We propose a novel Energy-Predictive Drone Service (EPDS) framework for efficient package delivery within a skyway network. The EPDS framework incorporates a formal modeling of an EPDS and an adaptive bidirectional Long Short-Term Memory (Bi-LSTM) machine learning model. This model predicts the energy status and stochastic arrival times of other drones operating in the same skyway network. Leveraging these predictions, we develop a heuristic optimization approach for composite drone services. This approach identifies the most time-efficient and energy-efficient skyway path and recharging schedule for each drone in the network. We conduct extensive experiments using a real-world drone flight dataset to evaluate the performance of the proposed framework.

We propose an integrity-preserving framework for managing trust information in crowdsourced IoT environments. The integrity of trust information is paramount for ensuring accurate trust assessment. Traditional trust frameworks assume that distributed storing entities of trust information are trustworthy, making them vulnerable to internal attacks. In this respect, entities responsible for storing trust data could tamper with information for personal gain and competitive advantage. Trust assessment using such tampered data could lead to inaccurate evaluations and may mislead IoT users within the environment. We propose a novel Tampering Detection Approach (TDA) to identify the tampering in trust information. Furthermore, we propose a technique to discover the tampering sophistication level. A set of experiments is conducted to evaluate the effectiveness and efficiency of the proposed approaches. Results demonstrate that our TDA achieves a 40% accuracy improvement in detecting tampered data compared to state-of-the-art methods.

We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks . We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring during both flight and at charging stations, providing a comprehensive evaluation of its effects on overall network performance. We conduct a comprehensive series of experiments across diverse scenarios to systematically understand and model the dynamics of inter-drone interference. Key metrics, such as power consumption and delivery times , are considered. This generates a comprehensive dataset for in-depth analysis of interference at both the node and segment levels. These findings are then formalized into a predictive model. The results validate the effectiveness of the developed model, demonstrating its potential to accurately forecast inter-drone interferences.

We propose Meta-DPMAPPO /metə,dipi'mæpəʊ/, a a metaverse multi-user service migration framework that combines Multi-Agent Proximal Policy Optimization (MAPPO) with Differential Privacy (DP)-enabled dual-domain perturbation. To maintain usability, we incorporate trajectory topology constraints that balance privacy strength with data availability. The framework enables dynamic service migration, i.e., transferring services to follow mobile users, to ensure low-latency access while safeguarding sensitive user data. We design a migration strategy with multiple migration actions (i.e.,reuse,follow, andnomigration) to minimize global delay and improve resource utilization. We conduct a series of experiments using a combination of public, collected, and synthetic datasets. The results demonstrate that our approach significantly reduces global migration delay in multi-user environments while ensuring privacy protection, and adapts well to different metaverse application scenarios.

We present a new service framework designed to determine the network provenance of social media images. This framework identifies the online platforms where an image has appeared, offering essential insights to accurately trace its origin. Ultimately, it assists in ascertaining the trustworthiness of online images. The innovative aspect of our design is its exclusive reliance on image metadata. We begin by analyzing how various social media platforms handle image metadata during the upload process. We then map these images into a high-dimensional Cartesian space, reflecting the metadata pruning that occur during upload. This projection captures the unique metadata pruning patterns of each platform, allowing us to identify their distinct fingerprints. This approach reveals the trajectory of images across various social media platforms. We conduct experiments on a subset of the Multimodal C4, Metadata Extractor and Image Ballistics on Social Data datasets. The results demonstrate almost 89 % accuracy in identifying the platform from which an image is sourced.

Fall detection is critical to support the growing elderly population, projected to reach 2.1 billion by 2050. However, existing methods often face data scarcity challenges or compromise privacy. We propose a novel IoT-based Fall Detection as a Service (FDaaS) framework to assist the elderly in living independently and safely by accurately detecting falls. We design a service-oriented architecture that leverages Ultra-wideband (UWB) radar sensors as an IoT health-sensing service, ensuring privacy and minimal intrusion. We address the challenges of data scarcity by utilizing a Fall Detection Generative Pre-trained Transformer (FD-GPT) that uses augmentation techniques. We developed a protocol to collect a comprehensive dataset of the elderly daily activities and fall events. This resulted in a real dataset that carefully mimics the elderly's routine. We rigorously evaluate and compare various models using this dataset. Exper-imental results show our approach achieves 90.72% accuracy and 89.33% precision in distinguishing between fall events and regular activities of daily living.

We propose a distributed trust information manage-ment framework for crowdsourced loT services. The distributed entities responsible for storing trust information may sometimes be unavailable or untrustworthy, meaning they might either withhold trust information or provide tampered trust data. In our framework, we introduce a trust information availability improvement approach to obtain additional trust information while detecting tampered trust records. Moreover, we present an adaptive trust information sufficiency estimate method to assess whether the available trust data is sufficient to compute a statisti-cally reliable trust score. A series of experiments were conducted to evaluate the effectiveness of the proposed approaches. The results demonstrate that the estimated trust scores deviate by no more than 2 % from the actual scores, confirming the effectiveness of our approach.

We propose a novel service-based interference resolution framework for drones operating in a shared skyway network. This network consists of interconnected line-of-sight segments between designated building rooftops that serve as charging and delivery stations for drones. We propose an approach that effectively and efficiently mitigates delays in service delivery caused by interference between drones operating in close proximity within skyway segments. We use a range of constraints to determine the likelihood of impactful interferences to map out proximity distances for the safe and efficient delivery of drone services. Experimental results conducted on real-world data validate the effectiveness of the proposed approach.

Precise drone landing remains a persistent challenge due to the high level of accuracy needed. We propose a novel technique that collects actual drone landing data and employs machine learning algorithms to predict errors in autonomous landing. Our model considers variables like battery charge, flight path, altitude, and velocity for prediction. Various trends associated with the drone’s flight and landing are determined and visualised. We propose neural network models that use time series data from the drone’s flight before landing to predict its landing position. Our best model reduced landing error to 2.34 cm, a 7% improvement over the baseline. • A novel framework for predicting drone landing errors precisely. • A comprehensive dataset of real drone landings to ascertain influential factors impacting landing accuracy. • Application of advanced machine learning approaches to forecast the exact landing coordinates of drones.

We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integratescloudcomputing,drones, andservicesas a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery.

We propose a novel change detection framework to identify changes in the long-term performance behavior of an Infrastructure as a Service (IaaS). An IaaS’s long-term performance behavior is represented by an IaaS performance signature. The proposed framework leverages time series similarity measures and a sliding window technique to detect changes in IaaS performance signatures. We introduce a new IaaS performance noise model that enables the proposed framework to distinguish between performance noise and actual changes in performance. The proposed framework utilizes a novel Signal-to-Noise Ratio-based approach to detect changes when prior knowledge about performance noise is available. A set of experiments is conducted using real-world datasets to demonstrate the effectiveness of the proposed change detection framework.

We propose a novel dynamic and immersive 3D framework designed to facilitate the setup and customization of drone scheduling algorithms for evaluating service-based drone delivery systems. This framework features a robust system architecture that supports user-defined behavior logic. It also incorporates real-time data communication protocols for relaying timely instructions to the drones. Additionally, it integrates a comprehensive drone energy consumption model that accurately simulates the physics of drone operations and accounts for both internal and external factors affecting energy usage. The framework includes a sophisticated 3D visualization component, depicting drone deliveries from source to destination through a realistic skyway network within an interactive virtual urban environment. It also enables automated data tracking, which is crucial for testing algorithms and collecting data to support data-driven decisions and optimizations. We evaluate the framework by conducting a comprehensive usability test to assess its user interface and overall user experience. Additionally, we test the framework using a drone swarm to execute delivery requests under both simple and complex energy consumption models. The results show that the framework has a user-friendly interface and effectively supports drone delivery simulations under the complex physics-based energy consumption model.

We propose a novelQuality of Experience (QoE)metric as a key criterion for optimizing the composition of energy services within a crowdsourced IoT environment. Two novel composition approaches, namely, Importance-based and Heuristic-based, are proposed to ensure the highest QoE for consumers. The Importance-based approach prioritizes time slots based on their significance. The Heuristic-based approach considers the importance of time slots and the availability of services to maximize QoE while minimizing service provisioning costs. We conduct extensive experiments using real-world datasets to evaluate the effectiveness and efficiency of the proposed approaches. The results demonstrate that both approaches enhance consumer satisfaction by optimizing energy allocation, with the Heuristic-based approach outperforming the Importance-based method in minimizing rewards.

We propose a novel service-based framework for drone service resilience. Our framework monitors inter-drone interference that may lead to drone service failure. We present a novel drone service interference taxonomy to formally identify different interference types in a skyway network. We then propose a heuristic-based approach that leverages spatio-temporal proximity analysis to detect the occurrence of inter-drone interference. In addition, we present an interference severity assessment to quantify their impact on drone services' efficiency. We conduct a set of experiments using real-world datasets to evaluate the effectiveness and efficiency of our proposed approach. The results indicate that the proposed heuristic-based approach detects the occurrence of inter-drone interferences with an accuracy of 95%. In addition, the proposed method is$\approx$70% more efficient than the baseline exhaustive approach and$\approx$48% faster than the K-means approach.

Abstract Social media platforms usually contain several modified versions of an image. This proliferation of versions questions the trust of social media images. We propose a novel framework to find modified versions of social media images using only their metadata. We consider several aspects to determine if an image is a modified version of another image. These aspects include topic of an image, spatio-temporal information, and semantic similarity. We first do topic modeling to find images linked to the same context. Secondly, we perform spatio-temporal clustering to group spatio-temporally close images. Finally, we perform hierarchical clustering to form more precise clusters of versions. Notably, the proposed framework also considers modifications introduced in an image’s metadata while determining versions of the image. Modifications in social media images pose a significant challenge to correctly cluster versions together as a version may exhibit significant deviations from its original image. We address this issue by exploring inconsistencies in the image metadata. These inconsistencies are reflective of the changes in an image. We validate our model on a fact-checked image verification corpus and the Multimodal C4 dataset. We achieve around 95% accuracy, validating the effectiveness of the proposed approach.

We propose a failure-resilient Swarm-based Drones-as-a-Service (SDaaS) framework for reliable delivery services. Our framework is specifically tailored to address soft failures, characterized by degradation in drone performance. Failures within drone swarms are an inherent part of real-world scenarios and should be factored to ensure the reliability of services. This framework emphasizes post-assessment of failures as a critical step in preplanning for subsequent service compositions. The framework detects soft service failures from tolerable behavior deviations using margin-aware pruning. Furthermore, a failure severity score is computed using signal-based heuristic. Our aim is to minimize the impact of soft failures on consumers by delivering services within the expected time. This is achieved through formation reordering and service recomposition. Experimental results prove the efficiency of the proposed model with respect to failure correlation scores and service delivery times.

We propose a novel impact conflict detection framework for IoT services in multi-resident smart homes. The proposed impact assessment model is developed based on the integral of a signal deviation strategy. We mine the residents’ previous service usage records to design a robust preference estimation model. We design an impact conflict detection approach using temporal proximity and preferential proximity techniques. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach.

We propose a novel failure-aware reactive UAV delivery service composition framework. A skyway network infrastructure is presented for the effective provisioning of services in urban areas. We present a formal drone delivery service model and a system architecture for reactive drone delivery services. We develop radius-based, cell density-based, and two-phased algorithms to reduce the search space and perform reactive service compositions when a service failure occurs. We conduct a set of experiments with a real drone dataset to demonstrate the effectiveness of our proposed approach.

This paper presents a novel probabilistic Quality of Service (QoS) monitoring method named DLSTM-BRPM (Double Long Short Term Memory (DouLSTM-Den) based Bayesian Runtime Proactive Monitoring) to accurately and efficiently monitor QoS in a mobile edge environment. This method consists of a DouLSTM-Den model and a Gaussian Hidden Bayesian classifier. The DouLSTM-Den model aims to predict a user’s future movement trajectory in real time and proactively monitor the spatio-temporal QoS performance of services based on the predicted trajectory. The Gaussian Hidden Bayesian classifier is employed to accurately monitor QoS by constructing parent attributes to reduce the interdependence between QoS attributes. Our experiments based on public synthetic datasets demonstrate the effectiveness of the proposed method over state-of-the-art solutions. We also conducted experiments in a real-world edge environment to validate the feasibility of the proposed method.

We propose a novel framework for achieving precision landing in drone services. The proposed framework consists of two distinct decoupled modules, each designed to address a specific aspect of landing accuracy. The first module is concerned with intrinsic errors, where new error models are introduced. This includes a spherical error model that takes into account the orientation of the drone. Additionally, we propose a live position correction algorithm that employs the error models to correct for intrinsic errors in real time. The second module focuses on external wind forces and presents an aerodynamics model with wind generation to simulate the drone’s physical environment. We utilize reinforcement learning to train the drone in simulation with the goal of landing precisely under dynamic wind conditions. Experimental results, conducted through simulations and validated in the physical world, demonstrate that our proposed framework significantly increases landing accuracy while maintaining a low onboard computational cost.

We propose a novel resident identification framework to identify residents in a multi-occupant smart environment. The proposed framework employs a feature extraction model based on the concepts of positional encoding. The feature extraction model considers the locations of homes as a graph. We design a novel algorithm to build such graphs from layout maps of smart environments. The Node2Vec algorithm is used to transform the graph into high-dimensional node embeddings. A Long Short-Term Memory model is introduced to predict the identities of residents using temporal sequences of sensor events with the node embeddings. Extensive experiments show that our proposed scheme effectively identifies residents in a multi-occupant environment. Evaluation results on two real-world datasets demonstrate that our proposed approach achieves 94.5% and 87.9% accuracy, respectively.

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis's validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster; 2) reduces dissemination costs by 48.21% to 91.87%; and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs.

We propose an innovative approach named MEC-RDESN /mek”r:dI’saIn/ (MECQoS forecasting based onRegion recognition andDynamicEchoStateNetwork) enabling mobility-aware and swift QoS forecasting in the mobile edge computing environment. MEC-RDESN offers efficient QoS forecasting while maintaining high accuracy. We can identify the edge region to which a user belongs in real time while moving by leveraging mobile sensing technology. We employ adynamic echo state networkcharacterized by multi-service adaptability to retain information about services invoked by users to ensure real-time training and forecasting accuracy. Our approach is validated through a series of experiments using both public and collected datasets. The experiments demonstrate that MEC-RDESN achieves the goal of fast forecasting while ensuring its forecasting accuracy in diverse application scenarios.

In the context of social media, the integrity of images is often dubious. To tackle this challenge, we introduce Exif2Vec , a novel framework specifically designed to discover modifications in social media images. The proposed framework leverages an image’s metadata to discover changes in an image. We use a service-oriented approach that considers discovery of changes in images as a service . A novel word-embedding-based approach is proposed to discover semantic inconsistencies in an image metadata that are reflective of the changes in an image. These inconsistencies are used to measure the severity of changes. The novelty of the approach resides in that it does not require the use of images to determine the underlying changes. We use a pretrained Word2Vec model to conduct experiments. The model is validated on two different fact-checked image datasets, i.e., images related to general context and a context-specific image dataset. Notably, our findings showcase the remarkable efficacy of our approach, yielding results of up to 80% accuracy. This underscores the potential of our framework.

Human mobility patterns refer to the regularities and trends in the way people move, travel, or navigate through different geographical locations over time. Detecting human mobility patterns is essential for a variety of applications, including smart cities, transportation management, and disaster response. The accuracy of current mobility prediction models is less than 25%. The low accuracy is mainly due to the fluid nature of human movement. Typically, humans do not adhere to rigid patterns in their daily activities, making it difficult to identify hidden regularities in their data. To address this issue, we proposed a web platform to visualize human mobility patterns by abstracting the locations into a set of places to detect more realistic patterns. However, the platform was initially designed to detect individual mobility patterns, making it unsuitable for representing the crowd in a smart city scale. Therefore, we extend the platform to visualize the mobility of multiple users from a city-scale perspective. Our platform allows users to visualize a graph of visited places based on their historical records using a modified PrefixSpan approach. Additionally, the platform synchronizes, aggregates, and displays crowd mobility patterns across various time intervals within a smart city. We showcase our platform using a real dataset.

We propose a novel failure-sentient framework for swarm-based drone delivery services. The framework ensures that those drones that experience a noticeable degradation in their performance (called soft failure) and which are part of a swarm, do not disrupt the successful delivery of packages to a consumer. The framework composes a weighted continual federated learning prediction module to accurately predict the time of failures of individual drones and uptime after failures. These predictions are used to determine the severity of failures at both the drone and swarm levels. We propose a speed-based heuristic algorithm with lookahead optimization to generate an optimal set of services considering failures. Experimental results on real datasets prove the efficiency of our proposed approach in terms of prediction accuracy, delivery times, and execution times.

We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is essential for the successful deployment of efficient energy service composition techniques. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment. The predicted battery levels are used to estimate the energy loss. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework. We conducted extensive experiments on real wireless energy datasets to demonstrate that our framework significantly outperforms existing methods.

We propose a novel activity-based profiling framework to estimate IoT users’ harvested energy based on their daily activities. Energy is harvested from natural sources such as the kinetic movement of IoT users. The profiling framework captures the users’ physical activity data to define activity-based profiles. These profiles are utilized to estimate the harvested energy by IoT users. We train and evaluate our framework based on a real Fitbit dataset.

Building a quality service-based system (SBS) is one of the most important research topics in software engineering. Many studies investigate intelligent methods to simplify the process of building SBSs. In particular, some keyword-based SBS building methods allow service users to automatically build an SBS by only providing a few of keywords. This type of work usually constructs a directed weighted graph of a service repository. A set of minimum-weight group Steiner trees (MSTs) is extracted from the graph to represent the service functions and their relations. However, to the best of our knowledge, none of the existing keyword-based SBS building methods allow the relaxation of the function requirements for a user. A relaxed SBS may achieve a comparable functionality versus a complete SBS containing all the query functions. To fill in the above gap, we define a new problem: a bounded skyline SBS building problem, whose solution is more adaptive and less limited than the traditional keyword-based SBS building methods. To solve this problem, we propose two algorithms based on skyline query, dynamic programming, and lower bound pruning. In the experiments, we collect real-world datasets and label the nodes with keywords. We conduct a comprehensive study to demonstrate the time efficiency of our algorithms on automatically finding SBSs. We make the annotated real-world datasets and our source code open to peer researchers.

Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users' geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41\% on average.

We propose a novel end-to-end trust management framework for crowdsourced Internet of Things (IoT) services. The framework targets three main aspects:trust assessment,trust information credibility and accuracy, andtrust information storage. We harness theusage patternsof IoT consumers to offer a trust assessment thatadaptsto IoT consumers’ uses. Additionally, our framework ascertains thecredibilityandaccuracyof trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures thecontextual fairnessbetween IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves theintegrityandavailabilityof trust information.

We propose a novel spatio-temporal service composition framework for crowdsourcingmultipleIoT energy services to cater tomultipleenergy requests. We define a new energy service model to leverage thewearable-basedenergy and wireless power transfer technologies. We reformulate the problem of spatio-temporal service composition to provision multiple energy requests asa matching problem. We leverage thefragmentednature of energy to offerpartialservices to maximize the utilization of energy services. We proposeEnergyFlowComp, a modified Maximum Flow matching algorithm that efficiently provisions IoT energy services to accommodate multiple energy requests. Moreover, we proposePartialFlowComp, an extension of theEnergyFlowCompapproach that considers thepartial-temporaloverlapbetween services and requests in provisioning. We conduct an extensive set of experiments to assess the effectiveness and efficiency of the proposed framework.

We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers variousintrinsic and extrinsicdelivery constraints. We propose to usesupport droneswhose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the next stations. We also use two settings to position the support drones in a flight formation for comparative purposes. Two novelenergy sharingmethods are proposed, namely, Priority-based and Fairness-based methods. A re-ordering method of the delivery drones is presented to facilitate the in-flight energy composition process. An enhanced A* algorithm is implemented to compose the optimal services in terms of delivery time. Experimental results prove the efficiency of our proposed approach.

We introduce the concept ofadaptive trustin crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers.Usage patternsof IoT consumers are exploited to provide an accurate trust value for service providers. A noveladaptive trust management frameworkis proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtaintrust indicatorsthat are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to buildservice-to-indicatormodel to evaluate a service’strust at each indicator. Similarly, ausage-to-indicatormodel is built to obtain theimportance of each trust indicatorfor a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework.

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

We propose a novel framework for crowdsourced images to determine the likelihood of an image beingfake. We use a service-oriented approach to model and represent crowdsourced images uploaded on social media, asimage services. Trust may, in some circumstances, be determined by using only the non-functional attributes of an image service, i.e., image metadata. We defineintention of changesas a key parameter to ascertain fake image services. A novel framework is proposed to estimate the intention of underlying changes considering change in semantics of an image. Our experiments show high accuracy using a large real dataset.

We propose a novel Quality of Experience (QoE) metric as a key criterion to optimize the composition of energy services in a crowdsourced IoT environment. A novel importance-based composition algorithm is proposed to ensure the highest QoE for consumers. A set of experiments is conducted to evaluate the proposed approaches’ effectiveness and efficiency.

We propose a novel framework for the recommendation of swarm-based drone delivery services based on the consumers preferences. We propose a density-based pruning approach that uses the concept of partnerships with charging station providers to reduce the search space of swarm-based drone service delivery providers. A weighted service composition algorithm is proposed that considers the providers capabilities and consumers’ preferences in selecting the best next service. We propose a voting-based recommendation algorithm to select the best providers. We conduct a set of experiments to evaluate the efficiency of the framework in terms of consumer satisfaction, run-time, and search space reduction cost.

We propose a novel service-oriented architecture for drone-based multi-package delivery. The proposed architecture provides a high-level design for deploying a skyway network in a city for the effective provisioning of drone-based service delivery. A graph-based heuristic is proposed to reduce the search space for optimal service selection in the skyway network. We then find an optimal solution using the selected drone services under a range of constraints. Experimental results demonstrate the efficiency and effectiveness of our proposed graph-based heuristic approach in terms of execution time and delivery time.

We present DIY-IPS - Do It Yourself - Indoor Positioning System, an open-source real-time indoor positioning mobile application. DIY-IPS detects users' indoor position by employing dual-band RSSI fingerprinting of available WiFi access points. The app can be used, without additional infrastructural costs, to detect users' indoor positions in real time. We published our app as an open source to save other researchers time recreating it. The app enables researchers/users to (1) collect indoor positioning datasets with a ground truth label, (2) customize the app for higher accuracy or other research purposes (3) test the accuracy of modified methods by live testing with ground truth. We ran preliminary experiments to demonstrate the effectiveness of the app.

Understanding human mobility is essential for the development of smart cities and social behavior research. Human mobility models may be used in numerous applications, including pandemic control, urban planning, and traffic management. The existing models' accuracy in predicting users' mobility patterns is less than 25%. The low accuracy may be justified by the flexible nature of human movement. Indeed, humans are not rigid in their daily movement. In addition, the rigid mobility models may result in missing the hidden regularities in users' records. Thus, we propose a novel perspective to study and analyze human mobility patterns and capture their flexibility. Typically, the mobility patterns are represented by a sequence of locations. We propose to define the mobility patterns by abstracting these locations into a set of places. Labeling these locations will allow us to detect close-to-reality hidden patterns. We present IMAP, an Individual huMAn mobility Patterns visualizing platform. Our platform enables users to visualize a graph of the places they visited based on their history records. In addition, our platform displays the most frequent mobility patterns computed using a modified PrefixSpan approach.

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2020.2974741]

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make realtime, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results based on public and self-collected data sets demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.

Edge Computing (EC) enables a new kind of caching system in close geographic proximity to end-users by allowing app vendors to cache popular data on edge servers deployed at base stations. This edge cache system can better support latency-sensitive applications. However, transmitting data from the centralized cloud to the edge servers without proper transmission strategies may cost app vendors dearly. Cost-effective data distribution strategies are of particular importance for applications, whose data to be cached at the edge often changes dynamically. In this paper, we study thisOnline Edge Data Distribution(OEDD) problem, aiming to minimize app vendors’ total transmission cost, while ensuring low transmission latency in the long term. We first model this problem and prove its$\mathcal {NP}$-hardness. We then combine Lyapunov optimization and game theory to propose a novel Latency-Aware Online (LAO) approach for solving this OEDD problem over time in a distributed manner with provable performance guarantees. The evaluation of LAO based on a real-world dataset demonstrates that it can help app vendors formulate cost-effective edge data distribution strategies in an online manner.

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leveragecrowdsourcedsocial media images to create a tapestry scene for scene analysis of designated locations and time intervals. The existing attempts however ignore the temporal-semantic relevance and spatio-temporal evolution of the images and direction-oriented scene reconstruction. We propose a novel social-sensor cloud (SocSen) service composition approach to form tapestry scenes for scene analysis. The novelty lies in utilising images and image meta-information to bypass expensive traditional image processing techniques to reconstruct scenes. Metadata, such as geolocation, time, and angle of view of an image are modelled as non-functional attributes of a SocSen service. Our major contribution lies on proposing a context and direction-aware spatio-temporal clustering and recommendation approach for selecting a set of temporally and semantically similar services to compose the best available SocSen services. Analytical results based on real datasets are presented to demonstrate the performance of the proposed approach.

We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits amulti-perspective trust modelthat captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set ofattributesthat contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate atrust modelfor crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.

We propose a novel framework to select IaaS providers according to a consumer’s long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider’s long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework.

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose anuncertainty-aware DaaS route-planning algorithmthat selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. Aspatiotemporal DaaS composerfirst selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. Apredictive DaaS composerthen utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users’ energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

We develop a novel framework for efficiently and effectively discovering crowdsourced services thatmovein close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT services considering quality parameters. Additionally, we develop a parallel flock-based service discovery algorithm as a ground-truth to measure the accuracy of the proposed approach. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.

Mobile Edge Computing is an emerging technology offering low latency responses by deploying edge servers near mobile devices. We propose a novel privacy-preserving QoS forecasting approach – Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments) to address the challenges of user mobility and information leakage encountered by QoS forecasting in mobile edge environments. Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users’ accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.

We propose a novel framework for the allocation of drone swarms for delivery services known as Swarm-based Drone-as-a-Service (SDaaS). The allocation framework ensures minimum cost (aka maximum profit) to drone swarm providers while meeting the time requirement of service consumers. The constraints in the delivery environment (e.g., limited recharging pads) are taken into consideration. We propose three algorithms to select the best allocation of drone swarms given a set of requests from multiple consumers. We conduct a set of experiments to evaluate and compare the efficiency of these algorithms considering the provider's profit, feasibility, requests fulfilment, and drones utilization level.

We propose a novel distributed integrity-preserving framework for storing trust information in crowdsourced IoT environments. The integrity and availability of the trust information is paramount to ensure accurate trust assessment. Our proposed framework leverages the blockchain to build a distributed storage medium for trust-related information that ensures its integrity. We propose a geo-scoping approach, which ensures that trust-related information is only available where needed, thus, enabling fast access and storage space preservation. We conduct several experiments using real datasets to highlight the effectiveness of our framework.

We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. An adaptive priority model is developed considering the residents' contextual factors (e.g., age, illness, impairment). The proposed priority model is designed using the concept of the analytic hierarchy process. A set of experiments on real-world datasets are conducted to show the efficiency of the proposed approach.

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home setting based on the context information. We propose a conflict taxonomy to capture different types of conflicts in a single resident smart home setting. A conflict detection algorithm is proposed to identify potential conflicts using the profiled knowledge graph. We conduct a set of experiments on real datasets and synthesized datasets to validate the effectiveness and efficiency of our proposed approach.

We propose a novel proactive composition framework of wireless energy services in a crowdsourced IoT environment. We define a new model for energy services and requests that includes providers' and consumers' mobility patterns and energy usage behavior. The proposed composition approach leverages the mobility and energy usage behavior to generate energy services and requests proactively. Preliminary experimental results demonstrate the effectiveness of generating proactive energy requests and composing proactive services.

We propose a novel robust composition framework for drone delivery services considering changes in the wind patterns in urban areas. The proposed framework incorporates the dynamic arrival of drone services at the recharging stations. We propose a Probabilistic Forward Search (PFS) algorithm to select and compose the best drone delivery services under uncertainty. A set of experiments with a real drone dataset is conducted to illustrate the effectiveness and efficiency of the proposed approach.

Most cloud computing platforms offer reactive resource auto-scaling mechanisms for dealing with variable traffic patterns to deliver the desired QoS properties while keeping low provisioning costs. However, a range of scenarios have not been fully addressed by the current auto-scaling solutions, particularly dealing with a rapid increase in workload and the risk of thrashing due to frequent workload variations. A reactive system is vulnerable in such conditions. Realizing the full potential of auto-scaling still remains challenging particularly due to the need of accurately estimating the application resource requirements for time-varying workload patterns. In this work, we propose and evaluate a novel method using only application access logs to estimate more accurately the hardware resource demands and application response time. In particular, we propose novel workload latent features which we compute by applying unsupervised learning on the access logs. We use these latent features to estimate the application hardware resource requirements and response time for various workload patterns. We evaluate the proposed method using multiple benchmark web applications and compare it with current state-of-the-art. Extensive experimental evaluations show an excellent performance of our proposed workload latent features in estimating response time, CPU, memory, and bandwidth utilization.

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.

We propose a novel framework for composing Swarm-based Drone-as-a-Service (SDaaS) for delivery. Two composition approaches, i.e., sequential and parallel are designed considering the different behaviors of drone swarms. The proposed framework considers various constraints, e.g., recharging time and limited battery to meet delivery deadlines. We propose SDaaS composition algorithms using a modified A* algorithm. A cooperative behavior model is incorporated to reduce recharging and waiting time in a delivery. Experimental results prove the efficiency of the proposed approach.

We propose just-in-time memoryless trust for crowdsourced IoT services. We leverage the characteristics of the IoT service environment to evaluate their trustworthiness. A novel framework is devised to assess a service's trust without relying on previous knowledge, i.e., memoryless trust. The framework exploits service-session-related data to offer a trust value valid only during the current session, i.e., just-in-time trust. Several experiments are conducted to assess the efficiency of the proposed framework.

We propose a novel framework to detect conflicts among IoT services in a multi-resident smart home. A novel IoT conflict model is proposed considering the functional and non-functional properties of IoT services. We design a conflict ontology that formally represents different types of conflicts. A hybrid conflict detection algorithm is proposed by combining both knowledge-driven and data-driven approaches. Experimental results on real-world datasets show the efficiency of the proposed approach.

We propose a novel approach to select IaaS cloud services for a long-term period where the service providers offer limited QoS information. The proposed approach leverages free short-term trials to obtain the previously undisclosed QoS information. A new significance-based trial scheme is proposed using frequency distribution analysis to test a consumer's long-term workloads in a short trial. We introduce a novel IaaS signature technique to uniquely identify the variability of a provider's QoS performance. A Signature-based QoS Performance Discovery (SPD) algorithm is proposed which leverages the combination of free trials and IaaS signatures. A set of exhaustive experiments with real-world datasets is conducted to evaluate the proposed approach.

We propose a novel game-theoretic approach for drone service composition considering recharging constraints. We design a non-cooperative game model for drone services. We propose a non-cooperative game algorithm for the selection and composition of optimal drone services. We conduct several experiments on a real drone dataset to demonstrate the efficiency of our proposed approach.

We propose a novel reliability model for composing energy service requests. The proposed model is based on consumers’ behavior and history of energy requests. The reliability model ensures the maximum incentives to providers. Incentives are used as a green solution to increase IoT users’ participation in a crowdsourced energy sharing environment. Additionally, adaptive and priority scheduling compositions are proposed to compose the most reliable energy requests while maximizing providers’ incentives. A set of experiments is conducted to evaluate the proposed approaches. Experimental results prove the efficiency of the proposed approaches.

Swarm of drones are increasingly being asked to carry out missions that can’t be completed by one drone. Particularly, in delivery, issues arise due to the swarm’s limited flight endurance. Hence, we propose a novel formation-guided framework for selecting Swarm-based Drone-as-a-Service (SDaaS) for delivery. A detailed study is carried out to highlight the effect of swarm formations on energy consumption. Two SDaaS selection approaches, i.e. Fixed and Adaptive, are designed considering the different formation decisions a swarm can take. The proposed framework considers extrinsic constraints including wind speed and direction. We propose SDaaS selection algorithms for each approach. Experimental results prove the efficiency of the proposed algorithms.

We propose a novel type of service composition, called elastic composition which provides a reliable framework in a highly fluctuating IoT energy provisioning settings. We rely on crowdsourcing IoT energy (e.g., wearables) to provide wireless energy to nearby devices. We introduce the concepts of soft deadline and hard deadline as key criteria to cater for an elastic composition framework. We conduct a set of experiments on real-world datasets to assess the efficiency of the proposed approach.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which resulted in a disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. In this paper, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical.

We introduce the notion of Adaptive Trust in crowdsourced IoT services; a usage-based trust that represents a service's trustworthiness based on consumers' uses. A novel four-stage framework is proposed to assess the dynamic service trust by leveraging how the service is being used. The first stage uses an algorithm to predict different trust factors that affect the overall trustworthiness of an IoT service. Trust factors are fed to the second stage to build a service-to-factor model that predicts the trustworthiness of a service at each given trust factor. A usage-to-factor model is built at the third stage, which detects the importance of each factor for a specific usage scenario. The last stage utilizes the two models to compute a trust value specifically tailored for a particular usage. Several experiments have been conducted using real dataset to ensure the efficiency of the proposed approach.

We propose a novel approach to select privacy-sensitive IaaS providers for a long-term period. The proposed approach leverages a consumer's short-term trial experiences for long-term selection. We design a novel equivalence partitioning based trial strategy to discover the temporal and unknown QoS performance variability of an IaaS provider. The consumer's long-term workloads are partitioned into multiple Virtual Machines in the short-term trial. We propose a performance fingerprint matching approach to ascertain the confidence of the consumer's trial experience. A trial experience transformation method is proposed to estimate the actual long-term performance of the provider. Experimental results with real-world datasets demonstrate the efficiency of the proposed approach.

We propose a novel composition framework for drone-based package delivery services termed as Drone-as-a-Service (DaaS). The proposed framework includes a spatio-temporal service model and a quality model for DaaS. A drone service selection algorithm is designed using 3D Rtree. We develop a Dijkstra-based and a heuristic-based drone service composition approach to meet users' delivery requirements, i.e., expected delivery time and cost. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.

We present a new spatio-temporal incentive-based approach to achieve a geographically balanced coverage of crowdsourced services. The proposed approach is based on a new spatio-temporal incentive model that considers multiple parameters including location entropy, time of day, and spatio-temporal density to encourage the participation of crowdsourced service providers. We present a greedy network flow algorithm that offers incentives to redistribute crowdsourced service providers to improve the crowdsourced coverage balance within an area. A novel participation probability model is also introduced to estimate the expected number of crowdsourced service providers’ movement based on spatio-temporal features. Experimental results validate the efficiency and effectiveness of the proposed approach.

We propose a new social-sensor cloud services trust model. We propose to represent social media data streams, i.e., images' meta-data and related posted information, as social-sensor cloud services. Images' meta-data and the related posted information are abstracted as the functional and non-functional aspects of the social-sensor cloud services. The trustworthiness of a social-sensor cloud service is measured based on the users' stance based trust model. We use the textual features of the social-sensor cloud services, i.e., comments and meta-data, e.g., spatio-temporal information to gather the trust-rate of the service. Analytical results are presented to show the performance of the proposed model with real datasets.

We propose a framework to discover proximate IoT service relationships based on spatio-temporal features. We introduce a spatio-temporal proximity model in terms of spatial-proximity and temporal-proximity to discard insignificant IoT service relationships. The proximity model focuses on quantifying the correlation strength among IoT services from time and location aspects. A new algorithm is proposed to discover proximate spatio-temporal IoT service relationships. We also present preliminary experimental results.

We propose a novel dynamic metaheuristic optimization approach to compose an optimal set of IaaS service requests to align with an IaaS provider's long-term economic expectation. This approach is designed for the context that the IaaS provisioning subjects to resource and QoS constraints. In addition, the IaaS service requests have the features of dynamic resource and QoS requirements and variable arrival times. A new economic model is proposed to evaluate the similarity between the provider's long-term economic expectation and a composition of service requests. The evaluation incorporates the factors of dynamic pricing and operation cost modeling of the service requests. An innovative hybrid genetic algorithm is proposed that incorporates the economic inter-dependency among the requests as a heuristic operator and performs repair operations in local solutions to meet the resource and QoS constraints. The proposed approach generates dynamic global solutions by updating the heuristic operator at regular intervals with the runtime behavior data of an existing service composition. Experimental results preliminarily prove the feasibility of the proposed approach.

IaaS cloud resources are offered by cloud providers using various pricing schemes such as reservation, scheduled, on-demand and spot pricing. Such pricing models suffer from either wasteful payment in case of underutilization of reserved/scheduled instances or volatile prices, particularly for spot instances. This paper explores using financial options for pricing IaaS cloud resources. We advocate using options to complement existing pricing models and to provide a cheaper way to gain access to resources without reserving them in advance while avoiding prices volatility of spot instances. An option provides the cloud consumer the right, but not the obligation, to buy or sell a cloud resource at a fixed price during a predetermined period. That means that the maximum loss associated with an option is limited to the premium paid. Various options pricing methods were studied and compared. Using extensive simulation, we show the practical utility of options particularly for handling the unpredictable workload and their efficacy in terms of cost savings.

We propose a new framework for social-sensor cloud services selection based on spatio-textual correlation between user's query and service. The proposed research defines a formal social-sensor cloud service model that abstracts the functional and non-functional aspects of social-sensor data on the cloud in terms of spatio-temporal, textual and quality of service parameters. Proposed framework is a 4-stage filtering algorithm, to select social-sensor cloud services based on user query and quality of service demands. 4-stage filtering is based on spatial correlation, textual correlation, visual features and quality of service parameters. Analytical results are presented to show the performance of the proposed approach.

We propose a 'Sentiment Analysis as a Service' (SAaaS) framework that abstracts sentiments from social information services, analyses and transforms into useful information. We propose a dynamic service composition mechanism for sentiment analysis based on the social information service classification. We also propose a new quality model to assess the quality of social information services. We use social media based public health surveillance as a motivating scenario. In particular, we focus on the spatio-temporal properties of social media users' sentiments to identify the locations of disease outbreaks. Experiments are conducted on the real-world datasets. Analytical results preliminarily show the performance of our proposed approach.

We investigate the use of subjective metrics in social media to evaluate cloud service performance in the market. We first examine the subjective factors that drive cloud consumers to/from purchasing cloud services. These include the ability to achieve greater scalability, security concerns, etc. according to several industry surveys. We then analyse the correlation between the consumers' perception on those factors and the cloud market revenue growth. This paper identifies the unique subjective metrics that are indicative of cloud service performance from the market perspective. The cloud consumers' perception is sourced from several particular social media using sentiment analysis techniques. We focus on consumers' perception on a leading cloud provider that holds the majority of the cloud market share. We find that subjective metrics are empirically proved to be applicable in evaluating the performance of cloud services in the market.

Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted significant attention from both industry and research communities. As online services may keep evolving over time and thus lead to a highly dynamic environment, service composition must be self-adaptive to tackle uninformed behavior during the evolution of services. In addition, service composition should also maintain high efficiency for large-scale services, which are common for enterprise applications. This article presents a new model for large-scale adaptive service composition based on multi-agent reinforcement learning. The model integrates reinforcement learning and game theory, where the former is to achieve adaptation in a highly dynamic environment and the latter is to enable agents to work for a common task (i.e., composition). In particular, we propose a multi-agent Q-learning algorithm for service composition, which is expected to achieve better performance when compared with the single-agent Q-learning method and multi-agent SARSA (State-Action-Reward-State-Action) method. Our experimental results demonstrate the effectiveness and efficiency of our approach.

We present a new two-level composition model for crowdsourced Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of sensors to select and compose Sensor-Cloud services. The quality model defines Coverage as a Service which is formulated as a composition of crowdsourced Sensor-Cloud services. We present two new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. Experimental results validate the performance of the proposed algorithms.

A service-oriented System of Systems (SoS) considers a system as a service and constructs a robust and value-added SoS by outsourcing external component systems through service composition techniques. Online reliability prediction for the component systems for the purpose of assuring the overall Quality of Service (QoS) is often a major challenge in coping with a loosely coupled SoS operating under dynamic and uncertain running environments. It is also a prerequisite for guaranteeing runtime QoS of a SoS through optimal service selection for reliable system construction. We propose a novel online reliability time series prediction approach for the component systems in a service-oriented SoS. We utilize Probabilistic Graphical Models (PGMs) to yield near-future, time series predictions. We assess the approach via invocation records collected from widely used real Web services. Experimental results have confirmed the effectiveness of the approach.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to find out some users whose profiles are similar to the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online data site, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This serious data breach has urged researchers to explore practical privacy protection for user profiles in online dating. In this paper, we give a privacy-preserving solution for user profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out some matching users with the help of the multiple servers without revealing to anyone privacy of the query and the queried user profiles. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our implementation and experiments demonstrate that our solution is practical.

With an increasing number of Web services on-line, service recommendation becomes an important approach to help user discover suitable services. However, current service recommendation approaches only consider user's historical preferences and functional or non-functional properties of services. The compositional side of services to form mashups is overlooked. However, the service composition information is a valuable information for recommendation since it gives an implicit measure of relatedness of services. In this paper, we recognize the importance of mashups on service recommendation and propose to jointly model historical preference of users on mashups and services, compositional information of services as mashups, and functional information of services and mashups in a single framework. Specifically, we extract topics from functional descriptions of services and model the relations between user, mashup and service and topics as a quadripartite graph. We use a personalized ranking on graph algorithm to learn the proposed model and simultaneously recommend services and mashups for user. We conduct a comprehensive experimental study using real world data from ProgrammbleWeb and results show that our recommendation method outperforms other representative recommendation approaches.

As web-enabled software becomes the standard for business processes, the ways organizations, partners and customers interface with it have become a critical differentiator in the market place, i.e., API Economy. With the rapid proliferation of APIs, it is increasingly important for users to effectively manage objective APIs in kinds of API markets, e.g., ProgramableWeb (PW), Mashape, etc. In this paper, to facilitate the process of API management, we propose a graphbased recommendation approach called ATRec to automatically assign tags to unlabeled APIs by exploiting both graph structure information and semantic similarity. Specifically, ATRec first leverages the multi-type relations (i.e., among APIs, mashups, and mashup assigned tags) to construct a heterogeneous network, in which a Random Walk with Restart (RWR) model is applied to alleviate the total cold start problem where no API has ever been tagged. Furthermore, we apply the recommended API tags in two API management scenarios (API search, API recommendation). Comprehensive experiments based on a real dataset crawled from PW demonstrate the effectiveness of the proposed approach.

We propose a model for clustering data with spatiotemporal intervals. This model is used to effectively evaluate clusters of spatiotemporal interval data. A new energy function is used to measure similarity and balance between clusters in spatial and temporal dimensions. We employ as a case study a large collection of parking data from a real CBD area. The proposed model is applied to existing traditional algorithms to address spatiotemporal interval data clustering problem. Results from traditional clustering algorithms are compared and analysed using the proposed energy function.

In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients' privacy.

We propose a cloud service composition framework that selects the optimal composition based on an end user's long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multivariate QoS analysis to predict the long-term QoS provisions from service providers' historical QoS data and short-term advertisements represented using Time Series. The quality of the QoS prediction is improved by incorporating QoS attributes' intra correlations into the multivariate analysis. To select the optimal service composition, the proposed framework uses QoS time series' inter correlations and performs a novel time series group similarity approach on the predicted QoS values. Experiments are conducted on real QoS dataset and results prove the efficiency of the proposed approach.

Recently, the paradigm of data mining-as-a-service in cloud computing environment has been attracting interests. In this paradigm, a company (data owner), lacking data storage, computational resources and expertise, stores its data in the cloud and outsources its mining tasks to the cloud service provider (server). In order to protect the privacy of the outsourced database and the association rules mined, k-anonymity, k-support, and k-privacy techniques have been proposed to perturb the data before it is uploaded to the server. These techniques are computationally expensive. If the data owner has resources to use these techniques, then it is often able to execute association rule mining locally. In this paper, we consider a scenario where a user (data owner) encrypts its data and stores it in the cloud. To mine association rules from its data, the user outsources the task to n (≥ 2) "semi-honest" servers, which cooperate to perform association rule mining on the encrypted data in the cloud and return encrypted association rules to the user. In this setting, we provide three solutions to protecting data privacy during association rule mining. Our solutions are built on the distributed ElGamal cryptosystem and achieve item privacy, transaction privacy and database privacy, respectively, as long as at least one out of the n servers is honest. To reduce the possibility that all servers are compromised, the user can use servers from different cloud providers. Our implementation and experiments demonstrate that our solutions are practical.

We propose a novel composition framework for an Infrastructure-as-a-Service (IaaS) provider that selects the optimal set of long-term service requests to maximize its profit. Existing solutions consider an IaaS provider's economic benefits at the time of service composition and ignore the dynamic nature of the consumer requests in a long-term period. The proposed framework deploys a new multivariate HMM and ARIMA model to predict different patterns of resource utilization and Quality of Service fluctuation tolerance levels of existing service consumers. The dynamic nature of new consumer requests with no history is modelled using a new community based heuristic approach. The predicted long-term service requests are optimized using Integer Linear Programming to find a proper configuration that maximizes the profit of an IaaS provider. Experimental results prove the feasibility of the proposed approach.

Due to the diversity and dynamic nature of cloud services, it is usually hard for potential cloud consumers to select the most suitable cloud service. This paper proposes CCCloud: a context-aware and credible cloud service selection model based on the comparison and aggregation of subjective assessments extracted from ordinary cloud consumers and objective assessments from quantitative performance testing parties. We propose a novel approach to evaluate cloud users' credibility, which not only can accurately evaluate how truthfully they assess cloud services, but also resist user collusion. In addition, in our model, objective assessments are used as benchmarks to filter out potentially biased subjective assessments, and then objective assessments and subjective assessments are aggregated to evaluate the overall performance of a cloud service. Furthermore, our model takes the contexts of objective assessments and subjective assessments into account. By calculating the similarity between different contexts, the benchmark level of objective assessments is dynamically adjusted according to context similarity, and the aggregated final scores of alternative cloud services are weighted by the similarity between the contexts of a potential cloud consumer and every testing party. This makes our cloud service selection model reflect potential cloud consumers' customized requirements more effectively. Finally, our proposed model is evaluated through the experiments conducted under different conditions. The experimental results demonstrate that our model significantly outperforms the existing work, especially in the resistance of user collusion.

We propose a new framework for composing Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. To evaluate spatio-temporal Sensor-Cloud services, two new quality attributes are introduced. We present a heuristic algorithm based on A* to compose Sensor-Cloud services in terms of spatio-temporal aspects. In addition, a new spatio-temporal technique based on 3D R-tree to access Sensor-Cloud services is proposed. Analytical and simulation results are presented to show the performance of the proposed approach.

This paper proposes a novel context-aware cloud service selection model based on the comparison and aggregation of subjective assessment extracted from cloud user feedback and objective assessment from quantitative performance testing. In this model, objective assessment provided by some professional testing parties is used as a benchmark to filter out potentially biased subjective assessment from cloud users, then objective assessment and subjective assessment are aggregated to evaluate the overall performance of cloud services according to potential cloud users' personalized requests. Moreover, our model takes the contexts of objective assessment and subjective assessment into account. By calculating the similarity between different contexts, the benchmark level of objective assessment is dynamically adjusted according to context similarity, which makes the following comparison and aggregation process more accurate and effective. After aggregation, the final results can quantitatively reflect the overall quality of cloud services. Finally, our proposed model is evaluated through the experiments executed in different conditions.

In the era of big data, data intensive applications have posed new challenges to the filed of service composition, i.e. composition efficiency and scalability. How to compose massive and evolving services in such dynamic scenarios is a vital problem demanding prompt solutions. As a consequence, we propose a new model for large-scale adaptive service composition in this paper. This model integrates the knowledge of reinforcement learning aiming at the problem of adaptability in a highly-dynamic environment and game theory used to coordinate agents' behavior for a common task. In particular, a multi-agent Q-learning algorithm for service composition based on this model is also proposed. The experimental results demonstrate the effectiveness and efficiency of our approach, and show a better performance compared with the single-agent Q-learning method.

This article considers cloud service composition from a decision analysis perspective. Traditional QoS-aware composition techniques usually consider the qualities available at the time of the composition because compositions are usually immediately consumed. This is fundamentally different in the cloud environment where the cloud service composition typically lasts for a relatively long period of time. The two most important drivers when composing cloud service are the long-term nature of the composition and the economic motivation for outsourcing tasks to the cloud. We propose an economic model, which we represent as a Bayesian network, to select and compose cloud services. We then leverage influence diagrams to model the cloud service composition. We further extend the traditional influence diagram problem to a hybrid one and adopt an extended Shenoy-Shafer architecture to solve such hybrid influence diagrams that include deterministic chance nodes. In addition, analytical and simulation results are presented to show the performance of the proposed composition approach.

The articles in this special issue focus on the use of cloud computing for social computing applications. Currently, two complimentary Internet based research areas are emerging: social computing and cloud computing. On the one hand, social computing empowers individual users with relatively low technological sophistication to use the web to engage in social interactions, contribute their expertise and share their content, experiences and opinions. On the other hand, cloud computing offers everyone sophisticated computing infrastructures and resources as utilities, so that individual users with relatively low computing knowledge can have at their disposal a high performing computing infrastructure with little investment. Together, these two complementary technological advances form the backbone of our digitized world, when coupled with the rise of sensors,mobile devices and the internet of things. Of course, they also face significant challenges. These articles look at each area and describe how they complement each other. The Social Web has become an important means of communication for everyone: individuals, organizations, and governments all use it to disseminate and share information, offer opinions and engage in discussions.

Service composition is emerging as an effective vehicle for integrating existing web services to create value-added and personalized composite services. As web services with similar functionality are expected to be provided by competing providers, a key challenge is to find the “best” web services to participate in the composition. When multiple quality aspects (e.g., response time, fee, etc.) are considered, a weighting mechanism is usually adopted by most existing approaches, which requires users to specify their preferences as numeric values. We propose to exploit the dominance relationship among service providers to find a set of “best” possible composite services, referred to as a composite service skyline. We develop efficient algorithms that allow us to find the composite service skyline from a significantly reduced searching space instead of considering all possible service compositions. We propose a novel bottom-up computation framework that enables the skyline algorithm to scale well with the number of services in a composition. We conduct a comprehensive analytical and experimental study to evaluate the effectiveness, efficiency, and scalability of the composite skyline computation approaches.

We propose a system, called EVolution of Long-term Composed Services (Ev-LCS), to address the change management issues in long-term composed services (LCSs). An LCS is a dynamic collaboration between autonomous web services that collectively provide a value-added service. It has a long-term commitment to its users. We first present a formal model, which provides the grounding semantics to support the automation of change management. We present a set of change operators that allow to specify a change in a precise and formal manner. We then propose a change enactment strategy that actually implements the changes. We develop a prototype system for the proposed Ev-LCS to demonstrate its effectiveness. We also conduct an experimental study to assess the performance of the change management approach.

Quality of service (QoS) is a major concern in the design and management of a composite service. In this paper, a systematic approach is proposed to calculate QoS for composite services with complex structures, taking into consideration of the probability and conditions of each execution path. Four types of basic composition patterns for composite services are discussed: sequential, parallel, loop, and conditional patterns. In particular, QoS solutions are provided for unstructured conditional and loop patterns. We also show how QoS-based service selection can be conducted based on the proposed QoS calculation. Experiments have been conducted to show the effectiveness of the proposed method.

Service-centric solutions usually require rich context to fully deliver and better reflect on the underlying applications. We present a novel use of context in the form of customized user interface services with the concept of User Interface as a Service (UIaaS). UIaaS takes user profiles as input to generate context-aware interface services. Such interface services can be used as context to augment semantic services with contextual information leading to UIaaS as a Context (UIaaSaaC). The added serendipitous benefit of the proposed concept is that the composition of a customized user interface with the requested service is performed by the service composition engine, as is the case with any other services. We use a special-purpose language (called User Interface Description Language (UIDL)) to model and realize user interfaces as services. We use a real-life e-government application, human services delivery for the citizens, as a proof-of-concept. We also present a comprehensive evaluation of the proposed approach using a functional evaluation and a nonfunctional evaluation consisting of an end user usability test and expert usability reviews.

We propose a trust prediction model for service Web using the Hidden Markov Model (HMM). The proposed model uses a dynamic training scheme that incorporates temporal sensitivity by using a dynamic training pool. The concept of the dynamically updated training pool enables the system to absorb newest information from data. We present the implementation of the proposed model and corresponding algorithm and their evaluation on real life data.

This paper presents a service-oriented digital government infrastructure focused on efficiently providing customized services to senior citizens. We designed and developed a Web Service Management System (WSMS), called WebSenior, which provides a service-centric framework to deliver government services to senior citizens. The proposed WSMS manages the entire life cycle of third-party web services. These act as proxies for real government services. Due to the specific requirements of our digital government application, we focus on the following key components of WebSenior: service composition, service optimization, and service privacy preservation. These components form the nucleus that achieves seamless cooperation among government agencies to provide prompt and customized services to senior citizens.

Classification is a widely used mechanism for facilitating Web service discovery. Existing methods for automatic Web service classification only consider the case where the category set is small. When the category set is big, the conventional classification methods usually require a large sample collection, which is hardly available in real world settings. This paper presents a novel method to conduct service classification with a medium or big category set. It uses the descriptive information of categories in a large-scale taxonomy as sample data, so as to disengage from the dependence on sample service documents. A new feature selection method is introduced to enable efficient classification using this new type of sample data. We demonstrate the effectiveness of our classification method through extensive experiments.

We propose an integrated framework that manages changes in long term composed services. The main procedure of change reaction is presented. One of the most challenging research issues of change management is how to automate the process of change reaction. To address this issue, we propose a semantic support, which centers around a tree-structured Web service ontology. The ontology is expected to provide sufficient semantic for change reaction. We propose a set of algorithms for efficiently querying semantics from the ontology. We conduct a set of experiments to evaluate the performance of the proposed algorithms.

We propose a fuzzy trust management framework for the Service Web. The proposed framework supports a natural way of representing and querying consumers' perception on services. We describe the underlying models, algorithms and an implementation architecture, mainly focusing on the key features and contributions of the proposed framework.

We propose a skyline computation approach that enables service users to optimally access sets of services as an integrated service package. We first present a one pass algorithm based on the observation that a multi-service skyline is completely determined by single service skylines. The skyline is returned after an enumeration on a significantly reduced candidate space. We then develop a dual progressive algorithm that is able to progressively report the skyline. We conduct an experimental study to assess the performance of the skyline computation approaches.

Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to extract a series of dominant subspaces of variable lengths from the whole visual feature space without the constraint of dimension consecutiveness. A stable visual dictionary is built by clustering the video keyframes over each dominant subspace. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a series of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video retrieval. The experimental results demonstrate the high effectiveness and efficiency of SUDS.

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies.

The performance of a service provider may fluctuate due to the dynamic service environment. Thus, the quality of service actually delivered by a service provider is inherently uncertain. Existing service optimization approaches usually assume that the quality of service does not change over time. Moreover, most of these approaches rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is rather a demanding task and an imprecise specification of the weights could miss user-desired services. We present a novel concept, called p-dominant service skyline. A provider S belongs to the p-dominant skyline if the chance that S is dominated by any other provider is less than p. Computing the p-dominant skyline provides an integrated solution to tackle the above two issues simultaneously. We present a p-R-tree indexing structure and a dual-pruning scheme to efficiently compute the p-dominant skyline. We assess the efficiency of the proposed algorithm with an analytical study and extensive experiments.

SERVICE-ORIENTED computing is gaining momentum as the next technological vehicle to leverage the huge investments in web application development. Web services are poised to take center stage as part of this adoption [4]. The ever-increasing number of web services will have the effect of transforming the web from a data-oriented repository to a service-oriented repository, also known as the Service Web [1]. In this new paradigm, existing business logic would be wrapped as web services to be accessible on the web via a web services middleware [2]. As the number of web services is expected to substantially increase, this would have the effect of introducing competition among web services that offer similar functionalities. Service users are enabled to select the “best” web services and/or their combinations with respect to their expected quality, such as price, response time, and reputation. There is a need to provide a sound framework to organize web services. This would form as a platform to query web services. Building this framework is especially important due to the ever-increasing large and heterogeneous web service deployment. A key ingredient of such a service framework is a formal service query model that can capture the key features of services to filter interactions and accelerate service searches. The query models must be congruent with the dynamic, active, autonomous, and highly heterogeneous nature of web services and their environment. Query languages and efficient selection techniques can then be developed once such a service model is in place. Existing service discovery technologies, such as service registries and service search engines, mainly support the simple keyword-based search on web services. However, keyword search cannot always precisely locate web services, partially because of the rich semantics embodied in these services. Due to the ambiguity of the keywords, which are typically described using natural language, either too many irrelevant services may be returned or some highly relevant services may be missed. As a key facilitator for application outsourcing, a common usage pattern of web services is to be programatically integrated into other applications (e.g., a travel package, navigation system, etc.). This further requires a service query mechanism that is more precise and reliable than keyword-based search. Query processing on web services is a novel concept that goes beyond the traditional data-centric view of query processing, which is mainly performance centered. It focuses on user quality parameters to select multiple services that are equivalent in functionality but exhibit a different quality of web service [3]. This special issue provides insights into the latest research on web service querying and efficient selection. Five articles were selected through a rigorous review process. They cover a set of key research topics including modeling techniques for web services, service query languages, algorithms for efficient service selection, as well as quality of web service modeling and quality-based service selection. The article by Skoutas et al., “Ranking and Clustering Web Services Using Multicriteria Dominance Relationships,” proposes a service selection framework that integrates the similarity matching scores of multiple parameters obtained from various matchmaking algorithms. The framework relies on the service dominance relationships to determine the relevance between services and users’ requests. Instead of using a weighting mechanism, the dominance relationship adopts a multi-objective strategy that simultaneously considers the matching scores of all the parameters for ranking the relevant services. A clustering algorithm is also proposed that captures the trade-offs among different parameters with respect to the considered matching criteria. The article by Grigori et al., “Ranking BPEL Processes for Service Discovery,” proposes a service discovery approach based on behavioral descriptions expressed in BPEL. Behavioral matchmaking goes beyond interface matchmaking as it considers the constraints on the invocation order of operations in service interfaces. Graph matching algorithms are applied, which enable the delivery of approximate behavioral matches. The article by Michlmayr et al., “End-to-End Support for QoSAware Service Selection, Binding, and Mediation in VRESCo,” describes a runtime environment for serviceoriented computing, called VRESCo. The proposed VRESCo framework provides a service metadata model. Service discovery and selection approaches are developed using this model. In addition, other important issues, such as QoS monitoring, dynamic binding, and service mediation, are addressed. The article by Barhamgi et al., “A Query Rewriting Approach for Web Service Composition,” proposes a service querying approach to compose dataproviding services. The data-providing services are modeled as RDF views over a mediated ontology specified in RDF to capture the consensual and shared knowledge in a IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. 3, NO. 3, JULY-SEPTEMBER 2010 161

We propose a subspace symbolization approach, namely SUDS, for content-based search on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary. With this dictionary, the video data are processed using string matching techniques with two-step data simplification. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces. Then, we present an innovative similarity measure called ED, which draws from the concept of the edit distance on strings to conduct video matching. The experimental results demonstrate the high effectiveness of SUDS with optimal parameters.

This paper investigates the problem of reputation management in composite services. Our focus is on developing a method of distribution of reputation received by a composite service to its component services. The proposed method enables the composite service to provide a fair distribution of reputation values so that a component service is neither penalized nor awarded for the bad and good performances respectively, of other component services. Experiment results show that the proposed technique propagates the "fair share" of reputation from the composite service to its component services.

Wireless Web services, also called mobile services or M-services, provide access to Web services through wireless networks. In this paper, we propose novel access methods and multi-channel organization for mobile users to effectively access composite M-services in wireless broadcast networks. We define a few semantics for accessing broadcast based M-services and study their impact on access efficiency.

BACKGROUND: Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. RESULTS: Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. CONCLUSION: Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper.

M-services provide mobile users wireless access to Web services. In this paper, we present a novel infrastructure for supporting M-services in wireless broadcast systems. The proposed infrastructure provides a generic framework for mobile users to look up, access, and execute Web services over wireless broadcast channels. Access efficiency is an important issue in wireless broadcast systems. We discuss different semantics that have impact on the access efficiency for composite M-services. A multiprocess workflow is proposed for effectively accessing composite M-services from multiple broadcast channels based on these semantics. We also present and compare different broadcast channel organizations for M-services and wireless data. Analytical models are provided for these channel organizations. Practical studies are presented to demonstrate the impact of different semantics and channel organizations on the access efficiency.

We propose a shot-based interest point selection approach for effective and efficient near-duplicate search over a large collection of video shots. The basic idea is to eliminate the local descriptors with lower frequencies among the selected video frames from a shot to ensure that the shot representation is compact and discriminative. Specifically, we propose an adaptive frame selection strategy called furthest point voronoi (FPV) to produce the shot frame set according to the shot content and frame distribution. We describe a novel strategy named reference extraction (RE) to extract the shot interest descriptors from a keyframe with the support of the selected frame set. We demonstrate the effectiveness and efficiency of the proposed approaches with extensive experiments.

The Web is transforming from a Web of data to a Web of both Semantic data and services. This trend is providing us with increasing opportunities to compose potentially interesting and useful services from existing services. While we may not sometimes have the specific queries needed in top-down service composition approaches to identify them, the early and proactive exposure of these opportunities will be key to harvest the great potential of the large body of Web services. In this paper, we propose a Web service mining framework that allows unexpected and interesting service compositions to automatically emerge in a bottom-up fashion. We present several mining techniques aiming at the discovery of such service compositions. We also present evaluation measures of their interestingness and usefulness. As a novel application of this framework, we demonstrate its effectiveness and potential by applying it to service-oriented models of biological processes for the discovery of interesting and useful pathways.

With a fast paced development of bioinformatics in recent years, we have witnessed a rapid growth of the number of databases and tools available for aiding in scientific research and knowledge discovery for bioinformaticians. Web service is an enabling technique to facilitate bioinformaticians in this discovery process by integrating the databases and tools. In this paper, we will propose a novel system, called bio-sense, for supporting the sharing and exploration in bioinformatics using semantic Web services. The promising features of bio-sense will be discussed in this paper.

We present a query algebra that supports optimized access of Web services through service-oriented queries. The service query algebra is defined based on a formal service model that provides a high-level abstraction of Web services across an application domain. The algebra defines a set of algebraic operators. Algebraic service queries can be formulated using these operators. This allows users to query their desired services based on both functionality and quality. We provide the implementation of each algebraic operator. This enables the generation of Service Execution Plans (SEPs) that can be used by users to directly access services. We present an optimization algorithm by extending the Dynamic Programming (DP) approach to efficiently select the SEPs with the best user-desired quality. The experimental study validates the proposed algorithm by demonstrating significant performance improvement compared with the traditional DP approach.

In this paper, we focus on the changes that trigger the modification of a service-oriented enterprisepsilas functionality. We present a framework that helps an SOE automatically modify its functional schema based on a change specification. The central component of this framework is a change reaction manager. It provides mechanisms to interpret a change specification, modify the member services in an SOE, and rearrange their cooperation correspondingly. A domain knowledge provider offers the semantics that facilitates the change reaction process. We also use a logging mechanism to keep track of the change reaction process.

A Service Oriented Enterprise (SOE) provides an efficient and flexible platform where multiple Web services can cooperate together to provide a value-added service. Change management is one of the fundamental issues in enabling SOEs. In this paper, we propose a framework that facilitates in automatically managing top-down changes in SOEs. We start with formalizing a SOE's schema since it is a central concept for specifying and managing top-down changes in SOEs. We then propose a change model as a guide to react to changes. Algorithms are proposed to implement changes by refining a SOE's behavior.

We present DIA, a Web services-based infrastructure for the Discovery, Integration, and Analysis of geoscience data, tools, and services. DIA provides a collaborative environment where scientists can share their resources (e.g., geochemical data, filtering services, etc.) by registering them through well-defined ontologies. We have developed a planetary materials ontology in OWL for this purpose. The ontology is used by different geoscientists (using Web services) to explore, extract, and integrate information from different heterogeneous data sets. The DIA system is now in its final pre-release phase. It is currently made accessible to a few geoscientists for conducting usability analyses, and it will eventually be made available to the community at large through the geoscience portal (GEON) at the San Diego Supercomputer Center.

We propose a service mining framework for exploring interesting compositions of existing Web services. The framework first screens Web services for composition leads using a "coarse-grained" filtering approach. It then verifies these leads based on runtime conditions. Top candidates are selected from the verified leads and evaluated for their interestingness. We present algorithms to automate the screening phase of the framework. Finally, we study the effects of key variables on lead compositions' interestingness. As a motivating example, we apply these algorithms to the field of biological pathway discovery and rely on knowledge obtained from reverse engineering online resources to assess their effectiveness.

introduction Introduction to special issue on semantic Web services Editors: Brahim Medjahed View Profile , Athman Bouguettaya View Profile , Boualem Benatallah View Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 8Issue 1pp 1–eshttps://doi.org/10.1145/1294148.1294149Published:01 November 2007Publication History 3citation881DownloadsMetricsTotal Citations3Total Downloads881Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A new generation of Web services called Mobile services has emerged to support wireless oriented services. One of the most important issues in M-services environments is to provide efficient access methods for discovering and accessing services. In this paper, we propose a multi-channel broadcast-based M-services infrastructure. We discuss a novel access method and compare it to other access methods. We present an analytical model and a performance evaluation for each method.

The emergence of Web databases has introduced new challenges related to their organization, access, integration, and interoperability. New approaches and techniques are needed to provide across-the-board transparency for accessing and manipulating Web databases irrespective of their data models, platforms, locations, or systems. In meeting these needs, it is necessary to build a middleware infrastructure to support flexible tools for information space organization, communication facilities, information discovery, content description, and assembly of data from heterogeneous sources. In this paper, we describe a scalable middleware for efficient data and application access that we have built using the available technologies. The resulting system is called WebFINDIT. It is a scalable and uniform infrastructure for locating and accessing heterogeneous and autonomous databases and applications.

Broadcasting is an important means of data dissemination in wireless environments. Data access methods are used to provide power efficient access to broadcast channels. In this paper, we propose a hybrid data access method which is built on the combination of an existing index tree based data access method and hashing techniques. Cost models are derived for the proposed method. Simulation experiments are also conducted to compare the hybrid method with the index tree based methods. We show that under a range of parameters the hybrid method exhibits better performance over the index tree based methods.

In this paper, we propose an approach for preserving trade secrets in B2B interactions among competitors. Customer information exchanged during Business-to-Business (B2B) collaborations is usually considered as a business asset not to be freely shared with other businesses. This customer information is in essence a business trade secret. The full automation of B2B interactions is now possible because of the wide deployment of such technologies as Web services. However, these advances also create greater opportunities for businesses in acquiring sensitive customer data from other transacting businesses, thus creating an impediment for potential B2B collaboration between competitors. This calls for techniques to protect disclosure of sensitive data. Our approach leverages psycholinguistic knowledge to perturb data to computationally impede the disclosure of privileged customer information. We present an analytical model and a set of experiments to demonstrate the robustness of the proposed techniques.

We propose a composability model to ascertain that Web services can safely be combined, hence avoiding unexpected failures at runtime. Composability is checked through a set of rules organized into four levels, syntactic, static semantic, dynamic semantic, and qualitative levels. We introduce the concepts of composability degree and /spl tau/-composability to cater for partial and total composability. We also propose a set of algorithms for checking composability. Finally, we conduct a performance study (analytical and experimental) of the proposed algorithms.

Power conservation and client waiting time reduction are two important aspects of data access efficiency in broadcast-based wireless communication systems. The intention of data access methods is to optimize client power consumption with the least possible overhead on client waiting time. We propose an adaptive data access method which builds on the strengths of indexing and hashing techniques. We show that this method exhibits better average performance over the well-known index tree-based access methods. A new performance model is also proposed. This model uses more realistic assessment criteria, based on the combination of access and tuning times, for evaluating wireless access methods. This new model provides a dynamic framework to express the degree of importance of access and tuning times in an application. Under this new model, the adaptive method performance also outperforms the other access methods in the majority of cases.

The Web is changing the way organizations are conducting their business. Businesses are rushing to provide modular applications, called Web services, that can be programmatically accessed through the Web. Despite the tremendous developments achieved so far, one of the most important, yet untapped potential, is the use of Web services as facilitators for inter-organizational cooperation. This promising concept, known as Web service composition, is gaining momentum as the potential silver bullet for the envisioned Semantic Web. The development of such integrated services has so far been ad hoc, time-consuming, and requires extensive low-level programming efforts. In this paper, we present WebBIS (Web Base of Internet-accessible Services), a generic framework for composing and managing Web services. We combine the object-oriented and active rules paradigms for such a task. We also provide a ontology-based framework for organizing the Web service space. We finally propose a peer-to-peer mechanism for reporting, propagating, and reacting to changes in Web services.

Mobile commerce (m-commerce) refers to the conduct of business using wireless devices and communications. Driven by the success of e-commerce and impressive progress in wireless technologies, m-commerce is rapidly taking place in the business forefront. However, most of the concepts developed for e-commerce may not be easily applicable to wireless environments. This is due to the peculiarities of these environments such as limited bandwidth, unbalanced client-server communication, and limited power supply. Web services are undeniably one of the most significant e-commerce concepts worth of being adapted to the wireless world. Mobile services, also called m-services, promise several benefits compared with their wired counterparts. They provide larger customer base and cater for "anytime and anywhere" access to services. In this paper, we propose an infrastructure for organizing and efficiently accessing m-services in broadcast environments. We define a multichannel model to carry information about m-services available within a given geographic area. The UDDI channel includes registry information about m-services. The m-service channel contains the description and executable code of each m-service. The data channel contains the actual data needed while executing the m-service. We also introduce three techniques to enable efficient access to wireless channels. These techniques extend well-known mobile databases' access methods to m-services: B+ tree, signature indexing, and hashing. We finally present an analytical model and conduct an extensive experimental study to evaluate and compare the proposed techniques.

The ubiquity of the World Wide Web offers an ideal opportunity for the deployment of highly distributed applications. Now that connectivity is no longer an issue, attention has turned to providing a middleware infrastructure that will sustain data sharing among Web-accessible databases. We present a dynamic architecture and system for describing, locating, and accessing data from Web-accessible databases. We propose the use of flexible organizational constructs service links and coalitions to facilitate data organization, discovery, and sharing among Internet-accessible databases. A language is also proposed to support the definition and manipulation of these constructs. The implementation combines Java, CORBA, database API (JDBC), agent, and database technologies to support a scalable and portable architecture interconnecting large networks of heterogeneous and autonomous databases. We report on an experiment to provide uniform access to a Web of healthcare-related databases.

We describe an architecture called WebFINDIT that allows dynamic couplings of Web accessible databases based on their content and interest. We propose an implementation using WWW, Java, JDBC, and CORBA's ORBs that communicate via the CORBA's IIOP protocol. The combination of these technologies offers a compelling middleware infrastructure to implement fluid-area enterprise applications. In addition to a discussion of WebFINDIT's core concepts and implementation architecture, we also discuss an experience of exiting WebFINDIT in a healthcare application.

article Free Access Share on World Wide Database—integrating the Web, CORBA and databases Authors: Athman Bouguettaya Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Boualem Benatallah Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Lily Hendra Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , James Beard Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Kevin Smith Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Mourad Quzzani Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile Authors Info & Claims ACM SIGMOD RecordVolume 28Issue 2June 1999 pp 594–596https://doi.org/10.1145/304181.304589Online:01 June 1999Publication History 4citation577DownloadsMetricsTotal Citations4Total Downloads577Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The popularity of the Internet has generated an explosion in the number of accessible information sources. In addition, recent advances in wide-area networking have led to a push for a logically-unified, yet physically distributed, information repository accessible through the Internet. The architecture of data resources and sources should be scalable and able to accommodate hundreds and thousands of databases. These are called Internet databases. This has to be achieved against a backdrop of accommodating database autonomy and bridging their heterogeneity with reasonable overhead. In addition, manual and evolution management needs a careful design as ad hoc management is clearly intractable in a highly dynamic environment as the Internet. Therefore, there is a need for supporting a self-adjustable model of evolution. We propose an architecture that has the ability to reflect upon its own state and to decide when to evolve. The use of flexible constructs like service links, coalitions, and co-databases provide a dynamic and portable model for eliciting data sharing. Further, we propose an implementation model using CORBA and Web related technologies.

The Internet and the World Wide Web (WWW) have elicited the explosion of accessible data repositories. Because high connectivity is now a reality, the challenge has been to take advantage of it to enable data sharing in a cost-effective way. Any proposed architecture would have to allow dynamic couplings of heterogeneous databases based on their content and interest. The authors propose an implementation using CORBA and Web technologies as a distributed infrastructure and platform to support the dynamic interconnection of heterogeneous and autonomous databases on the Web.

Workflow systems are receiving increased attention as they intend to facilitate the operations of enterprises by coordinating and streamlining business activities. The need for automated support and operational models that allow workflow applications to coordinate units of work across multiple servers-according to business defined rules and routes-is becoming critical for the proper management of such activities. In this paper, we describe a Transaction-Oriented Workflow Environment (TOWE) for programming workflow activities. The novelty of our approach resides in the proposed unified abstraction, class libraries, to support workflow activities. The fundamental concept used in the TOWE system is based on the symbiosis of object-oriented programming and interprocess communication concepts. In TOWE, the concurrency abstractions are represented by process objects, active objects acting as processes, which involve asynchronous, location-independent, and application specific process invocations.

Providing a general purpose programming environment that supports the definition of, and exercises control over, the flow of execution of long-running activities is highly beneficial for a variety of client/server distributed data-intensive applications. In this paper, we present a Transaction-Oriented Work-Flow Environment (TOWE) for the programming of long-lived activities through a set of class libraries. The TOWE is based on an amalgamation of object-oriented programming with distributed interprocess communication concepts. The concurrency abstractions provided by TOWE are objects, acting like processes, and involve an asynchronous, location-independent, mode of process invocation coupled with data-driven synchronization of processes.

The merging of distributed computing and object-oriented technology has resulted in novel ways to design and develop modern and aggregate architectures of databases. Distributed object management has emerged as the core technology of this trend, and the CORBA specifications attempt to manage such environments. In this paper, we examine the CORBA architecture, both as a distributed object management framework as well as a service to interoperate persistent objects. We report on using two off-the-shelf DBMSs (ONTOS and UniSQL) as a testbed for a feasibility study.

We focus on the stability and behavior of three widely used clustering algorithms. Various correlation coefficients are computed to help us understand the sensitivity of object clustering. Surprisingly, the results indicate that there is almost no difference among any clustering approach. Furthermore, all methods appear to be near stable. These findings tend to show that the clustering algorithms are independent of the way objects are inherently clustered.

The establishment of one logical database that spans countries and continents is increasingly becoming a realistic goal to achieve. This conceptual database would potentially consist of an ever growing number of component databases. We propose a scheme to build a Worldwide Database using a two-level approach. In particular, we describe how conglomerations (small and large) of databases are formed, modified and evolved.>