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.

Background and Aim: The Fog Computing is a highly virtualized platform that provides storage, computing and networking services between the Cloud data centers and end devices. Fog computing fits the characteristics of real-time health monitoring systems. In such systems, a large amount of data is acquired from a multitude of biological and environmental sensors. On the other hand, its distribution and open structure makes it vulnerable and weak to security threats. Therefore, the aim of this paper was to identify the security challenges in healthcare. Materials and Methods: This applied research has been done in three phases using mixed-method approach in 2019. In the first phase, security codes from library resources by content analysis was identified. In the second phase interpretation of experts by Delphi method, Panel of IT experts consists of twelve members who work on healthcare sector was evaluated. Finally, we used Analytic Hierarchy Process(AHP) method for ranking security codes. Results: According to fuzzy AHP results, attacks(0. 31), secure communications (0. 23), authentication and access control(0. 19), trust(0. 15) and privacy preservation (0. 12) are the most important issues in security challenges of fog computing. Conclusion: According to the results of this study, secure communications and network attacks are the major challenges in fog computing, because fog nodes are usually deployed in some places with relatively weak protection. They may encounter various malicious attacks. As a result, policymakers should be aware of the role of secure communications and network attacks in fog computing implementation.

Cloud computing and fog computing are deployed as computing storage and services for the end-users. Fog computing promotes task performance through storage, computing, and networking services. Instead of taking place in centralized cloud computing data centers, these services can be provided via near-edge devices. Efficient load balancing in distributed computing systems has been the main challenge. The load balancing algorithm has an important role in enhancing the Quality of Service (QoS), throughput, and resource utilization and diminishing the potential cost and its strategy and architecture completely depend on the centralized or distributed architecture of the system and the type of requests. Cloud computing and fog computing use centralized and distributed architectures, respectively. The load balancing algorithm in these two environments cannot be the same. Meanwhile, the demand for near real-time processing requests is drastically increasing; load balancing should be able to handle real-time requests. This paper reviews and investigates the modern and diverse load balancing aspects of fog and cloud computing systems. We also categorize the load balancing algorithms in cloud and fog computing: meta-heuristic algorithms, heuristic algorithms, learning algorithms, and customized algorithms. We propose different research classes about the algorithm's type, objectives, simulation tools, and so forth. This review demonstrates that the most prevalent categories of methods used in load balancing in fog and cloud computing are custom approaches and meta-heuristic algorithms, respectively. While the most renowned load balancing algorithms have not yet succeeded in fog environments, meta-heuristic algorithms have shown their competence in cloud environments impeccably.