Tendermint is the replication engine at the core of Cosmos, a network of proof-of-stake blockchains. In the lifespan of blockchains, Cosmos and Tendermint are mature technologies, currently used by more than a hundred businesses and deployed by hundreds of nodes. The system was designed to provide flexible deployment despite heterogeneous environments, scale performance with the number of nodes, and tolerate misbehaving participants. In this practical experience report, we overview Tendermint's main design goals and architecture, and present a detailed performance evaluation of the system in a realistic environment. We report results from a geographically distributed environment with up to 128 nodes, including failure-free executions and fail-prone scenarios, with both crash and Byzantine failures.

Blockchain systems have received much attention and promise to revolutionize many services. Yet, despite their popularity, current blockchain systems exist in isolation, that is, they cannot share information. While interoperability is crucial for blockchain to reach widespread adoption, it is difficult to achieve due to differences among existing blockchain technologies. This paper presents a technique to allow blockchain interoperability. The core idea is to provide a primitive operation to developers so that contracts and objects can switch from one blockchain to another, without breaking consistency and violating key blockchain properties. To validate our ideas, we implemented our protocol in two popular blockchain clients that use the Ethereum virtual machine. We discuss how to build applications using the proposed protocol and show examples of applications based on real use cases that can move across blockchains. To analyze the system performance we use a real trace from one of the most popular Ethereum applications and replay it in a multi-blockchain environment.

Classic state machine replication (SMR) does not scale well, since each replica must execute every command. To address this problem, several systems have investigated the use of state partitioning in the context of SMR, allowing client commands to be executed on a subset of replicas. Prior approaches range from completely static schemes, which do not adapt as workloads change, to dynamic schemes, which move data on-demand. This paper presents DynaStar, a new dynamic partitioning scheme for scaling state machine replication. In contrast to prior dynamic schemes, DynaStar uses a replicated location oracle to maintain a global view of the workload and inform heuristics about data placement. Using this oracle, DynaStar is able to adapt to workload changes over time, while also minimizing the number of state moves. The result is a practical technique that achieves excellent performance.

This paper introduces and evaluates a Traffic Sign Management Architecture (TSMA), which represents a paradigm shift for the deployment of traffic sign infrastructure in the context of Intelligent Transport Systems, Vehicular Networks and Smart Cities. The proposal addresses limitations of the current traffic control model by enabling remote updates of traffic signs and displaying them on the vehicular navigation system display to improve their legibility. TSMA is an architecture developed to provide V2I interaction using a commodity technology, Wi-Fi, through the beacon-stuffing technique. The initial design of TSMA's security mechanisms is also presented in this paper. Evaluations were performed on a developed prototype and simulation environments.

In mobile ad hoc networks (MANET) nodes are free to move on the environment and interact with each other and with the infra-structure, enabling a multitude of applications and services. However, the same mobility that is key to MANET is also the greater limiting factor in the quality of the services provided in this environment, since the infrastructure must keep adapting to the mobility. This paper describes algorithms for predicting the future position of mobile nodes in MANET, allowing the infrastructure to proactively adapt. Our algorithms maintain the recent movement history of nodes in a compact representation, a graph, based on stigmergy of ant colonies. Predictions are generated through a limited depth first search. We have experimented this method against real world data and the results show that the prediction is accurate for short time horizons (30 seconds) in a metropolitan area (Seattle) divided in a grid of two or three-block square cells, in 77.8% of the cases. This method may be used in optimizing MANET and enabling novel applications, for example, proactive hand-off, tailored content generation, and proactive routing.

Cellular automata (CA) are able to perform complex computations through local interactions. The investigation of how CA computations are carried out can be made by the usage of CA rules to solve specific tasks. The well-known problem called density classification task (DCT) is investigated, with focus on its two-dimensional version. Evolutionary algorithms have been widely used in the search for DCT rules. A sample of lattices with Gaussian distribution is commonly used to evaluate rule quality. However, uniform lattices are easier to classify, allowing an initial selective pressure needed to start the convergence. A comparative evaluation of three adaptive strategies is presented here: they start using easy lattices to classify and as effective rules are being obtained the difficult level is progressively increased toward the target evaluation. Several experiments were performed to evaluate the strategies efficiency and new rules were found, which outperform the best ones published.