Ad hoc mobile cloud computing networks are arranged with several wireless communicating nodes [1]. These networks define a concept from a source to targeting approach for on-demand contact to pooled services. The ongoing review of the system’s operational state is necessary to identify performance degradations and malfunctioning resources as fast as possible to manage the resources effectively. Any change in the load, computer state, or software code may affect the device status from regular to irregular. These changes deteriorate the service efficiency and quality. The supervision of Ad hoc mobile cloud computing networks is decentralized, and the nodes can move randomly [2]. Here, data transition and distributions from one node to another are achieved using a wireless link. This process facilitates the route identification for intruders in the network. Generally, the nodes in adhoc mobile cloud computing networks communicate without nearby base stations. For instance, the routing protocols in adhoc networks regulate the possible path for good communication through the participating mobile nodes [3]. Adhoc mobile cloud computing networks are utilized for many applications because of their decentralized peer-to-peer system. These networks are arranged in clusters from which one node is selected as the mobile cloud head, and the rest of the participating nodes are the cloud member nodes. Furthermore, mobile cloud clustering is applied to distinguish the decentralized peer-to-peer system from interconnected infrastructure, namely, adhoc mobile cloud computing networks. Each network in a particular environment has a mobile node, which is denoted as the mobile cloud head and performs the network route organizer’s function [4].

Additionally, intrusion detection methods are required to address security issues and confidentiality concerns. The hidden Markov model works with high stability in terms of exact data transition and distributions. This model helps identify and prevent intrusions to address the security issues and confidentiality concerns of adhoc mobile cloud computing networks [5]. Several protocols, like the ad hoc on-demand distance vector, the border gateway protocol, location aided routing, optimized link state routing, open shortest path first, the routing information protocol, and the zone routing protocol, are used to enhance data communication. However, they fail to decrease the routing overhead. Thus, the developed clustering-based storage and search optimization approach model has enhanced adhoc features, such as addressing delay, energy consumption, and flexibility, and our main research analysis is based on the following questions:

How can node stability be maintained while transferring data in an adhoc network during failure?

Contribution

In this paper, a new clustering-based storage and search optimization approach using cross-layer analysis based on intrusion detection methods is proposed. To avoid the delay in data packet of the cloud environment and a significant increase in the route efficiency of adhoc mobile cloud computing networks, the following contributions are made in our research work:

First, this research quantification converts individual mobile nodes into mobile cluster nodes using the proposed clustering approach based on the storage and search optimization technique.

The remainder of the paper is organized as follows. In Section 2, the previous clustering approach, the storage and search optimization approach, and the hidden Markov model are discussed. In Section 3, we describe the proposed cross-layer analysis of intrusion detection methods using the clustering approach based on the storage and search optimization approach employed to enhance the adhoc mobile cloud computing network’s performance. The experimental results are presented in Section 4, and the conclusions are shown in Section 5.

Storage using an autonomous mobile infrastructure-based phoenix technique allows a system to make opportunistic use of mobile computing devices and adhoc networking to provide quick storage services to clients in a localized geographical region. A large amount of potential data is lost due to node mobility. An internode communication system can overcome the potential data loss through distributed communication and a storage protocol.

Pudlewski et al. [6] proposed a cross-layer perspective scheme for video transmission over lossy wireless networks. Li et al. [7] presented the construction of a dynamic cloudlet-assisted energy-saving routing mechanism for mobile adhoc networks. This approach detects the target nodes, where the intrusion is happening in network. Zhang et al. [8] proposed a communication workflow technology known as lightweight service-oriented architecture (SOA)-based multi-engine architecture for mobile smart devices.

The proposed adhoc mobile cloud computing network is established using cross-layer analysis-based intrusion detection methods with hidden Markova model [17,18]. This cross-layer analysis runs on cloud computing mechanisms and mobile adhoc networks, which extend the functionality of the analysis. Here, a practical mobile adhoc network denotes data sharing between different mobile nodes on a cloud environment [19]. The node’s size is static for fast access between the cluster head and the nodes [20]. Hence, deciding which data should be present in the cloud or the mobile ad hoc network during interaction is crucial. Any mobile node can transfer stored data to the cloud. This facility allows unauthorized data (attacker data) to be stored in the cloud [21]. Each unauthorized data stored in the cloud will have a different token, which must be stored in the cloud [22]. The proposed clustering-based storage and search optimization approach (cross-layer analysis based on intrusion detection methods) was implemented on Network Simulator 3 (NS-3) [23]. This research employed the cross-layer analysis of intrusion detection (CLAID) methods as our key system for transferring and distributing data to the other nodes. Each node is identified by a token of its public/private key [24]. The complete application stack of any nearby node is illustrated in Fig. 2.

Fig. 2 describes the complete application stack of the node with smart routing using cloud software codes. These codes helps in verifying detail about any transmission and raise the token [25]. The CLAID cluster tool is used to coordinate nodes between the authorized mobile device and different nodes.

CLAID is the content-addressable cloud environment’s transmission model. This model is used to maintain the data transaction within the linked node on the cloud environment and unify all data transactions that link to the decentralized peer-to-peer cloud system [26]. Whenever the decentralized peer-to-peer cloud system receives a request from the clustering-based search optimization, the hidden Markov model first handles this request [27]. Further, CLAID forwards the request to the clustering-based storage and search optimization for the allocation of the route in the adhoc mobile cloud computing network. Later CLAID replies to its route history in network [28]. The data communication between the two authorized mobile devices is processed using the route request/route response (RREQ/RRES) signal. This system is distinguished for the hidden Markov Model based on the intrusion detection system’s cross-layer analysis [29,30].

The route between the source node and the destination/target node is connected by the cloud head for the clustering-based storage and search optimization [31–33]. This storage model allows decentralized routing peer-to-peer applications based on the following key terms:

Clustering-based Storage: Data is stored in the interaction history.

The developed clustering-based storage and search optimization approach (cross-layer analysis based on intrusion detection methods) is simulated using NS-3 to calculate the performance parameters, like the end-to-end delay, the energy consumption, the network lifetime, the packet delivery ratio, and the throughput. The simulation details using the proposed CLAID methods by the clustering approach based on the storage and search optimization approach are provided in Tab. 1.

Performance Metrics

The existing approaches such as MZBIDS [47] and EA-ICA [48] are compared with proposed CLAID technique which is based on storage and search optimization. CLAID calculates the QoS performance, such as the end-to-end delay, the energy consumption, the network lifetime, the packet delivery ratio, and the throughput. In addition, the area under the curve (AUC) is plotted for binary classification problems. AUC is the measure of a router’s ability to distinguish between routes and summarize the score chart.

AUC Chart: An accurate system will detect the chart’s intrusion, showing different values and stating the entire false positive and true positive rate (Fig. 4). In addition, the system also provides the accuracy of the proposed algorithm stated on the chart. With this data, the number of packets containing intrusion attacks is identified. To describe the efficiency established by CLAID methods’, fine-tuned accuracy is evaluated based on the following action to achieve an accurate result [49].

(4) Accuracy=True Negative+True PositiveTrue Negative+True Positive+False Negative+False Positive

Fig. 5 indicates that accuracy is assessed using Eq. (4) for storage and route sustentation, employing the proposed CLAID. Using the existing methods MZBIDS and EA-ICA in Tab.1, the proposed CLAID’s efficacy is approximately 97.76%. Furthermore, an accuracy of nearly 97% was achieved by our proposed CLAID [49].

Packet is measured using Eq. (5):

(5) P=Total no of participated data packetsTotal no of transmitted data  packets×100.

Tab. 2 proves that the proposed CLAID method can achieve a high packet delivery ratio. The evaluated measures indicate that the existing MZBIDS obtains 92% in packet delivery ratio, whereas CLAID obtains 97% in packet delivery ratio. Furthermore, a high PDR value of 97.856% was obtained by the proposed method, which is more powerful than the other methods (Fig. 6) [50].

Tab. 3 shows the throughput value of the proposed CLAID (Fig. 7). Throughputs of approximately 30 and 43 kbps are achieved through existing approaches, such as MZBIDS and EA-ICA. The efficiency of the proposed CLAID is also higher than the throughput performance of nearly 121 kbps. Fig. 8 and Tab. 4 describe the adequate energy used for any node in the transmission path and show the measured values. Furthermore, the comparisons between energy usage and current approaches are based on the comparison in Fig. 8.

The energy consumption of the nodes is estimated depending on the simulation time. The proposed CLAID model consumes minimal energy due to the minimal, efficient route. In an adhoc mobile cloud computing network, one of the required metrics to achieve successful connectivity is network lifetime. When the nodes’ energy consumption is high, connecting with nodes to impact the network’s lifespan is impossible. The energy consumption is minimal in this suggested method, indicating that the network’s lifespan increased.

Fig. 8 and Tab. 5 describe the network lifetime for many nodes and the testing with predominant approaches. When sending messages across 60 nodes, the existing MZBIDS and EA-ICA have network lifetimes of 220 and 350 s, but the proposed CLAID method’s maximum network lifetime 627.133 s. The delay is measured to identify the time required to reach the destination of the transmitted data. The ratio of time consumed for receiving packets to the time consumed by the transmitted packets is measured using Eq. (6).

(6) End-to-end delay=Time Duration (Received data packet)Time Duration (Transmitted data packets)

End-to-end delay value of the proposed CLAID method and compares the value with those of existing techniques. Here, MZBIDS and EA-ICA obtain delay values of approximately 59 and 43 s, respectively. The proposed CLAID method also obtains a delay value of 14 ms. In comparison, the proposed CLAID obtains a lower delay value of 0.6 s when transmitting 10 nodes.

Adhoc mobile cloud computing networks face various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment issues. This research proposes the CLAID methods using the clustering approach based on the storage and search optimization approach to overcome the above issues against the routing in the cloud environment. These approaches are formed by cross-layer analysis is based on intrusion detection methods for clustering and route maintenance in an adhoc mobile cloud computing network. Hence, the developed clustering-based storage and search optimization approach model was evaluated using NS-3, and the outcomes were compared with those of existing methods, such as MZBIDS and EA-ICA, to evaluate parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput.