Huge Number Of Devices Research Articles

Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements

Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.

Optimal Resource Management for Hierarchical Federated Learning Over HetNets With Wireless Energy Transfer

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in industrial Internet of Things (IoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of Machine and Deep Learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical Federated Learning (HFL) performs training at multiple layers offloading the tasks to nearby Multi-Access Edge Computing (MEC) units. In this paper, we propose a novel energy-efficient HFL framework enabled by Wireless Energy Transfer (WET) and designed for heterogeneous networks with massive Multiple-Input Multiple-Output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a Heuristic Resource Management Algorithm, namely H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.

Bit-Interleaved Multiple Access: Improved Fairness, Reliability, and Latency for Massive IoT Networks

Internet-of-Things (IoT) networks require massive connections in dense areas. Therefore, a resource efficient multiple access scheme seems inevitable to enable immense connectivity where multiple devices have to share the same resource block. Non-orthogonal multiple access (NOMA) has been considered as the strongest candidate in recent years. However, in this paper, by considering the practical implementation, we first provide a true power allocation (PA) constraint with finite alphabet inputs for conventional downlink NOMA and demonstrate that it cannot support massive connections in practical systems. To this end, we propose bit-interleaved multiple access (BIMA) scheme in downlink IoT networks. The proposed BIMA scheme implements bitwise multiaccess interleaving and deinterleaving at the transceiver ends and there are no strict PA constraints, unlike conventional NOMA, thus allowing a high number of devices in the same resource block. We provide a comprehensive analytical framework for BIMA by investigating all key performance indicators (KPIs) to present both information-theoretic (i.e., ergodic capacity EC and outage probability OP) and finite alphabet inputs (i.e., bit error rate BER) performance metrics with both instantaneous and statistical channel ordering. In addition, we define Jain’s fairness index and proportional fairness index in terms of all KPIs. Based on the extensive computer simulations, we reveal that BIMA outperforms conventional NOMA significantly, with a performance gain of up to 20-30 dB in terms of KPIs in some scenarios. In other words, compared to conventional NOMA schemes, the same KPIs are met in BIMA with 20-30 dB less transmit power, which is quite promising for energy-limited use cases. Moreover, this performance gain becomes greater when more IoT devices are supported. BIMA provides a full diversity order for all IoT devices and enables the implementation of an arbitrary number of devices and modulation orders, which is crucial for IoT networks where a huge number of devices should be supported in a single resource block in dense areas. In addition to the overall performance gain, BIMA guarantees a fairness system where none of the devices gets a severely degraded performance and the sum-rate is shared in a fair manner among devices. It guarantees QoS satisfaction for all devices. Finally, we provide an intense complexity and latency analysis for BIMA and demonstrate that it provides lower latency compared to conventional NOMA receivers, since it allows parallel computation at the receivers and no iterative operations are required. We show that compared to conventional NOMA receivers, BIMA reduces latency by up to 350% for specific IoT devices and 170% on average.

LIGHTWEIGHT CRYPTOGRAPHY METHOD IN THE INTERNET OF THINGS USING ELLIPTIC CURVE AND CROW SEARCH ALGORITHM

The Internet of Things (IoT) as an important technology consists of a heterogeneous and huge number of devices that generates an enormous amount of data in various applications. However, generating and transmitting huge amount of data in the IoT makes it crucial to implement a secure and safe data transmission scheme. Cryptography methods can secure the confidentiality, data integrity, access control, and authentication. Due to constrained resources in IoT devices, providing classical cryptography schemes isn’t efficient for IoT applications, so a lightweight cryptography scheme is one of the most important solutions to overcome security challenges in IoT. In this paper, a new security scheme called ECCCSASHA256 based on Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm (SHA-256) using Crow Search Algorithm (CSA) has been proposed for secure data transmission in IoT devices. The ECCCSASHA256 model uses the CSA for generating a private key to encode the elliptic curve. Furthermore, the proposed scheme uses SHA-256 model for hashing the incoming encoded data using ECC. The simulation results indicate that the average throughput of the proposed model was about 8.22% and 8.97% higher in encryption and 8.72% and 9.81% higher in decryption compared to 3DES&ECC&SHA-256 and RC4&ECC&SHA-256, respectively.

Real-Time Detection of DDoS Attacks Based on Random Forest in SDN

With the development of the Internet of Things, a huge number of devices are connected to the network, network traffic is exhibiting massive and low latency characteristics. At the same time, it is becoming cheaper and cheaper to launch DDoS attacks, and the attack traffic is becoming larger and larger. Software-defined networking SDN is proposed as a new network architecture. However, the controller as the core of SDN is vulnerable to DDoS attacks and causes a single point of failure in the network. This paper combines the ideas of distributed and edge computing, firstly, a DDoS attack detection algorithm using heterogeneous integrated feature selection and random forest algorithm is proposed. Then, this DDoS attack detection algorithm is distributed and deployed on the edge equipment switches of SDN to perform distributed edge parallel computing using the residual computing power of the switches for fast and accurate detection of DDoS attacks. Finally, simulation experiments are conducted in the SDN environment using the CIC-DDoS2019 dataset to evaluate the effectiveness and feasibility of the proposed scheme. The experimental results show that the performance evaluation metrics of this solution: accuracy, precision, recall and F-value all reach 99.99%, while the prediction time is only 0.4 s, all metrics are better than other DDoS attack detection methods in the same category. Therefore, this solution is able to detect DDoS attacks in a timely and accurate manner.

A Points-to-Sensitive Model Checker for C Programs in IoT Firmware

The Internet of Things (IoT) provides convenience for our daily lives via a huge number of devices. However, due to low-resource and poor computing capability, these devices have a high number of firmware vulnerabilities. Software verification is a powerful solution to ensure the correctness and security of IoT firmware programs. Unfortunately, due to the complex semantics and syntax of program languages (typically C), applying software verification in IoT firmware faces the tradeoff between efficiency and accuracy. One of the fundamental reasons is that verification methods cannot support verifying state transitions on the memory space caused by pointer operations well. To this end, by combining sparse value flow (SVF) analysis into model checking and optimizing computational redundancy among them, we design a novel points-to-sensitive model checker, called PCHECKER, which can provide a highly precise and efficient verification for IoT firmware programs. We first design a spatial flow model to effectively describe state behaviors of a C program both on the symbolic and memory space. We then propose a counterexample-guided model checking algorithm that can dynamically refine abstract precisions and update nondeterministic points-to relations. With a set of C benchmarks containing a variety of pointer operations and other complex C features, our experiments have shown that compared with state of the art (SOTA), PCHECKER can achieve outstanding results in the verification tasks of C programs that its verification accuracy is 95.9%, and its average verification time of each line of code is 1.27 ms, which are both better than existing model checkers.

ROLE OF 5G WIRELESS SYSTEMS IN FUTURE

5G goes far beyond what we think of today as cell phones. We’re at the start of a revolution in the world of connected things, with the first steps toward implementing 5G — the newest generation of telecom networks — already in full motion. To some, 5G promises to simply improve cellular capabilities, providing enhanced broadband power and boosting mobile capacity and data rates. In fact, 5G is 10 to 50 times faster than previous generations of cellular networks. Future 5G wireless networks will aspect new contests, as well as growing claim on network capacity to support a huge number of devices running application necessitating high data rates and always-on connectivity; hugely and supportive the emerging business models in the wireless network market demanding networks to be more open. New challenges initiative new resolutions and involve changed plans in the network positioning, management, and operation of future 5G wireless networks equated to those of current wireless networks. One of the key purposes of future 5G wireless networks is to compliantly provide service- customized networks to a wide variety of services using integrated cloud reserve and wireless/wired network possessions, which may be presented by several infrastructure providers and/or operators. Keywords: Future, 5G, Wireless, Capacity.

Home Security System using Arduino Uno

Abstract: This Project is based on implemented of an IOT based home security system, to be able to secure homes from anywhere in the world. The Major aim of this Project is it should be cost efficient and at the same time it should be secure, and this was achieved with the use of internet of things and some other electronics component. With the help of a mobile app and a keypad a working automatic door system was achieved.. IoT refers to the infrastructure of connected physical devices which is growing at a rapid rate as a huge number of devices and object are getting associated with the internet. Home Security has become a very important and useful application of IoT and we are using it to build an economical security system for homes as well as industrial use Keywords: Arduino, ESP32 CAM

Contextualized Design of IoT (Internet of Things) Finance for Edge Artificial Intelligence Computing.

With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively.

Guest Editor's Introduction: Special Section on Edge AI as a Service

In the last decade there has been a strong move towards mobile computing and the proliferation of the IoT (Internet of Things). A huge number of devices have been connected to the Internet and created zettabytes of data items. To extract value from such massive data volumes, processing power offered by cloud computing is often utilized. However, streaming data to the cloud exposes some limitations related to increased communication and data transfer, which introduces delays and consumes network bandwidth. Another limitation that cloud-based computing for IoT poses is a limited or no network connectivity. Other problems with cloud-based processing of IoT generated data regard the sensitivity of the information, because sending and storing so much information in the cloud involves privacy and security challenges, related to the protection of personally identifiable information, storing it in compliance with privacy laws, securing stored information, and preventing from being stolen, or accessed and shared illegally. The use of AI in edge processing resulted in a new interdisciplinary field that enables distributed intelligence with edge devices and is known as edge AI or edge intelligence. However, research on edge AI is still relatively new, and thus models, techniques, and protocols supporting intelligent management, querying and mining of large-scale amounts of data produced at the edge are required. A lot of challenges related to providing edge intelligence include training edge devices, so they can become more and more smart. There is also a need for the presentation of the most recent outcome of research of distributed intelligence. The papers in this special issue address many of the challenges we have outlined, and are briefly summarized.

Algorithm for Selection Equipment to Reduce the Technogenic Effect on the Environment

The work is devoted to the development of an algorithm for selection dust and gas purification equipment in order to reduce the anthropogenic impact on the environment from emissions of the heat, power and chemical industries, which are sources of complex environmental pollution. The need for the research was necessitated by the inefficiency of environmental measures taken due to the huge number of devices with different designs for emissions’ purification, their non-universality and low efficiency of the purification systems. The main goal of the study was to develop a software algorithm for the reasonable choice of the optimal dust and gas purification equipment, taking into account the actual conditions of the technological environment and the characteristics of pollutants. The goal was achieved using a logical and mathematical description of the pollutant parameters, environmental conditions and process equipment parameters. The uniqueness of the developed algorithm consisted of rechecking the compliance of each equipment parameter with environmental conditions and pollutant characteristics. The most important aims were to obtain an algorithm for selection efficient equipment, taking into account its parameters and initial characteristics of pollutants and environmental conditions, the simplicity and accessibility of its implementation for a wide number of industrial facilities in the heat, power and chemical industries. The proposed algorithm, in contrast to the approaches used in practice, was characterized by the work flexibility due to the possibility of supplementing and improving the databases of pollutants and equipment.

Exploiting Impacts of Antenna Selection and Energy Harvesting for Massive Network Connectivity

As a new energy saving approach for green communications, energy harvesting (EH) could be suitable technique to facilitate massive connections for large number of devices in such networks. The spectrum shortage occurs in huge number of devices which access with small-cell and macro-cell networks. To tackle these challenges, we develop a tractable framework relying on prominent techniques such as non-orthogonal multiple access (NOMA), antenna selection and energy harvesting. In this paper, we aim at practical scenarios of small cell networks by jointly evaluating capable of interference management and EH. We benefit from transmission approaches including full duplex (FD) and bi-directional transmission to improve the main performance system metrics such as outage probability and throughput. Three useful schemes are explored by considering EH and inter-cell interference. We derive the closed-form and asymptotic expressions for system metrics. We then perform extensive simulations with different system configurations to confirm the effectiveness of the proposed small-cell NOMA systems.

A Review on IoT Based Smart Security And Home Automation

Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for Internet-of-Things (IoT)-enabled home automation and security systems. These systems usually consist of sensing and actuating layer that is made up of sensors such as passive infrared sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances, and other IoT devices connect to the Internet through a home gateway. This project is based on implementation of an IoT based home security system, to be able to control entrance from anywhere in the world is the major aim of this project and at same time it should be secured, and this was achieved with the use of Internet of things and some other electronics components. An automatic door control system working through a mobile app and also through a keypad was achieved. IoT refers to the infrastructure of connected physical devices which is growing at a rapid rate as a huge number of devices and objects are getting associated with the Internet. Home security is a very useful application of IoT and we are using it to create an inexpensive security system for homes as well as industrial use.

Analysis of Power Allocation for Non-Orthogonal Multiple Access

Most multiple access schemes provide orthogonal access to the users in time, frequency, code and space, this is not true for NOMA, where each user operates in the same band and at the same time where they are distinguished by their power levels. It uses superposition coding at the transmitter such that the successive interference cancellation (SIC) receiver can separate the users both in the uplink and in the downlink channels. The users in (NOMA) are classified based on power, while in Orthogonal Multiple Access (OMA) it is classified based on time, frequency, and code. The NOMA system contains a power-delay tradeoff and hence power efficiency becomes critical for Ultra-Reliable Low Latency Communication (URLLC), especially where a huge number of devices are battery-powered. Combining these considerations, we simulate Dynamic Power Allocation (DPA) for power domain non-orthogonal multiple access (PD-NOMA) with user mobility. For small and clumsy battery-powered IoT devices, power efficiency becomes critical. Further, flexibility is also important to communicate with diverse machine-type devices as well as human users while meeting a variety of quality of service (QoS) requirements. The performance of the DPA is compared with Static Power Allocation under user mobility.

An Efficient and Secure Data Forwarding Mechanism for Opportunistic IoT

Internet of Things (IoT) is a heterogeneous network of interconnected things where users, smart devices and wireless technologies, collude for providing services. It is expected that a great deal of devices will get connected to the Internet in the near future. Opportunistic networks(OppNet) are a class of disruption tolerant networks characterized by uncertain topology and intermittent connectivity between the nodes. Opportunistic Internet of Things(OppIoT) is an amalgamation of the OppNet and IoT exploiting the communication between the IoT devices and the communities formed by humans. The data is exposed to a wide unfamiliar audience and the message delivery is dependent on the residual battery of the node, as most of the energy is spent on node discovery and message transmission. In such a scenario where a huge number of devices are accommodated, a scalable, adaptable, inter-operable, energy-efficient and secure network architecture is required. This paper proposes a novel defense mechanism against black hole and packet fabrication attacks for OppIoT, GFRSA, A Green Forwarding ratio and RSA (Rivest, Shamir and Adleman) based secure routing protocol. The selection of the next hop is based on node’s forwarding behavior, current energy level and its predicted message delivery probability. For further enhancing the security provided by the protocol, the messages are encrypted using asymmetric cryptography before transmission. Simulations performed using opportunistic network environment (ONE) simulator convey that GFRSA provides message security, saves energy and outperforms the existing protocols, LPRF-MC (Location Prediction-based Forwarding for Routing using Markov Chain) and RSASec (Asymmetric RSA-based security approach) in terms of correct packet delivery by 27.37%, message delivery probability is higher by 34.51%, number of messages dropped are reduced by 15.17% and the residual node energy is higher by 14.08%.

Revisiting Lightweight Block Ciphers: Review, Taxonomy and Future directions

Block ciphers have been extremely predominant in the area of cryptography and due to the paradigm shift towards devices of resource constrained nature, lightweight block ciphers have totally influenced the field and has been a go-to option ever since. The growth of resource constrained devices have put forth a dire need for the security solutions that are feasible in terms of resources without taking a toll on the security that they offer. As the world is starting to move towards Internet of Things (IoT), data security and privacy in this environment is a major concern. This is due to the reason that a huge number of devices that operate in this environment are resource constrained. Because of their resource-constrained nature, advanced mainstream cryptographic ciphers and techniques do not perform as efficiently on such devices. This has led to the boom in the field of ‘lightweight cryptography’ which aims at developing cryptographic techniques that perform efficiently in a resource constrained environment. Over the period of past two decades or so, a bulk of lightweight block ciphers have been proposed due to the growing need and demand in lightweight cryptography. In this paper, we review the state-of-the-art lightweight block ciphers, present a comprehensive design niche, give a detailed taxonomy with multiple classifications and present future research directions.

A Survey on Access Control in IoT: Models, Architectures and Research Opportunities

The rapid growth of smart devices and sensors industry has revolutionised many fields such as smart cities, healthcare, etc. Nowadays, the internet of things (IoT) interconnects theses devices making them able to exchange data which improves the delivery of various services. Although IoT represents a promising paradigm in almost all fields, the security of users' data is still a significant issue that should be thoroughly addressed. This is mainly required where sensitive information is being used such as in healthcare or military sectors. Access control is a fundamental security mechanism that has to be provided for IoT-based applications in order to limit access to users data to only authorised individuals. However, due to the high mobility and huge number of devices, controlling access is challenging. In particular, using cloud data centres inevitably leads to high delays and network overhead. This research examines the growing literature on access control for IoT with respect to security requirements.

Data processing by end devices in IoT systems

Internet of things (IoT) systems are making a significant contribution to the growth of global traffic. There is a tendency towards a decrease in the volume of transmitted and stored data, for which various approaches are used. Most of these approaches involve traditional cloud and gateway processing, leaving the endpoints idle in the process. The article discusses the correlation method of data processing on the end device. The results of the study of the maximum performance when implemented on FPGAs with different orders of the matched filter N and different bit widths of the input data stream are presented. A huge number of devices have been connected to the network for a long time. On the Internet of Things, communication must occur between things (without human intervention). This paper presents a correlation method for processing data on end devices and reducing the amount of data transmitted over the network. Instead of expensive and complex network devices, developers can use cheap and proven low-speed Internet of Things (ZigBee, NB IoT, BLE) solutions for data transfer. The novelty lies in one of the features of this approach: the use of components for analysis, rather than a complete copy of the signals, as well as processing directly on the sensor. The advantage of this approach allows you to reduce the number of operations and complexity of implementation, in contrast to other methods focused on the cloud computing paradigm. We provide results for correlation values and the number of logical elements (LE) when implemented on the FPGA, depending on the number of elements in the correlator. This allows to maintain a balance between the required calculation accuracy and spent hardware resources, as well as to simplify the end device.

Performance Evaluation of the M2M Communication in the 4G Network (LTE/LTE-A) With Real Background Traffics

The applications of the fifth generation in 5G communications depend on the Internet Of Things (IOT), meaning that every person and everything will be connected to the Internet, so any tool or device in the house or in the street or any work place will be connected to the internet, and this leads us to the term Smart cities, i.e. data is formed everywhere by any person or any machine and is analyzed in a short time to obtain useful information in a timely manner such as monitoring the health status of patients and the elderly, and monitoring devices and tools at home and determining whether there is a malfunction or a lack of a substance, As well as analyzing the traffic situation in the streets and assisting and warning drivers Non-visual risks, which pave the way towards self-driving cars. Here, machine-to-machine (M2M) mobile communications play a pivotal role in enabling the effective and safe transfer of this information from machine to machine without human intervention at full speed and with minimal delay. This poses more challenges for the future network that must accommodate mobile data and the huge number of devices and sensors deployed everywhere in order to be a large-scale network with high capacity and efficiency [16, RODRIGUEZ.J]. In this article, several scenarios have been tested to evaluate the performance of M2M technology within 4Generation LTE / LTE-A networks by adding an external simulated network load. The results showed that the amount of data sent by the MMS sensors is fully received by the remote host, so we get 100% productivity. As for other applications, the productivity is around 99% and the average delay is relatively small as long as the network operates within the available transfer rates. In addition, the process of exchanging packages takes place almost completely (a small amount of losses).

TARA: An Efficient Random Access Mechanism for NB-IoT by Exploiting TA Value Difference in Collided Preambles

To meet the tremendous demand of Internet of Things (IoT) applications, the 3rd Generation Partnership Project (3GPP) has specified the Narrowband IoT (NB-IoT) standard. However, collisions in the random access (RA) channel of NB-IoT can be severe due to mismatch between frequent random access attempts by a huge number of devices and the limited radio resources. In this paper, a new random access mechanism called time-alignment-value based random access (TARA) is designed to improve the efficiency of random access of NB-IoT. The key mechanism of TARA is to conduct quick retries upon failure by exploiting the difference of time-alignment (TA) values in collided preambles. To analyze TARA rigorously, a theoretical model of TARA is derived, and its validity is verified via simulations. Further simulations are carried out to evaluate TARA with respect to different system parameters. Comparisons with existing schemes are also conducted. Both analytical and simulation results show that, under various system parameters, TARA achieves 30% higher success probability of random access, 75% higher throughput, and 40% lower access delay than the original slotted Aloha mechanism of NB-IoT. It also highly outperforms other random access schemes.