Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and solutions to various research problems. However, the extensive computation required to train AI algorithms comes with a cost. Driven by the need to reduce the energy consumption, the carbon footprint and the cost of computers running machine learning algorithms, TinyML is nowadays considered as a promising AI alternative focusing on technologies and applications for extremely low-profile devices. This paper presents the results of a literature survey of all TinyML applications and related research efforts. Our survey builds a taxonomy of TinyML techniques that have been used so far to bring new solutions to various domains, such as healthcare, smart farming, environment, and anomaly detection. Finally, this survey highlights the remaining challenges and points out possible future research directions. We anticipate that this survey will motivate further discussions on the various fields of applications of TinyML and the synergy of resource-constrained devices and edge intelligence.

With the proliferation of embedded technologies and wireless capabilities, today's vehicles are no longer isolated mechanical machines. They become part of a hyper-connected system -Intelligent Transportation Systems (ITS)- that has the potential to support multiple levels of autonomy and intelligence improving considerably the safety, efficiency, and sustainability of transportation networks. However, this raises new security issues that make the whole system prone to cybersecurity attacks that threaten both the safety and privacy of all road-users. This article gives a short background tutorial on the main security issues and the different attacks that hinder Intelligent Transport Systems. To enable secure and safe ITS applications, this article provides a comprehensive analysis of existing solutions and highlights their strengths and limitations. Finally, this survey presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and car manufactures to improve the security of ITS.

Dynamic Service Function Chaining (SFC) is a technique that facilitates the enforcement of advanced services and differentiated traffic forwarding policies. It dynamically steers the traffic through an ordered list of Service Functions (SFs). Enabling SFC capabilities in the context of a Software Defined Networking (SDN) architecture is promising, as it takes advantage of the SDN flexibility and automation abilities to structure service chains and improve the delivery time. However, the delivery time depends also on the traffic steering techniques used by an SFC solution. This paper provides a closer look at the current SDN architectures for SFC and provides an analysis of traffic steering techniques used by the current SDN-based SFC approaches. This study presents a comprehensive analysis of these approaches using efficiency criteria. It concludes that the studied solutions are not efficient enough to be deployed in real-life networks, principally due to scalability and flexibility limitations. Accordingly, the paper identifies relevant research challenges.