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Mobile cloud computing (MCC) is one of the most visible efforts to take computing technology beyond the use of tools and achieve universal penetration. Mobile and cloud computing are becoming increasingly important in people's daily lives, highlighting the urgent need for new approaches. The motivation of mobile users and the nature of cloud computing make it ideal for developing mobile cloud services. The focus of this paper is on the cognitive, social, and technical motivations that mobile users experience when owning, interacting with, and using mobile devices through MCC. We also explore the moderating role of network externalities between perceived utility of MCC, usage attitudes, and actual use of MCC. The results show that the positive effects of perceived utility and attitudes on mobile cloud computing usage increase gradually with network externalities. This study provides a theoretical basis for academics and practical guidance for service providers to promote MCC. [ABSTRACT FROM AUTHOR]

This article has developed a novel dual-data sanitization and dual-data restoration model for privacy preservation in mobile cloud computing (MCC) environment. The original medical data have been sanitized at the transmitter end utilizing the newly suggested dual-data sanitization technique. The wireless channel transports the sanitized data from the transmitter to the receiver. The dual-sanitized input has been recovered at the other end. More significantly, the exact key generation, which has been found by a novel hybrid optimization model, is essential to both operations (dual-data cleaning and dual-data restoring). Electric fish customized dolphin swarm optimization (ECDO) is a suggested hybrid optimization model that is a conceptual merger of the electric fish optimization (EFO) and the dolphin swarm method. Ultimately, to confirm the effectiveness of the anticipated strategy, a study has been made. The present architecture has been contrasted against the previous models, including the DSOA, EFO, SMO, WOA, and J-SSO, respectively. The restoration effectiveness of the projected model is 57.84%, 26.3%, 5.26%, 31.5%, and 15.7% improved over DSOA, EFO, SMO, WOA, and J-SSO, respectively. As a result of the overall assessment, the proposed model appears to be extremely secure during data transfer. [ABSTRACT FROM AUTHOR]

Optimizing computational offloading in Mobile Edge Computing (MEC) environments presents a multifaceted challenge requiring innovative solutions. Soft computing, recognized for its ability to manage uncertainty and complexity, emerges as a promising approach for addressing the dynamic multi-objective evaluation inherent in computational offloading scenarios. This paper conducts a comprehensive review and analysis of soft computing approaches for Dynamic Multi-Objective Evaluation of Computational Offloading (DMOECO), aiming to identify trends, analyze existing literature, and offer insights for future research directions. Employing a systematic literature review (SLR) methodology, we meticulously scrutinize 50 research articles and scholarly publications spanning from 2016 to November 2023. Our review synthesizes advancements in soft computing techniques, including fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, as applied to computational offloading optimization within MEC environments. Within this comprehensive review, existing approaches are categorized and analyzed into distinct research lines based on methodologies, objectives, evaluation metrics, and application domains. The evolution of soft computing-based DMOECO strategies is emphasized, showcasing their effectiveness in dynamically balancing various computational objectives, including energy consumption, latency, throughput, user experience, and other pertinent factors in computational offloading scenarios. Key challenges, including scalability issues, lack of real-world deployment validation, and the need for standardized evaluation benchmarks, are identified. Insights and recommendations are provided to enhance computational offloading optimization. Furthermore, collaborative efforts between academia and industry are advocated to bridge the theoretical developments with practical implementations. This study pioneers the use of SLR methodology, offering valuable perspectives on soft computing in DMOECO and synthesizing state-of-the-art approaches. It serves as a crucial resource for researchers, practitioners, and stakeholders in the MEC domain, illuminating trends and fostering continued innovation in computational offloading strategies. [ABSTRACT FROM AUTHOR]

Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs' knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarers. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed. [ABSTRACT FROM AUTHOR]

Mobile devices (MDs) are used by mobile cloud computing (MCC) customers and by other users because of their portability, robust connectivity, and ability to house and operate third-party applications (apps). However, the apps installed on an MD may pose data security risks to the MD owner and to other MCC users, especially when the requested permissions include access to sensitive data (e.g., user's location and contacts). Calculating the risk score of an app or quantifying its potential harmfulness based on user input or on data gathered while the app is actually running may not provide reliable and sufficiently accurate results to avoid harmful consequences. This study develops and evaluates a risk assessment framework for Android-based MDs that does not depend on user input or on actual app behavior. Rather, an app risk evaluator assigns a risk category to each resident app based on the app's classification (benign or malicious) and the app's risk score. The app classifier (a trained machine learning model) evaluates the permissions and intents requested by the app. The app risk score is calculated by applying a probabilistic function based on the app's use of a set of selected dangerous permissions. The results from testing of the framework on an MD with real-life resident apps indicated that the proposed security solution was effective and feasible. [ABSTRACT FROM AUTHOR]

Aiming at the task unloading mode in cloud computing environment, the task unloading problem for IoT devices is studied. Through theoretical analysis, we can know that in the task unloading problem, it is usually contradictory to improve the utilization of cloud resources and reduce the task delay. In order to solve this problem, a task unloading scheme for Internet of things devices using deep reinforcement learning algorithm is proposed. The deep reinforcement learning algorithm is used to model the task unloading problem. The return value with weight is introduced into the algorithm, and the utilization rate of cloud resources and the delay of unloading task are weighed by adjusting the return value of the weight. First of all, the improved k-means clustering algorithm with weighted density is used to cluster the physical machines. The physical machines of each cluster have similar bandwidth and task waiting time. Then, deep reinforcement learning is used to select the best physical machine cluster from the current unloading tasks. Finally, the improved PSO algorithm is used to select the optimal physical machine from the optimal cluster, and Pareto is used to improve the convergence speed. Experimental results show that compared with the traditional method, the proposed algorithm has a good performance, and can achieve the goal of increasing the utilization of physical machine resources and reducing task delay. [ABSTRACT FROM AUTHOR]

The integration of mobile learning applications has emerged as indispensable in higher education, yet the widespread adoption of mobile cloud computing technology within university settings remains limited in developing countries. This research seeks to bridge this gap by elucidating the primary factors influencing the decision to adopt mobile cloud computing in universities. To attain this objective, a comprehensive model is proposed, encompassing seven established technological factors along with novel considerations such as quality of service and social influence. The study involves data collection from 210 students and academic staff An-Najah National University in Palestine. The investigation underscores the paramount influence of quality of service, perceived usefulness, perceived ease of use, facilitating conditions, social influence, management support, and privacy on mobile cloud adoption. Furthermore, the research brings to light the persistent concerns surrounding security and privacy, serving as significant impediments to mobile cloud adoption in Palestinian Universities. The implications of these findings extend to higher educational institutions, mobile cloud service providers, and decision-makers. The identified factors serve as critical considerations for successful implementation of mobile cloud computing technology. Moreover, the research sheds light on the societal and scientific significance by addressing the motivation behind the title--exploring the utility of Mobile Computing Technology in enhancing educational processes. [ABSTRACT FROM AUTHOR]

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

Scheduling of task is one of the most important aspects in cloud computing, edge computing, and mobile cloud computing. It becomes very prominent issue when a framework and its scheduling algorithm caters to task scheduling at the local cloud and remote cloud with multiple cloud environment. This research paper mainly focuses on scheduling of independent and workflow-based tasks at local cloud and remote cloud. It proposes an advanced scheduling mechanism enabled-scalable key parameter yield of resources framework which is an improved version of SKYR framework which has SKYR framework-based task allocation to resources (STAR) algorithm which manages the various aspects of task scheduling at both local and remote cloud. This framework along with its algorithm, manages heterogenous edge cloud at local level and also caters the multiple cloud environment at remote cloud which is the unique feature of the proposed work. This proposed STAR algorithm uses some prominent and established methods such as laxity-based priority access, constraint optimization algorithm based on ant colony system, execution time computation matrix, genome interpretation and crossover scheduling for efficient and cost-effective scheduling. Moreover, it also facilitates reliability and scalability in computation at both levels and also allows comprehensive interaction among various entities involved to provide effective computation to the multiple types of users. [ABSTRACT FROM AUTHOR]

Mobile cloud computing is a very attractive service paradigm that outsources users' data computing and storage from mobile devices to cloud data centers. To protect data privacy, users often encrypt their data to ensure data sharing securely before data outsourcing. However, the bilinear and power operations involved in the encryption and decryption computation make it impossible for mobile devices with weak computational power and network transmission capability to correctly obtain decryption results. To this end, this paper proposes an outsourcing decryption algorithm of verifiable transformed ciphertext. First, the algorithm uses the key blinding technique to divide the user's private key into two parts, i.e., the authorization key and the decryption secret key. Then, the cloud data center performs the outsourcing decryption operation of the encrypted data to achieve partial decryption of the encrypted data after obtaining the authorization key and the user's outsourced decryption request. The verifiable random function is used to prevent the semi-trusted cloud data center from not performing the outsourcing decryption operation as required so that the verifiability of the outsourcing decryption is satisfied. Finally, the algorithm uses the authorization period to control the final decryption of the authorized user. Theoretical and experimental analyses show that the proposed algorithm reduces the computational overhead of ciphertext decryption while ensuring the verifiability of outsourcing decryption. [ABSTRACT FROM AUTHOR]

Automobiles have undergone a transformation during the past two decades due to the merger of the electronics and automotive industries. The combination of autos and electronic sensors has resulted in a new generation of vehicles known as autonomous vehicles (AVs). These AVs have a few hundred thousand sensors, producing an enormous amount of raw data for computation. Data from the vehicular network can be offloaded to existing telecommunication infrastructure to address the problem of processing resources. In order to address vehicular network requirements, large-capacity servers deployed in major telecommunications networks are first used to offload resource-intensive tasks. Mobile Cloud Computing (MCC) is a critical enabling technology for 5 G networks, which has a key feature of offloading to divide application tasks into local and cloud server execution components. This paper proposes a novel Three TierEdge cloud computing (T2 EC2) system which uses an Energy-aware Dynamic Task offloading and collaborative task execution algorithm (EA-DTOCTE) for multilayer vehicular cloud computing networks. The EA-DTOCTE algorithm is included in the decision-making engine in the proposed system, which selects whether to offload the task to the remote environment or implement it locally. EA-DTOCTE focuses on consumption of energy by tasks both locally and remotely since its goal is to efficiently and dynamically split the application into tasks and schedule them on local devices and cloud resources. The proposed T2 EC2 has been evaluated in terms of parameters such as energy consumption, completion time, and throughput. Experimental results indicate that the proposed T2EC2 can save up to 28% of system energy consumption compared with other state-of-art techniques. [ABSTRACT FROM AUTHOR]

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users' context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC). [ABSTRACT FROM AUTHOR]

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future. [ABSTRACT FROM AUTHOR]

Mobile devices (MDs) are used by mobile cloud computing (MCC) customers and by other users because of their portability, robust connectivity, and ability to house and operate third-party applications (apps). However, the apps installed on an MD may pose data security risks to the MD owner and to other MCC users, especially when the requested permissions include access to sensitive data (e.g., user’s location and contacts). Calculating the risk score of an app or quantifying its potential harmfulness based on user input or on data gathered while the app is actually running may not provide reliable and sufficiently accurate results to avoid harmful consequences. This study develops and evaluates a risk assessment framework for Android-based MDs that does not depend on user input or on actual app behavior. Rather, an app risk evaluator assigns a risk category to each resident app based on the app’s classification (benign or malicious) and the app’s risk score. The app classifier (a trained machine learning model) evaluates the permissions and intents requested by the app. The app risk score is calculated by applying a probabilistic function based on the app’s use of a set of selected dangerous permissions. The results from testing of the framework on an MD with real-life resident apps indicated that the proposed security solution was effective and feasible.

Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs’ knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarers. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed.

Today, Mobile Cloud Computing (MCC) alone can no longer respond to the increasing volume of data and satisfy the necessary delays in real-time applications. In addition, challenges such as security, energy consumption, storage space, bandwidth, lack of mobility support, and lack of location awareness have made this problem more challenging. Expanding applications such as online gaming, Augmented Reality (AR), Virtual Reality (VR), metaverse, e-health, and the Internet of Things (IoT) have brought up new paradigms for processing big data. Some of the paradigms that have emerged in the last decade are trying to alleviate cloud computing problems jointly. Mobile Edge Computing (MEC) and Fog Computing (FC) are the most critical techniques that serve the IoT. One of the common points of the above paradigms is the offloading of IoT tasks. This paper reviews machine learning-based computation offloading mechanisms in the edge and fog environment. This review covers three significant areas of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We discuss various performance metrics, tools, and case studies and analyze their advantages and disadvantages. We systematically elaborate on open issues and research challenges that are crucial for the next decade. [ABSTRACT FROM AUTHOR]

Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobile devices, the growth rate of mobile data has proliferated exponentially. Consequently, processing the tasks of mobile users has become difficult due to the limitations of these devices, such as low computing power and low capacity. Therefore, the idea of mobile cloud computing (MCC) for mobile devices using cloud-based storage and computing resources was introduced. In MCC, processing information is transferred from the user's mobile devices to the cloud servers. This process is known as the tasks offloading and scheduling of mobile users. In this case, the task execution time, CPU power consumption, network bandwidth, and task allocation time must be specified. Due to many tasks and different resources, the process of task offloading and scheduling is considered a challenging subject in the field of MCC. Therefore, in this paper, a multi-objective parallel imperialist competitive algorithm (MPICA) is proposed. The main objective of this parallel algorithm is to reduce the algorithm's execution time for searching the problem space, reducing processing time, reducing energy consumption, and improving load balance. The simulation results of the proposed algorithm represent that the parallelization of the imperialist competitive algorithm (ICA) has a significant effect on reducing the execution time of the algorithm. In general, the proposed algorithm performs better than the state-of-the-art algorithms based on the proposed criteria. [ABSTRACT FROM AUTHOR]

Answering a query through a peer-to-peer database presents one of the greatest challenges due to the high cost and time required to obtain a comprehensive response. Consequently, these systems were primarily designed to handle approximation queries. In our research, the primary objective was to develop an intelligent system capable of responding to approximate set-value inquiries. This paper explores the use of particle optimization to enhance the system's intelligence. In contrast to previous studies, our proposed method avoids the use of sampling. Despite the utilization of the best sampling methods, there remains a possibility of error, making it difficult to guarantee accuracy. Nonetheless, achieving a certain degree of accuracy is crucial in handling approximate queries. Various factors influence the accuracy of sampling procedures. The results of our studies indicate that the suggested method has demonstrated improvements in terms of the number of queries issued, the number of peers examined, and its execution time, which is significantly faster than the flood approach. Answering queries poses one of the most arduous challenges in peer-to-peer databases, as obtaining a complete answer is both costly and time-consuming. Consequently, approximation queries have been adopted as a solution in these systems. Our research evaluated several methods, including flood algorithms, parallel diffusion algorithms, and ISM algorithms. When it comes to query transmission, the proposed method exhibits superior cost-effectiveness and execution times. [ABSTRACT FROM AUTHOR]

Mobile devices can improve their battery life by offloading their tasks to a nearby cloudlet instead of executing tasks on the mobile device. Because mobile devices have low-speed processors, small-size memory, and limited battery. As the mobile devices are moving, they are connected and disconnected from the cloudlets. So, their tasks are offloaded to the new cloudlets and also migrated from one cloudlet to another until the tasks finish their execution. Scheduling these tasks in the cloudlet will reduce the tasks' execution time and the mobile device's power consumption using this proposed new method (AGWO). The GWO algorithm is modified to accept the inputs from a two-dimensional array instead of sequence inputs and search for the prey within the two-dimensional array instead of an unknown circle area. This method deals with the arrival time of the task, task size, and big task. The migration of the partially executed task dynamically to other VMs is also examined. This proposed method also reduces the average scheduling delay and increases the percentage of requests executed by the cloudlet than other variations of GWO and other research algorithms.

The Internet of Things (IoT) was introduced as a recently developed technology in the telecommunications field. It is a network made up of real-world objects, things, and gadgets that are enabled by sensors and software that can communicate data with one another. Systems for monitoring gather, exchange, and process video and image data captured by sensors and cameras across a network. Furthermore, the novel concept of Digital Twin offers new opportunities so that new proposed systems can work virtually, but without differing in operation from a "real" system. This paper is a meticulous survey of the IoT and monitoring systems to illustrate how their combination will improve certain types of the Monitoring systems of Healthcare–IoT in the Cloud. To achieve this goal, we discuss the characteristics of the IoT that improve the use of the types of monitoring systems over a Multimedia Transmission System in the Cloud. The paper also discusses some technical challenges of Multimedia in IoT, based on Healthcare data. Finally, it shows how the Mobile Cloud Computing (MCC) technology, settled as base technology, enhances the functionality of the IoT and has an impact on various types of monitoring technology, and also it proposes an algorithm approach to transmitting and processing video/image data through a Cloud-based Monitoring system. To gather pertinent data about the validity of our proposal in a more safe and useful way, we have implemented our proposal in a Digital Twin scenario of a Smart Healthcare system. The operation of the suggested scenario as a Digital Twin scenario offers a more sustainable and energy-efficient system and experimental findings ultimately demonstrate that the proposed system is more reliable and secure. Experimental results show the impact of our proposed model depicts the efficiency of the usage of a Cloud Management System operated over a Digital Twin scenario, using real-time large-scale data produced from the connected IoT system. Through these scenarios, we can observe that our proposal remains the best choice regardless of the time difference or energy load. [ABSTRACT FROM AUTHOR]

On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains. Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’ cognitive capacity. However, the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines (VMs). Furthermore, any delays at the cloud end would further aggravate the miseries of real-time tasks. To resolve these issues, this paper proposes an auto-scaling framework (ACF) that strives to maintain the quality of service (QoS) for the end users as per the service level agreement (SLA) negotiated assurance level for service availability. In addition, it also provides an innovative solution for dealing with the VM startup overheads without truncating the running tasks. Unlike the waiting cost and service cost tradeoffbased systems or threshold-rule-based systems, it does not require strict tuning in the waiting costs or in the threshold rules for enhancing the QoS. We explored the design space of the ACF system with the CloudSim simulator. The extensive sets of experiments demonstrate the effectiveness of the ACF system in terms of good reduction in energy dissipation at the mobile devices and improvement in the QoS. At the same time, the proposed ACF system also reduces the monetary costs of the service providers. [ABSTRACT FROM AUTHOR]

Nowadays, mobile devices can run a wide range of programs, and they all require more and more processing power. Due to their limited resources, mobile devices often make use of cloud computing $'\text{s}$ offloading features to do more complex tasks. The offloading problem in Mobile Cloud Computing (MCC) is the task scheduling problem, which entails deciding where to dump work to maximize its value. The task scheduling problem in MCC is an NP-hard problem because of the difficulty in moving resources and the size of the search space required to find the ideal scheduler, making the use of extensive search techniques impractical. For this reason, metaheuristic search strategies are provided, to yield a best-case or near-best-case scenario in terms of job completion time and energy savings. This work provides a non-dominated multi-objective strategy based on the Harris Hawks Optimization (HHO) technique called Hybrid Multi-objective Harris Hawks Optimization (HMHHO) to handle the described issue in MCC. The objectives of this research were allocating jobs from mobile source nodes to processors in the public cloud, cloud patches, and processors in mobile resources. In comparison to the other four algorithms—the Genetic Algorithm (GA), the Ant Colony Optimization (ACO), the Particle Swarm Optimization (PSO), and the Cuckoo Search Algorithm (CSA) the proposed method completes jobs faster and uses less energy on average.

Mobile code offloading is a well-known technique for enhancing the capabilities of mobile platforms by transparently leveraging the resources to the cloud. Although this technique has been studied for years, little empirical evidence exists to demonstrate its alleged benefits in terms of performance in real-life situations. All studies conducted on this topic have so far been relegated to controlled environments in laboratory settings. As such, there is no evidence of how and how well this technique performs in real-life scenarios, where network unreliability is the norm. In this work, we present the first empirical study of an Android mobile application integrated with a code offloading framework being tested in the wild. We distributed an application that contains a set of benchmarks in APK format and deployed it on a wide gamut of Android devices to which we had no physical access. We carefully detail the methodology and infrastructure we used to monitor the benchmarks’ performance of 18 devices. Overall, our results show that the accuracy of the decision-making engine is heavily affected by a couple of factors, mainly the network diagnosis and connection type. Therefore, determining whether or not it is more convenient to execute a given task in the cloud is a difficult task. We summarize five lessons we learned by performing our experiment that we believe should be considered for future experiments in this area.

Abstract With the continuous spread of COVID-19 virus, how to guarantee the healthy living of people especially the students who are of relative weak physique is becoming a key research issue of significant values. Specifically, precise recognition of the anomaly in student health conditions is beneficial to the quick discovery of potential patients. However, there are so many students in each school that the education managers cannot know about the health conditions of students in a real-time manner and accurately recognize the possible anomaly among students quickly. Fortunately, the quick development of mobile cloud computing technologies and wearable sensors has provided a promising way to monitor the real-time health conditions of students and find out the anomalies timely. However, two challenges are present in the above anomaly detection issue. First, the health data monitored by massive wearable sensors are often massive and updated frequently, which probably leads to high sensor-cloud transmission cost for anomaly detection. Second, the health data of students are often sensitive enough, which probably impedes the integration of health data in cloud environment even renders the health data-based anomaly detection infeasible. In view of these challenges, we propose a time-efficient and privacy-aware anomaly detection solution for students with wearable sensors in mobile cloud computing environment. At last, we validate the effectiveness and efficiency of our work via a set of simulated experiments.

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), andUtility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit. [ABSTRACT FROM AUTHOR]

Healthcare is a fundamental part of every individual's life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture. We compare them, to identify the strengths and weaknesses of each algorithm; and provide their comparative results, to show latency and energy consumption performance. Challenging issues for cloudbased healthcare systems are discussed in detail. [ABSTRACT FROM AUTHOR]

Mobile cloud computing (MCC) is an emerging concept that is gaining popularity in the IT sector. It is a significant topic of debate because it is being discussed as one of the most important trends for the future. After the COVID-19 pandemic, we saw the rapid emergence of mobile computing, which also created massive hype over mobile application usage. This survey paper's introduction is a literature review of the latest advanced prominent publications, highlighting its definition, infrastructure, advantages/limitations, and challenges, and is followed up with a discussion and results section, concluding with discussions on the future of MCC work that will be undertaken in the coming years. [ABSTRACT FROM AUTHOR]