A Systematic Literature Review of Lightweight Blockchain for IoT

Abstract

The Internet of Things (IoT) has become an essential part of our society. IoT devices are used in our houses, hospitals, cars, industry, etc., making our lives easier. Nonetheless, there are a number of serious concerns about security, privacy and performance issues in IoT. It has been proven that the aforementioned issues are strictly related to the high degree of centralisation of current IoT architecture. Thus, there is an increasing interest in adopting blockchain in IoT. However, blockchain adoption is not straightforward due to the power, storage and computational limitations of IoT. Consequently, the concept of lightweight blockchain is getting more and more attention from researchers and engineers. In this paper, we conduct a systematic literature review on the lightweight blockchain concept for IoT following the PRISMA methodology. We systematically analyse "lightweight blockchain for IoT" proposals in order to better understand the limitations of blockchain for IoT, the characteristics of the current work on this topic and further research opportunities. Specifically, we analyse the definition of lightweight blockchain that other authors give, the characteristics of the reviewed proposals, their "lightweight" aspects and their evaluation. Finally, we discuss the results of the review along with further research opportunities. Consequently, this work is mostly focused on understanding the technical and performance-related aspects of blockchain for IoT as a prelude to more specific analysis such as security (i.e., attacks, vulnerabilities, etc.).

Quantifying IoT Security Parameters: An Assessment Framework

“…Only a few solutions applied metrics to the transport/network layer (IV-B3)(IV-B3)(IV-B3) [39], [47]. Most solutions focused on the other two layers, perception and application [2], [20], [22], [24], [27], [41], [42], [48], [49], [50].…”

“…Because most solutions use metrics working at the IoT device and application layers, the attack types that the above metrics can detect are common, including DoS/DDoS, botnet, man-in-the-middle (MITM), replay, masquerade, home invasions, trespass, unintentional damage or loss, disasters and outages, failures or malfunctions, unsecured wireless network problems, side-channel, identity theft, physical, advanced persistent threats (APT), weak passwords, and software-level attacks including code injection, function creep, and buffer overflow 8 [2], [4], [19], [20], [27], [39], [40], [42], [47], [48], [51]. Only a few studies aim to detect particular types, such as brute-force, TCP and UDP port scans [22], and RPL attacks [49].…”

Multi-State Merkle Patricia Trie (MSMPT): High-Performance Data Structures for Multi-Query Processing Based on Lightweight Blockchain

“…An experiment demonstrated, for instance, that a lightweight blockchainbased network could accommodate up to 1.34 million authentication and verification processes per second, which is sufficient for use in IoT networks with limited resources, especially in the healthcare industry [34]. To better understand the research opportunities and restrictions, the characteristics of current research and the limitations of blockchain for IoT on lightweight blockchain for IoT proposals must be rigorously studied [35].…”

Secure data access using blockchain technology through IoT cloud and fabric environment

“…To deal with unauthorized access and threats, additional security measures must be added to the hashing process. 3 However, as more devices are added, monitoring collaboration among them becomes easier, but scalability issues remain unaddressed. 4 Therefore, before integrating them into the working environment, it is imperative to carefully analyze various existing protocols and select an appropriate one.…”

“…To successfully advance the growth, it is also crucial to monitor air traffic factors when working with equipment like drones. To deal with unauthorized access and threats, additional security measures must be added to the hashing process 3 . However, as more devices are added, monitoring collaboration among them becomes easier, but scalability issues remain unaddressed 4 .…”