Connected Internet Of Things Devices Research Articles

EMERGING TECHNOLOGIES AND APPLICATIONS FOR SMART CITIES

The large deployment of the Internet of Things (IoT) is empowering Smart City tasks and activities everywhere throughout the world. Items utilized in day-by-day life are outfitted with IoT devices and sensors to make them interconnected and connected with the internet. Internet of Things (IoT) is a vital piece of a smart city that tremendously impact on all the city sectors, for example, governance, healthcare, mobility, pollution, and transportation. This all connected IoT devices will make the cities smart. As different smart city activities and undertakings have been propelled in recent times, we have seen the benefits as well as the risks. This paper depicts the primary challenges and weaknesses of applying IoT innovations dependent on smart city standards. Moreover, this paper points the outline of the technologies and applications of the smart cities.

Ethereum for Internet of Things security

The influence of Internet of Things (IoT) is growing exponentially in the consumer patterns and will continue to increase in the coming years. With millions of interconnected devices over the internet, IoT is currently running into its monumental security and centralization issues that can be resolved using Blockchain. This paper aims to demonstrate the use of a smart contract on a private Ethereum Blockchain (also known as permission less Blockchain) to check whether a user holds enough tokens to utilize a service. Private in this context implies a private instance of the Ethereum implementation in which we make use of the same security and principles of the Ethereum main chain on a personal network of connected IoT devices which improves the overall privacy and security of the system than the traditional schemes. The proposed model consists of a target IoT device controlled by a Raspberry Pi, running a client application that makes decisions based on the state of the smart contract. With this environment, we are able to achieve the following goals: (1) no data leaves the private network (2) all services are employed with transparency and accountability (3) only registered devices can issue requests for using a service. Although the demonstration is made on a home network, the model presented in this paper can be utilized in commercial environments where any service can be availed from a wide array of smart devices through a mobile application.

A New Design and Analysis of Power Saving for IoT Gateway

Internet of Things (IoT) enables the connection of physical objects to the internet for exchanging information. Due to the massive number of IoT devices, cellular traffic is increasing exponentially in wireless networks. IoT devices require low latency, high reliability and massive connectivity. Fifth Generation (5G) networks are expected to provide promising solutions to meet these demands and improve the performance of IoT systems. 5G offers low latency, high reliability, and efficient data transmissions for large number of connected IoT devices. IoT systems employ gateways for efficient connectivity of various devices. Gateways have capabilities of integrating various protocols, handling data storage temporarily, analyzing the data, and transmitting that data between edge devices and cloud. IoT devices transmit data in frequent intervals, causing the gateway to be active all the time, leading to the increase in power consumption of the gateways. In this article, we focus on applying Discontinuous Reception (DRX) mechanism to IoT gateways. The proposed DRX at IoT gateways reduces the power consumption and improves the energy efficiency of IoT gateways. Our proposed work improves the energy efficiency of gateways by almost 30% compared to Relay Energy Efficiency (REE) based scheme.

The Vulnerability of the Production Line Using Industrial IoT Systems under DDoS Attack

Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.

A secure blockchain-based group key agreement protocol for IoT

Since most Internet of things (IoT) devices are energy-limited, increasingly more manufacturers have chosen to develop IoT applications based on group communication. For these applications, a secure and effective authenticated group secret key is fundamental to whole group communication. A large number of related protocols have been proposed to address this security problem. However, most of them have a centralized architecture, which is vulnerable to a single point of failure. Blockchain technology has the potential to address the challenges mentioned above because of its distributed, secure, and private property. Thus, this paper proposes a blockchain-based authenticated group key agreement protocol for IoT. The proposed protocol introduces a new entity called the device manager, who acts as an intermediary to connect IoT devices with blockchain networks. Security analysis demonstrates the robustness of the proposed protocol to various kinds of attacks. Besides, the simulation results further show that the time costs of protocol operations are reasonable and suitable for IoT environments.

Enabling Full-duplex in MEC Networks Using Uplink NOMA in Presence of Hardware Impairments

To satisfy the fast growth of Internet of Things (IoT) or development of applications in the fifth generation (5G) wireless networks, the expected systems need two main functions such as massive connectivity of IoT devices and the low latency. Fortunately, non-orthogonal multiple access (NOMA) has been recommended as a promising approach for 5G networks to satisfy concerned requirements and to significantly improve the network capacity. Together with advances of NOMA scheme, mobile edge computing (MEC) is recognized as one of the key emerging approaches to improve the quality of service and reduce the latency for 5G networks. In order to capture the potential gains of NOMA-aided MEC systems, this paper proposes design of multiple antennas and full-duplex for an edge computing aware NOMA architecture. As expected, such NOMA-aided MEC systems can enjoy the benefits of uplink NOMA in reducing MEC users’ uplink outage probability. To this end, we derive expression of outage probability for signals corresponding two-user model at uplink. Especially, degraded performance might happens in practice under impact of hardware impairment.

A novel vector-space-based lightweight privacy-preserving RFID authentication protocol for IoT environment

Internet of Things (IoT) is a novel paradigm that connects several physical devices and the cyber world over the Internet. IoT technology is growing rapidly and soon will have an enormous innovation in our daily lives. With the increasing number of connected IoT devices making our daily lives more convenient, it puts personal data at serious risk too. Radio frequency identification (RFID) contributes to IoT applications in the automatic identification of the connected devices. However, the primary concerns with the RFID tag connected devices are their security and privacy. The communication channel is deemed to be wireless or insecure between the tags and the readers, which makes RFID system vulnerable to the various known attacks. To deal with these security flaws, we have put forward a novel vector-space-based lightweight RFID authentication protocol for IoT environment named VLPRAP. The proposed protocol integrates vector space, linear mapping and basis mechanism that achieves secure authentication and improves security and privacy without increasing the computational cost. The correctness of the proposed VLPRAP protocol has been validated using BAN logic inference rules. The formal and informal security analysis demonstrates that our proposed protocol resists various malicious security threats, providing better security and higher efficiency. Furthermore, the performance analysis of our proposed protocol has been measured in terms of computation operations, storage requirement and communication overhead, which shows that better performance as compared to related existing protocols.

Resource Management and Task Offloading Issues in the Edge–Cloud Environment

With the increasing number of Internet of Things (IoT) devices connected to the internet, a platform is required to support the enormous amount of data they generate. Since cloud computing is far away from the connected IoT devices, applications that require low-latency, real-time interaction and high quality of service (QoS) may suffer network delay in using the Cloud. Consequently, the concept of edge computing has appeared to complement cloud services, working as an intermediate layer with computation capabilities between the Cloud and IoT devices, to overcome these limitations. Although edge computing is a promising enabler for issues related to latency sensitivity, its deployment produces new challenges. Therefore, this paper presents and discusses the main factors of service latency for IoT applications, considering the impact of application characteristics (e.g., computation and communication demands), as well as resource utilization and heterogeneity at the edge level. Thus, a number of simulation experiments were set up to evaluate the influence of these factors. The outcomes of this research can be used to understand the complex interactions between many factors that affect the overall service time for latency-sensitive applications. Additionally, several open challenges are highlighted, serving as potential directions for future research.

A Survey of IoT Stream Query Execution Latency Optimization within Edge and Cloud

IoT (Internet of Things) streaming data has increased dramatically over the recent years and continues to grow rapidly due to the exponential growth of connected IoT devices. For many IoT applications, fast stream query processing is crucial for correct operations. To achieve better query performance and quality, researchers and practitioners have developed various types of query execution models—purely cloud‐based, geo‐distributed, edge‐based, and edge‐cloud‐based models. Each execution model presents unique challenges and limitations of query processing optimizations. In this work, we provide a comprehensive review and analysis of query execution models within the context of the query execution latency optimization. We also present a detailed overview of various query execution styles regarding different query execution models and highlight their contributions. Finally, the paper concludes by proposing promising future directions towards advancing the query executions in the edge and cloud environment.

Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments.

LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.

Toward Computing Resource Reservation Scheduling in Industrial Internet of Things

The Industrial Internet of Things (IIoT) is a critically important implementation of the Internet of Things (IoT), connecting IoT devices ubiquitously in an industrial environment. Based on the interconnection of IoT devices, IIoT applications can collect and analyze sensing data, which help operators to control and manage manufacturing systems, leading to significant performance improvements and enabling automation. IIoT systems are characterized by a variety of IIoT applications, which generate different computing tasks depending on their functionalities. Some tasks are time sensitive (TS), while others are not, and more importantly, some tasks are nonpreemptive in IIoT scenarios. Thus, processing the different IIoT applications efficiently in an IIoT environment is key to achieving automation. Since computing resources are limited in IIoT, how to rapidly process TS tasks is a critical issue. Although some existing scheduling schemes can deal with the latency requirements of TS tasks, they lack consideration for nonpreemptive tasks. To address this issue, in this article we consider a typical smart warehouse system as an example and propose a generic task scheduling scheme that reserves computing resources to wait for upcoming TS tasks in such an IIoT environment. In doing so, our proposed scheme is capable of minimizing the overall waiting time for TS tasks. To evaluate the proposed scheme, we have implemented a simulation platform for a smart warehouse and conducted extensive experiments. Our experimental results demonstrate the efficacy of our scheme, which can allocate computing resources so that the processing time for the TS tasks can be reduced. Additionally, we discuss some potential research directions toward improving performance in IIoT environments with respect to resource management, machine learning, and security and privacy.

Security Requirements and Solutions for IoT Gateways: A Comprehensive Study

The need for improving the security level of Internet-of-Things (IoT) systems is growing. Users may refrain from using such systems if they realize that security measures are not in place. In this context, one of the most important IoT components has not received the necessary attention by the community: the gateway. IoT gateways play a central role in an IoT system as they solve heterogeneity issues. However, if compromised, gateways can be a source of security threats, and these threats become more relevant due to the gateway's central role. Gateways connect IoT devices and cloud services, and successful attacks on this component may exploit this fact. Using a well-known security requirements (SRs) engineering approach, this article evaluates and prioritizes SRs specifically for IoT gateways. The prioritization highlights a set of SRs that must be observed when evaluating and improving the gateway security level. Also, current solutions are detailed to support the enforcement of such SRs. Finally, a set of open challenges are discussed to highlight current research gaps that must be addressed to support SRs.

Parallel Implementations of ARX-Based Block Ciphers on Graphic Processing Units

With the development of information and communication technology, various types of Internet of Things (IoT) devices have widely been used for convenient services. Many users with their IoT devices request various services to servers. Thus, the amount of users’ personal information that servers need to protect has dramatically increased. To quickly and safely protect users’ personal information, it is necessary to optimize the speed of the encryption process. Since it is difficult to provide the basic services of the server while encrypting a large amount of data in the existing CPU, several parallel optimization methods using Graphics Processing Units (GPUs) have been considered. In this paper, we propose several optimization techniques using GPU for efficient implementation of lightweight block cipher algorithms on the server-side. As the target algorithm, we select high security and light weight (HIGHT), Lightweight Encryption Algorithm (LEA), and revised CHAM, which are Add-Rotate-Xor (ARX)-based block ciphers, because they are used widely on IoT devices. We utilize the features of the counter (CTR) operation mode to reduce unnecessary memory copying and operations in the GPU environment. Besides, we optimize the memory usage by making full use of GPU’s on-chip memory such as registers and shared memory and implement the core function of each target algorithm with inline PTX assembly codes for maximizing the performance. With the application of our optimization methods and handcrafted PTX codes, we achieve excellent encryption throughput of 468, 2593, and 3063 Gbps for HIGHT, LEA, and revised CHAM on RTX 2070 NVIDIA GPU, respectively. In addition, we present optimized implementations of Counter Mode Based Deterministic Random Bit Generator (CTR_DRBG), which is one of the widely used deterministic random bit generators to provide a large amount of random data to the connected IoT devices. We apply several optimization techniques for maximizing the performance of CTR_DRBG, and we achieve 52.2, 24.8, and 34.2 times of performance improvement compared with CTR_DRBG implementation on CPU-side when HIGHT-64/128, LEA-128/128, and CHAM-128/128 are used as underlying block cipher algorithm of CTR_DRBG, respectively.

Blockchain-Based Continued Integrity Service for IoT Big Data Management: A Comprehensive Design

The state-of-the-art centralized Internet of Things (IoT) data flow pipeline has started aging since it cannot cope with the vast number of newly connected IoT devices. As a result, the community begins the transition to a decentralized pipeline to encourage data and resource sharing. However, the move is not trivial. With many instances allocating data or service arbitrarily, how can we guarantee the correctness of IoT data or processes that other parties offer. Furthermore, in case of dispute, how can the IoT data assist in determining which party is guilty of faulty behavior. Finally, the number of Service Level Agreement (SLA) increases as the number of sharing grows. The problem then becomes how we can provide a natural SLA generation and verification that we can automate instead of going through a manual and tedious legalization process through a trusted third party. In this paper, we explore blockchain solutions to answer those issues and propose continued data integrity services for IoT big data management. Specifically, we design five integrity protocols across three phases of IoT operations—during the transmission of IoT data (data in transit), when we physically store the data in the database (data at rest), and at the time of data processing (data in process). In each phase, we first lay out our motivations and survey the related blockchain solutions from the literature. We then use curated papers from our surveys as building blocks in designing the protocol. Using our proposal, we augment the overall value of IoT data and commands, generated in the IoT system, as they are now tamper-proof, verifiable, non-repudiable, and more robust.

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved.

Energy Efficient Constellation for Wireless Connectivity of IoT Devices.

Reducing energy consumption is one of the most important task of the approaching Internet of Things (IoT) paradigm. Existing communication standards, such as 3G/4G, use complex protocols (active mode, sleep modes) in order to address the waste of energy. These protocols are forced to transmit when one frame is only partially filled with information symbols. The hard task to adapt the power-saving mode with low latency to the discontinuity of the source is mainly due to the fact that the receiver cannot know a priori when the source has something to transmit. In this paper, we propose a modified signalling/constellation which can save energy by mapping a zero-energy symbol in the information source. This paper addresses the fundamentals of this new technique: the maximum a posteriori probability (MAP) criterion, the probability of error, the (energy) entropy, the (energy) capacity as well as the energy cost of the proposed technique are derived for the binary signalling case.

Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques

The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.

Emerging Smart Logistics and Transportation Using IoT and Blockchain

Transportation and logistics management play a vital role in the development of a country. With the advancement of the Internet of Things (IoT) devices, smart transportation is becoming a reality. However, these abundant connected IoT devices are vulnerable to security attacks. Recently, Blockchain has emerged as one of the most widely accepted technologies for trusted, secure and decentralized intelligent transportation systems. This research study aims to contribute to the field of logistics and transportation by exploring the potential of IoT and Blockchain technology in smart logistics and transportation. We propose a layered framework, namely BCTLF, for smart logistics and transportation that integrates IoT and Blockchain to provide an intelligent logistics and transportation system. Finally, we present two real-life IoT and Blockchain-based case studies to highlight the contribution of IoT and Blockchain in logistics and transportation.

Extended RECTANGLE Algorithm Using 3D Bit Rotation to Propose a New Lightweight Block Cipher for IoT

The Internet of Things (IoT) is a broad range of applications enabled by the connection of devices such as sensors, actuators, and monitors accessible through the Internet. Massive IoT device connectivity and vast data transmission have made the information susceptible to various types of attacks. Therefore, encryption is required for secure communication in an IoT ecosystem. An IoT system is constrained by its complexities that require small computing power. Thus, lightweight block cipher is chosen as the solution to IoT security issues. RECTANGLE block cipher has very efficient encryption speed performance among the existing lightweight algorithms. Although RECTANGLE achieves such high efficiency, lack of focus on its security aspect needs to be addressed. The algorithm is short of confusion and diffusion characteristics that should be offered by a block cipher as one of the cryptographic security properties. Therefore, we extended RECTANGLE using a 3D cipher to improve its security features by enhancing the algorithm confusion and diffusion properties. Security analysis and performance tests were performed to verify the strength of the proposed 3D RECTANGLE. The results show that 3D RECTANGLE performs better than its original version in terms of the correlation between data input and output with an increase of 1.58% for non-linearity results, records approximately 50% bit error rate for sensitiveness against both modifications of plaintext and key, increase of passing rate in the randomness test by 22.22%, and achieves competitive performance results against existing algorithms with 0.9516 ms execution speed and 67.26 bit/ms throughput.

Secure and lightweight communication in heterogeneous IoT environments

The Internet of Things (IoT) is a fast growing and rapidly evolving field. Everyday objects from smart home appliances (e.g. toasters and refrigerators) to health monitoring products (e.g. pacemakers and blood pressure monitors), to minuscule sensors and actuators, are being redesigned to be able to transfer data over a network. The connection of billions of these new objects to the Internet will cause a dramatic increase in the amount of shared data, which will simultaneously introduce a plethora of security risks. Understanding how to secure the data being sent over IoT links is necessary for the IoT paradigm to succeed. A popular data transfer protocol connecting IoT devices is the Constrained Application Protocol (CoAP). We used an open-source implementation of CoAP along with Datagram Transport Layer Security (DTLS) to implement secure data transfer between IoT devices. We studied the impact of DTLS on CoAP in the real IoT testbed we have developed using resource-constrained IoT devices and open-source software. Our tests showed that utilizing a CoAP-DTLS implementation with a symmetric key cipher suite resulted in noticeable performance costs. A secure connection with DTLS over CoAP used approximately 10% more energy than an unsecure connection. In addition, our latency tests revealed over a 100% increase in average latency time for secure messages compared to when no encryption is used. We also highlight some of the implementation challenges encountered while developing a real IoT testbed for secure experimentation.