Lightweight Internet Of Things Devices Research Articles

A Novel Lightweight Human Activity Recognition Method Via L-CTCN.

Wi-Fi-based human activity recognition has attracted significant attention. Deep learning methods are widely used to achieve feature representation and activity sensing. While more learnable parameters in the neural networks model lead to richer feature extraction, it results in significant resource consumption, rendering the model unsuitable for lightweight Internet of Things (IoT) devices. Furthermore, the sensing performance heavily relies on the quality and quantity of data, which is a time-consuming and labor-intensive task. Therefore, there is a need to explore methods that reduce the dependence on the quality and quantity of the dataset while ensuring recognition performance and decreasing model complexity to adapt to ubiquitous lightweight IoT devices. In this paper, we propose a novel Lightweight-Complex Temporal Convolution Network (L-CTCN) for human activity recognition. Specifically, this approach effectively combines complex convolution with a Temporal Convolution Network (TCN). Complex convolution can extract richer information from limited raw complex data, reducing the reliance on the quality and quantity of training samples. Based on the designed TCN framework with 1D convolution and residual blocks, the proposed model can achieve lightweight human activity recognition. Extensive experiments verify the effectiveness of the proposed method. We can achieve an average recognition accuracy of 96.6% with only 0.17 M parameter size. This method performs well under conditions of low sampling rates and a low number of subcarriers and samples.

Access control mechanism for the Internet of Things based on blockchain and inner product encryption

The dynamic, massive, and lightweight properties of the Internet of Things device nodes lead to complicated application environments, and traditional access control mechanisms cannot match the current security requirements of the Internet of Things. Although attribute encryption schemes support more fine-grained access control, their overt access policies can endanger users’ privacy. To address the above problems, this paper proposes an Internet of Things access control mechanism based on blockchain and inner product encryption. First, the mechanism employs blockchain technology to provide distributed and decentralized access control management in the Internet of Things, allowing for dynamic management of the Internet of Things data while avoiding the problem of a single point of failure. And utilizing the tamper-evident feature of blockchain, this system stores the hash value of the ciphertext in the third-party storage on the chain to ensure the integrity of the ciphertext so that the third-party storage can be trusted. Furthermore, malicious user access is prevented by deploying smart contracts on the blockchain, which provide automatic and traceable access control to massive amounts of Internet of Things data. Then, in order to guarantee data security and user privacy for lightweight Internet of Things devices, fine-grained access control and complete hiding of access policies are achieved using the property of vector representation of attributes in inner product encryption. Finally, the experimental findings reveal that the mechanism has high efficiency while providing security and can meet the particular access control requirements of the Internet of Things.

Dynamic Access Control and Trust Management for Blockchain-Empowered IoT

The Internet of Things (IoT), while providing comprehensive interconnection and ubiquitous services, poses security issues by enabling resources sharing among various devices from different untrusted authorities. Blockchain, as a distributed ledger, provides a traceable and verifiable platform to ensure the secure access control in IoT. The existing works based on blockchain may bring up intolerable computing overhead and delay to the lightweight IoT devices. In this article, we propose a dynamic and lightweight attribute-based access control framework for blockchain-empowered IoT, to achieve secure and fine-grained authorization. The proposed scheme allows access to resources by evaluating attributes, operations, and the environment relevant to a request. The access policy is executed through smart contract in blockchain for security and flexibility. To further adapt to IoT device constraints, we design a access control framework based on decentralized application (DApp), which can maintain tamper proof in a timely manner and be adapt to the delay-intolerant application. When delay-intolerant access is required, access can be allowed according to local replica of the blockchain, without a consensus of blockchain network. Considering the time-varying attributes of IoT devices, a trust management scheme is proposed based on the Markov chain to resist the security fluctuation caused by the vulnerability of IoT devices. In the experiments, we deploy our system prototype on Ethereum to evaluate the feasibility and effectiveness of the scheme. The results show the proposed scheme can achieve secure, high throughput, and flexible access control in IoT.

AxRLWE: A Multilevel Approximate Ring-LWE Co-Processor for Lightweight IoT Applications

This work presents a multilevel approximation exploration undertaken on the Ring-Learning-with-Errors (R-LWE)-based public-key cryptographic (PKC) schemes that belong to quantum-resilient cryptography algorithms. Among the various quantum-resilient cryptography schemes proposed in the currently running NIST’s post-quantum cryptography (PQC) standardization plan, the lattice-based learning-with-error (LWE) schemes have emerged as the most viable and preferred class for the Internet of Things (IoT) applications due to their compact area and memory footprint compared to other alternatives. However, compared to the classical schemes used today, R-LWE is much harder a challenge to fit on embedded IoT (end-node) devices, due to their stricter resource constraints (lower area, memory, and energy budgets) as well as their limited computational capabilities. To the best of our knowledge, this is the first endeavor exploring the inherent approximate nature of the LWE problem to undertake a multilevel approximate R-LWE (AxRLWE) architecture with respective security estimates opt for lightweight IoT devices. Undertaking AxRLWE on field-programmable gate arrays (FPGAs), we benchmarked a 64% area reduction cost compared to earlier accurate R-LWE designs at the cost of reduced quantum security. For the application-specific integrated circuits (ASICs) with 45-nm CMOS technology, AxRLWE was benchmarked to fit well within the same area budget of a lightweight ECC processor and consume a third of energy compared to special class of R-Binary LWE (R-BLWE) designs being proposed for an IoT, with a better security level.

Three-Dimensional Resource Matching for Internet of Things Underlaying Cognitive Capacity Harvesting Networks

In this paper, we propose a cognitive capacity harvesting network (CCHN) based Internet of Things (IoT) architecture, which allows the lightweight IoT devices without spectrum monitoring/sensing capabilities to enjoy the benefits of cognitive radio networks (CRNs). We investigate the sum-rate maximization of IoT links in this proposed architecture. In particular, we formulate the considered problem as a three-dimensional (3-D) resource matching between the IoT links, the CR links and the available CR spectrum blocks (CSBs). Then, two approaches, i.e., Hungarian based switching iteration (HBSI) approach and minimum interference clustering based Lagrange relaxation (MICBLR) approach, are proposed to obtain the near-optimal solution. In HBSI approach, the IoT and CR links are divided into a set of IoT and CR links clusters (ICCs). Based on the partition of ICCs, the considered problem can be simplified to a maximum weight bipartite-matching problem and solved by the Hungarian algorithm. Switching iteration is then used to improve the partition of ICCs. To achieve a better tradeoff between the performance and running time, we further propose the MICBLR approach, which contains IoT links clustering according to the minimum interference rule and a Lagrange relaxation (LR) algorithm used to solve the 3-D matching problem between the clusters of IoT links, the CR links, and the available CSBs. Simulations show that the performance of the proposed approaches is close to the exhaustive search (ES) method but with a much shorter running time. Compared with the Nearest sharing based Hungarian (NSBH), Furthest sharing based Hungarian (FSBH), and Random allocation (RA) policies, the proposed approaches can averagely improve the system performance by 33.68%-38.18%.

Enhancing AES Using Chaos and Logistic Map-Based Key Generation Technique for Securing IoT-Based Smart Home

The Internet of Things (IoT) has brought new ways for humans and machines to communicate with each other over the internet. Though sensor-driven devices have largely eased our everyday lives, most IoT infrastructures have been suffering from security challenges. Since the emergence of IoT, lightweight block ciphers have been a better option for intelligent and sensor-based applications. When public-key infrastructure dominates worldwide, the symmetric key encipherment such as Advanced Encryption Standard (AES) shows immense prospects to sit with the smart home IoT appliances. As investigated, chaos motivated logistic map shows enormous potential to secure IoT aligned real-time data communication. The unpredictability and randomness features of the logistic map in sync with chaos-based scheduling techniques can pave the way to build a particular dynamic key propagation technique for data confidentiality, availability and integrity. After being motivated by the security prospects of AES and chaos cryptography, the paper illustrates a key scheduling technique using a 3-dimensional S-box (substitution-box). The logistic map algorithm has been incorporated to enhance security. The proposed approach has applicability for lightweight IoT devices such as smart home appliances. The work determines how seeming chaos accelerates the desired key-initiation before message transmission. The proposed model is evaluated based on the key generation delay required for the smart-home sensor devices.

Trust Based Security Mechanisms for Resource-Constrained Internet of Things-A Review

The Internet of Things (IoT) defined as the assembling of real-world objects that are connected over the Internet to make human lives become well-being. The implementation and success of an IoT depend on how it’s secured. But providing security is a critical task because of various natures of IoT devices such as shared and open environment, a wide range of communication protocols, standards, self-organized, lack of central control, heterogeneity of devices, etc. To provide security solutions and mechanisms, key management, cryptographic mechanisms such as private and public-key cryptography, Intrusion Detection System (IDS), and hash functions have been used. Though it is working well, it is not suitable for lightweight IoT devices. Because of such mechanisms always demand high computational, memory, and processing. To overcome these issues, researchers have chosen trust management for providing security. In this article, some of the trust-based solutions have presented. Besides, security issues, security loopholes/attacks in various layers of IoT, security requirements, trust management in IoT, properties of trust, trust management building blocks, the role of trust management in IoT, basic trust calculation methods have presented.

Toward Next Generation of Blockchain Using Improvized Bitcoin-NG

The Bitcoin-Next Generation (NG) is a new blockchain protocol that is designed to scale the efficiency in terms of throughput and delay. Bitcoin-NG is a Byzantine fault-tolerant blockchain protocol robust to extreme churn and shares the same trust model as Bitcoin. Researchers are trying to extend their applications by integrating them with existing technologies, such as the Internet of Things (IoT). However, due to high computational power and high consensus delay, current blockchain protocols, such as Bitcoin, are not suitable for working with lightweight IoT devices. Moreover, they are not energy-efficient as the mining of blocks requires dedicated mining machines that consume electricity. However, protocols, such as Bitcoin-NG, are designed to reduce the consensus delay, but they still need high computational power and high energy. This article proposes a new mechanism for blockchain to reduce the consensus delay, reduce energy consumption, and increase the throughput by introducing a new leader election scheme in the blockchain.

Stabilizing Frame Slotted Aloha-Based IoT Systems: A Geometric Ergodicity Perspective

The explosive deployment of the Internet of Things (IoT) brings a massive number of light-weight and energy-limited IoT devices, challenging stable wireless access. Energy-efficient, Frame Slotted Aloha (FSA) recently emerged as a promising MAC protocol for large-scale IoT systems such as Machine to Machine (M2M) and Radio Frequency Identification (RFID). Yet the stability of FSA and how to stabilize it, despite of its fundamental importance on the effective operation in practical systems, have not been systematically addressed. In order to bridge this gap, we devote this paper to designing stable FSA-based access protocol (SFP) to stabilize IoT systems. We first design an additive active node population estimation scheme and use the estimate to set frame size and participation probability for throughput optimization. We then carry out theoretical analysis demonstrating the stability of SFP in the sense of geometric ergodicity of Markov chain derived from dynamics of the active node population and its estimate. Our central theoretical result is a set of closed-form conditions on the stability of SFP. We further conduct extensive simulations whose results confirm our theoretical analysis and demonstrate the effectiveness of SFP.

PCBChain: Lightweight Reconfigurable Blockchain Primitives for Secure IoT Applications

In the era of ubiquitous intelligence, the Internet of Things (IoT) holds the promise as a breakthrough technology to enable diverse applications that benefit societal problems. Yet interconnecting myriad heterogeneous IoT devices across various application domains remain a security challenge. Decentralized technology has recently emerged as a powerful primitive in building distributed applications to facilitate secure transactions between mutually distrustful parties in a trustworthy manner. Unfortunately, these decentralized protocols demand computing resources and power far beyond the reach of resource-constrained IoT devices, preventing the full adoption of distributed consensus platform in the IoT setting. In this article, we address the key bottleneck to enable blockchain in resource-constrained IoT devices. We propose a lightweight implementation of proof-of-work (PoW) mining with reconfigurable hardware primitives. By replacing the hash and cryptographic functions in classic blockchain protocol with secure and efficient hardware implementations, our proposed solution can significantly reduce hardware resources and power overheads of PoW mining, while improving the transaction speed of large-scale IoT systems. Finally, we demonstrate the algorithm by proposing an antispoofing solution for GPS navigation among lightweight IoT devices. As a replacement for position computation, a mining process generates the expected coordinates with the correct initial value and function configuration.

Testing the pseudorandom number generator as a component of the security of the internet of things

Internet of Things (IoT) devices collect some volumes of data, some of which will require protection based on sensitivity or compliance requirements. IoT data protection solutions must span edge to cloud, provide scalable encryption and key management, and not impede data analysis. An analysis of recent research and publications shows a great deal of interest in finding various ways to develop lightweight pseudorandom number generators that have been widely used on the Internet of Things or mobile devices. These so-called lightweight IoT devices have limited power, space, and computing resources. Therefore, there is a huge need to develop and test the quality of lightweight pseudorandom number generators, which is an important component of cybersecurity. The available approaches to testing random or pseudorandom sequences show low flexibility and versatility in the means of finding hidden patterns in the data. It is revealed that for sequences of length up to 100 bits there are not enough existing statistical packets. The available techniques show low flexibility and versatility in the means of finding hidden patterns in the data. Perspective direction of research — static testing of sequences using multidimensional statistics is considered. To solve this problem, it is suggested to use algorithms based on multidimensional statistics. In the work, formulas are given and theorem for testing sequences for randomness, using two or three-dimensional statistics that can be used for small and medium-sized sequences is formulated. The new technique of PRS testing is proposed in the paper, and several criteria for testing bit sequence of small length are considered, which, in comparison with one-dimensional statistics, gives a more accurate result. As a result of the implementation of this technique, an information system can be created that will allow analyzing the PRS of a small length and choosing a quality PRS for use in the Internet of Things Security.

A decentralized lightweight blockchain-based authentication mechanism for IoT systems

The Internet of Things (IoT) is an emerging paradigm branded by heterogeneous technologies composed of smart ubiquitous objects that are seamlessly connected to the Internet. These objects are often deployed in open environments to provide innovative services in various application domains such as smart cities, smart health, and smart communities. These IoT devices produce a massive amount of confidentiality and security-sensitive data. Thus, security of these devices is very important in order to ensure the safety and effectiveness of the system. In this paper, a decentralized authentication and access control mechanism is proposed for lightweight IoT devices and is applicable to a large number of scenarios. The mechanism is based on the technology of the fog computing and the concept of the public blockchain. The results gained from the experiments demonstrate a superior performance of the proposed mechanism when compared to a state-of-the-art blockchain-based authentication technique.

Secure Service Provisioning Scheme for Lightweight IoT Devices With a Fair Payment System and an Incentive Mechanism Based on Blockchain

The Internet of Things (IoT) industry is growing very fast to transform factories, homes, farms and practically everything else to make them efficient and intelligent. IoT is applied in different resilient scenarios and applications. IoT faces lots of challenges due to lack of computational power, battery and storage resources. Fortunately, the rise of blockchain technology facilitates IoT in many security solutions. Using blockchain, communication between IoT and emerging computing technologies is made efficient. In this work, we propose a secure service provisioning scheme with a fair payment system for Lightweight Clients (LCs) based on blockchain. Furthermore, an incentive mechanism based on reputation is proposed. We use consortium blockchain with the Proof of Authority (PoA) consensus mechanism. Furthermore, we use Smart Contracts (SCs) to validate the services provided by the Service Providers (SPs) to the LCs, transfer cryptocurrency to the SPs and maintain the reputation of the SPs. Moreover, the Keccak256 hashing algorithm is used for converting the data of arbitrary size to the hash of fixed size. AES128 encryption technique is used to encrypt service codes before sending to the LCs. The simulation results show that the LCs receive validated services from the SPs at an affordable cost. The results also depict that the participation rate of SPs is increased because of the incentive mechanism.

Privacy-Preserving Wildcards Pattern Matching Protocol for IoT Applications

With the continuous development of the Internet of Things (IoT), various IoT devices create an incomprehensible amount of data all the time. However, the IoT devices have limited computing and storage resources and are difficult to process massive data locally, so they often introduce servers to help them for calculating or analyzing data. At present, the “IoT + Cloud” mode has been widely accepted. How to protect users’ privacy in the public cloud environment has become critical. Among the common methods of processing data in the server, pattern matching is an important one which aims to identify the appearance and location of a string (called pattern) within a larger string or text. There are a lot of studies on privacy-preserving pattern matching protocols, but most protocols are constructed using heavy public-key cryptographic operations, which are not applicable to IoT devices. In this paper, we propose a new protocol using secret sharing and oblivious transfer (OT) and latter improve its efficiency with OT extension, so it is very efficient for lightweight IoT devices. In addition, our protocol also supports query with wildcards which can be used for the batch search. This protocol is provable-secure against honest-but-curious adversaries. Both the theoretical and experimental results show that our protocol can be used in real-world IoT applications.

BOAT: A Block-Streaming App Execution Scheme for Lightweight IoT Devices

The contradiction between the limited capability of lightweight Internet-of-Things (IoT) devices and ever-increasing user demands is a fundamental and challenging problem in the era of IoT. One of the most important challenges is that lightweight IoT devices are generally embedded with fixed applications on resource-constrained hardware, and cannot enable on-demand service provisioning. In this paper, we propose BOAT, a block-streaming application execution scheme based on transparent computing (TC), which can remotely retrieve the necessary parts of traditional applications from edge servers on demand and run them on IoT devices locally. Specifically, we first exploit TC to build a scalable IoT system, where the applications of lightweight IoT devices are stored in edge servers or cloud but can be dynamically loaded on IoT devices in a block-streaming way. Moreover, we present a code partition approach to split the codes of a whole service into numerous functional blocks at the edge side. Such that, IoT devices only need to load necessary blocks of an application to obtain the requested services without loading the whole application codes. A lightweight I/O virtualization mechanism and a fine-grained relocation technology are then developed to support the block-streaming service loading. Our experimental results on a lightweight wearable device demonstrate that the proposed scheme can efficiently achieve flexible service provisioning with improved scalability and reduced service loading delay and energy consumption.

Heterogeneous Distributed Shared Memory for Lightweight Internet of Things Devices

Despite the promising applicability of the Internet of Things (IoT), such as for home automation and health monitoring, it is laborious to develop an IoT application, because programmers should explicitly manipulate communication for shared data across heterogeneous distributed IoT devices. Recent proposals simplify the communication management with graphical views or language extensions but still require programmers to explicitly manage communication. To liberate programmers from explicit communication management, this article revisits heterogeneous distributed shared memory (HDSM) for IoT devices and proposes a compiler-runtime cooperative HDSM framework, called IoT-HDSM. The framework's compiler unifies heterogeneous memory layouts of different IoT devices, and the runtime system provides a shared memory view for the devices. To support lightweight devices that cannot execute the runtime, the compiler automatically inserts explicit communication instructions into the IoT application. Moreover, to efficiently control concurrent memory accesses without locks, the authors also propose a new annotation called the memory branch, which provides a memory snapshot for each device. This article describes the implementation of three IoT services with IoT-HDSM and shows that IoT-HDSM simplifies shared data management without harming QoS.

A Built-in-Self-Test Scheme for Online Evaluation of Physical Unclonable Functions and True Random Number Generators

In the emerging era of Internet of Things (IoT) where various physical entities are spontaneously communicating with each other and sharing sensitive information, it is prohibitive to have a global entity for maintaining the security of the complex web against environmental variations and active attacks. Therefore, it is crucial that each entity has the capability of safeguarding its security features on its own. Methods based on harnessing the random identification and authentication from the physical device and environment, such as physical unclonable functions (PUFs) and True Random Number Generators (TRNGs), if securely run, are promising primitives for protecting lightweight IoT devices. This paper presents the first Built-In-Self-Test scheme for on-the-fly evaluation of PUFs that can also be utilized for assessing the desired statistical properties of TRNGs. Unlike earlier known PUF evaluation suites that were software-based and offline, our methodology enables online assessment of the pertinent statistical and security properties all in hardware. Specifically, the BIST structure is designed to evaluate two main properties of PUFs: unpredictability and stability. Our work is the first online test suite that thoroughly evaluates the internal health of the entropy source of TRNGs along with the statistical properties of the generated bit stream. Comprehensive real-time evaluation by the BIST method is able to ensure robustness and security of both TRNG and PUF in the face of operational, structural, and environmental fluctuations due to variations, aging, or adversarial acts. Proof-of-concept implementation of our BIST methodology in FPGA demonstrates its reasonable overhead, effectiveness, and practicality.