The growing use of wireless communications increases the challenge of spectrum usage efficiency challenge. Cognitive radio technology has developed as a productive solution for allowing other users, named secondary users (SUs) or cognitive radio users, to share the underused spectrum, offering that there will be no intrusion with primary users (PUs) [1]. When SU is detected, the PU will have appeared; it has to switch to other available channels but not employed by PU. Dynamic spectrum accessibility refers to a spectrum-efficient interaction pattern for Wireless Sensor Networks (WSN) [2]. Such later face an augmented level of intrusions from several wireless mechanisms functioning on the available frequency band like Bluetooth, WiFi, WIMAX, etc. A Cognitive Radio Sensor Network (CRSN) becomes a novel sensor network pattern that accepts the cognitive radio abilities of sensor network systems [3]. CRSNs will be a solution to unscrupulously use the idle parts of the approved spectrum. Presenting to sensor nodes (SNs) temporary use of the accessible licensed networks advances the utility efficacy of the spectrum itself. It offers enhanced quality of service (QoS) regarding prevailing wireless technologies [4]. Cognitive radio users can access any part of the spectrum. Significant interference is made to approved and other users. Schedule-related MAC protocol for cognitive radio networks was devised to solve this complexity. Similarly, there were several difficulties which should be solved [5–7]. Fig. 1 illustrates the infrastructure of WSN.

The common control channel issue is mostly unresolved in cognitive radio technology. Then, it was proved that single-user detection methods must execute more effectively to find primary user activity. Lastly, several solutions were devised for only a limited-sized network. A probable solution for such problems was splitting the system into clusters [8]. Unlike earlier studies, that allows different channels to adjacent clusters to avoid collision and income from the whole spectrum remaining by the PUs to raise the correspondence of interactions done by SUs.

Logically consolidating and grouping the same SNs in their closeness with some objects is termed node clustering [9]. A gathered WSN structure becomes beneficial to a non-cluster-related structure in many ways. This non-cluster-related structure is termed a single-tier network structure and depends on flat topologies. Node clustering allows bandwidth reprocessing and effectual resource distribution so that it could enhance system capability [10]. Predominantly, a dense sensor network and, on a large scale, single-tier networks could be overloaded the gateway node, resulting in congestion and communication delay. These single-tier networks were not ascendable for a large set of sensors positioned in a big area. Clustering in CR-WSNs becomes infancy [11]. There was enormous work in clustering for cognitive radio networks (CRNs), mobile ad hoc networks (MANET), and WSNs. Though certain clustering complexities were solved in the study, clustering will remain a vast unexplored field in CR-WSNs [10].

The contribution of the paper is given as follows. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN. Besides, the MDMO-EAC algorithm is based on the dwarf mongoose optimization (DMO) algorithm design with oppositional-based learning (OBL) concept. In addition, the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency. The presented model is validated using a comprehensive range of experiments, and the results are inspected under varying measures.

Prajapat et al. [12] introduce a neighbour discovery technique and 2 greedy k-hop clustering methods (k-SACB-WEC and k-SACB-EC) for CRSN to concentrate on IoT application that needs constant intercluster and intracluster interactions. The researchers concentrate on attaining channel connectivity while optimizing network lifetime. In this clustering, several variables, like nodes’ remaining energy, spectrum awareness, appearance possibility of PUs channel, channel qualities, strength on the arrival of PUs, and the Euclidean distance among nodes were considered for selecting the common channels and hop count for clusters. Bhagyalakshmi et al. [13] provide the optimizing capability of the network lifespan via joint routing and resource allotment with an isolated nodes approach (JR-IN) among isolated nodes and cluster head in a cognitive oriented WSN. In the JR-IN algorithm, the network area can be separated into distinct layers, and cluster size can be developed in every layer so that the cluster size will remain unequal whenever it transfers against the sink. Later the cluster size was large in the outer layer when a comparison was made with the cluster size in the inner layers.

Stephan et al. [14] devise an energy and spectrum-aware unequal clustering (ESAUC) protocol that jointly overwhelms the limits of spectrum and energy for optimizing the CRSN span. This devised protocol enhances equality by attaining remaining energy equilibrium between the SNs and improves the network lifespan by reducing general energy utilization. The deep Belief Networks technique was used for predicting the spectrum holes. ESAUC enhances the cluster constancy through the adjustment of the common channel count optimally. Zheng et al. [15] devise a new stability-aware cluster-related routing (SACR) protocol for CRSNs. The major novelty of SACR is the unified incorporation of opportunistic sending and a stable clustered structure. In cluster creation, the novelists considered energy consumption and spectrum dynamics in the clustering procedure. The resulting clustered structure can be stable, evading large interaction overhead because of high clustering frequency.

In [16], a new technique—energy preservation and network critics-related channel scheduling (EPNCS) approach in CRSNs was devised that regulates the slot time for SNs. Dependent on ecological data traffic, the sleeping period of SNs can be changed, which diminishes energy stylization. A scalable, dynamic slot is calculated for every SN related to the average buffer occupancy, resulting in optimum channel usage. An RF EH-related multi-hop clustering routing protocol (RFMCRP) related to the non-linear EH method was devised in [17]. At First, by using statistical analysis and curve fitting tool, the most reasonable non-linear EH method can be detected and was used by RFMCRP for measuring the harvested energy precisely. Second, the optimum cluster number was hypothetically extracted, and its value was employed as a benchmark for assessing the proposal’s validity. Then, the energy control system was presented for managing node state, which could help enhance cluster building stability. Zheng et al. [18] suggest a short preamble cognitive MAC (SPC-MAC) protocol for CRSNs. The main input of SPC-MAC was the smart grouping of short opportunistic forwarding and preamble sampling. So, SPC-MAC can support fast spectrum access and be reliable whenever minimizing power usage. Additionally, SPC-MAC was a distributed cognitive MAC protocol deprived of any common control channel.

In this study, a new MDMO-EAC technique has been projected for CRWSN. The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN. Besides, the MDMO-EAC algorithm is based on the design of the DMO algorithm with the OBL concept. In addition, the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency. Primarily, the nodes are randomly deployed in the target area, and the initialization phase occurs where the nodes exchange information with their neighbours. Moreover, the BS executes the clustering process and advertises the CHs.

Here, the experimental results of the MDMO-EAC approach are examined under several aspects. The parameter setting is as follows: target region: 200 * 200 m², number of sensor nodes: 100–500, number of primary users: 5, number of available channels: 5, and data packet size: 50bytes.

Table 1 and Fig. 3 portray the energy level analysis of CH nodes (ELCHN) of the MDMO-EAC model with compared methods on 100 nodes [19]. The experimental values indicated that the MDMO-EAC model had shown improved output with increased ELCHN value. On 20 nodes, the MDMO-EAC approach has obtained increased ELCHN of 97.75%, whereas the NCP-CRWSN, SAC, LEACH, and RATE models have attained reduced ELCHN of 92.92%, 78.05%, 74.33%, and 68.39% respectively. Similarly, with 40 nodes, the MDMO-EAC approach has acquired a higher ELCHN of 97.38%, whereas the NCP-CRWSN, SAC, LEACH, and RATE methodologies have achieved reduced ELCHN of 95.15%, 73.96%, 70.62%, and 66.16% correspondingly. Also, with 60 nodes, the MDMO-EAC method has attained increased ELCHN of 94.40%, whereas the NCP-CRWSN, SAC, LEACH, and RATE algorithms have obtained reduced ELCHN of 89.57%, 68.76%, 67.64%, and 61.32% correspondingly.

A detailed energy consumption (ECOM) examination of the MDMO-EAC model with recent models is performed under 100 nodes in Table 2 and Fig. 4. The simulation values pointed out the supremacy of the MDMO-EAC model with minimal ECOM values. For instance, with 300 s simulation time, the MDMO-EAC model has resulted in a minimal ECOM of 18 J, whereas the NCP-CRWSN, SAC, LEACH, and RATE models have reached maximum ECOM of 25, 40, 46, and 52 J respectively. Furthermore, with 600 s simulation time, the MDMO-EAC approach has resulted in minimal ECOM of 21 J, whereas the NCP-CRWSN, SAC, LEACH, and RATE techniques have achieved maximum ECOM of 34, 48, 58, and 59 J correspondingly. In the meantime, with 900 s simulation time, the MDMO-EAC method has resulted in a minimal ECOM of 58 J, whereas the NCP-CRWSN, SAC, LEACH, and RATE approaches have attained maximum ECOM of 85, 104, 114, and 125 J correspondingly.

Table 3 and Fig. 5 represent the lifetime time (LTT) of the MDMO-EAC algorithm with compared methodologies on 100 nodes. The experimental values highlighted the MDMO-EAC approach had displayed improved output with increased LTT value. On 20 nodes, the MDMO-EAC methodology has attained an increased LTT of 1582 s, whereas the NCP-CRWSN, SAC, LEACH, and RATE methodologies have gained reduced LTT of 1456, 1225, 1046, and 1019 s correspondingly. Further, with 40 nodes, the MDMO-EAC method has reached an increased LTT of 1708 s, whereas the NCP-CRWSN, SAC, LEACH, and RATE approaches have gained reduced LTT of 1522, 1244, 1059, and 960 s correspondingly. Similarly, with 60 nodes, the MDMO-EAC technique has obtained an increased LTT of 1615 s, whereas the NCP-CRWSN, SAC, LEACH, and RATE algorithms have reduced LTT of 1516, 1225, 1053, and 920 s correspondingly.

Table 4 and Fig. 6 portray the ELCHN of the MDMO-EAC technique with compared approaches on 500 nodes. The experimental values indicate the MDMO-EAC approach has exhibited improvised output. On 100 nodes, the MDMO-EAC algorithm has achieved an increased ELCHN of 94.82%, whereas the NCP-CRWSN, SAC, LEACH, and RATE methodologies have achieved reduced ELCHN of 90.76%, 78.58%, 71.94%, and 64.93% correspondingly. Likewise, with 200 nodes, the MDMO-EAC technique has reached an increased ELCHN of 94.08%, whereas the NCP-CRWSN, SAC, LEACH, and RATE methodologies have gained reduced ELCHN of 90.39%, 79.32%, 71.20%, and 64.93% correspondingly. Also, with 300 nodes, the MDMO-EAC approach has reached an ELCHN of 94.08%, whereas the NCP-CRWSN, SAC, LEACH, and RATE algorithms have gained reduced ELCHN of 87.81%, 74.52%, 67.14%, and 62.72% correspondingly.

Table 5 and Fig. 7 illustrate the ECOM of the MDMO-EAC method with compared methodologies on 500 nodes. The experimental values denote the MDMO-EAC approach has displayed superior performance with increased ECOM value. On 300 nodes, the MDMO-EAC algorithm has attained an increased ECOM of 16 J, whereas the NCP-CRWSN, SAC, LEACH, and RATE approaches have acquired reduced ECOM of 30, 41, 50, and 67 J correspondingly. Similarly, with 600 nodes, the MDMO-EAC methodology has attained an increased ECOM of 19 J, whereas the NCP-CRWSN, SAC, LEACH, and RATE methods have attained reduced ECOM of 37, 60, 69, and 93 J correspondingly. Moreover, with 900 nodes, the MDMO-EAC method has outperformed the increased ECOM of 44 J, whereas the NCP-CRWSN, SAC, LEACH, and RATE methods have reached reduced ECOM of 72, 104, 117, and 127 J correspondingly.

Table 6 and Fig. 8 describe the LTT of the MDMO-EAC technique with compared methods on 500 nodes. The experimental values indicate the MDMO-EAC algorithm has exhibited exceeding performance with increased LTT value. Under 100 nodes, the MDMO-EAC technique has attained an increased LTT of 1682 s, whereas the NCP-CRWSN, SAC, LEACH, and RATE methodologies have reached reduced LTT of 1473, 1217, 1132, and 910 s correspondingly. Additionally, with 200 nodes, the MDMO-EAC technique has attained an increased LTT of 1689 s, whereas the NCP-CRWSN, SAC, LEACH, and RATE methods have attained reduced LTT of 1512, 1224, 1073, and 903 s correspondingly. Also, with 300 nodes, the MDMO-EAC technique has gained an increased LTT of 1663 s, whereas the NCP-CRWSN, SAC, LEACH, and RATE approaches have achieved reduced LTT of 1479, 1217, the 1080, and 884 s correspondingly.

An effective MDMO-EAC technique has been developed for CRWSN. The MDMO-EAC technique focused on clustering sensor nodes into several clusters to accomplish energy efficiency in the CRWSN. The MDMO-EAC algorithm is primarily based on the design of the DMO algorithm with the OBL concept. In addition, the presented MDMO-EAC algorithm computed a multi-objective fitness function for improved network efficiency. The presented model is validated using a comprehensive range of experiments, and the outcomes were reviewed in varying measures. The comparison study stated the improvements of the MDMO-EAC approach over other recent methods. The comparison study stated the improvements of the MDMO-EAC approach over other recent methods. In the future, data aggregation protocols will be designed to enhance the efficacy of the network.