Modern smart city services necessitate complex technological infrastructure with heterogeneous compute servers, networks, and communication protocols. However, there are many research issues in heterogeneous computing infrastructure for Intelligent transport systems (ITS) when using the services in the network. Therefore, the main objective of this paper is to intelligent transportation services and their underlying infrastructure, built on an amalgamation of the Internet of Things (IoT), cloud and fog computing, and associated technologies. Specifically, this paper investigates the challenging issues of security, processing costs, and communication delays that frequently occur during the communication of data and messages. We propose novel, secure, and cost-effective schemes based on blockchain-assisted homomorphic encryption techniques. A Secure, Cost-Optimal Workload Assignment (SCWA) algorithm and a blockchain scheme made possible by Partially Hashing Homomorphic Encryption and Decryption (PHHE/D) are designed to distribute workload efficiently. We developed a simulator, MOTEL, that simulates the different functions of the proposed schemes and all the necessary components. Using MOTEL and data sets from real transport companies, the proposed approach is tested and evaluated using various experiments. The results demonstrate that, compared to existing solutions, the proposed approach significantly reduces processing costs and delays while maintaining an appropriate level of security in transport services.

Cloud, Internet of Things (IoT), and big data have not only become the fundamental technologies and platforms in computing and network communication, but they have greater potential in a wide range of other areas such as business, finance, transportation, healthcare, social media, and engineering, among others. Cloud computing provides a large pool of compute and storage resources and enables transparent access to such resources through the Internet. IoT is used to connect physical devices through the Internet that can generate large volume of (big) data. For instance, IoT can connect most modern devices such as smart phones, smart TVs, traffic and weather sensors, and other smart digital devices that generate large volumes of the data. Thus cloud, IoT, and big data can be used together to offer new and exciting services in the aforementioned areas. This special issue has been organized to solicit papers via an open call as well as selected papers from the 7th International Conference on Future Internet of Things and Cloud (FiCloud-2019), IEEE TCI, and the 5th International Conference on Big Data Innovations and Applications (Innovate-Data 2019), Springer, which were held during August 26–28, 2019, in Istanbul, Turkey. The conferences were attended by a large number of participants from different countries across the world. The conferences featured a number of technical sessions of research papers, industry talks, and keynote talks. A number of workshops and symposia were also organized alongside the main conferences. This special issue aims to foster the dissemination of high-quality research with new ideas, approaches, theories and practice to solve the challenging issues in the design and development of new platforms and application areas related to the convergence of cloud, IoT, and big data. Some of the cutting-edge topics covered by the research articles in this special issue include: the design and development of new models and architectures, analysis and evaluation of security, privacy and trust models, performance evaluation, and data mining techniques. A number of papers were submitted to this special issue and each was reviewed by multiple referees. Based on the reviews, 13 papers were accepted for this special issue. The work presented in these papers is summarized as follow. Stavrinides and Karatza1 highlighted the significance of supplementary resources from public cloud to effectively handle the increasing IoT workflow jobs in a fog environment. As the cloud involves higher data transfer latency and monetary cost, the proposed approach takes into account these two factors, in addition to the real-time constraints of the workload. The proposed scheduling heuristic is based on the trade-off between performance and monetary cost. The proposed scheduling strategy is compared against a baseline policy that utilizes only the fog resources. The simulation experiments have been carried out under varying sizes of workflow input data and for workloads with soft and hard deadlines. Barati and Rana2 presented an investigation into the privacy of data in an emerging composite service provision from visible and invisible cloud service providers. This article presented an architecture based on blockchain and smart contracts to understand a challenging issue of the impact of users' data privacy in such environment. The implementation of the proposed architecture demonstrated effective access control management. The article also described the scalability and cost of undertaking this access control mechanism. The proposed approach can be used to support service aggregation across both private and public clouds. Afzal et al.3 carried out investigations into the challenges involved in unlicensed radio spectrum usage by increasing IoT devices leading to increased collisions with impact on the quality of service. This article proposed a cognitive radio (CR) based solution to address this problem. The network, formed by CRs, allows an unlicensed user to occupy and use the spectrum of a licensed user/primary user (PU) when the spectrum is found to be free. These two technologies are combined to maximize their advantages in an ad hoc manner while relating this concept with trunking theory. Experimental results show that this integration is beneficial for the survival of future wireless technologies. Onan4 analyzed sentiment as one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject can be extracted. The work presented in this article states that the recognition of sentiment can be helpful for individual decision makers, business organizations, and governments. A deep learning-based approach to analyze sentiments on product reviews obtained from Twitter has been proposed. The presented architecture combines TF-IDF weighted Glove word embedding with CNN-LSTM architecture. The empirical results indicate that the proposed deep learning architecture outperforms the conventional deep learning methods. Rjoub et al.5 highlighted the importance of deriving appropriate task scheduling mechanisms that can minimize tasks' execution delay and unnecessary cloud resources consumption. This article proposed four deep and reinforcement learning-based scheduling approaches to automate the process of scheduling large-scale workloads onto cloud computing resources, while reducing both the resource consumption and task waiting time. The proposed approaches include reinforcement learning (RL), deep Q networks, recurrent neural network long short-term memory (RNN-LSTM), and deep reinforcement learning combined with LSTM (DRL-LSTM). Experiments conducted using real-world datasets from Google Cloud Platform revealed that DRL-LSTM outperforms the other three approaches. Samir and Pahl6 addressed a critical problem of auto-scaling of cloud resources in a containerized cluster environment. A self-healing model for an identified anomalous behavior (e.g., overload, underload) to auto-scale compute resources in a containerized cluster environment has been proposed. The performance of the model has been demonstrated through an evaluation with different auto-scaling strategies based on the number of created/terminated containers, container migration, resource utilization, and response time. Singh et al.7 carried out investigations into the suitability of energy harvesting sensor (EHS) nodes, powered by indoor lighting and vibrations, for a simple temperature monitoring application. This article proposed an autoregressive (AR) model based adaptive sampling algorithm that switches to different sampling rates and resolutions depending on the energy available at the node and the variations in temperature. Experimental results showed that adaptive sampling and transmissions based on regulating function not only save energy at the sensor nodes, but also reduce the amount of data generated and accumulated in a network. Çiçek and Gören8 proposed a vision-based automated mobile cloud parking management solution, which can find road side parking spots in any street. This solution employs both object detection and road segmentation methods. Thus, it does not need to manually label and train for every distinct street and does not mark out parking spot boundaries and surrounding road areas. In this approach, fully convolutional networks, specifically the FCN-VGG16 model and KITTI road dataset are used for road segmentation, whereas Faster Region-based Convolutional Neural Networks and the Microsoft Common Objects in Context dataset are used for object detection. The main contribution is to differentiate the parked cars from the moving cars on the road and detect the available parking spot between the parked cars on the roadside, and direct the driver to the nearest spot. AlSuwaidan9 carried out an investigation into the role of data management solutions for Industrial Internet of Things (IIoT) which is an underlying platform for various smart manufacturers seeking effective and powerful structures. This article discussed the role of data management in supporting the IIoT and smart manufacturing and proposed a taxonomy to classify and categorize various important parameters such as data sources, machine learning, performance management, business intelligent, and big data. Using the oil and gas industry in Saudi Arabia as a case study, this work has explored various challenges for proposing an IoT-based reliable architecture in order to monitor the flow of operations in the oil and gas industry. Aslaoui Mokhtari et al.10 carried out an investigation into privacy, transparency, and fairness of big data which are considered critical for collecting and analyzing large scale data. This article proposed a novel framework that monitors personal data usage in a big data platform in order to ensure that data usage is compliant with the transparency and fairness as stipulated by Privacy Transparency Fairness Agreement (PTFA) rules. The evaluation of the proposed framework using multi-nodes cluster with millions of lines of data and thousands of contracts demonstrates its efficiency and scalability when monitoring transparency and fairness in big data collection, analysis, and usage. Perente and Serif11 addressed an interesting problem of proposing a robot-aided signal mapping system for indoor environments to replace time-consuming human interaction based processes. The proposed approach has been implemented using a low-cost, low-power, not off-the-shelf signal data collection robot. Its signal collection speed and location estimation precision are then compared with the manual method both in a small and large environments. The experimental results clearly demonstrate performance gains. Staifi and Belguidoum12 investigated the design of an adapted smart system for elderly health-state monitoring. The research work addressed some of the disadvantages of existing e-health solutions, such as the precision in monitoring patient health, security, accuracy of adapted service delivery, and the lack of patient-awareness. An improved smart contract, and service level agreement based system has been proposed to address the short comings of the existing approaches. The proposed system automatically assessed the degree of dependence by analyzing the activities of daily and domestic life. Hamd et al.13 addressed an important issue of load balancing in cloud computing due to an increasing number of users and the dynamic nature of the volume of traffic. This article proposed combining LTE-based multicellular 4G technologies. The investigation into this combined approach demonstrated the performance of different load balancer mechanisms, namely, static and semi-dynamic, using simulation based scenarios. The experimental results have been analyzed considering the Quality of Service (QoS) and computational resources for a deployed Web application. The papers presented in this special issue are related to the emerging topics in cloud computing, IoT, and big data. The topics covered in this collection include scheduling, security, formation of community, service negotiation, and data control, among others. We hope the readers will benefit from the research findings included in this special issue. We would like to express our special thanks to all authors who contributed to this special issue. We are also very thankful to all reviewers for participating in a rigorous review process and providing useful feedback to authors to enhance their work. Finally, we would like to express our special gratitude to the Editor in Chief, for providing us with this unique opportunity to present state of the art research work in a special issue of the international journal of Concurrency and Computation: Practice and Experience. We are also grateful to the publishing team for their hard work, timely response, and continued support to prepare the special issue at a very high standard.

Summary Big data comprises large volume of complex, (semi) structured, and unstructured data which are processed and analyzed in order to help organizations in making strategic decisions. This paper investigates into privacy, transparency, and fairness of big data. It contends that these factors are crucial and must be taken into account when collecting, analyzing, and using large scale personal data. It proposes a novel framework that monitors personal data usage in big data platform in order to ensure that data usage is compliant with the transparency and fairness as stipulated by Privacy Transparency Fairness Agreement (PTFA) rules. The proposed framework is implemented using Python and Java. It is evaluated using multi‐nodes cluster with millions of lines of data and thousands of contracts. Various experiments are conducted in order to evaluate the efficiency and scalability of the proposed framework. These show that the proposed framework is efficient and scalable when monitoring transparency and fairness in big data collection, analysis, and usage. Visualization experiments are also conducted in order to derive relevant indicators, for instance, to monitor non‐compliance of PTFA rules, visualize the alerts, and show the proportion of personal data being violated as compared to the number of contracts.

Cloud computing has become an exciting platform in modern IT era that provides on-demand services and resources such as compute power, memory, storage, networking, and various software services. A large number of small and medium size companies and big organizations have been exploiting cloud computing services for various applications in order to save time and cost as they do not have to maintain their own IT infrastructure. Cloud computing has various benefits such as flexibility, scalability, automatic maintenance of software, and anywhere and anytime service provisioning over the Internet.1 This special issue was organized to solicit papers via an open call as well as selected papers from the IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud 2016) and the 13th International Conference on Mobile Web and Intelligent Information Systems (MobiWis 2016), which were held in Vienna, Austria, 22-24 August 2016. The conferences were attended by a large number of participants from different countries across the world. The conferences featured a number of technical sessions of research papers, industry talks, and keynote talks. A number of workshops and symposia were also organized alongside the FiCloud and MobiWis conferences. This special issue focused on the emerging challenges and frontiers in cloud computing. The objective of this special issue is to showcase the most recent developments and research in the area of cloud computing such as cloud models and architectures, eg, Software as a Service (SaaS), Software-defined network (SDN), security in cloud computing, mobile cloud, cloud networking, and communication and cloud REST services. A number of papers were submitted to this special issue. Each of the paper was reviewed by multiple reviewers. Based on the reviews, eight papers were accepted for this special issue. The work presented in these papers is summarized as follows. Stavrinides and Karatza2 carry out investigations into the challenges involved in Software as a Service cloud computing. These challenges are mainly due to heterogeneity and multi-tenancy in the host environment. Authors argue that according to the Service Level Agreement (SLA) between the cloud providers and the end-users, the execution of applications must be time constrained. It has been claimed that the effective scheduling of multiple parallel applications is one of the most important aspects of SaaS in order to avoid any violation of SLA. Their work focuses on the enhancement of the most commonly used scheduling algorithms by utilizing approximate computation and evaluating the impact of the computational demand variability on the performance of the employed scheduling strategies. The simulated-based experimental results give a very interesting insight. One of the main objectives of Software-Defined Networking (SDN) is to enable cloud computing to be flexible and agile in order to support virtualized servers and data storage infrastructure of the data centers. Benkhelifa et al3 underpin a very interesting issue of combining mobile computing with cloud computing. The basic idea in this research is to create a cloud-computing environment by exploiting localized mobile devices. Through application of social models, they claim that the mobile devices can employ cooperative strategies for effective sharing of resources. They propose the design of a system that employs aggregated user and application resource profiling. This determines the optimal place for data processing and leads to finding a balance between optimum energy consumption and efficient resource utilization. Vinh et al4 investigate the security aspects of mobile cloud computing. One of the key challenges highlighted in this work is the assurance of trust on mobile devices, which play a key role in mobile cloud computing. Mobile device attestation has been recognized as a way to tackle this problem. The main contribution presented in this paper includes the property-based token attestation (PTA), which takes the advantage of existing techniques, such as Trusted Platform Module (TPM), Bring Your Own Device (BYOD), etc, to secure the mobile devices. A detailed analysis of the security threats has been included. This is followed by the proposed attestation schema for tackling these threats. Omezzine et al5 highlight the complexity of a cloud operational environment where the application providers want to maximize their profit and the end user demands high quality efficient application provisioning. The significance of negotiation has been addressed as a way forward to satisfy both service providers and service users. Two of the main characteristics of cloud application provisioning, which have been identified in this work, have been neglected in the current negotiation models. This work addresses both these properties and proposes a multi-layered negotiation framework that includes a generic negotiation model for cloud provisioning. The paper also presents an instantiation among cloud layers for efficient SaaS provisioning to demonstrate maximization of provider profit and increased satisfaction of users. Simulation-based experiments using multi-agent system technology demonstrate that negotiation improves the application provisioning in terms of profit for the SaaS provider and in terms of user satisfaction. Bull et al6 investigate the performance of periodic data flows on the real SDN hardware in a variety of situations. The work uses a Preemptive Flow Installation Mechanism (PFIM) to improve SDN performance for periodic traffic by limiting the number of controller look-ups through predicting when a flow is likely to need a particular rule. The experimental results demonstrate that the system can detect the majority of temporary periodic flows and preemptively install a flow rule, facilitating performance comparable to layer two switching speeds. Kim et al7 investigated the transmission of data in IoT devices using Low Power Wide Area Network (LPWAN), which is managed by a centralized server in the cloud. Their research highlights the vulnerability of the server in terms of response time and bandwidth utilization. To address this issue, the authors have proposed an interesting approach to move the control of LPWAN to the edge cloud. In the proposed approach, an LPWAN gateway is integrated with the edge cloud and LPWAN data is cached at the edge cloud in the gateway for LPWAN control. The proposed approach has demonstrated performance gains over the existing approaches and improves the data transmission response time while enhancing the bandwidth utilization. Khosrowshahi-Asl et al8 investigate a very complex issue of forming communities of identical or complementary cloud services for better visibility and efficiency. Contrary to most of the existing models, which require real-time global knowledge of services, this work proposes an efficient yet computationally cost-effective model based on partially available information to form communities. The proposed strategic Distributed Decision-making Mechanism (DDM) regulates the cloud services decision-making process. DDM generates an initial set of data based on information obtained from existing cloud services regarding their single and cooperative efficiency. This is then analyzed based on the distance function followed by the implementation of communities formation. The experimental results clearly indicate the performance gains over the existing solution. Mesfin et al9 work underpins an interesting framework of the REST services development for smartphones. The main objective of this work is to enhance the usability of composing services on smartphones. This work employed the design science research methodology to describe the new framework, REST4Mobile. The framework's usability evaluation is conducted using a systems usability scale. The evaluation results reveal that the guidelines in the REST4Mobile framework can enhance usability of REST services on smartphones. This encourages further studies on design approaches for enterprise level mobile end-users. The papers presented in this special issue are related to the emerging topics in cloud computing. The topics covered in this collection include scheduling, security, formation of community, service negotiation, and Data control, among others. We hope the readers will benefit from the research findings included in this special issue. Irfan Awan is a Professor of Computer Science at the University of Bradford. His research focuses on Network Security, Cyber Security, Performance Modeling of Cloud, and Communication Networks. His research work won Best Paper Awards from 24th IEEE AINA 2010, Australia and 8th BWCCA 2013, France. His research work has been published in IEEE Transactions, IEE Proceedings, IEEE Computer Magazine, among others. He is a member of editorial board of the Elsevier journal of Simulation, Modeling and Practice and served as a guest editor for various special issues. Professor Awan organized and chaired several international conference and workshops and edited proceedings. He has successfully supervised 34 Ph.D. students. Professor Awan is a fellow of HEA, fellow of BCS, and member IEEE. Muhammad Younas is a Reader in Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University, UK. His research interests include Web technologies, cloud and services computing, and Big data. He received a Ph.D. in Computer Science from the University of Sheffield, UK. He has published more than hundred papers in international journals and conferences. He is on the editorial and advisory boards of international journals and is also involved in the steering, organizing, and program committees of refereed international conferences and workshops. Farookh Khadeer is Associate Professor at the School of Computer Science, University of Technology, Sydney, Australia. He received the B.Tech. degree in Computer Science and Computer Engineering and the M.S. degree in Information Technology from the La Trobe University, Melbourne, Australia, and the Ph.D. degree in Information Systems from Curtin University of Technology, Perth, Australia, in 2006. His areas of active research are cloud computing, services computing, trust and reputation modeling, semantic web technologies, and industrial informatics. He works actively in the domain of cloud computing and business intelligence. In the area of business intelligence, the focus of his research is to develop smart technological measures such as trust and reputation technologies and semantic web for enhanced and accurate decision making. We would like to express our special thanks to all authors who contributed to this special issue. We are also very thankful to all reviewers for participating in a rigorous review process and providing useful feedback to authors to enhance their work. Finally, we would like to express our special gratitude to Professor Geoffrey Fox, the Editor-in-Chief, for providing us with this unique opportunity to present state-of-the-art research work in a special issue of the international journal of Concurrency and Computation: Practice and Experience.

Timely detection of a malicious piece of code accurately, in an enterprise network or in an individual device, before it propagates and mutate itself, is one of the most challenging tasks in the domain of cyber security. Millions of variants of each latest malware are released every day and each of these variants have a unique static signature. Conventional anti-malware tools use signatures and static heuristics of malware to segregate them from legitimate files, which is not an effective technique because of the number of malware variants released every passing day. To overcome the fundamental flaw of operational techniques, we propose a framework that generalizes the static and dynamic malware features that are used to train multiple machine learning algorithms. The generalization of clean and malicious features enables the framework to accurately differentiate between clean and malicious files.

Cloud service selection is complicated by the prospect that there exist a large number of services and each service is characterized by multiple QoS attributes. Various commercial tools have been developed in order to help cloud consumers with selecting best cloud services. This paper provides an in-depth analysis of the three commercial cloud service selection tools as well as the way they represent QoS attributes. Accordingly, it proposes a new model that succinctly represents QoS attributes which cloud consumers can easily use (and understand) when selecting cloud services. The proposed model also classifies the QoS attributes into four main categories of technical, strategic & organizational, economic and political & legislative. These QoS attributes can also be seamlessly fed into the multi-criteria decision (e.g., MCDM) - which compares and ranks different QoS attributes of multiple alternatives in order to decide which services are most suitable for cloud consumers.

Protection strategy against malware attacks, whether in enterprise networks or on personal computers is generally divided into two main aspects; the detection rate and the resource consumption. Having a better detection rate is not the solution unless it is complimented by energy efficiency with respect to its CPU resource consumption of the host machine. This paper presents a cloud-based energy efficient hosting model for an intelligent malware detection framework, which amalgamates different components of Amazon's cloud services to develop a unique and dynamically scalable hosting model. The hosting model is then evaluated separately for its CPU utilization and in comparison with the conventional security software. The experimental results demonstrate significant energy efficiency in terms of CPU utilization by the hosting model.

REST service are generally more appropriate for smartphones than its counterpart, SOAP. However, the flexibility, scalability and performance gained through REST services are never enough to meet the goal of addressing the complex and mobile relationships among people. That is, usability of the REST service must be enhanced for involving none-programmers to compose services on their smartphones thereby address the complex and mobile relationships among people. In this paper, we explored the factors influencing usability of the REST web service on the smartphone environment. Accordingly, the breadth and depth of URIs namespace, number of request parameters and response fields, and the naming of URIs, parameters and response fields are found to be the factors that influence the usability of composing REST services on smartphones.

NoSQL databases provide high availability and efficiency in data processing but at the expense of weaker consistency. In this paper, we propose a new approach in order to test NoSQL key/value databases in general and their CRUD operations in particular. We design a new context-aware model that takes into account the contextual requirements of clients (users) and the NoSQL database system. Accordingly, we develop a transaction model and testing criteria in order to test NoSQL databases by taking into account transactional and non-transactional CRUD operations. Results from testing criteria are used to analyse the trade-off between availability and consistency of NoSQL databases. In addition, these are used to help NoSQL database users and developers to choose between transactional and non-transactional CRUD operations.

Software-as-a-Service (SaaS) is a model of cloud computing in which software functions are delivered to the users as services. The past few years have witnessed its global flourishing. In the foreseeable future, SaaS applications will integrate with the Internet of Things, Mobile Computing, Big Data, Wireless Sensor Networks, and many other computing and communication technologies to deliver customizable intelligent services to a vast population. This will give rise to an era of what we call Big SaaS systems of unprecedented complexity and scale. They will have huge numbers of tenants/users interrelated in complex ways. The code will be complex too and require Big Data but provide great value to the customer. With these benefits come great societal risks, however, and there are other drawbacks and challenges. For example, it is difficult to ensure the quality of data and metadata obtained from crowd sourcing and to maintain the integrity of conceptual model. Big SaaS applications will also need to evolve continuously. This paper will discuss how to address these challenges at all stages of the software lifecycle.

Tenant level check pointing is a novel fault-tolerance technique proposed in our previous work for large-scale software-as-a-service applications. This paper evaluates the technique by a theoretical analysis of the technique and an empirical study using workload benchmarks of real large-scale cluster systems. We propose the notion of accumulated delay as a measurement of the performance of check pointing techniques. It reflects the effect of check pointing on system performance as experienced by the end users. Both theoretical analysis and empirical study show that tenant level check pointing significantly outperforms traditional bulk check pointing as measured by the amount of accumulated delays.

Summary Web services transactions are used to build efficient and reliable Web applications that are distributed across the Internet and are accessed by multiple simultaneous users. Current research develops various models and protocols to improve the performance and reliability of Web services transactions. However, there is little research on testing the different models and protocols of Web services transactions. This paper presents an abstract transaction model that patterns different Web services transactions standards. This model is capable of deriving concrete models to automatically generate test cases for different Web services transactions standards. The proposed model is implemented as a prototype system and is evaluated using a case of the Jboss Transaction. The evaluation shows that the proposed system has the capability to automatically generate test cases and detect possible failures of transactions running under different Web services transactions standards. Copyright © 2012 John Wiley & Sons, Ltd.

Summary Big Data encompasses large volume of complex structured, semi‐structured, and unstructured data, which is beyond the processing capabilities of conventional databases. The processing and analysis of Big Data now play a central role in decision making, forecasting, business analysis, product development, customer experience, and loyalty, to name but a few. In this paper, we examine the distinguishing characteristics of Big Data along the lines of the 3Vs:variety,volume, andvelocity. Accordingly, the paper provides an insight into the main processing paradigms in relation to the 3Vs. It defines a lifecycle for Big Data processing and classifies various available tools and technologies in terms of the lifecycle phases of Big Data, which include data acquisition, data storage, data analysis, and data exploitation of the results. This paper is first of its kind that reviews and analyzes current trends and technologies in relation to the characteristics, evolution, and processing of Big Data. Copyright © 2014 John Wiley & Sons, Ltd.

Modern smartphones have a rich spectrum of increasingly sophisticated features, opening opportunities for software-led innovation. Of the large number of platforms to develop new software on, in this paper we look closely at three platforms identified as market leaders for the smartphone market by Gartner Group in 2013 and one platform, Firefox OS, representing a new paradigm for operating systems based on web technologies. We compare the platforms in several different categories, such as software architecture, application development, platform capabilities and constraints, and, finally, developer support. Using the implementation of a mobile version of the tic-tac-toe game on all the four platforms, we seek to investigate strengths, weaknesses and challenges of mobile application development on these platforms. Big differences are highlighted when inspecting community environments, hardware abilities and platform maturity. These inevitably impact upon developer choices when deciding on mobile platform development strategies.

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This paper explores the notion of cross-source integration of cloud-based, context-aware information. The objective is to develop a new solution that incorporates remote, web based configuration of Android phones and advances the research area of context-aware information and web applications. It expands the notion of context-awareness harvesting from web applications, enabling automatic adaptation and behaviour altering of smartphones in accordance with implicit user needs. Results from the conducted experiment show the viability of tailoring contextual information through a web application to provide users with timely, relevant and adapted application behavior and content. Through the developed application suite, we present a remote configurable, adaptive application solution to demonstrate the validity of our proposal. The novelty in this work is the new proposed solution and design whereby context-aware information is harvested from several dimensions to allow web application executions to build rich foundations on which algorithms for context-aware computation can be based.

As Mobile Ad hoc Networks (MANETs) are attracting attention for their potential use in several fields such as collaborative computing and multimedia communications, there is an increasing need to minimize the overhead introduced by routing protocols in the network. In this paper, we analyze the performance of a MANET by simulations, in terms of RREQ, RREP and RERR, considering Random Waypoint Mobility (RWM) model. We consider the cases when all nodes are moving and source and destination nodes are static. We evaluate the performance by measuring the throughput and AODV control packets.

In this paper, we investigate the performance of OLSR and AODV protocols in a VANET crossroad scenario. The mobility patterns of vehicles are generated by means of CAVENET (Cellular Automaton based Vehicular Network) and as communication protocol simulator, is used NS3(Network Simulator 3). For the simulations, we used IEEE802.11p standard, TwoRayGroundPropagationLossModel and sent multiple CBR flows over UDP between ten pairs source-destination. We use Packet Delivery Ratio (PDR), throughput and delay as evaluation metrics. We compared the performance of both protocols and the simulation results shows that for big values of transmission rates, OLSR performs better than AODV. For small transmission rates, the delay is less than 0.1sec and both protocols can be used for real time applications such as safety applications. For big values of transmission rate, the delay is higher than 1 sec and these protocols can be used for applications that tolerate this delay such as streaming and entertainment.

In Mobile Ad hoc Networks (MANETs), mobile wireless devices communicate together in an autonomous fashion. The transmission is done by multi-hop routes. Because of mobility and limited resources in MANET, it is difficult to guarantee the QoS requirements. In this work, we propose a fuzzy-based system, which improves the QoS of MANETs via data replication. We aim to support critical requirement applications based on MANETs such as disaster management scenarios in which data retrieval is an important requirement to support real time decision making. There are several techniques to optimize data replication strategy for this task. In this paper, we use fuzzy logic and build a system which has three input linguistic parameters and one output linguistic parameter. We evaluate by simulations the proposed system. The simulation results show that the proposed system has a good decision.

Transactions are a key issue in the reliability of distributed applications because they ensure all the participants achieve a mutually agreed outcome. However, current research has given little attention to testing transactions in web services. This paper presents a conceptual framework, inspired in risk-based methodologies, to address this gap. It also reports on preliminary results and identifies future work.

Transactions are a fundamental technology for building efficient and reliable web service based applications. Various models and protocols have been developed by academic and industrial research community in order to effectively manage web services transactions. We propose a novel abstract model for dynamically modeling distinct web services transaction protocols. Model-based testing techniques can be used on the abstract model in order to automatically generate test scenarios.

With the ubiquity of handheld devices people can now access information and conduct online transactions virtually anywhere and anytime. In such flexible but less reliable environment transaction management technology is believed to provide service reliability and data consistency. Existing models developed for mobile transactions mainly focus on variation of conventional commit procedures and give little or no attention to the context-awareness which has become a crucial part of modern mobile applications. Given the high availability of context-aware services, mobile transactions should not only acquire required services but should also comply with the service context. This paper exploits the service-oriented computing technology and accordingly proposes a novel approach that manages transactions using the contextual information of mobile services. The proposed approach has several advantages. It manages transactions such that they adapt to the required service context as well as user’s needs. It also ensures the reliability of transactions and also increases their resilience to failures.

Modern E-commerce services are offered in a flexible but complex setup involving multiple websites, for example, business web portals or price comparison websites, which are used in drawing the prices of a variety of online products from related websites and presenting them to millions of customers. Though this modern style of service provisioning is very attractive it significantly increases load on the web servers implementing the E-commerce services. The concern is that overloaded servers will become unresponsive and will drop requests which are beyond their capacity. This paper presents a new approach to the synthesis of performance management mechanisms, which we contend, provides more effective way for managing the performance of modern E-commerce services. The proposed approach is formally specified and is implemented as a Java-based prototype system. A number of experiments are conducted in order to test the performance management of the proposed approach.

Web portals work as a point of access to a large volume of information on the Web. This paper focuses on the performance of Web portals in an E-commerce environment which involves the processing of a large number of users' requests. It proposes a class-based priority scheme which classifies users' requests into high and low priorities. In E-commerce, some requests (e.g.\ buy) are generally considered more important than others (e.g.\ search or browse). We contend that the requests received from a Web portal should generally get higher priority as such requests are more likely to lead to purchases. We believe that assigning such priorities at multiple service levels can improve the performance of Web portals' requests of higher priority. The proposed scheme is formally specified and implemented, and performance results are obtained and compared to a server that does not prioritise requests. The results show significant performance improvements in the processing of high priority requests.

Choreography describes the observable behaviour of component Web services that collaborate in a peer-to-peer (P2P) manner. The highly distributed and dynamic nature of Web services results in performance degradation of choreography. This paper presents a new protocol which is based on the P2P paradigm. It exploits the capabilities of underlying networks such that part of the processing is carried out at the network nodes. The proposed protocol is formally specified using pi-calculus on which the Web services choreography description language (WS-CDL) is based. Efficiency of the proposed protocol is tested through various simulation experiments. These experiments show that the proposed protocol significantly improves performance by reducing the system response time in the choreography of Web services.

Abstract Mobile computing greatly facilitates the provision of mobile services using portable and handheld devices which are connected through wireless networks. A pre‐requisite for the provision of an effective service is to ensure quality of service (QoS) during the concurrent execution of requests and system or network failures. This paper presents a new transaction management approach for the provision of mobile services which is based on advanced transaction models and fuzzy selection criteria. It employs a novel QoS consensus moderation approach in order to select preferred mobile services from among various alternatives, thus providing multiple pathways to the completion of mobile service transactions. Unlike traditional transaction management schemes, the proposed approach takes into account QoS attributes in the commit process of mobile service transactions. The proposed approach is implemented as a prototype tool and evaluated through a number of experiments. Experimental results show that the proposed approach effectively selects mobile services and significantly increases the throughput (i.e. commit rate) of mobile service transactions. Copyright © 2007 John Wiley & Sons, Ltd.

Ensuring consensus on QoS attributes is imperative for the discovery of web services. Inconsistent views of service providers and consumers on QoS attributes could be problematic, even though they have consensus on characteristics. For instance, they may have distinct views of the QoS attribute of service reliability, wherein a consumer considers that a service is reliable if its success rate is 99.9% or 100%, while providers may consider its service as reliable if its success rate is higher than 90%. Different expectations in service discovery could hinder prevailing of the web services. This paper proposes a QoS Consensus Moderation Approach (QCMA) in order to perform QoS consensus and to alleviate the differences on QoS requirements in the web services discovery. The proposed approach is implemented and evaluated through an example of hotel booking web service.

Transaction commit protocols have widely been used to ensure the correctness and reliability of distributed applications. This paper investigates into the performance of such protocols within the context of composite web services. It presents a new commit protocol which aims to improve the performance of composite web services transactions. The proposed protocol is tested through various analytical experiments. These experiments reveal that the proposed protocol significantly improves the performance in committing a composite web service transaction. The experiments also exhibit the processing overhead of the proposed protocol in the case of unsuccessful execution of a web service transaction

While we have seen a successful move towards a rapid proliferation of CSCW integration through Web technology including artificial intelligence techniques, still a significant work needs to be done in order to provide quality of service (QoS), particularly addressing the issues of QoS in terms of user requirements. In this paper, we present an approach based on ethnography and multi-agents systems to address these issues in an effective way by mapping a user mental model onto agents. We apply ethnographic approach in order to understand and explain the semantics, functionality and detailed user requirements of CSCW systems. Secondly, we employ artificial intelligent agents for communication and collaboration purposes based on user profile and preferences. In this paper, we also demonstrate the use/illness of this approach by presenting a real life case study of two CSCW (computer supported cooperative work) systems

To fully realise the potential of Web services it is necessary to develop viable dynamic discovery and composition techniques. Matchmaking is considered as one of the crucial factors to ensure dynamic discovery and composition of Web services. Current matchmaking methods are inadequate given their inability to abstract and classify the underlying data of Web services. Instead, they classify Web services based on the capability of services, software signatures, and so on. This paper proposes a novel framework which exploits fuzzy logic in order to abstract and classify the underlying data of Web services as fuzzy terms and rules. The aim is to increase the efficiency of the discovery of Web services and to allow imprecise or vague terms in the search query.

Hidden Web databases maintain a collection of specialised documents, which are dynamically generated in response to users' queries. The categorisation of such databases into a set of predefined categories has been widely employed to assist users in their information searches. In this paper we present a technique that automatically categorises a document database through its content summary and concepts described by their specificity and coverage. Experimental results show that our approach categorises databases with a larger number of relevant categories.

Grid services are software components that provide seamless access to a variety of grid resources such as computational resources and data sources. We present a new multi-agent framework which aims to ensure effective mediation of grid services. The framework comprises different services including search and discovery, planning and coordination, and service reliability and security. The main objectives of the framework are to dynamically discover grid services, devise effective planning and coordination strategies for the discovered services, and securely and reliably utilise those services.

Current CSCW models give no preferential treatment to the processing of collaborative applications. However, to ensure effective collaboration among large number of diverse and distributed applications, it becomes imperative that some applications should be treated differently than others. This paper proposes a new Web services-based model that enables differentiated treatment of collaborative applications. The proposed model is based on three-layered architecture such that it processes various and diverse in nature collaborative applications. It assigns different grades of services to different collaborative applications. This model provides collaborative applications with better quality of service such as improved performance and prioritised service.

The open grid services infrastructure (OGSI) defines a distributed system framework by integrating grid and Web services technologies to facilitate resource sharing. In OGSI, Web services are supplemented with additional features in order to meet the requirements of grid computing. However, the issue of grid service composition is not well addressed in the OGSI framework. We apply BPEL4WS (business process execution language for Web services) as a business workflow description language for the composition of grid services. We provide an in depth analysis of BPEL4WS and OGSI in terms of their similarities and differences in areas such as life cycle management, Web service instantiation and instance group management. Based on our analysis we propose a high-level architecture to compliment OGSI with BPEL4WS for defining process workflow among grid services. We describe a prototype system which shows how the proposed architecture can be used in modelling or orchestrating grid services with BPEL4WS.

We present an active network-based approach in order to improve the performance of mobile transactions. This approach employs preemptive resume scheduling mechanism which reduces queuing delays involved in the processing of mobile transactions at each network node. Such reduction of queuing delay significantly improves performance of mobile transactions given the heavy network traffic and the lower network bandwidth. We have conducted several experiments using analytical modelling approach in order to determine the impact of priority scheduling mechanism on the performance of mobile transactions. Experiments show significant performance improvements of the proposed approach over existing approaches of mobile transactions. It has also been shown that the proposed approach is robust in that it can be applied to improve the performance of current mobile transaction approaches.

The larger amount of information on the Web is stored in document databases and is not indexed by general-purpose search engines (i.e., Google and Yahoo). Such information is \ndynamically generated through querying databases — which are \nreferred to as Hidden Web databases. Documents returned in \nresponse to a user query are typically presented using templategenerated Web pages. This paper proposes a novel approach that identifies Web page templates by analysing the textual contents and the adjacent tag structures of a document in order to extract query-related data. Preliminary results demonstrate that our approach effectively detects templates and retrieves data with high recall and precision.

Transaction management is a major issue in Mobile Commerce (M-commerce). It enables people to order goods and access information in an anywhere and anytime fashion. Given the nature of mobile computing, there is a need for a generic approach that adapts to the needs of M-commerce applications. We propose a new multi-agent approach that dynamically manages mobile transactions which adapt to the processing environment of M-commerce. We also present techniques that deal with the issues of handoff, disconnection, or system failures. Potential advantages of the proposed approach include enhanced flexibility, improved performance and fault tolerance.

This paper proposes an approach for the efficient commit processing of Web transactions. The approach is based on the priority active network scheduling mechanism. This approach takes into account the actual queuing delay of the network nodes involved in the commit processing of Web transactions. Using this approach, the queuing delays for the high priority messages (such as commit/abort) can significantly be reduced. Consequently, the performance of the commit processing is improved as response time of the nodes responsible for making decisions is reduced. This approach can be used to improve the performance of the existing commit protocols of Web transactions.

Web-based engineering design activities require fault tolerance and concurrent access to shared resources such as databases and Web servers. Such activities are generally dynamic, cooperative, long-lived, interactive, and non-prescriptive. We propose a new multi-agent transaction model, which is based on extended transactions and multi-agent technologies. The novelty of this model is that it automatically customises transactions to the requirements of the design activities. In addition, this model is believed to improve concurrency and fault tolerance, facilitate interaction and co-operation of participating systems involved in design activities.