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Recently, most modern cars can be controlled with the Remote Keyless System (RKS). A Remote Keyless System consists of a key fob that communicates wirelessly with the car transceiver that is used to control and secure access to the vehicle. The keyless entry systems are based on RFID communication and are equipped with limited computation and power resources. It was proven to be a target for cybercriminals. In this work, we proposed the use of the LED block cipher for data encryptiondecryption of the keyless entry systems. The proposed hardware architecture of the LED algorithm was optimized to fit the limited resources of the keyless entry systems. The proposed 8bit sequential architecture contains 627 LUTs+FFs. To further reduce hardware resources, we proposed a 4bit architecture for the MixColumns subfunction, which gives good results in occupying hardware resources by reducing it to 597 LUTs+FFs. This leads to a good effect on the area implementation and power consumption of keyless entry systems. Thus, the proposed LED hardware architecture may be applied to lightweight applications that demand a high level of secrecy such as the key fob. In order to adopt the proposed design of the LED block cipher as a security system for keyless entry systems, we examine the security of the proposed LED architecture using five metrics; entropy analysis, histogram analysis, correlation analysis, NPCR, and UACI. As a result, the LED block cipher has a good ability to encrypt any data against any attack.
lightweight cryptography, hardware implementation, LED block cipher, key fob, highperformance, security analysis, lowresource
Modern cars are becoming smarter through a collection of automotive systems such as braking, power, locks, and so on. As the number of functionalities in vehicles explodes, the software needed to manage that functionality will highly increase, presenting the best opportunities for hackers looking to attack vehicles. As important systems, key fobs are becoming more complex. In addition to unlocking the vehicle and enabling the ignition, key fobs can be used to control windows and mirrors, set seats, and enable radio [1]. As an example, Tesla key fobs can be used to start automated parking and parking.
Recently, keyless vehicles have been increasingly targeted by burglars. Theft of automobiles is accomplished via the reprogramming of remoteentry keys [2]. For this reason, several insurance companies have rejected the insurance for this issue. Keyless entry systems are a system that has an RFID (RadioFrequency IDentification) tag for wireless communication to the vehicle. As a result of RFID technology, keyless entry systems are becoming more vulnerable to several security threats [3]. It is vulnerable to a Scan Attack, Playback Attack, TwoThief Attack, Challenge Forward Prediction Attack, and Dictionary Attack [4]. Also, OnBoardDiagnose (OBD) key programmers are another risk to Remote Keyless Entry Systems (RKES). For systems that employ the rolling code technique, the scanning attack is very effective [5]. It is possible to launch the scan attacks against these systems, by sending several codes to the automobile transceiver and making sure they match the transceiver's code. Using the OBD key programmers [6], can be used to unlock vehicles, where an attacker can replicate all key information to a keyless entry system that has not been programmed.
To solve these problems, an encryption system is an efficient security system to ensure data protection for the keyless entry system. The mentioned features make a keyless entry system encryption an attractive target for cyberattacks. So, hackers will try to break the encryption and clone the RKES. Glocker et al. [4] proposed the symmetric encryption algorithms as a solution to the keyless system. As a result, the use of symmetric encryption algorithms in the keyless system gives a good level of security to protect private data. However, it hurts hardware resources and power consumption.
As the keyless entry systems are based on a RFID transponder to communicate with the vehicle, the available computation resources are very limited which prevents the implementation of powerful encryption algorithms [6]. So, the invention of lightweight cryptography algorithms is considered a powerful solution. In the last decade, a large number of research work has already been carried out to propose a lightweight security system that is suitable for resourceconstrained applications. Lightweight encryption was provided as a security solution for highly constrained devices and embedded systems [7]. In this field, several lightweight encryption algorithms have been proposed, for instance, PRESENT [8], LED [9], Piccolo [10], KATAN [11], and SPARK [12].
The main objective of this work is to propose an efficient hardware architecture of the lightweight block cipher for implementation on a keyless entry system. To that end, we propose a hardwareserial architecture to improve the LED block cipher in terms of area implementation for use as a security system on a keyless entry system. The LED algorithm was designed especially for embedded devices with limited computation resources (e.g., Memory, area, and power). The LED block cipher uses input data of 64 bits and a key length of 128 bits. The main idea of this work was to read the data by 8 bits through 16 cycles. Then, to speed up the processing time, an 8bit architecture of the MixColumns process was proposed to reduce the latency.
The following is a summary of the paper's contributions:
The rest of the paper is organized as follows: The description of LED Block Cipher is presented in Section 2. The proposed hardware designs of the LED algorithm are detailed in Section 3. The performance and the security analysis of the proposed designs are presented in Section 4. Finally, the conclusion is drawn in Section 5.
Already an important aspect of keyless entry systems, an LED block cipher is a lightweight block cipher designed for resourceconstrained environments, making it suitable for small devices such as key fobs and smart cards. The LED algorithm provides efficiency and a lightweight design, making it suitable for keyless entry systems with limited resources. However, its security is limited by its small block size, and it may not be appropriate for applications requiring high levels of security. When LED block cryptography is used to ensure the security of keyless entry systems, the keyless entry systems are robust against sidechannel and similar attacks.
LED (lightweight encryption devices) is a lightweight encryption algorithm based on an SPN (substitutionpermutation network). It was proposed by Guo et al. [9] in 2011. LED is an algorithm that needs 64 bits to implement the encryption or decryption process. LED block cipher can be used with two versions of key lengths (46bit or 128bit). To generate the ciphertext, the encryption process for the LED algorithm requires 32 clock cycles or 48 clock cycles for a 64bit or 128bit key length. The encryption and decryption process of the LED algorithm is shown in Figure 1.
In order to understand how the data processing through the LED algorithm, Figure 2 shows the graph of a single LED encryption round, in which the encryption process has five subfunctions: AddRoundKey, AddConstants, SBoxes, ShiftRows, and MixColumns.
In each round, the input data M (i.e., the 64bit data path) consisting of 16 blocks (A0, through A15) is arranged in a fourbyfour bits matrix. The key addition layer (AddRoundKey) is the first operation of the LED encryption process, it is to add a 64bit of the subkey Ski with a 64bit of state. The two inputs are combined through a bit XOR operation, where the XOR operation is equal to addition. Then, the round constants RCST are combined with the output of the first process. The round constant RCST is defined as follows in (1):
$R C S T=\left(\begin{array}{llll}0 \oplus\left(K s_7\left\K s_6\right\ K s_5 \ K s_4\right) & \left(R C_5\left\R C_4\right\ R C_3\right) & 0 & 0 \\ 1 \oplus\left(K s_7\left\K s_6\right\ K s_5 \ K s_4\right) & \left(R C_2\left\R C_1\right\ R C_0\right) & 0 & 0 \\ 2 \oplus\left(K s_3\left\K s_2\right\ K s_1 \ K s_0\right) & \left(R C_5\left\R C_4\right\ R C_3\right) & 0 & 0 \\ 3 \oplus\left(K s_3\left\K s_2\right\ K s_1 \ K s_0\right) & \left(R C_2\left\R C_1\right\ R C_0\right) & 0 & 0\end{array}\right)$ (1)
As shown in Figure 2, the third layer in each round is the 4bit Substitution operation. The Substitution or Sbox layer can be viewed as a row of 16 parallel Sboxes. Each element of the state Ai is replaced by another element Bi using lookup tables with special mathematical properties, as given in Table 1.
Table 1. 4bit Substitution table for the LED algorithm
X 
0 
1 
2 
3 
4 
5 
6 
7 
8 
9 
A 
B 
C 
D 
E 
F 
S 
C 
5 
6 
B 
9 
0 
A 
D 
3 
E 
F 
8 
4 
7 
1 
2 
Figure 1. The encryption and decryption process of the LED algorithm
Figure 2. Graph of a single LED encryption round
The diffusion layer of the LED algorithm consists of two sublayers: ShiftRows and MixColumn transformations. The diffusion layer is an important layer to enhance the security level of any encryption algorithms, it is the influence of individual bits on the entire state.
The ShiftRows operation is a cyclic shift, shifting the second row of the state matrix cyclically by two elements to the right. The third and fourth rows are cyclically shifting two and three items to the right, respectively.
The MixColumn operation is the main diffusion element in the LED algorithm where every change in a 4bit input directly affects the 16bit output. Each 16bit column of the state after the ShiftRows operation is considered a vector and multiplied by the MDS matrix. The matrix MDS contains constant entries, as presented in Eq. (2).
$M D S=\left(\begin{array}{llll}4 & 1 & 2 & 2 \\ 8 & 6 & 5 & 6 \\ B & E & A & 9 \\ 2 & 2 & F & B\end{array}\right)=\left(\begin{array}{llll}0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ 4 & 1 & 2 & 2\end{array}\right)^2$ (2)
The encryption process of the LED algorithm consists of three blocks: an encryption round block, a control block, and a key scheduler. The key scheduler is a process that is used to generate 48 subkeys for 48 LED rounds using the original input key, which has a length of 128 bits. Due to the key required for key whitening in the final key addition layer, the number of subkeys is equal to the number of rounds plus one. As a result, for a 128bit of key length, the key scheduler process generates 48+1 subkeys, each of 64 bits. The subkeys are alternatively equal to the left part or the right part for the original key.
As mentioned in Section 2, the round block of the LED algorithm consists of five layers: 1AddRoundKey, 2AddConstants, 3SBox, 4ShiftRows, and 5MixColumns. All operations require 64 bits of input to generate 64 bits of output, so one round operation takes one clock cycle. In order to propose an efficient hardware design for the LED block cipher, a hardware architecture for the LED algorithm (128bit key length) is designed. The proposed hardware architecture is illustrated in Figure 3.
In the proposed architecture, the round block is designed on 8bit for the datapath encryption, in which most of the LED subfunctions are reduced to 8 bits (AddRoundKey, AddConstants, and SBox). Thus, these processes take 8 clock cycles. ShiftRows is a process that requires only 64bit input to generate 64bit output. For this reason, the ShiftRows process is performed on a oneclock cycle that is separate from other processes.
The MixColumns layer requires a minimum of 16bit input to be multiplied by the MDS matrix. So, it needs sixteen clock cycles.
$\left(\begin{array}{l}C_0 \\ C_4 \\ C_8 \\ C_{12}\end{array}\right)=\left(\begin{array}{llll}0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ 4 & 1 & 2 & 2\end{array}\right)^2 \times\left(\begin{array}{c}S_0 \\ S_4 \\ S_8 \\ S_{12}\end{array}\right)$ (3)
To achieve efficient hardware implementation of the LED algorithm, two architectures are designed for the MixColumns process. Figure 3 shows the proposed architectures of the MixColumns process for the LED algorithm.
As shown in Figure 4, the 4bit architecture of the MixColumns process takes 16 clock cycles, which affects the performance of the proposed hardware architecture of the LED algorithm. As a result, one round operation of the proposed hardware architecture of the LED algorithm takes 25 clock cycles and requires 1200 total cycles to generate the ciphertext. To reduce the latency of this process, we propose an 8bit architecture for the MixColumns process. The proposed design takes 8 clock cycles, which can reduce the total latency of the proposed LED design to 17 cycles, and requires 816 total cycles to generate the ciphertext.
The 8bit design for the LED block cipher was carefully considered based on several factors that make it advantageous for our specific application. While there are various design possibilities, this design offers key benefits over others:
Efficiency for ResourceConstrained Devices: Our primary consideration was the efficiency of the design, especially for resourceconstrained devices commonly found in keyless entry systems. The 8bit design minimizes computational overhead, making it wellsuited for devices with limited processing power and memory.
RealTime Requirements: Keyless entry systems often require realtime responsiveness, and the 8bit design ensures rapid encryption and decryption. This is crucial for the seamless and quick operation of such systems.
Compatibility and Integration: Many existing keyless entry systems utilize 8bit microcontrollers. Choosing an 8bit design ensures compatibility and smooth integration with these systems, minimizing the need for extensive hardware upgrades.
Figure 3. Proposed efficient hardware architecture of the LED algorithm
(a) 4bit architecture
(b) 8bit architecture
Figure 4. The proposed architecture of the MixColums process for the LED algorithm
4.1 Security analysis
To protect sensitive data, it is required to deploy cryptography algorithms to encrypt data. However, the recent IoT applications are featured with limited resources devices that require lightweight algorithms. In this part, we evaluate the security of the LED algorithm against a recognized statistical analysis. One of the most significant methods of representing information is an image, which is frequently utilized in fields including telemedicine, medical imaging, and military communication. Over unsecured networks, images are often shared between two parties. As a result, experts and academics are now studying how to safeguard image data from being intercepted, copied, and destroyed. The process of changing the original picture into one that is unintelligible, unrecognizable in appearance, disordered, and unsystematic is known as image encryption. In order to evaluate the security level provided by the LED algorithm (128bit key length), we perform a statistical study of the different evaluation properties of the encrypted image by the LED algorithm, using 5 metrics: Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), image entropy, image histogram, and Correlation analysis.
A robust encryption algorithm is sensitive to a light input fluctuation, even to a onebit change in the input image. A little change in the input image should result in a large change in the encrypted image as a prerequisite for the image encryption technique to resist the differential attack (NPCR & UACI). The number of pixels change rate (NPCR) and unified average changing intensity (UACI), are used to assess the impact of a onepixel change in the encrypted image.
NPCR (Number of Pixel Change Rate) is a metric used to evaluate the sensitivity of the encryption algorithm to changes in the input data. It quantifies the percentage of differing pixel values in two ciphertexts generated from plaintexts with a onebit difference. A high NPCR value, approaching 100%, indicates that the encryption algorithm is highly sensitive to changes in the plaintext, which is generally desirable for cryptographic algorithms. NPCR is calculated using the following equation [13]:
$\begin{gathered}N P C R=\frac{\sum_{i=1}^m \sum_{j=1}^n \theta(i, j)}{m \times n} \times 100 \% \\ \theta(i, j)=\left\{\begin{array}{l}1 \text { if } c_1(i, j) \neq c_2(i, j) \\ 0 \text { if } c_1(i, j)=c_2(i, j)\end{array}\right.\end{gathered}$ (4)
UACI (Unified Average Changing Intensity) is another important metric used to assess the diffusion properties of an encryption algorithm. It measures the average intensity change in the encrypted image when a onebit change is made in the plaintext. A lower UACI value indicates that the encryption algorithm achieves a better diffusion effect with smaller intensity changes in the ciphertext. UACI is determined using the following equation [14] :
$\begin{gathered}U A C I=\frac{\sum_{i=1}^m \sum_{j=1}^n\leftc_1c_2\right}{m \times n \times 255} \times 100 \% \\ \theta(i, j)=\left\{\begin{array}{l}1 \text { if } c_1(i, j) \neq c_2(i, j) \\ 0 \text { if } c_1(i, j)=c_2(i, j)\end{array}\right.\end{gathered}$ (5)
To evaluate the effect of changing a single pixel in the input image on the encrypted image, we performed the analysis using both NPCR and UACI for different images. Table 2 provides a summary of the achieved NPCR and UACI values. According to the results, even with a onebit difference in the input images, the proportion of pixels altered in the encrypted images is greater than 90.6% for NPCR and greater than 30.9% for UACI, which indicates that the LED algorithm is sensitive to even little changes in the input image.
Table 2. Results of the NPCR and UACI metrics for the LED algorithm

Peppers 
Boats 
Baboon 
NPCR 
90.782% 
90.639% 
91.125% 
UACI 
31.344% 
30.984% 
31.152% 
In cryptographic security analysis, entropy is a useful measure used to measure the randomness of data output to create an effective cryptographic system. In image processing, entropy is used to measure how much information is contained in the image output of the cryptography algorithm. In general, it is impossible to extract any significant properties from data that have a full entropy. To hide private information, a highly secure algorithm must get high entropy values close to 8. The entropy provides insight into the level of uncertainty. A high entropy value often indicates a great deal of uncertainty, and the ability to predict the following extracted values is greatly increased by a low entropy value. For the security of hash functions and cryptographic systems, entropy analysis is highly recommended. Entropy methods are introduced by Shannon which can be calculated as follows [15]:
$e(x)=\sum_{i=0}^M P\left(x_i\right) \times \log _2\left(P\left(x_i\right)\right)$ (6)
where, $\mathrm{e}(\mathrm{x})$ is the entropy of the cipher image, $P(x i)$ is the probability of the image pixel intensity $x i$, and $\mathrm{M}$ is the total pixel of the cipher image $x$. The entropy values of the tested images are shown in Table 3. The average entropy value for the different encrypted images is $7.99 \approx 8$. The results obtained indicate that information leakage from encrypted images is negligible, which means that the encryption algorithm LED is secure against entropybased attacks.
The histogram analysis is particularly used to lower the likelihood of image attacks and to conceal the private information in input images [16]. The histogram is used to assess how well the encryption process is working. It displays the distribution of gray pixel values in the image, which should be fairly similar to a uniform distribution. In most cases, the recovered histogram of the encrypted image differs greatly from the original image. When the histogram of the encrypted image is completely different and flat than the histogram of the input image, successful attacks might not be feasible in this situation. This type of analysis is highly recommended to reduce the risk of attacks, as the histogram of the encrypted image should be close to uniform distribution. Figures 57 illustrate the histogram analysis of the original images and their encrypted using the LED algorithm (128bit key length). The histogram results of the encrypted images are approximated by a uniform distribution. They are completely different and flat than the histogram of the input image. The uniformity is presented by the chisquare test in Eq. (7):
$\chi^2=\sum_{k=1}^{256} \frac{\left(v_k256\right)}{256}$ (7)
where represents the observed occurrence frequencies of each gray level (0255), $\mathrm{k}$ represents the total number of gray levels (256), and the expected occurrence frequency of each gray level is (256). When comparing the histograms of the encrypted images with those of the input images, we observed several differences in the histogram results between the plainimage and encrypted images. In the case of input images, the histograms often displayed peaks and valleys, indicating varying levels of pixel value concentration. However, the histograms of the encrypted images exhibited a more uniform distribution. The absence of prominent peaks in the encrypted image histograms suggests that the LED algorithm effectively disperses pixel values, minimizing any patterns that could be exploited.
Correlation analysis is used to assess how similar two neighboring pixels are to one another. A low correlation value between adjacent pixels is required to get a secure system. A numerical measure preceding a relationship between two variables is shown by the correlation metric. The link between the original data and its encryption should be eliminated via good encryption. As a result, no meaningful information can be recovered and the link between the plaintext and its encryption cannot be established. in this study, we determined the relationship between the original image and its encrypted output using the LED algorithm. The correlation coefficient is calculated as [17]:
$\begin{aligned} & r_{u, v}=\frac{\operatorname{cov}(u, v)}{\sqrt{D(u) D(v)}}, \\ & D(u)=\frac{1}{N} \sum_{i=1}^N\left(u_i\frac{1}{N} \sum_{i=1}^N u_i\right)^2, \\ & \operatorname{cov}(u, v)=\frac{1}{N} \sum_{i=1}^N\left(u_iE(u)\right)\left(v_iE(v)\right), \\ & E(u)=\frac{1}{N} \sum_{i=1}^N u_i\end{aligned}$ (8)
where, $u$ and $v$ are grayscale values of two adjacent pixels in the image. For the perfect cipher, the correlation $r(u, v)$ should be equal to 0 and it will be equal to 1 for the worst cipher. This criterion is best explained by the theory of Shannon [17]. Table 3 shows the correlation coefficient values for three encrypted images (Pepper image, Boat image, and Baboon image) using the above formulas and the correlations of adjacent pixels are illustrated in Figure 8.
4.2 Performance analysis
A hardware implementation of the proposed designs of the LED algorithm was carried out using VHDL description language. The Xilinx ISE 14.7 was used to simulate the proposed implementations. To evaluate the performance of the proposed designs, we used a Spartan3 FPGA device. Table 4 presents the obtained results for the proposed designs on the Spartan3 FPGA and an existing FPGA implementation for some block cipher algorithms.
To evaluate the performance of the proposed implementations, we focus on the area consumption expressed as the number of slices, flipflops (FFs), and LUTs consumed for each design. Also, the speed performance is evaluated and expressed in terms of latency (clock cycles) and throughput (Mbps). The proposed 4bit MixColumns have a higher latency than the proposed 8bit MixColumns, which directly affects the performance of the proposed hardware implementation of the LED algorithm. The proposed designs have approximately the same Slices for resources as those used in FPGAs (Spartan3).
Figure 5. Histogram results of Pepper image for the LED algorithm. (a) Pepper plainimage, (b) histogram of Pepper plainimage, (c) Pepper cipherimage, and (d) histogram of Pepper cipherimage
Figure 6. Histogram results of Boat image for the LED algorithm. (a) Boat plainimage, (b) histogram of Boat plainimage, (c) Boat cipherimage, and (d) histogram of Boat cipherimage
Figure 7. Histogram results of Baboon image for the LED algorithm. (a) Baboon plainimage, (b) histogram of Baboon plainimage, (c) Baboon cipherimage, and (d) histogram of Baboon cipherimage
Figure 8. Correlations of adjacent pixels for (a) Pepper plainimage, (b) Pepper cipherimage, (c) Boat plainimage, (d) Boat cipherimage, (e) Baboon plainimage, (f) Baboon cipherimage
Table 3. Correlation and entropy results for Peppers, boats, and baboons of size 256 × 256

Peppers 
Boats 
Baboon 


Plainimage 
Cipherimage 
Plainimage 
Cipherimage 
Plainimage 
Cipherimage 
$e(x)$ 
7.577 
7.998 
7.158 
7.998 
7.228 
7.998 
$r_{u, v}$ 
0.964 
0.001 
0.927 
0.002 
0.874 
0.002 
Table 4. Results of various hardware implementations of lightweight encryption algorithms on FPGA
Design 
Size (bits) 
Area 
Speed 
Efficiency 
FPGA Device 

Key 
Datapath 
Slices 
FFs 
LUTs 
LUTs+FFs 
Clock Cycles 
Freq. (MHz) 
Throughput (Mbps) 
Eff. (Mbps/slices) 

Proposed 4bit 
128 
8 
210 
212 
385 
597 
1200 
102.89 
5.48 
0.02 
Spartan3 XC3S505 
Proposed 8bit 
128 
8 
222 
216 
411 
627 
816 
123.22 
9.66 
0.04 

Piccolo [18] 
128 
4 
265 
260 
442 
702 
496 
45.85 
5.92 
0.02 

Piccolo [18] 
128 
64 
397 
207 
757 
964 
31 
81.82 
168.9 
0.49 

AES [19] 
128 
8 
393 
 
 
 
534 
 
16.86 
0.04 

Klein [20] 
80 
4 
 
194 
597 
791 
 
116 
26 
 

Lilliput [20] 
80 
4 
 
205 
592 
797 
 
119.2 
28 
 

Piccolo [21] 
128 
8 
271 
260 
512 
772 
248 
47.83 
12.34 
0.04 

Piccolo [21] 
128 
16 
281 
241 
532 
773 
124 
47.63 
24.58 
0.08 

Piccolo [21] 
128 
32 
301 
248 
575 
823 
62 
48.23 
49.78 
0.16 

PRESENT V1 [22] 
80 
16 
271 
145 
524 
669 
250 
141.26 
36.16 
0.13 
Spartan3E 500FG3205 
PRESENT V2 [22] 
80 
16 
256 
98 
478 
576 
132 
132.19 
64.09 
0.25 
The results of FPGA implementations for the proposed LED architectures are compared with other implementations of Piccolo, PRESENT, AES, Klien, and Lilliput block ciphers in this section. The proposed designs of the LED algorithm have fewer hardware resources compared to other hardware implementations of block ciphers algorithms, which indicates that the proposed designs have low power consumption compared to those designs.
To propose an efficient hardware implementation of the LED algorithm, we have proposed an 8bit serial architecture with two designs of MixColumns(4bit and 8bit). The proposed designs were implemented on the FPGA (Spartan3 XC3S50 device). The proposed designs have different latencies to perform the encryption process. From the result in Table 4, the proposed serial architecture with 8bit MixColumns is the best design. It presents a higher performance and efficiency compared to the proposed serial architecture with 4bit MixColumns. Compared to existing block cipher implementations, the proposed designs are characterized by lower energy consumption thanks to the lower use of resources.
To evaluate the level of security of the LED algorithm, we used five metrics such as entropy analysis, histogram analysis, correlation analysis, NPCR, and UACI. As a result, the LED algorithm has a good ability to encrypt any data against any attack. The 8bit designs offer a higher speed compared to the 8bits ones. Indeed, the first one speed’s is 9.66 compared to 5.48 for the second design.
In future work, we plan to implement the efficient design (the proposed serial architecture with 8bit) of the LED algorithm on the RKS to ensure the security of vehicle data. A key fob that wirelessly connects with the automobile transceiver used to regulate and secure access to the vehicle makes up a Remote Keyless System. The RFIDbased keyless entry systems have a finite amount of processing power and energy available to them. It has been established that cybercriminals are after it.
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