# Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks
###### Abstract  
Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster's trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% like in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity. Internet of Things; modified gray wolf optimization; artificial bee colony optimization; trustworthy nodes; edge node +
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transmission are more prone to attacks and faults. Some of the applications' requirements are not only limited to security but also mandate the mitigation of delay, energy usage, and packet loss. Some of the issues such as congestion, delay in transmission, and energy consumption can be dealt with using existing routing algorithms [4; 5; 6]. Figure 1 shows the cloud-based sensor network having EDGE nodes to send the data to the cloud server for real-time data analysis. Edge technology, which serves as an extension of the cloud, shows an enormous technological improvement by shifting the burden of storage and computing to the edge network that is confined to an edge layer near the sensing devices. This technique not only alleviates the resource constraints at the end devices but also uplifts the system performance [7]. The Edge network can be easily integrated with IoT networks and achieves a remarkable success in obtaining desired QoS parameters. The trust issues can also be resolved using this technology [8]. There has been enormous research conducted to solve the issue of security by using edge node approaches and trust mechanisms such as [9; 10; 11]. In [10], the best route path with trustworthy nodes is generated and a mobile edge node visits each sensor with a significant trust value and collects data. Some research works have focused on establishing route paths using trust-based schemes. In [11], a belief-based trust approach to identify malicious nodes is proposed that guarantees defense against Denial of service (DoS), Bad-mouth, and On-Off attacks. The direct and indirect trust values are gathered using the Bayesian estimation technique to determine the correlation in time series data, which can be used to estimate inaccurate knowledge thereby avoiding data from malicious nodes. The issues encountered while designing a network for data collection using trust-based scenarios such as cross-layer optimized networks, 3D underwater sensor networks, UAV-based edge networks, etc. are also major concerns while designing a sensor network.