# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 4 RQ 3--BIoT Integration: Future Concerns and Trends
### Artificial Intelligence  
With a big volume of shared data of good quality and a strong BC-based distributed computing system, BC may make it feasible to move AI closer to the IoT [36; 116]. To make intelligent judgments for improving automated chores such as scheduling and energy transactions, AI methods can aid IoT to a large extent [8; 26]. These approaches can enhance edge computing scalability and increase the compute capacity for IoT data processing [97]. IoT devices run the learning models locally and communicate updates to several dispersed combination servers [176]. These servers exchange their related IoT devices learning models before performing global model aggregation [177]. There is no need for a centralized aggregation server, in this way. Several studies have used a federated learning model in IoT devices to address this issue. For example, [PERSON] et al. [177] developed a BC-enabled federated learning method for FC. To provide resilience, this method aggregates local learning models in a dispersed way rather than using a unified server. All updates from local fog servers, in the end, are provided to all IoT devices that interact throughout the learning process. In another example, [PERSON] et al. [176] proposed a federated learning strategy that relied on cooperation between IoT devices, without the need for a fog server,to avoid the situation when IoT devices are unable to communicate with their fog server owing to communication restrictions.  
Furthermore, by utilizing resource optimization strategies and scalable ML approaches, AI can assist in optimizing QoS and reducing breaches of services [82]. ML and BC have recently become increasingly popular. For example, [PERSON] et al. [178] proposed a dynamic access control system based on BC, with access control strategies implemented as smart contracts. Smart contracts are updated based on reinforcement learning algorithms, which are utilized to improve and change access control rules. [PERSON] et al. [179] suggested combining ML with complex event processing techniques for real-time threat detection and effective event management.  
However, the IoT application determines the federated local learning model type that should be utilized. According to [99], a feed-forward neural network (FNN) learning model is suitable for DDoS attacks applications, a convolutional neural network (CNN) model suits smart car infotainment and image-based disease diagnosis applications, a support vector machine (SVM) model is suitable for detecting malware and slicing network applications, a long-short term memory (LSTM) model is suitable for edge caching and traffic prediction applications, and a K-means model suits IoT networks proactive caching and sensor network clustering applications. Because noisy or low-quality data can drastically damage the learning process, the algorithm requires a solid data sample to produce appropriate training datasets, which is not the case in BIoT [36,98]. Accordingly, decentralizing IoT with ML faces a significant barrier in terms of training dataset authenticity [68,102]. Furthermore, if the adversaries are conscious of the attacking nature and have access to the training dataset, this may easily change the attack or attack type [73,180]. As a result, determining the precise nature of an attack to properly discern between desired and undesirable network states is another difficult problem that requires additional exploration [25].