Large-scale Internet Of Things Networks Research Articles

Lifetime maximization of IoT-enabled smart grid applications using error control strategies

Recently, with the advancement of Internet of Things (IoT) technology, IoT-enabled Smart Grid (SG) applications have gained tremendous popularity. Ensuring reliable communication in IoT-based SG applications is challenging due to the harsh channel environment often encountered in the power grid. Error Control (EC) techniques have emerged as a promising solution to enhance reliability. Nevertheless, ensuring network reliability requires a substantial amount of energy consumption. In this paper, we formulate a Mixed Integer Programming (MIP) model which considers the energy dissipation of EC techniques to maximize IoT network lifetime while ensuring the desired level of IoT network reliability. We develop meta-heuristic approaches such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) to address the high computation complexity of large-scale IoT networks. Performance evaluations indicate that the EC-Node strategy, where each IoT node employs the most energy-efficient EC technique, yields a minimum of 8.9% extended lifetimes compared to the EC-Net strategies, where all IoT nodes employ the same EC method for a communication. Moreover, the PSO algorithm reduces the computational time by 77% while exhibiting a 2.69% network lifetime decrease compared to the optimal solution.

Proactive blockchain deployment mechanism in resource-constrained rate-splitting multiple access IoT networks

In this paper, we propose an innovative blockchain deployment mechanism tailored for resource-constrained rate-splitting multiple access (RSMA) Internet of Things (IoT) networks. To address the storage limitations and security concerns inherent in IoT environments, our approach includes several advanced techniques. First, we utilize a Block Allocation Strategy Contract (BASC) to manage storage efficiently. Second, we employ a deep reinforcement learning (DRL) model to dynamically perform block assignment, ensuring optimal storage utilization. To accommodate the resource constraints of IoT devices, we adopt the Smart Byzantine Fault Tolerance (SBFT) consensus mechanism, which offers low latency and energy efficiency. Our framework demonstrates superior performance in storage optimization and reduced running time compared to existing methods, making it well-suited for large-scale IoT networks. Through extensive simulations, we validate the effectiveness of our proposed solution in enhancing security and operational efficiency in RSMA IoT networks.

DSME-FOTA: Firmware over-the-air update framework for IEEE 802.15.4 DSME MAC to enable large-scale multi-hop industrial IoT networks

The Internet of Things (IoT) is rapidly expanding in applications ranging from smart homes and smart cities to various other domains, including agriculture, transportation, industrial automation, and environmental monitoring. Regardless of the application domain, firmware update over-the-air (FOTA) capability is critical for any IoT network, and its importance is particularly evident in the field of industrial IoT. This paper presents a comprehensive solution designed for large-scale industrial IoT networks, with a special focus on Deterministic Synchronous Multi-channel Extension (DSME)-based multi-hop networks, called DSME-FOTA. The paper provides a detailed FOTA protocol design, a numerical analysis, and proof-of-concept experiments. The analytical and experimental results show that the total firmware update time of DSME-FOTA exhibits a first-order dependence on both the number of hops and the firmware binary size.

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management.

State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.

Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication

In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms.

HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks

Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing. Despite numerous advantages, low computing resources at IoT devices and high communication costs for exchanging model parameters make applications of FL in massive IoT networks very limited. In this work, we develop a novel compression scheme for FL, called <i>high-compression federated learning (HCFL)</i>, for very large scale IoT networks. HCFL can reduce the data load for FL processes without changing their structure and hyperparameters. In this way, we not only can significantly reduce communication costs, but also make intensive learning processes more adaptable on low-computing resource IoT devices. Furthermore, we investigate a relationship between the number of IoT devices and the convergence level of the FL model and thereby better assess the quality of the FL process. We demonstrate our HCFL scheme in both simulations and mathematical analyses. Our proposed theoretical research can be used as a minimum level of satisfaction, proving that the FL process can achieve good performance when a determined configuration is met. Therefore, we show that HCFL is applicable in any FL-integrated networks with numerous IoT devices.

An Advanced Strategy for Addressing Heterogeneity in SDN-IoT Networks for Ensuring QoS

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller’s bandwidth and find that the system throughput decreases when the controller’s bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics.

Agentless Approach for Security Information and Event Management in Industrial IoT

The Internet of Things (IoT) provides ease of real-time communication in homes, industries, health care, and many other dependable and interconnected sectors. However, in recent years, smart infrastructure, including cyber-physical industries, has witnessed a severe disruption of operation due to privilege escalation, exploitation of misconfigurations, firmware hijacking, malicious node injection, botnets, and other malware infiltrations. The proposed agentless module for Wazuh security information and event management (SIEM) solution contributes to securing small- to large-scale IoT networks of industry 4.0. An agentless module is implemented by vigilantly examining the IoT device traffic without installing any agent or software on the endpoints. In the proposed research scheme, a module sniffs the network traffic of IoT devices captured from the gateway and passes it to a machine learning model for initial detection and prediction. The output of the ML model is embedded in the JSON log format and passed through the Wazuh agent to the Wazuh server where a decoder is added that decodes the network traffic logs. For event monitoring in Wazuh, industrial protocols are also thoroughly analyzed, and the feature set is determined. These features are used to write rules which are tested on the SWaT dataset, utilizing a common industrial protocol (CIP) for communication. Custom and dynamic rules are written at the Wazuh end to generate alerts to respond to any anomaly detected by the machine learning (ML) model or in the protocols used. Finally, in case of any event or an attack is detected, the alerts are fired on the Wazuh dashboard. This agentless SIEM solution has practical implications for the security of the industrial control systems of industry 4.0.

LightTrust: Lightweight Trust Management for Edge Devices in Industrial Internet of Things

The phenomenal increase in the usage of Internet promotes the quality of trust in the scope of the Internet of Things (IoT). Trust is beneficial in the provision of an effective, reliable, scalable, and trustworthy environment to users of the IoT network, where they can share their private information with each other on a secure communication platform. For successful communications among the Internet users, trust is an important factor to provide them with private infrastructures and secure environments, where exchanging data among devices becomes more easy and trustworthy. Therefore, trust management is a backbone for the successful and secure transmission of data among various nodes in a large-scale IoT network. To overcome the security issues, latency, and risk of malicious activities, a lightweight approach is proposed for those nodes in Industrial IoT that cannot maintain security. LightTrust utilizes a centralized trust agent to generate and manage trust certificates that allow nodes to communicate for a specific time without performing trust computations. Trust agents also maintain a trust database to store the current trust degree for the aggregation/propagation purposes. Trust between two nodes is developed by direct observations in terms of compatibility, cooperativeness, and delivery ratio, whereas recommendations are used to develop trust in the context of indirect observations, i.e., experience or previous knowledge. The comparative simulations of the proposed and existing approaches are also performed whereby the results illustrate that the proposed approach efficiently maintains resilience and robust environments.

Trust2Vec: Large-Scale IoT Trust Management System Based on Signed Network Embeddings

A trust management system (TMS) is an integral component of any Internet of Things (IoT) network. A reliable TMS must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices’ reputations, which can help predict the future behaviors of network entities and subsequently judge the reliability of different entities in the IoT networks. Many TMSs have been proposed in the literature, these systems are designed for small-scale trust attacks and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this article, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate large-scale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, such as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network settings.

Intelligent Resource Allocation in LoRaWAN Using Machine Learning Techniques

With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power wide-area network (LPWAN) technologies will inevitably face performance degradation due to congestion and interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. Meanwhile, new solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.

Device Selection and Beamforming Optimization in Large-Scale mmWave IoT Networks

The joint provision of higher data rates and massive Internet of Things (IoT) connectivity has been identified as one of the key milestones toward beyond 5G (B5G). To this end, we investigate the issue of device selection and beamforming (BF) optimization assuming a large-scale IoT network using mmWaves. We formulate the considered problem as a network sum-rate maximization problem under Access Points’ load constraints, and where the BF parameters belong to discrete sets, as in practical cases. First, we mathematically prove the submodularity of the objective function, under specific yet reasonable assumptions. Based on the identified features of the problem at hand, we propose three different approaches to tackle this intricate optimization problem: 1) a Branch-and-Bound-based; 2) a Lagrangian Relaxation-based; and 3) a Greedy-based approach inspired by the submodular objective. The numerical results validate the three approaches, as they achieve a near-optimal sum rate in small network cases, and largely outperform benchmark schemes in terms of sum rate and individual rates. Among them, the proposed Greedy-based approach achieves the best sum rate with very low complexity, thereby providing excellent scalability.

Hybrid Bayesian optimization hypertuned catboost approach for malicious access and anomaly detection in IoT nomalyframework

The successful applications and diversified popularity of the Internet of Things (IoT) present various advantages and opportunities in broad characteristics of our lives. However, unfortunately, the IoT is allied with several types of defenseless attacks and illegitimate exploits. Security specialists specify voluminous threats imposed by the IoT devices in various aspects. Therefore, security and intrusion detection have constantly been growing areas of distress for any field of IoT research. This paper introduces a new hybrid ensemble hyper-tuned model (i.e., Catboost) that efficiently recognizes IoT sensor attacks and anomalies. The hyper-parameters are optimized with Bayesian optimization to develop security-based models effectively. The significant contributions of this work are the design of an intelligent model-based security framework based on the advanced ensemble learning Catboost model for detecting malicious IoT activities in the IoT network, the use of a Bayesian optimization approach to find an optimal set of Catboost hyper-parameters, and evaluate the model with a new real dataset (DOS2DOS) from a large-scale IoT network. The performance of the proposed model is compared with other state-of-the-art approaches, and the experimental results are evident towards a high detection rate of 99.9%.

Data Aggregation in Regular Large-Scale IoT Networks: Granularity, Reliability, and Delay Tradeoffs

This article studies data aggregation in large-scale regularly deployed Internet of Things (IoT) networks. The data granularity, in terms of information content and temporal resolution, is parameterized by the sizes of the generated packets and the average interpacket generation time. The generated data packets at the devices are aggregated through static terrestrial gateways. Universal frequency reuse is adopted across all gateways and randomized scheduling is utilized for the IoT devices associated with each gateway. Such network model finds applications in environmental sensing, precision agriculture, and geological seismic sensing to name a few. To this end, we develop a novel spatiotemporal mathematical model to characterize the interplay between data granularity, transmission reliability, and delay. The developed model accounts for several IoT design parameters, which include packet sizes, average generation duty cycle, devices and gateways spatial densities, transmission rate adaptation, power control, and antenna directivity. For tractable analysis, we propose two accurate approximations, based on the Poisson point process (PPP), to characterize the signal-to-interference-plus-noise-ratio (SINR)-based transmission reliability. For the delay analysis, we propose a phase-type arrival/departure (PH/PH/1) queueing model that accounts for packet generation, transmission scheduling, and rate-sensitive SINR-based packet departure. The developed model is utilized to obtain the optimal transmission rate for the IoT devices that minimizes delay. The numerical results delineate the joint feasibility range of packet sizes and interarrival times for data aggregation and reveal significant gains when deploying directional antennas.

Fast and Scalable Distributed Consensus Over Wireless Large-Scale Internet of Things Network

Due to the rapid paradigm shift in Internet of Things networks from wired and centralized to flexible wireless and decentralized networks, building effective and reliable distributed consensus mechanisms over wireless is becoming essential. Especially, since the performance of consensus over communication endpoints in a large-scale wireless network is limited by their communication capability, it requires a careful co-design of communication and consensus to attain a fast and scalable distributed wireless consensus mechanism with high resiliency against faulty nodes. Within this context, this article addresses such problem by designing two wireless consensus mechanisms that well-suit in large-scale wireless networks. On the one hand, as a reinterpretation of the conventional referendum consensus (RC) in a large-scale wireless network, gossip-broadcasting-based RC (GB-RC) is proposed. On the other hand, to overcome the scalability issue of the GB-RC, cooperative-broadcast-based electoral-college consensus (CB-EC) is proposed. By mathematically analyzing the performance of both of the consensus mechanisms, in terms of consensus latency and resiliency against the faulty nodes, we show that the GB-RC outperforms the conventional RC, while the CB-EC significantly reduces the consensus latency compromising the stochastic resiliency. We further evaluate their performance numerically to show their effectiveness and feasibility under realistic large-scale wireless environments.

A DAG Blockchain-Enhanced User-Autonomy Spectrum Sharing Framework for 6G-Enabled IoT

The rapidly growing number of Internet-of-Things (IoT) devices poses new challenges for spectrum management in future wireless communication networks. It is critical to achieve efficient and dynamic spectrum management in the sixth-generation (6G) wireless communication networks era. To tackle the challenges of managing a large-scale IoT network with heterogeneous devices, we propose a directed acyclic graph (DAG) blockchain-enhanced user-autonomy spectrum sharing model. As the proposed consensus rule is closely related to system utility, the swarm intelligence of users gradually reaches the point of convergence in the process of blockchain consensus. We analyze the effect of the tip selection method of the DAG blockchain on spectrum allocation utility. A dynamic tip selection method is proposed to enhance the global utility, which is related to the spectrum supply–demand. In addition, the ring signature technique is utilized to realize privacy protection during the sharing process. Simulation indicates that the proposed tip selection method achieves a 10% enhancement in terms of the global utility. Furthermore, significant reductions in administrative expense and reliability improvement are demonstrated by simulation results. The stability of the tip number in the proposed model has been proved theoretically, which is also validated by simulation experiments.

UGV-Assisted Wireless Powered Backscatter Communications for Large-Scale IoT Networks

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency.

Modeling the Greedy Behavior Attack and Analyzing its Impact on IoT Networks

One of the major security issues in the Internet of Things (IoT) is maintaining the network availability against attacks and traffic congestion. In practice, the greedy behavioral attack is considered as an intelligent Denial of Service (DoS), which aims to compromise the availability of the network by consuming as much as possible the bandwidth of the deployed network. This attack is achieved by tuning the CSMA-CA network communication parameters that plays at the physical layer. In this paper, we propose an efficient modeling technique of attacks proper to the behavior of the greedy node in IoT networks while respecting unslotted IEEE 802.15.4. In fact, our developed greedy nodes algorithm relies on CSMA-CA protocol. This fashioned way of attack representation helped us to easily detect greedy nodes on large-scale IoT networks through simulations. Indeed, the obtained numerical results of different scenarios allow us to validate our approach and showed that the greedy nodes can monopolize the transmission channel during a significant period of time. Various relevant parameters (the number of sent/lost packets, the collision rate, and energy consumption) are considered to analyze and evaluate the impact of selfish nodes on the IoT networks.

Federated Deep Reinforcement Learning for Online Task Offloading and Resource Allocation in WPC-MEC Networks

Mobile edge computing (MEC) is considered a more effective technological solution for developing the Internet of Things (IoT) by providing cloud-like capabilities for mobile users. This article combines wireless powered communication (WPC) technology with an MEC network, where a base station (BS) can transfer wireless energy to edge users (EUs) and execute computation-intensive tasks through task offloading. Traditional numerical optimization methods are time-consuming approaches for solving this problem in time-varying wireless channels, and centralized deep reinforcement learning (DRL) is not stable in large-scale dynamic IoT networks. Therefore, we propose a federated DRL-based online task offloading and resource allocation (FDOR) algorithm. In this algorithm, DRL is executed in EUs, and federated learning (FL) uses the distributed architecture of MEC to aggregate and update the parameters. To further solve the problem of the non-IID data of mobile EUs, we devise an adaptive method that automatically adjusts the FDOR algorithm’s learning rate. Simulation results demonstrate that the proposed FDOR algorithm is superior to the traditional numerical optimization method and the existing DRL algorithm in four aspects: convergence speed, execution delay, overall calculation rate and stability in large-scale and dynamic IoT.

Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things

In this paper, we propose a lightweight physical layer aided authentication and key agreement (PL-AKA) protocol in the Internet of Things (IoT). The conventional evolved packet system AKA (EPS-AKA) used in long-term evolution (LTE) systems may suffer from congestion in core networks by the large signaling overhead as the number of IoT devices increases. Thus, in order to alleviate the overhead, we consider cross-layer authentication by integrating physical layer approaches to cryptography-based schemes. To demonstrate the feasibility of the PL-AKA, universal software radio peripheral (USRP) based tests are conducted as well as numerical simulations. The proposed scheme shows a significant reduction in the signaling overhead, compared to the conventional EPS-AKA in both the simulation and experiment. Therefore, the proposed lightweight PL-AKA has the potential for practical and efficient implementation of large-scale IoT networks.