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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]

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy -hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption. [ABSTRACT FROM AUTHOR]

Cloud computing is an on-demand model of computing that utilizes virtualization expertise to offer cloud resources such as CPU, memory, storage, and network in virtual machines. As a result, most big data analytics in many modern enterprise applications are run from the cloud. Since resources in these private clouds are limited, getting the most out of resource applications and providing guaranteed user service by efficiently scheduling tasks and resources is the ultimate goal. However, existing big data processing system schedulers need to consider application performance and resource utilization when performing allocations. Therefore, it is difficult to design workflows for low turnaround time and high resource consumption in extensive data systems. In this paper, we propose a resource management system for efficient job scheduling, called RMS, which dynamically schedules big data jobs in Kubernetes cluster nodes for Spark applications and autonomously adjusts scheduling policies in heterogeneous node clusters to enhance application execution and resource consumption. The RMS mechanism will ensure adequate guidance and resources available in its planning objectives and satisfactory resource utilization. The experimental analysis of different RMS and performance preferences using different methods depends on the predicted completion time and the benchmark statistical result of different significant data performance indicators traces. The results show that RMS decreases the cost and scheduling overhead and improves job execution performance. [ABSTRACT FROM AUTHOR]

In the context of the 6th generation (6G) network, UAV-aided networking is emerging as a promising solution to burst traffic offloading and on-demand service coverage. Given the dynamic networking environment, a UAV migration scheme needs to be as flexible, efficient, and adaptive as possible. To address this issue, a UAV migration-based decision-making scheme is put forward to make a tradeoff between migration costs and the satisfaction of service requirements. In the scheme, a UAV adaptive migration strategy (UAMS) is adopted to improve the migration efficiency. Meanwhile, the corresponding signaling interaction process is designed according to different UAV migration scenarios. The experimental results show that the proposed approach makes a significant improvement in the performance of the whole system and takes obvious advantage over traditional solutions. [ABSTRACT FROM AUTHOR]

Quantum computers (QCs) aim to disrupt the status-quo of computing – replacing traditional systems and platforms that are driven by digital circuits and modular software – with hardware and software that operate on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve 'quantum computational supremacy' over traditional, i.e., digital computing systems. Currently, the issues that impede mass-scale adoption of quantum systems are rooted in the fact that building, maintaining, and/or programming QCs is a complex and radically distinct engineering paradigm when compared to the challenges of classical computing and software engineering. Quantum service orientation is seen as a solution that synergises the research on service computing and quantum software engineering (QSE) to allow developers and users to build and utilise quantum software services based on pay-per-shot utility computing model. The pay-per-shot model represents a single execution of instruction on quantum processing unit and it allows vendors (e.g., Amazon Braket) to offer their QC platforms, simulators, and software services to end-users. This research contributes by (i) developing a reference architecture for enabling Quantum Computing as a Service (QCaaS), (ii) implementing microservices with the quantum-classic split pattern as an architectural use-case, and (iii) evaluating the architecture based on practitioners' feedback. The proposed reference architecture follows a layered software pattern to support the three phases of service lifecycle namely development , deployment , and split of quantum software services. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting QCaaS. [ABSTRACT FROM AUTHOR]

Cloud computing represents one of major innovations in Information Technology (IT). It is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. Elasticity is one of the most important characteristic in the cloud. Due to the enormous amounts of energy consumed by a cloud, it becomes difficult to build an elasticity system that satisfies all the requirements that might arise during its lifetime. Different approaches are proposed to address the energy-efficiency in the modelling of elasticity system at multiple layers of cloud services. Most of the existing approaches measure the overall hardware energy consumption, instead of the energy consumption of the software. Understanding how energy is consumed by cloud with elastic scaling mechanism is a key for managing better energy efficient software. This work proposes an architecture for modelling elasticity and energy-efficiency in the application layer. This architecture combines both the characteristics of adaptation (with Autonomic Computing) and variability (with Feature Model) into a single solution. The feature model considers the variability in the structural modelling and the behaviour one. We show the feasibility of the proposed approach by analysing the smart university applications associated with the Znn.com scenario. [ABSTRACT FROM AUTHOR]

Now more than ever, optimizing resource allocation in cloud computing is becoming more critical due to the growth of cloud computing consumers and meeting the computing demands of modern technology. Cloud infrastructures typically consist of heterogeneous servers, hosting multiple virtual machines with potentially different specifications, and volatile resource usage. This makes the resource allocation face many issues such as energy conservation, fault tolerance, workload balancing, etc. Finding a comprehensive solution that considers all these issues is one of the essential concerns of cloud service providers. This paper presents a new resource allocation model based on an intelligent multi-agent system and reinforcement learning method (IMARM). It combines the multi-agent characteristics and the Q-learning process to improve the performance of cloud resource allocation. IMARM uses the properties of multi-agent systems to dynamically allocate and release resources, thus responding well to changing consumer demands. Meanwhile, the reinforcement learning policy makes virtual machines move to the best state according to the current state environment. Also, we study the impact of IMARM on execution time. The experimental results showed that our proposed solution performs better than other comparable algorithms regarding energy consumption and fault tolerance, with reasonable load balancing and respectful execution time. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly approaching its capability as the next Digital uprising. It enables smart objects to interact and collaborate, allowing us to share information that helps us to maintain a healthy lifestyle. Cloud computing, on the other hand, delivers ondemand, flexible, and adaptable network access, permitting devices to access computational resources which facilitates the integration of unstructured information from multiple data sources. Cloud and IoT services have profound influences that can have a significant effect on today's ever-growing digital life, as well as the need to address a myriad of challenges for each system, namely scalability, security, and reliability with real-world objects in a more versatile and decentralized manner. Therefore, IoT requires more data storage and computation power. Cloud computing delivers on-demand accessibility of network assets especially for data storage and computing power to share the resources. Several architectures have been undergone with the literature to integrate these two technologies by using current computing paradigms, namely FOG and EDGE computing. This platform can use Cloud resources and services to gather, transmit, analyze, process, and store data created by a variety of sources. With this integration, we can resolve storage and computation issues. Moreover, in many applications, the integration plays a vital role in producing automated services and solutions without any delay. [ABSTRACT FROM AUTHOR]

Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms. [ABSTRACT FROM AUTHOR]

Cloud Computing is one amid emerging technology greatly necessitated aiding computing on demand services by letting users for subsequent pay-per-use-on-demand scheme. The service cloud providers have non insignificant impacts on ideal resources exploitation and cost benefit in case of Energy aware task scheduling in cloud. Presently Minimum Migration Time (MMT) policy was employed for Virtual Machines migration and offering an energy proficient cloud service. Nonetheless prevailing methodologies never concentrated on any security for cloud. The confidential data protection is highly demanded now-a-days due to increasing users for cloud computing. Hence robust security system for cloud computing is greatly demanded by various cloud researchers. An enhanced approach is presented for mitigating these concerns in which Artificial Bee Colony Optimization (ABC) is deployed for queuing all incoming tasks into multi-level. Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms are checked initially. The SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms integration is done for evading job starva¬tion probability in SJF-MMBF. The over utilized host detection is achieved through Adaptive Neuro Fuzzy Inference System (ANFIS) and Virtual Machines (VMs) migration is attained via Minimum Migration Time (MMT) policy from over-utilized hosts to other hosts for energy consumption reduction. Also, security in cloud is greatly achieved by presenting a novel cryptographic technique. There are several advantages such as sharing hardware, software and losing data fear deficiency and due to which current demand for cloud computing is greatly necessitated. The significant information on cloud is maintained by business person, hence data security is vital concern as there is hacking and unauthorized access probability. Here cloud data encryption is attained through elliptic curve cryptography, hence successful and secure storage on cloud is accomplished thereby. The authorized user might access cloud data via key in the suggested system. [ABSTRACT FROM AUTHOR]

Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO. [ABSTRACT FROM AUTHOR]

This paper mainly proposes and solves a two-phase truck-cargo (T-C) matching model for truck alliance and task set in drop-and-pull (D-P) transport. Firstly, the candidate trucks were selected for each task through feature matching between the set of trucks and the set of tasks. Next, the trucks were grouped into a truck alliance; the demand-capacity fitness (DCF) between the truck alliance and task set was computed based on the demand vector of the task set and the demand vector of the truck alliance; a T-C matching model for truck alliance and task set was established to maximize the DCF. Finally, the GA was introduced to solve the proposed model on MATLAB with and without truck alliance. The solving results show that the truck alliance can effectively improve the DCF, and optimize the allocation of social resources. [ABSTRACT FROM AUTHOR]

Cloud computing is a hybrid paradigm which makes use of utility computing, high performance cluster computing and grid computing and it offers various benefits such as flexibility, expandability, little or almost no capital investment, disaster recovery, moveable work space and much more. However, due to constantly increasing number of data centers worldwide, the issue of energy consumption by these data centers has attracted attention of researchers. Resource allocation and resource utilization are the major criterion in which the problem of energy efficiency can be addressed. In this research, we aim to provide energy-efficient resource allocation using Multi-Objective Optimization (MOO) method. Further, We propose MOO-based virtual machine (VM) allocation policy and implement it in CloudSim environment. The results are compared with existing policies. The results depict that MOO-based policy leads to saving in energy due to efficient resource allocation, without compromising performance of data center operations. [ABSTRACT FROM AUTHOR]

Cloud computing has become a standard and promising distributed computing framework for the provision of on-demand computing resources and pay-per-use concepts. Operations of these computing resources result in maximum power consumption, enraged cost and high Co 2 emission to the environment. The major difficulties faced when accessing cloud data center are SLA violations, increased time, less utilization of resources, high consumption of power and energy. Hence, considering these difficulties, a novel virtual machine (VM) selection approach is proposed to minimize the constraints while maintaining the SLA. First, based on the assumptions of VMs and physical machines (PMs), the overutilized hosts are detected using a static threshold approach, while underutilized hosts are identified based on the utilized resources. After load detection, the VMs that need to be migrated over other PMs are selected using the tweaked chimp optimization algorithm (TCOA). After selecting VMs without influencing the capacity of other VMs, the placement process is performed over other PMs using a power aware best fit decreasing approach. The proposed approach can greatly improve the QoS by selecting the optimal VMs that need to be migrated. Cloudsim is used as a simulation tool, and the results are compared with existing techniques in terms of migration time, energy consumption, SLA violation per host and so on to prove the superiority. The energy consumption of the proposed model is obtained to be 195.3 kWh, the overall SLA violation rate is attained to be 0.032%, and the migration time for 500 virtual machines is 8.72 s • A novel virtual machine (VM) selection approach is proposed to minimize the constraints while maintaining the SLA. • The VMs that needs to be migrated over other PMs are selected using the tweaked chimp optimization algorithm (TCOA). • The placement process is performed over other PMs using a power aware best fit decreasing approach. [ABSTRACT FROM AUTHOR]

The rapid expansion of technologies, especially Internet and mobile technologies has enabled the development of many mobile applications that facilitate travel and tourism. This study proposes and empirically tests the extended expectation-confirmation model (EECM) with two additional constructs—self-efficacy and perceived value—to explain the continued usage of on-demand ride services/ride hailing applications (ODRHA) by riders. Results strongly support the integrated model of TAM and ECM for predicting post adoption behavior of ODRHA customers. They also show that perceived value, self-efficacy and satisfaction contribute significantly to the continued usage of app-based services. The study offers valuable insights for ODRHA service providers in understanding rider behavior and following best practices to achieve higher rate of continued usage of ODRHA services through mobile applications. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the paradigms that have undertaken to deliver the utility computing concept. It views computing as a utility similar to water and electricity. We aim in this paper to make an investigation of two highly efficacious Cloud platforms: Microsoft Azure (Azure) and Amazon Web Services (AWS) from users' perspectives the point of view of users. We highlight and compare in depth the features of Azure and AWS from users' perspectives. The features which we shall focus on include (1) Pricing, (2) Availability, (3) Confidentiality, (4) Secrecy, (5) Tier Account and (6) Service Level Agreement (SLA). The study shows that Azure is more appropriate when considering Pricing and Availability (Error Rate) while AWS is more appropriate when considering Tier account. Our user survey study and its statistical analysis agreed with the arguments made for each of the six comparisons factors. [ABSTRACT FROM AUTHOR]

The growth in customer requirements, big data analysis and pressures on response time, high costs of network platforms pushed companies to migrate to Cloud Computing providing on demand internet hosted IT services. The increase of Cloud users and their interactions with the Cloud infrastructure raise the risk of resources faults. Such a problem can lead to a bad reputation of the Cloud environment, which slows down the evolution of this paradigm. The dynamic architecture and the complex system of the Cloud should be taken into account. In fact, this paradigm Cloud requires that resources protection and healing must be effective, transparent and without external intervention. Thus, it is essential the use fundamental aspects of autonomic Computing in the Cloud to deal with the self-healing of Cloud security. The high degree of match between autonomic Computing systems and multi-agent systems permit to create an intelligent architecture Cloud that support autonomic aspects. Therefore, we propose a cooperative framework based on Hybrid intrusion detection system (Hy-IDS), mobile Agents and Firewall, which permit to detect both insider and outsider attacks with high detection accuracy in Cloud environment. In this paper, we propose a Cloud Computing framework offering the access security, ease of resources management using mobile agents and service availability in a reliable structure with lower cost. [ABSTRACT FROM AUTHOR]

The Cloud Computing is growing rapidly in the current IT industry. Cloud computing has become a buzzword in relation to Grid & Utility computing. It provides on demand services to customers and customers will pay for what they get. Various "Cloud Service Provider" such as Microsoft Azure, Google Web Services etc. enables the users to access the cloud in cost effective manner. However, security, privacy and integrity of data is a major concern. In this paper various security challenges have been identified and the survey briefs the comprehensive overview of various security issues in cloud computing. The classification of security issues in cloud computing have been studied. In this paper we have discussed security challenges in cloud computing and also list recommended methods available for addressing them in the literature. [ABSTRACT FROM AUTHOR]

Compute Cloud comprises a distributed set of High-Performance Computing (HPC) machines to stipulate on-demand computing services to remote users over the internet. Clouds are capable enough to provide an optimal solution to address the ever-increasing computation and storage demands of large scientic HPC applications. To attain good computing performances, mapping of Cloud jobs to the compute resources is a very crucial process. Currently we can say that several effcient Cloud scheduling heuristics are available, however, selecting an appropriate scheduler for the given environment (i.e., jobs and machines heterogeneity) and scheduling objectives (such as minimized makespan, higher throughput, increased resource utilization, load balanced mapping, etc.) is still a diffcult task. In this paper, we consider ten important scheduling heuristics (i.e., opportunistic load balancing algorithm, proactive simulation-based scheduling and load balancing, proactive simulation-based scheduling and enhanced load balancing, minimum completion time, Min-Min, load balance improved Min-Min, Max-Min, resource-aware scheduling algorithm, task-aware scheduling algorithm, and Sufferage) to perform an extensive empirical study to insight the scheduling mechanisms and the attainment of the major scheduling objectives. This study assumes that the Cloud job pool consists of a collection of independent and compute-intensive tasks that are statically scheduled to minimize the total execution time of a workload. The experiments are performed using two synthetic and one benchmark GoCJ workloads on a renowned Cloud simulator CloudSim. This empirical study presents a detailed analysis and insights into the circumstances requiring a load balanced scheduling mechanism to improve overall execution performance in terms of makespan, throughput, and resource utilization. The outcomes have revealed that the Suffer age and task-aware scheduling algorithm produce minimum makespan for the Cloud jobs. However, these two scheduling heuristics are not effcient enough to exploit the full computing capabilities of Cloud virtual machines. [ABSTRACT FROM AUTHOR]

• Achieving sustainability in affordable housing remains a challenge. • Modern construction methods are proposed to address this challenge. • There is no comprehensive index to validate the sustainability of construction methods. • A sustainable innovative affordable housing (SIAH) index has been developed. • Monolithic prefabricated structures have shown the highest sustainability performance. Recently, efforts to mainstream sustainability principles into affordable housing projects through the adoption of modern methods of construction, like innovative prefabricated construction methods (IPCMs), have been observed. However, limited studies have evaluated the sustainability performance of IPCMs deployed to affordable housing projects in Southern Africa, where the delivery of sustainable affordable housing remains a regional imperative. This study bridges this gap by assessing the sustainability performance and index scores of six IPCM variants implemented on affordable housing projects. The study adopted a two-phased quasi-experimental research design. In the first phase, the technical specification, lifecycle energy and cost and actual performance of these variants under varying conditions pertaining to sustainability were assessed and compared. The Sustainable, Innovative, Affordable Housing (SIAH) framework was adjusted to enhance its utility for computing sustainability index scores using expert opinions. Afterwards, the experts computed the sustainability index score for each case using the adjusted SIAH framework. Subsequently, the Edge App and SimaPro were used to simulate the sustainability performance of these variants under varying conditions. The study's results indicate higher overall sustainability performance of IPCMs compared to conventional methods. Moreover, the research results proved that the monolithic IPCMs were more suitable for net-zero carbon buildings. The study highlights the SIAH framework's usefulness as an assessment tool for determining the sustainability performance of affordable housing projects and for successfully selecting optimum IPCMs to implement towards eliciting such performance. It is expected that this study's results will contribute towards advancing the SIAH agenda in Southern Africa. [ABSTRACT FROM AUTHOR]

Cloud computing has a model for utility computing that promotes on-demand scalability, flexible application deployment and reuse. It also promotes multi-tenancy for efficient resource utilization by sharing hardware and software infrastructure among multiple users. Multi-tenant applications running on a cloud infrastructure are provided to clients as Software-as-a-Service (SaaS) over the network. In this context, Software product lines (SPL) promote reusable application development for product families. On the other hand, business processes of most organizations are automated to support the advanced workflow management systems. In the context of the e-learning applications, it is perfectly suitable to manage the e-learning application by automating the e-learning process to effectively manage multiple tenants’ (institutions/teachers) ever changing requirements. As the available LMSs such as Moodle, Claroline and WebCT do not satisfy all the needs of different institutions/teachers, their demand is to develop their own systems. Cloud-based applications can be modeled as Business Process-as-a-Service (BPaas) families similar to the SPL products. As SPL development techniques rely on feature models to describe the commonality and variability of family member applications, such techniques can be used to model variability in BPaas. This paper proposes a generative multi-tenant e-learning process, that utilizes Business Process Feature Model (BPFM) approach which is a combination of SPL approach and BPM approach for modeling the reconfigurable e-learning process with multi-tenancy capabilities in cloud computing (BPaas). [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) technology enables Dynamic Spectrum Access (DSA) to improve the efficiency of under-utilized licensed bands and overcrowded unlicensed bands. However, providing an acceptable service for cognitive users requires more sophisticated approaches due to the existence of Primary Users (PU) with high priority over licensed bands. Additionally, it is of paramount importance that interference with PUs be reduced so that they can communicate without interruption. In order to meet requirements of users as much as possible and reduce interference with PUs, a new Class-based Multi-path Ondemand Routing protocol for Cognitive radio networks (CMORC) is proposed. CMORC is a multipath routing protocol, which defines two classes of routing to make delay or interference optimum. To the best of our knowledge, it is the first multipath protocol that takes into consideration both route discovery and rate allocation. CMORC finds the optimum sending rates for each path by solving an optimization problem for each routing class. Since solving these optimization problems is shown to be easy, CMORC is an efficient and practical solution for radio equipment with limited hardware. The simulation results reveal that CMORC outperforms the other recent multipath routing protocol, D2CARP, in terms of interference, delay and packet delivery ratio depending on class type. [ABSTRACT FROM AUTHOR]

The measurement of an individual’s heart rate has many relevant theoretical implications for communication research. However, there are reasons to believe that the most common way of reporting cardiac response—beats per minute (BPM)—might not be appropriate for all methodological situations. This article presents an overview of the historical use of cardiac activity within communication research, and provides a summary of the current conceptualizations of attention and resource allocation. To fully understand the difference between BPM and interbeat interval (IBI)—an alternate method for reporting cardiac response—a comparative analysis was performed on a data set from a previously published study. This article concludes with a set of suggestions that researchers may want to consider when utilizing BPM or IBI within communication research. [ABSTRACT FROM AUTHOR]

This paper explores the context of remote monitoring of parameters acquired through SCADA (Supervisory Control and Data Acquisition) systems, with particular focus on parameters that change infrequently (temperature, light levels, humidity, etc.). We aim to design a simple and cost-effective framework that allows a DAS (Data Acquisition System) with networking capabilities to make its collected data available to multiple distributed clients. [ABSTRACT FROM AUTHOR]

Cloud computing brings efficiency improvement on resource utilization and other benefits such as on-demand service provisioning, location independence and ubiquitous access, elastic resource pooling, pay as usage pricing mode, etc. However, it also introduces new security issues because the data management and ownership are separated, and the management is operated on a virtualized platform. In this paper, a novel dynamic secure interconnection (DSI) mechanism is proposed to isolate the cloud computing system into a couple of dynamic virtual trust zones with different security policies implemented for different customers so as to enhance security. Experimental results are presented to demonstrate the feasibility and effectiveness of the DSI mechanism. [ABSTRACT FROM AUTHOR]

Wireless Mesh network (WMN) is multi-hop heterogeneous network, which breaks shortcomings of traditional wireless network. WMN comes into sight with more advantages. To the multi-radio multi-channel (MRMC) wireless mesh networks (MRMCWMN), routing distribution, channel assignment and rate allocation can be united to optimize network performance. MRMC resource optimization method based on convex theory for WMN is presented in this paper. According to convex theory, we use the convex optimization function to get the optimal solution in the limited network supporting by Ad hoc On-Demand Distance Vector (AODV) routing method. At the same time, we resolve the optimal solution into three simple sub-problems according to the Lagrange duality method supporting by multi-radio multi-channel AODV (MAODV). We use MATLAB simulation tools to simulate the new and improved optimization method. The experimental results show that our method has better network performance in the applications of WMN. [ABSTRACT FROM AUTHOR]

The article presents a case study analyzing of on-demand work platforms such as ridesharing service provider Uber and Amazon Mechanical Turk. Topics discussed include terms of service or participation agreements that both parties have to agree to, legal issues concerning status and statutory protection of workers and indicators of treatment of sickness, benefits and overtime.

In TV White Space, a secondary user must access periodically to a geolocated database to acquire the spectrum availability information. Furthermore, it can access on-demand to the database to update such an information. The more frequent are the on-demand accesses, the higher are the communication opportunities available to the secondary user but the higher is the induced overhead. Hence, in this manuscript, the on-demand access is investigated to a-priori determine whenever it is advantageous to perform it by accounting for the correlation exhibited by primary traffic patterns. To this aim, first the on-demand access is modeled through the general notions of reward and cost. Then, it is proved that the on-demand access maximizing the total reward available to the secondary user is a Markov Decision Process. Stemming from these results, a computational-efficient algorithm is designed. Finally, the theoretical analysis is validated through numerical simulations. [ABSTRACT FROM AUTHOR]

The exponential data growth and demands for high computing resources lead to excessive resource use in cloud data centers, which cause an increase in energy consumption and high carbon emissions in the environment. So, the high energy consumption, inefficient resource usage, and quality of service assurance (QoS) are major challenges for cloud data centers. The dynamic consolidation of Virtual Machines (VMs) is proven to be an efficient way to tackle these issues while reducing energy consumption and improving resource utilization in data centers. It reduces the number of active hosts for energy efficiency by switching under-utilized and idle hosts into lower power mode. So, several heuristics and Artificial Intelligence (AI) based VM consolidation approaches have been published in papers. Most existing approaches rely on the aggressive consolidation of VMs for energy efficiency, thus causing performance degradation and high SLA violation. However, an automated solution is needed to reduce energy consumption and SLA violation by ensuring efficient resource usage in the cloud data center environment. Therefore, this article proposes an energy-efficient autonomous VM consolidation (AVMC) mechanism that has Deep Reinforcement Learning (DRL) based agent for performing VM consolidation decisions. The DRL agent learns the optimal distribution of VMs in the data center, considering energy efficiency and QoS assurance. The real-time workload traces from PlanetLab have been used to validate the proposed mechanism. Experimental results reveal the superiority of the proposed AVMC system over the existing models. AVMC reduced the energy consumption and SLA violation rate significantly. • DRL-based Autonomous VM consolidation (AVMC) mechanism is presented for cloud data centers. • The main goal is to maximize energy saving and minimize SLA violations. • Real workload traces have been used for the experimental evaluation. • Several performance metrics have been used to compare the AVMC method with existing approaches. [ABSTRACT FROM AUTHOR]

The software industry faces a fundamental change from delivering software On-premises to delivering software On-demand. Well-established companies have to shift their focus from the (still) profitable On-premises market to an, as yet unprofitable, On-demand market to survive. This requires strategies to handle the transformation process. Based on five case studies and following Christensen’s disruptive innovation theory, we learn the successful strategies that software companies can adopt to handle a change from On-premises to On-demand delivery. These strategies include the general ideas that Christensen proposes and separate extensions for the software industry, suggesting an emerging area of IS research. [ABSTRACT FROM AUTHOR]

Cloud computing is the delivery of on-demand computing resources – everything from applications to datacenters over the internet on pay per use basis. Service composition in cloud is an important mechanism usually done through several scheduling policies or by dynamic resource allocation techniques. Services delivered to the users lack Quality of Service during this composition phase. Hence, an Agent-based cloud computing paradigm for resource allocation is proposed that consist of designing and developing software agents for cloud service discovery, finding appropriate service and service composition. Here, two types of agents are created – consumer agent that process the user requirements and service provider agent that clusters the output from consumer agent. Finally, each user is mapped to each producer. Experimental results shows efficient execution of user requests using agent with 100% success execution rate in a parallel manner than that of using Hadoop. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc networks (MANET) allow a set of wireless hosts to exchange information without any special infrastructure. The projct entitled “ Energy Optimization in Manets using On-Demand Routing Protocol” motivates the need for energy management in ad hoc wireless networks. Limited battery power is one of the most important issues in mobile ad hoc network as the mobile nodes operate in limited battery power. Also there occurs a problem of broken links due to the lack of energy which cause disorder in network system. Such problem occurs due to the unawareness of energy of mobile neighbor nodes. This paper presents the implementation of Adaptive HELLO messaging scheme to determine the local link connectivity information for monitoring the link status between nodes along with the incorporation of Dynamic On Demand Routing Protocol to reduce the energy consumption of mobile nodes to certain extent. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multi-resource allocation problem, a unique feature of which is that the multiple resources can compensate each other while achieving the desired system performance. In particular, power and time allocations are jointly optimized with the target of energy efficiency under the resource-limited constraints. Different from previous studies on the power-time tradeoff, we consider a multi-server case where the concurrent serving users are quantitatively restricted. Therefore user selection is investigated accompanying the resource allocation, making the power-time tradeoff occur not only between the users in the same server but also in different servers. The complex multivariate optimization problem can be modeled as a variant of 2-Dimension Bin Packing Problem (V2D-BPP), which is a joint non-linear and integer programming problem. Though we use state decomposition model to transform it into a convex optimization problem, the variables are still coupled. Therefore, we propose an Iterative Dual Optimization (IDO) algorithm to obtain its optimal solution. Simulations show that the joint multi-resource allocation algorithm outperforms two existing non-joint algorithms from the perspective of energy efficiency. [ABSTRACT FROM AUTHOR]

With the commercialization of fifth generation mobile networks (5G) and continuous innovation of the Internet of Things (IoT), crowdsensing, as a new emerging IoT application, has further deepened our knowledge, utilizing various smart objects distributed throughout the whole industrial system for data collection and processing. To avoid a single point of failure and ensure data integrity, blockchain technology was widely applied. However, due to the demand for computing power and resources, IoT devices with limited capabilities and resources are unsuitable for crowdsensing implementation in the blockchain network. Therefore, this paper proposes a lightweight blockchain-based crowdsensing model named CrowdLBM with a novel consensus mechanism based on global reputation to resolve the issues raised above. Furthermore, we propose a two-stage scheme and two types of smart contracts to support the crowdsensing process automatically without the intervention of any third party. Finally, we implement a blockchain prototype on Hyperledger Sawtooth, extensive experimental results show that the proposed CrowdLBM outperforms other crowdsensing systems in terms of both scalability and security. [ABSTRACT FROM AUTHOR]

Healthcare is of particular importance in everyone's life, and keeping the advancement of it on a good pace is a priority of any country, as it highly influences the overall well-being of its citizens. Each government strives to build a modern, intelligent medical system that provides maximum population coverage with high-quality medical services. The development of Information and Communication Technologies (ICT) significantly improves the accessibility and effectiveness of the healthcare system by forming the eHealth environment, thus, providing an opportunity to enhance the quality of patient care and significantly speed up the work of medical experts and reduce costs for medical services. Shifting medical services to digital and remote operations requires a lot of computational capabilities. Implementing new computing paradigms is prominent — remote services face new requirements due to the increasing data and demand for new computing solutions. Computing paradigms, e.g., Cloud, Edge, Mobile Edge Computing, besides others, are used to process the collected medical data, improving patient healthcare quality. This paper focuses on computing solutions for medical use cases by offering a comprehensive survey on standardization aspects, use cases, applicable computing paradigms, security limitations, and design considerations within the ICT usages for medical applications. Finally, it outlines the most critical integration challenges and solutions from the literature. [ABSTRACT FROM AUTHOR]

The Reverse k Nearest Neighbor (RkNN) query is valuable for finding objects influenced by a specific object and is widely used in both scientific and commercial systems. However, the influence level of each object is unknown, information that is critical for some applications (e.g. target marketing). In this paper, we propose a new query type, Ordered Reverse k Nearest Neighbor (ORkNN), and make efforts to adapt it in an on-demand scenario. An Order-k Voronoi diagram based approach is used to answer ORkNN queries. In particular, for different values of k, we pre-construct only one Voronoi diagram. Algorithms on both the server and the clients are presented. We also present experimental results that suggest our proposed algorithms may have practical applications. [ABSTRACT FROM AUTHOR]

The biggest advantage of employing virtualization in cloud is the ability to provision resource flexibly which makes "pay-as-use" model possible. However, since the workload of virtual machine constantly changes, it is still a challenge that efficiently schedule resource by migrating virtual machines among lots of hosts, especially with multi-objective to meet, like power and QoS constraints. In this paper, we present a dynamic resource scheduling solution, named Smart-DRS, which can well determines when to schedule, which to schedule, and where to schedule. It is an integrated framework that aims to deal well with hotspot mitigation, load balancing and server consolidation which are classic problems to solve in on-demand clouds. The experimental results show that our framework strikes a balance between efficiency, overhead and instantaneity. [ABSTRACT FROM AUTHOR]

In this paper we present a detail account of Mobile Ad-hoc On-Demand Data Delivery Protocol (MAODDP) power efficiency mechanism. MAODDP belongs to on-demand data delivery routing protocol family of Mobile ad-hoc network. It establishes route and deliver data simultaneously one after the other. The key feature of MAODDP is the integrated approach that enables the protocol to address various issues besides routing. Power conservation in mobile ad-hoc network is an important aspect to be considered. Unlike some of the traditional routing protocol MAODDP has its own power saving mechanism. MAODDP was implemented in Java and evaluated under Scalable Wireless Network Simulator (SWANS). A series of tests were run to monitor the efficiency of power aware operation of MAODDP. Evaluation results indicate that MAODDP is applicable in all types of simulation environments. Statistics showed that MAODDP saved 22 % more memory with an increased data delivery of 5 % under power saving mode. [ABSTRACT FROM AUTHOR]

Abstract: New markets for personalized and efficient transport are creating a need for on-demand mobility services. To rigorously analyse new control mechanisms for these services, we introduce an open-source agent-based simulation testbed that allows users to eva- luate the performance of multi-agent, on-demand transport schemes. In particular, our testbed provides a framework to compare both centralized and decentralized, static and dynamic passenger allocation and vehicle routing mechanisms under various con- ditions; including varying vehicle fleets, road network topologies and passenger demands. Our testbed supports all stages of the experimental process; from the implementation of control mechanisms and the definition of experiment scenarios, through to simu- lation execution, analysis, and interpretation of results. Ultimately, the testbed accelerates the development of control mechanisms for emerging on-demand mobility services and facilitates their comparison using well-defined benchmarks. [Copyright &y& Elsevier]

Abstract: A Virtual Organization (VO) is logical orchestration of globally dispersed resources to achieve common goals fostering new computing paradigms as Utility Computing, Grid Computing, Autonomic Computing, Clusters Computing and Cloud computing. The Computational Intelligence community is striving hard to build an online community to share resources such as data, paradigms, human expertise, models and methods. We build up on the N2Sky system, a Virtual Organization architecture for the Computational Intelligence community, which provides sharing and exchange of neural network resources, neural network specific knowledge, neural network objects and paradigms. In this paper we present novel extensions to N2Sky: automatic exploitation of parallelization capabilities of modern hardware (Multi-core servers, GPGPU) and softwares (OpenMP, CUDA), SLA management for Quality-of-Service guarantees of Cloud resources, and NN2Query, a novel query interface facilitating the user to find solutions to problems by smart searching of neural network resources using semantic web technology. [Copyright &y& Elsevier]

Abstract: The paper presents two mechanisms for designing an on-demand, reliable and efficient collection protocol for Wireless Sensor Networks. The former is the Bidirectional Link Quality Estimation, which allows nodes to easily and quickly compute the quality of a link between a pair of nodes. The latter, Hierarchical Range Sectoring, organizes sensors in different sectors based on their location within the network. Based on this organization, nodes from each sector are coordinated to transmit in specific periods of time to reduce the hidden terminal problem. To evaluate these two mechanisms, a protocol called HBCP (Hierarchical-Based Collection Protocol), that implements both mechanisms, has been implemented in TinyOS 2.1, and evaluated in a testbed using TelosB motes. The results show that the HBCP protocol is able to achieve a very high reliability, especially in large networks and in scenarios with bottlenecks. [Copyright &y& Elsevier]

Abstract: This paper proposes a three-stage approach to solve the order fulfillment problem of on-demand delivery service with large quantities of orders for bento (boxed meal) industry. At the first stage, a geographic information system (GIS) is used to locate the delivery destinations designated by customers. Then the k-means algorithm is utilized to cluster customer orders based on locations and according to the number of delivery vehicles. At the final stage, a genetic algorithm (GA) is employed to minimize the total travel distance of deliveries. Experimental results show that the proposed approach is highly feasible and very potential in dealing with the present order fulfillment problem. In addition, the influences of increasing the number of clusters on the profit and the service level are discussed. [Copyright &y& Elsevier]

Abstract: The software industry is in the middle of a paradigm shift from desktop to mobile and web-based software. In the new era, applications increasingly live on the Web as services that lend themselves for runtime configuration. The associated delivery model, referred to on-demand software, or Software-as-a-Service (SaaS), implies that applications do not require installation or manual upgrades by the end users, as they are loaded on the fly. Furthermore, applications that build on resources offered by other applications, referred to as mashups, offer increasingly interesting opportunities. We believe that the trend towards using the web as an application platform will only strengthen in the future, and that instead of individual applications, it will also be possible to use application components in the same way we today download complete applications – in essence the Web is used as an architecture and transport for distributed applications, similarly to e.g. Corba at the level of principal idea. In this paper, we provide an overview for mashware computing, where downloadable components form applications in a piecemeal fashion, and enable rich access to resources of devices in a programmer-friendly way. Furthermore, applications can be easily built by components created by others and available as services. Finally, we demonstrate the capabilities of the concept with a sample application and discuss the lessons learned during the design process. [Copyright &y& Elsevier]