Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

The objective of this position paper is to present Fluidware, a proposal towards an innovative programming model for the IoT, conceived to ease the development of flexible and robust large-scale IoT services and applications. The key innovative idea of Fluidware is to abstract collectives of devices of the IoT fabric as sources, digesters, and targets of distributed 'flows' of contextualized events, carrying information about data produced and actuating commands. Accordingly, programming services and applications implies declaratively specifying 'funnel processes' to channel, elaborate, and re-direct such flows in a fully-distributed way, as a means to coordinate the activities of devices and realize services and applications. The potential applicability of Fluidware and its expected advantages are exemplified via example in the area of ambient assisted living

An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks

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A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)-edge-fog-cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for 'pulverization': applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT-edge-fog-cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings

Towards a Dynamic Edge AI Framework applied to autonomous driving cars

[EN] This work proposes an innovative solution in the field of Edge AI in order to efficiently exploit new hardware components available on the market at low cost. Edge AI means that algorithms are processed locally on a hardware device. The algorithms use data (sensor data or signals) that are created on the own device. The idea of this paper focuses on demonstrating the validity of the proposed solution by implementing an autonomous driving system that exploits communication between intelligent agents. In this case, our self-driving cars are equipped with a low-cost device that allows you to recognise objects along the way and consequently take actions by running a machine learning model. The presence of a machine learning model also allows the developer to modify it by extending the flexibility and application possibilities of the proposed solution.This work was partly supported by: ERASMUS+ Programme, KA1 Istruzione Superiore, Carta Erasmus+: 29388-EPP-1-2014-1-IT-EPPKA3-ECHE, ACCORDO PER LA MOBILITÀ ERASMUS PER STUDIO - a.a. 2019/2020, Progetto n o 2019-1-IT02-KA103-061203 - CUP: H25J19000080006, Generalitat Valenciana (PROMETEO/2018/002). Universitat Politecnica de Valencia Research Grant PAID-10-19.Muratore, G.; Rincón Arango, JA.; Julian Inglada, VJ.; Carrascosa Casamayor, C.; Greco, G.; Fortino, G. (2020). Towards a Dynamic Edge AI Framework applied to autonomous driving cars. Springer. 406-415. https://doi.org/10.1007/978-3-030-51999-5_34S406415Chang, A.: The role of artificial intelligence in digital health. In: Wulfovich, S., Meyers, A. (eds.) Digital Health Entrepreneurship. HI, pp. 71–81. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12719-0_7Yang, L., Henthorne, T.L., George, B.: Artificial intelligence and robotics technology in the hospitality industry: current applications and future trends. In: George, B., Paul, J. (eds.) Digital Transformation in Business and Society, pp. 211–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-08277-2_13Khayyam, H., Javadi, B., Jalili, M., Jazar, R.N.: Artificial intelligence and internet of things for autonomous vehicles. In: Jazar, R.N., Dai, L. (eds.) Nonlinear Approaches in Engineering Applications, pp. 39–68. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-18963-1_2Li, H., Ota, K., Dong, M.: Learning iot in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018)Alonso, R.S., Sittón-Candanedo, I., Rodríguez-González, S., García, Ó., Prieto, J.: A survey on software-defined networks and edge computing over IoT. In: De La Prieta, F., et al. (eds.) PAAMS 2019. CCIS, vol. 1047, pp. 289–301. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24299-2_25Wang, T., Mei, Y., Jia, W., Zheng, X., Wang, G., Xie, M.: Edge-based differential privacy computing for sensor-cloud systems. J. Parallel Distrib. Comput. 136, 75–85 (2020)Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. arXiv preprint arXiv:1905.10083 (2019)Sittón-Candanedo, I., Alonso, R.S., Corchado, J.M., Rodríguez-González, S., Casado-Vara, R.: A review of edge computing reference architectures and a new global edge proposal. Future Gener. Comput. Syst. 99, 278–294 (2019)Ke, R., Zhuang, Y., Pu, Z., Wang, Y.: A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. arXiv preprint arXiv:2001.00269 (2020)Mazzia, V., Khaliq, A., Salvetti, F., Chiaberge, M.: Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application. IEEE Access 8, 9102–9114 (2020)Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861 (2017)Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.or

SAJaS: enabling JADE-based simulations

Multi-agent systems (MAS) are widely acknowledged as an appropriate modelling paradigm for distributed and decentralized systems, where a (potentially large) number of agents interact in non-trivial ways. Such interactions are often modelled defining high-level interaction protocols. Open MAS typically benefit from a number of infrastructural components that enable agents to discover their peers at run-time. On the other hand, multi-agent-based simulations (MABS) focus on applying MAS to model complex social systems, typically involving a large agent population. Several MAS development frameworks exist, but they are often not appropriate for MABS; and several MABS frameworks exist, albeit sharing little with the former. While open agent-based applications benefit from adopting development and interaction standards, such as those proposed by FIPA, MABS frameworks typically do not support them. In this paper, a proposal to bridge the gap between MAS simulation and development is presented, including two components. The Simple API for JADE-based Simulations (SAJaS) enhances MABS frameworks with JADE-based features. While empowering MABS modellers with modelling concepts offered by JADE, SAJaS also promotes a quicker development of simulation models for JADE programmers. In fact, the same implementation can, with minor changes, be used as a large scale simulation or as a distributed JADE system. In its current version, SAJaS is used in tandem with the Repast simulation framework. The second component of our proposal consists of a MAS Simulation to Development (MASSim2Dev) tool, which allows the automatic conversion of a SAJaS-based simulation into a JADE MAS, and vice-versa. SAJaS provides, for certain kinds of applications, increased simulation performance. Validation tests demonstrate significant performance gains in using SAJaS with Repast when compared with JADE, and show that the usage of MASSim2Dev preserves the original functionality of the system. © Springer-Verlag Berlin Heidelberg 2015

The Medical Device Dongle: An Open-Source Standards-Based Platform for Interoperable Medical Device Connectivity

Emerging medical applications require device coordination, increasing the need to connect devices in an interoperable manner. However, many of the existing health devices in use were not originally developed for network connectivity and those devices with networking capabilities either use proprietary protocols or implementations of standard protocols that are unavailable to the end user. The first set of devices are unsuitable for device coordination applications and the second set are unsuitable for research in medical device interoperability. We propose the Medical Device Dongle (MDD), a low-cost, open-source platform that addresses both issues

An AI approach to Collecting and Analyzing Human Interactions with Urban Environments

Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens’ quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens’ well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41% acceleration factor and a 75% reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizen’s well-being projects, and sociology, to mention but a few cases

Internet of Things for Sustainable Community Development: Introduction and Overview

The two-third of the city-dwelling world population by 2050 poses numerous global challenges in the infrastructure and natural resource management domains (e.g., water and food scarcity, increasing global temperatures, and energy issues). The IoT with integrated sensing and communication capabilities has the strong potential for the robust, sustainable, and informed resource management in the urban and rural communities. In this chapter, the vital concepts of sustainable community development are discussed. The IoT and sustainability interactions are explained with emphasis on Sustainable Development Goals (SDGs) and communication technologies. Moreover, IoT opportunities and challenges are discussed in the context of sustainable community development