatio-temporal cues, extracting highly discriminative features is still quite challenging. To deal with this problem, numerous methods have been published based on the attention mechanism and skeleton modality. Instead of focusing on data pre-processing, we shed light on the feature map and concentrate on extracting highly discriminative features. First, we introduce Batch-wise Entropy Supervised Stream (BESS) to extend feature discrimination similar to the uncertainty of the corresponding batch. Secondly, to obtain a more generalized model, we propose a Stream to Harmonize the feature discrimination by Augmenting both Features (HAFS) of ResNext101 and BESS. These two streams are hallucinated by the distillation and feature fusion technique effectively into HAFS. We introduce a new metric to assess the characteristics of the feature map. This metric depicts the relationship between the feature discrimination and recognition accuracy. Finally, we comprehensively evaluate our approach on two benchmark datasets, HMDB51 and UCF101. Experimental results demonstrate that, extending and then harmonizing the feature discrimination is one of the effective ways of generating highly discriminative features. Experimental outcomes indicate the superiority of our proposed technique over the existing state-of-the-art methods.

In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

Vehicle ad-hoc networks (VANETs), which support various important applications for intelligent transportation systems (ITSs), consist of vehicle-to-vehicle and vehicle-to-infrastructure communications based on vehicles and roadside units (RSUs). Medium access control (MAC) plays a critical role in providing efficient broadcast services for VANETs. Unlike other types of networks, VANETs suffer from rapid changes in topology, resulting in frequent network disconnections caused by the high mobility of nodes. Hence, adaptive MAC protocols, which dynamically adjust the interval according to the traffic conditions, are essential to VANETs. This study proposes a cooperative and reliable RSU-assisted IEEE 802.11p-based multi-channel MAC protocol for VANETs, called RAM. In our proposal, an RSU is used to both calculate the optimized interval and keep track of the safety packet transmission. We also present a cooperative scheme for the retransmission of safety packets that failed to broadcast because of hidden nodes. The simulation results show that the RAM not only allows safety packets to be broadcast more efficiently using the existing MAC protocols, but also outperforms the existing MAC protocols in terms of the packet delivery ratios for safety and control packets.

Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire the maximum utility for each vehicle. However, we used a best response offloading strategy algorithm for the task-offloading game in order to achieve a unique and stable equilibrium. Numerous simulation experiments affirm that our proposed scheme fulfills the performance guarantees and can reduce the response time and task-failure rate by almost 47.6% and 54.6%, respectively, when compared with the local RSU computing (LRC) scheme. Moreover, the reduced rates are approximately 32.6% and 39.7%, respectively, when compared with a random offloading scheme, and approximately 26.5% and 28.4%, respectively, when compared with a collaborative offloading scheme.

Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one.

The linked open data (LOD) cloud is a global information space with a wealth of structured facts, which are useful for a wide range of usage scenarios. The LOD cloud handles a large number of requests from applications consuming the data. However, the performance of retrieving data from LOD repositories is one of the major challenge. Overcome with this challenge, we argue that it is advantageous to maintain a local cache for efficient querying and processing. Due to the continuous evolution of the LOD cloud, local copies become outdated. In order to utilize the best resources, improvised scheduling is required to maintain the freshness of the local data cache. In this paper, we have proposed an approach to efficiently capture the changes and update the cache. Our proposed approach, called application-aware change prioritization (AACP), consists of a change metric that quantifies the changes in LOD, and a weight function that assigns importance to recent changes. We have also proposed a mechanism to update policies, called preference-aware source update (PASU), which incorporates the previous estimation of changes and establishes when the local data cache needs to be updated. In the experimental evaluation, several state-of-the-art strategies are compared against the proposed approach. The performance of each policy is measured by computing the precision and recall between the local data cache update using the policy under consideration and the data source, which is the ground truth. Both cases of a single update and iterative update are evaluated in this study. The proposed approach is reported to outperform all the other policies by achieving an F1-score of 88% and effectivity of 93.5%.

A medium access control (MAC) protocol is designed to disseminate safety messages reliably and rapidly to improve the safety and efficiency of vehicles on the road in vehicular ad-hoc networks (VANETs). VANETs, which are created by moving vehicles, have specific properties, such as high node mobility with constrained movements and quick topology changes. Hence, MAC protocols should be designed to adapt to the changing data traffic patterns due to vehicle densities in the VANET environment. The latest multi-channel MAC protocols based on IEEE 802.11p and IEEE 1609.4 standards have higher performance than that of single-channel MAC protocols in every key performance indicator. Specifically, the multi-channel MAC protocols, which adapt themselves to different vehicular traffic densities, can guarantee a bounded transmission delay of real-time safety applications and an increased throughput for non-safety applications. In this paper, we focus on the three following perspectives: First, the multi-channel MAC protocols are studied under saturated and non-saturated data traffic conditions; Second, we study the multi-dimensional Markov chains (up to three dimensions) used in the MAC protocols; and Third, the considered Markov models are compared with real-life application requirements to improve the existing analytical models and protocol designs. Finally, we summarize our findings and discuss the open issues concerning multi-channel MAC protocols for VANETs as a part of the Intelligent Transportation System.

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.

Over the decades, robotics technology has acquired sufficient advancement through the progression of 5G Internet, Artificial Intelligence (AI), Internet of Things (IoT), Cloud, and Edge Computing. Though nowadays, Cobot and Service Oriented Architecture (SOA) supported robots with edge computing paradigms have achieved remarkable performances in diverse applications, the existing SOA robotics technology fails to develop a multi-domain expert with high performing robots and demands improvement to Service-Oriented Brain, SOB (including AI model, driving service application and metadata) enabling robot for deploying brain and a new computing model with more scalability and flexibility. In this paper, instead of focusing on SOA and Robot as a Service (RaaS) model, we propose a novel computing architecture, addressed as Brainware Computing, for driving multiple domain-specific brains one-at-a-time in a single hardware robot according to the service, addressed as Brain as a Service (BaaS). In Brainware Computing, each robot can install and remove the virtual machine, which contains SOB and operating applications from the nearest edge cloud. Secondly, we provide an extensive explanation of the scope and possibilities of Brainware Computing. Finally, we demonstrate several challenges and opportunities and then concluded with future research directions in the field of Brainware Computing.

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Recently, vehicular ad hoc networks, which organize communications between vehicles and between vehicles and infrastructures, have played an important role in providing safe transportation and infotainment applications. To satisfy the various requirements of applications such as delay-bounded transmission of safety packets, a broad bandwidth of non-safety packets, known as medium access control has been developed. In addition, to cope with the frequent topology changes of vehicular ad hoc networks, adaptive IEEE 802.11p–based multi-channel medium access control protocols have been proposed, which can adapt to different traffic conditions. In this article, we focus on such protocols and classify them according to interval division methods. Here, we perform comparisons between these protocols following three main categories: the fixed safety transmission interval, safety and Wireless Access in Vehicular Environment Service Advertisement/Acknowledgment transmission interval, and safety and Wireless Access in Vehicular Environment Service Advertisement transmission interval. We performed simulations in NS-3 under the assumption of saturated throughput for Wireless Access in Vehicular Environment Service Advertisement packets and a Poisson arrival process for the safety packets. From the simulation results, it is evident that the design of a new multi-channel medium access control protocol based on IEEE 802.11p needs to balance the trade-off between transmission delay and packet delivery ratio to be efficient, reliable and adaptive. Moreover, such a medium access control protocol can provide efficient vehicle-to-vehicle and vehicle-to-infrastructure communications, which affect the stability of the vehicular cloud.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

The rapid development of Internet of Things applications, along with the limitations of cloud computing due mainly to the far distance between Internet of Thing devices and cloud-based platform, has promoted a newly distributed computing platform based on collaboration between cloud computing and fog computing. Fog computing helps to reduce transmission latency and monetary cost for cloud resources, while cloud computing helps to fulfill the increasing demands of large-scale compute-intensive offloading applications. In this article, we study the tradeoff issue between the makespan and cloud cost when scheduling large-scale applications in such a platform. We propose a scheduling algorithm called Cost-Makespan aware Scheduling heuristic whose major objective is to achieve the balance between the performance of application execution and the mandatory cost for the use of cloud resources. Additionally, an efficient task reassignment strategy based on the critical path of the directed acyclic graph modeling the applications is also proposed to refine the output schedules of the Cost-Makespan aware Scheduling algorithm to satisfy the user-defined deadline constraints or quality of service of the system. We also verify our proposal by extensive simulations, and the experimental results show that our scheduling approach is more cost-effective and achieves better performance compared to others.

Technology provides ample opportunities for the acquisition and processing of physical, mental and social health primitives. However, several challenges remain for researchers as how to define the relationship between reported physical activities, mood and social interaction to define an active lifestyle. We are conducting a project, ATHENA(activity-awareness for human-engaged wellness applications) to design and integrate the relationship between these basic health primitives to approximate the human lifestyle and real-time recommendations for wellbeing services. Our goal is to develop a system to promote an active lifestyle for individuals and to recommend to them valuable interventions by making comparisons to their past habits. The proposed system processes sensory data through our developed machine learning algorithms inside smart devices and utilizes cloud infrastructure to reduce the cost. We exploit big data infrastructure for massive sensory data storage and fast retrieval for recommendations. Our contributions include the development of a prototype system to promote an active lifestyle and a visual design capable of engaging users in the goal of increasing self-motivation. We believe that our study will impact the design of future ubiquitous wellness applications.

Information technology (IT) is pushing ahead with drastic reforms of modern life for improvement of human welfare. Objects constitute “Information Networks” through smart, self-regulated information gathering that also recognizes and controls current information states in Wireless Sensor Networks (WSNs). Information observed from sensor networks in real-time is used to increase quality of life (QoL) in various industries and daily life. One of the key challenges of the WSNs is how to achieve lossless data transmission. Although nowadays sensor nodes have enhanced capacities, it is hard to assure lossless and reliable end-to-end data transmission in WSNs due to the unstable wireless links and low hard ware resources to satisfy high quality of service (QoS) requirements. We propose a node and path traffic prediction model to predict and minimize the congestion. This solution includes prediction of packet generation due to network congestion from both periodic and event data generation. Simulation using NS-2 and Matlab is used to demonstrate the effectiveness of the proposed solution.

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) service placement determining services to be placed in each MEC node under its storage capacity constraint, and (ii) request scheduling determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

This paper explores data caching as a key factor of edge computing. State-of-the-art research of data caching on edge nodes mainly considers reactive and proactive caching, and machine learning based caching, which could be a heavy task for edge nodes. However, edge nodes usually have relatively lower computing resources than cloud datacenters as those are geo-distributed from the administrator. Therefore, a caching algorithm should be lightweight for saving computing resources on edge nodes. In addition, the data caching should be agile because it has to support high-quality services on edge nodes. Accordingly, this paper proposes a lightweight, agile caching algorithm, EDCrammer (Efficient Data Crammer), which performs agile operations to control caching rate for streaming data by using the enhanced PID (Proportional-Integral-Differential) controller. Experimental results using this lightweight, agile caching algorithm show its significant value in each scenario. In four common scenarios, the desired cache utilization was reached in 1.1 s on average and then maintained within a 4−7% deviation. The cache hit ratio is about 96%, and the optimal cache capacity is around 1.5 MB. Thus, EDCrammer can help distribute the streaming data traffic to the edge nodes, mitigate the uplink load on the central cloud, and ultimately provide users with high-quality video services. We also hope that EDCrammer can improve overall service quality in 5G environment, Augmented Reality/Virtual Reality (AR/VR), Intelligent Transportation System (ITS), Internet of Things (IoT), etc.

Wireless Sensor Networks (WSNs) are gaining tremendous importance thanks to their broad range of commercial applications such as in smart home automation, health-care and industrial automation. In these applications multi-vendor and heterogeneous sensor nodes are deployed. Due to strict administrative control over the specific WSN domains, communication barriers, conflicting goals and the economic interests of different WSN sensor node vendors, it is difficult to introduce a large scale federated WSN. By allowing heterogeneous sensor nodes in WSNs to coexist on a shared physical sensor substrate, virtualization in sensor network may provide flexibility, cost effective solutions, promote diversity, ensure security and increase manageability. This paper surveys the novel approach of using the large scale federated WSN resources in a sensor virtualization environment. Our focus in this paper is to introduce a few design goals, the challenges and opportunities of research in the field of sensor network virtualization as well as to illustrate a current status of research in this field. This paper also presents a wide array of state-of-the art projects related to sensor network virtualization.

IP based Wireless Sensor Networks (IP-WSNs) are being used in healthcare, home automation, industrial control and agricultural monitoring. In most of these applications global addressing of individual IP-WSN nodes and layer-three routing for mobility enabled IP-WSN with special attention to reliability, energy efficiency and end to end delay minimization are a few of the major issues to be addressed. Most of the routing protocols in WSN are based on layer-two approaches. For reliability and end to end communication enhancement the necessity of layer-three routing for IP-WSNs is generating significant attention among the research community, but due to the hurdle of maintaining routing state and other communication overhead, it was not possible to introduce a layer-three routing protocol for IP-WSNs. To address this issue we propose in this paper a global addressing scheme and layer-three based hierarchical routing protocol. The proposed addressing and routing approach focuses on all the above mentioned issues. Simulation results show that the proposed addressing and routing approach significantly enhances the reliability, energy efficiency and end to end delay minimization. We also present architecture, message formats and different routing scenarios in this paper.

IP based Wireless Sensor Networks (IP-WSNs) are gaining importance for their broad range of applications in health-care, home automation, environmental monitoring, industrial control, vehicle telematics and agricultural monitoring. In all these applications, mobility in the sensor network with special attention to energy efficiency is a major issue to be addressed. Host-based mobility management protocols are not suitable for IP-WSNs because of their energy inefficiency, so network based mobility management protocols can be an alternative for the mobility supported IP-WSNs. In this paper we propose a network based mobility supported IP-WSN protocol called Sensor Proxy Mobile IPv6 (SPMIPv6). We present its architecture, message formats and also evaluate its performance considering signaling cost, mobility cost and energy consumption. Our analysis shows that with respect to the number of IP-WSN nodes, the proposed scheme reduces the signaling cost by 60% and 56%, as well as the mobility cost by 62% and 57%, compared to MIPv6 and PMIPv6, respectively. The simulation results also show that in terms of the number of hops, SPMIPv6 decreases the signaling cost by 56% and 53% as well as mobility cost by 60% and 67% as compared to MIPv6 and PMIPv6 respectively. It also indicates that proposed scheme reduces the level of energy consumption significantly.

In vehicular ad hoc networks (VANETs), many schemes for a multi-channel media access control (MAC) protocol have been proposed to adapt to dynamically changing vehicle traffic conditions and deliver both safety and non-safety packets. One such scheme is to employ both time-division multiple access (TDMA) and carrier-sense multiple access (CSMA) schemes (called a hybrid TDMA/CSMA scheme) in the control channel (CCH) interval. The scheme can adjust the length of the TDMA period depending on traffic conditions. In this paper, we propose a modified packet transmitted in the TDMA period to reduce transmission overhead under a hybrid TDMA/CSMA multi-channel MAC protocol. Simulation results show that a MAC protocol with a modified packet supports an efficient packet delivery ratio of control packets in the CCH. In addition, we analyze the hybrid TDMA/CSMA multi-channel MAC protocol with the modified packet under saturated throughput conditions on the service channels (SCHs). The analysis results show that the number of neighbors has little effect on the establishment of the number of time slots in TDMA periods and on SCHs under saturated throughput conditions.

These days, the advancing of smart devices (e.g. smart phones, tablets, PC, etc.) capabilities and the increase of internet bandwidth enables IPTV service provider to extend their services to smart mobile devices. User can just receive their IPTV service using any smart devices by accessing the internet via wireless network from anywhere anytime in the world which is convenience for users. However, wireless network communication has well a known critical security threats and vulnerabilities to user smart devices and IPTV service such as user identity theft, reply attack, MIM attack, and so forth. A secure authentication for user devices and multimedia protection mechanism is necessary to protect both user devices and IPTV services. As result, we proposed framework of IPTV service based on secure authentication mechanism and lightweight content encryption method for screen-migration in Cloud computing. We used cryptographic nonce combined with user ID and password to authenticate user device in any mobile terminal they passes by. In addition we used Lightweight content encryption to protect and reduce the content decode overload at mobile terminals. Our proposed authentication mechanism reduces the computational processing by 30% comparing to other authentication mechanism and our lightweight content encryption reduces encryption delay to 0.259 second.

With the integration of mobile computing and cloud computing, more diverse services can be provided to the users, supporting the mobility feature of mobile computing along with the power of cloud computing. This new paradigm still faces challenges, especially in terms of performance. When it comes to multimedia data communication, thin clients (such as smart phones and tablets) suffer because of performance and power constraints. Previously done studies have trivially addressed this problem. Therefore, in our paper, we present a framework in which thick clients (laptop or desktop computers) are incorporated into mobile cloud paradigm with attention paid to user mobility. Its objective is to optimize the distribution of multimedia content between the cloud and the thin clients. Our work comes up with both numerical analysis and simulation to justify the validity and the effectiveness of the proposal approach.