Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications

Abstract

The design of mobility-aware framework for edge/fog computing for IoT systems with back-end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2-D space, typically over the road-network. The framework analyses the spatio-temporal mobility data (GPS logs) along with the other contextual information and deploys machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real-time. The accumulated spatio-temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT-Edge-Fog-Cloud architecture to provide better QoS in real-time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and energy consumption. The performance evaluations yield that the proposed mobility prediction algorithm has approximately 93% accuracy and reduced the delay and power by approximately 23-26% and 37-41% respectively than compared to the existing mobility-aware task delegation system.

Designing an efficient clustering strategy for combined Fog-to-Cloud scenarios

“…A different example of the clustering problem in distributed computing paradigms can be found in [20]. That work proposes a framework in a 4-layers environment (i.e., from down to top: IoT, edge, fog and cloud), which, by analyzing spatio-temporal mobility data and other contextual information, and by means of a machine learning algorithm, the location of IoT and edge devices that are on the move can be predicted.…”

“…Focusing on the main objective of the FCC problem (i.e., reducing the average distance to management functions), in view of equation (2), it is clear that, depending on the distance to cloud (d ic ) and between edge devices (d ij ), which in turn determine the cost of cloud connections ( ) and fog connections ( ), respectively, as well as on the weights α and β, the number of clusters (which is not known in advance) will vary. To evaluate the impact of these parameters, equation (19) shows the average weighted cost of a connection from a node to cloud, , while equation (20) shows the average weighted cost of a (fog) connection between two nodes as:…”

“…Thirdly, all nodes marked as unassigned in the previous two steps are assigned to the closest cluster leader which is reachable and has enough capacity to serve it. If the whole procedure is successful (i.e., all nodes are connected to cloud directly or via a cluster leader), the procedure returns the set of leaders, otherwise it returns an empty set (lines [17][18][19][20].…”

An Innovative Industry 4.0 Cloud Data Transfer Method for an Automated Waste Collection System

“…A huge amount of data are available in manufacturing systems, waste management systems, etc., but the data and information should be properly collected, encoded and transmitted to the cloud application in order to be processed and to allow a real-time monitoring of equipment and systems. The major challenges are represented by: data acquisition from smart sensors, actuators and PLCs; data conversion; data security and privacy; data encoding, encryption and decryption [7][8][9][10].Software architectures of the big data processing platforms are analyzed in the literature, including solutions for job scheduling for big data applications [11], solutions which offer a flexible co-programming architecture able to support the life cycle of time-critical cloud native applications [12], mobility-driven cloud-fog-edge collaborative real-time framework solution, which has IoT, Edge, Fog and Cloud layers and which exploits the mobility dynamics of the moving agent [13].Several data encoding and encryption solutions for cloud applications are presented in the literature [14][15][16][17][18][19][20][21][22], but without including detailed case studies to present identified problems, propose solutions, and implement results.The main problem is the identification of the right solution that will ensure the data transmission and processing in a secure way, quickly, efficiently and with low costs. The proposed solution presented in Chapter 2 can be used both in smart factories and for smart city applications including automated waste collection systems.Waste management comprises prevention, collection (metal, paper, cardboard, aluminum cans plastic), removal, recycling, reuse, and safe disposal of toxic and dangerous waste.…”

“…Software architectures of the big data processing platforms are analyzed in the literature, including solutions for job scheduling for big data applications [11], solutions which offer a flexible co-programming architecture able to support the life cycle of time-critical cloud native applications [12], mobility-driven cloud-fog-edge collaborative real-time framework solution, which has IoT, Edge, Fog and Cloud layers and which exploits the mobility dynamics of the moving agent [13].…”