# Analysis of Lightweight Cryptographic Algorithms on IoT Hardware Platform +
## 2 Background  
The research and development of lightweight cryptography for use on resource-constrained IoT devices have advanced quickly over the past decade. The main goal is to create and use simple cryptographic algorithms that may be applied to such applications while providing the appropriate levels of security. The implementations for IoT applications can be categorized as either software or hardware solutions. For the hardware implementation, the speed, the area, and the energy consumption are taken into consideration. For the software implementation, the required memory size (ROM and RAM) of the embedded software is taken into consideration. When selecting the proper security algorithm to be utilized for resource-constrained devices, these restrictions must be respected [13]. With a focus on LED [14], Piccolo [15], and PRESENT [16], the authors in [17] investigated various software implementations of lightweight ciphers for x86 processors. First, they examined table-based implementations and then offered a theoretical model to forecast how different potential trade-offs will behave in relation to the processor cache delay profile. For IoT platforms, such as resource-constrained devices (8-bit AVR and 32-bit ARM Cortex-M3) and Application-Specific Integrated Circuits, the authors in [18] studied the lightweight properties of the HIGHT [19] block cipher and offered the optimized implementations of both software and hardware for IoT platforms, such as resource-constrained devices (8-bit AVR and 32-bit ARM Cortex-M3) and Application-Specific Integrated Circuits. Utilizing a unique, lightweight reconfigurable processor, the authors in [20] enabled this comparison in their study. Six ciphers including AES, SIMON, SPECK, PRESENT, LED, and TWINE were implemented in the hardware with a Register-Transfer-Level (RTL) design [21] and in the software with a specially designed reconfigurable processor. A direct comparison of the area, throughput, power, energy, and throughput-to-area (TP/A) ratio was performed. Both hardware and software versions were implemented in an identical Xilinx Kintex-7 FPGAs SIMON [22], a lightweight block cipher designed for hardware implementation. Implementing, optimizing, and modeling the SIMON cipher design for resource-constrained devices with a focus on energy and power were the goals of the research conducted by the authors in [23]. The scalar and pipelines design implementations FPGA technology were the two types that were explored in this research [24]. The hardware implementation of the block cipher RECTANGLE with various data paths was the focus of the authors in [25]. They devised, constructed, and assessed the five most effective RECTANGLE [26] cipher data paths for various data bus sizes. The same implementation conditions were used for all of these data paths when they were implemented on various FPGA platforms, and the results were compared across all performance metrics. The ideal architecture for an application can be chosen based on the device and desired performance metrics. Meanwhile, in [27], the authors used Artix-7, Spartan-6, and Cyclone-V FPGAs to implement the six NIST LWC round 2 candidate ciphers, SpoC, GIFT-COFB [28], COMET-AES [29], COMET-CHAM [29], Ascon [30], and Schwaemm and Esch [31]. Among all the schemes, it was clear that SpoC had the lowest area and power consumption, while Ascon had the highest throughput-to-area (TPA) ratio. KLEIN-80, TWINE-80, Piccolo-80, SPECK (64, 96), and SIMON (64, 96) were among the choices of lightweight block ciphers that were implemented on the Atmega128 processor in the AVR studio 5.1 simulation environment performed by the authors in [32]. The evaluation's findings indicate that the SPECK (64,96) cipher was the most energy efficient and is suitable for wireless sensor networks. Meanwhile, the implementation of the TWINE-80 was the most appropriate with regard to memory utilization. The authors in [32] studied the efficiency of lightweight block ciphers when used in resource-constrained environments.