In the last decades, the demand for electronics and, therefore, electronic waste, has increased. To reduce this electronic waste and the impact of this sector on the environment, it is necessary to develop biodegradable systems using naturally produced materials with low impact on the environment or systems that can degrade in a certain period. One way to manufacture these types of systems is by using printed electronics because the inks and the substrates used are sustainable. Printed electronics involve different methods of deposition, such as screen printing or inkjet printing. Depending on the method of deposition selected, the developed inks should have different properties, such as viscosity or solid content. To produce sustainable inks, it is necessary to ensure that most of the materials used in the formulation are biobased, biodegradable, or not considered critical raw materials. In this review, different inks for inkjet printing or screen printing that are considered sustainable, and the materials that can be used to formulate them, are collected. Printed electronics need inks with different functionalities, which can be mainly classified into three groups: conductive, dielectric, or piezoelectric inks. Materials need to be selected depending on the ink’s final purpose. For example, functional materials such as carbon or biobased silver should be used to secure the conductivity of an ink, a material with dielectric properties could be used to develop a dielectric ink, or materials that present piezoelectric properties could be mixed with different binders to develop a piezoelectric ink. A good combination of all the components selected must be achieved to ensure the proper features of each ink.

Advances in Artificial intelligence (AI) and embedded systems have resulted on a recent increase in use of image processing applications for smart cities’ safety. This enables a cost-adequate scale of automated video surveillance, increasing the data available and releasing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Additionally, new lightweight open-source middleware for constrained resource devices, such as EdgeX Foundry, have appeared to facilitate the collection and processing of data at sensor level, with communication capabilities to exchange data with a cloud enterprise application. The objective of this work is to show and describe the development of two Edge Smart Camera Systems for safety of Smart cities within S4AllCities H2020 project. Hence, the work presents hardware and software modules developed within the project, including a custom hardware platform specifically developed for the deployment of deep learning models based on the I.MX8 Plus from NXP, which considerably reduces processing and inference times; a custom Video Analytics Edge Computing (VAEC) system deployed on a commercial NVIDIA Jetson TX2 platform, which provides high level results on person detection processes; and an edge computing framework for the management of those two edge devices, namely Distributed Edge Computing framework, DECIoT. To verify the utility and functionality of the systems, extended experiments were performed. The results highlight their potential to provide enhanced situational awareness and demonstrate the suitability for edge machine vision applications for safety in smart cities.