# Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks
## 2 Related Works
This approach is efficient but computationally heavy and resource hungry. The trustworthiness is also helpful in Industrial IoT for secure applications [28]. In addition to the trust evaluation of the sensor nodes, the routing of nodes is another important aspect of the cloud-enabled sensor network. There have been many optimization algorithms implemented by researchers to procure optimal routing for the nodes. The use of optimization also plays a vital role in establishing optimal paths for the nodes in IoT-based sensor networks. The hybrid algorithms for clustering, namely, K-means with Ant lion optimization (ALO-K) and modified bee colony optimization with K-means, are proposed in [22; 23], respectively. In [29], Black Widow (BWO) optimization and ABC protocol are utilized for optimized clustering and routing in a resource-limited sensor network for IoT applications. In [24], the authors use the modified version of artificial bee (ABC) colony optimization called parallel ABC algorithm to detect various attacks and ensure successful communication in edge-enabled industrial IoT. A blockchain approach is used in [26] to address the trustworthiness of IoT agriculture applications in [30]. This approach is cost-efficient but computationally complex. Similarly, ref. [31] also used blockchain technology to address trustworthiness in digital twins. In [25], authors use the Ant Colony Optimization approach (ACO) to fetch data from the rendezvous points in an optimally routed sensor network that generates uneven data from the sensors. In [26], authors use a hybrid approach by implementing Principal Component Analysis (PCA) along with the gray wolf optimizer to optimize the parameters of the Deep neural network, which enables detecting the intrusion in the Internet of Medical Things (IoMT). The authors in [27] incorporate a hybrid metaheuristic approach to choose optimal cluster heads to obtain energy efficiency in the network. This approach uses a hybrid algorithm called Whale Optimization and Simulated Annealing (WOA-SA). The above-discussed methods deal with the problem of security and energy consumption by providing various solutions such as trust-based data collection, optimized routing, and edge-node-enabled secure data transmission. However, a trade-off has been observed between the efficiency and security of the algorithms, as mentioned above. This is because some researchers use the edge nodes for secure and prosperous transmission based on the trust values of nodes, and some use optimized routing to enhance the system's QoS parameters. So, a hybrid approach that uses edge node computing and optimized route establishment for data transmission needs to be developed in clustered IoT-enabled sensor networks. This paper uses a modified gray wolf optimizer (GWO) for the clustering process. It provides high convergence with no local optima problem. It also employs the trust evaluation approach to pick trustworthy nodes. The route path is devised by implementingthe artificial bee colony optimizer, enabling the mobile edge node to collect data optimally from all trustworthy nodes.