We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, it deals with permutation, ordered, and continuous parameter types along with both known and unknown parameter constraints. To reason about these parameter types and efficiently deliver high-quality code, BaCO uses Bayesian optimization algorithms specialized towards the autotuning domain. We demonstrate BaCO's effectiveness on three modern compiler systems: TACO, RISE & ELEVATE, and HPVM2FPGA for CPUs, GPUs, and FPGAs respectively. For these domains, BaCO outperforms current state-of-the-art auto-tuners by delivering on average 1.36X--1.56X faster code with a tiny search budget, and BaCO is able to reach expert-level performance 2.9X--3.9X faster.

Design problems are ubiquitous in scientific and industrial achievements. Scientists design experiments to gain insights into physical and social phenomena, and engineers design machines to execute tasks more efficiently. These design problems are fraught with choices which are often complex and high-dimensional and which include interactions that make them difficult for individuals to reason about. In software/hardware co-design, for example, companies develop libraries with tens or hundreds of free choices and parameters that interact in complex ways. In fact, the level of complexity is often so high that it becomes impossible to find domain experts capable of tuning these libraries [1].

We demonstrate attribute-based authentication and access control schemes tailored to resource-constrained devices typical of IoT home environments. The demo shows how IoT devices would be managed and controlled in such scenario. The underlying cryptographic scheme relies on an Attribute-Based Cryptosystem (ABC) to cryptographically implement an authenticated Attribute-Based Access Control (ABAC) mechanism.

The research community has been investigating the potential of processing and wireless communication of vehicles in a transportation system. In this case, VANETs aim to exploit the communication and sensing capabilities of vehicles to feed data into applications and services. VANETs also contribute to the improvement of Advanced Driver Assistant Systems and Intelligent Transportation Systems, which aim to provide services to users such as safer and more comfortable trips. Many of these systems need to authenticate their users, but they do so in a way that an attacking driver can use them. This work explores the driver identification as an extra authentication factor to local services and vehicular networks. In this respect, a virtual sensor was developed to determine the driver's identity, with precision above 98% in our experiments, using embedded sensor data. This virtual sensor was also used to identify a suspected driver. Besides, based on the suspect's identification, we discuss the impacts of these drivers in the data dissemination in a vehicular network.

The consumer electronics industry is witnessing a surge in Internet of Things (IoT) devices, ranging from mundane artifacts to complex biosensors connected across disparate networks. As the demand for IoT devices grows, the need for stronger authentication and access control mechanisms is greater than ever. Legacy authentication and access control mechanisms do not meet the growing needs of IoT. In particular, there is a dire need for a holistic authentication mechanism throughout the IoT device life-cycle, namely from the manufacturing to the retirement of the device. As a plausible solution, we present Authentication of Things (AoT), a suite of protocols that incorporate authentication and access control during the entire IoT device life span. Primarily, AoT relies on Identity- and Attribute-Based Cryptography to cryptographically enforce Attribute-Based Access Control (ABAC). Additionally, AoT facilitates secure (in terms of stronger authentication) wireless interoperability of new and guest devices in a seamless manner. To validate our solution, we have developed AoT for Android smartphones like the LG G4 and evaluated all the cryptographic primitives over more constrained devices like the Intel Edison and the Arduino Due. This included the implementation of an Attribute-Based Signature (ABS) scheme. Our results indicate AoT ranges from highly efficient on resource-rich devices to affordable on resource-constrained IoT-like devices. Typically, an ABS generation takes around 27 ms on the LG G4, 282 ms on the Intel Edison, and 1.5 s on the Arduino Due.