Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things

Abstract

The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) increases the efficiency of communication networks due to its low costs and simple management. However, it has been demonstrated that many systems still need an intelligent strategy for green computing. Establishing reliable connectivity in Green-IoT (G-IoT) networks is another key research challenge. With the integration of edge computing, this study provides a Sustainable Data-driven Secured optimization model (SDS-GIoT) that uses dynamic programming to provide enhanced learning capabilities. First, the proposed approach examines multi-variable functions and delivers graph-based link predictions to locate the optimal nodes for edge networks. Moreover, it identifies a sub-path in multistage to continue data transfer if a route is unavailable due to certain communication circumstances. Second, while applying security, edge computing provides offloading services that lower the amount of processing power needed for low-constraint nodes. Finally, the SDS-GIoT model is verified with various experiments, and the performance results demonstrate its significance for a sustainable environment against existing solutions.

Research Trends in the Use of the Internet of Things in Sustainability Practices: A Systematic Review

“…These models can use data to predict and monitor various factors. Recent research has focused on using this technology to develop energy-efficient networks [2,66] or optimize data-based networks for ecological IoT use [67], among other applications. The second quadrant groups less common but emerging terms in the field of study.…”

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

“…∇𝒲 (1) = ∑𝜀 (𝑙+1) ⊙ (𝑓(𝑧 (𝑙) )) 𝑇 (∇𝐵 𝑡∈ℐ(𝑇 ℐ ) , ∇𝑈 𝑡∈ℒ(𝑇 ℐ ) ) = 𝐻𝑇 − 𝐵𝑃(∇𝒲 (1) ) ∇𝑏 = 𝜀 (𝑙+1) ; ∇𝜃 = {∇𝑏, ∇𝐵 𝑡∈ℐ(𝑇 ℐ ) , ∇𝑈 𝑡∈ℒ(𝑇 ℐ ) } End…”

“…Input: l, 𝜃,𝑧 (𝑙) ,𝜀 (𝑙+1) Output:∇𝜃, 𝑧 (𝑙) If layer (l)=convolutional then 𝜀 (𝑙) = (𝒲 (1) ) 𝑇 * 𝜀 (𝑙+1) ⋅ 𝑓 ′ (𝑧 (𝑙+1) )…”

“…IoT has changed many aspects of modern life, including business as well as customer service, based on the ubiquitous sensing as well as computing capabilities of mobile devices like smartphones, laptops, and tablets [1]. In order to gain insights from the data produced by these mobile devices, artificial intelligence (AI) fields like machine learning (ML) is extensively utilized in process of training data methods for intelligent applications.…”