Fog computing providers have started providing services in closer proximity to the users by leasing the unused computational resources of users' devices. The leased devices are increasingly used for many time-sensitive and IoT applications. Despite the advantages of Fog, due to the highly mobile and dynamic nature of Fog devices, the resources or devices may fail at any time or may not always be available for the processing of the applications, which leads to degradation of service quality and an increase in application processing time. Hence, effective selection of the resources in the Fog computing environment by considering mobility, heterogeneity, and failure of devices is a complex task. Traditional resource selection techniques may not be applied directly in Fog computing environments due to their dynamic and unique resource characteristics. Therefore, this article proposes a Markov chain-based resource selection model to improve the quality of service by minimizing disruptions and managing device failures. The effectiveness of the proposed algorithm is evaluated using simulations, which take failure traces, current resource usage, and mobility as input from a dataset archive. Our results demonstrate the effectiveness of the proposed algorithm in terms of average disruption rates, average latency, and average overutilization. Our analysis shows significant improvements in average latency reduction of approximately 11.83%, and an average overutilization improvement by 16.17%.

Fog computing is an increasingly popular method with which to process the huge amount of data generated by the Internet of Things (IoT) devices and applications at the edge-level, using the heterogeneous autonomous end-devices of the participating users. To meet the requirements of the IoT and time-sensitive applications, a Fog computing platform needs to select appropriate resources, the availability of which can be guaranteed during the execution of the application. For the proper selection of resources, the platform must be able to predict future availability. Hence, a proper resource availability model which provides knowledge about the future availability of resources in the Fog computing environment is required. However, designing an efficient resource availability model, in a highly distributed and mobile environment like the Fog, is a complex task due to the multidimensional characteristics of Fog devices, such as mobility, lack of centralised control, limited resources, and being battery powered. Existing resource availability models did not consider all the characteristics of a real Fog environment. Therefore, this study aims to provide a generic continuous-time Markov chain (CTMC), based resource availability model for Fog computing environments. The applicability of the model is shown by integrating the model input with the nearest-location best fit (NLBF) and Best-Fit resource selection policies.

The paper considers the detection of beetle infestations in North American pine forests using high temporal resolution, coarse spatial resolution MODIS remotely sensed satellite images. Two methods are proposed to detect beetle infestation, both applying a triply modulated cosine model. The first method uses an Extended Kalman Filter (EKF) for estimating model parameters, and the second a Least Squares estimator. When beetles infest a forest, the changes in the affect large geographical area. Therefore, the change detection metrics are based on the time series of each pixel, and do not utilize information from neighboring pixels. Using data from the Rocky Mountain region of the United States and of British Columbia in Canada, we show that our methods are highly effective at detecting beetle infestations.