# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 5 Future Roadmap
### A Future Look-Ahead: The Promise of AI
#### 5.2.1 AI-Assisted IoT Extensions
In a study conducted in [220], the authors propose STM32 micro controller combined with temperature, a sphyg-momanometer cuff, and a pulse sensor to create an effective prediction scheme. The scheme uses supervised learning that applies regression analysis. The model is trained to find correlations between independent and dependent variables in order to create a neural network capable of identifying cardiovascular disease based on information collected from IoMT. This study shows the effectiveness of the proposed model and its applications in vascular heart disease scanning. Blockchain and federated learning are commonly used today in creating a secure data sharing model. The main issue with current models is they do not perform well when data in clusters is sparse. To improve this a larger network consisting of more devices can be used but this can increase latency. The authors of [221] propose a way to fix this by taking larger clusters and splitting them into smaller clusters based on geography. The scheme used is a cross-cluster federated learning system. The scheme takes advantage of cross-chain technology to improve the functionality. Experimental analysis proves the feasibility and efficiency of the proposed model. AI-Assisted Internet of Health Things  
Since most devices in the IoHT are limited in storage and computing resources they need to be able to efficiently offload to more capable systems. Edge computing is one solution that is used in many IoHT networks. Issues such as competition among devices,communication restraints, and lack of global state information (GSI) make these systems ineffective. The authors of [5] look at the offloading problem in edge computing as an adversarial multi-armed bandit (MAB) problem. To solve the problem, the authors create an ultra-reliable and low latency communication (URLLC) aware task offloading scheme. The system is based on the exponential weight algorithm that is for the use of exploration and exploitation. URLLC is a set of constraints that goes beyond what most schemes focus on in terms of efficiency and reliability. The use of URLLC awareness is proposed by using a queue on URLLC constraints to more accurately categorize them. The scheme uses learning-based task offloading to find optimal performance, keeping in mind the URLLC constraints using location information. This is performed by first introducing a problem based on Lyapunov optimization. URLLC task offloading-GSI is used with the ideal case of GSI. The authors extend this by allowing for it to work with non ideal cases of GSI. Intensive simulations and analysis show that the proposed scheme can reduce task queue backlog, eliminate URLLC constraint therefore reducing queuing delay and improving convergence speed, and reduce energy consumption. Wearable devices such as smartwatches have allowed for an increase in human activity recognition (HAR). In [222] the authors propose a deep-learning enhanced HAR for use in IoHT. The system uses a semi-supervised design in conjunction with an auto-labeling module. This is then used with a long short-term memory (LSTM)-based classification module to analyze weakly labeled data to better train a classifier. The auto-labeling scheme uses a deep Q-network (DQN) applied to a distance-based reward system, which can more efficiently categorize weakly labeled data. The use of LSTM allows the model to better collect sequential data and find hard to see patterns. When tested and compared to other similar methods, the proposed scheme is shown to be effective.