# 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
The use of deep reinforcement learning is proven to outperform deep deterministic policy gradient and a deep Q-network algorithms when tested in this application. Another application of UAVs is in the field of monitoring large fields of agriculture. Due to the large amount of data collected, nodes can not meet the needs of the drones. To fix the related issues, the authors of [7] propose a data collection method for air-to-ground communication. The proposed scheme is a bandwidth-weighted traffic pushing optimization (BWPTO) algorithm that uses a deep Q-learning (DQL) network to allow for the network to adapt to future changes. The ability of the network to adapt allows for maximum efficiency in data distribution. Mathematical experiments show optimal routing and a dynamic data collection system that is effective in air-to-ground communication. In a study conducted in [227], the authors use UAVs to track target vehicles by employing a pretrained CNN. This method is highly effective but consumes large amounts of resources due to the need for accuracy and delay requirements. The authors employ the use of a mobile edge computing scheme to offload the heavy computational cost of the CNN. The system will allow for the UAVs to do low level computations but pass off more complex and consuming tasks of the CNN to edge nodes in the server. To effectively distribute the tasks a weighted-sum cost algorithm is used taking the parameters of tracking delay and energy consumption. The algorithm is able to maximize quality and avoid interference errors. Numerical and analytical results show the effectiveness when comparing this model against a model that does not offload high computational tasks of the CNN. AI-Assisted Internet of Remote Things  
The Internet of Remote Things (IoRT) has many challenges due to its remote location sensors. One solution to solving inaccessibility is to use satellites that offer a wide area of coverage. The issues to overcome for satellites to be viable include dynamic environments, which affect solar in-feed and channel conditions. The authors of [228] offer the solution of a model-free reinforcement learning (RL) framework that can be used for joint resource scheduling. This would allow the most amount of data to be transmitted over the network. For effective use of model-free RL the authors make several contributions. The first contribution is formulating a resource allocation for the IoRT data scheduling (RAIDS) problem. This allows for model-free RL to adapt to realistic environmental factors and is rewarded for optimizing performance based on conditions. The system is rewarded based on a state-action reward-state-action (SARSA)-based actor-critic reinforcement learning strategy. This will allow the system to optimize resource allocation and create an environment that allows for the most data to be uploaded in a single period. Simulations have proven the effectiveness of the scheme in terms of learning and resource allocation. AI-Assisted Internet of Underwater Things  
The IoUT is an area that struggles with localization due to its mobility, asynchronous time clock, and stratification. Sensor node estimation has to be fairly exact for IoUT to be effective at sensing and communication. In [229] the authors propose some concepts to solve issues related to the uncertainties of location estimation. The authors provide a hybrid network that includes surface buoys, autonomous underwater vehicles (AUV), and sensor nodes. The scheme employs an AUV-aided asynchronous localization protocol that is able to use ray compensation and motion to handle the asynchronous clock, mobility, and stratification in IoUT. An RL-based localization algorithm can be used to attain the most optimized solution for communication between sensor nodes, AUVs, and surface vehicles. When tested the scheme is proven to have stronger localization performance than other similar methods. AI-Assisted Internet of Controllable Things  
Underwater communication is important to the exploration of the world's oceans.