In recent years, the number of objects connected to the internet have significantly increased. Increasing the number of connected devices to the internet is transforming today’s Internet of Things (IoT) into massive IoT of the future. It is predicted that, in a few years, a high communication and computation capacity will be required to meet the demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill a part of these requirements by providing a data rate of up to terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constraints. On the other hand, the next generation of software-defined networking (SDN) with emerging cloudrelated technologies (e.g., fog and edge computing) can play an important role in supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid cloud-fog systems to support 5G and beyond-enabled applications.

Finding an available parking place has been considered a challenge for drivers in large-size smart cities. In a smart parking application, Artificial Intelligence of Things (AIoT) can help drivers to save searching time and automotive fuel by predicting short-term parking place availability. However, performance of various Machine Learning and Neural Network-based (MLNN) algorithms for predicting parking segment availability can be different. To find the most suitable MLNN algorithm for the above mentioned application, this paper evaluates performance of a set of well-known MLNN algorithms as well as different combinations of them (i.e., known as Ensemble Learning or Voting Classifier) based on a real parking datasets. The datasets contain around five millions records of the measured parking availability in San Francisco. For evaluation, in addition to the cross validation scores, we consider resource requirements, simplicity and execution time (i.e., including both training and testing times) of algorithms. Results show that while some ensemble learning algorithms provide the best performance in aspect of validation score, they consume a noticeable amount of computing and time resources. On the other hand, a simple Decision Tree (DT) algorithm provides a much faster execution time than ensemble learning algorithms, while its performance is still acceptable (e.g., DT’s accuracy is less than 1% lower than the best ensemble algorithm). We finally propose and simulate a recommendation system using the DT algorithm. We have found that around 77% of drivers can not find a free spot in their selected destinations (i.e., street or segment) and estimated that the recommendation system, by introducing alternative closest vacant locations to destinations, can save, in total, 3500 min drivers searching time for 1000 parking spot requests. It can also help to reduce the traffic and save a noticeable amount of automotive fuel.