## 5 Model Limitations  
One of the main limitations of the ECC algorithm, even optimized, is the need for higher computing power and energy consumption compared to the initial requirements of some low-power IoT devices (LPD). Devices with limited microcontrollers (for example, ARM Cortex-M0) may encounter difficulties in supporting cryptographic implementations that, although efficient, require complex mathematical processes.  
Although the proposed ECC algorithm provides a balance between security and energy efficiency, key exchange on resource-constrained devices can still introduce latency in large device networks. In extended IoT scenarios, where devices need to communicate frequently for secure data exchange, this can cause delays and possible deadlocks.  
Another limitation is related to the length of the keys and the hashing algorithms used. Shortening key lengths to reduce computational requirements can affect the security level of the system, making it vulnerable to more sophisticated attacks. In networks with mixed devices (low and high power), this limitation can become a problem, because the trade-off between security and efficiency must be managed to avoid weak points in the network.  
In a complex IoT environment, key synchronization and management become a significant challenge. Key exchange between LPD and HPD devices requires a synchronization mechanism that can introduce additional vulnerabilities into the network. The ECC algorithm requires frequent updates of session keys to maintain secure communications, but this can become a risk factor if devices fail to synchronize keys at correct intervals.  
ECC algorithm optimized for IoT can work well in smaller networks; however, as the number of devices increases, key management costs and processing requirements increase exponentially. This limits the scalability of the IoT network in an ECC-based configuration, suggesting that additional optimizations may be required to cope with network growth without compromising performance.  
An important limitation of any cryptographic system in the IoT environment is the vulnerability of devices to physical attacks. IoT devices are often located in insecure environments and can be exposed to attacks by tampering with hardware or by extracting sensitive data through side-channel attacks. This vulnerability must be taken into account, because software protection can become ineffective if an attacker gains physical access to the device.  
The integration of hardware acceleration for ECC computations, such as dedicated cryptographic coprocessors, could significantly enhance the performance of low-power IoT devices. Exploring these optimizations will be a priority for future work to address current limitations more effectively.  
In conclusion, although the ECC algorithm optimized for IoT offers an increased level of security and is relatively energy efficient, there are challenges related to its application in large and diverse networks of devices. The need for additional optimization for low-power devices, efficient key management, and ensuring synchronization between IoT devices are some of the limitations that must be addressed to turn this model into a viable solution at scale.  
One potential avenue to address the vulnerability to physical attacks is the incorporation of tamper-resistant hardware, which can detect or prevent unauthorized physical access to devices. This approach, combined with lightweight cryptographic protocols, could significantly enhance the resilience of IoT devices in resource-constrained environments. Future research will focus on designing tamper-resistant IoT hardware that can withstand side-channel and physical attacks. Initial approaches may include integratingsensors that detect physical tampering or employing hardware-based secure enclaves for cryptographic key storage.