Secured Data Collection With Hardware-Based Ciphers for IoT-Based Healthcare

Tao, Hai; Bhuiyan, Zakirul Alam; Abdalla, Ahmed N.; Hassan, Mohammad Mehedi; Zain, Jasni Mohamad; Hayajneh, Thaier

doi:10.1109/jiot.2018.2854714

Cited by 162 publications

(62 citation statements)

References 31 publications

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“…This paper validate the complexity of the proposed algorithm based on [25]. According to (17) and (20), Table 1 lists the computational complexity of the proposed algorithm and the existing algorithm. [3] 2( − 1)…”

Section: Resultsmentioning

confidence: 60%

“…The lower limit of the conditional average signal to interference and noise ratio (SINR, Signal-Interference-Noise-Ratio) of the subcarriers on the antenna is the criterion correction frequency offset, but it has two disadvantages. the first, when the frequency deviation between the transmitting antennas increases, the lower limit of the conditional average SINR becomes more and more slack [4,16,17]. At this time, the frequency offset is corrected by maximizing the lower limit of the conditional average SINR.…”

Section: Introductionmentioning

confidence: 99%

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Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

Husein¹

2020

IJECE

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In the distributed transmit antenna MIMO OFDM system, each transmitting antenna has different frequency offset between each transmitting antenna and receiver due to the use of independent crystal oscillator. This paper proposes Polynomial-time algorithm for correcting the frequency offset in a received signal by maximizing the conditional average signal. The algorithm focus on reducing to interference and noise ratio of each subcarrier on the receiving antenna by frequency offset. The simulation result shows the performance of the proposed algorithm is slightly improved compared with the existing frequency offset correction algorithm, and the complexity is reduced by 50% or more.

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Section: Resultsmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

Husein¹

2020

IJECE

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“…The dynamically reconfigurable security algorithm is presented by Yao et al [20] for IoT, which includes AES and TDES algorithms are used to realize the dynamic switching in the reconfigurable partition, and core controller controls it. Tao et al [21] explain about hardware-based block cipher with secured data collection for IoT based Healthcare, and KATAN security algorithm is used to provide crypto information from the patients and to doctor via a secured share cloud server. Ahmed et al [22] presents the lightweight encryption algorithm for secured IoT on the Matlab platform and also implemented on 8-bit Microcontroller.…”

Section: A the Backgroundmentioning

confidence: 99%

LEA-SIoT: Hardware Architecture of Lightweight Encryption Algorithm for Secure IoT on FPGA Platform

Bharathi¹,

Parvatham²

2020

IJACSA

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The Internet of Things (IoT) is one of the emerging technology in today's world to connect billions of electronic devices and providing the data security to these electronic devices while transmission from the attacks is a big challenging task. These electronic devices are smaller and consume less power. The conventional security algorithms are complex with its computations and not suitable for IoT environments. In this article, the hardware architecture of the new Lightweight encryption algorithm (LEA) for the secured Internet of things (SIoT) is designed, which includes Encryption, decryption along with key generation process. The New LEA-SIoT is a hybrid combination of the Feistel networks and Substitutionpermutation Network (SPN). The encryption/decryption architecture is the composition of Logical operations, substitution transformations, and swapping. The encryption/decryption process is designed for 64-bit data input and 64-bit key inputs. The key generation process is designed with the help of KHAZAD block cipher algorithm. The encryption and key generation process are executing in parallel with pipelined architecture with five rounds to improve the hardware and computational complexity in IoT systems. TheLEA-SIoT is designed on the Xilinx platform and implemented on Artix-7 FPGA. The hardware constraints like area, power, and timing utilization are summarized. The Comparison of the LEA-SIoT with similar security algorithms is tabulated with improvements.

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“…With an increase in urbanization, it is expected that a massive number of such devices will be connected to the Internet. The presence of these devices makes a city smarter by generating a huge amount of data that can be leveraged for various services such as, safety and privacy, infotainment, better health facilities, greener environment and better waste management [3,20]. To realize these services, the voluminous data generated by smart devices need to be managed in an efficient manner at the edge of the network.…”

Section: Related Workmentioning

confidence: 99%

SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application

Jan

Zhang

Usman

et al. 2019

Journal of Network and Computer Applications

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The Internet of Things (IoT) has the potential to transform communities around the globe into smart cities. The massive deployment of sensor-embedded devices in the smart cities generates voluminous amounts of data that need to be stored and processed in an efficient manner. Long-haul data transmission to the remote cloud data centers leads to higher delay and bandwidth consumption. In smart cities, the delay

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Secured Data Collection With Hardware-Based Ciphers for IoT-Based Healthcare

Cited by 162 publications

References 31 publications

Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

LEA-SIoT: Hardware Architecture of Lightweight Encryption Algorithm for Secure IoT on FPGA Platform

SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application

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