We present a blockchain-based traceability framework for pharmaceutical supply chains, equipped with an automated arbitration mechanism. The framework integrates four core components: (i) a blockchain infrastructure that guarantees immutability, transparency and public verifiability of critical supply chain metadata; (ii) a publicly accessible Cryptographically Controlled Append-only Filesystem that enforces cryptographic access control to ensure secure and auditable raw data storage; (iii) a Secure Leader Election protocol that reliably selects an arbitration coordinator under adversarial conditions; and (iv) an automated arbitration protocol that resolves disputes by evaluating evidence drawn from both the blockchain and the publicly accessible filesystem. We formally define the security properties required for the arbitration protocol. Our integrated design establishes a robust foundation for trustworthy, efficient and autonomous governance in pharmaceutical supply chains.

In order to solve the problem that high-priority tasks can not be processed timely and reliably due to the disorder of multi-task and dynamicity in Power Internet of Things(PIoTs), a high-reliability small-area task offloading mechanism with trust evaluation and fuzzy logic(HRSATF) is proposed. First, considering task priority, preemptive priority queue is introduced to ensure high-priority tasks processed preferentially, and minimum resource allocation coefficients(MRACs) of tasks are solved to ensure the effectiveness of offloading. Second, the trust model between smart device(SD) and edge server(ES) is established, and ESs are divided into three priorities based on trust value and computing power by fast non-dominated sorting. Thirdly, fuzzy logic is applied to select target ES when the priorities of task and ES do not match or the ES is offline, and MRAC is used to schedule tasks between SD and ES. Finally, NSGA2 is modified (MNSGA2) to verify the effectiveness of HRSATF in terms of success rate, time, power consumption and load balancing, where success rate is increased by$102.3\%$, and time and power consumption are decreased by$90.7\%$,$89.3\%$at most, respectively.

Smart firefighting utilizes advanced information technologies to enhance disaster prevention and emergency response capabilities. The deployment of Service Function Chains (SFCs) in smart firefighting involves orchestrating a series of Virtual Network Functions (VNFs) as software instances on edge servers, enabling flexible and efficient service provisioning for firefighting applications. Security-enhancing VNFs, such as data encryption modules and firewalls, are deployed to strengthen the security of the smart firefighting system. A significant challenge in smart firefighting SFCs is ensuring strict low-delay and reliability constraints while optimizing energy efficiency. Traditional sequential SFCs introduce substantial end-to-end delays, rendering them unsuitable for delay-sensitive applications. Parallelized SFC addresses this issue by multiple independent VNFs in an SFC to run in parallel. In this paper, the deployment of parallelized SFCs for smart firefighting is formulated as an Integer Linear Programming (ILP) model. Since the NP-hard nature of the ILP model, we proposed a heuristic scheme named EADRC to minimize energy consumption while satisfying reliability and end-to-end delay constraints. Extensive simulations demonstrate the effectiveness of EADRC, achieving significant reductions in energy consumption, as well as improved SFC acceptance ratio and reliability compared to baseline approaches.

Healthcare systems are increasingly adopting IoT-enabled solutions, from wearables to remote surgery platforms; thus, we can expect enormous cybersecurity and data protection risks. In this paper, we propose a penetration testing framework for healthcare IoT environments to assess a wide range of vulnerabilities from communication, device control, and data integrity perspectives. Abstract State Machine (ASM) modelling is used by the framework to evaluate real-world attack scenarios, and this helps improve security and compliance against HIPAA and GDPR. Gives tangible methods to improve patient safety while protecting confidential health information.

Abstract With more and more interconnected smart devices (ISDs) accessing the Internet of Things (IoT), massive and diverse tasks need to be transformed and computed. Mobile edge computing enables the offloading of tasks to nearby servers to enhance processing efficiency, which makes ISDs idle, causing resource waste and failing to satisfy the high real‐time requirements of tasks. Besides, when tasks with different priorities are processed in the order they are generated, it will be difficult for IoT to guarantee a timely response to high‐priority tasks. To address the aforementioned issues, we establish an edge‐terminal‐local architecture by software‐defined networking to centrally manage idle ISD resource (2ISDR). Then the proposed two‐step scheduling mechanism with preemptive priority queue ensures the real‐time responses to high‐priority tasks, and the minimum resource allocation coefficients make offloading effective. Finally, we also propose a modified NSGA‐III algorithm named MNSGA‐III, which is designed to make decisions about offloading and solve resource allocation for tasks, and we correct infeasible solutions by a two‐step correction function to ensure the feasibility of MNSGA‐III. Experimental results show that the method can ensure a timely response to high‐priority tasks and optimize processing time, energy consumption, and economic cost through the utilization of 2ISDR.