The Internet of Things and Services (IoTS) has encouraged the development of service provisioning systems in respect to Smart City topics. Most of them are operated as heterogeneous systems which limits end customers’ access and contradicts with IoTS principles. In this paper, we discuss and develop a reference model of an interconnected service marketplace ecosystem. The prototypical implementation incorporates findings from an empirical study and lessons learned from research projects. The elaborated ecosystem enables service request roaming between different parties across system boundaries. The paper presents a feasible centralized architecture, introduces involved parties and parts of a developed message protocol. Why a contracting mechanism is indispensable for request roaming is also outlined. The model’s feasibility is demonstrated by means of a current electric mobility use case: providing access to foreign charging infrastructure without multiple registrations. This work contributes to simplify the data exchange between service platforms to improve Smart City solutions and to support travelers with intelligent mobility applications.

Femtocells are widely used to improve poor indoor coverage and decrease the cellular networks high cost. The fact that femtocells and macrocells share similar frequency bands leads to a major challenge in time/frequency resources allocation. Addressing fairness among femtocells and improving time/frequency resources utilization are the main objectives of the previous studies. The balance between femtocell level fairness and utilization is considered in some of the previous studies. Providing USER level fairness with respect to USER Equipments (UEs) DEMANDs is the issue that has not received adequate attention so far.Here, a centralized resource allocation algorithm is proposed to improve the balance between USER level fairness and radio resource utilization where the DEMAND of UEs for radio resources are involved. In this algorithm, two independent phases are followed. The first phase assigns resources to femtocells in a greedy manner to increase the reused spectrum utilization based on the proposed priority. The second phase is planned in a manner to guarantee the fairness between UEs by considering their required resources whenever the residual resources are not proportional to the unmet requirements. Compared to the conventional methods, this proposed approach leads to an improvement in terms of utilization, fairness, and average amount of satisfied DEMANDs.

In the current era of online learning, the recommendation system for the eLearning process is quite important. Since the COVID-19 pandemic, eLearning has undergone a complete transformation. Existing eLearning Recommendation Systems worked on collaborative filtering or content-based filtering based on historical data, students’ previous grade, results, or USER profiles. The eLearning system selected courses based on these parameters in a generalized manner rather than on a personalized basis. Personalized recommendations, information relevancy, choosing the proper course, and recommendation accuracy are some of the issues in eLearning recommendation systems. In this paper, existing conventional eLearning and course recommendation systems are studied in detail and compared with the proposed approach. We have used, the dataset of USER Profile and USER Rating for a recommendation of the course. K Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Nave Bayes, Linear Regression, Linear Discriminant Analysis, and Neural Network were among the Machine Learning techniques explored and deployed. The accuracy achieved for all these algorithms ranges from 0. 81 to 0. 97. The proposed algorithm uses a hybrid approach by combining collaborative filtering and deep learning. We have improved accuracy to 0. 98 which indicate that the proposed model can provide personalized and accurate eLearning recommendation for the individual USER.