Industrial vehicles equipped with CAN bus devices transmit large volumes of IoT signals. The analysis of CAN bus data can be helpful to monitor the current vehicles’ workload, namely the vehicle duty levels, in an automated fashion. Despite the use of machine learning techniques to automatically detect vehicle duty levels is particularly appealing, existing approaches are challenged by the high cost of human data annotation and by the high heterogeneity of the analyzed vehicles types and models. In this paper, we present a self-supervised approach to automatically detect vehicles’ duty levels based on contrastive learning. The multivariate CAN Bus signals are first divided into fixed-sized segments and then embedded into a vector space shared by all vehicles of the same model by leveraging a constrastive approach with a mixup augmentation strategy. The key idea is to embed similar segments in close proximity by self-learning a model-specific clustering, which allows automatic duty level assignment with minimal human supervision. We validate the proposed approach in a real industrial use case, analyzing CAN Bus data acquired from test heavy-duty vehicles. Data were provided by a multinational Internet-of-Things company specialized in telematics solution. The experiments show clustering performance superior to state-of-the-art models and as well as an higher ability to differentiate between Moving and Working duty levels.

In this paper, we consider a scenario where nodes in a MANET disseminate data chunks using rateless codes. Any node is able to successfully decode any chunk by collecting enough coded blocks from several other nodes without any coordination. We consider the problem of identifying malicious nodes that launch a pollution attack by deliberately modifying the payload of coded blocks before transmitting. It follows that the original chunk can only be obtained if there are no malicious nodes among the chunk providers. In this paper we propose SIEVE, a fully distributed technique to infer the identity of malicious nodes. A node creates what we termed a check whenever a chunk is decoded; a check is a pair composed of the set of other nodes that provided coded blocks used to decode the chunk (the chunk uploaders) and a flag indicating whether the chunk is corrupted or not. SIEVE exploits rateless codes to detect chunk integrity and belief propagation to infer the identity of malicious nodes. In particular, every node autonomously constructs its own bipartite graph (a.k.a. factor graph in the literature) whose vertexes are checks and nodes, respectively. Then, it periodically runs the belief propagation algorithm on its factor graph to infer the probability of other nodes being malicious. We show by running detailed simulations using ns-3 that SIEVE is very accurate and robust under several attack scenarios and deceiving actions. We discuss how the topological properties of the factor graph impacts SIEVE performance and show that nodes speed in the MANET plays a role on the identification accuracy. Furthermore, an interesting trade-off between coding efficiency and SIEVE accuracy, completeness, and reactivity is discovered. We also show that SIEVE is efficient requiring low computational, memory, and communication resources.

In this paper we consider the following problem: nodes in a MANET must disseminate data chunks using rateless codes but some nodes are assumed to be malicious, i.e., before transmitting a coded packet they may modify its payload. Nodes receiving corrupted coded packets are prevented from correctly decoding the original chunk. We propose SIEVE, a fully distributed technique to identify malicious nodes. SIEVE is based on special messages called checks that nodes periodically transmit. A check contains the list of nodes identifiers that provided coded packets of a chunk as well as a flag to signal if the chunk has been corrupted. SIEVE operates on top of an otherwise reliable architecture and it is based on the construction of a factor graph obtained from the collected checks on which an incremental belief propagation algorithm is run to compute the probability of a node being malicious. Analysis is carried out by detailed simulations using ns-3. We show that SIEVE is very accurate and discuss how nodes speed impacts on its accuracy. We also show SIEVE robustness under several attack scenarios and deceiving actions.

Interconnection of multiple P2P networks has recently emerged as a viable solution to increase system reliability and fault-tolerance as well as to increase resource availability. In this paper we consider interconnection of large scale unstructured P2P networks by means of special nodes (called synapses) 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.