Number Of Internet Of Things Devices Research Articles

IoT & Privacy – Issues in Modern World

IoT is considered a worldwide network of individually connected devices that communicate with each other, passing data across the internet. This data is collected about individual users and in some cases may contain very personal and private information about the user. Because this data can also be used for malicious purposes it presents security and Privacy Issues with the Internet of Things (IoT) devices. The number of IoT devices is quite large. IoT devices are typically dispersed across the globe, allowing for instant communication between connected devices. Domestic appliances, automobiles, TVs and other devices are collecting and transmitting their data across the internet in order to complete a variety of tasks. Users are typically unaware of how much data is actually collected and how the data is used or shared with others. As a result of the collecting, passing and sharing of data among these networked devices and data recipients there is a concern among security and privacy experts about how this data is collected, shared and used.

When Mobile-Edge Computing (MEC) Meets Nonorthogonal Multiple Access (NOMA) for the Internet of Things (IoT): System Design and Optimization

Mobile-edge computing (MEC) is considered as a promising technology to enable low latency applications while consuming less energy, and nonorthogonal multiple access (NOMA) is regarded as a hopeful method of increasing spectrum efficiency and the wireless network capacity. In this article, we consider a NOMA-MEC-based Internet-of-Things (IoT) network, and propose a joint optimization framework to maximize the effective system capacity, i.e., the number of IoT devices whose tasks are processed successfully, and meanwhile to maximize the total energy saving. First, we concentrate on improving the effective system capacity from the wireless side by introducing NOMA, and from the IoT device side by task offloading decision optimization, where distributed optimization is conducted and closed-form solution is obtained. Then, we maximize the total energy saving also from two aspects, i.e., the device-side computation resource allocation, and the wireless side joint admission control, user clustering, orthogonal subcarrier assignment, and transmit power control, where we resort to graph theory and propose a low-complexity heuristic algorithm to solve it. Abundant simulation results demonstrate our proposed joint optimization algorithm performs well in both effective system capacity optimization and energy saving maximization.

Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation

This paper focuses on proposing a new access barring scheme for internet of things (IoT) devices in long term evolution advanced (LTE/LTE-A) and 5G networks. Massive number of IoT devices communicating simultaneously is one of the hallmarks of the future communication networks such as 5G and beyond. The problem of congestion also comes with this massive communication for which access barring is one of the solutions. So, it is required that sophisticated access barring techniques are designed such that the congestion is avoided and these devices get served in less time. Legacy access barring schemes like access class barring (ACB) and extended access barring (EAB) suffer from high energy consumption and high access delay respectively. However, our proposed scheme provides less energy consumption than ACB while giving less access delay than EAB. The proposed scheme maximizes the success probability while reducing the number of collisions at the same time. The scheme is based on an approximation of the number of IoT devices based on details available to the eNodeB of the number of idle, successful and collided preambles. Extensive Matlab simulations are performed to validate our claims and analysis.

Blockchain-Based Secure Mist Computing Network Architecture for Intelligent Transportation Systems

Conventional centralized architectures are sufficiently educated to provide high scalability, availability, and low latency and bandwidth usages for the Internet of Things (IoT) network. The exponential increase in volume and number of IoT devices in Intelligent Transportation Systems (ITS) turns our physical world into the cyber world. Security and privacy in the ITS network have become the main concern. To address these issues and challenges, a secure distributed mist computing network architecture for ITS is proposed by leveraging the features of blockchain technology. In this model, we present IoT user/device registration and authentication algorithms and enable the computing resources at the extreme edge of the network by deploying a smart contract. The proposed model uses an aggregate signature scheme to generate a signature for multiple IoT devices. To evaluate the proposed model, we performed an experimental analysis based on various performance measures. The proposed model gains 81% of lower median latency at local nodes compared to the core model. The result shows that the model performed effectively and a suitable solution for various ITS applications.

Fog Fragment Cooperation on Bandwidth Management Based on Reinforcement Learning.

The term big data has emerged in network concepts since the Internet of Things (IoT) made data generation faster through various smart environments. In contrast, bandwidth improvement has been slower; therefore, it has become a bottleneck, creating the need to solve bandwidth constraints. Over time, due to smart environment extensions and the increasing number of IoT devices, the number of fog nodes has increased. In this study, we introduce fog fragment computing in contrast to conventional fog computing. We address bandwidth management using fog nodes and their cooperation to overcome the extra required bandwidth for IoT devices with emergencies and bandwidth limitations. We formulate the decision-making problem of the fog nodes using a reinforcement learning approach and develop a Q-learning algorithm to achieve efficient decisions by forcing the fog nodes to help each other under special conditions. To the best of our knowledge, there has been no research with this objective thus far. Therefore, we compare this study with another scenario that considers a single fog node to show that our new extended method performs considerably better.

Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

IoT integrates and connects intelligent devices or objects with varied architectures and resources. The number of IoT devices is growing exponentially. Due to the massive wave of IoT objects, their diversity and heterogeneity among their architectures, the existing communication protocols for wireless networks become ineffective in the context of IoT. Wireless Sensor Network (WSN) has the potential to be integrated to the internet of things (IoT). The issues of the routing of WSNs impose nearly similar prerequisites for IoT routing technique. Most of the traditional routing protocols are not appropriate for WSNs and IoT because of resource constraints, computational overhead and environmental interference and do not take into account the different factors affecting energy parameter and do not accommodate node mobility. Routing algorithms must ensure the data transmission in an efficient way, having proper knowledge of the IoT system. For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network's dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. The proposed routing algorithm is simulated using MATLAB for performance evaluations. The evaluation results have recorded an improvement in conservation of energy, of almost 50% less consumed energy even with an increase in the number of nodes, by comparing with an existing routing technique based on ant system, a current routing protocol for IoT and the conventional ACO algorithm. © 2020 ASTES Publishers. All rights reserved.

A secure and collaborative data aggregation scheme for fine‐grained data distribution and management in Internet of Things

AbstractOne of the greatest threats to the Internet of Things (IoT) is the increase in the number of IoT devices, leading to an exponential increase in the traffics on the communication networks. Asides this, the recent rise in ransomware, which is becoming better at evading detection, is posing security and privacy threats on IoT. With these, there is a need for effective security and aggregation solutions capable of securing data, protecting privacy, and minimizing traffic on the networks. Most of the existing security approaches are network and application‐centric and not effective for IoT due to the heterogeneity and resource constraints of its devices. To address these, we propose a security and aggregation solution, which not only reduces the traffic on the communication network but also enforces integrity and confidentiality on IoT. Our scheme is capable of securing, aggregating, and separating heterogeneous IoT devices' data. Besides, the scheme can detect and filter injected false data during in and out‐of‐network data access through the location‐based parametric‐attribute. Security and performance analysis show that our proposed scheme is secure and efficient.

Guest Editorial: Introduction to the Special Section on Edge Computing for Internet of Things

The papers in this special section focus on edge computing for Internet of Things (IoT). The IoT is expected to improve the quality of human lives through billions of Internet-based devices that connect the physical and digital worlds. To satisfy the computation and storage requirements of IoT, cloud computing has served as the most important computing infrastructure. However, with the explosion of the number of devices in IoT and the resulting a large volume of raw data continuously generated by IoT devices, cloud computing has significant challenges to efficiently an efficiently and securely handle the data volume. In particular, cloud computing is limited in terms of network bandwidth, security and privacy protection in IoT. To address these challenges, many researchers have attempted to move data computation and service provisioning from the cloud to the edge, which results in the area of edge computing. Early-stage research has indicated that edge computing could potentially enable IoT applications to meet their latency/delay requirements, improve the scalability and energy efficiency of IoT systems, and facilitate contextual information processing. Nevertheless, a series of challenging problems needs to be addressed in order to fully utilize edge computing for IoT.

IoVT: Internet of Vulnerable Things? Threat Architecture, Attack Surfaces, and Vulnerabilities in Internet of Things and Its Applications towards Smart Grids

In recent years, people have witnessed numerous Internet of Things (IoT)-based attacks with the exponential increase in the number of IoT devices. Alongside this, the means to secure IoT-based applications are maturing slower than our budding dependence on them. Moreover, the vulnerabilities in an IoT system are exploited in chains to penetrate deep into the network and yield more adverse aftereffects. To mitigate these issues, this paper gives unique insights for handling the growing vulnerabilities in common IoT devices and proposes a threat architecture for IoT, addressing threats in the context of a three-layer IoT reference architecture. Furthermore, the vulnerabilities exploited at the several IoT attack surfaces and the challenges they exert are explored. Thereafter, the challenges in quantifying the IoT vulnerabilities with the existing framework are also analyzed. The study also covers a case study on the Intelligent Transportation System, covering road transport and traffic control specifically in terms of threats and vulnerabilities. Another case study on secure energy management in the Smart Grid is also presented. This case study covers the applications of Internet of Vulnerable Things (IoVT) in Smart energy Grid solutions, as there will be tremendous use of IoT in future Smart Grids to save energy and improve overall distribution. The analysis shows that the integration of the proposed architecture in existing applications alarms the developers about the embedded threats in the system.

Age of Information for Multicast Transmission With Fixed and Random Deadlines in IoT Systems

In this article, we consider the multicast transmission of a real-time Internet-of-Things (IoT) system, where an access point (AP) transmits timestamped status updates to multiple IoT devices. Different from the existing studies that only considered multicast transmission without deadlines, we enforce a deadline for the service time of each multicast status update, taking into account both the fixed and randomly distributed deadlines. In particular, a status update is dropped when either its deadline expires or it is successfully received by a certain number of IoT devices. Considering deadlines is important for many emerging IoT applications, where the outdated status updates are of no use to IoT devices. We evaluate the timeliness of the status update delivery by applying a recently proposed metric, named the Age of Information (AoI), which is defined as the time elapsed since the generation of the most recently received status update. After deriving the distributions of the service time for all possible reception outcomes at IoT devices, we manage to obtain the closed-form expressions of both the average AoI and the average peak AoI. Simulations validate the performance analysis, which reveals that the multicast transmission with deadlines achieves a lower average AoI than that without deadlines and there exists an optimal value of the deadline that can minimize the average (peak) AoI. Results also show that the fixed and random deadlines have respective advantages in different deadline regimes.

Massive MIMO enabled joint unicast transmission to IoT devices and mobile terminals

We investigate the viability of downlink (DL) unicast transmission from a base station (BS) with massive number of antennas jointly to support machine-centric communication among the internet-of-things (IoT) devices and human-centric communication among the mobile terminals. Specifically, we derive a new expression for the DL sum spectral efficiency (SE) for the IoT devices with maximum ratio precoding when channel estimates are acquired via the proposed distance-dependent grouping based hybrid pilot assignment strategy. We also derive a new DL sum SE expression for IoT devices based on non-orthogonal pilot assignment. We show that under statistical channel inversion based power control at the BS, the proposed strategy yields the highest sum SE and can serve the largest number of IoT devices when compared against orthogonal, non-orthogonal and distance-independent grouping based hybrid strategies. Furthermore, we also analyse the DL sum SE of mobile terminals in the presence of IoT devices when every mobile is given an orthogonal pilot and prove that the sum SE is independent of the pilot assignment strategy employed by the IoT devices. We also obtain the max–min rate for the IoT devices and prove that it is independent of the pilot assignment strategy used by the devices.

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

Service discovery in the Internet of Things: review of current trends and research challenges

Recent technologies have made the life of people more comfortable and more straightforward than it was before. With the development of information technology, the Internet of Things (IoT) as an emerging technology has been entered into a lane of development. With the advent of IoT, data sharing, and connections among systems, devices, and people have been facilitated, and daily devices have been equipped with sensors and applications to provide their functionality through services. As a matter of fact, IoT provides a platform where everyday objects become smarter than before, everyday communication becomes informative, and everyday processes become intelligent. In this regard, to provide novel IoT services, numerous heterogeneous frameworks and protocols have been proposed. Since the number of IoT devices or objects is growing day by day, and the number of services is also increasing, discovering and locating appropriate services becomes a vital challenge, and the traditional service discovery strategies are not efficient enough to handle this issue. Service discovery refers to the process of finding suitable services according to clients' requests. Although the service discovery problem has essential impacts on the IoT, there is not any detailed and systematic study of the existing methods in this field. Therefore, this paper aims to find, categorize, and investigate all the effective and valid papers in the field of service discovery in the IoT using a systematic method. The selected papers are discussed based on various service discovery metrics and other criteria such as adopted architecture, search method, service description, discovery scope, adopted simulation tools, and datasets. The advantages and weaknesses of each reviewed paper are specified. Moreover, an abreast comparison of the selected papers is presented, in which the aforementioned methods are evaluated considering the mentioned metrics and criteria. Finally, future research directions and challenging problems are outlined to help researchers improve their innovations.

Virtualization Based Efficient Service Matching and Discovery in Internet of Things

Internet of Things (IoT) is getting more popular day by day, which triggers its adoption for solving domain specific problems. Cities are becoming smart by gathering the context knowledge through sensors and controlling specific parameters through actuators. Dynamically discovering and integrating different data streams from different sensors is a major challenge these days. In this paper, a service matchmaking algorithm is presented for service discovery utilizing IoT devices and services in a particular geographic area. It helps us to identify services based on a variety of parameters (location, query size and processing time, etc.). Customization of service selection and discovery are also explored. The conceptual framework is provided for the proposed model along with a matchmaking algorithm based on IoT devices virtualization. The simulation results elaborate the increased complexity of processing time with respect to the increasing pool of available services. The average processing time varies as the number of conditions are multiplied. Query size and complexity increases with additional number of filters and conditions which results in the reduction of the number of matching services. Moreover, upon decreasing the radius of geographic search area, the number of candidate services decreases for service matching algorithm. This is based on the assumption that IoT devices and services are evenly distributed in a given geographic area. Similarly, the remaining energy of IoT devices is also assumed to be uniformly distributed and, therefore, if we are interested in IoT devices or services with more residual energy, then a limited number of IoT devices or services will fulfill this criterion.

Nonorthogonal Multiple Access for Servicing the Internet of Things and Web Traffic in Wi-Fi Networks

Abstract—Nowadays, many devices require a wireless Internet connection, while the available frequency spectrum is limited. The nonorthogonal multiple access (NOMA) is a multiplexing technology that can significantly improve spectral efficiency and thus solve this problem. In this paper, the use of NOMA in Wi-Fi networks with two traffic types, Web traffic and Internet of Things (IoT) traffic, is investigated. Web traffic is one of the main Internet traffic types. It consists of relatively small data flows that need to be quickly delivered to users. IoT traffic consists of very short data flows compared to Web traffic, but there can be a lot of IoT flows due to a large number of IoT devices. In this paper, we propose an algorithm for resource allocation in Wi-Fi networks with the NOMA. This algorithm takes into account the traffic of both types and its performance is investigated by simulation.

A Security Transmission and Storage Solution about Sensing Image for Blockchain in the Internet of Things.

With the rapid development of the Internet of Things (IoT), the number of IoT devices has increased exponentially. Therefore, we have put forward higher security requirements for the management, transmission, and storage of massive IoT data. However, during the transmission process of IoT data, security issues, such as data theft and forgery, are prone to occur. In addition, most existing data storage solutions are centralized, i.e., data are stored and maintained by a centralized server. Once the server is maliciously attacked, the security of IoT data will be greatly threatened. In view of the above-mentioned security issues, a security transmission and storage solution is proposed about sensing image for blockchain in the IoT. Firstly, this solution intelligently senses user image information, and divides these sensed data into intelligent blocks. Secondly, different blocks of data are encrypted and transmitted securely through intelligent encryption algorithms. Finally, signature verification and storage are performed through an intelligent verification algorithm. Compared with the traditional IoT data transmission and centralized storage solution, our solution combines the IoT with the blockchain, making use of the advantages of blockchain decentralization, high reliability, and low cost to transfer and store users image information securely. Security analysis proves that the solution can resist theft attacks and ensure the security of user image information during transmission and storage.

Blockchain-Based Distributed Firmware Update Architecture for IoT Devices

The Internet of Things (IoT) which creates a hyper-connected society is playing a major role in the 4th industrial revolution. The IoT is being leveraged across various fields of business globally and the number of IoT devices is causing serious security concerns. Since the firmware update of an IoT device is necessary for its lifecycle, secure firmware update of the IoT device is being brought as the first step in IoT security. The Internet Engineering Task Force (IETF) Software Updates for Internet of Things (SUIT) working group has started to specify a software update architecture for IoT devices. However, the current SUIT working group adopts a traditional client-server model to distribute firmware images, which potentially causes security risks. The current approach of the SUIT working group is unable to solve a targeting issue and an author-disappearing issue, which is suggested in this paper. Therefore, in this work, we introduce a distributed firmware update architecture based on the SUIT firmware update architecture applying blockchain. Our update architecture can prevent the issues we concern through the characteristics of blockchain, such as decentralization, transparency, and irreversibility. The blockchain network has registration nodes that process registration of manifest and firmware image files from authors, and retrieval nodes that process downloading manifest and firmware image files. The firmware image files are stored in a distributed file system and the hash values of firmware image chunks are stored on the blockchain with manifest files. The proposed architecture in this paper enables the irreversible downloads even in the author-disappearing state and tolerant to a single point of failure.

Multi-Channel Joint Forecasting-Scheduling for the Internet of Things

We develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels, as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand, Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance, thus obviating contention, collision and handshaking, which are found in reactive protocols. In this paper, we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system, first, we design a heuristic, called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL), that solves the general form of the scheduling problem. Second, for the special case of identical channels, we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB), both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption, and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore, we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.

Guest Editorial: Special Issue on Large-Scale Wireless-Powered Networks With Backscatter Communications

The Proliferation of connected and embedded devices has played a critical role in the development of next generation Internet of Things (IoT), and consequently, the diffusion of low power networks. The sustainability of such networks entails energy aware devices that are capable of self-sustainable operations by harvesting and recycling energy from various sources. Accordingly, integration of wireless-powered networks (WPN) with ambient backscatter communications (AmBC), in which a backscatter transmitter conveys data to a destination by backscattering ambient signals, has recently emerged as a promising key technology for low power wireless networks. However, these technologies are not optimized and/or designed for large-scale networks and a massive number of IoT devices.

A Hopping Umbrella for Fuzzy Joining Data Streams From IoT Devices in the Cloud and on the Edge

Internet of Things (IoT) is a new technology that changes the image of the current world, yielding new possibilities, but also proliferating data. IoT devices may constantly produce enormous amounts of data as data streams that can be analyzed in real time and also collected for further exploration in data lakes in huge data centers. Due to their scaling capabilities, these data centers are frequently located in the Cloud. However, recent rapid growth in the number of IoT devices and their applications in manufacturing, transport, and health care motivates moving the burden of data processing and analysis to the Edge. One of the phases of data processing is combining data streams from two (or more) IoT devices that monitor the same object and work asynchronously. Since they generate sensor readings at various moments of time, their data streams must be properly combined in order to obtain a complete image of the monitored object or process. In this article, we present the idea of a hopping umbrella which fuzzifies timestamps from sensor readings while joining data streams from asynchronous IoT devices in a flexible way. In contrast to processing data at rest, the hopping umbrella implements the fuzzy join operation in time windows for data streams (data in motion). By using fuzzy sets, the hopping umbrella not only allows combining asynchronous events from multiple sensors, but also facilitates evaluation of the degree of matching of the combined sensor readings, and consequently allows for reduction of the output stream size. Our experiments performed in Cloud and on Edge devices proved that with the use of this idea, we are able to properly join the best matching sensor readings and in some scenarios, reduce the number of data transferred to the Cloud data center without significant overhead in resource utilization of stream processing units.