The industrial control anomaly detection algorithm effectively monitors and identifies anomalies in industrial control systems, ensuring timely issue detection to maintain system reliability, safety, and regular operation. Researchers often integrate a large number of high-precision, high-sampling-frequency sensors into the system to obtain comprehensive real-time equipment operating data. However, deploying consumer-grade high-performance computing devices in large-scale industrial control systems is often impractical, rendering existing anomaly detection systems unusable in practical environments. Most existing sensor feature selection algorithms are unsuitable for industrial control time series datasets, and some suffer from issues such as a limited reduction in the number of sensors and a decrease in anomaly detection accuracy after reduction. We propose an offline industrial control anomaly detection algorithm based on the Maximum Information Coefficient (MICHC-NN) to address these challenges. We utilize the Maximum Information Coefficient to compute correlations among sensors, addressing the issue of existing feature selection algorithms relying on anomaly labels. Additionally, we introduce a key sensor selection algorithm based on correlation, identifying interrelated sensors in an industrial control system and determining the key sensors most susceptible to system anomalies. These key sensors allow the anomaly detection algorithm to be deployed with a smaller footprint in real industrial control environments. Finally, we employ an autoencoder-decoder model with attention mechanisms to train the anomaly detection network. Experimental results demonstrate that our proposed anomaly detection algorithm achieves performance comparable to using all sensors, with a 75% reduction in the number of sensors employed.

Deep learning (DL)-based physical-layer authentication (PLA) has shown promising performance in Industrial Internet of Things (IIoT) scenarios. Existing methods formulate PLA as a single multi-classification task with a static training dataset and implicitly assume the training and authentication data are drawn from the same distribution. However, user mobility over time in mobile IIoT leads to temporal distribution shift in Channel Impulse Response (CIR), severely degrading authentication performance. To address this challenge, we propose TMML-PLA, a Temporal Meta-Metric Learning method that reformulates multi-user PLA as a sequence of temporal few-shot classification tasks based on the intrinsic temporal correlation characteristic of CIR. When new CIR samples require authentication, the model dynamically constructs a task using a small set of the most recent CIR samples of each user as the support set to ensure distributional consistency. A temporal meta-metric learning scheme based on the Prototypical Network is introduced to tackle these tasks under evolving CIR distributions, and a temporal decay mechanism is designed to emphasize recent training samples. Experiments on a real-world IIoT dataset demonstrate that TMML-PLA significantly improves both accuracy and robustness. For instance, when the number of users is 15 and the authenticating CIR samples increase over time, TMML-PLA maintains stable accuracy from 88.20% to 85.75% while the DL-based ResNet drops sharply from 83.93% to 46.95%, confirming its robustness and accuracy in mobile IIoT scenarios.

Secure authentication and key agreement across heterogeneous trust domains is essential for reliable collaboration in the Industrial Internet. However, existing cross-domain AKA schemes still face certificate-management overhead, key-escrow risk, insufficient identity privacy, and high computational cost. To address these issues, this paper proposes LBCAK, a lightweight blockchain-assisted anonymous cross-domain AKA scheme. LBCAK combines a certificateless-style key construction with AuthLedger-based public-state coordination, where the blockchain maintains current registration states for cross-domain credential validation while online authentication and session-key establishment remain off-chain. Credential-state-specific protected identifiers are further used to reduce on-chain linkability without exposing raw identities or private credential materials. Security is analyzed under an eCK-style leakage model in the random-oracle setting and further examined using ProVerif, showing that LBCAK provides mutual authentication, identity privacy, untraceability, forward secrecy, and resistance to major active and key-leakage attacks. Performance evaluation shows that LBCAK reduces computational and communication overheads by 14.3% and 4.2%, respectively, demonstrating its practicality for resource-constrained Industrial Internet deployments.

Blockchain has been increasingly adopted in the Industrial Internet of Things (IIoT) for cross domain authentication, enabling trusted collaboration among multiple management domains and supporting decentralized trust management. However, existing authentication schemes, particularly those based on classical public-key cryptography such as RSA and ECC, are vulnerable to quantum attacks and still face significant challenges in computation overhead. These limitations make them insufficient for the practical demands of complex, multi-domain environments, especially in the context of future quantum threats. To address these challenges, this article proposes PQEdgeAuth, an edge-assisted authentication scheme for IIoT that integrates post-quantum secure authentication with an optimized consensus protocol. The scheme incorporates post-quantum cryptographic techniques to ensure long-term security against quantum adversaries, while employing location-based grouping and aggregation signatures to reduce consensus communication overhead and enhance scalability. Experimental results show that PQEdgeAuth reduces computation time by up to 35.23% and improves throughput by up to 54.38% compared to existing schemes, demonstrating its potential to offer a secure, scalable, and efficient solution for cross domain authentication in IIoT environments.

CSI-based physical-layer authentication (PLA) is a promising solution for securing access in wireless communication systems. However, distribution shifts caused by time-varying CSI and computational limitations due to constrained edge resources jointly degrade authentication accuracy. Existing methods lack effective modeling of the temporal dynamics and long-range dependencies in time-varying CSI and high computational costs, resulting in limited robustness and generalization in dynamic environments. To address these challenges, we propose LRTV-PLA, a lightweight and robust PLA with time-varying distribution shift in dynamic wireless environments. Specifically, we propose temporal interpolation with contrastive regularization (TICR) to address sparse CSI sequences by interpolating missing data and constraining cross-time variation. We also adopt a shallow autoencoder for nonlinear dimensionality reduction (SAE-NLDR) to reduce cost while preserving discriminative features. We further introduce LinA-BiLSTM, which combines bidirectional LSTM for short-term dependencies with linformer-based mulit-head attention for efficient long-range modeling, reducing attention complexity from$O(n^{2})$to$O(n)$and enabling lower latency and memory usage on edge devices. Finally, extensive experiments conducted in real-world scenarios demonstrate that the proposed method outperforms existing approaches in accuracy, robustness, and resource efficiency. For example, in the street scenario, our method improves authentication accuracy by 1.47% compared to the best baseline, shortens authentication time by approximately 60%,decreases memory usage by around 36%, and reduces the standard deviation from 1.52 to 0.95.

With the rapid advancement of industrial structures, many factories are deploying anomaly detection systems. However, most existing anomaly detection algorithms are unsuitable for high-noise industrial control system (ICS) environments and constantly changing sensor patterns, resulting in low detection accuracy. To address these challenges, we propose a hierarchical temporal memory-based online anomaly detection algorithm for ICS, incorporating cumulative distribution functions and Gaussian convolution kernels. We enhanced the encoding process of hierarchical temporal memory, enabling it to quickly fit the physical processes of ICS and remember the system’s previous operating states for anomaly recognition. Additionally, our anomaly calculation algorithm, based on cumulative distribution functions and Gaussian kernel convolution, effectively addresses the adaptability and accuracy challenges posed by high-noise ICS environments. Experimental results demonstrate that our method outperforms the best baseline algorithms. While maintaining good time stability, our method achieves a 9$\%$improvement in detection accuracy, highlighting its significant advantage in balancing detection precision and time performance.

The convenience of wireless communication has led to its widespread adoption across various application scenarios. However, existing Physical Layer Authentication (PLA) schemes still have limitations in terms of adaptability. This paper introduces a novel method based on key-less channel-based authentication framework designed to enhance PLA performance under different mobility patterns. Our method combines the sliding window with a model based on a siamese neural network, enabling a fully connected neural network classifier to effectively distinguish between legitimate transmitters and potential attackers, achieving an accuracy of 97.91%. Experiments conducted in various environments demonstrate the robustness and high performance of the proposed method, making it well-suited for deployment in time-varying environments with high-security demands and resource constraints.

The emergence of digital twin technology offers a promising solution to address the limitations of traditional methods on early diagnosis and accurate propagation analysis of flight ground service delays. However, the application of digital twin technology in the civil aviation domain still stays at the lower maturity of the L2 level, which focuses on physical assets, operational data, and maintenance planning at airports, and failed to achieve the integration of flight ground operation mechanism and real‐time data, making it difficult to realize timely delay diagnosis. The simulation model is also limited to the offline simulation technology, which cannot connect to real‐time data for simulation from intermediate processes. In this work, we developed an advanced L3‐level airport digital twin system for flight ground service processes delay diagnosis and propagation, which focused on real‐time data‐driven simulation models and machine learning applications to meet the timely and precision requirements. First, we used the Unity3D platform to construct static three‐dimensional models of flight ground service objects on the airport cloud server. By parsing these behavioral state interfaces and mapping real‐time dynamic data from the airport sensing and business systems, we achieved accurate visualization of the airport’s dynamic operational processes. Then, a vehicle delay tree–based Bayesian diagnostic model was proposed in the digital twin system to analyze the relationships between multiple flights and service processes, which enables proactive diagnosis of the operation status and provides delay warning information. To improve the accuracy of propagation analysis, we proposed a “breakpoint” simulation method that enables real‐time simulation starting from an intermediate moment, facilitating the inference of flight ground service delays since the early warning moment. In addition, two delay tracing and propagation algorithms were proposed to identify delays and investigate propagation paths. Leveraging real‐time operational information, our approach provides valuable feedback for decision‐making, empowering the airport manager to formulate precise optimization strategies. Experiments on real‐world airport data have validated the effectiveness of our proposed method and provided practical recommendations for airport managers to reduce aircraft delays and improve airport operation efficiency.

Reliable and robust airport flight ground service resources collaborative scheduling has emerged as an issue of major concern at congested airports, which has great significance on airport service quality and operation efficiency. Due to the uncertainty of the service process of multiple vehicles in airport ground operations, the combination of simulation and optimization (sim-opt) techniques has been proven to be an effective means to solve the collaborative scheduling problem. However, existing models still have limitations on the reliability of feasible solutions and the robustness of evaluations. In this work, we developed a conflict resolution based sim-opt framework that combines the search capability of mathematical optimization models with the ability of simulation models to describe uncertainty. The dynamic window is introduced to divide the flight zone into multiple sub-intervals, which allows for iterative search and evaluation of the optimal schedule solution while reducing model calculation time. Additionally, a matching algorithm based on slack and tight constraints is proposed to optimize the possible resource requirements of each vehicle, thereby improving the initial schedule solution’s search depth and reliability. Furthermore, a conflict resolution feedback mechanism between adjacent windows is constructed to optimize the scheduling plan, reducing misjudgment and omission of the optimal solution and enhancing the sim-opt framework’s robustness. Finally, experiments on real airport datasets of three different scales demonstrate that our proposed method efficiently allocates resources with a smaller flight delay time as well as reducing total vehicle cost time during the flight ground service process.

Industrial control systems rely on wireless devices and sensors, necessitating critical security. Physical layer authentication (PLA) is a promising mechanism for device authentication, utilizing its unique spatiotemporal characteristics and channel state randomness, which offers unforgeability and high informatics security with low computational overhead and efficiency in resource-constrained scenarios. However, existing PLA mechanisms face challenges in complex industrial wireless environments, including insufficient accuracy, computational complexity, inadequate noise consideration, and poor performance. To address these challenges, we propose a convolutional denoising autoencoder (CDAE) model that reduces feature dimensions, eliminates noise, and extracts key vectors. The weighted$k$-nearest neighbor algorithm classifies the extracted vectors for comprehensive authentication in control system networks. Accurate authentication enables efficient detection of malicious attacks. Simulation experiments show that using CDAE-extracted feature vectors achieves over 95% accuracy with only 1% training samples, surpassing channel state information-based authentication by 46.15%, validating the proposed mechanism’s effectiveness.

Unlike conventional IT systems, industrial control systems (ICSs) requires tailored attack detection methods due to its unique communication protocols. Existing attack detection methods lack the ability to consider both detection accuracy and time performance, particularly for highly stealthy fake data injection attacks (FDIAs). To address these challenges, this work proposes an online parallel attack detection method for ICS based on multibandpass filter. By building multiple adaptive filters based on energy equilibrium and time–frequency domain data transformation, we implement multifrequency band data segmentation. Hierarchical temporal memory (HTM) models are employed to parallelly fit the segmented data and detect anomalies. Simulation experiments demonstrate that our method outperforms the state-of-the-art Numenta method, achieving a 9% higher detection accuracy while reducing detection time to just 1/14 of Numenta’s. These results highlight the significant advantages of our method in striking a balance between detection accuracy and time performance. Our proposed method fills the gap in ICS attack detection and offers substantial improvements over existing techniques.

In recent years, industrial control systems (ICSs) security incidents have revealed vulnerabilities in the system hardware, user programs, and communication protocols. The various components of the ICS are connected by the Industrial Internet of Things (IIoT) protocol. Nevertheless, malicious attackers can exploit vulnerabilities in IIoT protocol to manipulate the ICS, potentially causing damage to the associated ICS equipment. This work focuses on the challenge of identifying vulnerabilities in IIoT protocols, aiming to enhance the system security through advanced fuzz testing techniques. To address the limitations of current fuzz testing in IIoT protocols, such as short prediction sequence lengths and low recognition rates, this work proposes a novel fuzz testing model based on the long attention mechanism, named TXL-Fuzz. This model is capable of handling longer protocol sequences and improving the diversity of the generated test cases. Experimental results demonstrate that the model outperforms the existing fuzz testers in test case recognition rate (TCRR) for the protocols of different lengths. Notably, TXL-Fuzz achieves a bits-per-character (BPC) of approximately 0.5, significantly lower by nearly 0.3 compared to the Anti-Sample Fuzzer, the long short-term memory network (LSTM)-based model, and GRU-based model. Furthermore, it exhibits a TCRR that is 5% to 15% higher than Peach Fuzzer, Anti-Sample Fuzzer, and BLSTM-DCNNFuzz under similar conditions.

Mobile edge computing (MEC) can sufficiently meet the computing demands of complex application consists of multiple interdependent tasks which can be represented by a directed acyclic graph (DAG). For tasks in a DAG, different scheduling orders and offloading decisions will generate different completion time, which further affects the Quality of Experiences (QoEs). So it is important to study the scheduling and offloading schemes for tasks in MEC scenarios. To this end, we first designed a scheme that schedules tasks with the highest response ratio and offloads tasks to the optimal processor with the optimization method for a DAG, which is termed as HRRO algorithm. Then, considering the complexity of the reality, we extended the HRRO to the ultradense MEC system and achieved the optimal joint scheduling and offloading scheme for multi-DAG based on the genetic algorithm, which can be concluded as HRRO based on the genetic algorithm (HRRO-GA). Subsequently, to evaluate the performance of the algorithms, we conducted amounts of the simulation experiments and compared the results with several state-of-the-art algorithms, including distributed earliest finish-time offloading (DEFO), potential game-based offloading algorithm (PGOA), and GA-based multiuser earliest finish time (GA-MEFT). Meanwhile, we selected some random strategies to verify the schemes of HRRO-GA are the best. Finally, we concluded that HRRO-GA is more suitable for the ultradense MEC system.

Industrial control technology faces serious communication security threats. Authenticated key agreement(AKA) protocols are essential to secure communication between industrial control nodes, but they must be efficient and robust due to resource constraints. Existing AKA protocols based on encryption algorithms have limitations such as vulnerability to clone attacks. Also, there is a lack of protocol’s research that fully integrate advantages of physical and upper layer. We propose a novel cross-layer AKA protocol that leverages channel state information(CSI), which has uniqueness and real randomness, and is unforgeable, to enhance security and reduce computational overhead. Our protocol only requires simple operations such as algebraic, Hash and XOR. We introduce a new security verification model, superCK, which extends the well-known eCK and relaxes assumptions on attackers. Our protocol achieves 12 fully provable key security capabilities, and has 75.91% less computational overhead and 6.99% less communication overhead than the best ones, achieving an optimal trade-off between security and efficiency.

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This article proposes a physical layer key generation scheme for wireless LAN MIMO systems based on feature fusion autoencoder (FFAEncoder) to address the issue of a high key disagreement rate (KDR). Our approach involves extracting amplitude and phase features separately, fusing them through multiplication operator in a neural network, and using an autoencoder to extract common features. The proposed scheme was evaluated on multiple data sets in different real-world scenarios, and it was found that the transmitter and receiver codeword’s mean squared error (MSE) and mean absolute error (MAE) were smaller than those of the current models, indicating better key generation performance. Additionally, the proposed scheme’s KDR was smaller, with a decay rate faster than that of the other two models in the same environment, and the primary key bits were 1/2 and 1/3 that of the other models, respectively, as the signal-to-noise ratio (SNR) increased.

Physical-layer secret key generation (PLKG) is a new research area that has emerged in recent years. It is aimed at scenarios where legitimate IoT devices communicate directly, interacting with confidential information for lower overhead and higher security by using wireless channel. When applying it to wireless feature extraction, noise removal is not taken into account in current deep learning networks. To address these problems, the PLKG scheme based on bidirectional convergence feature learning convolutional network (BCFL-based scheme) is proposed, which consists of neural network called BCFL and a new quantization method to achieve better secret key generation. Unlike existing PLKG schemes that enabling both parties to communicate for obtaining higher channel feature similarities, when training it, channel state information (CSI) obtained by channel estimation for two legitimate devices during coherent time is used as inputs; and mean square error (MSE) between two outputs is used as a result of loss function for iterative training. Thus, it can obtain better denoising ability with guaranteed low computational resource consumption, and two legitimate devices can obtain highly correlated channel features. Multiple quantization method is also proposed to address low secret key generation rate (KGR) and low-secret key randomness (KR). The results show that the proposed BCFL-based scheme has a lower MSE than other schemes in different scenarios, indicating that it has better capability to learn channel reciprocity; and secret key error rate (KER) and time consumption are only about 50% of other schemes, which is a significant performance improvement.

With the development of big data and blockchain, an increasing number of scholars have begun to study blockchain for data sharing. By studying data sharing models that are based on blockchain, we find that almost all of them have the following problems: 1) it is difficult to protect the privacy and integrity of users’ data, along with users’ data ownership; 2) the storage burden of blockchain is heavy, and blockchain lacks a mechanism for dealing with data with diverse types and inconsistent formats; and 3) the consensus mechanism has low fairness or low efficiency. Therefore, we propose a data-sharing privacy protection model (DS2PM) that is based on blockchain and a federated learning mechanism for solving these problems. The safety analysis and experimental results show that the DS2PM outperforms the previously established schemes.

Nowadays, Industrial Internet of Things (IIoT) technology has made a great progress and the industrial control systems (ICSs) have been used extensively, which has brought more and more serious information security threats to the ICS at the same time. The authenticated key agreement (AKA) protocol is a common method to ensure the communication security. This work proposes a lightweight AKA protocol based on the elliptic curve cryptography (ECC) algorithm to adapt to the resource-constrained environment. We only employ hash operation, XOR operation, and ECC algorithm to encrypt the data in the authentication and key agreement phase, and avoid involving the register center while proceeding the key agreement, to give consideration to both performance and security. The security analyses indicate that our protocol can meet nine critical security requirements, more than all of the existing protocols, and the performance analysis carried out indicates that our protocol has less computational and communication overheads in contrast to other corelative protocols.

Advanced metering infrastructure (AMI) is a vital component of the smart grid (SG) for real-time data access and bidirectional communication. An authentication and key agreement (AKA) protocol is needed for AMI systems to ensure the confidentiality and integrity of communication data. Since the devices are connected to the open network and generally deployed outdoors with limited computation, communication, and storage, designing a suitable AKA protocol is a challenging task. Researchers are still looking for good ways to make the SG secure and efficient simultaneously. To remedy the situation, in this article, we advance a security-enhanced elliptic-curve-cryptography-based AKA protocol, and the security has been proven rigorously under the random oracle model and verified with the ProVerif tool. Furthermore, performance comparison validates the proposed protocol in affording improved security features with lower computation and communication cost. In addition, the proposed scheme is implemented practically on a testbed, which is deployed usingRaspberry Pi 3 Model B+for smart meters.

With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data. First, we integrate the incentives based on reputation and payment and introduce “credibility coins” as a cryptocurrency for data-sharing transactions, to encourage users to participate honestly in the data-sharing process based on federated learning. Second, we propose a dynamic incentive model based on the evolutionary game theory to model the game process of users in data sharing and analyze the stability of their strategies. Finally, based on the results of this analysis, we use the blockchain-based smart contract technology to dynamically adjust the participation benefits of users under different conditions in order to promote users to join consortium blockchains more often and steadily to participate in model training for federated learning and obtain better model accuracy. Our work is the first to apply the evolutionary game theory to the study of incentives in federated learning, and plays a leading role in the study of incentives in federated learning. Experimental simulation validation shows that our DIM-DS model can adequately motivate users to participate in the collaborative task of data sharing and maintain stability. The model can maximize the effectiveness of the federated learning model.

Mobile-edge computing (MEC) has been garnering considerable level of interests by processing computation tasks nearby mobile devices (MDs). With limited computation and communication resources and strict task deadline, balancing the energy consumption and time delay of computational tasks will be highly focused. MDs deployed energy harvesting (EH) modules can always provide service to continuous task requests, and finer-grained offloading schemes of the MEC system will significantly affect the time delay of computation tasks. However, when combined them together, the energy causal constraint and the coupling between offloading ratios and resources allocation will cause new challenges for the computation offloading problem. To address these issues, we investigate the partial computation offloading schemes for multiple MDs enabled by harvested energy in MEC. Specifically, we build models for two computing modes and EH process. Subsequently, we formulate a nonconvex optimization problem by minimizing the energy consumption of all the MDs while satisfying the constraint of time delay. Furthermore, we propose and design a novel algorithm based on the Lyapunov optimization to achieve optimal solution, that is, Lyapunov-optimization-based partial computation offloading for multiuser (LOMUCO). Then, we take the long-term average energy consumption and the discarding ratio of computation tasks as the quantitative metrics and conduct extended simulation experiments to confirm the performance of LOMUCO. Finally, compared to several baseline or state-of-the-art algorithms, including local computing all (LCA), offloading computing all (OCA), randomly partial computation offloading (RPCO), and Lyapunov-optimization-based dynamic computation offloading (LODCO), we can demonstrate the superiority of LOMUCO.

Currently, available contactless continuous authentication (CA) techniques depend on physiological biometrics to identify individuals at long intervals through complex feature extraction, resulting in poor accuracy, high computation costs, and security vacuums during lengthy intervals. To address these issues, we propose WiPT, a WiFi-based contactless CA system that utilizes contextual features and behavioral biometrics to optimize contactless CA technology. Specifically, we designed a low-computation two-step user state detection (TUSD) mechanism that continuously monitors user states in real-time. It locks the system when the registered user leaves and allows user authentication only when the departing user returns. Therefore, it eliminates pointless periodic re-authentication and results in considerably shorter monitoring intervals while significantly reducing computation. Subsequently, benefiting from contextual features, WiPT can identify individuals using more detectable behavioral biometrics. We built a one-class classification model based on the Convolutional Autoencoder to automatically extract rich representations of WiFi signals associated with postural transition movements, resulting in lower authentication delay, higher accuracy, and anti-interference. WiPT was implemented by the widely available 802.11n devices and has been extensively evaluated with typical sit-to-stand postural transitions. WiPT achieves an average accuracy of 96.63% in authentication and 99.78% in defense across 30 subjects with an authentication delay of 5.59 milliseconds and a monitoring interval of 2 seconds. They are 4.87% and 5.03% more accurate and dozens of times less time-consuming than existing WiFi-based CA solutions.

Videos have become the new preference comparing with images in recent years. However, during the recording of videos, the cameras are inevitably occluded by some objects or persons that pass through the cameras, which would highly increase the workload of video editors for searching out such occlusions. In this paper, for releasing the burden of video editors, a frame-level video occlusion detection method is proposed, which is a fundamental component of automatic video editing. The proposed method enhances the extraction of spatial-temporal information based on C3D yet only using around half amount of parameters, with an occlusion correction algorithm for correcting the prediction results. In addition, a novel loss function is proposed to better extract the characterization of occlusion and improve the detection performance. For performance evaluation, this paper builds a new large scale dataset, containing 1,000 video segments from seven different real-world scenarios, which could be available at: https://junhua-liao.github.io/Occlusion-Detection/. All occlusions in video segments are annotated frame by frame with bounding-boxes so that the dataset could be utilized in both frame-level occlusion detection and precise occlusion location. The experimental results illustrate that the proposed method could achieve good performance on video occlusion detection compared with the state-of-the-art approaches. To the best of our knowledge, this is the first study which focuses on occlusion detection for automatic video editing.

The Internet of Things (IoT) has become more popular over the past decade. For the IoT to be successful, it is vital to track the location of these things (sensors or actuators). In this article, based on a new IoT underlying architecture, a narrowband-IoT (NB-IoT)-aided, bounding-based iterative and range-free method, named BBIL, is proposed to localize things. We make use of the location information of all anchor regular nodes that can help improve the localization accuracy as much as possible. Specifically, not only single-hop and multihop anchor things but also single-hop and multihop regular things are used for localization. In addition, the communication and computational loads of the local network are greatly decreased because the anchor things can directly access the Internet using the NB-IoT modules; hence, data can be sent to the Internet through a small number of hops in the local network and processed in the cloud/edge computing facilities in a centralized way. To balance the location accuracy and energy consumption of BBIL, we propose a theoretical model to obtain the optimal number of the anchor things. BBIL is evaluated and compared with the existing methods. The simulation results indicate that the average localization error of BBIL is less than 11.6%. Also, it performs well in anisotropic networks. In addition, we verified the validity of our method in real-world scenario.

Neighbor discovery is a prerequisite procedure in communication among energy-limited Internet-of-Things (IoT) devices. Discovering neighbors should be achieved in an energy-efficient way. Communication between heterogeneous IoT devices is a common phenomenon for achieving extensive connections among IoT devices. Generally, existing asymmetric neighbor discovery methods are evolved from the symmetric methods and lack specialized designs for heterogeneous IoT devices which led to relatively poor energy-efficient performance. In this article, we use a generalized slot model using pure sending (PS) slot and pure listening (PL) slot, called PSPL, to convert existing neighbor discovery algorithms (symmetric and asymmetric) to highly energy-efficient asymmetric ones. The core idea of PSPL is that to achieve highly energy-efficient one-way discovery, PS slot and PL slot are only used in devices with smaller and larger energy budgets, respectively, and the length of PL slot is much larger than that of PS slot. In this way, the energy efficiency of asymmetric neighbor discovery can be substantially improved. After implementing one-way discovery, two-way discovery can be easily achieved with mutual assistance. Two examples of how to convert existing algorithms into highly efficient asymmetric ones are presented, the one is to convert Disco to Disco+PSPL and the other one is to convert Griassdi to Griassdi+PSPL. The theoretical analysis results indicate that with the same energy budget, Disco+PSPL and Griassdi+PSPL reduce the worst case latency bounds by up to 82.28% and 87.69%, respectively, compared with the best known asymmetric solutions. The simulation evaluation verifies the effectiveness of our designs.

Neighbor discovery, meaning that a node receives other nodes' radio frequency (RF) signals to be aware of their existence, is an indispensable procedure in Internet of Things (IoT)-oriented peer-to-peer (P2P) networks with energy-limited nodes. The main objective of neighbor discovery methods is to improve the energy efficiency, since node energy is usually very limited. As the neighborship maintaining time is very short in the mobile networks, neighbor discovery should be achieved in a very energy-efficient manner. The existing neighbor discovery methods only consider the received RF signals from other nodes (or neighbor table information derived from the received RF signals) as the basis of neighborship evaluation. The neighborship evaluation is inaccurate when the single source of neighbor information is employed. Inaccurate neighborship causes incorrect active slot scheduling and low energy efficiency of neighbor discovery. This paper proposes a generalized and probabilistic neighborship evaluation model to unify a variety of neighborship information in IoT-oriented P2P networks into neighborship probability values. Based on the model, we propose an energy-efficient neighbor discovery middle-ware algorithm by carefully replanning active slots of nodes according to the neighborship probability. The simulation evaluation results show that our proposed method decreases the average discovery delay by up to 11.46%, approximately, compared with other methods at the same energy budget.