# Energy-Efficient Edge Optimization Embedded System Using Graph Theory with 2-Tiered Security
## 2 Literature Work  
The IoT provides an internet-connected civilization where smart objects gather data and monitor environmental conditions. IoT deployment is hard and needs edge computing for the processing of real-time data and improved system performance [20; 21; 22]. Energy efficiency is one of the most demanding issues in IoT scenarios, as sensors, actuators, and smart devices are battery-powered devices with limited constraints. In [23] authors discuss utilizing an edge-IoT platform and a social computing framework to design a smart energy-efficient building system. The system was evaluated in a public venue, and the results illustrate edge computing's energy efficiency and framework benefits. The benefits include reduced IoT-edge-to-cloud data transfer, computation, and network costs. The authors of [24] studied the possibilities of merging blockchain with SDN. They presented a safe, energy-efficient blockchain-enabled SDN controller architecture for IoT networks utilizing a cluster topology and novel routing protocol. With the elimination of proof-of-work (POW) and the adoption of an effective authentication strategy with distributed trust, the design uses both public and private blockchains for peer-to-peer (P2P) communication between IoT devices and SDN controllers. In addition, the cluster-based routing protocol provided better throughput, lower latency, and lower energy consumption than EESCFD, SMSN, AODV, AOMDV, and DSDV. Network devices provide real-time data to gateways and remote servers for processing and display. Data transferred by these devices are subject to hostile attacks; thus, privacy and integrity must be maintained. In [25], the authors introduced LightIoT, a lightweight and secure connectivity method for healthcare infrastructure devices. LightIoT was initialized, paired, and authenticated. These steps assure data reliability by creating safe sessions among communication entities (wearable, gateways, and a remote server). Statistical findings showed that their technique was lightweight, robust, and resilient against various adversarial attacks and incurred substantially lower computational and communication costs for transmitted data than previous approaches. The authors of [26] also presented a cooperative offloading approach based on the Lagrangian suboptimal convergent computation offloading algorithm (LSCCOA) for multiaccess MEC in a dispersed Internet of Things (IoT) network. A computational rivalry amongst SCDs for limited resources impeding job offloading for MEC is explored in a high-demand IoT network. The proposed suboptimal computational approach is applied to accomplish task offloading that is optimized at the cloud edge server without transferring it to the centralized network. In the form of a mixed-integer optimal solution, this led to a minimum weighted total of transmit power consumption and outputs. In addition, the resulting fast-convergent suboptimal method is used to solve the non-deterministic polynomial-time (NP) hard problem. To increase lifespan using a trustworthy approach, trust-based energy efficient data collecting with an unmanned aerial vehicle (TEEDC-UAV) scheme is proposed [27]. Firstly, an ant colony-based unmanned aerial vehicle (UAV) trajectory optimization technique is developed. This approach forms the most data anchors in the working field with the shortest route and improves network lifetime. To increase the trust for collected data, trust reasoning and evolution are presented and ensure the degree of privacy in sensor nodes. The issue of network throughput optimization for an IRS-assisted multihop MEC network is investigated in [28], where it is necessary to jointly optimize the IRS's phase shifts and the relays' resource allocation. However, it is challenging to address the studied problem by simply using known optimization techniques because of the coupling among the transmission lines of different hops generated by the utilization of the IRS and the complex multi-hop network topology.