Global industry is undergoing major transformations with the genesis of a new paradigm known as the Internet of Things (IoT) with its underlying technologies. Many company leaders are investing more effort and money in transforming their services to capitalize on the benefits provided by the IoT. Thereby, the decision makers in public waste management do not want to be outdone, and it is challenging to provide an efficient and real-time waste management system. This paper proposes a solution (hardware, software, and communications) that aims to optimize waste management and include a citizen in the process. The system follows an IoT-based approach where the discarded waste from the smart bin is continuously monitored by sensors that inform the filling level of each compartment, in real-time. These data are stored and processed in an IoT middleware providing information for collection with optimized routes and generating important statistical data for monitoring the waste collection accurately in terms of resource management and the provided services for the community. Citizens can easily access information about the public waste bins through the Web or a mobile application. The creation of the real prototype of the smart container, the development of the waste management application and a real-scale experiment use case for evaluation, demonstration, and validation show that the proposed system can efficiently change the way people deal with their garbage and optimize economic and material resources.

The Internet of Things (IoT) has become a rapidly growing research field. This is due to the advancement of digital technologies, miniaturization, and the reduction of the cost of IoT devices and wireless connectivity, among others. Despite the plethora of technologies used for the Internet of Things, the trade-off between long data transmission range and low power consumption was not found until the advent of Low Power Wide Area Network (LPWAN) technologies. This paper reviews the main aspects of LPWANs and their technologies based on an exhaustive search in several online scientific databases, such as Springer, IEEE Xplore, the ACM digital library, and Google Scholar. This research methodology enabled us to gather recent work on LPWANs, which forms the basis of this article. It is informative and knowledge-updating support in the LPWANs’ environment that broadly covers LPWANs. This research work has developed the characteristics of LPWANs and the techniques used to achieve long-range energy efficiency, high scalability, and low cost. In addition, it presents the application areas of LPWAN technologies with use-case network architectures for each area, addresses spectrum and energy optimization, and discusses open research challenges that need to be focused to provide guidelines for further contributions.

The evolution of surveillance technologies allows a reduction in human interaction with the process, since most of the monitoring functions performed by an individual can be replaced by detection and recognition techniques in real-time. This paper proposes the development of a surveillance system, which uses these techniques to identify individuals present within the field of view of camera. A combination of the Histogram of Oriented Gradient and Support Vector Machine techniques is applied for face detection, while a Residual Network is used during the stage of recognizing individuals. This shows the possibility of implementing this set of techniques, even in hardware with processing limitations.