Internet of Things (IoT) applications consist of diverse resource-constrained/rich devices with a considerable portion being mobile. Such devices demand lightweight, loosely coupled interactions in terms of time, space, and synchronization. IoT protocols at the middleware layer support several interaction types (e.g., asynchronous messaging, streaming, etc.) ensuring successful interactions between devices that use the same protocol. Additionally, they introduce different Quality of Service (QoS) delivery modes for data exchange with respect to available device and network resources. On the other hand, interconnecting heterogeneous IoT devices requires mapping both their functional and QoS properties. This calls for advanced interoperability solutions integrated with QoS modeling and analysis techniques. This paper introduces an automated synthesis of QoS-aware mediating artifacts. Such mediators enable the interconnection between IoT devices employing heterogeneous middleware protocols. Additionally, representative QoS models are synthesized. Leveraging these models, system designers can evaluate the effectiveness of the interconnection in terms of end-to-end QoS. We evaluate the usefulness of our approach through experimentation with a case study employing heterogeneous middleware protocols. In particular, we statistically analyze through simulations the effect of varying system parameters on the end-to-end QoS.

Mobile crowdsensing allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, a major challenge facing mobile crowdsensing is to guarantee privacy preservation to the contributing users. Privacy preservation in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies “anonymizing” the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users’ own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we leverage the ℓ-Completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with ℓ−1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure ℓ-Completeness when analyzing the data while allowing the data to be used for as many tasks as possible, and promoting the accuracy of the resulting knowledge. This is achieved through a clustering algorithm sensitive to the data distribution, which optimizes data reuse and utility. Nevertheless, it is critical to allow the deployment of such a solution even in the presence of a malicious adversary able to act on the server side, for which we introduce a privacy-by-design architecture leveraging Trusted Execution Environments. The implementation of a prototype using SGX enclaves further allows running experiments that show that our system incurs a reasonable performance overhead, while providing strong security properties against a malicious adversary.

THE COVID-19 pandemic has brought the global society to a historic turning point. While the current pandemic is transforming our normal lives in an unprecedented way, the computing and information services ecosystem have provided the much-needed support to the continuity of the social interactions and the organizational functions. As COVID-19 continues to transform the global society, it is also exposing the weaknesses of the current services ecosystems and newer research challenges to further leveraging emerging computing and services technologies to provide secure, privacy-aware and resilient infrastructure and services during pandemics. The goal of this special issue has been to solicit impactful research papers that will be of immediate value to the ongoing COVID-19 pandemic as well as for future pandemic and crises.

Crowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies "anonymizing" the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the ℓ-completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with ℓ - 1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure ℓ-completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application.

The papers in this special section focus on secure and emerging collaborative computing and intelligent systems. The Internet, coupled with recent advances in computing and information technologies, such as IoT, mobile edge/ cloud computing, cyber-physical-social systems, and artificial intelligence/machine learning/deep learning, have paved the way for creating next-generation smart and intelligent systems and applications that can have transformative impact in our society while accelerating rapid scientific discoveries and innovations. Unprecedented cyber-social and cyber-physical infrastructures and systems that span geographic boundaries are possible because of the Internet and the growing number of collaboration-enabling technologies. With newer technologies and paradigms getting increasingly embedded in the computing platforms and networked information systems/ infrastructures that form the digital foundation for our personal, organizational, and social processes and activities, it is increasingly becoming critical that the trust, privacy, and security issues in such digital environments are holistically addressed to ensure the safety and well-being of individuals as well as our society.

Real-time event detection and targeted decision making for emerging mission-critical applications require systems that extract and process relevant data from IoT sources in smart spaces. Oftentimes, this data is heterogeneous in size, relevance, and urgency, which creates a challenge when considering that different groups of stakeholders (e.g., first responders, medical staff, government officials, etc.) require such data to be delivered in a reliable and timely manner. Furthermore, in mission-critical settings, networks can become constrained due to lossy channels and failed components, which ultimately add to the complexity of the problem. In this article, we propose PrioDeX, a cross-layer middleware system that enables timely and reliable delivery of mission-critical data from IoT sources to relevant consumers through the prioritization of messages. It integrates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics through a queueing analytical model. PrioDeX proposes novel algorithms that utilize the results of this analysis to tune data exchange configurations (event priorities and dropping policies), which is necessary for satisfying situational awareness requirements and resource constraints. PrioDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate our approach using both simulated and prototype-based experiments in a smart building fire response scenario. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

Crowdsensing allows citizens to contribute to the monitoring of their living environment using the sensors embedded in their mobile devices, e.g., smartphones. However, crowdsensing at scale involves significant communication, computation, and financial costs due to the dependence on cloud infrastructures for the analysis (e.g., interpolation and aggregation) of spatio-temporal data. This limits the adoption of crowdsensing by activists although sorely needed to inform our knowledge of the environment. As an alternative to the centralized analysis of crowdsensed observations, this paper introduces a fully distributed interpolation-mediated aggregation approach running on smartphones. To achieve so efficiently, we model the interpolation as a distributed tensor completion problem, and we introduce a lightweight aggregation strategy that anticipates the likelihood of future encounters according to the quality of the interpolation. Our approach thus shifts the centralized post-processing of crowdsensed data to distributed pre-processing on the move, based on opportunistic encounters of crowdsensors through state-of-the-art D2D networking. The evaluation using a dataset of quantitative environmental measurements collected from 550 crowdsensors over 1 year shows that our solution significantly reduces –and may even eliminate– the dependence on the cloud infrastructure, while it incurs a limited resource cost on end devices. Meanwhile, the overall data accuracy remains comparable to that of the centralized approach.

Opportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens’ active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resourceefficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the workload in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly one million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach.

Civic engagement refers to any collective action towards the identification and solving of public issues. Current civic technologies are traditional Web-or mobile-based platforms that make difficult, or just impossible, the participation of citizens via different communication technologies. Moreover, connected objects sensing physical-world data can nourish participatory processes by providing physical evidence to citizens; however, leveraging these data is not direct and still a time-consuming process for civic technologies developers. This paper introduces the concept of social middleware for civic engagement. Social middleware allows citizens to engage in participatory processes - supported by civic technologies-via their favorite communication tools, and to interact not only with other citizens but also with relevant connected objects and software platforms. The mission of social middleware goes beyond the connection of all these heterogeneous entities. It aims at easing the implementation of distributed applications oriented toward civic engagement by featuring dedicated built-in services.

The Internet of Things is expected to contribute to a "smarter world" by connecting the physical to the virtual, i.e., enabling advanced knowledge engineering over the big data gathered about the physical world. However, such a promise comes along with high resource consumption, spanning the network, storage and computational resources, not to mention possible security and privacy threats. As a result, it tends to be admitted that the IoT smartness will not be accommodated at scale by a centralized cloud-based approach. Instead, the deployment of IoT systems needs to leverage a highly distributed system architecture, which optimizes the distribution of the computation -from the edge to the cloud-according to the unique business requirements in terms of financial cost, latency, availability, etc. Toward that goal, this paper introduces the LATTICE framework, which aims at taming the complexity of configuring edge-based IoT systems. LATTICE builds upon ontologies that have proven useful to characterize the constituents of IoT systems in the required domain-specific way. However, LATTICE also revisits the exploitation of ontologies -i.e., the formal description of the real world, spanning the physical and cyber entities-across the development life-cycle of the IoT systems. As a first evidence, this paper introduces an automated approach to the optimization of IoT system configurations at the edge, provided the ontological description of the target IoT system.

Emerging applications in smart cities and communities require massive IoT deployments using sensors/actuators (things) that can enhance citizens' quality of life and public safety. However, budget constraints often lead to limited instrumentation and/or the use of low-cost sensors that are subject to drift and bias. This raises concerns of robustness and accuracy of the decisions made on uncertain data. To enable effective decision-making while fully exploiting the potential of low-cost sensors, we propose to send mobile units (e.g., trained personnel) equipped with high-quality (more expensive) and freshly-calibrated reference sensors so as to carry out calibration in the field. We design and implement an efficient cooperative approach to solve the calibration planning problem, which aims at minimizing the cost of the recurring calibration of multiple sensor types in the long-term operation. We propose a two-phase solution that consists of a sensor selection phase that minimizes the average cost of recurring calibration, and a path planning phase that minimizes the travel cost of multiple calibrators which have load constraints. We provide fast and effective heuristics for both phases. We further build a prototype that facilitates the mapping of the deployment field and provides navigation guidance to mobile calibrators. Extensive use-case-driven simulations show our proposed approach significantly reduces the average cost compared to naive approaches: up to 30% in a moderate-sized indoor case, and higher in outdoor cases depending on the scale.

Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal datasets. Detecting sensing contexts in the wild is a challenging task and requires features from smartphone sensors that are not always available. In this paper, we propose three heuristic algorithms for detecting sensing contexts such as in/out-pocket, under/over-ground, and in/out-door for crowdsourced datasets that are destined for human mobility mining. These are unsupervised binary classifiers with a small memory footprint and execution time. Using a segment of the Ambiciti real dataset - a feature-limited crowdsourced dataset - we report that our algorithms perform equally well in terms of balanced accuracy (within 4.3%) when compared to machine learning (ML) models reported by an AutoML tool.

Online Social Network Services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the past few decades, an aggressive market rivalry has led to the emergence of multiple competing, “closed” OSNSs. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about what they can do with their personal data, the people they can interact with, and the information they get access to. As an alternative to the platform lock-in, “open” OSNSs promote the adoption of open, standardized APIs. However, users still massively adopt closed OSNSs to benefit from the services’ advanced functionalities and/or follow their “friends,” although the users’ virtual social sphere is ultimately limited by the OSNSs they join. Our work aims at overcoming such a limitation by enabling users to meet and interact beyond the boundary of their OSNSs, including reaching out to “friends” of distinct closed OSNSs. We specifically introduceUniversal Social Network Bus (USNB), which revisits the “service bus” paradigm that enables interoperability across computing systems to address the requirements of “social interoperability.” USNB featuressynthetic profilesandpersonaefor interaction across the boundaries of closed and open and profile- and non-profile-based OSNSs through areference social interaction service. We ran a 1-day workshop with a panel of users who experimented with the USNB prototype to assess the potential benefits of social interoperability for social network users. Results show the positive evaluation of users for USNB, especially as an enabler of applications for civic participation. This further opens up new perspectives for future work, among which includes enforcing security and privacy guarantees.

The widespread deployment of connected Things in our cities allows for the enhanced management of the urban space. However, due to environmental constraints, the connected Things are often battery-powered, which affects the lifetime of the IoT Networks. We believe that the urban IoT networks must leverage the power of the crowd, leading to combine the fixed urban IoT networks with the crowdsensing capabilities of the citizens' smartphones. That is, we integrate the offloading of a portion of the traffic generated by the fixed network to mobile crowdsensing devices that also contribute with relevant urban observations (e.g., for noise pollution monitoring). This paper highlights the key technical features of the resulting solution, whose evaluation shows that we are able to extend the lifetime of the battery-operated IoT network by a up to 7 factor.

Real-time event detection and targeted decision making for emerging mission-critical applications, e.g. smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. In this paper, we propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We design an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate its performance through simulated experiments in a smart building fire response scenario. Our results demonstrate significant improvement to mission-critical data delivery under a variety of conditions. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

Mobile IoT applications are typically deployed on resource-constrained devices with intermittent network connectivity. To support the deployment of such applications, the Publish/Subscribe (pub/sub) interaction paradigm is often employed, as it decouples mobile peers in time and space. Pub/sub middleware protocols and APIs consider the Things' hardware limitations and support the development of effective applications by providing QoS features. These features aim to enable developers to tune an application by switching different levels of response times and success rates. However, the profusion of pub/sub protocols coupled with intermittent connectivity result in non-trivial application tuning. In this paper, we model the performance of middleware protocols found in IoT, which are classified within the pub/sub interaction paradigm. We model reliable and unreliable protocols, by considering QoS semantics for data validity, buffer capacities as well as the intermittent availability of peers. Finally, we perform statistical analysis by varying these QoS semantics, demonstrating their significant effect on the rate of successful interactions. We showcase the application of our analysis in concrete scenarios relating to Traffic Information Management systems, that integrate both reliable and unreliable participants. The consequent PerfMP performance modeling pattern may be tailored for a variety of deployments, in order to control fine-grained QoS policies.

Mobile Phone Sensing offers a great opportunity toward the large scale monitoring of urban phenomena, such as the exposure of the population to environmental noise. Our research aims to make available a supporting mobile application together with the associated platform for the analysis of the contributed observations. The technological issues arising have been partly solved but a gap remains between the need for the massive collection of relevant data, and the quantity and accuracy of the measurements that are actually gathered. This paper presents our iterative research process to tackle this challenge, which combines technological innovation and social design. The presented research results contribute to a better understanding of why and how people use mobile phone sensing applications; the results also inform how to best leverage mobile crowd-sensing in the development of smart cities and how it may serve addressing urban challenges related to, e.g., public health or urban planning.

I am very happy to report that TSC has gained an Impact Factor (IF) of 3.520 and the 5-year IF of 4.245, both of which represent significant increases from the previous years. This further speaks to the global reputation of the journal and the amazing work done by the past EICs, all the current and past EB members, and reviewers - all of whom have volunteered their precious time despite their very busy schedule to support and contribute to the growth of this journal. I hope to count on your continued engagement for the future growth of this journal. Over this past year, several esteemed EB members have completed their terms of service to TSC after serving for several years. On behalf of the Services Computing community and the TSC EAB, I would like to thank the following Associate Editors who retired from TSC EB in 2017 for their invaluable service and contributions to the journal. Overall, I am very proud of the success that TSC has achieved in 2017. This would not have been possible without the continued support of the authors, readers, reviewers, TSC EAB, TSC EB, and the staff of IEEE and IEEE Computer Society. I look forward to exploring ways to further enhance the reputation and impact of our journal. I would love to hear your suggestions and comments, and I hope to have your continued support.

Internet of Things (IoT) applications consist of diverse Things (sensors and devices) in terms of hardware resources. Furthermore, such applications are characterized by the Things' mobility and multiple interaction types, such as synchronous, asynchronous, and streaming. Middleware IoT protocols consider the above limitations and support the development of effective applications by providing several Quality of Service (QoS) features. These features aim to enable application developers to tune an application by switching different levels of response times and delivery success rates. However, the profusion of the developed IoT protocols and the intermittent connectivity of mobile Things, result to a non-trivial application tuning. In this paper, we model the performance of the middleware overlay infrastructure using Queueing Network Models (QNMs). To represent the mobile Thing's connections/disconnections, we model and solve analytically an ON/OFF queueing center. We apply our approach to Streaming interactions with mobile peers. Finally, we validate our model using simulations. The deviations between the performance results foreseen by the analytical model and the ones provided by the simulator are shown to be less than 5%.

While bringing massive-scale sensing at low cost, mobile participatory sensing is challenged by the low accuracy of the sensors embedded in and/or connected to the smartphones. The mobile measurements that are collected need to be corrected so as to accurately match the phenomena being observed. This paper addresses this challenge by introducing a multi-hop, multiparty calibration method that operates in the background in an automated way. Using our method, sensors that are within a relevant sensing (and communication) range coordinate so that the observations of the participating (previously) calibrated sensors serve calibrating the other participants. As a result, our method is particularly well suited for participatory sensing within crowd meetings, as for instance within public spaces. Our solution leverages multivariate linear regression, together with robust regression so as to discard the measurements that are of too low quality for being meaningful. To the best of our knowledge, we are the first to introduce a multiparty calibration algorithm, while previous work in the area focused on pairwise calibration. The paper further introduces a supporting prototype implemented over Android, and related experiment in the context of noise sensing.We show that the proposed multiparty calibration system enhances the accuracy of the mobile noise sensing application.

Systems deployed in mobile environments are typically characterized by intermittent connectivity and asynchronous sending/reception of data. To create effective mobile systems for such environments, it is essential to guarantee acceptable levels of timeliness between sending and receiving mobile users. In order to provide QoS guarantees in different application scenarios and contexts, it is necessary to model the system performance by incorporating the intermittent connectivity. Queueing Network Models (QNMs) offer a simple modeling environment, which can be used to represent various application scenarios, and provide accurate analytical solutions for performance metrics, such as system response time. In this paper, we provide an analytical solution regarding the end-to-end response time between users sending and receiving data by modeling the intermittent connectivity of mobile users with QNMs. We utilize the publish/subscribe (pub/sub) middleware as the underlying communication infrastructure for mobile users. To represent the user's connections/disconnections, we model and solve analytically an ON/OFF queueing system by applying a mean value approach. Finally, we validate our model using simulations with real-world workload traces. The deviations between the performance results foreseen by the analytical model and the ones provided by the simulator are shown to be less than 5% for a variety of scenarios.

Nowadays, various static wireless sensor networks (WSN) are deployed in the environment for many purposes: traffic control, pollution monitoring, and so on. The willingness to open these legacy WSNs to the users is emerging, by integrating them to the Internet network as part of the future Internet of Things (IoT), for example, in the context of smart cities and open data policies. While legacy sensors cannot be directly connected to the Internet in general, emerging standards such as 6LoWPAN are aimed at solving this issue but require us to update or replace the existing devices. As a solution to connect legacy sensors to the IoT, we propose to take advantage of the multi-modal connectivity as well as the mobility of smartphones to use phones as opportunistic proxies , that is, mobile proxies that opportunistically discover closeby static sensors and act as intermediaries with the IoT, with the additional benefit of bringing fresh information about the environment to the smartphones’ owners. However, this requires us to monitor the smartphone’s mobility and further infer when to discover and register the sensors to guarantee the efficiency and reliability of opportunistic proxies. To that end, we introduce and evaluate an approach based on mobility analysis that uses a novel path prediction technique to predict when and where the user is not moving, and thereby serves to anticipate the registration of sensors within communication range. We show that this technique enables the deployment of low-cost resource-efficient mobile proxies to connect legacy WSNs with the IoT.

The increased adoption of mobile devices and social networking is drastically changing the way people monitor and share knowledge about their environment. Here, information and communication technologies (ICT) offer significant new ways to support social activism in cities by providing residents with new digital tools to articulate projects and mobilize activities. However, the development of ICT for activism is still in its infancy, with activists using basic tools stitched together in an ad hoc manner for their needs. Still, Internet-based technologies and related software architectures feature various enablers for civic action beyond base social networking. To that end, this paper discusses the vision and initial details of AppCivist, a platform that builds on cross-domain research among social scientists and computer scientists to revisit service-oriented architecture and relevant services to further social activism. We discuss the ICT challenges inherent in this project and present our recent work to address them.

Service composition is a widely used method in ubiquitous computing that enables accomplishing complex tasks required by users based on elementary (hardware and software) services available in ubiquitous environments. To ensure that users experience the best Quality of Service (QoS) with respect to their quality needs, service composition has to be QoS-aware. Establishing QoS-aware service compositions entails efficient service selection taking into account the QoS requirements of users. A challenging issue towards this purpose is to consider service selection under global QoS requirements (i.e., Requirements imposed by the user on the whole task), which is of high computational cost. This challenge is even more relevant when we consider the dynamics, limited computational resources and timeliness constraints of ubiquitous environments. To cope with the above challenge, in this paper we present QASSA, an efficient service selection algorithm that provides the appropriate ground for QoS-aware service composition in ubiquitous environments. The contribution of QASSA is three-fold. First, it formulates service selection under global QoS requirements as a set-based optimisation problem, benefiting from recent proposals in the domain of multi-objective optimisation. Second, QASSA resolves this problem in an efficient way using clustering techniques, namely the K-Means algorithm. Third, QASSA is devised in two versions, viz., centralised and distributed versions, so that it can be executed on top of centralised and decentralised infrastructures in ubiquitous environments. Results of experimental studies are presented to illustrate the timeliness and optimality of QASSA.

Large-scale mobile environments are characterized by, among others, a large number of mobile users, intermittent connectivity and non-homogeneous arrival rate of data to the users, depending on the region's context. Multiple application scenarios in major cities need to address the above situation for the creation of robust mobile systems. Towards this, it is fundamental to enable system designers to tune a communication infrastructure using various parameters depending on the specific context. In this paper, we take a first step towards enabling an application platform for large-scale information management relying on `mobile social crowd-sourcing'. To inform the stakeholders of expected loads and costs, we model a large-scale mobile pub/sub system as a queueing network. We introduce additional timing constraints such as (i) mobile user's intermittent connectivity period; and (ii) data validity lifetime period (e.g. that of sensor data). Using our MobileJINQS simulator, we parameterize our model with realistic input loads derived from the D4D dataset (CDR) and varied lifetime periods in order to analyze the effect on response time. This work provides system designers with coarse grain design time information when setting realistic loads and time constraints.

Software cohesion concerns the degree to which the elements of a module belong together. Cohesive software is easier to understand, test and maintain. In the context of service-oriented development, cohesion refers to the degree to which the operations of a service interface belong together. In the state of the art, software cohesion is improved based on refactoring methods that rely on information, extracted from the software implementation. This is a main limitation towards using these methods in the case of web services: web services do not expose their implementation; instead all that they export is the web service interface specification. To deal with this problem, we propose an approach that enables the cohesion-driven decomposition of service interfaces, without information on how the services are implemented. Our approach progressively decomposes a given service interface into more cohesive interfaces; the backbone of the approach is a suite of cohesion metrics that rely on information, extracted solely from the specification of the service interface. We validate the approach in 22 real-world services, provided by Amazon and Yahoo. We assess the effectiveness of the proposed approach, concerning the cohesion improvement, and the number of interfaces that result from the decomposition of the examined interfaces. Moreover, we show the usefulness of the approach in a user study, where developers assessed the quality of the produced interfaces.

Interoperability is a major concern for the software engineering field, given the increasing need to compose components dynamically and seamlessly. This dynamic composition is often hampered by differences in the interfaces and behaviours of independently-developed components. To address these differences without changing the components, mediators that systematically enforce interoperability between functionally-compatible components by mapping their interfaces and coordinating their behaviours are required. Existing approaches to mediator synthesis assume that an interface mapping is provided which specifies the correspondence between the operations and data of the components at hand. In this paper, we present an approach based on ontology reasoning and constraint programming in order to infer mappings between components' interfaces automatically. These mappings guarantee semantic compatibility between the operations and data of the interfaces. Then, we analyse the behaviours of components in order to synthesise, if possible, a mediator that coordinates the computed mappings so as to make the components interact properly. Our approach is formally-grounded to ensure the correctness of the synthesised mediator. We demonstrate the validity of our approach by implementing the MICS (Mediator synthesis to Connect Components) tool and experimenting it with various real-world case studies.

Task mapping, which basically consists of mapping a set of tasks onto a set of nodes, is a well-known problem in distributed computing research. As a particular case of distributed systems, the Internet of Things (IoT) poses a set of renewed challenges, because of its scale, heterogeneity and properties traditionally associated with wireless sensor networks (WSN), shared sensing, continous processing and real time computing. To handle IoT features, we present a formalization of the task mapping problem that captures the varying consumption of resources and various constraints (location, capabilities, QoS) in order to compute a mapping that guarantees the lifetime of the concurrent tasks inside the network and the fair allocation of tasks among the nodes. It results in a binary programming problem for which we provide an efficient heuristic that allows its resolution in polynomial time. Our experiments show that our heuristic: (i) gives solutions that are close to optimal and (ii) can be implemented on reasonably powerful Things and performed directly within the network, without requiring any centralized infrastructure.

Quality of Service (QoS) in orchestrated web services compositions have been well studied with probabilistic and multi-dimensional models. Choreographies that involve message passing among services, on the other hand, require further analysis. In this paper, we begin with the set of QoS domains that may be studied in case of choreographies and the algebraic rules for their composition. As choreographies manage QoS composition in a distributed fashion, techniques to enrich functional specifications with QoS are examined using the model proposed in the CHOReOS project. These are further analyzed with choreographies that may reconfigure due to functional or QoS requirements. Studies on the effects of such reconfiguration on multiple QoS domains can lead to better understanding of optimal runtime configurations along with associated tradeoffs. A goal programming approach is also proposed to choose Pareto optimal solutions with respect to diverse QoS domains.

One of the main benefits of mobile participatory sensing becoming a reality is the increased knowledge it will provide about the real world while relying on a large number of mobile devices. Those devices can host different types of sensors incorporated in every aspect of our lives. However, given the increasing number of capable mobile devices, any participatory sensing approach should be, first and foremost, scalable. To address this challenge, we present an approach to decrease the participation of (sensing) devices in a manner that does not compromise the accuracy of the real-world information while increasing the efficiency of the overall system. To reduce the number of the devices involved, we present a probabilistic registration approach, based on a realistic human mobility model, that allows devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path. We present the design and implementation of a registration middleware based on our techniques, using which mobile devices can base their registration decision. Through experiments performed on real and simulated datasets, we show that our approach scales, while not sacrificing significant amounts of sensing coverage.

The computing and networking capacities of today's wireless portable devices allow for ubiquitous services, which are seamlessly networked. Indeed, wireless handheld devices now embed the necessary resources to act as both service clients and providers. However, the ubiquitous networking of services remains challenged by the inherent mobility and resource constraints of the devices, which make services a priori highly volatile. This paper discusses the design, implementation, and experimentation of the ubiSOAP service-oriented middleware, which leverages wireless networking capacities to effectively enable the ubiquitous networking of services. ubiSOAP specifically defines a layered communication middleware that underlies standard SOAP-based middleware, hence supporting legacy Web Services while exploiting nowadays ubiquitous connectivity.

Several lines of research rely on the concept of service abstractions to enable the organization, the composition and the adaptation of services. However, what is still missing, is a systematic approach for extracting service abstractions out of the vast amount of services that are available all over the Web. To deal with this issue, we propose an approach for mining service abstractions, based on an agglomerative clustering algorithm. Our experimental findings suggest that the approach is promising and can serve as a basis for future research.

The four papers in this special section focus on topics related to exception handling.

The CONNECT European project that started in February 2009 aims at dropping the interoperability barrier faced by todaypsilas distributed systems. It does so by adopting a revolutionary approach to the seamless networking of digital systems, that is, synthesizing on the fly the connectors via which networked systems communicate. CONNECT then investigates formal foundations for connectors together with associated automated support for learning, reasoning about and adapting the interaction behavior of networked systems.

In this paper, we propose a framework that reduces the complexity of service substitution. The framework is based on two substitution relations and corresponding theorems. The proposed relations and theorems allow organizing available services into groups. Then, the complexity of retrieving candidate substitute services for the target service and generating corresponding adapters scales up with the number of available groups, instead of scaling up with the number of available services.

In pervasive computing systems, a higher number of interactions will be mediated by computers, amplifying the menace to privacy. Privacy protection in pervasive environments is still a big issue, despite the growing number of works on this subject as evidenced by this survey. In this paper, we propose a taxonomy for privacy invasion attacks, classify existing privacy enhancing technologies according to the protection provided for those attacks, and introduce a service-oriented privacy-enhanced architecture for pervasive computing.

Service discovery is a critical functionality of emerging pervasive computing environments. In such environments, service discovery mechanisms need to (i) overcome the heterogeneity of hardware devices, software platforms, and networking infrastructures; and (ii) provide users with an accurate selection of services that meet their current requirements. To address these issues, we have developed the Multi- Protocol Service Discovery and Access (MSDA) middleware platform^2, which provides context-aware service discovery and access in pervasive environments. This paper primarily focuses on the design and implementation of the context-awareness support of MSDA. Context-awareness not only provides a more accurate service selection, but also enables a more efficient dissemination of service requests across heterogeneous pervasive environments. We present the design and prototype implementation of MSDA, along with experimental results that demonstrate the advantages derived by introducing context awareness.

Pervasive computing environments bring new challenges for client/service interactions due to the heterogeneity of both the interconnected networks and the middleware platforms used. In order for clients to truly benefit from all the networked services embedded into the environment, an innovative solution is required that overcomes both the lack of interoperability of the existing discovery and access protocols, and the limited interconnectivity between the different networks available at a location. This paper introduces the MUSDAC middleware platform that interoperates with existing middleware protocols, and manages the dissemination of service information in dynamic multi-network environments. A generic service is thus provided that enables clients to discover and interact with services in the entire environment. A prototype implementation of our platform is further presented, enabling us to demonstrate the flexibility of our approach

The wide deployment of mobile networks and the emergence of powerful portable devices has given core to pervasive computing. As particularly addressed by Beyond 3G (B3G) networks, the recent evolution of mobile networks introduces the convergence of wireless technologies, where several radio interfaces are to be used concurrently. Thus, B3G-aware applications shall make the most effective use of this connectivity. In pervasive environments, mobile users may discover and access services offered on the networks using Service Discovery Protocols (SDPs). Several SDPs are currently in use, each one designed for specific target network architecture and setting. Thus, in a multi-radio environment, each SDP does not equally suit each radio interface. In order to provide effective service discovery in multi-radio networks, the most resource efficient interface shall be chosen with respect to two main criteria: the adequacy of the radio interface against the SDP to be used, and energy saving, which is crucial for battery-powered devices. Toward this goal, this paper assesses how to exploit multiple radio interfaces from the standpoint of service discovery and access with respect to energy consumption, and the adequacy of the legacy SDPs with the various networks, so as to classify the most appropriate networks for each SDP.

The vision of ubiquitous computing is becoming a reality thanks to the advent of portable devices and the advances in wireless networking technologies. It aims to facilitate user tasks through seamless utilization of services available in the surrounding environments. In such distributed environments featuring openness, interactions, especially service provision and consumption, between entities that are unknown or barely known to each other, are commonplace. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of ubiquitous computing. It is, however, faced by the problems of how to stimulate reputation information sharing and honest recommendation elicitation. We present in this paper an incentive compatible reputation mechanism to facilitate the trustworthiness evaluation in ubiquitous computing environments. It is based on probability theory and supports reputation evolution and propagation. Our reputation mechanism not only shows robustness against lies, but also stimulates honest and active recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can elicit the most honest (helpful) recommendations and thus suffer the least number of wrong trust decisions, as validated by simulation based evaluation.

Due to the large success of wireless networks and handheld devices, the ambient intelligence (AmI) paradigm is becoming a reality. One of the most challenging objectives to achieve in AmI environments is to enable a user to perform a task by composing on the fly networked services available at a specific time and place. Towards this goal, we propose a solution based on semantic Web services, and we show how service capabilities described as conversations can be integrated to perform a user task that is also described as a conversation, further meeting the QoS requirements of the user task. Experimental results show that the runtime overhead of our algorithm is reasonable, and further, that QoS-awareness improves its performance.

Mobile ad hoc networks (MANET) offer a convenient basis toward realization of pervasive computing, due to its ease of deployment and inherent support for anytime, anywhere network access for mobile users. However, the development of applications over such networks is faced by the challenge of network dynamics attributed to node mobility and the scalability issue. Group management poses as a promising paradigm to ease the development of distributed applications for dynamic, mobile networks. Specifically, group management makes transparent the failures due to node mobility and assembles mobile nodes to meet target functional and non-functional properties. Various network-level grouping schemes over MANET have been investigated over the last couple of years. In this paper, we introduce the design and implementation of a generic group service for MANET, defined with respect to the various attributes of relevance. Generic group management is further demonstrated with its support of scalable service discovery in MANET.

Mobile Ad hoc NETworks (MANETs) conveniently complement infrastructure-based networks, allowing mobile nodes to spontaneously form a network and share their services, including bridging with other networks, either infrastructure-based or ad hoc. However, distributed service provisioning over MANETs requires adequate support for service discovery and invocation, due to the network's dynamics and resource constraints of wireless nodes. While a number of existing service discovery protocols have shown to be effective for the wireless environment, these are mainly aimed at infrastructure-based and/or 1-hop ad hoc wireless networks. Some discovery protocols for MANETs have been proposed over the last couple of years but they induce significant traffic overhead, and are thus primarily suited for small-scale MANETs with few nodes. Building upon the evaluation of existing protocols, we introduce a scalable service discovery protocol for MANETs, which is based on the homogeneous and dynamic deployment of cooperating directories within the network. Scalability of our protocol comes from the minimization of the generated traffic, and the use of compact directory summaries that enable to efficiently locate the directory that most likely caches the description of a given service

Service discovery is an important part of realization towards ubiquitous computing with self-configurable mobile ad hoc networks. However, it is faced by the challenge of network dynamics due to node mobility. In this paper, we propose a signal strength based service discovery framework in mobile ad hoc networks. Service discovery is carried out selectively along reliable links by monitoring the qualities of wireless connections to neighbor nodes. We show by simulation that, compared to non-selective distributed service discovery, our approach achieves higher service success ratio, less message overhead and shorter service discovery latency.

The Amigo project aims to develop a networked home system enabling the ambient intelligence / pervasive computing vision by effectively integrating devices and their hosted services in today’s home. The Amigo system architecture poses limited technology-specific restrictions, supporting interoperability among heterogeneous services.

A group key agreement (GKA) protocol is a mechanism to establish a cryptographic key for a group of participants, based on each one's contribution, over a public network. The key, thus derived, can be used to establish a secure channel between the participants. When the group composition changes (or otherwise), one can employ supplementary GKA protocols to derive a new key. Thus, they are well-suited to the key establishment needs of dynamic peer-to-peer networks as in ad hoc networks. While many of the proposed GKA protocols are too expensive to be employed by the constrained devices often present in ad hoc networks, others lack a formal security analysis. We present a simple, secure and efficient GKA protocol well suited to dynamic ad hoc networks. We also present results of our implementation of the protocol in a prototype application.

Pervasive environments bring new challenges for service management, as clients have to handle large collections of services offered by concurrent networks at their location, as well as services on networks in known remote locations. To enable service discovery in such service-rich environments, we introduce a multiprotocol service discovery (MSD) platform that interacts with existing SD protocols and extend their service descriptions with context information. Service information and discovery requests are then disseminated among interconnected networks based on service and network profiles.

Abstract From a real‐time perspective, the garbage collector (GC) introduces unpredictable pauses that are not tolerated by real‐time tasks. Real‐time collectors eliminate this problem but introduce a high overhead. Another approach is to use memory regions (MRs) within which allocation and deallocation is customized. This facility is supported by the memory model of the Real‐Time Specification for Java (RTSJ). RTSJ imposes strict access and assignment rules to avoid both the dangling inter‐region references and the delays of critical tasks of the GC. A dynamic check solution can incur high overhead, which can be reduced by taking advantage of hardware features. This paper provides an in‐depth analytical investigation of the overhead introduced by dynamic assignments checks in RTSJ, describing and analysing several solutions to reduce the introduced overhead. Copyright © 2005 John Wiley & Sons, Ltd.

The advent of light-weight terminals (e.g., PDA) with integrated communication capabilities facilitates service access and hosting anytime, anywhere. However, effective service access requires dealing with the service's QoS, including related resource consumption. This paper introduces a comprehensive framework for QoS-aware service location in ad hoc networks. Our framework divides into: (i) QoS specification that is expressive enough to capture most significant QoS-related properties, and concise enough to have minimal computation cost associated with QoS management; and (ii) a benefit function for evaluating the overall benefit of service instances available in the network from the standpoint of QoS regarding both the user's perspective and resource consumption on hosts. Application of the proposed QoS framework is addressed in the context of the mobile ADHoc WS system, which supports QoS-aware location of Web services deployed in mobile ad hoc networks.

Today's wireless networks and devices support the dynamic composition of mobile distributed systems, according to device connectivity. This has in particular led to the introduction of a number of supporting middleware. However, such solutions need to be complemented with adequate modeling and verification support towards enforcing the correctness of the dynamic mobile systems with respect to both functional and nonfunctional properties. Building on the elegant properties of software architecture modeling, this paper introduces base modeling of mobile software components, which integrates key features of the wireless infrastructure and allows for reasoning about the behavior of dynamically composed systems.

This paper presents the ADHOCFS file system for mobile users, which realizes transparent, adaptive file access according to the users' specific situations (e.g., device in use, network connectivity, etc). The paper concentrates more specifically on the support of ADHOCFS for collaborative file sharing within ad hoc groups of trusted nodes that are in the local communication of each other using the underlying ad hoc network, which has not been addressed in the past.

This paper proposes a solution based on forward error recovery, oriented towards providing dependability of composite Web services. While exploiting their possible support for fault tolerance (e.g., transactional support at the level of each service), the proposed solution has no impact on the autonomy of the individual Web services, our solution lies in system structuring in terms of co-operative atomic actions that have a well-defined behavior, both in the absence and in the presence of service failures. More specifically, we define the notion of Web Service Composition Action (WSCA), based on the Coordinated Atomic Action concept, which allows structuring composite Web services in terms of dependable actions. Fault tolerance can then be obtained as an emergent property of the aggregation of several potentially non-dependable services. We further introduce a framework enabling the development of composite Web services based on WSCAs, consisting of an XML-based language for the specification of WSCAs.

The paper addresses the issue of improving the performance of memory management for real time Java applications, building upon the Real-Time Specification for Java (RTSJ) from the Real-Time Java Expert Group. In a first step, a thorough analysis of the parameters influencing the performance of memory management together with ways of improvement are presented. The implementation of a memory management solution, which is compliant with the RTSJ and integrating the proposed improvements is then sketched.

Good quality models for the analysis of complex enterprise information systems (EIS) are hard to build and require lots of experience and effort, which are not always available. A possible solution to deal with the previous issue is to build automated procedures for quality model generation. Such procedures will encapsulate previous existing knowledge on quality modeling and their use will reduce the cost of developing quality models. The authors concentrate on the performance and reliability of EIS and investigate the automatic generation of quality models from EIS architectural descriptions comprising additional information related to the aspects that affect the quality of the EIS.

When designing complex software systems that provide multiple non-functional properties, it is usual to try to reuse (and finally compose) simpler existing designs, which deal with each of these properties in solitude. The paper describes a method for automatically and quickly identifying all the different ways one can compose such designs, with the aid of a model checker.

Java is a programming language with features not found in traditional languages, such as platform independence and dynamic loading. Because of this, the use of Java is extending and it is beginning to be used in many new environments. In particular, the advantages that Java provides make it a good candidate for distributed, real-time and embedded systems. However, Java presents some problems regarding its use in embedded and real-time environments. In this paper, we examine the state of the art in the development of embedded real-time systems using Java. We analyse the limits that the Java language and its execution environment present in developing real-time and embedded systems, and we present current research in this area that is aimed at solving these limits.

A method is presented for the automatic construction of all possible valid compositions of different middleware software architectures. This allows reusing the latter in order to create systems providing a set of different non-functional properties. These compositions are constructed by using only the structural information of the architectures, i.e. their configurations. Yet, they provide a valuable insight on the different properties of the class of systems that can be constructed when a particular set of non-functional properties is required.

This paper provides a method to verify that the properties we consider critical to build a correct multimedia application are met. These properties are the compatibility of the protocols employed by the computing elements to interact with each other and the timeliness of the application. The method relies on (i) an original model that enables a software architect to specify the basic elements of its multimedia application according to the two aforementioned properties, and (ii) two algorithms that determine the best level of perceived quality of service that can be guaranteed by the application.

This paper introduces the Aster distributed configuration-based-programming system that is aimed at easing the development of emerging distributed applications having quality of service requirements. Our approach is based on high-level customization: given the specification of application requirements using the Aster interconnection language, a distributed runtime system, customized for meeting these requirements is built. So as to make the customization process sound, we propose a formal method that allows one to reason about specification matching of a customized distributed runtime system with the application's requirements.

Recent advances in computing, like high-speed networks and data-compression, make extensible distributed multimedia applications a challenging application-domain of distributed systems. Such applications like VoD (Video on Demand) or real-time conferencing are characterized by QoS (quality of service) requirements which depend on the quality of video and sound transmitted to the client and on the respect of time constraints associated to video and audio data. Much work has been done in order to provide system support aimed at meeting these requirements. However, existing proposals do not integrate the consequence of failure occurrence on the guaranteed QoS. To deal with this issue, we propose a resource reservation model that integrates availability requirements of multimedia services in addition to the QoS constraints introduced above. Our paper details the resulting model together with its integration in a distributed system. In particular we show how the model implementation can be customized in the case of a VoD server.

This paper presents the design and implementation of a mechanism aimed at enhancing the performance of distributed object-based applications. This goal as achieved by means of a new algorithm implementing placement of method executions that adapts to processors' load and to objects' characteristics, the latter allowing to approximate the cost of methods' remote execution. The behavior of the proposed placement algorithm is examined by providing performance measures obtained from its integration within a customizable distributed object-based runtime system. In particular, the cost of method executions using our algorithm is compared with the cost resulting from the standard placement technique that consists of executing any method on the storing node of its embedding object.

This paper sketches our experience with the design and implementation of a parallel object-oriented language and it distributed run-time system. The language integrates two original mechanisms for concurrency control: a synchronization mechanism that does not interfere with inheritance nor with subtyping, and a mechanism that serves for managing object groups. Because of the increasing power of interconnection networks, the language's run-time system has been designed for a distributed architecture instead of a single multiprocessor machine. Furthermore, in order to ease the development of correct applications, we have chosen to rely on the run-time system to provide the required efficiency instead of offering the programmer low level primitives to be used for producing efficient code.>

This paper addresses extensions to be made to a basic remote procedure call system for the integration of primitive fault tolerance measures. Our main design goal is to not introduce performance penalty for remote procedure calls executing in the absence of failures, and to not impose significant overhead by the treatment of failures. Basically, extensions include a simple algorithm that finds and eliminates orphans, and a mechanism that detects abnormally terminated remote calls. Our solution for orphan detection as based on the extermination approach, its efficiency coming from a minor addition to the system architecture that allows the implementation of high speed stable storage. Performance measures given by the implementation of our reliability mechanisms on top of the Mach 3.0/BSD UX36 operating system show that the mechanisms are responsible for adding only 1% overhead on the operating system's base remote procedure call.>