Resource-constrained Internet Of Things Devices Research Articles

Wireless IoT Networks Security and Lightweight Encryption Schemes- Survey

— IoT networks suffered from different kinds of attacks. The main issue of IoT security is the limitation of its node’s energy ability to perform big tasks such as encryption. This paper provides a comprehensive overview of the dynamic landscape of lightweight cryptography research, particularly in the context of real-time traffic security and Internet of Things (IoT) networks. The aim of this research is to identify the gaps in IoT research efforts and draw up ways to address it. The objectives are highlighting the global collaboration and standardization initiatives that have shaped its development. Also, it emphasizes the importance of balancing cryptographic strength with practical considerations such as encryption latency, energy efficiency, and memory constraints, particularly crucial in the context of resource constrained IoT devices. Furthermore, it highlights innovative approaches proposed by researchers to enhance the security of cryptographic operations while minimizing overhead and ensuring compatibility with evolving wireless environments. It calls for sustained research efforts to address these challenges and foster the development of robust cryptographic solutions capable of meeting the stringent security requirements of modern digital ecosystems.

A Survey on Lightweight Cryptographic Algorithms in IoT

Abstract The Internet of Things (IoT) will soon penetrate every aspect of human life. Several threats and vulnerabilities are present due to the different devices and protocols used in an IoT system. Conventional cryptographic primitives or algorithms cannot run efficiently and are unsuitable for resource-constrained devices in IoT. Hence, a recently developed area of cryptography, known as lightweight cryptography, has been introduced, and over the years, numerous lightweight algorithms have been suggested. This paper gives a comprehensive overview of the lightweight cryptography field and considers various popular lightweight cryptographic algorithms proposed and evaluated over the past years for analysis. Different taxonomies of the algorithms and other associated concepts were also provided, which helps new researchers gain a quick overview of the field. Finally, a set of 11 selected ultra-lightweight algorithms are analyzed based on the software implementations, and their evaluation is carried out using different metrics.

Analysis and Performance Comparison of IoT Message Transfer Protocols Applying in Real Photovoltaic System

The adoption of reliable and real-time communication technology is an absolute necessity for the advancement of Internet of Things (IoT) applications. Messaging protocols such as MQTT, AMQP, and HTTP are frequently used for communication with resource-constrained IoT devices. However, choosing a suitable and effective messaging protocol presents a daunting challenge for organizations, as it depends on the specific characteristics and messaging requirements of the IoT system. Therefore, it is crucial to have a comprehensive understanding of three established messaging protocols, such as the Hypertext Transfer Protocol (HTTP), the Message Queuing Telemetry Transport (MQTT), and the Advanced Message Queuing Protocol (AMQP), to appropriately apply them in practical projects. In this paper, information technology solutions are provided for a chain of solar farms to improve harvest productivity, facilitate warning notifications, and enable remote control. Subsequently, a detailed comparative analysis is performed, considering various interconnected criteria, to gain valuable insight into the strengths and limitations of these protocols. The results show that MQTT and AMQP play a role in enhancing overall efficiency and speed within the framework of our suggested photovoltaic system.

Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices

In the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.

Lightweight Strong PUF for Resource-Constrained Devices

Physical Unclonable Functions are security primitives that exploit the variation in integrated circuits’ manufacturing process, and, as a result, each instance processes applied stimuli differently. This feature can be used to provide a unique fingerprint of the electronic device, or as an interesting alternative to classic key storage methods. Due to their nature, they are often considered an element of the Internet of Things nodes. However, their application heavily depends on resource consumption. Lightweight architectures are proposed in the literature but are technology-dependent or still introduce significant hardware overhead. This paper presents a lightweight, Strong PUF based on ring oscillator architecture, which offers small hardware overhead and sufficient security levels for resource-constrained Internet of Things devices. The PUF design utilizes a Linear Feedback Shift Register-based scramble module to generate many challenge–response pairs from a small number of ring oscillators and a control module to manage the response generation process. The proposed PUF can be used as a Weak PUF for key generation or a Strong PUF for device authentication.

Efficient privacy preserving communication protocol for IOT applications

The proliferation of Internet of Things (IoT) devices has led to an unprecedented increase in data generation and communication, raising concerns about the security and privacy of transmitted information. This paper presents a novel Message Authentication Protocol (MAP) specifically designed for IoT devices, addressing the dual challenges of ensuring data integrity and preserving user privacy. The proposed protocol employs advanced cryptographic techniques to authenticate messages securely while incorporating privacy-preserving mechanisms to safeguard sensitive user information.The protocol utilizes a hybrid approach, combining symmetric and asymmetric cryptographic primitives to achieve efficient and robust message authentication. Symmetric key algorithms are employed for fast and lightweight authentication, ensuring minimal computational overhead for resource-constrained IoT devices. Additionally, asymmetric key techniques are integrated to facilitate secure key exchange and protect against key compromise.Privacy preservation is a paramount concern in IoT ecosystems, where devices often handle sensitive data. The protocol incorporates anonymization and differential privacy techniques to conceal the identity of users and the specific content of transmitted messages. This ensures that even if an adversary intercepts communication, extracting meaningful information about the users or their data remains a formidable challenge.To evaluate the effectiveness of the proposed protocol, we conducted comprehensive simulations and real-world experiments. The results demonstrate that the protocol achieves a balance between security, efficiency, and privacy preservation. It outperforms existing solutions in terms of computational efficiency and provides a robust defence against various common attacks, such as replay attacks and message tampering.In conclusion, the presented Secure and Efficient Message Authentication Protocol for IoT Devices with Privacy Preservation offers a viable solution to the evolving security and privacy challenges in IoT environments. By leveraging cryptographic techniques and privacy-preserving mechanisms, the protocol provides a strong foundation for safeguarding sensitive information while ensuring the integrity of communication in the vast and interconnected landscape of IoT devices.

DRcipher: A pseudo-random dynamic round lightweight block cipher

The Internet of Things (IoT) has gained popularity in various fields, including components such as embedded devices and wireless sensors. However, ensuring the security of data transmission from these devices is of critical importance. In light of these challenges, a novel lightweight encryption method called DRcipher is proposed in this paper for resource-constrained IoT devices. DRcipher has a 64-bit block size and supports either 96-bit or 128-bit key sizes. In order to improve the security, DRcipher employs a pseudo-random number of encryption rounds determined by the primary key. DRcipher adopts the structure of Generalized Feistel Network (GFN) with 4 branches, and its round functions consist of F-function, FF-function and RP permutation components. In particular, there is a negative feedback mechanism between the FF-function and the overall round of encryption functions. In addition, DRcipher is synthesised using Synopsys Design Compiler version A-2007.12-SP1 and the UMCL18G212T3 standard cell library. DRcipher-96 has an area footprint of 1546 Gate Equivalents (GE), while DRcipher-128 has a slightly larger area footprint of 1646 GE. Moreover, a comprehensive security analysis shows that the proposed DRcipher ensures high-level security redundancy against differential cryptanalysis, linear cryptanalysis, and so on.

Securing micro transactions in the Internet of Things with cryptography primitives

The growing number of IoT devices, microtransaction security in the Internet of Things (IoT) is a critical issue. In order to improve the security of microtransactions in Internet of Things ecosystems, this article investigates the usage of cryptographic primitives. Strong safeguards against possible risks including data manipulation, eavesdropping, and unauthorized access are provided by cryptography, which provides a foundation for guaranteeing the confidentiality, integrity, and validity of transaction data. We address the special problems of power and processing limits by delving into the implementation of lightweight cryptography algorithms and protocols designed for resource-constrained IoT devices. Our goal is to create a trust framework that guarantees the security and privacy of data exchange by incorporating cryptographic primitives into IoT microtransactions. By encouraging trust and dependability in microtransactions, which are essential to the future of networked smart devices, our research helps to build a more secure and robust Internet of Things environment.

Fog-Enabled Secure and Efficient Fine-Grained Searchable Data Sharing and Management Scheme for IoT-Based Healthcare Systems

In recent times, fog computing has emerged as a helpful extension of cloud computing. It can efficiently handle the prevalent issue of managing silos of data generated by today's digital healthcare services. Moreover, the application of the Internet of Things (IoT) in the development of smart healthcare systems further adds tons of data tirelessly to these silos, thus making the cloud congested. To manage such continuously growing data, the concept of adding a fog layer between the cloud and the end-users (EUs) proved to be beneficial. These intermediary fog nodes (FNs) can handle and store data, and thus facilitate the cloud and alleviate the burden from the EUs. Most of the existing search schemes for encrypted data have been developed for the cloud platform and ignored this helpful extension, which can improve the scheme's efficiency by delegating most of the heavy computations to the intermediary FNs. In this article, a fine-grained searchable data sharing scheme has been proposed using the fog computing platform. The resulting scheme is efficient and lightweight because the FN facilitates EUs by performing computationally intensive tasks on their behalf. A significant reduction in storage and computational cost has been achieved by the proposed scheme at the data owner's end, representing the resource-constrained IoT devices. The storage cost has been reduced to two source group elements, and the computational cost has been reduced to three exponent operations in the source group and one hash operation. Furthermore, the proposed scheme is secure against the selectively chosen keyword attack in the generic bilinear group model.

A review of on-device machine learning for IoT: An energy perspective

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications.

T2S-MAKEP and T2T-MAKEP: A PUF-based Mutual Authentication and Key Exchange Protocol for IoT devices

Nowadays, more constrained devices are becoming connected, building an extensive Internet of Things (IoT) network, but suffering from many security issues. In particular, authentication has become a severe research challenge for IoT systems. Furthermore, confidentiality, integrity, and availability are considered the core underpinnings of information security in general. Unfortunately, deploying conventional authentication protocols for IoT devices in practice is challenging for two main reasons. First, IoT devices have limited memory capacity, processing power, and energy resources. Second, these protocols store secret keys in the IoT devices’ volatile memory, making them vulnerable to physical attacks. Luckily, Physical Unclonable Functions (PUF) has emerged as promising low-cost security primitive. A PUF eliminates the need to store secret keys in device memory, making it a potential alternative to deploying more secure and low-cost authentication protocol schemes for IoT systems. Thing-to-Thing (T2T) or direct connection between IoT devices represents a promising technique to enable things to communicate directly without the need for a trusted third party. This paper proposes two novel lightweight Mutual Authentication and Key Exchange Protocols (MAKEP) for IoT devices using PUF. The first scheme, called T2S-MAKEP, ensures secure communication for Thing-to-Server (T2S). The second, called T2T-MAKEP, allows two endpoints of resource-constrained IoT devices, each with an embedded PUF circuit, to communicate securely. Both proposed protocols, T2T-MAKEP and T2S-MAKEP, allow for robust authentication without storing any information on the device’s memory and simultaneously establish the session key exchange. Our proposed protocols have been verified and validated using the automatic security analysis checker, Verifpal.

Behavioral fingerprinting to detect ransomware in resource-constrained devices

The Internet of Things (IoT), a network of interconnected devices, has grown and gained traction over the last few years. This paradigm can impact our lives while also providing significant economic benefits. However, although resource-constrained IoT devices offer numerous advantages, they are also vulnerable to cyberattacks. As a result, ransomware severely threatens IoT devices managing sensitive and relevant information. Solutions based on Machine and Deep Learning (ML/DL) that consider behavioral data have been identified as promising. However, most detection solutions have been developed for Windows-based systems, which generally have more resources than IoT devices. As a result, these solutions are not suitable for resource-constrained components. In addition, no solution compares the pros and cons of different behavioral dimensions of resource-constrained devices. Thus, this work presents a framework that combines three different behavioral sources with supervised and unsupervised ML/DL algorithms to detect and classify heterogeneous ransomware impacting resource-constrained spectrum sensors. A pool of experiments has demonstrated the suitability of the proposed solution and compared its performance with a rule-based system. In conclusion, the usage of resources combined with local outlier factor and decision tree are the most promising combinations to detect anomalies and classify ransomware while consuming CPU, RAM, and time of devices in a reduced manner.

Multi-objective task scheduling method for cyber–physical–social systems in fog computing

Cyber–physical–social systems refer to new systems that involve a large number of Internet of Things (IoT) devices connected through an interconnected network. These systems gather data for various services such as transportation, energy, healthcare, etc. This data is then transmitted to the cloud layer for storage, processing, and access. To handle the significant workload offloaded from resource-constrained IoT devices without latency or bandwidth issues, a technology called fog computing has emerged as an intermediary layer between the cloud and IoT devices. The quality of service (QoS) provided by fog computing relies on efficient task scheduling to optimize energy consumption and maximum execution time. This task scheduling problem is known to be NP-hard and cannot be solved with high precision in a reasonable time using traditional techniques or existing metaheuristic-based task schedulers. Therefore, this study proposes a new multi-objective task scheduling approach based on a modified marine predators algorithm (MMPA) to simultaneously minimize energy consumption and make-span under the Pareto optimality theory. This variant is named the multi-objective MMPA (M2MPA). M2MPA enhances the performance of the MPA by incorporating the polynomial crossover operator to improve its exploration operator and the adaptive CF parameter to enhance its exploitation operator. The effectiveness of M2MPA is evaluated using eighteen tasks with different scales (small, large, and medium) and heterogeneous workloads assigned to two hundred fog devices with varying processing speeds. Finally, M2MPA is compared with six well-established techniques using various performance indicators, including carbon dioxide emission rate, flowtime, make-span, and energy consumption. The experimental findings demonstrate the substantial superiority of M2MPA over all competing algorithms across different performance indicators and the Wilcoxon rank-sum test. Quantitatively, M2MPA achieves an average make-span of 10.095, average energy consumption of 3.77E+06, average flowtime of 8.39E+03, and average carbon dioxide emission rate of 8.88E+06.

Stochastic gradient descent classifier-based lightweight intrusion detection systems using the efficient feature subsets of datasets

The Internet of Things (IoT) has become an essential part of our daily lives. However, with the increasing use of IoT, the number of botnet attacks targeting resource-constrained IoT devices is also on the rise. To mitigate these threats, intrusion detection systems (IDSs) have been developed. However, traditional IDSs based on heavyweight deep/machine learning, fuzzy logic, rough set theories, or data mining techniques, often lack in detection accuracy and energy efficiency. Therefore, there is a crucial need for more lightweight, accurate, and energy-efficient IDSs capable of detecting a broad spectrum of cyber attacks. This paper presents a solution to these challenges by introducing lightweight and accurate IDSs that use a stochastic gradient descent classifier (SGDC) and four feature-selection algorithms based on a ridge regressor. The hyperparameters of the SGDC algorithm and ridge regressor model were fine-tuned to enhance the accuracy of IDSs while reducing computational complexity. Moreover, the fine-tuned feature selectors were used to decrease the dataset’s dimensionality and improve the accuracy of IDSs. To evaluate the proposed IDSs, three network traffic datasets (KDD-CUP-1999, BotIoT-2018, and N-BaIoT-2021) were used. The systems achieved an average accuracy of 92.69%, and the number of features was reduced by an average of 79.93%. The results demonstrate that the proposed systems can be utilized for lightweight IDSs on resource-constrained IoT devices. Overall, this paper presents a significant contribution to the field of IDSs for IoT devices by offering an efficient and accurate solution. The proposed lightweight IDSs have the potential to enhance IoT security and privacy, safeguarding sensitive IoT data.

Fair Cloud Auditing Based on Blockchain for Resource-Constrained IoT Devices

Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> air <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> loud <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> uditing proposal by employing the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> lockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced fog node, significantly reducing the local burden. Considering that fog nodes may behave dishonestly during auditing, FCAB enables the IoT device to verify the audit result's authenticity by extracting reliable checking records from the blockchain, thereby achieving auditing fairness, which ensures that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">honest</i> cloud and fog node will gain the corresponding reward. Finally, FCAB is proved to satisfy tag unforgeability, proof unforgeability, privacy preserving, and auditing fairness. Experiment evaluations affirm that FCAB is computationally and communicationally efficient and retains a smaller and fixed computation locally at the data processing stage (mainly including tag computation) than existing auditing methods.

A Novel Approach of a Low-Cost Voltage Fault Injection Method for Resource-Constrained IoT Devices: Design and Analysis.

The rapid development of the Internet of Things (IoT) has brought about the processing and storage of sensitive information on resource-constrained devices, which are susceptible to various hardware attacks. Fault injection attacks (FIAs) stand out as one of the most widespread. Particularly, voltage-based FIAs (V-FIAs) have gained popularity due to their non-invasive nature and high effectiveness in inducing faults by pushing the IoT hardware to its operational limits. Improving the security of devices and gaining a comprehensive understanding of their vulnerabilities is of utmost importance. In this study, we present a novel fault injection method and employ it to target an 8-bit AVR microcontroller. We identify the optimal attack parameters by analyzing the detected failures and their trends. A case study is conducted to validate the efficacy of this new method in a more realistic scenario, focusing on a simple authentication method using the determined optimal parameters. This analysis not only demonstrates the feasibility of the V-FIA but also elucidates the primary characteristics of the resulting failures and their propagation in resource-constrained devices. Additionally, we devise a hardware/software countermeasure that can be integrated into any resource-constrained device to thwart such attacks in IoT scenarios.

On the Efficiency of a Lightweight Authentication and Privacy Preservation Scheme for MQTT

The Internet of Things (IoT) deployment in emerging markets has increased dramatically, making security a prominent issue in IoT communication. Several protocols are available for IoT communication; among them, Message Queuing Telemetry Transport (MQTT) is pervasive in intelligent applications. However, MQTT is designed for resource-constrained IoT devices and, by default, does not have a security scheme, necessitating an additional security scheme to overcome its weaknesses. The security vulnerabilities in MQTT inherently lead to overhead and poor communication performance. Adding a lightweight security framework for MQTT is essential to overcome these problems in a resource-constrained environment. The conventional MQTT security schemes present a single trusted scheme and perform attribute verification and key generation, which tend to be a bottleneck at the server and pave the way for various security attacks. In addition to that, using the same secret key for an extended period and a flawed key revocation system can affect the security of MQTT. To address these issues, we propose an Improved Ciphertext Policy-Attribute-Based Encryption (ICP-ABE) integrated with a lightweight symmetric encryption scheme, PRESENT, to improve the security of MQTT. In this work, the PRESENT algorithm enables the secure sharing of blind keys among clients. We evaluated a previously proposed ICP-ABE scheme from the perspective of energy consumption and communication overhead. Furthermore, we evaluated the efficiency of the scheme using provable security and formal methods. The simulation results showed that the proposed scheme consumes less energy in standard and attack scenarios than the simple PRESENT, Key Schedule Algorithm (KSA)-PRESENT Secure Message Queue Telemetry Transport (SMQTT), and ECC-RSA frameworks, with a topology of 30 nodes. In general, the proposed lightweight security framework for MQTT addresses the vulnerabilities of MQTT and ensures secure communication in a resource-constrained environment, making it a promising solution for IoT applications in emerging markets.

MBSNN: A multi-branch scalable neural network for resource-constrained IoT devices

Recent breakthroughs in artificial intelligence promote the development of deep neural networks (DNNs)-based intelligent applications in the Internet of Things (IoT). However, these powerful DNNs have considerable model parameters, computational complexity, and storage usage, which impose heavy costs on resource-constrained IoT devices and incur unendurable latency in smart applications. To overcome these challenges, numerous works aim to design lightweight DNNs, but existing works encounter two severe bottlenecks: one is the acute accuracy degradation when reducing model parameters, and the other is the failure to balance the accuracy and resource usage under dynamic workloads.In this paper, we investigate the problems of designing and deploying DNNs on resource-constrained IoT devices. Specifically, we first design a novel multi-branch scalable neural network (MBSNN) architecture, which features multiple subnets to provide optional intelligent services of different accuracy. Then, we propose a stepwise incremental training technique and a learning rate dual decay strategy that can efficiently train MBSNN and increase accuracy. Afterwards, we develop a threshold selection-based adaptive inference mechanism, which can wisely select thresholds for MBSNN to achieve optimal accuracy under given timing constraints. Extensive experiments show that compared to benchmarking methods, our MBSNN improves accuracy by up to 1.63%, reduces computational complexity by up to 32.7%, and decreases model parameters by up to 41.9%.

A quantum resistant multi-user signcryption scheme featuring key invisibility for Internet of Things

With the advent of ubiquitous computing, multiple heterogeneous devices have evolved with increased connectivity, yielding the Internet of Things (IoT). For establishing secure communication, IoT has effectively become a primary security concern. The massive connectivity of the resource-constrained devices in IoT and enormous data on the air have made information vulnerable to several types of attacks. Although several resource-intensive cryptographic algorithms have been proposed to combat this security threat, signcryption has been identified as a sound solution for such an application.In this work, we propose an isogeny-based signcryption scheme by integrating HyPKE, an isogeny-based encryption scheme with an isogeny-based signature SeaSign based on Fiat Shamir with aborts. Apart from confidentiality and authenticity requirements, our scheme also achieves ciphertext anonymity and key invisibility, unlike the existing signcryption schemes in the post-quantum world. More positively, our security model considers insider attacks, supports multi-users setting and offers non-repudiation. Our work also studies the key privacy notion of the isogeny-based encryption scheme Hashed-PKE, which can be of independent interest in designing several anonymous channels or privacy-preserving protocols. More precisely, we showed Hashed-PKE achieves indistinguishability of keys against chosen-plaintext attack. This leads to the indistinguishability of keys against chosen-ciphertext attack security of HyPKE, a hybrid encryption scheme that is obtained by applying the Fujisaki Okamoto transformation to Hashed-PKE. We have proved our result in the standard model assuming the hardness of the Commutative Supersingular Decisional Diffie–Hellman assumption.

Spectrum-Efficient Analog Pulse Index Modulation for High-Volume Wireless Data Telemetry

Spectrum-efficient, high-density, and high-speed data transmission are essential to store and exchange sensitive information through Internet-of-Things (IoT) networks. However, the resource-constrained IoT devices and the limited existing frequency spectrum pose a significant challenge to employing the more efficient technique for the next-generation technology. This paper presents an innovative pulse index modulation (PIM) architecture with a randomization technique for analog pulse-based packet-less high-volume data transmission. The pulse-based compression simplifies the encoding and decoding schemes for short-range wireless telemetry. The pulse randomization technique creates the pulse blinding by reordering the pulse positions and adds an extra level of physical layer security. This paper describes the design, simulation, and prototype development of PIM to validate the feasibility and compatibility of pulse-based data telemetry using the standard architecture. Simulation results show that the proposed PIM increases the data compression ratio by 2.61 times and improves the processing time by 5.56 time compared to the Huffman coding for a 3-bit system. Test results show that the BER is 7.5×10-4 at 10.0 dB SNR, which satisfies the lower bound for data communication and increases the transmission speed k-times by supporting 2k*k bits data using 2k number of pulses, where k is a non-negative integer number. The proposed PIM has the potential to improve data rate with added security to support the next-generation IoT networks.