# Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things
## 2 Literature Review  
The development of information and communication technologies over the past few decades has sparked a trend toward smartening everyday things to improve human comfort. The paradigm of smart cities is a reaction to the objective of building future cities for quality assurance and sustainable development [22; 23; 24]. Smart devices and dynamic wireless systems offer various benefits for developing heterogeneous networks. Using these kinds of technologies, smart sensing, network automation, and resource management is made feasible around us. However, due to the energy and other constraints on IoT devices, researchers are still searching for recommended green solutions for IoT networks. Another important research goal is to ensure the safety of network data from potential threats [25; 26]. In [27], the authors presented a power-efficient tree-based routing algorithm that decreases end-to-end latency in energy-efficient green-IoT networks with a mobile sink. The proposed protocol offers two new distinct ways of controlling network routing. The first mechanism uses a more dependable and energy-efficient version of the geographic routing algorithm. The second mechanism uses a tree-based structure, which can be built with the fewest possible control packets and updated efficiently. According to simulation findings, the proposed routing protocol is superior to the existing solutions in terms of energy consumption, network longevity, delay, and throughput. The authors in [28] developed a Mobility-Aware Dynamic Clustering-based Routing (MADCR) protocol for the Internet of Vehicles (IoV) to optimize the lifetime of networks and decrease the end-to-end communication latency. The MADCR protocol includes methods for cluster formation and Cluster Head (CH) selection. The formation of a cluster is based on Euclidean distance. The CH is then selected using the Mayfly Optimization Algorithm (MOA). The CH then receives vehicle data and transmits this information to the Road Side Unit (RSU). In addition, the proposed MADCR protocol reduced the end-to-end delay and improved the packet delivery ratio as compared to other studies. Authors in [29] proposed a Secure Routing Protocol by using Multi-objective Ant-colony-optimization (SRPMA) for WSN. They upgraded the ant colony method to be a multi-objective routing algorithm using the residual energy of nodes. First, a routing path is formed by exploring multi-pheromone and multi-heuristic information. The node trust assessment model enhancesthe D-S evidence concept with conflict preparation. Second, multi-objective routing results were generated by employing crowd distance criteria. A novel routing protocol termed Secured QoS aware Energy Efficient Routing Protocol is presented to improve WSN security and energy efficiency [30]. Trust modeling employed a key-based authentication approach to provide trust ratings in this suggested study. This study calculated direct, indirect, and total trust ratings to improve communication security. They also proposed a cluster-based safe routing method where the cluster head is chosen based on QoS measurements and trust ratings. The ultimate secure routing route is based on path trust, energy, and hop count. The Software-Defined Network (SDN) approach has recently been combined with IoT to address prospective scalability and flexibility needs to create SDN-IoT. As SDN-IoT grows, efficient routing methods with low latency and robust security are required. However, the default SDN routing protocols are subject to dynamic flow control rule changes when the network is under attack. The Internet of Things Cloud (IoTC) may enhance the sensing capabilities of IoT devices, while fog computing can circumvent IoT devices' processing and storage constraints. Accordingly, the authors [31] proposed a fog-assisted IoTC data collecting and delivery architecture to minimize IoTC-based data transfer costs and increase success rates.