LiteEncrypt : A Lightweight Block Cipher for Secure Communication in IoT Enabled Sensor

The Internet of Things (IoT) has become an integral part of future solutions, ranging from industrial to everyday human life applications. Adding a new level of intelligence to objects and automating decisions make this new technology appealing to everyone. However, applications that involve data are more vulnerable to various types of attacks. As a result, researchers are constantly exploring secure connections between IoT edge nodes. On one hand, suitable IoT nodes should be cheap and require low power, which means lower computational performance. On the other hand, a secure connection layer is power hungry and requires powerful hardware resources. Lightweight cryptography (LWC) algorithms are a promising solution to reduce computation complexity while maintaining a desired level of security. In the presented work, we attempt to address the issue of adding security to the IoT network layer by comparing the performance of 32 LWC algorithms with currently well-known algorithms on multiple IoT platforms (Raspberry Pi 3, Raspberry Pi Zero W, and iMX233). These 32 authenticated encryption with associated data algorithms have been selected from the second round of the LWC standardization process conducted by the National Institute of Standards and Technology. Power consumption, random access memory usage, and execution time are measured for these algorithms using the targeted embedded platforms that are used as IoT sensor nodes. The results of this study will assist researchers in choosing a suitable platform and optimal LWC algorithm for IoT applications.

In the last few years, a large amount of private and sensitive data generated by Internet of Things (IoT) devices need to be transmitted, stored, and processed in security, which become a serve challenge because these resource-constrained IoT devices cannot satisfy the requirements of conventional encryption ciphers. In this article, we propose the extended Type-1 generalized Feistel networks (Type-1 EGFNs) to improve the diffusion speed of Type-1 generalized Feistel networks (Type-1 GFNs). We instantiate Type-1 EGFNs into a lightweight block cipher that is named ALLPC, and analyze the performance of the cipher in terms of software implementation, hardware implementation, and security. The Type-1 EGFNs overcome the shortcoming of slow diffusion of Type-1 GFNs, and the ALLPC cipher is quite suitable for the resource-constrained IoT environment. Compared with other existing lightweight block ciphers, the ALLPC cipher can achieve compact hardware and low-cost software (e.g., low memory) while having low execution time, high throughput, and low energy consumption on the 32-bit ARM-Cortex M3 STM32F103 platform. In our security analysis, the ALLPC cipher can attain sufficient security degree against differential cryptanalysis, zero-correlation attack, and linear cryptanalysis, and owns a meaningful avalanche effect. Experimental results shows that our ALLPC cipher outperforms other existing lightweight ciphers in throughput, execution time and energy consumption.

Internet-of-Things (IoT) is growing network paradigm which enables mutual communication between the user and smart devices using the internet. The IoT devices are susceptible to the security threats, due to placement of restricted computational capabilities of the computing devices in IoT. The conventional encryption algorithm utilizes the high amount of resource block in it which increases the area and power. Moreover, Two Factor Authentication (TFA) scheme based authentication protocols does not have the efficiency to secure the data. Because the random number generated by the TFA is ideal for all IoT devices which are easy to hack by the unauthorized persons. In this Research paper, the Linear Feedback Shift Register (LFSR) based Reconfigurable Physical Unclonable Function (RPUF) is proposed to overcome the security issues caused in the IoT communication. The RPUF is designed based on the LFSR to generate the random number for every clock cycle. Normally, reconfigurable process helps to generate the different output values for every clock cycle. But, it failed to generate different outputs for same input values. Here, LFSR based RPUF helps to generated the different response values even the same challenge is given to the input side. The Lightweight TFA scheme is presented for IoT, where PUF has been considered as one of the major authentication factors. At last, Spartan 6 and Virtex 6 Field Programmable Gate Array (FPGA) performances are calculated for proposed TFA-RPUF-IoT and existing TFA-PUF-IoT protocols. In Spartan 6, TFA-RPUF-IoT protocol occupied 11 slices, 31 LUTs, 42 flip flops which are less compared to conventional TFA-PUF-IoT.

With increased Internet of Things (IoT) devices, complexity and protection are more challenging. Lightweight cryptographic algorithms are secure and suitable for limited-resource environments; however, their hash functions provide encrypted data but not integrity. Strong security features are available, but setup is difficult and expensive. Network security mechanisms increase power consumption and latency. As IoT networks grow, managing cryptographic keys and securely authenticating large numbers of devices become complex tasks. Efficient key management strategies are required to ensure the scalability required. Existing state-of-the-art solutions lack standardization, scalability, complex and costly. Thus, this research proposes a secure solution for IoT resource-constrained devices, combining strong data integrity and lightweight encryption, and is thus named a hybrid. This hybrid approach integrates SHA-512 and the present cipher in our proposed approach and thus ensuring higher security than state-of-the-art models. This intelligent combination not only enhances the algorithm’s resistance against cryptographic attacks but also improves its processing speed. The proposed approach is used to reduce the processing time for encryption in the IoT platform and to preserve the trade-off between security and efficiency. In terms of memory use, execution time, and precision, the proposed approach is compared with recent state-of-the-art research. The experimental results indicate that our approach is efficient using the avalanche, authentication success rate, collision events, and execution time. The efficiency is 53% to 65%, and the avalanche effect indicates sensitivity to input variations, suggesting moderate-to-considerable reactivity to small data changes. The experimental tests conducted across 10,000 and 80,000 runs reveal no collisions and found that the proposed approach is resilient in managing unique IDs. Moreover, our approach performs consistently, with an average execution time of 0.088246 s, ranging from 0.075954 to 0.094583 s. Finally, our approach provides a practical and scalable solution for securing IoT devices in resource-constrained environments, addressing practical problems for IoT devices.

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

In the past decade, the Internet of Things (IoT) has emerged and has been of great importance in the digital world arousing the interest of the scientific community. The requirements of new small intelligent devices brought numerous security and privacy concerns. In order to secure the exchanged data through the IoT, integrity and authentication protocols are required and are usually constructed under hash functions. We notice that the most conventional cryptographic standards are not suitable for multiple platforms hardware/software. The expressed need is to construct lightweight functions and algorithms suitable for constrained environments including small devices. In this context, we propose a review of the research progress on IoT architecture. Further, we present and compare some selected lightweight cryptographic algorithms regarding security, performance, and resource requirements. The main goal of the present paper is to highlight the need to provide security systems for IoT applications through lightweight cryptographic mechanisms.

In the rapidly developing world of the Internet of Things (IoT), data security has become increasingly important since massive personal data are collected. IoT devices have resource constraints, which makes traditional cryptographic algorithms ineffective for securing IoT devices. To overcome resource limitations, lightweight cryptographic algorithms are needed. To identify research trends and patterns in IoT security, it is crucial to analyze existing works, keywords, authors, journals, and citations. We conducted a bibliometric analysis using performance mapping, science mapping, and enrichment techniques to collect the necessary information. Our analysis included 979 Scopus articles, 214 WOS articles, and 144 IEEE Xplore articles published during 2015–2023, and duplicates were removed. We analyzed and visualized the bibliometric data using R version 4.3.1, VOSviewer version 1.6.19, and the bibliometrix library. We discovered that India is the leading country for this type of research. Archarya and Bansod are the most relevant authors; lightweight cryptography and cryptography are the most relevant terms; and IEEE Access is the most significant journal. Research on lightweight cryptographic algorithms for IoT devices (Raspberry Pi) has been identified as an important area for future research.

The Internet of Things creates advanced application systems requiring minimum human interaction by integrating entities having unique addressing mechanisms with digital processing platforms by communicating in a network. The Internet of Things devices being resource constrained are prone to attacks and security breaches. Conventional cryptographic procedures are not viable for Internet of Things and embedded applications as the computations involved with them require higher processing power, memory and energy. Lightweight cryptographic techniques can be used in resource constrained Internet of Things devices to mitigate the security issues. These lightweight cryptographic solutions use smaller key sizes, smaller block sizes and lesser number of rounds. This paper uses a lightweight cryptographic algorithm, AES (Advanced Encryption Algorithm) which is one of the efficient and robust algorithms, to secure communication in the wireless sensor networks. The experimentation uses UDP protocol for transmissions and Routing Protocol for Low-Power and Lossy networks (RPL) for packet delivery from source to destination. Later, the performance of different variants of AES is checked by performing the power analysis. The results demonstrate that AES-192 and AES-256 with 8 rounds show optimized results in terms of power, thus making it suitable for power constrained devices.

The Internet of Things (IoT) based Smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

Cyclic Redundancy Check (CRC) is a widely used error detection code in digital communication systems to detect errors in data transmitted between transmitter and receiver. With the development of Internet-of-Things (IoT) technology, where many devices communicate wirelessly, the application of error detection codes such as CRC is essential to obtain reliable communication. The computation of CRC can be implemented in hardware using a linear feedback shift register (LFSR) or in software with shift and bitwise exclusive OR (XOR) operations for polynomial division. Due to the frequent use of CRC in wireless communication for IoT, research for the design and implementation of CRC continues to this day. This study aims to design and implement the encoder and decoder of CRC-8 with generator polynomial 0x07 on IoT devices. Two algorithms for computing the CRC, namely bitwise computation using LFSR and another one using lookup tables, are implemented on the Arduino Uno R3 board. Two Arduino boards connected in serial were used to verify the encoder and decoder implementation in the data transmission and error detection process. The experiment results showed that the encoder could correctly calculate the CRC value from the input data. Furthermore, the computation using lookup tables takes about four times faster than the bitwise method but requires more memory. In contrast, the bitwise computation method requires less memory but slower computation time. When testing using input data of 128 characters, the bitwise-based encoder requires a computation time of 2.37 milliseconds, while the lookup table-based encoder utilizing a lookup table requires a computation time of 0.5 milliseconds. In the error detection test, the results demonstrated that the receiver could detect errors in the transmitted data with a percentage of 100%.

This paper investigates the skills needed by Higher Education (HE) students to become successful Internet of Things (IoT) future leaders/developers. The paper focuses on the programming and Maths aspects, being fundamental to IoT development, in addition to computational thinking and coding concepts for the HE students. The research objective is to build a framework for developing IoT skills within higher education to establish the computing competencies needed for this rapidly expanding technology. The investigation included the most widely used programming language for IoT, Python, and the Maths skills at the HE level, which are essential for developing successful infrastructure, functionality, security algorithms and encryption tools for IoT. Quantitative research methodology is applied on empirical data obtained on non-Computer Science students (Non-CS) in a HE institution in UAE. There is a correlation between in university (InUni) Maths skills and Python programming course's performance. Compared to Engineering students, Business students may found it more challenging to learn Python programming language, which also applies to their Maths skills. This suggests better IoT competencies developed by Engineering students. The paper concluded that N on-CS students are able to obtain sufficient knowledge of, and skills in, using Python within one semester. The future study should consider other factors, such as general knowledge, business awareness, technology applications and legal issues.

Internet of Things (IoT) is a rapidly developing technology that enables “devices” to communicate and share information amongst them without human control. The devices have the features of internet connectivity and networking. Due to the increasing demands of a secure environment in IoT application, security has become a crucial aspect on which researchers have been increasingly focused. Connecting devices to the internet can facilitate intruders to attack devices as they can access the data from anywhere in the globe. In this work, an encryption–decryption process-based stream cipher has been used. The messages between IoT nodes were encrypted using One Time Pad (OTP) and DNA computing. Furthermore, the required key sequence was generated using a linear feedback shift register (LFSR) as a pseudo number key generator. This key sequence was combined to generate a unique key for each message. The algorithm was implemented using source python and tested on a Raspberry pi under Linux open operation system.

This article proposes a novel deep learning-based robust Internet of Things (IoT) sensor data transmission period control (DL-RDTPC) framework in an IoT edge computing system. In general, as the data transmission period of IoT sensors increases, the energy consumption of IoT sensors is reduced, and contrarily, the amount of un-transmitted data (i.e., missing values) becomes continuously accumulated. Therefore, the IoT server is in charge of accurately imputing these missing data for reliable data analysis. By addressing this issue, we newly design the imputation accuracy prediction (IAP) module, which captures the complicated relationships between the imputation accuracy and the data transmission period, in order to estimate the imputation accuracy, precisely. For constructing the IAP, three submodules, which include a stacked bidirectional long short-term memory (Bi-LSTM) model, a multihead convolutional neural network (CNN), and a neural network-based period information encoding network (PIEN) are leveraged. To balance the tradeoff between the imputation accuracy and energy consumption regarding the data transmission period, the multiobjective optimization problem is formulated for minimizing the maximum value of both: 1) the energy consumption of IoT sensors obtained from the analytical model and 2) the imputation accuracy predicted from IAP module. The optimal solution is consequently obtained by utilizing the bisection search algorithm. Extensive performance evaluations validate the effectiveness of the proposed RDTPC algorithm in terms of both the average energy consumption (maximum 68% reduction) and missing data imputation accuracy (maximum 64% RMSE reduction) over other benchmarks. Finally, this article provides a practical implementation of the proposed RDTPC framework via the HTTP protocol under the IEEE 802.11-based WLAN network, as well as interworking with the commercial cloud server.

Data security in Internet of Things (IoT) devices is a critical challenge due to their inherent limitations in memory, energy, and processing capacity. Traditional security mechanisms are often unfeasible, driving the search for lightweight cryptographic algorithms. In this context, DNA-based cryptography stands out as a promising bioinspired approach. This study presents an empirical and direct comparison of four DNA-based cryptographic algorithms, implemented on a real resource-constrained IoT device, the ESP8266. The analysis quantified performance and efficiency in both encryption and decryption modes, using instrumented measurements of energy consumption (mJ), execution time (𝜇s), electric current (mA), and RAM usage (B). The results reveal statistically significant differences among the algorithms, confirmed by Shapiro-Wilk and Wilcoxon tests. Alg_3 demonstrated the highest efficiency in time and energy, being the fastest and with the lowest total energy consumption, although it required the most RAM. The research highlights the potential of bioinspired cryptography and reinforces the importance of considering the constraints of the target environment when selecting a cryptographic scheme.

With the proliferation of ubiquitous Internet of things (IoT) sensors and smart devices in a wide array of industries, involving transportation, agriculture, and healthcare, a huge amount of data is being generated, which requires ever-increasing calculations and services both at the cloud and at the edge of the network. Because cloud servers are specifically located a significant distance from IoT devices, time-sensitive Internet of things applications must be extended to the cloud architecture for the timely delivery of important services. Although IoT services can be allocated to the right edge nodes, ensuring low latency and efficient resource use is still challenging. To overcome these challenges, a novel resource allocation using clustering enabled optimization in IoT (RACE-IoT) has been proposed in this paper. This work introduces a novel Shannon–dragonfly (Sha-Dragon) optimization algorithm, which combines the dragonfly algorithm with the Shannon entropy for resource scheduling. The proposed RACE-IoT balances the load by reallocating the resources to maintain the same load ratio over all servers. The developed technique has been compared with the previous strategies involving PBSM, HRL-Edge-Cloud, and ERAM-EE in terms of specific parameters such as energy consumption, throughput, response time, end-to-end delay, resource utilization, makespan, and success rate. The outcomes demonstrate the efficiency of the developed RACE-IoT technique. The proposed technique achieves higher success rates of 35%, 17%, and 18% than existing techniques such as PBSM, HRL-Edge-Cloud, and ERAM-EE, respectively.