Fog Computing over Challenged Networks: a Real Case Evaluation

Fog computing enables a multitude of resource-constrained end devices (e.g., sensors and actuators) to benefit from the presence of fog nodes in their close vicinity, which can provide the required computing and storage facilities instead of relying on a distant Cloud infrastructure. However, guaranteeing stable communication between end devices and fog nodes is often not trivial. Indeed, in some application scenarios such as mining operations, building sites, precision agriculture, and more, communication occurs over Challenged Networks e.g., because of the absence of a fixed and reliable network infrastructure. This paper analyzes the applicability of Fog Computing in a real Industrial Internet of Things (IIoT) environment, providing an architecture that enables disruption-tolerant communication over Challenged Networks and evaluating the achieved performance on an open-source prototype implementation

Profiling industrial vehicle duties using CAN bus signal segmentation and clustering

Industrial vehicles working in construction sites show rather heterogeneous usage patterns. Depending on its type, model, and context of usage, the vehicle workload may vary from light to heavy with variable periodicity. Duties summarize the current state of a vehicle according to its usage level. They are usually set up manually vehicle by vehicle according to the specifications of the manufacturer. To automate the definition of per-vehicle duty levels, this paper explores the use of clustering techniques applied to CAN bus signals. It first performs a segmentation of the CAN bus signals to identify specific working cycles. Then, it clusters the segments to support the definition of vehicle-specific duty levels. The preliminary results, acquired on real vehicle usage data, show the applicability of the proposed approach

Modeling and Analysis of Large Scale Interconnected Unstructured P2P Networks

Short paper + posterInternational audienceInterconnection of multiple P2P networks has recently emerged as a viable solution to increase system reliability and fault-tolerance as well as to increase resource avail- ability. In this paper we consider interconnection of large scale unstructured P2P networks by means of special nodes (called synapses) [1] that are co-located in more than one overlay. Synapses act as trait d'union by sending/forwarding a query to all the P2P networks they belong to. Modeling and analysis of the resulting interconnected system is crucial to design efficient and effective search algorithms and to control the cost of interconnection. Yet, simulation and/or prototype deployment based analysis can be very difficult - if not impossible - due to the size of each component (we consider large scale systems that can be composed of millions of nodes) and to the complexity arising from the interconnection of several such complex systems. To overcome this strong limitation, we developed a generalized random graph based model that is validated against simulations and it is used to investigate the performance of search algorithms for different interconnection costs and to provide some insight in the characteristics of the interconnection of a large number of P2P networks

Preventive maintenance for heterogeneous industrial vehicles with incomplete usage data

Large fleets of industrial and construction vehicles require periodic maintenance activities. Scheduling these operations is potentially challenging because the optimal timeline depends on the vehicle characteristics and usage. This paper studies a real industrial case study, where a company providing telematics services supports fleet managers in scheduling maintenance operations of about 2000 construction vehicles of various types. The heterogeneity of the fleet and the availability of historical data fosters the use of data-driven solutions based on machine learning techniques. The paper addresses the learning of per-vehicle predictors aimed at forecasting the next-day utilisation level and the remaining time until the next maintenance. We explore the performance of both linear and non-liner models, showing that machine learning models are able to capture the underlying trends describing non-stationary vehicle usage patterns. We also explicitly consider the lack of data for vehicles that have been recently added to the fleet. Results show that the availability of even a limited portion of past utilisation levels enables the identification of vehicles with similar usage trends and the opportunistic reuse of their historical data

Providing crowd-sourced and real-time media services through a NDN-based platform

International audienceThe diffusion of social networks and broadband technologies is letting emerge large online communities of people that stay always in touch with each other and exchange messages, thoughts, photos, videos, files, and any other type of contents. At the same time, due to the introduction of crowd-sourcing strategies, according to which services and contents can be obtained by soliciting contributions from a group of users, the amount of data generated and exchanged within a social community may experience a radical increment never seen before. In this context, it becomes essential to guarantee resource scalability and load balancing to support real time media delivery. To this end, the present book chapter aims at investigating the design of a network architecture, based on the emerging Named Data Networking (NDN) paradigm, providing crowd-sourced real-time media contents. Such an architecture is composed by four different entities: a very large group of heterogeneous devices that produce media contents to be shared, an equally large group of users interested in them, a distributed Event Management System that creates events and handles the social community, and a NDN communication infrastructure able to efficiently manage users requests and distribute multimedia contents. To demonstrate the effectiveness of the proposed approach, we have evaluate its performance through a simulation campaign using real-world topologies

CCN-TV: a data-centric approach to real-time video services

International audienceContent-Centric Networking (CCN) is a promising data-centric architecture, based on in-network caching, name-driven routing, and receiver-initiated sessions, which can greatly enhance the way Internet resources are currently used, making support for a broader set of users with increasing traffic demands possible. The CCN vision is, currently, attracting the attention of many researchers across the world, since it has all the potential to become ready to the market, to be gradually deployed in the Internet of today, and to facilitate a graceful transition from a host-centric networking rationale to a more effective data-centric working behaviour. At the same time, several issues have to be investigated before CCN can be safely deployed at the Internet scale. They include routing, congestion control, caching operations, name-space planning, and application design. With reference to application-related facets, it is worth noticing that the demand for TV services is growing at an exponential rate over time, thus requiring a very careful analysis of their performance in CCN architectures. To this end, in the present contribution we deploy a CCNTV system, capable of delivering real-time streaming TV services, and we evaluate its performance through a simulation campaign based on real-world topologies

Techniques de modélisation et d’analyse pour l’amélioration de la robustesse des systèmes distribués

The original “selling point” for decentralized networks has been file exchange, using the decentralized approach to diffuse the bandwidth cost between all the participating nodes, augmenting the robustness by avoiding single point of failures and control by sharing the responsibility between all nodes. While the most decentralized approaches are very efficient in terms of resilience they are, by the same reason, more performance limited and harder to analyze. This analysis is usually the sole realm of simulation tools, a quite in- efficient way to analyze the possibility space. We thus developed and present here a mathematical model for network interconnection, enabling the study and exploration of equilibriums and, by virtue of the abstraction of the model, perfectly applicable to any interconnection of networks, be them communication networks, social networks or, for example, water distribution networks. We also focused on decentralized networks, called MANETs, where communication between mobile nodes is purely ad-hoc based (eg.: two cars passing each other and communicating while in range), exploit- ing rateless coding to increase their robustness by minimizing data loss due to transmission unreliability, and detecting malicious nodes sending corrupted packets, a hard to detect and prevent problem in a strongly distributed environments, using SIEVE, a custom developed algorithm.Le point de départ pour les systèmes décentralisés a été l’échange des fichiers, en utilisant cet approche i) pour distribuer la bande passante entre tous les nœuds concernés et ii) pour augmenter la robustesse en éliminant autant que possible les points individuels de défaillance et de contrôle et iii) en partageant également les responsabilités entre les nœuds. Si les approches le plus décentralisés sont très efficaces en termes de résilience aux pannes, pour la même raison, les performances sont limités et difficiles à analyser quand on observe plusieurs réseaux interconnectés entre eux, configurations qui peuvent être analysés à travers des outils de simulation, souvent peu efficaces dans l’analyse de l’espace de possibilités. Dans cette thèse on a développé un modèle mathématique pour la modélisation de l’interconnexion des réseaux en permettant l’étude et l’exploration d’équilibres qui grâce à l’abstraction du modèle peuvent s’appliquer à l’interconnexion des réseaux de communications, réseaux de distribution de marchandise ou réseaux de distribution d’eau. La thèse se focalise aussi sur les réseaux décentralisés MANET, ou` la communication entre nœuds mobiles est purement ≪ ad-hoc ≫ (ex: deux voitures communiquant entre eux quand ils sont proches) en utilisant i) des ≪ rateless coding ≫ pour augmenter la robustesse et minimiser la perte ou la corruption de données causées par la non fiabilité du moyen de transmission et ii) des algorithmes de ≪ pollution détection ≫, par exemple de détection de nœuds malveillants ou de paquets corrompus, cette détection et prévention étant très difficile dans des environnements fortement distribués

Modeling and analysis of techniques to increase robustness in distributed systems

Le point de départ pour les systèmes décentralisés a été l’échange des fichiers, en utilisant cet approche i) pour distribuer la bande passante entre tous les nœuds concernés et ii) pour augmenter la robustesse en éliminant autant que possible les points individuels de défaillance et de contrôle et iii) en partageant également les responsabilités entre les nœuds. Si les approches le plus décentralisés sont très efficaces en termes de résilience aux pannes, pour la même raison, les performances sont limités et difficiles à analyser quand on observe plusieurs réseaux interconnectés entre eux, configurations qui peuvent être analysés à travers des outils de simulation, souvent peu efficaces dans l’analyse de l’espace de possibilités. Dans cette thèse on a développé un modèle mathématique pour la modélisation de l’interconnexion des réseaux en permettant l’étude et l’exploration d’équilibres qui grâce à l’abstraction du modèle peuvent s’appliquer à l’interconnexion des réseaux de communications, réseaux de distribution de marchandise ou réseaux de distribution d’eau. La thèse se focalise aussi sur les réseaux décentralisés MANET, ou` la communication entre nœuds mobiles est purement ≪ ad-hoc ≫ (ex: deux voitures communiquant entre eux quand ils sont proches) en utilisant i) des ≪ rateless coding ≫ pour augmenter la robustesse et minimiser la perte ou la corruption de données causées par la non fiabilité du moyen de transmission et ii) des algorithmes de ≪ pollution détection ≫, par exemple de détection de nœuds malveillants ou de paquets corrompus, cette détection et prévention étant très difficile dans des environnements fortement distribués.The original “selling point” for decentralized networks has been file exchange, using the decentralized approach to diffuse the bandwidth cost between all the participating nodes, augmenting the robustness by avoiding single point of failures and control by sharing the responsibility between all nodes. While the most decentralized approaches are very efficient in terms of resilience they are, by the same reason, more performance limited and harder to analyze. This analysis is usually the sole realm of simulation tools, a quite in- efficient way to analyze the possibility space. We thus developed and present here a mathematical model for network interconnection, enabling the study and exploration of equilibriums and, by virtue of the abstraction of the model, perfectly applicable to any interconnection of networks, be them communication networks, social networks or, for example, water distribution networks. We also focused on decentralized networks, called MANETs, where communication between mobile nodes is purely ad-hoc based (eg.: two cars passing each other and communicating while in range), exploit- ing rateless coding to increase their robustness by minimizing data loss due to transmission unreliability, and detecting malicious nodes sending corrupted packets, a hard to detect and prevent problem in a strongly distributed environments, using SIEVE, a custom developed algorithm