Efficient distribution of service requests between fog and cloud nodes considering USER mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of USERs, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of USERs and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile USER leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

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.

Introduction: Choosing the right type of device is important to support USERs of Health Information Systems. In addition to cost, durability and simplicity, staffing levels and hospital workflows affect in the selected. The purpose of this study was to identify the preferences of USERs in device selection.Methods: This applied research with qualitative method, Focus group, was conducted in 2014. Population research are USERs of HIS. They have been divided into three distinct groups based on their computer skills and medical knowledge. The sixteen USERs, were invited to three focus group meetings and have been asked to explain their criteria for selection of the device. Questionnaires were also used in order to compare the results of discussions quantitatively. The reliability and validity of the questionnaire were confirmed by Cronbach’s alpha (0.86). The analysis was performed using descriptive statistics.Results: The mean response of participants shows that end-USERs with high level of medical knowledge and computer skills prefer to use tablets (3.75) and then PCs (3.67). Medical staff without computer skills selected tablets (4), workstation on wheels (WOW) (2.5), wall mounted workstation (2.5) and PCs (2.25). Ultimately members with skills in using the computer and lack of medical knowledge, selected PCs (3.37), tablets (3.29), Personal Digital Assistant (PDA) (3.17), wall mounted workstation (4.2) and WOW (2.67).Conclusion: This study showed that the best device for the medical staff is Tablet and PC is the best device for administrative staff.

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.