Teaching Learning Based Optimization (TLBO) for energy efficiency in Fog Computing

Fog Computing is eminent to ensure quality of service in handling huge volume and variety of data and to display output, or for closed loop process control. It comprises of fog devices to manage huge data transmission but results in high energy consumption, end-to end-delay, latency. In this paper, an energy model for fog computing environment has been proposed and implemented based on teacher student learning model called Teaching Learning Based Optimization (TLBO) to improve the responsiveness of the fog network in terms of energy optimization. The results show the effectiveness of TLBO in choosing the shortest path with least energy consumption.

Performance Evaluation of Broadcast Domain on the Lightweight Multi-Fog Blockchain Platform for a LoRa-Based Internet of Things Network

The Internet of Things (IoT) is a technology that allows every object or item to become part of the Internet and interact with each other. One of the technologies based on the IoT is Long Range (LoRa). Apart from the increasing number of IoT services, security aspects become a separate issue in the development of the IoT. One of the solutions is to utilize blockchain technology in the IoT topology to secure the data and transactions that occur in the IoT network. The blockchain can take minutes to compute a cryptographic chain. It also needs sufficient computing resources. This problem gave rise to the idea of establishing a lightweight blockchain platform with low latency that could run on devices with low computing resources as well as IoT devices. We offered a technology called Lightweight Multi-Fog (LMF) in our previous publication that is implemented using the Lightweight Scalable Blockchain (LSB) algorithm and the fog network on the IoT to solve the problem of integrating a blockchain with the IoT. In this paper, we simulate how the broadcast domain on LMF works and verify the results in lower latency and energy transmission compared to the standard blockchain model. The results showed that the average increase of the total delivery time (Taverage) on the LMF platform was smaller than the average increase of the total delivery time (Taverage), which was 0.53% for the variations in the number of nodes and 0.27% for the variations in the number of brokers/miners. Regarding the average increase of the total energy delivery (Eaverage), the Proof of Work (PoW) platform has a smaller increase of the total energy delivery (Eaverage), which is 1.68% during the variations in the number of nodes. In contrast, the LMF platform has a smaller average increase of the total shipping energy (Eaverage), which is 0.28% for the variations in the number of brokers/miners.