This article is to propose a consensus algorithm, called Proof of Chance (PoCh), which is designed for the industrial Internet of Things (IIoT). The PoCh protocol is designed to be scalable and extensible, with a controllable conformance delay and low hardware and computation requirements. To reach a consensus, PoCh uses chance rather than computing power: “if condition $_{1}$ , I am a candidate; if condition $_{2}$ , I am the miner.” During every consensus iteration, the condition $_{1}$ is updated, and a single miner is chosen using condition $_{2}$ . Those conditions are randomized without the node generating any value and without assigning any weight to such value. The fault tolerance of PoCh is $5f/3 + 1$ , meaning that PoCh can successfully achieve consensus as long as more than 40% of nodes are functioning properly, compared to 50% in the Proof of Stake (PoS) protocol.

Several trusted tasks use consensus algorithms to solve agreement challenges. Usually, consensus agreements are used to ensure data integrity and reliability in untrusted environments. In many distributed networking fields, the Proof of Work (PoW) consensus algorithm is commonly used. However, the standard PoW mechanism has two main limitations, where the first is the high power consumption and the second is the 51% attack vulnerability. In this paper, we look to improve the PoW consensus protocol by introducing several proof rounds. Any given consensus node should resolve the game of the current round $Roun{d}_i$ before participating in the next round $Roun{d}_{i+1}$. Any node that resolves the game of $Roun{d}_i$ can only pass to the next round if a predetermined number of solutions has been found by other nodes. The obtained evaluation results of this technique show significant improvements in terms of energy consumption and robustness against the 51% and Sybil attacks. By fixing the number of processes, we obtained an energy gain rate of 15.63% with five rounds and a gain rate of 19.91% with ten rounds.

The rapid proliferation of internet of things (IoT) devices has ushered in a new era of technological development. However, this growth has also exposed these devices to various cybersecurity risks, including command and control (C&C) attacks. C&C attacks involve unauthorized entities taking control of IoT devices to carry out malicious activities. Traditional cybersecurity measures often fall short in addressing these evolving threats. To enhance IoT security and counter C&C threats, this study explores the potential of supervised learning, a subfield of machine learning. Supervised learning, a method that utilizes past data to train machine learning models capable of independently identifying patterns indicative of C&C threats in real time, offers additional protection to IoT networks. This article delves into the advantages and drawbacks of this approach, considering factors such as the need for well-defined labeled datasets, resource constraints of IoT devices, and ethical considerations surrounding data security.

In electronic healthcare, patient medical imaging data is critical for remote diagnostic procedures. The increasing demand to harness the potential of these medical images necessitates their secure sharing among various entities, including hospitals, medical institutions, and insurance companies. However, third-party access and possible manipulation make it challenging to maintain the ownership and integrity of this data. This study introduces a novel approach that combines compression, digital watermarking, symmetric encryption, and blockchain technology to protect medical images from unauthorized third-party interventions. Using the Discrete Wavelet Transform, our proposed technique embeds a compressed watermark into the host image. Specifically, the watermark is encoded into vectors and inserted into the second-level approximation, i.e., the Low-Low of the image using the Least Significant Bit, producing a watermarked image. The watermarked data is encrypted and stored on a blockchain to further safeguard these images’ integrity. This multi-layer security framework not only preserves the integrity and confidentiality of the data but also facilitates transparent and secure sharing among stakeholders. The proposed method achieves a peak signal-to-noise ratio of 63.24dB and a structural similarity index of 1. These results demonstrate the robustness of our solution in protecting and exchanging medical images within the digital healthcare ecosystem, positioning it as an advanced and reliable option for secure data management.