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Ensuring the reliability and trustworthiness of massive IoT-generated data processed in cloud-based systems is paramount for data integrity in IoT-Cloud platforms. The integration of Blockchain (BC) technology, particularly through BC-assisted data Edge Verification combined with a consensus system, utilizes BC's decentralized and immutable nature to secure data at the IoT network's edge. BC has garnered attention across diverse domains like smart agriculture, intellectual property, and finance, where its security features complement technologies such as SDN, AI, and IoT. The choice of a consensus algorithm in BC plays a crucial role and significantly impacts the overall effectiveness of BC solutions, with considerations including PBFT, PoW, PoS, and Ripple in recent years. In this study, I developed a Football Game Algorithm with Deep learning-based Data Edge Verification with a Consensus Approach (FGADL-DEVCA) for BC assisted IoT-cloud platforms. The major drive of the FGADL-DEVCA algorithm was to incorporate BC technology to enable security in the IoT cloud environment, and the DL model could be applied for fault detection efficiently. In the FGADL-DEVCA technique, the IoT devices encompassed considerable decentralized decision-making abilities for reaching an agreement based on the performance of the intrablock transactions. Besides, the FGADL-DEVCA technique exploited deep autoencoder (DAE) for the recognition and classification of faults in the IoT-cloud platform. To boost the fault detection performance of the DAE approach, the FGADL-DEVCA technique applied FGA-based hyperparameter tuning. The experimental result analysis of the FGADL-DEVCA technique was performed concerning distinct metrics. The experimental values demonstrated the betterment of the FGADL-DEVCA approach with other existing methods concerning various aspects.

Ensuring the reliability and trustworthiness of massive IoT-generated data processed in cloud-based systems is paramount for data integrity in IoT-Cloud platforms. The integration of Blockchain (BC) technology, particularly through BC-assisted data Edge Verification combined with a consensus system, utilizes BC's decentralized and immutable nature to secure data at the IoT network's edge. BC has garnered attention across diverse domains like smart agriculture, intellectual property, and finance, where its security features complement technologies such as SDN, AI, and IoT. The choice of a consensus algorithm in BC plays a crucial role and significantly impacts the overall effectiveness of BC solutions, with considerations including PBFT, PoW, PoS, and Ripple in recent years. In this study, I developed a Football Game Algorithm with Deep learning-based Data Edge Verification with a Consensus Approach (FGADL-DEVCA) for BC assisted IoT-cloud platforms. The major drive of the FGADL-DEVCA algorithm was to incorporate BC technology to enable security in the IoT cloud environment, and the DL model could be applied for fault detection efficiently. In the FGADL-DEVCA technique, the IoT devices encompassed considerable decentralized decisionmaking abilities for reaching an agreement based on the performance of the intrablock transactions. Besides, the FGADL-DEVCA technique exploited deep autoencoder (DAE) for the recognition and classification of faults in the IoT-cloud platform. To boost the fault detection performance of the DAE approach, the FGADL-DEVCA technique applied FGA-based hyperparameter tuning. The experimental result analysis of the FGADL-DEVCA technique was performed concerning distinct metrics. The experimental values demonstrated the betterment of the FGADL-DEVCA approach with other existing methods concerning various aspects. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with time windows (VRPTW) is a famous problem in logistics and transportation that has many applications in real life. The objective of the problem is to find the minimum distance covered by a set of vehicles that set to start together from the warehouse and visit some prefixed customers within certain time frames. In this paper, an efficient optimization algorithm, called modified football game algorithm (MFGA), is proposed to solve the VRPTW as an NP-hard problem. MFGA imitates the behavior of football players during a game to find the best positions to score a goal under the supervision of a team coach. In the presented algorithm, a new method is presented to create an initial solution for the VRPTW problem. In addition, a more efficient method has been designed to create a new location for the player, which makes the algorithm more efficient. The performance of the proposed MFGA is validated against Solomon’s VRPTW benchmark instances. Experimental results confirm that MFGA produces competitive results compared to several state-of-the-art algorithms in terms of various solution quality indicators. The proposed algorithm obtains 17 best-known solutions (BKSs) and improves 12 BKSs in the literature.

The Capacitated Vehicle Routing Problem (CVRP) plays an important role in the logistics transportation sector. Determining the proper route will reduce the company's operational costs. In CVRP, a number of vehicles have a capacity limit that can serve all customers. This research completes a real case study on a bottled drinking water company where the company still uses the subjective method of the driver to determine the transportation route. Based on the conditions in the company, the selection of the best route will consider vehicle capacity and demand to determine the shortest route. The execution of this case study uses the Football Game Algorithm (FGA) which was first initiated by Fadakar Ebrahimi which proved promising and had the strongest performance in all cases. FGA is expected to be able to determine the shortest distribution route from the existing cases to reduce the distribution costs incurred. This study takes data from 4 days of delivery that served 78 customers. The average daily transportation cost savings result is 42%. This amount indicates that the FGA algorithm is effective for completing a real case study in CVRP.

The vehicle routing problem with time windows (VRPTW) is a famous problem in logistics and transportation that has many applications in real life. The objective of the problem is to find the minimum distance covered by a set of vehicles that set to start together from the warehouse and visit some prefixed customers within certain time frames. In this paper, an efficient optimization algorithm, called modified football game algorithm (MFGA), is proposed to solve the VRPTW as an NP-hard problem. MFGA imitates the behavior of football players during a game to find the best positions to score a goal under the supervision of a team coach. In the presented algorithm, a new method is presented to create an initial solution for the VRPTW problem. In addition, a more efficient method has been designed to create a new location for the player, which makes the algorithm more efficient. The performance of the proposed MFGA is validated against Solomon's VRPTW benchmark instances. Experimental results confirm that MFGA produces competitive results compared to several state-of-the-art algorithms in terms of various solution quality indicators. The proposed algorithm obtains 17 best-known solutions (BKSs) and improves 12 BKSs in the literature. [ABSTRACT FROM AUTHOR]