Given population growth and its increasing concentration in urban areas, traffic congestion increasingly affects various aspects of daily life. From long commutes to stressed workers, traffic congestion reduces mobility, limits accessibility, and negatively impacts the environment and urban quality of life. Intersections play a critical role in managing traffic flow, and many approaches leverage technology to optimize Traffic Signal Control (TSC). However, most adaptive models in the literature focus only on pedestrian and vehicle demand, overlooking broader contextual factors. In this paper, we propose SIAGeTrans, an adaptive system based on IoT and Game Theory that aims to consider inputs beyond demand fluctuations, including factors like weather, flooding, and emergency vehicles. As a result, it reduces overall waiting time by up to one-third and significantly lowers fuel consumption and emissions compared to the fixed-time approach.

The vision of 6G goes beyond incremental improvements in network performance, representing a paradigm shift toward a programmable, intelligent “network of networks.” In this context, digital twins (DTs) emerge as key enabling technologies for 6G, providing virtual replicas of physical, virtual, and logical infrastructures. The capabilities of 6G, such as ultra-high-speed transmission, massive connectivity, and integration with Artificial Intelligence, are essential for enabling accurate, real-time, and synchronized DTs. However, developing DT systems poses significant challenges, including integrating heterogeneous data sources, ensuring real-time synchronization, and ensuring interoperability across distributed environments. This article presents MidDiTS, a middleware solution that supports the modeling, synchronization, and management of DT systems. MidDiTS provides a comprehensive set of services, including physical entity connection, model and instance management, data ingestion, API integration, and device management. By abstracting away system complexities and promoting interoperability, MidDiTS enables developers to focus on higher-level business logic and accelerates the development of robust, synchronized DT systems for next-generation networked environments. We validated MidDiTS in a 6G-based scenario, demonstrating significant improvements in timeliness, reliability, and operational stability. The results highlight the role of advanced network capabilities in sustaining real-time DT synchronization and confirm the suitability of MidDiTS for supporting DT systems under next-generation network conditions.

Network Functions Virtualization (NFV) virtualizes network functions typically provided by hardware, such as firewalls and routers, using Virtualized Network Functions (VNFs). Service Function Chains (SFCs) are sequences of VNFs that process user data streams, which can span different autonomous and competing infrastructure providers. Blockchain technology has been used several times for resource allocation solutions for VNFs, such as in BRAIN, using reverse auctions. This paper presents BRAIN*, an enhanced version of BRAIN, capable of managing SFC auctions in a single smart contract using consortiums. In addition, it proposes an interface for auction contracts and performs experiments to validate the proposed solution. Experimental results shows that BRAIN* con efficiently handles a high number of simultaneous auctions without incurring additional costs. The results also show that the proposed solution is more effective in scenarios with a high number of providers participating in the auctions, returning lower prices to users and maintain a block rate below 10%.

The combination of both Network Function Virtualization (NFV) and Edge Computing facilitates the Quality of Service (QoS) fulfillment of latency-stringent applications by taking advantage of the proximity of the devices at the edge and the user, and provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models for 5G environments. However, to achieve those benefits, VNFs must be deployed so that system resource utilization is optimized and service QoS is maintained. In this context, the selection of the most suitable edge nodes to deploy VNFs is a challenging research issue because multiple — and often conflicting — objectives must be considered by the VNF placement algorithm. The challenges become greater when considering the high dynamism of the edge environment. The availability of edge nodes and users’ locations vary over time, making original allocation decisions ineffective. In this paper, we propose DSP, a Dynamic Smart VNF Placement algorithm that considers the dynamic nature of a 5G edge environment. Our proposal focuses on optimizing resource utilization with an online placement approach to handle continuously arriving service requests. Furthermore, we also account for user mobility providing an approach that reduces the performance impact caused by users’ movement. Additionally, a two-level resource-sharing mechanism is provided, minimizing the cost for the infrastructure provider with low impact on QoS.

Emerging IoT applications with stringent requirements on latency and data processing have posed many challenges to cloud-centric platforms for Smart Cities. Recently, Fog Computing has been advocated as a promising approach to support such new applications and handle the increasing volume of IoT data and devices. The Fog Computing paradigm is characterized by a horizontal system-level architecture where devices close to end-users and IoT devices are used for processing, storage, and networking functions. Fog Computing platforms aim to facilitate the development of applications and systems for Smart Cities by providing services and abstractions designed to integrate data from IoT devices and various information systems deployed in the city. Despite the potential of the Fog Computing paradigm, the literature still lacks a broad, comprehensive overview of what has been investigated on the use of such paradigm in platforms for Smart Cities and open issues to be addressed in future research and development. In this paper, a systematic mapping study was performed and we present a comprehensive understanding of the use of the Fog Computing paradigm in Smart Cities platforms, providing an overview of the current state of research on this topic, and identifying important gaps in the existing approaches and promising research directions.

The post-discharge support is increasingly suggested for stroke patients to be discharged earlier and start rehabilitation at home. Considering that stroke patients usually have a high chance of recurrence, a good prognostic program is essential to improve diagnostic capabilities while reducing readmission rate to further save medical sources. In this context, various machine learning methods have been leveraged to obtain diagnostic findings and guide further treatments. However, those approaches mainly focus on performing analysis using a single data source obtained from the hospital, which could ignore the information complementarity between different groups of features and several subtle and discrete differences of physical interpretation among them. In this paper, we propose an Edge-based system design for post-stroke surveillance and warning prediction, called PSMART (Post-Stroke Mobile Auxiliary Rudiment Treatment), for processing enriched pathogenic factors of ischemic stroke from multi-sensors (views) to make readmission warning predictions. Our approach can considerably enrich the distinctive features from raw data, as well as exploit the consistency and complementary proprieties of different views, leading to better learning results. We evaluate the performance of the proposed approach on a real-world dataset, and the accuracy can reach up to 98.98%. Moreover, experiment results also show that our proposed approach can provide better accuracy when compared to the single-view ones.

In the context of the Internet of Things (IoT), a new service model has emerged, namely, the Sensing as a Service (S2aaS). In such model, sensing data is available for external users as resources, and can be provided on demand, as services. Typically, the devices providing sensing resources are powered by batteries, thus the lifetime of devices becomes a concern to IoT service providers. A common strategy to reduce the number of tasks to be performed by the devices, thus saving energy, is caching data, reusing a prior response message to satisfy a current request. However, on the side of IoT service consumers, the freshness of the data is also a critical concern. That is, the time elapsed since the data is collected until it is delivered to the requesting user should be small. In high workload scenarios, addressing both lifetime and data freshness requirements may be challenging, mainly because commonly used application protocols, such as the Constrained Application Protocol (CoAP), only allow specifying the data freshness requirement in a strict way. This paper introduces an extension to CoAP, namely, CoAP-XED, aiming at supporting the relaxation of requests to sensing resources. Simulation results show that our approach supports maximizing the lifetime of devices and, at the same time, fulfill data freshness requirements.

The emerging Internet of Things (IoT) paradigm creates a growing need to analyze a significant amount of data produced by the interconnected IoT devices. Since IoT devices have limited computation capabilities, Fog Computing is a natural complement, to provide distributed, location-aware, and easy-to-access computation resources. In this work, we address the problem of application processing and data offloading in a Fog-integrated IoT ecosystem. By leveraging the Lyapunov optimization technique, we design an online and distributed system control policy called the Distributed Weighted Backpressure (DWB) policy that asymptotically minimizes the cost of IoT devices. A three-way tradeoff among queue backlogs, communication cost, and computation cost is then investigated. Finally, simulation study has been conducted to validate the correctness and usefulness of the proposed DWB policy.

The Internet of Things (IoT) is a paradigm thatrefers to the ubiquitous presence around us of physical objects equipped with sensing, networking and processing capabilities that allow them to cooperate with each other and with their environment to reach common goals. General objectives of IoT are improving the quality of human life and optimizing industrial processes, by automating tasks that humans must perform. Such benefits are obtained by developing IoT applications. The heterogeneity and interdisciplinary present in the development of any IoT application classifies such a task as a Systems Engineering problem. However, to the best of our knowledge, no Systems Engineering approach to develop IoT applications has been proposed so far. The objective of this paper is to introduce a Model-Based Systems Engineering methodology for IoT application development, focusing on the design phase, which aims at helping organizations to develop IoT applications to fully achieve the benefits of this new paradigm. The approach is comprised of a method and a tool, which are aligned with proven concepts from Systems Engineering and Software Architecture literature.

Internet of Things (IoT) application development is technically challenging and complex due to several factors such as the presence of different IoT protocols, interoperability issues, lack of industry standards and dealing with the heterogeneity that exists both in Physical and Internet worlds. Furthermore, developers involved in the IoT application development have to address issues pertaining to different life-cycles ranging from design, implementation to deployment (deployment complexities due to the possibilities of wide range of devices). Manual effort involved in all above three phases for heterogeneous devices is a time-consuming and error-prone process. We demonstrate IoTSuite which is an application development platform consisting of a suite of tools motivated by the need to simplify IoT application development. The IoTSuite supports several features such as automatic code generation and integration with a set of modeling languages. It takes high-level specification as input that abstract heterogeneity related complexity. It integrates compiler and deployment module to provide automation at different phases of application development process.

The steep rise of Internet of Things (IoT) applications along with the limitations of Cloud Computing to address all IoT requirements promotes a new distributed computing paradigm called Fog Computing, which aims to process data at the edge of the network. With the help of Fog Computing, the transmission latency and monetary spending caused by Cloud Computing can be effectively reduced. However, executing all applications in fog nodes will increase the average response time since the processing capabilities of fog is not as powerful as cloud. A tradeoff issue needs to be addressed within such systems in terms of average response time and average cost. In this paper, we develop an online algorithm, unit-slot optimization, based on the technique of Lyapunov optimization. It is a quantified near optimal solution and can online adjust the tradeoff between average response time and average cost. We evaluate the performance of our proposed algorithm by a number of experiments. The experimental results not only match up the theoretical analyses properly, but also demonstrate that our proposed algorithm can provide cost-effective processing while guaranteeing average response time.

The unprecedented capabilities of monitoring and responding to stimuli in the physical world of wireless sensor and actuator networks (WSAN) enable these networks to provide the underpinning for several Smart City applications, such as structural health monitoring (SHM). In such applications, civil structures, endowed with wireless smart devices, are able to self-monitor and autonomously respond to situations using computational intelligence. This work presents a decentralized algorithm for detecting damage in structures by using a WSAN. As key characteristics, beyond presenting a fully decentralized (in-network) and collaborative approach for detecting damage in structures, our algorithm makes use of cooperative information fusion for calculating a damage coefficient. We conducted experiments for evaluating the algorithm in terms of its accuracy and efficient use of the constrained WSAN resources. We found that our collaborative and information fusion-based approach ensures the accuracy of our algorithm and that it can answer promptly to stimuli (1.091 s), triggering actuators. Moreover, for 100 nodes or less in the WSAN, the communication overhead of our algorithm is tolerable and the WSAN running our algorithm, operating system and protocols can last as long as 468 days.

Wireless Sensor and Actuator Networks (WSANs) are highly heterogeneous regarding hardware, software, and application requirements. Developing WSAN applications is a hard task, as it requires domain and network knowledge and there is a lack of methodologies, architectures, and patterns to help software developers with such task. Moreover, non-functional requirements play an essential role to ensure the application and network performance, and must be taken into account from the early stages of application development. We propose a ModelDriven Architecture framework to develop WSAN applications that enables developers to express functional and non-functional requirements. The framework encompasses a generic middleware meta-model including: (i) features shared among different WSAN middleware, (ii) meta-model of a target middleware including features of a specific WSAN middleware, and (iii) model transformations. We evaluate our approach by verifying the support to specify non-functional requirements and the automatic code generation of a middleware tailored to the requirements ofa target application.

This paper presents FraSEE, a framework whose goal is to increase the energy efficiency (EE) of smart grid applications, specifically for the Power Quality (PQ) application domain. The key idea is to use a Wireless Sensor and Actuator Network (WSAN) and decentralize the decision processes of such applications among WSAN nodes through information fusion. Experiments assessed FraSEE effectiveness in increasing the EE of a PQ application, its accuracy, i.e. correct PQ disturbance detections, considering the influence of environmental parameters in decision process, its overhead, i.e. WSAN resources consumption; and delay for performing control actions in response to decisions.

The Internet of Things (IoT) has emerged as a paradigm in which smart things actively collaborate among them and with other physical and virtual objects available in the Web in order to perform high-level tasks. IoT environments are typically characterized by a high degree of heterogeneity, thus encompassing devices with different capabilities, functionalities, and network protocols. In such a scenario, it is necessary to provide abstractions for physical devices and services to applications and end-users, as well as means to manage the interoperability between such heterogeneous elements. In this context, we introduce EcoDiF (Web Ecosystem of Physical Devices), a Web-based platform for integrating heterogeneous physical devices with applications and users in order to provide services to support real-time data control, visualization, processing, and storage. In this paper, we present the main features of EcoDiF and detail its architecture and implementation, which is based on well-known Web technologies such as HTTP, REST, EEML, and EMML. Furthermore, we present a preliminary evaluation of an EcoDiF prototype through proof-of-concept applications from different domains as well as a performance analysis of the platform.

The use of Representational State Transfer (REST) as an architectural style for integrating services and applications brings several benefits, but also poses new challenges and risks. Particularly important among those risks are failures to effectively address quality attribute requirements such as security, reliability, and performance. An architecture evaluation early in the software life cycle can identify and help mitigate those risks. In this paper we present guidelines to assist architecture evaluation activities in REST-based systems. These guidelines can be systematically used in conjunction with scenario-based evaluation methods to reason about design considerations and trade-offs. This paper also presents a proof of concept to describe how to use the guidelines in the context of an Architecture Trade-off Analysis Method (ATAM) evaluation.

SUMMARY Multi‐functional wireless sensor network (WSN) system is a new design trend of WSNs, which are evolving from dedicated application‐specific systems to an integrated infrastructure that supports the execution of multiple concurrent applications. Such system offers inherent advantages in terms of cost and flexibility because it allows the effective utilization of available sensors and resource sharing among multiple applications. However, sensor nodes are very constrained in resources, mainly regarding their energy. Therefore, the usage of such resources needs to be carefully managed, and the sharing with several applications imposes new challenges in achieving energy efficiency in these networks. In order to exploit the full potential of multi‐functional WSN systems, it is crucial to design mechanisms that effectively allocate tasks onto sensors so that the entire system lifetime is maximized while meeting various application requirements. However, it is likely that the requirements of different applications cannot be simultaneously met. In this paper, we present the Multi‐Application Requirements Aware and Energy Efficiency algorithm as a new resource allocation heuristic for multi‐functional WSN system to maximize system lifetime subject to various application requirements. The heuristic effectively deals with different quality of service parameters (possibly conflicting) trading those parameters and exploiting heterogeneity of multiple WSNs. Copyright © 2013 John Wiley & Sons, Ltd.

Most Wireless Sensor Network (WSN) applications require distributed signal and collaborative data processing. One of the critical issues for enabling collaborative processing in WSNs is how to schedule tasks in a systematic way, including assigning tasks to sensor nodes, and determining their execution and communication sequence. Since WSN nodes are very resource constrained, mainly regarding their energy supply, one major concern when scheduling tasks in such environments is to minimize and balance the energy consumption, so that the system operational lifetime is maximized. We propose a heuristic-based three-phase algorithm (TPTS) for allocating tasks to multiple clusters in hierarchical WSNs that aims at finding a scheduling scheme that minimizes the overall energy consumption and balances the workload of the system while meeting the application's deadline. The performance of the proposed algorithm and the effect of several parameters on its behavior were evaluated by simulations, with promising results. The experimental results show that the time and energy performance of TPTS are close to the time and energy of benchmarks in most cases, while load balance is always provided.

This work presents an adaptation (and enhancement) of a well-known fusion technique in order to deal with multiple applications simultaneously in a context of Shared Sensor and Actuator Networks (SSAN). SSAN allow the sensing infrastructure to be shared among multiple applications that can potentially belong to different users instead of assuming an application-specific network design. We also present simulation and tests conducted with the proposed solution on real nodes to validate our proposal.

Cyber physical systems (CPS) have recently emerged as a promising approach to improve the synergy between physical and virtual worlds. CPS applications can be built on top of Wireless Sensor Networks (WSNs) exploiting the physical information collected by them to bridge real and cyber realms. In this paper, we investigated the emerging problem of how to schedule multiple applications onto cyber-physical systems encompassed of multiple WSNs, while meeting demands of these applications. Our goal is to find out an optimal scheduling scheme for each application so that: (i) it minimizes the overall energy consumption, (ii) it meets the application's deadline, (iii) it provides the required data accuracy to applications, and (iv) it balances the workload of the system. To achieve such goals, we developed a polynomial-time three phase task scheduling heuristic, named HTPTS. Experimental results show that the time and energy performance of HTPTS are close to the time and energy of the benchmark in most of the cases, while load balance is satisfied.

With the advance of the Cloud Computing paradigm, new challenges in terms of models, tools, and techniques to support developers to design, build and deploy complex software systems that make full use of the cloud technology arise. In the heterogeneous scenario of this new paradigm, the development of applications using cloud services becomes hard, and the software product lines (SPL) approach is potentially promising for this context since specificities of the cloud platforms, such as services heterogeneity, pricing model, and other aspects can be catered as variabilities to core features. In this perspective, this paper (i) proposes a seamless adaptation of the SPL-based development to include important features of cloud-based applications, and (ii) reports the experience of developing HW-CSPL, a SPL for the Health Watcher (HW) System, which allows citizens to register complaints and consult information regarding the public health system of a city. Several functionalities of this system were implemented using different Cloud Computing platforms, and run time specificities of this application deployed on the cloud were analyzed, as well as other information such as change impact and pricing.

This paper describes the architecture, implementation and illustrates the usage of WebFlowAH, an environment for ad-hoc specifying and executing Web services-based business processes. WebFlowAH build on the adoption of common domain ontology to describe Web services and business processes. It enables the specification of processes in terms of high level users' goals that are expressed based on the concepts of such common domain ontology, thus independently on the syntax of the service provided operations. WebFlowAH provides a unique environment for specifying and executing goal-oriented business processes, allowing services discovery, composition and invocation on the fly.

This paper presents a novel approach, Fuzz-SSVS, to improve the secondary structure prediction of proteins. In this work, a Sugeno based Fuzzy System is trained to act as a voting system to combine results of several secondary structure prediction techniques and produce superior answers. Fuzz-SSVS is tested with three of the well-known benchmarks in this field. The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.

Ubiquitous environments still suffer from low availability given that any device may fail and it is hard to replace a failed element. In this paper we present AdaptUbiFlow (Adaptive Ubiquitous Workflow), an OpenCOPI´s element that aims to increase the availability of an ubiquitous system. When a device fails, AdaptUbiFlow supports the automatic reconfiguration of the system replacing the failed device (or service) by an equivalent one, this makes the system fault-tolerant without the need of any manual intervention. The replacing of a device/service is chosen taking into account not only the QoS and QoC (Quality of Context) provided but also the application's execution flow to ensure that the best adaptation option will be chosen. AdaptUbiFlow evaluation showed encouraging results.

Developing Wireless Sensor Networks (WSN) applications is not an easy task, since it demands different expertise and knowledge. To address this problem, we propose a model-driven approach to build WSN applications. It promotes the separation of concerns between two levels of requirements involved: (i) domain experts, with no knowledge on WSN platforms, contribute to the development of applications, (ii) network experts, with no knowledge on the application domain, program nodes to meet application requirements. Our approach also promotes the reuse of software artifacts: an application model can be reused on different sensor platforms and a platform model can be reused for different WSN applications.

This work presents an algorithm that aims at maximizing the network efficiency while meeting QoS requirements for different classes of users mobile in a scenario that supports the integration of wireless broadband networks such as WiMAX. The presented algorithm exploits the behavior of the mobile user, depicted in a user profile, for (i) refining the set of cells foreseen for the next handover process, taking into account the class of user as well as both the service and the network contexts, and (ii) proactive reserving resources for the cell chosen for the next handover. This algorithm is validated through an extensive simulation approach, corroborating the expected improvements.

We propose a fuzzy-based decision-making mechanism for selecting data dissemination protocols in wireless sensor networks (WSNs). Its goal is to select the most efficient protocol considering network performance and application-specific requirements. The mechanism relies on performing simulations and on defining and executing a two-tier fuzzy system. First, well-known WSN protocols are simulated over different scenarios and application requirements to feed a knowledge base. Then, a set of fuzzy systems are built based on the simulation results. A methodology for guiding the building of the knowledge base was developed. A case study is described to validate the mechanism and demonstrate its employment and usefulness.