A Decentralized Resource Allocation in Edge Computing for Secure IoT Environments

Abstract

The expansion of Internet of Things (IoT) devices and their integration into a variety of vital sectors has created serious concerns regarding data protection, privacy, and resource management. As a promising model, edge computing has the ability to overcome these difficulties by putting the computing power closer to IoT devices. This article presents a novel approach for decentralized resource allocation in edge computing settings, with the goal of improving the security and efficiency of IoT systems. Edge nodes are critical in our proposed framework for managing and assigning computing resources to IoT devices, minimizing latency, and optimizing network traffic. The decentralization of resource distribution promotes resilience in the event of network outages or cyberattacks and provides robustness against single points of failure. We created a proof-of-importance (PoI) consensus mechanism for creating new blocks in the blockchain integrated edge-computing IoT devices. Therefore, the consensus mechanism will ensure the trust and security of IoT devices by authentication of each user in the network. We performed a series of experiments in a simulated edge-computing setting to assess the feasibility of our proposed method. We analyze the proposed system model based on the operation of three different file delivery and transactions. The simulation outcomes show that the blockchain system efficiently delivers the files and increases the transmission rate. We also compared our file delivery and transmission rate with existing techniques, and our proposed model provides a better result. Finally, we compared the power consumption of creating IoT nodes based on proof-of-work (PoW), proof-of-stake (PoS), and PoI. The proposed PoI consensus mechanism consumes less power than the other two methods.

IBMESR: Towards Next-Generation Big Data Security with An Integrated Blockchain Model for Efficient, Scalable, and Robust Operations

“…The delay required for serving cloud requests without attacks is a critical performance metric in the evaluation of security models for big data environments. In the provided data, we have compared four different models: STFM [5], DLBI [12], PPQCM [18], and the proposed model MAQSPBT. The delay values (D) are measured in milliseconds (ms) for different request loads, ranging from 45,000 to 450,000 requests.…”

“…Jitter obtained for serving requests under attacks is a critical metric for assessing the stability and predictability of performance in security models when faced with adversarial conditions. In this analysis, we compare four different models: STFM [5], DLBI [12], PPQCM [18], and the proposed model MAQSPBT. Jitter values (J) are measured in milliseconds (ms) for various request loads, ranging from 45,000 to 450,000 requests, with approximately 10% of these requests simulated as attack requests.…”

“…This is done via use of Blockchain-Based Privacy-Aware Contextual Online Learning (BPACOL) operations. Data integrity, trust among interacting parties, and the availability of services are just a few facets that require undivided attention from a security standpoint under different use cases [10,11,12].…”