# 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
Location, speed, and directions are all factored into the equation in determining the best connection. When compared to similar schemes, the proposed method uses fewer paths of connection, ensuring more reliable communication. The DDoS attack is currently one of the biggest threats to the IoV, flooding networks by exhausting caches and using up computing resources. This can, in most cases, be solved with a strongly developed intrusion detection system. Many of the challenges include the amount of resources IDS eats up, short links to vehicles, and limited cache and storage. In [216] the authors propose a way of fixing these issues through an effective data-driven IDS. This scheme will monitor RSU links instead of nodes on the network to avoid using up limited node resources. A convolutional neural network (CNN) is used for intrusion detection with seven layers. The CNN is constructed as a Bayesian hierarchical model to prove the convergence of the architecture. AI-Assisted Internet of Connected Vehicles  
Localization services are important to today's IoCVs and can improve the quality of transportation. The issue is that with most systems there are large amounts of overhead costs that prohibit them from being effective. The authors of [217] design a Behavior-based Clustering Method (BCM) to be a solution. The method observes vehicles in different time intervals and puts them in non-overlapping clusters based on their behavior. The authors then propose a Clustering-learning-based Predictive Localization (CLPL) algorithm to predict distribution based on the BCM. This allows it to make long-term predictions. The scheme is compared to other approaches such as Co-operative Vehicle Localization Algorithm (CVLA) and neighbor-aided localization (NAL). The proposed algorithm outperforms them in terms of quickly stabilizing smaller clusters that include larger amounts of vehicles. Localization services provide many different types of advantages to the IoCVs. One such advantage is the ability to collect data and map traffic. Traffic forecasting is not possible without internal connections between vehicles. Many of the current systems that do this are not dynamic enough to accurately predict realistic traffic scenarios. To solve this problem, the authors of [218] propose a graph neural network-driven traffic forecasting model (Gra-TF). The scheme uses graph convolutional networks (GCNs), Chebyshev networks (ChebNets), and graph attention networks (GATs) to find different predictions, and then the results are fused to build a more accurate and dynamic model. Based on conditions each model is weighed differently in the neural network when training. The authors believe this is the first application of a distributed learning-based approach to this problem. When tested on real traffic data from Los Angeles and compared to GATs and GCNs, the proposed scheme is able to perform thirty to forty percent more accurately. AI-Assisted Internet of Medical Things  
In the realm of IoMT, AI has the ability to predict the chance a patient gets lung cancer by looking at computed tomography. Current applications are still in development and have a low detection rate. The authors of [219] propose an integrated deep learning algorithm. CT images are first preprocessed through a variety of methods, including segmentation, and then, using intelligent algorithms, lung parenchyma is extracted. CNN and a residual network are trained using the images on a variety of cancer positive and negative images. Once trained, the features from the networks are imported into a long short-term memory (LSTM) model. Testing of the model has proven it to be promisingly effective in detection and is able to mitigate issues of interference. Deep learning has many applications for finding and predicting health irregularities. One such application in the early detection of cardiovascular disease.