Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance complexity, and some security related issues. These limitations prevent its adoption in mainstream applications. Artificial Intelligence (AI) can help addressing some of these limitations. This survey provides a detailed overview of the different blockchain AI-based optimization and improvement approaches, tools and methodologies proposed to meet the needs of existing systems and applications with their benefits and drawbacks. Afterward, the focus is on suggesting AI-based directions where to address some of the fundamental limitations of blockchain.

Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.

Fog Computing extends Cloud computing capabilities by providing computational resources closer to end users. Fog Computing has gained considerable popularity in various domains such as drones, autonomous vehicles, and smart cities. In this context, the careful selection of suitable Fog resources and the optimal assignment of services to these resources (the service placement problem (SPP)) is essential. Numerous studies have attempted to tackle this issue. However, to the best of our knowledge, none of the previously proposed works took into consideration the dynamic context awareness and the user preferences for IoT service placement. To deal with this issue, we propose a hybrid recommendation system for service placement that combines two techniques: collaborative filtering and content-based recommendation. By considering user and service context, user preferences, service needs, and resource availability, the proposed recommendation system provides optimal placement suggestions for each IoT service. To assess the efficiency of the proposed system, a validation scenario based on Internet of Drones (IoD) was simulated and tested. The results show that the proposed approach leads to a considerable reduction in waiting time and a substantial improvement in resource utilization and the number of executed services.

Due to its features regarding immutability, transparency and traceability, blockchain has emerged as a promising solution in various application domains. However, despite its advantages, blockchain suffers from major drawbacks in terms of scalability and performance. This is due to blockchain’s ever-growing nature. Several research studies focus on optimizing blockchain by adopting new techniques and solutions such as pruning, sharding, SegWit and off chain storage solutions. However, so far, most of the suggested solutions entail massive technical burden, increase complexity and reduce transparency and security. In this work, after conducting a comparison among existing blockchain pruning solutions, we present a new intelligent pruning technique that only prunes spent transactions or previous versions of smart contracts. In the proposed approach, data are pruned according to data usage, allowing each participant to independently provide a data list that should be pruned. Our solution involves an intelligent selection process based on particle swarm optimization that uses node parameters to select the best list from all agents lists, this list will be adopted by all the blockchain network members. The approach is implemented in our ABISchain blockchain. ABIshchain pruning solution will reduce the storage requirements for nodes, increase the overall performance and security level as joining nodes will need only to fetch a pruned blockchain (which is more secure than joining blockchain network with a blockchain snapshots provided by other nodes).

This article proposes a formal specification method for Web services composition based on context-aware semantic planning graph technique. The authors first use a graph planning technique to conceive an adaptable semantic Web services composition. They use an ontology based context model for extending Web services descriptions with information about the most suitable context for its use. Then, the composition problem is transformed into a semantic context aware graph planning problem to build a set of best-composed Web services based on the user's context. Because of the error-prone nature of the Web services composition process, and with the integration of context information in this process, the complexity of Web services-based systems is increasing, hence the need for formal specification and verification approaches of such systems. The authors seek to use CCA calculus, to formalize Web services composition expressed in terms of adaptable OWL-S, and transform the description of workflow patterns into the description patterns in terms of CCA, a calculus for context-aware ambients.

In this paper, we introduce a new registry that provides publishing adaptable composition Web services (CAC-WSR). Our registry allows to publish and to find composition Web services not only through the functional criterion but also through users contexts and profiles. Our approach refers to service composition being performed on user demand, at run-time. This is suitable for exploiting the current state of available services and making adaptations based on run time parameters, such as the available bandwidth and the cost of executing the various sub-components.