Internet of Things (IoT) devices increasingly contribute to critical infrastructures, necessitating robust security measures. LoRaWAN, a low-power IoT network, employs the Advanced Encryption Standard (AES) with a 128-bit key for encryption and integrity, balancing efficiency and security. As computational capabilities of devices advance and recommendations for stronger encryption, such as AES-256, emerge, the implications of using longer AES keys (192 and 256 bits) on LoRaWAN devices’ energy consumption and processing time become crucial. Despite the significance of the topic, there is a lack of research on the implications of using larger AES keys in real-world LoRaWAN settings. To address this gap, we perform extensive tests in a real-world LoRaWAN environment, modifying the source code of both a LoRaWAN end device and open-source server stack to incorporate larger AES keys. Our results show that, while larger AES keys increase both energy consumption and processing time, these increments are minimal compared to the time on air. Specifically, for the maximum payload size we used, when comparing AES-256 to AES-128, the additional computational time and energy are, respectively, 750 $\mathrm{m}$$\mathrm{s}$ and 236 μJ. However, in terms of time on air costs, these increases represent just 0.2% and 0.13%, respectively. Our observations confirm our intuition that the increased costs correlate to the number of rounds of AES computation. Moreover, we formulate a mathematical model to predict the impact of longer AES keys on processing time, which further supports our empirical findings. These results suggest that implementing longer AES keys in LoRaWAN is a practical solution enhancing its security strength while not significantly impacting energy consumption or processing time.

High performance computing has been more important in the past decade. In the present day, data used for processing becomes enormous. Where a high performance computing resource is needed to help process the data. Some scientific experiments involving big data. Which requires high speed data processing cannot be done by an ordinary computer system. Also, there is a need for support of parallel processing. The solution starts by dividing the job into a number of sections to be processed into parts and the processing unit each processing unit of data at the same time. Then, the system sends the calculated result back to the compiled. This mechanism will speed up the processing time to complete the task and generate more output at the same time. Therefore, a solution in this study is to maximize efficiency when using the resources of the computer which involves the processing power of the processor (CPU Cores).When the HPC system has a large number of concurrent users and requests processing resources that do not match the actual usage. Therefore requires a system to detect job requests that use inefficient computing resources to help users and system administrators to work effectively.