# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 5 Future Roadmap
### A Future Look-Ahead: The Promise of AI
#### AI-Assisted Internet of Energy
Most communication suffers from large latency due to the environment it is communicating in. Underwater optical communication is a way of communicating through the use of light. This allows for a very low latency and an easy way to transmit data. To be able to do this the authors of [230] propose a communication system based on single and superposition of Laguerre-Gaussian modes. The system uses a CNN for the purpose of identifying the mode in an underwater environment. The CNN is trained using transfer learning to increase the training speed. When tested the system is shown to have a package drop rate of 0% in clear and turbid water. The scheme is 90% accurate in different water turbulent conditions. URLLC is extremely important to communication between devices in the Internet of Controllable Things. The authors of [231] propose a fast-convergent deep reinforcement learning for joint subcarrier-power allocation. This will be used in building a device-to-device (D2D) network. Each subcarrier is able to connect to multiple D2D pairs, and D2D pairs can connect to multiple subcarriers. The authors model the problem as a Markov decision model. The proposed solution is a double deep Q-network (DQN)-based resource allocation algorithm. If a system is missing full channel state information (CSI) then DQN-based resource allocation still allows for the system to be optimized. The system is very scalable as most communication happens between neighbors, vastly reducing overhead. D2D pairs act as learning agents for the system, allowing it to train quickly. Simulation testing shows that the subcarrier-power allocation can be performed in real time, given delayed CSI and quality of service feedback. AI-Assisted Internet of Vessels  
As discussed in previous sections, communication in underwater environments is difficult as it changes rapidly. The authors of [232] propose an orthogonal frequency division multiplexing (OFDM) auto-encoder that uses CNN-based channel estimation. The CNN allows the OFDM auto-encoder to optimize learning and improves block error rate. LSTM layers are used for channel coding gain. The LSTM layers encode the message before transmission and are able to extract correlated information for detection. The proposed method solves gradient vanishing, which occurs in CNNs and has a high level of accuracy. AI-Assisted Internet of Video Things  
The Internet of Video Things (IoVT) has many applications in many different field ranging from vehicles to surveillance. One such category of study is using the IoVT to understand human behavior. A system able to do this requires a large amount of computational resources, which many IoVTs do not have. In [233], the authors propose a novel practical hierarchical and multi-modal architecture to understand human behavior. To encode the spatial and temporal information from videos, the scheme uses a deep learning model. For the purpose of language description, the model uses deep RL. Tensor-Train decomposition is able to factorize a hidden inference weight matrix, reducingthe training cost of the model. To optimize tensor decomposition further, a genetic algorithm is used to find the best rank. When tested, the scheme is proven to be able to compete with other state-of-the-art schemes.