The Internet of Things (IoT) is widely recognized as the driving force behind the "third industrial revolution," enabling devices to collect, share, and respond to physical data via the Internet. This revolution has grown at an impressive rate, from two billion connected objects in 2006 to an anticipated 200 billion by 2020, transforming fields such as education, finance, energy, healthcare, transportation, and urban planning into “smart” sectors. With these advancements, however, ensuring security within IoT networks has become essential to prevent data breaches and protect users. The rise of interconnected IoT applications, such as smart homes and vehicles, also increases the risk of cyber-attacks, highlighting the need for robust defenses. A single vulnerability within an IoT system can compromise not just one component but potentially the entire network. For example, the 2016 Dyn cyberattack exploited IoT devices through malware (Mirai) to create large botnets, underscoring IoT systems' susceptibility to evolving cyber threats. Wireless Sensor Networks (WSNs), comprising nodes that gather and transmit data from various sensors (e.g., temperature, pressure), are particularly vulnerable because of their open wireless nature [1], [2].

In defense and surveillance contexts, WSNs are valuable for detecting environmental changes and unauthorized activity, although securing these systems with minimal energy use is challenging. Mobile adversaries can target WSNs by intercepting data, modifying sensor behavior, or compromising node functionality, all of which impact network performance and reliability [3], [4]. To address these risks, Intrusion Detection Systems (IDS) are commonly employed to monitor and safeguard network integrity. While IDS technologies are traditionally used in wired networks, advancements in data mining, machine learning, and deep learning are paving the way for adaptive IoT security solutions. Researchers such as Agarwal et al. and Buczak et al. have investigated these approaches, while others, like Fadlullah and Hodo, explore deep learning's role in intrusion detection. This research aims to extend these methods for IoT, adapting IDS to dynamically detect and prevent security threats within complex IoT ecosystems [5–7].

In this section, we dive into the unique dance of algorithms designed to guide and safeguard routes within wireless sensor networks. Imagine the route identification Request (RREQ) as the opening move, setting the stage for secure data passage. Following this, the initial selection phase chooses the strongest, most reliable pathways, ensuring a smooth journey ahead [8–11].

Then, enter the secure monitors, chosen specifically to oversee multiple communication channels. Their role is akin to vigilant sentinels, stationed across the network to keep threats at bay. When needed, the algorithm initiates on-demand route maintenance, like a rapid response team, stepping in to fix and reroute data pathways in real time. With secure monitors at the helm, secure transmission becomes more than just a passage; it’s a fortified journey, shielded against potential vulnerabilities. The monitors generate aggregated reports, track each channel’s status, and provide constant updates. Additionally, channel-dependent key shares further strengthen this communication, uniquely tailored to each channel to enhance confidentiality.

Together, these algorithms not only secure the communication within the network but also ensure that every bit of data travels safely and efficiently, demonstrating the intricate metrics at play in secure routing protocols. Peer-to-Peer (P2P) systems have expanded rapidly due to their ability to distribute and share resources across large decentralized networks. A central challenge in these systems is the selection of high-capacity nodes to serve as super-peers, a process complicated by heterogeneous node capabilities and the dynamic nature of P2P environments. Recent studies highlight the need for capacity-aware protocols that can accurately identify the most suitable nodes for this role. Approaches based on SG-2 models and Gossip-based mechanisms— utilizing broadcast services, delay-distance metrics, and status exchanges among neighboring nodes—have demonstrated improved efficiency in selecting powerful super-peers. Such methods enhance overall network performance and reduce convergence time toward an optimal overlay structure, making them valuable for modern distributed architectures.

This section presents the simulation results for the proposed secure routing system, implemented using **Network Simulator Version 2.34 (NS-2.34) ** with OTCL (Object Tool Command Language). NS-2.34 was selected for its detailed capabilities in simulating mobile ad hoc networks, offering comprehensive insights into network topology, sensor nodes, network links, application protocols, and queuing mechanisms. The NS-2.34 simulation environment allowed for precise evaluation of the proposed routing protocol’s performance and functionality.

The secure framework centered on three core components: detection, prediction, and isolation, with a focused analytical approach examining traffic generation patterns and routing mechanisms. Alternate NS versions proved inadequate for managing the required end-to-end connectivity and handling network load under various traffic conditions. Consequently, NS-2.34 was chosen to implement a secure hybrid routing protocol, which combines features from Multipath Optimized Link State Routing (OLSR) and Ad hoc On-Demand Multipath Distance Vector (AOMDV) protocols. This hybrid protocol is designed to monitor sensor-node activity effectively, ensuring network security and stability [15].

Table 8 shows the simulation parameters. The MAC layer uses IEEE 802.11b, providing a transmission range of 140 meters. Node speeds vary between 0 m/s and 5 m/s, and their high mobility results in cluster formation. Testing included both existing and proposed routing protocols; however, neither performed optimally in highly dynamic environments. At the transport layer, the protocols utilize User Datagram Protocol (UDP), transmitting data at a constant bit rate (CBR) of 512 or 1024 bytes. Key communication metrics, including monitoring rate and detection ratio, were evaluated over a 500-second simulation period. Nodes were distributed within a 600 x 600-meter area, with malicious nodes randomly placed to study the impacts of mobility and data collision on network security.

Algorithm 6. Details of simulation parameters.

Constraints Values

Network Size 600 m x 600 m

Number of Nodes 200 Nos.

Initial Energy 10 Joules

Simulation Time 100 S

Security Module HMAC, TF-256 Bits, Key Generation

Routing Protocol DSDV/OLSR/AOMDV/TARCS/Secure Hybrid Routing

Channel Model Two-Ray Ground Propagation

Data Transmission Mode Constant Bit Rate (CBR)

Antenna Omni-Directional

Data Packet Size 512 / 1024 Bytes

Data Transmission Rate 16 Kbps

MAC Protocol IEEE 802.11b, i.e., Distributed Coordination Function (DCF)

Range of Transmission 140m

The proposed hybrid routing and monitoring mechanism has been developed and implemented with dynamically selected sensor monitor nodes in ad hoc sensor networks to enhance secure data transmission. To ensure flexible security, a secure routing and monitoring protocol utilizing multi-variant tuples has been introduced, employing symmetric key algorithms such as MARS, RC6, Serpent, and Two-fish. This approach effectively identifies and mitigates adversarial threats within the global sensor network. Built on the Authentication and Encryption Model (ATE), the mechanism incorporates an Eligibility Weight Function (EWF) to select sensor guard nodes using a complex symmetric-key approach. By leveraging the strengths of MARS, RC6, Serpent, and Two-fish, the hybrid system uses a node-selection algorithm that strengthens security through the strategic choice of sensor monitor nodes. This innovative design facilitates secure data transmission in the ad hoc sensor network while minimizing the impact of malicious activities. Extensive simulations show that the proposed hybrid secure routing protocol achieves a higher monitoring rate and detection ratio than existing protocols.

Looking ahead, plans are in place to establish a real-time testbed to implement this hybrid routing and monitoring mechanism, enabling validation of the simulation results. Furthermore, this mechanism will be applied in IoT-based Wireless Sensor Networks (WSNs) to ensure the security and confidentiality of application environments, such as smart cities.

I, K. S.: conceptualized the study, developed the methodology, wrote the initial draft, analyzed the data, implemented the necessary algorithms, and reviewed the final manuscript.

This research received no external funding.

The data used and analyzed during the current study are available from the corresponding author upon reasonable request.

The authors declare no conflicts of interest regarding the publication of this paper.