Large-scale Internet Of Things Systems Research Articles

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

An effective technique to schedule priority aware tasks to offload data on edge and cloud servers

Recent advancements in the Internet of Things (IoT) have enhanced the quality of life globally. Billions of devices are brought under the ambit of IoT to make them smarter. IoT-based applications are generating voluminous data and managing this widespread amount of data in real-time through Cloud Technology, which offers high computational and storage facilities. However, sending all data to the cloud can bring serious concerns for applications, which are critical and require instant action without any delay. Edge computing has recently emerged as an effective technology to handle the instant processing of tasks of IoT-based applications locally. Additionally, an important concern in IoT networks is response to emergency tasks on time to increase the performance of large-scale IoT systems. As such, scheduling of tasks becomes vital, where emergency and non-emergency tasks can be prioritized to offload data to the nearby edge and cloud servers respectively and enhance Quality of Service (QoS). The execution order of tasks and allocating resources for computation to avoid delays are two of the most important factors that must be addressed during task scheduling in Edge Computing. With the aforementioned issues, we design a Priority aware Task Scheduling (PaTS) algorithm for sensor networks to schedule priority aware tasks to offload data on edge and cloud servers. The problem is formulated as a multi-objective function and the efficiency of the proposed algorithm is evaluated using the Bio-inspired NSGA-2 technique. The overall improvement for average queue delay, computation time, and energy obtained for 200 tasks is 17.2%, 7.08% and 11.4%, respectively. The results obtained show significant improvement when compared with the benchmark algorithms demonstrating the effectiveness of the proposed solution. Similarly, comparative results for tasks when increased from 200 to 1000 tasks also shows subsequent improvements.

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.

Experimental Performance Analysis of a Scalable Distributed Hyperledger Fabric for a Large-Scale IoT Testbed.

Blockchain technology, with its decentralization characteristics, immutability, and traceability, is well-suited for facilitating secure storage, sharing, and management of data in decentralized Internet of Things (IoT) applications. Despite the increasing development of blockchain platforms, there is still no comprehensive approach for adopting blockchain technology in IoT systems. This is due to the blockchain’s limited capability to process substantial transaction requests from a massive number of IoT devices. Hyperledger Fabric (HLF) is a popular open-source permissioned blockchain platform hosted by the Linux Foundation. This article reports a comprehensive empirical study that measures HLF’s performance and identifies potential performance bottlenecks to better meet the requirements of blockchain-based IoT applications. The study considers the implementation of HLF on distributed large-scale IoT systems. First, a model for monitoring the performance of the HLF platform is presented. It addresses the overhead challenges while delivering more details on system performance and better scalability. Then, the proposed framework is implemented to evaluate the impact of varying network workloads on the performance of the blockchain platform in a large-scale distributed environment. In particular, the performance of the HLF is evaluated in terms of throughput, latency, network size, scalability, and the number of peers serviceable by the platform. The obtained experimental results indicate that the proposed framework can provide detailed real-time performance evaluation of blockchain systems for large-scale IoT applications.

Federated Anomaly Detection on System Logs for the Internet of Things: A Customizable and Communication-Efficient Approach

Runtime log-based anomaly detection is one of several key building blocks in ensuring system security, as well as post-incident forensic investigations. However, existing log-based anomaly detection approaches that are implemented on large-scale Internet of Things (IoT) systems generally upload local data from edge devices to a centralized (cloud) server for processing and analysis. Such a workflow incurs significant communication and computation overheads, with potential privacy implications. Hence, in this paper, we propose a customizable and communication-efficient federated anomaly detection scheme (hereafter referred to as FedLog), designed to facilitate the identification of abnormal log patterns in large-scale IoT systems. Specifically, we first craft a Temporal Convolutional Network-Attention Mechanism-based Convolutional Neural Network (TCN-ACNN) model, to effectively extract fine-grained features from system logs. Second, we develop a new federated learning framework to support IoT devices in establishing a comprehensive anomaly detection model in a collaborative and privacy-preserving manner. Third, a lottery ticket hypothesis based masking strategy is designed to achieve customizable and communication-efficient federated learning in handling non-Independent and Identically Distributed (non-IID) log datasets. We then evaluate the performance of our proposed scheme with those of DeepLog (published in CCS, 2017) and Loganomaly (published in IJCAI, 2019) in both centralized learning and federated learning settings, using two publicly available and widely used real-world datasets (i.e., HDFS and BGL). The findings demonstrate the utility of the proposed FedLog scheme, in terms of log-based anomaly detection.

Delegated Anonymous Credentials With Revocation Capability for IoT Service Chains (DANCIS)

This article deals with providing privacy-preserving access control in Internet of Things (IoT) systems. Here, a user/IoT device requests access to services provided by other IoT devices and multiple requests are combined to a request-specific service chain. An anonymous delegated credential-based system architecture is proposed, where the requester’s identity is not exposed to the services. The article presents the proposed architecture’s various components including the security aspects. Various options for implementing the architecture on resource-full and resource-constrained services are presented. A prototype of the proposed architecture is then implemented using Linux-based containers to emulate the services. Two representative systems, namely, a small-scale home automation system using a short service chain and a large-scale industrial automation system using a long service chain are considered. Timing measurements from the implementation are presented to demonstrate that the architecture is feasible and can be adapted for practical use in large-scale IoT systems.

Fog-based distributed trust and reputation management system for internet of things

Internet of things (IoT) provides connectivity between different smart devices. IoT systems aim to make data collection, and processing easier. Studies show that we can expect over 75 Billion IoT devices to be active by 2025. There is an increase in the interest in IoT systems, due to their ability to provide a better quality of services to end users. However, many critical challenges arise when deployed in the various applications. Among these issues are security, bandwidth, scalability, and network latency. Security is one of the most critical issues in IoT applications. Moreover, IoT devices can have different computational capabilities, and might be as simple as sensors nodes, or as complex as smart devices. As a result, it is not feasible to adopt standard security methods into IoT devices. In this paper, we propose a new solution that is suitable to IoT systems. The proposed solution is a Fog computing based trust and reputation system. Using Fog nodes, each IoT device evaluates trust towards other IoT devices and only proceed with an interaction with a device, if it meets a certain threshold trust value. This evaluation is necessary to eliminate any malicious devices from affecting the system and quality of service. It will also help protect the system from many attacks such as Bad Mouthing, On Off, and Self Promoting attacks. Simulation results are provided to highlight the behavior of the system under these attacks. Moreover, the proposed solution is suitable for large-scale IoT systems. A comparison between the proposed model and the related works show that the proposed model outperforms the previous works in terms of suitability to IoT systems, and security.

Protocol-Based and Hybrid Access Control for the IoT: Approaches and Research Opportunities.

Internet of Things (IoT) applications and services are becoming more prevalent in our everyday life. However, such an interconnected network of intelligent physical entities needs appropriate security to sensitive information. That said, the need for proper authentication and authorization is paramount. Access control is in the front line of such mechanisms. Access control determines the use of resources only to the specified and authorized users based on appropriate policy enforcement. IoT demands more sophisticated access control in terms of its usability and efficiency in protecting sensitive information. This conveys the need for access control to serve system-specific requirements and be flexibly combined with other access control approaches. In this paper, we discuss the potential for employing protocol-based and hybrid access control for IoT systems and examine how that can overcome the limitations of traditional access control mechanisms. We also focus on the key benefits and constraints of this integration. Our work further enhances the need to build hierarchical access control for large-scale IoT systems (e.g., Industrial IoT (IIoT) settings) with protocol-based and hybrid access control approaches. We, moreover, list the associated open issues to make such approaches efficient for access control in large-scale IoT systems.

A Fast Hierarchical Physical Topology Update Scheme for Edge-Cloud Collaborative IoT Systems

The awareness of physical network topology in a large-scale Internet of Things (IoT) system is critical to enable location-based service provisioning and performance optimization. However, due to the dynamics and complexity of IoT networks, it is usually very difficult to discover and update the physical topology of the large-scale IoT systems in real-time. Considering the stringent latency requirements in IoT systems, while the initial processing time for topology discovery can be tolerated, latency due to real-time topology update constitutes an even higher level of challenge. In this paper, a novel fast hierarchical topology update scheme is proposed for the large-scale IoT systems enabled by using the edge-cloud collaborative architecture. Specifically, an event-driven neighbor update algorithm, termed as TriggerOn, is firstly developed to update the local neighbor table of the end devices when device association or disassociation occurs. Based on the updated neighbor tables, the physical topology update of the subnet is conducted at the coordinated edge device, where a hybrid multidimensional scaling (MDS) based 3D localization algorithm is developed to locate the newly associated devices. Simulation results have indicated that as compared to the benchmark methods, the neighbor discovery latency has been reduced dramatically, and the 3D localization accuracy has been improved. Furthermore, the overall latency incurred by the proposed hierarchical physical topology update scheme is significantly lower than the distributed consensus-based update scheme, especially for the large-scale IoT subnets.

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

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

PCBChain: Lightweight Reconfigurable Blockchain Primitives for Secure IoT Applications

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

Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

For sustainable Internet-of-Things (IoT) systems, the solar power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions with volatile weather, this requires taking the weather forecast into account. The problem is how to provide such solar energy predictions with high accuracy for large-scale IoT systems with various devices in an autonomous way, without manual adaptation effort. We present a detailed study on machine-learning approaches for the prediction of solar power intake for large-scale IoT systems. We examine which machine learning models, feature sets, and sampling rates gain the best results for a medium-term forecasting horizon. We also explore an operational setting in which devices are deployed without prior data and machine learning models are retrained for each sensor continuously as a form of online learning. Our results show that prediction errors can be reduced by 20 % compared to the state of the art, despite strong weather volatility.

PoBT: A Lightweight Consensus Algorithm for Scalable IoT Business Blockchain

Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.

Vehicles joint UAVs to acquire and analyze data for topology discovery in large-scale IoT systems

Billions of sensing devices have been connected to the Internet of Things (IoT), generating a large volume of data that can be turned into valuable insights for many applications. Location information is critical for many IoT applications. However, most sensor devices are randomly deployed and locations are unknown. Thus, it is a challenging issue to discover the physical topology of the IoT system consisted of thousands of low-cost sensor devices. In this paper, a Vehicles joint UAVs Topology Discovery (VUTD) scheme is proposed that can discover the physical topology with low-cost and accuracy. There are two main steps in VUTD scheme: (1) Vehicles are used as mobile anchors to assist adjacent sensor devices in positioning. They are also used to collect logical topology information of the IoT system. The collected logical topology information and location information can be combined into physical topology information that will be sent to the cloud platform through vehicles. (2) The cloud platform analyzes the received information to determine the area where the physical topology discovery is not completed. Then, the cloud platform dispatches the UAV as a flight anchor to locate these points. Experiments based on realworld taxi trajectory are conducted to verify the effectiveness of VUTD scheme. The experimental results show that the VUTD scheme has better performance. Compared with the VTD scheme, the localization ratio is increased by up to 13.6%, and the mean localization error is reduced by up to 90.78%. Compared with UTD, the cost of location discovery is reduced by up to 77.7%.

A Hardware-assisted Heartbeat Mechanism for Fault Identification in Large-scale IoT Systems

With increased inter-connectivity among disparate devices, such as Internet-of-Things (IoT) devices, including those deployed in a nation's critical infrastructure, there is a need to ensure that any failure in the deployed devices can be detected. The capability to automatically detect device failures is particularly crucial in a large-scale, complex IoT system, since it can be very time-consuming and challenging to investigate a large number of geographically-dispersed devices that are also of different makes and types. In this paper, we present a faulty-device identification technique that is designed to achieve lightweight processor-level architectural support. Specifically, a hardware-based monitoring agent is incorporated within a processor and connected to a separate monitoring program when an examination is required. By analyzing information collected by the agent, the monitoring program determines whether the device being monitored is functioning. Findings from our detailed evaluation demonstrate that the proposed approach can detect around 90% of the failures with minimal hardware overhead of approximately 5k gates. This area overhead is reasonable and amounts to 7.69% of the ARM Cortex-M4 a lightweight IoT-class processor that has a total area (excluding optional caches and scratch-pad memory) of 65k gates.

Collective Remote Attestation at the Internet of Things Scale: State-of-the-Art and Future Challenges

In recent years, the booming of Internet of Things (IoT) has populated the world with billions of smart devices that implement novel services and applications. The potential for cyberattacks on IoT systems have called for new solutions from the research community. Remote attestation is a widely used technique that allows a verifier to identify software compromise on a remote platform (called prover). Traditional challenge-response remote attestation protocols between the verifier and a single prover face a severe scalability challenge when they are applied to large scale IoT systems. To tackle this issue, recently researchers have started developing attestation schemes, which we refer to as Collective Remote Attestation (CRA) schemes, that are capable of remotely performing attestation of large networks of IoT devices. In this paper, after providing the reader with a background on remote attestation, we survey and analyze existing CRA schemes. We present an analysis of their advantages and disadvantages, as well as of their effectiveness against a reference attacker model. We focus our attention on CRA schemes’ characteristics and adversarial mitigation capabilities. We finally highlight open research issues and give possible directions for mitigating both the limitations of existing schemes, and new emerging challenges. We believe this work can help guiding the design of current and future proposals for CRA.

Two-level distributed clustering routing algorithm based on unequal clusters for large-scale Internet of Things networks

According to the recent advancements in communication technologies and the widespread use of smart devices, our environment can be transforming into the Internet of Things (IoT) because it can connect the physical, cyber, and biological world via smart sensors for different purposes. Wireless sensor networks are considered as one of the main infrastructures in the IoT systems. Therefore, decreasing the total energy consumption of sensor nodes and prolonging the network longevity are two important challenges that should be considered. To increase energy efficiency and to improve the network longevity, a two-level distributed clustering routing algorithm based on unequal clusters has been proposed for large-scale IoT systems. The main idea is to decrease the data transmission distances between member nodes and cluster heads to mitigate the hot spot problem by distributing two cluster heads in each cluster, which in turn leads to energy conservation and load balancing. The clustering method is two level due to the benefits it offers for the sensor nodes. First, each node can transfer its data to the nearest cluster head because a primary cluster head and a secondary cluster head have been considered for each cluster. Therefore, the nodes far from the primary cluster head can be organized based on their distances to the closest cluster head to reduce their data transmission distances to the cluster heads. Second, two cluster heads can be replaced with each other in different circumstances. This reduces the overhead of the cluster head selection algorithm in the proposed scheme. Third, the sensor nodes can benefit from the primary and secondary cluster heads to transfer the data to the sink through different paths with the minimum energy consumption. Simulation results indicate that the proposed algorithm has better performance in terms of total energy consumption, total network energy, and network longevity compared to previous similar schemes.

UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size $\boldsymbol K$ -means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.

A secure IoT cloud storage system with fine-grained access control and decryption key exposure resistance

Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in IoT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user secret keys due to different reasons. In this paper, we present a practical attribute-based access control system for IoT cloud by introducing an efficient revocable attribute-based encryption scheme that permits the data owner to efficiently manage the credentials of data users. Our proposed system can efficiently deal with both secret key revocation for corrupted users and accidental decryption key exposure for honest users. We analyze the security of our scheme with formal proofs, and demonstrate the high performance of the proposed system via experiments.

Activity Detection for Massive Connectivity Under Frequency Offsets via First-Order Algorithms

Activity detection in machine-type communication (MTC) has been recognized as an effective way to support massive connectivity of the Internet-of-Things (IoT) devices. However, due to the sporadic traffic pattern of the MTC, only a small portion of the massive potential devices are active, making the activity detection a challenging large-scale sparsity-constrained problem. On the other hand, since the low-cost IoT devices are commonly equipped with cheap crystal oscillators, the resulting frequency offsets would intensify the multi-user interference during the activity detection and invalidate existing detection methods that are designed under ideal frequency synchronization. To fill this gap, this paper proposes two methods for activity detection under unknown frequency offsets: a Lasso-based method and a sparsity-constrained method. Both the methods are first-order algorithms, making them suitable for large-scale IoT systems. Furthermore, the sparsity-constrained method can be executed in parallel and is proved to converge to a set of critical points. The simulation results show that both the proposed methods achieve much better detection performance than a two-stage approach that separately performs frequency synchronization and activity detection. Moreover, the proposed sparsity-constrained method is shown to perform better than two competing algorithms exploiting hierarchical sparsity.