One of the main trends in the evolution of smart grids is transactive energy, where distributed energy resources, e.g. smart meters, develop towards Internet-of-Things (IoT) devices enabling prosumers to trade energy directly among each other, without the need of involving any centralised third party. The expected advantages in terms of cost-effectiveness would be significant, indeed technical solutions are being investigated and large-scale deployment are planned by major utilities companies. However, introducing transactive energy in the smart grid entails new security threats, such as forging energy transactions. This paper introduces an infrastructure to support reliable and cost-effective transactive energy, based on blockchain and smart contracts, where functionalities are implemented as fully decentralised applications. Energy transactions are stored in the blockchain, whose high replication level ensures stronger guarantees against tampering. Energy auctions are carried out according to transparent rules implemented as smart contracts, hence visible to all involved actors. Threats deriving from known vulnerabilities of smart meters are mitigated by temporarily keeping out exposed prosumers and updating their devices as soon as security patches become available.

Distributed stream processing frameworks are designed to perform continuous computation on possibly unbounded data streams whose rates can change over time. Devising solutions to make such systems elastically scale is a fundamental goal to achieve desired performance and cut costs caused by resource over-provisioning. These systems can be scaled along two dimensions: the operator parallelism and the number of resources. In this paper, we show how these two dimensions, as two symbiotic entities, are independent but must mutually interact for the global benefit of the system. On the basis of this observation, we propose a fine-grained model for estimating the resource utilization of a stream processing application that enables the independent scaling of operators and resources. A simple, yet effective, combined management of the two dimensions allows us to propose ELYSIUM, a novel elastic scaling approach that provides efficient resource utilization. We implemented the proposed approach within Apache Storm and tested it by running two real-world applications with different input load curves. The outcomes backup our claims showing that the proposed symbiotic management outperforms elastic scaling strategies where operators and resources are jointly scaled.

As data is having an increasingly relevant role in di erent business fields, ensuring integrity has become fundamental. Modern databases rely on transaction history written on redo logs to allow for data restore. However, if redo logs are (maliciously) forged, data can actually be lost or altered. Due its strong data integrity guarantees, blockchain technology can be employed to ensure log integrity, but its current performance limitations hinder actual exploitations.In previous work, we proposed a layered blockchain-based architecture for distributed (federated) database redo logs: a fast first layer blockchain, anchored to a secure second layer blockchain, based on proof-of-work to achieve strong integrity. Here, we present an implementation and an experimental evaluation of a prototype of that architecture, which employs a total consensus algorithm on the first layer blockchain. Finally, to improve availability and scalability, we refine our solution by investigating, respectively, a Byzantine Fault Tolerant consensus and a Distributed Hash Table solution to shard the first layer blockchain ledger among available nodes.

Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of casualties has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance. Nonetheless, being able to detect and localize buried landmines with high precision in an automatic fashion is still considered a challenging task due to the many different boundary conditions that characterize this problem (e.g., several kinds of objects to detect, different soils and meteorological conditions, etc.). In this article, we propose a novel technique for buried object detection tailored to unexploded landmine discovery. The proposed solution exploits a specific kind of convolutional neural network (CNN) known as autoencoder to analyze volumetric data acquired with ground penetrating radar (GPR) using different polarizations. This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas. The system then recognizes landmines as objects that are dissimilar to the soil used during the training step. Experiments conducted on real data show that the proposed technique requires little training and no ad hoc data preprocessing to achieve accuracy higher than 93% on challenging data sets.