This paper proposes a hybrid IoT-blockchain architecture designed to ensure the security and value enhancement of flue gas desulfurization (FGD) gypsum throughout its entire lifecycle. At the edge, sensor data is encrypted using AES-256, with RSA-2048 handling key exchange, achieving a hybrid encryption overhead of 1.84 milliseconds per kilobyte. A permissioned Proof-of-Authority consensus mechanism delivers$\text{1, 7 0 0}$transactions per second with a confirmation time of just 0.59 seconds. An immutable ledger records purity, moisture, volume, and origin data; smart contracts automatically execute compliance checks and quality balance reconciliations. During a$\text{1 2}$-month field deployment at a 1.2-million-ton coal-fired power plant, the system reduced unauthorized access attempts by 94.7%, lowered transportation quality disputes by 80%, and improved downstream price stability by 18%. Scalable to 145,000 daily records, the system supports sub-second queries for$\text{8 5 {\%}}$of calls and achieves post-quantum security through zero-knowledge proof integration. This framework transforms industrial byproduct tracking into a verifiable, real-time asset valuation tool.
Abdelrahman E. E. Eltoukhy, Mohamed Hussein, Min Xu, Felix T.S. Chan
In modular integrated construction (MiC), the resource allocation problem (RAP) adopted by construction sites and vehicle routing problem (VRP) adopted by logistics companies are highly interdependent. However, this interdependence has been overlooked in the literature. Thus, the plans determined by each problem cannot be achieved practically. Moreover, there is no existing VRP model suitable for the MiC application. This study aims to propose a VRP model suitable for MiC, investigate the interdependence between the RAP and proposed VRP model, and develop a blockchain system to secure information sharing between the RAP and VRP. The first two objectives are achieved by developing a coordinated system formulated as a leader-follower Stackelberg game model (LFSGM). This model is presented as a bi-level optimisation model and solved using a nested ant colony optimisation-based algorithm. The effectiveness of the LFSGM is validated using a real case study. The results show that using the traditional approach (i.e. a separate RAP and proposed VRP) succeeds in providing a plan for the construction sites but not for the logistics company. In contrast, the LFSGM offers applicable plans for both the construction sites and logistics company. Lastly, some managerial implications are identified and discussed.
Lili Zhang, Wenhao Guo, Wenwen Yang, Di Su · 5 authors
As a decentralized and distrusted distributed ledger technology, blockchain is gradually applied in the IOT. Cost overrun are inherent part of most smart “IOT+ blockchain” projects. In order to guarantee a successful delivery of a smart “IOT+ blockchain” project with the ideal budget, with respect to the minimum cost of the forward problem is still higher than the approved budget, this research proposes a re-verse optimization method of 0-1 mixed-integer, bi-level programming model for reverse-inferring duration and personnel re-assignment. Based on a numerical experiment to a “IOT+ blockchain” construction project, the comparative results show that the reverse optimization method is superior to the forward method in terms of total cost reduction and can further shorten the duration. The result indicates that the reverse optimization methodology can be applied in scenarios which need to guarantee the objective value achieved through the proposed reverse modelling methodology by optimizing parameters and decision variables.
We study a two-level system having N local systems in the lower level subordinate to a central system in the higher one, such that both central and local systems have decision-making units. The central system is a coordinating agency and the local ones are semi-autonomous operating devisions. The basic principle of planning for this organization is that the central system allocates resources so as to optimize its own objective, while the local ones optimize their own objectives using the given resources. A local objective function, fn, is a function of the lower level decision variable vector x=(x1,・・・, xN) and the higher level one a=(a1,・・・, aN), where an is a resource vector allocated to the local system n. Since the functions ■ are mutually independent, the lower level composes a multi-objective system, in which the lower level decision-makers minimize a vector objective function f =(f1,・・・,fN) with respect to x in cooperation with each other. Thus, the lower level generates a set of noninferior (i.e. Pareto optimal) solutions ■(a) being parametric with respect to a. The central decision-maker, then, chooses the optimal resource allocation a⁰ and the best noninferior solution ■⁰ corresponding to a⁰ from among a set of ■(a). The above problem becomes a decentralized two-level optimization, when the local system contains only its own variables (xn, an). Several theorems and iterative algorithms for the formulated problems are obtained by use of mathematical programming techniques.