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Multiaccess edge computing (MEC) enables autonomous vehicles to handle time-critical and data-intensive computational tasks for emerging Internet-of-Vehicles (IoV) applications via computation offloading. However, a massive amount of data generated by colocated vehicles is typically redundant, introducing a critical issue due to limited network bandwidth. Moreover, on the edge server side, these computation-intensive tasks further impose severe pressure on the resource-finite MEC server, resulting in low-performance efficiency of applications. To solve these challenges, we model the data redundancy and collaborative task computing scheme to efficiently reduce the redundant data and utilize the idle resources in nearby MEC servers. First, the data redundancy problem is formulated as a set-covering problem according to the spatiotemporal coverage of captured images. Next, we exploit the submodular optimization technique to design an efficient algorithm to minimize the number of images transferred to the MEC servers without degrading the quality of IoV applications. To facilitate the task execution in the MEC server, we then propose a collaborative task computing scheme, where an MEC server intentionally encourages nearby resource-rich MEC servers to participate in a collaborative computing group. Accordingly, a cost model is formulated as an optimization problem, the objective of which is to prompt the MEC server to judiciously allocate computing tasks to nearby MEC servers with the goal of achieving the minimal cost while the latency of tasks is guaranteed. Experimental results show that the proposed scheme can efficiently mitigate data redundancy, conserve network bandwidth consumption, and achieve the lowest cost for processing tasks.

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.

Currently, matching the incoming Internet of Things applications to the current state of computing and networking resources of a mobile edge orchestrator (MEO) is critical for providing the high quality of service while temporally and spatially changing the incoming workload. However, MEO needs to scale its capacity concerning a large number of devices to avoid task failure and to reduce service time. To cope with this issue, we propose MEO with fuzzy-based logic that splits tasks from mobile devices and maps them onto the cloud and edge servers to reduce the latency of handling these tasks and task failures. A fuzzy-based MEO handles the multi-criteria decision-making process to decide where the offloaded task should run by considering multiple parameters in the same framework. Our approach selects the appropriate host for task execution and finds the optimal task-splitting strategy. Compared to the existing approaches, the service time using our proposal can achieve up to 7.6%, 22.6%, 38.9%, and 51.8% performance gains for augmented reality, healthcare, compute-intensive, and infotainment applications, respectively.

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.

Today's Internet and enterprise networks are so popular as they can easily provide multimedia and ecommerce services to millions of users over the Internet in our daily lives. Since then, security has been a challenging problem in the Internet's world. That issue is called Cyberwar, in which attackers can aim or raise Distributed Denial of Service (DDoS) to others to take down the operation of enterprises Intranet. Therefore, the need of applying an Intrusion Detection System (IDS) is very important to enterprise networks. In this paper, we propose a smarter solution to detect network anomalies in Cyberwar using Stacking techniques in which we apply three popular machine learning models: k-nearest neighbor algorithm (KNN), Adaptive Boosting (AdaBoost), and Random Decision Forests (RandomForest). Our proposed scheme uses the Logistic Regression method to automatically search for better parameters to the Stacking model. We do the performance evaluation of our proposed scheme on the latest data set NSLKDD 2019 dataset. We also compare the achieved results with individual machine learning models to show that our proposed model achieves much higher accuracy than previous works.

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.

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 B atch-wise E ntropy S upervised S tream (BESS) to extend feature discrimination similar to the uncertainty of the corresponding batch. Secondly, to obtain a more generalized model, we propose a S tream to H armonize the feature discrimination by A ugmenting both F eatures (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.

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.

Mobile edge computing (MEC) is used to offload services (tasks) from cloud computing in order to deliver those services to mobile Internet of Things (IoT) devices near mobile edge nodes. However, even though there are advantages to MEC, we face many significant problems, such as how a service provider (SP) deploys requested services efficiently on a destination MEC node, and how to discover existing services in neighboring MEC nodes to save edge resources. In this paper, we present a decentralized and revised content-centric networking (CCN)-based MEC service deployment/discovery protocol and platform. We organized a gateway in every area according to a three-tiered hierarchical MEC network topology to reduce computing overhead at the centralized controller. We revised CCN to introduce a protocol to help SP deploy their service on MEC node and assist MEC node discover services in neighboring nodes. By using our proposed protocol, MEC nodes can deploy or discover the requested service instances in the proximity of IoT devices to reduce transmission delay. We also present a mathematical model to calculate the round trip time to guarantee quality of service. Numerical experiments measure the performance of our proposed method with various mobile IoT device services. The results show that the proposed service deployment protocol and platform reduce the average service delay by up to 50% compared to legacy cloud. In addition, the proposed method outperforms the legacy protocol of the MEC environment in service discovery time.

Vehicular Edge Computing (VEC) is a new leading technology to enhance the vehicular performance through task offloading where resource-confined vehicles offload their computing task to the vehicular multi-access edge computing (MEC) networks in proximity. However, the environment of vehicular task offloading is extremely dynamic and faces some challenges to determine the location of processing the offloaded task. As a result, to achieve optimal performance by using traditional VEC system is difficult because in advance we don't know the demand of vehicles. Therefore, a non-cooperative game theory-based efficient task offloading (NGTO) scheme is proposed in this study where the offloading decisions are taken either the MEC server or remote cloud server through the game-theoretic approach. To reduce the processing latency of the vehicles' computation tasks and assure the maximum utility of each vehicle, we used a distributed best response offloading strategy. Our proposed strategy accommodates its offloading probability to achieve a unique equilibrium under certain conditions. Detailed performance evaluation affirms that our proposed NGTO scheme can outperform in all scenarios. It can minimize the response time at almost 41.2 % and average task failure rate at approximately 56.3% when compared with a local roadside unit computing (LRC) scheme. The reduced response time and task failure rates are approximately 25.2% and 20.4%, respectively, when compared with a collaborative (LRC with cloud via roadside unit) offloading scheme.

Proactive content caching in self-driving cars poses several challenges, particularly because of the dynamic nature of content popularity, heterogeneity in user preferences, and privacy issues for data sharing. To tackle these issues, in this paper, we study the significance of proactive content caching strategy in self-driving cars for optimizing content retrieval cost and quality-of-experience (QoE) with the edge cloud infrastructure. To that end, we propose a low-complexity content popularity prediction mechanism in a federated setting where we extract local content popularity patterns in the self-driving cars using long short-term memory (LSTM)-based prediction mechanism. Then, we leverage the privacy-preserving distributed model training paradigm of Federated Learning (FL) to create a global model by applying the Federated Averaging (FedAvg) algorithm on local LSTM models to create a regional content popularity prediction model. With extensive simulations on real-world datasets, we show the obtained global model helps to improve the local cache hit ratio, cache space utilization, and correspondingly minimize latency overhead at the self-driving cars.

The goal of sixth-generation (6G) wireless communication network is towards space-atmosphere-land-submarine connectivity. 6G communications are mainly characterized by massive connectivity, ultra-reliability, low latency, and extreme high bandwidth to meet the requirements of practical applications such as robotics and autonomous vehicles, transportation management systems, healthcare as well as telepresence, virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications. Multi-Access edge computing (MEC) is a key component of future 6G wireless networks, enabling latency-sensitive, bandwidth-intensive, and compute-intensive devices. This article presents an overview of the technologies assisting, challenges, and opportunities for envisioning MEC in future 6G wireless networks.

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.

Multi-Access Edge Computing (MEC) is a promising candidate to handle the enormous computation demands of many emerging applications and the ever-growing user's quality-of-service (QoS) requirements. However, due to the limitation of computing resource capacity of a distinct edge server, most of the previous studies have proposed a collaboration approach. For collaboration, they considered vertical offloading between mobile with edge computing or edge with cloud computing for taking the advantages of both these technologies. Therefore, these approaches ignored the neighboring edge server having spare computing resources in the same tier. This paper thus proposes edge orchestration based computation peer offloading (EOPO) scheme among the edge servers in the same tier. The main objective is to share the computation resources among the edge servers. Our proposed approach selects the optimal computational node for task offloading based on fuzzy rules. Simulation results corroborate that fuzzy decision based computation peer offloading scheme significantly improves the performance of edge computing. Our proposed EOPO scheme outperformed the two reference schemes which can reduce the average task completion time and task failure rate at approximately 36% and 80.5% respectively when compared with the local edge offloading (LEO) scheme; and at approximately 25.4% and 67.2% respectively when compared with two-tier based offloading between edge with cloud (TTO) scheme.

Mobile Ad hoc Network (MANET) is easy to be attacked than wired networks due to its characteristics of open network topology, high mobility, lack of physical security and independent management. This paper analyzes the impact of routing attacks on the performance of the AODV and AOMDV routing protocol under several collaborative routing attack scenarios (blackhole and wormhole). We conclude that AOMDV is better than AODV in case of collaborative attacks and we show that collaborative Blackhole and Wormhole attacks can affect much performance of the network, especially in the case when we do not recognize the location area of malicious nodes.

In the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks such as object detection, object segmentation, video coding, image re-targeting, image-quality assessment, and image compression. Traditional models focus on improving detection accuracy at the cost of high complexity. However, these models are computationally expensive for real-world systems. To cope with this issue, we propose a fast-motion saliency method for surveillance systems under various background conditions. Our method is derived from streaming dynamic mode decomposition (s-DMD), which is a powerful tool in data science. First, DMD computes a set of modes in a streaming manner to derive spatial–temporal features, and a raw saliency map is generated from the sparse reconstruction process. Second, the final saliency map is refined using a difference-of-Gaussians filter in the frequency domain. The effectiveness of the proposed method is validated on a standard benchmark dataset. The experimental results show that the proposed method achieves competitive accuracy with lower complexity than state-of-the-art methods, which satisfies requirements in real-time applications.

Wi-Fi-based indoor positioning for determining accurate wireless indoor location information has become crucial in meeting increasing demands for location-based services by leveraging the Internet of Things (IoT) and ubiquitous connectivity. Most Wi-Fi-based indoor positioning techniques using wireless received signal strength (RSS)-based methods are affected by the indoor environment and depend on the respective signals from at least three reference access points. In this paper, we propose a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation through a Wi-Fi located on a one-hop wireless network. Our cloudlet-based cloud computing system provides the reference point data and real-time interactive response for a self-driving indoor cart. The system was tested in a real environment with the following results: (1) our system autonomously performed actions, such as turning right or left or going straight according to a movement decision algorithm and determined the position within a stable range of Wi-Fi coverage; (2) the cloudlet and core cloud can track navigation for an indoor self-driving cart; (3) the global and local positions designed for reference access points and a specific position can navigate the self-driving cart to a particular position accurately; (4) the moving edge clouds play a role in deciding three action movements (go straight, turn left, and turn right), as well as managing the local position of the items; and (5) a core cloud is deployed to store all information for the items, such as their positions and corresponding Wi-Fi locations. A core cloud manages items that have the same position (i.e., a global position) defined as the corresponding Wi-Fi location. Finally, the practical results have significance in designing a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation.

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.

Under the dynamically changing topology of Vehicular Ad-hoc NETworks (VANETs), the restricted intervals in Medium Access Control (MAC) protocols cannot provide sufficient capacity to carry both safety and non-safety applications. One approach which can solve these issues is a dynamic MAC protocol that can adapt itself to the vehicle density or traffic conditions. In this survey, we, therefore, study various techniques for dynamic intervals used in MAC protocols, their advantages, and disadvantages. First, we classify these protocols into three following categories: 1) contention-based, 2) contention-free, and 3) a hybrid between contention-free and contention-based medium access methods. Second, in each medium access method, we classify the methodologies depending on their operating principles. The conclusions of our study are as follows: 1) Adaptive MAC protocols improve the channel utilization and adapt themselves to various traffic states, 2) Adaptive MAC protocols perform better than protocols using fixed intervals, 3) Currently, MAC protocols using dynamic intervals, which are suitable to VANET standards, are only applicable to optimize control channel interval; therefore, it is necessary to expand to both optimize control channel interval and service channel interval according to network and traffic condition. Finally, we discuss some open issues and whether designing MAC protocols using dynamic intervals can meet the QoS requirements for different applications in the future.

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&rsquo, 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.

Phishing detection is recognized as a criminal issue of Internet security. By deploying a gateway anti-phishing in the networks, these current hardware-based approaches provide an additional layer of defense against phishing attacks. However, such hardware devices are expensive and inefficient in operation due to the diversity of phishing attacks. With promising technologies of virtualization in fog networks, an anti-phishing gateway can be implemented as software at the edge of the network and embedded robust machine learning techniques for phishing detection. In this paper, we use uniform resource locator features and Web traffic features to detect phishing websites based on a designed neuro-fuzzy framework (dubbed Fi-NFN). Based on the new approach, fog computing as encouraged by Cisco, we design an anti-phishing model to transparently monitor and protect fog users from phishing attacks. The experiment results of our proposed approach, based on a large-scale dataset collected from real phishing cases, have shown that our system can effectively prevent phishing attacks and improve the security of the network.

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.

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%.

Cloud computing is a mature technology that provides a huge leap in elastic computation, and new development trends are shaped to compliment the cloud computing paradigm. Cisco recently introduced the concept of Fog Computing to enable applications on billions of devices that are already connected and form the Internet of Things at the edge of the network. With the view point that the fog computing paradigm will be the future of computing technology, we look at its strong characteristics and propose a novel approach to enable a new kind of service called Offload As A Service (OaaS). Offload computation has been an active research area for many years and provides the capability to extend mobile resources limitations in terms of CPU, GPU, memory, storage and battery energy. Fog computing paradigm is a good synergy for offload computation technology with its low delay and close proximity features. To realize the enabling of OaaS in a fog computing environment, we propose a novel framework for communication and an offloading mechanism between different layers of the fog infrastructure, using matching algorithm to handle the fair mapping between users and service providers. Simulation results are provided to validate the effectiveness of our proposal. Simulation results of our work have shown great potential and value based on the prototype implementation, especially in the running time of tasks, which was reduced significantly with improvements of up to 37 and $$12\%$$12% observed for photos offloaded to "PC" and "Odroid", respectively, compared to the local running time on the user device.

In recent years, power consumption has become one of the hottest research trends in computer science and industry. Most of the reasons are related to the operational budget and the environmental issues. In this paper, we would like to propose an energy-efficient solution for orchestrating the resource in cloud computing. In nature, the proposed approach firstly predicts the resource utilization of the upcoming period based on the Gaussian process regression method. Subsequently, the convex optimization technique is engaged to compute an appropriate quantity of physical servers for each monitoring window. This quantity of interest is calculated to ensure that a minimum number of servers can still provide an acceptable quality of service. Finally, a corresponding migrating instruction is issued to stack the virtual machines and turn off the idle physical servers to achieve the objective of energy savings. In order to evaluate the proposed method, we conduct the experiments using synthetic data from 29-day period of Google traces and real workload from the Montage open-source toolkit. Through the evaluation, we show that the proposed approach can achieve a significant result in reducing the energy consumption as well as maintaining the system performance. Balance between energy efficiency and quality of service in the cloud computing.Apply prediction technique to enhance the usefulness of monitoring statistics.Design optimal energy efficiency architecture to orchestrate the cloud system.After VM consolidation, adaptively turn-off idle physical machines to save energy.

The cognitive femtocell network (CFN) integrated with cognitive-radio-enabled technology has emerged as one of the promising solutions to improve wireless broadband coverage in indoor environments for next-generation mobile networks. In this paper, we study a distributed resource allocation that consists of subchannel- and power-level allocation in the uplink of the two-tier CFN comprised of a conventional macrocell and multiple femtocells using underlay spectrum access. The distributed resource allocation problem is addressed via an optimization problem, in which we maximize the uplink sum rate under constraints of intratier and intertier interference while maintaining the average delay requirement for cognitive femtocell users. Specifically, the aggregated interference from cognitive femtocell users to the macrocell base station (MBS) is also kept under an acceptable level. We show that this optimization problem is NP-hard and propose an autonomous framework, in which the cognitive femtocell users self-organize into disjoint groups (DJGs). Then, instead of maximizing the sum rate in all cognitive femtocells, we only maximize the sum rate of each DJG. After that, we formulate the optimization problem as a coalitional game in partition form, which obtains suboptimal solutions. Moreover, distributed algorithms are also proposed for allocating resources to the CFN. Finally, the proposed framework is tested based on the simulation results and shown to perform efficient resource allocation.

To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.

Mobile Edge Computing (MEC) enhances mobile cloud computing capabilities by fetching services close to the edge of the network, which adds 4C (Computing, Communication, Control and Caching) to the edges. MEC-enabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS). The research work regarding MEC-enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS-MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency-sensitive tasks and resources-hungry mobile applications in small-cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS-MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS-MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS-MEC with cloud scheme will provide lower task execution latency than others in small-cell networks.

The indoor positioning system has seen an explosion of advances in recent years, particularly in demands for location-based services. One such important location-based service is indoor navigation for robots based on an indoor positioning system, which is used to guide blind people and locate patients in a hospital. However, most indoor navigation systems are based on location information depended on respect signal from at least three reference access points. In this paper, we design a cloud-based navigation system for indoor robot through a Wi-Fi located at the one-hop wireless network. We compare our proposal and baseline techniques in the real-world indoor environment. The practical results show that the robot using the fixed edge cloud can move in accuracy achieved position and efficiently reach to the given destination position. Moreover, an indoor robot using a single access point and cloud service is more suitable and straightforward than using existing models.

Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. In this paper, we investigate the performance of MEC-assisted algorithms and compare their performance with the client based adaptation logic. We conduct extensive experiments and quantify benefits and drawbacks of edge computing-assisted adaptation algorithms. The results from our experiments reveal that MEC-assisted algorithms outperforms the purely client-based heuristics in most of the video quality metrics. However, the results also show that the MEC-assisted algorithms are not able to protect the playback buffer from drying up under different network settings.

As the edge cloud technology evolves, research is underway to provide customers with a serverless architecture-based FaaS (Function as a Service) on the edge nodes. However, serverless architecture-based FaaS on the edge nodes has a problem with resource management due to edge nodes with relatively lower computing power than the cloud datacenters. And also it has a late service provision problem due to time to prepare cloud instances for customer API call requests. In this paper, we propose the method of dynamic container layer replacement considering a resource-limited environment on the edge nodes while mitigating cold start and then shows the performance.

In the edge cloud, when two types of service providers provide edge nodes, each service provider has different logging areas, making it difficult to access QoS violations and complying with service level agreements (SLAs). Thus, the third-party broker plays a role of confirming QoS violations and compliance between the two providers, thereby securing reliability between the CSP and NP. In addition, by improving the existing logging technique in the edge cloud with limited resources, it reduces the CPU usage to improve the availability of cloud services. Therefore, we propose a broker-based framework that improves the service availability by reducing the CPU usage and secures QoS compliance and violations.

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&rsquo, 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.

Screen content images (SCI) are composed of texts, graphics, images, emails, web pages, and other computergenerated components which make it different from the pure natural images. As a result, image quality assessment (IQA) methods designed for natural images are not always suitable for screen content images. However, as time goes, many screen contents are being generated which share several properties of natural images. As a result, IQA methods for SCI need to be more generic while giving priority to the prominent SCI features. The image gradient is a very important feature for all kinds of images, especially for SCI. In this paper, we proposed a novel image gradient based similarity index (CGSI) for screen content images where the gradient is used both as a quality map and extractor for another feature map. Our previous studies show that the center part of an image is visually the most important part and HVS is more sensitive to any distortion in the middle area. To address this issue, the center area of both gradient and feature similarity maps are raised by element-wise squaring. Eventually, the final quality score is calculated using a summation of standard deviation based pooling strategy. We evaluated our proposed method on three large scale SCI databases and compared with 8 other state-of-the-art IQA methods designed for both natural and screen content images. Results show that the proposed approach provides competitive performance and defeats all other methods with a large gap in overall performance. Also, the relatively faster running time makes it suitable for most of the real-time applications.

State-of-the-art neural language models highly rely on fixed-size subword vocabulary based pre-training process to improve the performance of language understanding. Existing subword segmentation algorithms have been researched to generate fixed-size vocabulary by frequency in large text pool without considering the linguistic structures. Also, most of research is focused on widely-used languages such as English and Chinese. However, for Korean, a segmentation algorithm considering the linguistic structure is required, and we thus propose the algorithm considering the characteristics of Korean. For example, Korean words include the special type of suffix called “Josa” that adds grammatical meaning. We also propose subword regularization algorithm based on mutual information, which is interdependence between words. The regularization algorithm customizes the size of vocabulary on demand. In addition, we present an experiment analysis of subword segmentation and interdependent regularization by testing neural language model. It can achieve better performance by small changes of vocabulary.

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&ndash, 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.

In this paper, we propose a new accuracy measurement model for the video stabilization method based on background motion that can accurately measure the performance of the video stabilization algorithm. Undesired residual motion present in the video can quantitatively be measured by the pixel by pixel background motion displacement between two consecutive background frames. First of all, foregrounds are removed from a stabilized video, and then we find the two-dimensional flow vectors for each pixel separately between two consecutive background frames. After that, we calculate a Euclidean distance between these two flow vectors for each pixel one by one, which is regarded as a displacement of each pixel. Then a total Euclidean distance of each frame is averaged to get a mean displacement for each pixel, which is called mean displacement error, and finally we calculate the average mean displacement error. Our experimental results show the effectiveness of our proposed method.

This paper proposes An Architecture of IPTV Services based on PVR-Micro Data Center and PMIPv6 in Cloud Computing. The proposed architecture addresses important problems exist in normal PMIPv6 (Proxy mobile IPv6) environment which is not today addressed by normal PMIPv6. The first issue is user devices secure authentication, registration and fast handover in both inter-domain/intra-domain of PMIPv6. The second issues is multimedia delivery in inter-domain/intra-domain of PMIPv6. The third issue is tunneling overhead in inter-domain/intra. Today smart devices are mobile and user use them to access the internet from anywhere at any time using different connection method while they are traveling. Providing security measures such as user device authentication, registration as well as hand over requires different security measures and configuration in inter-domain/intra-domain of PMIPv6. Furthermore we provide new method to reduce tunneling overhead by allowing multimedia content to be delivered from many different Micro data center as well as Mega data center by using their own unique addresses to create tunneling to transfer multimedia content. Our goal is not only to reduce authentication delay, handover delay and reduce signaling cost using the intelligent utilization of AAA server (Authentication, Authorization, and Accounting Server), binding cache entry (BCE), and proxy notification update (PNU) and proxy notification acknowledgment (PNA) but also to provide fast delivery of multimedia content. Our result shows better performance when compare with normal PMIPv6. We used network simulator 3 (NS3) to generate our experiment result.

This paper studies price competition in a heterogeneous cloud market formed by public providers and a cloud broker, all of which are also known as cloud service providers (CSPs). We formulate the price competition between CSPs as a two-stage noncooperative game. In stage I, in which CSPs set their service prices to maximize their revenues, we model the pricing game using the noncooperative static game. We provide sufficient conditions for the existence and uniqueness of Nash equilibrium prices, which can be obtained using an iterative algorithm. The convergence properties of the iterative algorithm are characterized using the contract mapping theorem. In stage II, given the prices set by CSPs, cloud users can select the services that provide them the best payoff in terms of performance (i.e., delay) and price. We apply an evolutionary game to study the evolution and dynamic behavior of cloud users. Furthermore, we use the Wardrop equilibrium and replicator dynamics to determine the equilibrium and its convergence properties of the service selection game. To attract users to the equilibrium, we implement the service selection algorithms using population evolution and reinforcement learning approaches. Numerical results illustrate that our game models can provide comprehensive understanding of the heterogeneous CSPs market and service selection in cloud computing. HighlightsA game theoretical model in a Cloud Service Provider market in which there are two stages of competition.In stage I: formulate the competition among CSPs for selling service opportunities as a noncooperative game.In stage II, a Wardrop equilibrium is achievable by cloud users in the service selection game.

Media content in its digital form has been rapidly scaling up, resulting in popularity gain of cloud computing. Cloud computing makes it easy to manage the vastly increasing digital content. Moreover, additional features like, omnipresent access, further service creation, discovery of services, and resource management also play an important role in this regard. The forthcoming era is interoperability of multiple clouds, known as cloud federation or inter-cloud computing. With cloud federation, services would be provided through two or more clouds. Once matured and standardized, inter-cloud computing is supposed to provide services which would be more scalable, better managed, and efficient. Such tasks are provided through a middleware entity called cloud broker. A broker is responsible for reserving resources, managing them, discovering services according to customer's demands, Service Level Agreement (SLA) negotiation, and match-making between the involved service provider and the customer. So far existing studies discuss brokerage in a narrow focused way. In the research outcome presented in this paper, we provide a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement. A unique feature of this study is that we have considered dynamic management of customer's characteristics and historical record in evaluating the economics related factors. Additionally, a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities. For practical implications, the framework is modeled on Amazon Elastic Compute Cloud (EC2) On-Demand and Reserved Instances service pricing. For certain features required in the model, data was gathered from Google Cluster trace.

The number of applications based on Apache Hadoop is dramatically increasing due to the robustness and dynamic features of this system. At the heart of Apache Hadoop, the Hadoop Distributed File System (HDFS) provides the reliability and high availability for computation by applying a static replication by default. However, because of the characteristics of parallel operations on the application layer, the access rate for each data file in HDFS is completely different. Consequently, maintaining the same replication mechanism for every data file leads to detrimental effects on the performance. By rigorously considering the drawbacks of the HDFS replication, this paper proposes an approach to dynamically replicate the data file based on the predictive analysis. With the help of probability theory, the utilization of each data file can be predicted to create a corresponding replication strategy. Eventually, the popular files can be subsequently replicated according to their own access potentials. For the remaining low potential files, an erasure code is applied to maintain the reliability. Hence, our approach simultaneously improves the availability while keeping the reliability in comparison to the default scheme. Furthermore, the complexity reduction is applied to enhance the effectiveness of the prediction when dealing with Big Data.

The Internet of Things (IoT) is a technological revolution that represents the future of connectivity and reachability. In the IoT, things refer to any object on the face of the Earth, whether it is a communicating device or a noncommunicating "dumb" object. From a smart device to a leaf of a tree or a beverage bottle, anything can be part of the Internet. The objects become communicating nodes over the Internet through data communication means, primarily through radio frequency identification (RFID) tags. The IoT includes smart objects as well. Smart objects are digital, physical entities, which perform some tasks for humans and/or the environment. This is why the IoT is not only a hardware and software paradigm but also includes interaction and social aspects.

Digital media has been increasing very rapidly, resulting in cloud computing's popularity gain. Cloud computing provides ease of management of large amount of data and resources. With a lot of devices communicating over the Internet and with the rapidly increasing user demands, solitary clouds have to communicate to other clouds to fulfill the demands and discover services elsewhere. This scenario is called inter-cloud computing or cloud federation. Inter-cloud computing still lacks standard architecture. Prior works discuss some of the architectural blue-prints, but none of them highlight the key issues involved and their impact, so that a valid and reliable architecture could be envisioned. In this paper, we discuss the importance of inter-cloud computing and present in detail its architectural components. Inter-cloud computing also involves some issues. We discuss key issues as well and present impact of storage heterogeneity. We have evaluated some of the most noteworthy cloud storage services, namely: Dropbox, Amazon CloudDrive, GoogleDrive, Microsoft OneDrive (formerly SkyDrive), Box, and SugarSync in terms of Quality of Experience (QoE), Quality of Service (QoS), and storage space efficiency. Discussion on the results shows the acceptability level of these storage services and the shortcomings in their design.

Video stabilization methods cannot compensate entire unwanted motions of an unstable video. In this paper, we present a new performance metric which can effectively measure the stability of a video. Our hypothesis is that in case of non dynamic background, the background motion is zero without the foregrounds of a stable video when a video taken by a fixed camera. Firstly, the foregrounds of a stabilized video are discarded, then, each background pixel motion is determined between the two consecutive background frames in two directions separately, afterwards, an average motion of each pixel is computed. These mean motions determine the stability of a video. The more unstable video will have more background motions. The background motion is a criterion to evaluate the stability of a video. The experimental results prove the efficacy of our proposed approach.

Internet of Things applications produce enormous amounts of data that need to be collected, analyzed, transferred, and stored at best-suited places. An interplay among Cloud Computing and Fog Computing proves to be a promising solution for the Internet of Things (IoT). Publish/Subscribe (Pub/Sub) systems have been widely deployed in many IoT projects. IoT devices often directly connect to their closest brokers to send and receive data. Building an efficient broker overlay network for Pub/Sub systems to support wide geo-distribution IoT platforms is a matter of immediate concern. In this paper, we propose an efficient Edge-Cloud Pub/Sub model that coordinate brokers in the system for large-scale IoT application. Our simulation results prove that our broker overlay network can support low delay data delivery for these applications in a high scalable manner.