An ECC algorithm is a public key encryption method based on the mathematics of elliptic curves over finite fields. It tempts a high-security level, creating an elliptic curve data key that safeguards the transmission and optimizes computational efficiency. EC cryptography is used extensively in the vast network of IoT to help support secure communication, establishment of keys, and data integrity. ECC is a public key cryptography algorithm, and this model detects several kinds of attacks, including man in the middle, denial of service, and weak authentication. The ECC model utilizes two keys: the public key and the private key. The public key encrypts the real information; the private key decrypts the original information [2].

Step 1. Select an elliptic curve: initialize a curve equation elliptic curve over a finite field), which is used to encrypt the messages. The curve is designed to have parameters aaa and bbb that meet security requirements. Step 2. Generate key pairs: 1) Private Key: choose a large random number for that private key. 2) ECC functions have properties that range from private keys to public Keys. — Public Key => Compute the public key and multiply it with your Private Quadratic Residue Q on the Elliptic Curve. It is a predefined and shared base point that provides an initial key generation starting point.

Step 3. Encrypt the data: the sender creates a new temporary private key and derives the corresponding temporary public key.

This data message is then encoded using the sender's temporary public key, combined with a public uplink secure layer enterprise PK and eventually the recipient's private enterprise key, to ensure that only this specific person can decrypt it.

Step 4. Transmit encrypted data: Sender: the sender sends back encrypted data and a temporary public key. The recipient decrypts the message using this temporary public key and their private key.

Step 5. Decrypt the data: with some math help from ECC, the recipient rebuilds the original message using her private key in combination with the sender's public key.

The AES is a symmetric encryption algorithm widely used for securing data in WSNs and IoT applications. Due to its balance of security and efficiency, AES is ideal for IoT devices with constrained resources. It uses a block cipher approach, dividing data into fixed-size blocks (typically 128-bit) and encrypted in rounds. One of the most widely studied encryption algorithms is the AES, which offers robust security and efficiency in securing digital communications. AES, developed by Daemen and Rijmen, supports key lengths of 128, 192, and 256 bits, with AES 256-bit encryption providing the highest level of security [3]. The AES Algorithm, adopted by the U.S. government in 2001, is a block cipher that transforms 128-bit data blocks under a 128- bit,192- bit or 256-bit secret key by means of permutation and substitution [4].

Step 6. Key expansion: through key expansion, a 128-bit secret key creates a series of round keys. These round keys are used in each encryption round to ensure the data's secure transformation.

Step 7. Initial round: add round key: the initial plaintext block is XORed with the initial round key, creating the first transformation of the data.

Step 8. Encryption rounds (typically 10 rounds for 128-bit AES).

I. SubBytes: each byte in the block is substituted using a predefined substitution table (S-Box).

II. ShiftRows: rows of the block are shifted cyclically to the left to create data diffusion.

III. MixColumns: columns of the block are mixed by applying a mathematical transformation to increase diffusion.

IV. Add round key: the current state is XORed with the round key for each round.

Step 10. Final round: the final round involves SubBytes, ShiftRows, and Add Round Key steps but omits MixColumns. The output after this round is the encrypted ciphertext.

Step 11. Data decryption: the decryption process is the reverse of encryption, where each step (Add Round Key, MixColumns, ShiftRows, SubBytes) is applied in reverse order. Fig. 4 below shows the algorithm's entire

The LAP is designed for resource-constrained IoT devices, providing mutual authentication between devices and servers with minimal computational and memory requirements. LAP is widely used in WSNs to prevent unauthorized access and secure data exchange. In LAP-IoHT, the gateway authenticates all participants, including the users and wearable sensors. Subsequently, a shared session key is established for each communication session. LAP-IoHT encrypts the biometric features of the users to ensure anonymity [5]. Step 12. Initialize parameters: the device and server establish shared parameters, including a secret key and unique device identifiers.

Step 13. Device sends authentication request: the device initiates an authentication request by generating a unique nonce (random number) and combining it with the device's identifier, creating a message sent to the server.

Step 14. Server authentication: the server verifies the device’s identifier and generates a response by encrypting the device’s nonce with the shared key. This encrypted response is then sent back to the device. Step 15. Device verification: the device decrypts the server's response using the shared key and confirms its authenticity by comparing the decrypted nonce with its original nonce.

Step 16. Mutual authentication success: if the device's verification is successful, both entities can securely exchange data, as mutual authentication has been established.

IoT-based WSNs often consist of small, low-power devices with limited processing capabilities, memory, and battery life. This restricts the implementation of complex security protocols and robust encryption algorithms, making it challenging to achieve strong security without compromising performance or efficiency.

As the number of devices in IoT networks grows, scalability becomes a concern. Adding new devices requires the security system to handle increased authentication requests, key management complexities, and data integrity checks, which can overload networks and lead to security vulnerabilities.

IoT-based WSNs are vulnerable to attacks like eavesdropping and data interception, compromising data integrity and privacy. Ensuring data protection in transit and at rest, particularly within low-power sensors, is challenging due to their limited storage and processing capabilities.

IoT sensors are often deployed in remote or unsecured areas, making them vulnerable to physical tampering and node capture attacks. Attackers can gain direct access to devices, extract encryption keys, or tamper with data, creating significant security risks for the network.

IoT security for WSNs often requires a multi-layered approach, encompassing device-level, network-level, and application-level security. Implementing cohesive security measures across these layers is challenging due to device diversity and varying communication protocols, which may lead to gaps in protection.

IoT in smart manufacturing has led to a major improvement in product quality and efficiency of the manufacturing process. However, enormous uncertainty issues can still arise during its implementation. Some of the main challenges include the uncertainty of machine designers, builders, and even the end users to contrive this technology, as each enterprise has its own design requirements and process. This also creates the need for a customized design, which is usually expensive and requires experts in the related domain [6].

Lightweight cryptographic algorithms designed for IoT devices aim to provide strong security with minimal resource consumption. Research into these algorithms, such as ChaCha20 and SPECK, continues to evolve, making it possible to balance security and efficiency for resource-limited WSNs.

Artificial Intelligence (AI) and Machine Learning (ML) are becoming integral to identifying unusual patterns and detecting security breaches in real-time. Emerging AI-driven intrusion detection systems can help WSNs identify threats early and respond autonomously, enhancing network resilience without constant human intervention.

Blockchain technology can address IoT security issues through decentralized data storage, making it difficult for attackers to compromise the network. Using blockchain, each sensor's data can be securely verified across the network, enhancing trust and data integrity without relying on a central authority.

Although still emerging, quantum cryptography holds promise for secure communication in IoT networks by enabling unbreakable encryption techniques. Quantum key distribution (QKD) is a potential solution for future IoT and WSN security, providing theoretically unbreakable encryption for data transmitted across networks. The field of quantum cryptocurrency encompasses the implementation of quantum cryptography protocols, which are designed to ensure secure transactions that can withstand potential threats posed by quantum computing technology [8].

Context-aware security protocols adapt security measures based on device location, data sensitivity, and environmental conditions. This approach allows WSNs to apply stricter security for more sensitive data while minimizing resource use for less critical applications, making security more adaptable and efficient.

To counter resource constraints, this approach introduces adaptive encryption methods that adjust encryption strength based on data sensitivity and network load. Lightweight encryption techniques, such as ECC-based variations and optimized AES, will be used to achieve secure data transmission without overwhelming device resources.

This proposal includes a hierarchical key management system where cluster heads manage encryption keys for their nodes to manage scalability. This structure reduces the need for individual devices to handle complex key exchanges, improving security scalability while distributing computational load across the network.

To ensure data integrity and privacy, this solution proposes an end-to-end protocol that validates data at both source and destination points. Combining digital signatures and hash-based data verification can create a secure path, enabling strong integrity without continuous encryption at each node.

For physical security, tamper-resistant hardware modules in IoT sensors are proposed. These modules can store encryption keys securely, making it harder for attackers to extract data through physical attacks. In conjunction, these modules would trigger alarms upon physical tampering, adding a layer of security to network nodes.

This proposal advocates for a cross-layer security framework to address the complexity of multi-layered security requirements. By integrating security protocols across the physical, network, and application layers, this framework can reduce security gaps and offer a unified approach to effectively handle diverse WSN protocols and device types.