# 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  
Energy generation by renewable sources tends to be uncertain as it relies on natural factors such as the wind, sun, and weather. To better improve the stability of power systems, distributed generation units (DGs) are used with virtual power plants (VPP) to allow for large scale networks of DGs. The effectiveness of these systems is based on reliable connections between DGs and between the generation side and load side. In [223], the authors propose a deep reinforcement learning algorithm to find online economic solutions in VPPs. The first contribution is the use of a non-linear function to allow for better reliability in a realistic VPP setting. Due to the randomness of DGs in a system the use of a deep learning reinforcement algorithm is able to effectively solve the economic dispatch problem for VPPs. To reduce latency further, the system used to implement this will take advantage of edge computing in three layers. The first two layers are edge computing, while the third layer is cloud computing. The first layer collects information from the generation side and load side of the network. The second layer manages demand responses and energy transfer in a local area. The third layer is used for training the central node and further processing the entire network's communication. When tested against the deterministic policy gradient (DPG) algorithm and deep DPG, the proposed algorithm outperformed them in terms of time complexity. In the Internet of Energy (IoE), microgrids have become an increasingly popular subject. They are a hierarchical distribution of different types of DGs. Because of the uncertainties tied to renewable energy these systems lack the capability of accurately scheduling power. A hierarchical microgrid model is proposed by the authors of [224]. To allow for a realistic model, the authors consider communication uncertainty. Similar to the previous model, this scheme uses a distribution deep reinforcement learning algorithm to solve the renewable energy accommodation capacity in the model. The scheme uses a temporal difference generation adversarial network as opposed to the previous scheme's edge computing network. The proposed network is able to adapt to the deep reinforcement algorithm, improving its speed and allowing it to use the optimal strategy. Simulations show the effectiveness of the proposed scheme to increase the capacity of power scheduling and renewable energy accommodation. Safe operation of power systems relies on many aspects one being transient stability assessment (TSA). One of the biggest threats to a system is a major collapse. Other methods are only able to assess the state of the TSA. They are unable to infer events that could lead to such a collapse. In [225] the authors propose a Gated Graph Neural Network to track TSA as well as infer events that could lead to collapse. A Conditional Generative Adversarial Network (CGAN) is used to create a training set that allows for effective recognition of transient instability. The model was tested using the New England 39-bus system and was able to effectively identify the event that could lead to instability in the network. AI-Assisted Internet of UAVs  
UAVs have become an emerging technology with lots of applications and one such application is in sensing. With the use of a cellular Internet of UAVs they could openly communicate across cellular networks and allow them to communicate with base stations further out. To take advantage of cellular networks the authors of [226] propose a scheme that allows UAVs to use cooperative sensing and transmission to decrease latency in information communication. The main challenge the scheme faces is cooperative sensing and transmission can interfere with trajectory of UAVs. The model uses a trajectory design problem based on the Markov decision process and use a deep reinforcement learning algorithm to solve it.