The rapid proliferation and vast diversity of Internet of Things (IoT) devices present significant challenges to achieving seamless interoperability within modern networks. Many legacy IoT systems still rely on outdated protocols, rendering them incompatible with newer devices and creating communication bottlenecks. Additionally, the need for real-time traffic adaptation and modification further complicates the integration of these heterogeneous systems. This paper explores the potential of data plane programmability as a solution to address these challenges, enabling dynamic control over network behavior and improving compatibility between diverse IoT devices. By allowing real-time adjustments to traffic flows and adapting protocols on the fly, programmable data planes offer a flexible and scalable approach to ensuring interoperability across both modern and legacy IoT systems. Experimental results demonstrate the feasibility and effectiveness of this approach, highlighting its potential to enhance IoT network performance and longevity.

The increasing computational capabilities of IoT end devices push the deployment of application logic tasks directly on the extreme edge rather than the cloud or edge nodes. However, there are still unresolved issues on the Over-The-Air (OTA) software update operations for IoT end devices: (i) the hardware heterogeneity in IoT settings requires custom code for each different device type; (ii) the growing complexity of microcontroller code couples the development of high-level processing tasks with low-level operations; (iii) efficient methods for updating target IoT devices in a specific geographical area are absent. To address these issues, we propose an OTA firmware update framework that utilizes WebAssembly (WASM) and incorporates location-aware features. We split the application logic in WASM from the rest of the firmware written in native code, in order to create a greater separation of concerns. WASM's platform independence creates an abstraction layer for the underlying hardware, allowing the same application logic to be deployed virtually to any IoT device. We integrate a location-aware extension of the MQTT protocol in our framework to enable software updates targeting devices in specific geographical areas. Finally, our experiments demonstrate that location awareness does not add significant overhead to the system and that the performance of WASM in a microcontroller is comparable to native code and superior to Micropython.

The rapid expansion of the Internet of Things (IoT) has led to significant data reliability and system transparency challenges, aggravated by the centralized nature of existing IoT architectures. This centralization often results in siloed data ecosystems, where interoperability issues and opaque data handling practices compromise both the utility and trustworthiness of IoT applications. To address these issues, we introduce ZONIA (Zero-trust Oracle Network for IoT Applications), a novel blockchain oracle system designed to enhance data integrity and decentralization in IoT environments. Unlike traditional approaches that rely on Trusted Execution Environments and centralized data sources, ZONIA utilizes a decentralized, zero-trust model that allows for anonymous participation and integrates multiple data sources to ensure fairness and reliability. This paper outlines ZONIA’s architecture, which supports semantic and geospatial queries, details its data reliability mechanisms, and presents a comprehensive evaluation demonstrating its scalability and resilience against data falsification and collusion attacks. Both analytical and experimental results demonstrate ZONIA’s scalability, showcasing its feasibility to handle an increasing number of nodes in the system under different system conditions and workloads. Furthermore, the implemented reputation mechanism significantly enhances data accuracy, maintaining high reliability even when 40% of nodes exhibit malicious behavior.

In edge-cloud IoT scenarios, proactive caching strategies constitute an effective solution to optimize the use of resources while ensuring adequate Age of Information (Aol). However, the implementation of these strategies introduces significant privacy constraints, primarily stemming from the transmission of sensitive data to the cloud. To address such issue, Federated Learning (FL) has emerged as a promising approach which processes data at the edge, transmitting only the model updates to the cloud. This paper introduces CACHUUM (Cache Architecture for Cloud and Heterogeneous edge in the ContinUUM), a proactive and privacy-aware architecture designed to facilitate the deployment of various edge caching strategies within distributed edge environments. Our architecture supports three families of strategies: local, global and federated, each tailored to meet specific privacy requirements. Furthermore, our architecture is continuum-aware, accommodating different data caching locations, whether it be at the edge node, in the cloud, or somewhere in between. We demonstrate the effectiveness of CACHUUM on simulated IoT environments, by collecting metrics on forecast accuracy, caching precision and data overhead, for different strategies. The latter anticipate the optimal cache update timings for each IoT device, ensuring that Aol aligns with application requirements upon data request.

This innovative practice full paper describes the challenge of engaging students in computer network education, where conveying complex concepts can be difficult. The proposed solution, P4Checkers, integrates the game of checkers with P4 programming to teach networking concepts interactively. Using the P4Docker simulation environment, the game transforms network packets into game actions, providing hands-on experience with network protocols. The effectiveness of P4Checkers was evaluated using the Post-Study System Usability Questionnaire (PSSUQ), showing positive results in improving student engagement and understanding of network concepts. This study highlights the potential of serious games like P4Checkers to enhance network education.

The Cloud-to-Things (C2T) continuum combines the proximity of edge infrastructure to the devices with cloud resources to optimize data processing and response time in the Internet of Things (IoT). Proactive edge caching is a potential solution for meeting latency constraints in C2T environments, enabling efficient data processing and storage while reducing redundant computation and cost. However, while 5G/6G infrastructural aspects and caching strategies are extensively studied, no frameworks facilitate the design and deployment of caching strategies or address IoT’s unique requirements. This paper proposes CACHE-IT, a proactive edge caching framework that decouples the caching strategy algorithm from the underlying architecture, enabling customization based on application-specific requirements. Through extensive simulations, we analyzed the impact of different configurations on system metrics and verified that the CACHE-IT positively impacts the system latency and hit rate. By implementing a scenario-specific caching strategy, we illustrate the CACHE-IT deployment in a real-world Structure Health Monitoring (SHM) system. The evaluation demonstrates that CACHE-IT impacts positively in terms of latency, hit rate, and the number of requests sent to data providers.

As delivery companies continue to explore the use of drones, the need for efficient and safe operation in urban environments becomes increasingly critical. Market-wide versions of drone delivery services will necessarily spread many drones, especially in big cities. In this scenario, avoiding collisions with other drones or typical obstacles in urban spaces is fundamental. This paper proposes and evaluates, via simulation and analytical modeling, an aerial delivery service scenario and three autonomous geometric approaches for collision avoidance. We compare our approaches with three simple methods – DoNothing (not detouring), Random, and aviation-like Rightward – and two state-of-the-art geometric approaches. Simulation experiments consider different fleet sizes with constant and Poisson drone arrival rates and drones randomly choosing one of different altitudes for the cruise flight. Contrary to our expectations, the Random and Rightward approaches increase the collisions compared with DoNothing, making the latter our baseline. Our approaches significantly reduce collisions in all experiments and deal with more drones within the detection radius, showing that collisions are more complex to avoid. Comparing the collision rate, successful trips, and the number of flying drones reveals that the efficiency in avoiding collisions reduces the number of successful trips by increasing the number of active drones. Regardless of the expected reduction in collisions, more altitudes do not eliminate them. These results indicate the need for more sophisticated approaches to reduce or eliminate collisions. The analytical modeling using Markov Chains corroborates the simulation results by shedding some light on and helping explain the simulation results.

Online social networks are at the limelight of the public debate, where antagonistic groups compete to impose conflicting narratives and polarize the discussions. This article proposes an approach for measuring network polarization and political sectarianism in Twitter based on user interaction networks. Centrality metrics identify a small group of influential users (polarizers and unpolarizers) who influence a larger group of users (polarizees and unpolarizees) according to their ideological stance (left, right, and undefined). This network polarization is computed by the Bayesian probability using typical actions such as following, tweeting, retweeting, and replying. The measurement of political sectarianism also uses Bayesian probability and words extracted from the tweets to quantify the intensity of othering, aversion, and moralization in the debate. We collected Twitter data from 33 conflicted political events in Brazil during 2020, strongly influenced by the COVID-19 pandemic. Based on our methodology and polarization score, our results reveal that the approach based on user interaction networks leads to an increasing understanding of polarized conflicts in Twitter. Also, a small number of polarizers is enough to represent the polarization and sectarianism of Twitter events.

The Internet of Things is getting momentum and generating new demands over infrastructure, systems, and platforms. One of the main aspects that hamper the large-scale development of IoT-based systems is the lack of interoperability. IoT Platforms aim to solve this issue by providing a uniform interface to access data from heterogeneous sources. However, integrating new protocols and applications can impose additional overhead, hindering the platform’s overall performance and scalability. This study provides an insight into the trade-off between interoperability and performance of IoT platforms. First, we present a qualitative analysis of three open-source platforms - FIWARE, ThingsBoard, and Konker - analyzing their interoperability features. Second, we conduct a performance evaluation emulating two IoT-based environments – smart cities and smart health – to understand each platform’s scalability, response time, and computer resource usage. Finally, we analyze the possible trade-offs between interoperability features and scalability based on the qualitative and quantitative analysis. The results show that interoperability features do not have a direct impact on the performance of the platform.

Virtualization and softwarization have driven the widespread adoption of the cloud computing paradigm, with savings in CAPEX, OPEX, and environmental footprint, by transferring to and sharing computational resources in datacenters. Represented by some leading technologies - as Software Defined Networks, Network Function Virtualization, and Service Function Chaining - they bring new network and service management challenges. The autonomous resource elasticity that demands new techniques to detect overloaded functions, also known as bottlenecks, is a challenge. Most research efforts in this area have focused on traditional hardware metrics like CPU, memory, throughput, and tailored application metrics. These approaches may be imprecise and hard to transfer between different scenarios. This paper presents the Network Queue Assessment (NQA) technique for detecting bottlenecks in Service Function Chaining based on network queue occupation, which requires no end-user feedback or knowledge about its internal architecture, and no definition of thresholds. Our results confirm the accuracy of NQA for bottleneck detection in a testbed evaluation with virtualized versions of five network functions: Intrusion Detection System (IDS), Network Address Translation (NAT), Virtual Private Network (VPN), Proxy-cache, and Firewall. Also, we combine the previously evaluated functions in four more complex scenarios to confirm our conclusions.

With the increasing adoption of cloud computing, microservice architecture, and network function virtualization (NFV), addressing scalability and elasticity management becomes essential. The high demand for these services challenges the research community to create new automated management techniques, from which an essential part is the detection of bottlenecks in infrastructures and application boxes. The traditional approach based on hardware resource metrics (CPU and RAM) is the most straightforward strategy, providing independence from particular applications but may not capture the application's behavior in terms of workload variations. On the other hand, using an application-oriented approach provides a significant correlation with the end-user quality of experience but needs to be tailored for each case. We propose the Network Queuing Assessment (NQA) that breaks away with this tradeoff, capturing the application's workload variations and providing a significant correlation with the end-user quality of experience. Also, similarly to CPU and RAM, it is independent of particular applications. Our performance analysis results for CPU, RAM, and NQA metrics using virtualized applications and network functions in a cloud environment confirm this approach's usefulness.

The Internet of Things (IoT) is nowadays affected by significant interoperability issues. One of the most popular countermeasures is the Web of Things (WoT), proposed recently in a consistent standardization effort. On the other hand, several IoT-oriented frameworks are already established in industrial scenarios and provide SOA-like features such as discovery and orchestration. In this paper, we study how to bridge these two worlds by proposing a tool that enables a two-way translation between a WoT ecosystem and a System-of-Systems composed of well-described Web services. We evaluate the efficiency and scalability of our solution over the Eclipse Arrowhead framework through a series of experiments that assess the scalability of our solution under realistic workloads.

Fog and mist computing support the autonomy of distributed IoT devices, maintaining system execution temporally during the cloud network disconnection. However, fog and mist infrastructures should keep data flow continuity, especially if an IoT application requires real-time analysis. We introduce and assess the TW-Fogginess system, which deploys data filtering, resilience, and security mechanisms in a mist-fog-based IoT system. TW-Fogginess contains mist and fog agents, storing data during a network disconnection and reducing transmitted data volume when the communication returns. We use 5,000 simulated sensors in stable connection and during fog and cloud disconnection scenarios. We observe that mist and fog reduce the data size by 89.57% and 95.55%, respectively, in a network reconnection. We evidence that the mist reduces the encrypted data batch transfer time by 23.33% or by 97.11% in a specific situation. Fog reduces the same metric by 40% using RSA encryption and 63.7% with no data encryption.

The rapid and exponential growth of the Internet of Things (IoT) has been generating a new breed of technologies that introduce several different protocols and interfaces. The Web of Things (WoT) architecture stands out as an emerging and potential solution to improve interoperability across IoT platforms by describing well-defined software interfaces. However, few studies analyze and compare WoT to other interoperability solutions proposed in the IoT literature. In this paper, we attempt to bridge the gap by three main contributions. First, we qualitative compare the WoT approach with the well-known FIWARE-based interoperability solution.Second, based on the previous analysis, we design and implement a connector to bridge the WoT architecture to the FIWARE ecosystem. Third, we conduct a performance analysis emulating a real IoT-based environment to understand scalability, response time, and computer resource usage of the two interoperability solutions. The results reveal that conceptual design choices impact the applications’ performance: the WoT architecture effectively enables interoperability across IoT Platforms, though it incorporates several characteristics that hinder the implementation of applications. On the other hand, the FIWARE IoT Agent solution is platform-specific. Hence new implementations are needed for each different IoT data model.

The concepts of Software Defined Networks (SDN) and Network Function Virtualization (NFV) have promoted network chaining, or Service Function Chain (SFC), quickly and simply. In dynamic infrastructure scenarios, the management of SFC introduces challenges both for the connection of these elements and for understanding their behavior when automatic elasticity is required. Currently, most existing approaches have addressed this challenge with probabilistic heuristics or AI-based techniques, considering only static management. This paper presents an approach for profiling SFC that can be used for predictive NFV orchestration purposes. We conducted a performance evaluation study based on testbed experimentation and queueing modeling. Our results confirm that an analytical model can be used for managing SFC orchestration, not only as a validation technique for NFV, but also as a heuristic for predictive resource allocation in production environments.

The Internet of Things (IoT) is getting momentum, which drives us to design solutions able to deal with huge amounts of data coming from different sorts of sensors in order to make decisions to adapt system behavior automatically. While in recent years many IoT-based reasoning systems have already been proposed, there are no comprehensive results reporting their performance, particularly in complex environments. As an answer to that challenge, developers often choose an architecture design based on previous experience that have an impact on the system performance and scalability. This paper shows experimental results of a performance analysis study of different implementations of context-aware management architectures for IoT-based smart cities. Results show that different architectural choices affect system scalability and that automatic real time decision-making is feasible in an environment composed of dozens of thousands of sensors continuously transmitting data.

The advent of the Internet of Things (IoT) and its innumerous applications for Smart Cities emphasizes the need for context-aware systems able to adapt behavior automatically to instant environment conditions. Currently, there is a gap in terms of understanding how context information is interrelated, as well as tracking which events occurred under which conditions within certain context scopes. Visualizing, specifying, tracking and monitoring typical contexts involved in IoT-based applications are still challenging activities since context modeling remains a low level process that requires much human expertise. In this paper we propose a new context life cycle that involves context design and context tracking. We developed a context-aware management framework, where contexts are modeled as graphs and can be explicitly designed and their occurrences can be tracked down. We believe that this feature of designing and tracking context graphs may help developers, administrators, and end-users in harnessing the wealth of information generated by highly scalable IoT systems.

Cloud computing requires elasticity management for dynamically allocating and releasing resources. Even though the adoption of cloud services has been growing, there is little knowledge available for guiding users when they need to manage elasticity. This paper analyzes elasticity in private and hybrid clouds, using a university lab, PlanetLab and Amazon EC2. Results show that the choice of metrics and thresholds plays a key role in meeting performance levels and controlling costs and that cloudburst can be effectively used for a hybrid cloud but the choice of the type of virtual machine in the provider has a significant impact.

Performing distributed experimentation in an Internet-like scale is essential for the introduction of innovative protocols, applications, and techniques in future networks. In the past, having access to different servers in different locations in the world used to be a very hard task. This scenario has changed in recent years with the introduction of Planet Lab, a distributed laboratory with 1000+ servers spread in the five continents for developing experiments and measurements in a planetary scale. However, Planet Lab currently has some limitations that sometimes prevent researchers to perform reliable experiments. This paper presents a large-scale measurement study in Planet Lab aiming at shedding some light into the availability of resources for performing experimental research on it. Results show that users should take extra care when designing and deploying experiments in Planet Lab, due to high instability and heterogeneity of the available resources.

Dynamic system adaptation to continuously changing business requirements and environment conditions has been a common demand for IT over the last years. In spite of constant innovation in service computing, current technology is not able to keep up with business ever growing need faster time to market. The Service Refinement Cycle (SRC) has been proposed for providing flexibility and power for creating new services by composing existing services with integrated policies. We developed and deployed a Service Execution Environment (SEE) for the SRC using existing technology, such as Web Services, XACML and WS-BPEL. As a proof of concept prototype, we also developed an Online Radio Service that makes extensive use of the key SRC features and is executed by our SEE.

The Internet has rapidly evolved, becoming a virtual place where it is simple to handle tasks that were known to be cumbersome or complex beforehand. However the development of new services remains a time consuming activity. This paper presents E2ECloud, a computing environment that allows the development of solutions for creation and provision of composite services in the cloud. Users create a new service through a SaaS tool, which generates a Service Code and deploys it onto an IaaS to be executed. A prototype of E2ECloud was developed and qualitative and quantitative evaluations were conducted, highlighting the potential of our approach to the creation of a new market for cloud service development.

This paper presents two tools developed to facilitate the use and automate the process of using Virtual Worlds for educational purposes. The first tool has been developed to automatically create the classroom space, usually called region in the virtual world, which means, a region in the virtual world used to develop educational activities between professors, students and interactive objects. The second tool helps the process of creating 3D interactive objects in a virtual world. With these tools educators will be able to produce 3D interactive learning objects and use them in virtual classrooms improving the quality and appeal, for students, of their classes.

Social virtual worlds (MMOW) allow users to dynamically create new content with high degree of personalization and immersion in shared real time environments. Popular MMOWs, such as Second Life (SL), are based on a Client/Server (C/S) approach for guaranteeing a shared consistent view of the virtual world, thus generating high network traffic volumes. This is due to the intrinsic features of MMOWs, where servers keep most information of the virtual environment, unlike some other types of virtual worlds, such as Massively Multiplayer Online Games (MMOG), such as World of Warcraft, where clients keep all objects and textures stored locally. Although the C/S approach is universally used because it is reliable and easily controlled, it poses a high burden on server hardware and bandwidth. In this paper, we propose SimP2P, a peer- to-peer approach for sharing textures among clients for MMOW environments, such as OpenSim. Our results show that SimP2P is able to decrease significantly the traffic sent by the OpenSim server, as well as to provide a much better Quality of Experience (QoE) for users, measured by texture response time.

Service composition is an important feature enabled by Service Oriented Computing but it remains mainly a manual process. Proposals for complete or partial automation of composition exist, the majority relying on artificial intelligence-based planning, automated proof techniques, or graph-based solutions. Although sound, these approaches present practical issues that prevent their use in production scenarios. This paper presents a composition tool that allows for partial service specification along with a composition algorithm based on the semantic matching of services inputs and outputs. Early performance numbers are also presented.

Service composition is today mostly a manual process and automated service creation techniques are not ready for prime time in IT and network system development. This paper presents the Service Refinement Cycle as an integrated framework for dealing with the service lifecycle composed of stages like service creation, deployment, execution and management. The main realization of the cycle is the Service Code, which contains executable specifications (code) that may run at any compatible engine.

Ambient Networks propose the concept of network composition for providing dynamic and instant resource access for roaming users. policy-based management (PBM) is a good candidate for a service management platform in such scenarios. However, PBM infrastructures for large-scale service management in 3G/4G environments are still to be developed. This paper presents a prototype implementation of a PBM approach for network composition that uses a tree-based information model mapped on a peer-to-peer storage mechanism. A preliminary, measurement-based performance analysis of the prototype is presented along with a discussion on the strategies used, their benefits and pitfalls.

The management of large-scale 3G and 4G wireless systems, such as ambient networks (AN), in the Internet is a challenge, due to their inherent heterogeneous and distributed nature. In this paper we present our experience with the design and implementation of PBMAN, a Policy-based Management framework for AN, which gives special attention to network composition. We discuss the main choices as well as the lessons we learned during this process and the next steps.

Recently, peer-to-peer (P2P) streaming applications have drawn attention to both users and research community. The P2P approach for IPTV has the appealing feature of providing TV-like quality content distribution service (for both on-demand and live TV), which has been attracting a large number of users from across the globe. However, there is very little understanding about their network architecture and resource consumption. This work aims at having an in-depth understanding of Joost, a video-on-demand P2P system for distributing TV content, created by Skype and Kazaa founders. Results show that Joost relies on UDP for content delivery with an asymmetric bandwidth consumption of until 750 Kbps (download) and 480 Kbps (upload). We also evaluated the Joost control plane, which is mainly based on TCP. Along with other P2P IPTV technologies, such systems could have a great impact on network bandwidth utilization, even more than P2P file transfer ones.