The mobile transient and sensor network's routing algorithm detects available multi-hop paths between source and destination nodes. However, some methods are not as reliable or trustworthy as expected. Therefore, finding a reliable method is an important factor in improving communication security. For further enhancement of protected communication, we suggest a trust cluster based secure routing (TCSR) framework for wireless sensor network (WSN) using optimization algorithms. First, we introduce an efficient cluster formation using a modified tug of war optimization (MTWO) algorithm, which provides loadbalanced clusters for energy-efficient data transmission. Second, we illustrate the optimal head selection using multiple design constraints received signal strength, congestion rate, data loss rate, and throughput of the node. Those parameters are optimized by a butterfly optimal deep neural network (BO-DNN), which provides first-level security towards the selection of the best head node. Third, we utilize the lightweight signcryption to encrypt the data between two nodes during data transmission, which provides second-level security. The model provides an estimation of the trust level of each route to help a source node to select the most secure one. The nodes of the network improve reliability and security by maintaining the reliability component. Simulation results showed that the proposed scheme achieved 45.6% of delivery ratio.

In wireless sensor network (WSN), the gateways which are placed far away from the base station (BS) forward the collected data to the BS through the gateways which are nearer to the BS. This leads to more energy consumption because the gateways nearer to the BS manages heavy traffic load. So, to overcome this issue, loads around the gateways are to be balanced by presenting energy efficient clustering approach. Besides, to enhance the lifetime of the network, optimal routing path is to be established between the source node and BS. For energy efficient load balancing and routing, multi objective based beetle swarm optimization (BSO) algorithm is presented in this paper. Using this algorithm, optimal clustering and routing are performed depend on the objective functions routing fitness and clustering fitness. This approach leads to decrease the power consumption. Simulation results show that the performance of the proposed BSO based clustering and routing scheme attains better results than that of the existing algorithms in terms of energy consumption, delivery ratio, throughput and network lifetime. Namely, the proposed scheme increases throughput to 72% and network lifetime to 37% as well as it reduces delay to 37% than the existing optimization algorithms based clustering and routing schemes.