WSNs have various applications in several fields [1–3]. Batteries usually give power to the sensor. They are small and affordable devices that capture valuable data and send it across wireless networks from the source region to sink nodes. The cells in these sensing nodes, on the other hand, are not interchangeable. The link terminates due to charge degradation. These sensors can communicate with one another or with the sink directly. As a result, optimize resource allocation and node interaction to lengthen the lifespan of the channel.

Furthermore, due to the limited computational capabilities and buffer features of WSNs, various challenges exist. A selection of appropriate limitations, routing techniques to reduce the power consumption, and grouping to ensure correct network functioning are some of the multiple methods to lengthen network life [4–7]. WSN routing techniques divide into two categories: multilevel routing and flat routing. The balanced routing algorithm considers all terminals simple because they are all placed at the same tier. Furthermore, restrictions over scalability because of the overhead packet management, and as the channel capacity grows, it increases energy usage. Therefore, the system divides into clusters according to hierarchical routing. Every group has a CH node, which collects data from different nodes in the group and transmits it to the access point. As a result, extending the network duration and the energy demand in WSN is optimally balanced. When evaluating the efficiency and versatility of flat vs. multilevel routing protocols, hierarchical (multilevel) routing protocols stand ahead—many researchers and scientists concentrating on cluster-based routing strategies [8–10].

WSNs, on either hand, focuses primarily on how to advance energy conservation while minimizing transmission overhead expenses. Scalability, low costs, accuracy, stability, and ease of delivery are significant benefits of WSN applications over traditional network technology. However, power utilization is a unique resource in WSN circumstances considering the limited restrictions, and it must be dealt with smartly to improve network life and route navigation accuracy. Furthermore, due to the prominent dynamic properties of sensors, conventional and unicast routing techniques are not viable for sensor-based technologies [10–12]. As a result, various academics have recently focused on creating adaptive and robust routing methods to increase power consumption and locate optimal routes to destinations.

Routing strategies play an essential role in WSN since they facilitate low power dissipation, reduced latency, and high bandwidth Quality of Service (QoS). Numerous methods have been designed for application-specific WSN to address the restrictions encountered while delivering datagrams that demand node geolocation data for additional processing. It can use the data or information to calculate the gap between two nodes and even the amount of energy expended. Multi-hop routing enables the system to route outside the energy-constrained network area [13]. The latency has now decreased at the expense of increased power consumption; thus, we must devise routing strategies to concise energy.

This study aims to reduce power consumption by adopting efficient ECH selection, resulting in more extended alive nodes. We organize the remaining portions of this work as follows: The literature review summary is in Section 2, and the discussion of driving factors and research problems is in Section 3. Section 4 describes the collaborative suggested method, Section 5 shows the outcomes and inferences, and Section 5 concludes with future direction.

Shende et al. [14] overcame the difficulties by introducing an energy-aware multichannel routing mechanism relying on whale optimization algorithm (WOA) and crow search algorithm (CSA) were combined in the crow whale energy trust routing optimizer [14] depending on power and trust parameters. To determine the paths picked effectively with crow whale optimizer algorithm (CWOA), trustworthiness, and power were calculated. Chose this channel as the best for data transfer; each node’s reliability and capacity are updated at the end of each transaction, ensuring that selected trustworthy nodes boost the channel’s security. The investigation with 100 and 200 nodes, with and without threats, yielded the shortest latency and highest power bandwidth.

The glowworm swarming optimization model (LBR-GSO) was invented by Sampathkumar et al. [15] and proved beneficial in load balancing and navigation. This strategy combined a pseudo-random pathfinding mechanism with an improved pheromone trail-based update strategy to deal with nodes’ power consumption. Furthermore, for route optimization, use feasible heuristics update strategy, including cost-effective power measures. Finally, the LBR-GSO rendered energy-based broadcasts method decreased control overhead power usage. They examined LBR-GSO for various situations using metrics such as power consumption, power efficiency, and network throughput improvement, and it outperformed the competition.

First, utilizing the WOA by Mohan et al. [16] to select MANET’s maximally protected route. Next, the trust feature and the range among nodes assess the routing’s efficiency. Next, The k-disjoint path is identified, followed by optimal route analysis and interpretation of the assessed trustworthiness and range measurements. The performance indicators used to test WOA were increased power, bandwidth, and PDR. Finally, Ram et al. [17] suggested a hybrid M-Lion Whale optimizer that incorporated the lion approach and the WOA to find the optimal MANET routing plan for safe routing. Also, various QoS metrics such as range, power, lifespan, reputation, and latency examine these multiple objective approaches. In addition, designed fitness values are to find an optimal pathway with the parameters given. As a result, we attain the highest residual energy, performance, and PDR using this method.

Kumar et al. [18] developed a new exponentially ant colony optimizer (EACO) method to expose connectivity in WSNs after selecting CHs using the fractional artificial bee colony (FABC) strategy. First, CHs employed the fitness value, which takes a range, latency, and power into account. Second, to find multi-path routes, improve the ant colony optimizer (ACO) technique using an exponential smoothing prototype. Finally, EACO weighed several criteria such as power, range, and a revised fitness value to anticipate inter-cluster and intra-cluster delay to find the optimum data transmission paths from every node to BS. They also integrated the artificial bee colony (ABC) and ACO techniques to develop a new hybrid ABC-ACO system [19] that solved WSNs’ non-deterministic polynomial constraints. The main functions of this approach are to select the optimal number of sub-regions, choose CH using the ABC algorithm, and effectively transfer data using the ACO strategy. Next, hierarchical clustering aids data transfer based on the cutoff. Finally, CH found the best path for transmitting data to BS using the ACO technique. This technology established a framework for wildfire detection and management. In addition, the ABCACO method increased robustness and performance.

Linear/Nonlinear Programming developed by Kuila et al. [20] created navigation methods that combined the techniques of multi-objective fitness value and particle swarm optimization (PSO). The clustering plan concentrates on load balance to conserve transmission power. By taking into account power consumption, network lifespan, dead sensor network, and packet delivery, the testing results demonstrated the superiority of this strategy.

Pachlor et al. [21] created a central routing scheme called Basestation controlled active clustering technique, which dispersed power dissipation uniformly between all nodes, conserving power and thus increasing network lifespan. Srbinovska et al. [22] proposed a stochastic optimization technique that used a genetic strategy to compute power consumption depending on transmitting duration and intensity—solely tested this optimization method in various settings, which increased in communication range. Del-Valle-Soto et al. [23] developed a power model to assess the amount of energy required by the multi-Parent hierarchical routing mechanism and other widely used communication sensors routing algorithms. Conduct experimentation with a random architecture design and two collector nodes. Different wireless systems, including Bluetooth, Zigbee, and LoRa over WiFi, are used by each node. This project met its goal of examining the efficiency of a power model constructed using multiple routing algorithms of various kinds, Behera et al. [24] focused on developing a strategy for selecting CH and rotating its positioning among nodes with the highest power. Primary and residual power and the optimum value of CH were taken into account while selecting suitable CHs for IoT systems.

The above study clearly shows that effective power utilization combined with secure routing is an important area of research. Many previous approaches, it has been discovered, are unable to alter routing efficiency concerning the prevailing situation and limited capabilities of WSN. Furthermore, contemporary work misses next-hop choice based on the best conclusion, and such techniques reduce network-wide performance with accuracy over routing. In addition, while data relay, routing pathways are reconstructed regularly, which adds to the effort and expense of communication. This inefficiency happens by the connection field’s periodical transit of forwarding and control signals.

Furthermore, most previous work causes congestion issues and raises packet loss ratio in higher network-dense scenarios. As a result, we must explore the domain of power consumption centered on information gathering and transmission using a lightweight approach to improve network life while maintaining a consistent transmission rate. As a result, this research study proposes an energy-aware and dependable routing algorithm for heterogeneous networks that establish geographically scaled cluster centers by exploiting node positions to solve the challenges mentioned above. Furthermore, depending on a fitness value, the suggested protocol employs up-to-date neighborhood knowledge to uncover more efficient power shorter and far fewer congestion pathways over routing. Several aspects are associated with transmission power, the weighting value of Round Trip Time (RTT), and hop-count encompassed in the fitness weights. The RTT factor is vital in packet forwarding since it helps to avoid unreachable and distant companions and reduces the likelihood of repeated path re-discoveries [25]. Because the quantity of RTT changes from time to time, the suggested approach calculates a weighting factor.

Moreover, rather than flood the overall network field with route request RREQ packets to facilitate data delivery, the suggested technique sends unicast messages to a single next-hop, reducing navigation overhead expenses and power consumption. As a result, the recommended protocol enables dependable next-hop determination, which substantially affects data passing and network connectivity. In addition, overweight wireless routes, identify the nodes and restructure the routed patterns according to network connection and node capabilities to improve route lifespan and packet transmission performance [26].

With WSN optimization, We designed a novel energy-concise, multi-objective safe routing strategy. The suggested SECRET method, which included the route revamping methodologies, was used to perform multi-hop navigation. We created the suggested SECRET with many aims to enable safe energy-concise multi-hop path discovery in WSN. We depict the framework of the SECRET technique in Fig. 1.

The section compares previous works Taylor Based Grey Wolf Optimization Algorithm (TGWOA) [27], Reliable Cluster-based Energy-aware Routing protocol (RCER) [28] with our SECRET protocol. Using the NS2 simulator to analyze the routing performance of the SECRET protocol and various experiments relies on a node with high-density and variable network load are carried out during the evaluation. The NL, RL, network throughput, Power saving, and PDR are all used to measure the SECRET protocol’s performance. Sensor nodes are stationary and distributed at random in a square-shaped network environment. In the beginning, it assigns power levels in the range of 2j to 6j to nodes to start transmission. In addition, the nodes’ broadcast range is set to 25 m to compare the proposed protocol to existing work, and the simulation time is 1500 s as mentioned in Tab. 1.

We suggest a SECRET method for increasing the network’s lifespan in this research. This technique presents a new fitness value for ECH classification. The route optimization technique relies on the nodes’ residual energy, their ED from BS and the cluster head, and their relative ED. The routing protocol approach conserves energy by eliminating long distances between the transmitter and the receiver. We have proved that the SECRET outperforms other algorithms based on PDR, network lifespan, Route Lifespan, energy savings, and performance. Because more scientific studies are published annually, there would be plenty of future opportunities to suggest sophisticated meta-heuristic algorithms to improve WSN coverage and connection. The goal functions addressed here are range and latency and might include other essential restrictions to boost performance. Despite their suitability for handling the location-based sensor deployment challenge, existing algorithms affect the initiation and number of iterations. As a result, choosing the proper initialization process and convergence rate can attain the goals at low computational complexity. It is something that can research thoroughly in the future.