Secure indoor ranging is a critical component of modern industrial systems, with applications in access control, contactless payments, and industrial automation. This article presents the first comprehensive survey focusing exclusively on secure-ranging protocols for Bluetooth, ultrawideband (UWB), and Wi-Fi. Unlike localization, which relies on multiple anchors, ranging involves only two devices; therefore, popular attack detection methods based on multianchor data do not generalize to secure ranging. This article begins by detailing industry-standard protocols such as IEEE 802.15.4a for UWB, Bluetooth, and IEEE 802.11 for Wi-Fi, followed by an analysis of their vulnerabilities and distance manipulation attacks. Common attack strategies, including overshadow, early path injection, and relay attacks, are explored. To mitigate these threats, we review countermeasures grouped into two main categories: randomization and channel integrity verification, which incorporate techniques from machine learning, threshold-based anomaly detection, and cryptography. Finally, we highlight cross-technology insights, discuss lessons learned, and propose future research directions to address unresolved challenges in indoor-ranging security.

The carbon-factor accounting method is widely used for carbon emissions (CEs) evaluation and carbon quotas (CQs) allocation in integrated energy systems (IESs). However, its linear mapping model cannot capture the real-time influence of external and environmental factors, which weakens the constraint effect of CQs on CEs and limits the overall energy–carbon optimization capability. To address this issue, this article proposes a large language model (LLM)-assisted deep reinforcement learning (DRL) optimization method to enhance the constraint effect of CQs on CEs in IESs. First, a nonlinear CQ modeling method based on LLM semantic reasoning is proposed, breaking the dependence of the linear carbon-factor method on expert experience. Second, considering information including energy structure, market changes, policy orientation, and environmental constraints, an interpretable nonlinear CQ accounting method is designed based on LLM to enhance the constraint effect of CQs on CEs. Finally, a trigger mechanism is designed to achieve collaborative optimization through automatic interaction between LLM and DRL. Simulation results indicate that the optimized CQ mechanism enforces a more effective constraint on CE behaviors, enabling timelier response and enhanced energy–carbon optimization performance.

Physical-layer key generation holds significant potential in establishing cryptographic key pairs for emerging LoRa networks. Nevertheless, current key generation solutions may underperform due to critically impaired channel reciprocity, attributed to the low data rate and long range inherent in LoRa networks. In this study, we presentChirpKey, a novel key generation scheme for LoRa networks. We pinpoint the key hurdles as the coarse-grained channel measurement, inefficient quantization methods, and out-of-range device constraints. To capture fine-grained channel information, we introduce a unique, LoRa-specific channel measurement method that focuses on analyzing chirp-level variations in LoRa packets. We also propose a LoRa channel state estimation algorithm to neutralize asynchronous channel sampling. Instead of the traditional quantization approach, we propose an innovative key delivery method based on perturbed compressed sensing, offering enhanced robustness and security. For LoRa devices beyond each other's communication reach, we integrate relay nodes to ensure reliable key generation. To foster secure group communication, we formulate two protocols that facilitate collaborative key generation across both star and chain configurations. Evaluation across diverse real-world scenarios reveals thatChirpKeyenhances the key matching rate by 11.03–26.58% and increases the key generation rate by 27–49× in comparison to existing leading systems. Our security analysis shows thatChirpKeycan effectively withstand a variety of prevalent attacks. Furthermore, we implement aChirpKeyprototype, demonstrating its capability to operate within 0.2 s.

Salient Object Ranking (SOR) aims to study human attention shifts across different objects in the scene. It is a challenging task, as it requires comprehension of the relations among the salient objects in the scene. However, existing works often overlook such relations or model them implicitly. In this work, we observe that when Large Vision-Language Models (LVLMs) describe a scene, they usually focus on the most salient object first, and then discuss the relations as they move on to the next (less salient) one. Based on this observation, we propose a novel Language-Guided Salient Object Ranking approach (named LG-SOR), which utilizes the internal knowledge within the LVLM-generated language descriptions, i.e., semantic relation cues and the implicit entity order cues, to facilitate saliency ranking. Specifically, we first propose a novel Text-Guided Visual Modulation (TGVM) module to incorporate semantic information in the description for saliency ranking. TGVM controls the flow of linguistic information to the visual features, suppresses noisy background image features, and enables the propagation of useful textual features. We then propose a novel Text-Aware Visual Reasoning (TAVR) module to enhance model reasoning in object ranking, by explicitly learning a multimodal graph based on the entity and relation cues derived from the description. Extensive experiments demonstrate superior performances of our model on two SOR benchmarks.

With advances in deep learning models and the availability of large-scale 3D datasets, we have recently witnessed significant progress in single-view 3D reconstruction. However, existing methods often fail to reconstruct physically based material properties given a single image, limiting their applicability in complicated scenarios. This paper presents a novel approach (named MAGE) for generating 3D geometry with realistic decomposed material properties given a single image as input. Our method leverages inspiration from traditional computer graphics deferred rendering pipelines to introduce a multi-view G-buffer estimation model. The proposed model estimates G-buffers for various views as multi-domain images, including XYZ coordinates, normals, albedo, roughness, and metallic properties from a single-view RGB image. To address the inherent ambiguity and inconsistency in generating G-buffers simultaneously, we also formulate a deterministic network from the pretrained diffusion models and propose a lighting response loss that enforces consistency across these domains using PBR principles. Finally, we propose a large-scale synthetic dataset rich in material diversity for our model training. Experimental results demonstrate the effectiveness of our method in producing high-quality 3D meshes with rich material properties. Our code and dataset can be found at https://www.whyy.site/paper/mage.

Low Power Wide Area Network (LPWAN) has been one of the most widely applied wireless technologies supporting large-scale IoT applications in Industry 4.0 era. In which, 3 major LPWAN protocols play leading roles in actual deployments: Long Range (LoRa), NarrowBand-IoT (NB-IoT), and Sigfox. Currently, promoting interoperability among different LPWANs is regarded as an evolution for IoT constructions. Hitherto, there lacks of an industrial standard to produce multi-protocol LPWANs in systematical level, subsequently introduces challenges to interoperate LPWANs. To address this challenge, this article proposes an IEEE 2668 based LPWAN infrastructure to improve interoperability regarding the in-compliance issue raised by different LPWAN standards. Thus, an interoperable framework for LPWANs (IF-LPWANs) is presented to embrace and cooperate multiple LPWANs. The framework was tested to provide a 8.3% energy consumption reduction and a 13% PID transmission latency reduction. Such a development is the first of its kind in both industrial and research area.

This paper presents SeHDR, a novel high dynamic range 3D Gaussian Splatting (HDR-3DGS) approach for generating HDR novel views given multi-view LDR images. Unlike existing methods that typically require the multi-view LDR input images to be captured from different exposures, which are tedious to capture and more likely to suffer from errors (e.g., object motion blurs and calibration/alignment inaccuracies), our approach learns the HDR scene representation from multi-view LDR images of a single exposure. Our key insight to this ill-posed problem is that by first estimating Bracketed 3D Gaussians (i.e., with different exposures) from single-exposure multi-view LDR images, we may then be able to merge these bracketed 3D Gaussians into an HDR scene representation. Specifically, SeHDR first learns base 3D Gaussians from single-exposure LDR inputs, where the spherical harmonics parameterize colors in a linear color space. We then estimate multiple 3D Gaussians with identical geometry but varying linear colors conditioned on exposure manipulations. Finally, we propose the Differentiable Neural Exposure Fusion (NeEF) to integrate the base and estimated 3D Gaussians into HDR Gaussians for novel view rendering. Extensive experiments demonstrate that SeHDR outperforms existing methods as well as carefully designed baselines.

Generative 3D modeling has made significant advances recently, but it remains constrained by its inherently ill-posed nature, leading to challenges in quality and controllability. Inspired by the real-world workflow that designers typically refer to existing 3D models when creating new ones, we propose Phidias, a novel generative model that uses diffusion for reference-augmented 3D generation. Given an image, our method leverages a retrieved or user-provided 3D reference model to guide the generation process, thereby enhancing the generation quality, generalization ability, and controllability. Phidias integrates three key components: 1) meta-ControlNet to dynamically modulate the conditioning strength, 2) dynamic reference routing to mitigate misalignment between the input image and 3D reference, and 3) self-reference augmentations to enable self-supervised training with a progressive curriculum. Collectively, these designs result in significant generative improvements over existing methods. Phidias forms a unified framework for 3D generation using text, image, and 3D conditions, offering versatile applications.

The increasing commercial viability of body area network (BAN) devices has expanded their applications beyond smartwatches and smartphones to include wearables for industrial safety, healthcare monitoring, and augmented reality systems. Secure communication between these devices remains a challenge, as traditional cryptographic key exchange protocols impose significant computational and power constraints. Physically Derived Symmetric Key (PDSK) generation offers a lightweight alternative by leveraging the shared wireless channel characteristics to establish encryption keys without prior trust. Recent studies have demonstrated the effectiveness of PDSK in ultra-wideband (UWB) systems, as UWB’s high-resolution channel measurements capture high-entropy yet highly correlated signal variations at different devices. In this work, we evaluate the feasibility of UWB-based PDSK in BAN wearable devices. Our results show that secret keys can be reliably generated, achieving a bit agreement rate (BAR) of 84.14% and a key success rate (KSR) of 92.7% while generating 240-bit keys in 0.35s. We further analyse key generation performance across different wearable placements and movement conditions, demonstrating that UWB-based PDSK is a viable and practical solution for secure communication in BAN applications.

Building-Wide Virtual Private Network (VPN) systems leverage the current communication infrastructure to connect various IoT devices and management systems through encrypted tunnels. These systems are essential for protecting sensitive data and ensuring operational security but face imminent threats from quantum computers capable of breaking widely-used public key cryptography. In this paper, we propose a lightweight hybrid post-quantum security framework specifically designed for building-wide IoT VPN systems, which combines classical cryptographic primitives (e.g., ECDH) with selected post-quantum crypto (PQC) algorithms (e.g., Kyber, Dilithium). This dual-layer architecture ensures protection against both current and future quantum threats, relying on the hardness of at least one underlying cryptographic assumption for security. Furthermore, the framework incorporates lightweight design principles tailored for resource-constrained IoT environments. These principles emphasize minimal computational demands, reduced communication costs, and limited protocol complexity. Specifically, its lightweight nature reflects simplification to the VPN protocol’s operations and the selected integration of post-quantum algorithms. This optimized application of PQC simplifies the cryptographic workload for key exchange and signature, thereby balancing security guarantees with feasible deployment on IoT hardware. Our analysis and experimental results demonstrate that the proposed architecture achieves quantum resistance with manageable performance impact compared to traditional VPNs, making it a practical and scalable solution for migrating existing building management infrastructures towards post-quantum security across diverse IoT devices.

NLOS and multipath propagation are the main hurdles for accurate localization with time-based localization systems. Over the years, researchers have proposed myriad approaches to mitigate the effects of Non-Line-of-Sight (NLOS) and multipath propagation for accurate indoor localization. Residual Weighting (Rwgh) is a widely used technique for NLOS mitigation in literature. This is because it does not require expensive site surveys or statistical information of the channel. Also, position estimation is possible even if all the nodes (anchors) are in NLOS. We observed that with Ultra-Wideband (UWB), the ranging results in Line-Of-Sight conditions are stable compared to NLOS and multipath ranging scenarios. Based on this, we propose a Rwgh approach by exploring the stability of the range measurements obtained during the localization phase. The stability of the range measurement gives an indication of the veracity of the introduced residuals, which is then weighted to mitigate the NLOS effects directly during localization. We test the performance of the proposed approach with data collected from two distinct real-world environments with heavy NLOS and multipath presence instead of simulated data as used in the preponderance of Rwgh-based approaches.

Spatio-temporal video grounding (STVG) aims at localizing the spatio-temporal tube of a video, as specified by the input text query. In this paper, we utilize multimodal large language models (MLLMs) to explore a zero-shot solution in STVG. We reveal two key insights about MLLMs: (1) MLLMs tend to dynamically assign special tokens, referred to as \textit{grounding tokens}, for grounding the text query; and (2) MLLMs often suffer from suboptimal grounding due to the inability to fully integrate the cues in the text query (\textit{e.g.}, attributes, actions) for inference. Based on these insights, we propose a MLLM-based zero-shot framework for STVG, which includes novel decomposed spatio-temporal highlighting (DSTH) and temporal-augmented assembling (TAS) strategies to unleash the reasoning ability of MLLMs. The DSTH strategy first decouples the original query into attribute and action sub-queries for inquiring the existence of the target both spatially and temporally. It then uses a novel logit-guided re-attention (LRA) module to learn latent variables as spatial and temporal prompts, by regularizing token predictions for each sub-query. These prompts highlight attribute and action cues, respectively, directing the model's attention to reliable spatial and temporal related visual regions. In addition, as the spatial grounding by the attribute sub-query should be temporally consistent, we introduce the TAS strategy to assemble the predictions using the original video frames and the temporal-augmented frames as inputs to help improve temporal consistency. We evaluate our method on various MLLMs, and show that it outperforms SOTA methods on three common STVG benchmarks.

Creating 3D assets that follow the texture and geometry style of existing ones is often desirable or even inevitable in practical applications like video gaming and virtual reality.While impressive progress has been made in generating 3D objects from text or images, creating style-controllable 3D assets remains a complex and challenging problem. In this work, we propose StyleSculptor, a novel training-free approach for generating style-guided 3D assets from a content image and one or more style images.Unlike previous works, StyleSculptor achieves style-guided 3D generation in a zero-shot manner, enabling fine-grained 3D style control that captures the texture, geometry, or both styles of user-provided style images. At the core of StyleSculptor is a novel Style Disentangled Attention (SD-Attn) module, which establishes a dynamic interaction between the input content image and style image for style-guided 3D asset generation via a cross-3D attention mechanism, enabling stable feature fusion and effective style-guided generation.To alleviate semantic content leakage, we also introduce a style-disentangled feature selection strategy within the SD-Attn module, which leverages the variance of 3D feature patches to disentangle style- and content-significant channels, allowing selective feature injection within the attention framework. With SD-Attn, the network can dynamically compute texture-, geometry-, or both-guided features to steer the 3D generation process. Built upon this, we further propose the Style Guided Control (SGC) mechanism, which enables exclusive geometry- or texture-only stylization, as well as adjustable style intensity control. StyleSculptor does not require prior training and enables instant adaptation to any reference models while maintaining strict user-specified style consistency. Extensive experiments demonstrate that StyleSculptor outperforms existing baseline methods in producing high-fidelity 3D assets. Code will be available at the project page.

A major challenge to Ultra-Wideband (UWB) positioning, especially in indoor environments, is the prevalent presence of Non-Line-Of-Sight (NLOS) and multipath propagations, degrading localization performance. Moreover, indoor settings tend to constantly change, making it difficult to characterize NLOS signals each time these changes occur. Promising approaches have been proposed to identify NLOS signals before localization; however, their performance is limited to the specific environment where measurements were carried out and cannot be extended to new or different environments. This can be attributed to feature discrepancies causing domain shifts and distribution divergence, owing to differences in environments captured by the Channel Impulse Response (CIR) waveforms. In this paper, we propose a domain Adaptive NLOS (AdaLOS) identification framework for UWB positioning systems. First, adaptation knowledge is obtained to align the source and target domains’ CIRs to mitigate the domain shift problem via cross-domain mappings. The distribution gap of the CIRs is further reduced by minimizing the marginal and conditional distribution divergence between the two domains (environments) by employing the Maximum Mean Discrepancy (MMD) criterion. A joint optimization procedure is then formulated to minimize the domain shift, marginal, and conditional distribution differences between CIRs from the two domains simultaneously. After minimizing the domain difference, Transformed Representative Features (TRF) of the source and target domains are obtained to train a domain-invariant classifier. We perform extensive simulations with CIR information collected from four distinct indoor environments characterized by different relative permittivity, signal distortions, noise, etc. AdaLOS is effective in adapting to new, distinct environments and significantly outperforms state-of-the-art works for NLOS identification.

Physical layer key sharing has become a popular topic in today's literature, as it could provide an alternative to computationally expensive key-sharing protocols. Its importance is emphasized by several resource-constrained, battery-powered autonomous robots, and personal devices that must communicate within modern-day industrial complexes. Physical layer key sharing has been explored using temporally and spatially variant characteristics of signals to produce the same secret keys at different devices. In this paper, we propose a novel method for ultra-wideband (UWB)-based physical layer key sharing, leveraging an off-the-shelf plug-and-play UWB module and utilizing the frequency domain of the Channel Impulse Response (CIR) magnitude. The frequency domain effectively aligns and denoises CIR samples, increasing the spatial and temporal uniqueness of channel characteristics. In turn, our approach offers the advantage of creating high-entropy keys with a 92% success rate. Our paper is the first to examine employing an UWB module for key sharing under different dynamic scenarios. Finally, our system maintains a higher level of security against several types of attackers compared to standard CIR methods.

Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate color tone distortion in under-exposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and over-exposed regions display opposite color tone distribution shifts, which may not be easily normalized in joint modeling as they usually do not have “normal-exposed” regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the bright-ened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches. Project web-page: https://github.com/yiyulics/CSEC.

This paper presents a vision-based people identification system designed for industrial environments, focusing on continuous user identification through gait recognition. Leveraging the CASIA-B gait dataset, a renowned resource in gait recognition research, our system adopts an enhanced spatiotemporal representation known as the Gait Energy Image (GEI), a binary silhouette-based gait representation, as its primary feature. The gait recognition model is built upon a ResNet architecture, integrating all essential components for effective identification. Our system demonstrates superior or comparable performance to existing algorithms in the literature for normal walking styles, with opportunities for further enhancements in the bag sequence identification.

Long Range Wide Area Network (LoRaWAN) is a wireless communication protocol used in open Radio Frequency (RF) communication links. However, this openness also makes LoRaWAN vulnerable to Denial of Service (DoS) attacks. LoRaWAn DoS attacks can result in increased packet errors, delays, and additional energy costs, posing risks to the reliability and effectiveness of Industry 5.0 automation. To address these threats, this paper proposes a novel evaluation model that combines the entropy weighting method (EWM) with grey relational analysis (GRA). The model focuses on an intelligent healthcare scenario as a representative application of Industry 5.0. It assesses LoRaWAn performance by considering metrics such as Packet Error Rate (PER), latency, and energy consumption.

Neural Radiance Fields (NeRFs) have shown remarkable performances in producing novel-view images from high-quality scene images. However, hand-held low-light photography challenges NeRFs as the captured images may simultaneously suffer from low visibility, noise, and camera shakes. While existing NeRF methods may handle either low light or motion, directly combining them or incorporating additional image-based enhancement methods does not work as these degradation factors are highly coupled. We observe that noise in low-light images is always sharp regardless of camera shakes, which implies an implicit order of these degradation factors within the image formation process. This inspires us to explore such an order to decouple and remove these degradation factors while training the NeRF. To this end, we propose in this paper a novel model, named LuSh-NeRF, which can reconstruct a clean and sharp NeRF from a group of hand-held low-light images. The key idea of LuSh-NeRF is to sequentially model noise and blur in the images via multi-view feature consistency and frequency information of NeRF, respectively. Specifically, LuSh-NeRF includes a novel Scene-Noise Decomposition (SND) module for decoupling the noise from the scene representation and a novel Camera Trajectory Prediction (CTP) module for the estimation of camera motions based on low-frequency scene information. To facilitate training and evaluations, we construct a new dataset containing both synthetic and real images. Experiments show that LuSh-NeRF outperforms existing approaches. Our code and dataset can be found here: https://github.com/quzefan/LuSh-NeRF.

This paper explores the weakly-supervised referring image segmentation (WRIS) problem, and focuses on a challenging setup where target localization is learned directly from image-text pairs. We note that the input text description typically already contains detailed information on how to localize the target object, and we also observe that humans often follow a step-by-step comprehension process (\ie, progressively utilizing target-related attributes and relations as cues) to identify the target object. Hence, we propose a novel Progressive Comprehension Network (PCNet) to leverage target-related textual cues from the input description for progressively localizing the target object. Specifically, we first use a Large Language Model (LLM) to decompose the input text description into short phrases. These short phrases are taken as target-related cues and fed into a Conditional Referring Module (CRM) in multiple stages, to allow updating the referring text embedding and enhance the response map for target localization in a multi-stage manner. Based on the CRM, we then propose a Region-aware Shrinking (RaS) loss to constrain the visual localization to be conducted progressively in a coarse-to-fine manner across different stages. Finally, we introduce an Instance-aware Disambiguation (IaD) loss to suppress instance localization ambiguity by differentiating overlapping response maps generated by different referring texts on the same image. Extensive experiments show that our method outperforms SOTA methods on three common benchmarks.

Positioning with ultrawideband (UWB) is prominent among industrial localization systems, due to its high-range resolution attributes and lower cost. However, one notable challenge with UWB positioning in industrial environments is the prevalent presence of nonline-of-sight (NLOS) components or signals that degrade localization performance drastically. Coupled with this, industrial settings tend to constantly change making NLOS signals challenging to characterize each time these changes occur. Recently, promising approaches have been proposed to identify NLOS components, however their performances are limited to the specific environment where measurements were performed. Their performance cannot be extended to other unknown environments due to the distribution divergence problem, owing to differences in environments captured by the channel impulse response (CIR) waveforms. This therefore requires laborious processes of data collection and training environment-specific models for NLOS identification. In this article, we propose a robust transfer learning-based NLOS identification approach, which harnesses transition information via cross-domain mappings from both source and target domains, to construct representative homogeneous features of both domains. The representative homogeneous features capture discriminative information of both domains, while reducing the distribution divergence between the domains, making it easy to classify LOS and NLOS components from both environments together. To test the robustness of our approach, we perform extensive simulations with CIR data collected from two distinct environments—“hard NLOS” (characterized by high relative permittivity of surrounding objects, e.g., thick concrete walls, metallic objects, etc.) and “soft NLOS” (characterized by low relative permittivity of surrounding objects, e.g., plasterboard walls). Our proposed approach is not just effective in transferring knowledge between distinct environments, but significantly outperforms state-of-the-art works to NLOS identification in UWB positioning networks, while reducing the laborious process of data collection in the target domain.

Small cell (SC) and device-to-device (D2D) communications can fulfill high-speed wireless communication in indoor industrial Internet-of-Things (IIoT) services and cell-edge devices. However, controlling interference is crucial for optimizing resource sharing (RS). To address this, we present an adaptive interference avoidance and mode selection (MS) framework that incorporates MS, channel gain factor (CGF), and power-allocation (PA) techniques to reduce reuse interference and increase the data rate of IIoT applications for 5G D2D-enabled SC networks. Our proposed approach employs a two-phase RS algorithm that minimizes the system's computational complexity while maximizing the network sum rate. First, we adaptively determine the D2D user mode for each cell based on the D2D pair channel gain ratios of the cellular and reuse mode. We compute the CGF for each cell with a D2D pair in reuse mode (RM) to select the reuse partner. Then we determine the optimal distributed power for the D2D users and IoT-user equipment using the Lagrangian dual decomposition method to maximize the network sum rate while limiting the interference power. The simulation results indicate that our proposed approach can maximize system throughput and signal-to-interference plus noise ratio, reducing signaling overhead compared to other algorithms.

Harmonizing cut-and-paste images into perceptually realistic ones is challenging, as it requires a full understanding of the discrepancies between the background of the target image and the inserted object. Existing methods mainly adjust the appearances of the inserted object via pixel-level manipulations. They are not effective in correcting color discrepancy caused by different scene illuminations and the image formation processes. We note that image colors are essentially camera ISP projection of the scene radiance. If we can trace the image colors back to the radiance field, we may be able to model the scene illumination and harmonize the discrepancy better. In this paper, we propose a novel neural approach to harmonize the image colors in a camera-independent color space, in which color values are proportional to the scene radiance. To this end, we propose a novel image unprocessing module to estimate an intermediate high dynamic range version of the object to be inserted. We then propose a novel color harmonization module that harmonizes the colors of the inserted object by querying the estimated scene radiance and re-rendering the harmonized object in the output color space. Extensive experiments demonstrate that our method outperforms the state-of-the-art approaches.

Existing Referring Image Segmentation (RIS) methods typically require expensive pixel-level or box-level annotations for supervision. In this paper, we observe that the referring texts used in RIS already provide sufficient information to localize the target object. Hence, we propose a novel weakly-supervised RIS framework to formulate the target localization problem as a classification process to differentiate between positive and negative text expressions. While the referring text expressions for an image are used as positive expressions, the referring text expressions from other images can be used as negative expressions for this image. Our framework has three main novelties. First, we propose a bilateral prompt method to facilitate the classification process, by harmonizing the domain discrepancy between visual and linguistic features. Second, we propose a calibration method to reduce noisy background information and improve the correctness of the response maps for target object localization. Third, we propose a positive response map selection strategy to generate high-quality pseudo-labels from the enhanced response maps, for training a segmentation network for RIS inference. For evaluation, we propose a new metric to measure localization accuracy. Experiments on four benchmarks show that our framework achieves promising performances to existing fully-supervised RIS methods while outperforming state-of-the-art weakly-supervised methods adapted from related areas. Code is available at https://github.com/fawnliu/TRIS.

Shadow detection methods rely on multi-scale contrast, especially global contrast, information to locate shadows correctly. However, we observe that the camera image signal processor (ISP) tends to preserve more local contrast information by sacrificing global contrast information during the raw-to-sRGB conversion process. This often causes existing methods to fail in scenes with high global contrast but low local contrast in shadow regions. In this paper, we propose a novel method to detect shadows from raw images. Our key idea is that instead of performing a many-to-one mapping like the ISP process, we can learn a many-to-many mapping from the high dynamic range raw images to the sRGB images of different illumination, which is able to preserve multi-scale contrast for accurate shadow detection. To this end, we first construct a new shadow dataset with ~ 7000 raw images and shadow masks. We then propose a novel network, which includes a novel adaptive illumination mapping (AIM) module to project the input raw images into sRGB images of different intensity ranges and a shadow detection module to leverage the preserved multi-scale contrast information to detect shadows. To learn the shadow-aware adaptive illumination mapping process, we propose a novel feedback mechanism to guide the AIM during training. Experiments show that our method outperforms state- of-the-art shadow detectors. Code and dataset are available at https://github.com/jiayusun/SARA.

With 2-factor authentication practices becoming ever more popular, the need for developing new authentication procedures is gaining ever more traction. Rhythm-based gesture authentication appears to be a promising area of research as it encompasses two of the three main authenticator factors: something you know(the chosen rhythm) and something you are(how you enter that rhythm). Rhythm-based authentication approaches have mostly been achieved using touchscreen functions. But accelerometers and gyroscope sensors have the unique ability to capture the small differences in how users enter a set rhythm and how they hold their phone. Additionally, these sensors do not limit our approach to mobile devices with mobile screens but can be expanded to headsets, smart glasses, and screen-less fitness trackers. It also circumvents issues like wearing face masks and gloves. All 12 of our participants were asked to input the same tapping rhythm consisting of 7 taps, 50 times, totaling 600 samples to be used for trial and testing. If our system is able to perform well under these conditions it proves that even an attacker who knows your rhythm, wouldn't be able to access your device. This could be equated to you telling someone your password, but them still not being able to gain access to your phone. Our models were able to achieve an authentication accuracy of 99.65% only using 10 valid samples for training and an identification accuracy of 99.17 %.

In the fourth industrial revolution (Industry 4.0), robotics and autonomous navigational systems are essential for smart agriculture. Industrial smart agriculture relies heavily on autonomous navigational systems, which have important uses in unmanned farms, industrial supply chains, etc. For effective navigation of autonomous robotic systems in industrial settings, indoor-based positioning and navigation technologies play key roles. However, positioning performance is severely impacted by the abundance of Non-Line-Of-Sight (NLOS) components due to the dynamic nature of industrial environments. Different approaches have been proposed in literature for identifying and mitigating NLOS components in UWB positioning systems. However, the performance of these proposed approaches on par in multiple distinct environments is unknown. In this paper, we experimentally investigate and compare the performance of recent UWB-based state-of-the-art approaches to NLOS identification and mitigation on par, in distinct and dynamic obstructed settings to ascertain two key insights: i) how the performance of existing approaches change based on the environment type; ii) the impact of the environment type on NLOS identification and mitigation.

LoRaWAN is an overwhelmingly popular wide area networking protocol that is capable of deploying city-wide Internet of Things (IoT) networks. Increase on the deployment of LoRaWAN in the last few years has led to exponential increase on the number of LoRa sensory and actuating end devices and results in more congestion in LoRaWAN network traffic. Moreover, the evolving AI-IoT (AIoT) applications (e.g., AI-powered environment monitoring system) create needs for more computing power with low latency requirements. To mitigate the high latency issue and the shortage of computing power due to continuously demanding quality of service (QoS) requirements of IoT applications, the Edge-Cloud server structure has been explored in a thorough manner in research area in recent years. An IEEE 2668 standardized IoT infrastructure system is presented in this work focusing on analyzing the performance of applying Edge-Cloud network server structure to LoRaWAN networks and provide standardized edge computing LoRaWAN infrastructure. This work uses queuing network to quantitively analyze the performance of an edge computing infrastructure applied to LoRaWAN and uses IEEE 2668 to quantitively present the performance and applicability of the Edge-Cloud LoRaWAN infrastructure.

Physical-layer key generation is promising in establishing a pair of cryptographic keys for emerging LoRa networks. However, existing key generation systems may perform poorly since the channel reciprocity is critically impaired due to low data rate and long range in LoRa networks. To bridge this gap, this paper proposes a novel key generation system for LoRa networks, named ChirpKey. We reveal that the underlying limitations are coarse-grained channel measurement and inefficient quantization process. To enable fine-grained channel information, we propose a novel LoRa-specific channel measurement method that essentially analyzes the chirp-level changes in LoRa packets. Additionally, we propose a LoRa channel state estimation algorithm to eliminate the effect of asynchronous channel sampling. Instead of using quantization process, we propose a novel perturbed compressed sensing based key delivery method to achieve a high level of robustness and security. Evaluation in different real-world environments shows that ChirpKey improves the key matching rate by 11.03–26.58% and key generation rate by 27–49× compared with the state-of-the-arts. Security analysis demonstrates that ChirpKey is secure against several common attacks. Moreover, we implement a ChirpKey prototype and demonstrate that it can be executed in 0.2 s.

Solar insecticidal lamps (SILs) Internet of Things is an emerging and environmentally friendly technology for preventing and controlling agricultural pests. As the disk coverage model only provides a coarse approximation of the sensing area in reality, the probabilistic coverage model (PCM) is appropriate for the deployment of SILs. However, most of the current studies on coverage problem under PCM have focused on point$\epsilon $-probability coverage, whereas a few referred to the area coverage problem since it is extremely difficult to verify the coverage of a complete continuous area under PCM, especially for irregular shaped area. In this article, we study how to deploy the minimum number of SILs with PCM to provide complete area$\epsilon $-probability coverage for actual farmland with irregular shape, where the locations used to deploy SILs are a limited set of candidates located on field ridges. We first formulate the complete area$\epsilon $-probability coverage problem into the minimum point$\epsilon $-probability coverage problem and prove that it is NP-complete. After that, we present an approximation algorithm with provable approximation rations to our problem. Finally, we analyze the performance of the proposed algorithm theoretically and perform extensive simulations to demonstrate its effectiveness.

Adjusting the photo color to associate with some design elements is an essential way for a graphic design to effectively deliver its message and make it aesthetically pleasing. However, existing tools and previous works face a dilemma between the ease of use and level of expressiveness. To this end, we introduce an interactive language-based approach for photo recoloring, which provides an intuitive system that can assist both experts and novices on graphic design. Given a graphic design containing a photo that needs to be recolored, our model can predict the source colors and the target regions, and then recolor the target regions with the source colors based on the given language-based instruction. The multi-granularity of the instruction allows diverse user intentions. The proposed novel task faces several unique challenges, including: 1) color accuracy for recoloring with exactly the same color from the target design element as specified by the user; 2) multi-granularity instructions for parsing instructions correctly to generate a specific result or multiple plausible ones; and 3) locality for recoloring in semantically meaningful local regions to preserve original image semantics. To address these challenges, we propose a model called LangRecol with two main components: the language-based source color prediction module and the semantic-palette-based photo recoloring module. We also introduce an approach for generating a synthetic graphic design dataset with instructions to enable model training. We evaluate our model via extensive experiments and user studies. We also discuss several practical applications, showing the effectiveness and practicality of our approach. Please find the code and data at https://zhenwwang.github.io/langrecol.

Novel-view prediction of a scene has many applications. Existing works mainly focus on generating novel-view images via pixel-wise prediction in the image space, often resulting in severe ghosting and blurry artifacts. In this paper, we make the first attempt to explore novel-view prediction in the layout space, and introduce the new problem of novel-view scene layout generation. Given a single scene layout and the camera transformation as inputs, our goal is to generate a plausible scene layout for a specified viewpoint. Such a problem is challenging as it involves accurate understanding of the 3D geometry and semantics of the scene from as little as a single 2D scene layout. To tackle this challenging problem, we propose a deep model to capture contextualized object representation by explicitly modeling the object context transformation in the scene. The contextualized object representation is essential in generating geometrically and semantically consistent scene layouts of different views. Experiments show that our model outperforms several strong baselines on many indoor and outdoor scenes, both qualitatively and quantitatively. We also show that our model enables a wide range of applications, including novel-view image synthesis, novel-view image editing, and amodal object estimation.

Alertness detection is an important component of systems used to detect driver fatigue in order to prevent road accidents. Various approaches have been proposed in literature, including the use of sensors that track physiological markers and camera based systems that rely on computer vision techniques to track facial markers. In this paper, we adopt liveness detection to determine alertness. Liveness detection is a method used in biometrics to determine if a face, or fingerprint, is from a live person. The results show that the approach is robust to different lighting conditions.

In the past few years, smart healthcare has become a popular topic. Many users wear smart devices to monitor their health conditions. To provide information for health analysis, many sensors are installed in smart wearable devices to collect personal health data. Due to the limitation of space and computational power, private health data is not processed locally. Instead, it is usually sent to a mobile phone for further analysis, which required wireless data exchange between the mobile phone and smart wearable devices. To protect a user’s privacy, it is important to guarantee the connection security of the devices’ network as well as prevent information leakage. A possible method to secure the data exchange process is symmetric encryption. In this paper, we investigate the feasibility of symmetric key generation for communication between a mobile phone and smart wearable device using angular velocity data collected by gyroscopes as data source. We collected over 1000 samples of gait data, totally more than 20000 seconds of movements, using two industrial products including a smart watch and mobile phone placed on wrist and in pocket respectively. We successfully generated the same random number for mobile phone and smart wearable device in 78% samples.

The physical layer secret key generation scheme is a preferred solution designed for resource-constrained Internet of Things (IoT) devices. But it suffers from a severe attack, the signal manipulating attack, which aims at controlling the generated key. The existing solutions either cannot prevent all kinds of signal manipulation attacks or require working in full-duplex mode, which is not suitable for resource-constrained IoT devices. In this article, we introduce a secret key generation scheme with the help of an untrusted relay to address this dilemma. Also, our method can protect the privacy of legitimate users from the untrusted relay. We conclude a general signal manipulation attack model from existing practical signal manipulation attacks and analyze the security strength and privacy preserving ability of our scheme based on this model. Finally, we compare our method with existing signal manipulation attack solutions. The result shows that our method is the best solution for resource-constrained IoT systems.

Nowadays billions of smart objects are connected to the internet and interact with the cloud. Remote monitoring, control systems, and data analysis becomes more intelligent with the huge amount of data been collecting and crowdsourcing. To reduce data transmission and improve executive efficiency, computing and storing functions are executed toward edge devices. However, the great convenience raises numerous issues including reliability and security of the sensors and the control systems because these issues have not always been considered the top priority. Accordingly, many new research opportunities and challenges for intelligent sensing and control have arisen.

The connectivity and intelligence of Internet of Things (IoT) equipment offer improved services, but several technical challenges have emerged in recent years that hinder the widespread application of IoT, e.g., security and safety. Cyber-security and privacy countermeasures are widely used in IoT equipment, and many studies have been conducted. However, an important aspect that is often overlooked in security literature is IoT equipment’s physical security and safety, namely, preventing IoT equipment from vandalism and theft. Therefore, this article provides an overview of IoT equipment’s physical security and safety to draw attention to new research opportunities in this area. Afterward, we discuss, among other aspects, antitheft and antivandalism schemes along with circuit and system design, additional sensing devices, biometry and behavior analysis, and tracking methods. Besides, we summarize the artificial intelligence solutions for the physical security and safety of IoT equipment. Finally, we conclude with four future research opportunities.

Distance-bounding (DB) protocols are used to verify the physical proximity of two devices. DB can be used to establish trusted ad-hoc connections in the industrial Internet-of-Things, e.g., nodes can verify they are deployed in the same location and monitoring the same piece of equipment. Thresholds and error correction codes (ECCs) are two methods to provide error-resilience for DB protocols working in noisy environments. However, the threshold method adds overheads and the ECC method increases the adversary success probability, compared to threshold, when implemented in precommitment DB protocols. In this article, we investigate the ECC method and demonstrate that designers can mitigate increased adversary success probability by using nonsystematic codes. To demonstrate this idea, we compare a prominent precommitment protocol by Brands and Chaum (BC) integrated with different types of ECCs with two existing error-resilience methods, showing how nonsystematic codes provide improved protocol security. Moreover, We further evaluate the BC protocol with nonsystematic ECCs and discuss how to configure protocols to minimize the protocol failure rate, while maintaining adequate attack success probability.

Causally-taken images often suffer from flare artifacts, due to the unintended reflections and scattering of light inside the camera. However, as flares may appear in a variety of shapes, positions, and colors, detecting and removing them entirely from an image is very challenging. Existing methods rely on predefined intensity and geometry priors of flares, and may fail to distinguish the difference between light sources and flare artifacts. We observe that the conditions of the light source in the image play an important role in the resulting flares. In this paper, we present a deep framework with light source aware guidance for single-image flare removal (SIFR). In particular, we first detect the light source regions and the flare regions separately, and then remove the flare artifacts based on the light source aware guidance. By learning the underlying relationships between the two types of regions, our approach can remove different kinds of flares from the image. In addition, instead of using paired training data which are difficult to collect, we propose the first unpaired flare removal dataset and new cycle-consistency constraints to obtain more diverse examples and avoid manual annotations. Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively. We also show that our model can be applied to flare effect manipulation (e.g., adding or changing image flares).

The rapid development of the Internet of Things (IoT) has brought challenges to IoT platforms for high-efficiency deployments and low-budget management. Identifying IoT devices is the prerequisite for monitoring, protecting, and managing them. Considering different providers and IoT device renovation, centralized device identification solutions require large amounts of training data and frequent model updates. Traditional solutions based on machine learning cannot preserve identification precision for the long term at a low cost in reality. In this paper, we propose a multi-level IoT device identification framework, alleviating the problem of novel class detection and large-scale updating of IoT models in IoT device identification. The proposed framework improves the usability of device identification technology in the real world. We also designed an IoT device identification method, achieving an average identification accuracy of 93.37 %. With this proposed multi-level IoT device identification framework, IoT device identification can achieve a high precision over a long time.

Kyber, an IND-CCA-secure key encapsulation mechanism (KEM) based on the MLWE problem, has been shortlisted for the third round evaluation of the NIST Post-Quantum Cryptography Standardization. In this paper, we explored the optimizations of Kyber in high-performance processors from the ARM Cortex-A series, which are widely used in mainstream mobile phones. To improve the performance of Kyber, we utilized the powerful SIMD instruction set NEON in an ARMv8-A to parallelize the core modules of Kyber, i.e., modular reduction and NTT. Specifically, we specially designed the optimized implementation based on the characteristic of the NEON instruction set for the Barrett and Montgomery reduction algorithms. To make full use of the computing power of NEON instructions, we proposed a novel strategy for computing the 16-bit Barrett reduction without handling the 32-bit intermediate result. Our Barrett and Montgomery reduction showed 8.52 and 8.89 times faster than the reference implementation. As for NTT/INTT, we adopted the 2+5 layer merging strategy on an ARMv8-A to implement NTT/INTT after carefully analyzing the register occupancy of various layer merging techniques. Thanks to the selected layer merging strategy, our NTT and INTT achieved 11.89 and 13.45 times speedups compared with the reference implementation. Our optimized software achieved 1.77×, 1.85×, and 2.16× speedups for key generation, encapsulation, and decapsulation compared with Kyber's reference implementation.

Mobile phones are becoming a common tool for people to make the payments. The current peer-to-peer mobile based payment system allows two parties to use physical location-limited interaction between their devices to facilitate secure payment. This physical location-limited interaction is ensured by enabling wireless proximity through Near Field Communication (NFC) technology embedded in mobile phones. However, the NFC technology is vulnerable to relay attacks which is a very challenging security problem. This paper proposes a novel new proximity channel based on vibration for peer-to-peer mobile payment system. The built-in vibrator and accelerometer on the phone are used to send and receive the information. The communication ensures the payment is conducted at a close distance. This paper also compares and analyse the method with the other method such as the phone vibration data and encrypt One Time Password (OTP) as the vibration pattern for payment. The result shows that the proposed method is able retrieve the information successfully from the other smartphones and guarantees the transaction is safe from relay attacks and eavesdropping.

One of the major challenges in implementing a group key establishment and management scheme to provide security solutions for group communication in the Internet of Things (IoT) is the limited resource availability of the nodes such as memory, computation, and energy. To ensure security such as confidentiality, integrity of the transmitting messages in a certain IoT group, a feasible group key establishment and management scheme is necessary which uses minimum resources but provides high scalability and strong security. In this paper, we propose a symmetric group key establishment scheme that uses the secrecy guarantee provided by One Time Pad (OTP) and performs computations like bitwise Exclusive OR (XOR) and bit shifting of randomly generated binary vectors to produce random different keys for different sessions of message transmission. We show that our scheme is lightweight to support the resource-constrained nature of IoT nodes by using only primitive operations and scalable to support the dynamic constellation of IoT network groups where nodes can join and exit frequently. We prove that our scheme is secure under a designed threat model where a similar existing scheme fails by a detailed analysis.

An unmanned aerial vehicle (UAV) network is an emerging industrial IoT network for collaborative UAV communication and management. The open architecture and dynamic topology, which provide functional benefits, unfortunately make UAVNs more vulnerable to a variety of attacks. In UAVNs, malicious nodes not only eavesdrop the communications between UAV nodes but also attempt to attack the entire network by injecting or modifying messages. This work proposes a provenance-aware distributed trust model, named UAV-pro, for UAVNs that aim to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing the message delay and communication cost under resource-constrained network environments. Provenance refers to the history of ownership of messages transmitted on the network. The behavior of message creators and operators can be effectively evaluated based on message integrity, then generate the observational evidence. We collect the observational evidence for distributed trust evaluation, then identify malicious nodes in the network and isolate them from the network. UAVN-pro takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes while ensuring the safe transmission of data by digital signature technology. The experimental results show that UAVN-pro works are compatible with the existing UAV network routing protocols, and can effectively identify attacks, such as the black hole, gray hole, message modification, fake recommendation, and fake identity in UAV networks. UAVN-pro is superior to the existing security model in terms of detection rate, delivery rate, and system energy consumption in most cases.

When authenticating a group of radio-frequency identification tags, a common method is to authenticate each tag with some challenge-response exchanges. However, sequentially authenticating individual tags one by one might not be desirable, especially when considering that a reader often has to deal with multiple tags within a limited period, since it will incur long scanning time and heavy communication costs. To address these problems, we put forward a novel efficient group authentication protocol, where a group of tags can be authenticated simultaneously with only one challenge and one response. The protocol is built on a newly designed symmetric key-based algorithm and the bit-collision pattern technique, so that authentication responses transmitted by multiple tags in a group at the same time will result in a verifiable bit-collision pattern that represents the authentication response for the entire group. The proposed approach can significantly reduce the authentication time and communication cost in the sense that the verifier can authenticate the entire group within a period that is comparable to the time taken to perform a single-tag authentication and requires only one challenge. In addition, we extend our protocol to support the privacy-preserving property, which prevents the tagged items from being tracked by illegitimate parties. A thorough security analysis shows that the proposed protocol can resist common practical attacks and experimental results show that the protocol is very efficient in terms of time and communication costs. We also discuss important practical aspects that should be considered when implementing these protocols.

Recently there has been a growing interest in short-range communication using audio channels for device pairing and as a self-jamming communication medium. Given that such channels are audible to participants they are considered more resistant to active attacks, i.e., the attack signal would be heard by the participants. In this paper, we investigate the validity of this assumption using two prominent acoustic self-jamming systems implementations. We show that basic overshadowing attacks are possible in these systems and that these attacks cannot be effectively detected by the participants if the attacker is close to the receiving device. Finally, we propose a novel physical-layer solution for effectively detecting overshadowing attacks, which can improve state-of-the-art acoustic self-jamming systems by ensuring channel integrity while not requiring fundamental modifications to these schemes.

The papers in this special section focus on sustainable and intelligent precision agriculture. Human society has experienced three industrial revolutions from mechanization, and electricity to information automation. Every industrial revolution significantly alters the form of agricultural industry from labor-intensive farming, mechanized production, precision agriculture to large-scale fine grained industrial agriculture. However, the agricultural industry at current stage still faces many challenges, such as global food security, food safety, poverty reduction, and sustainable natural resource management. Now the fourth industrial revolution is ongoing, that is characterized by a fusion of emerging technologies such as Industry 4.0, Internet of Things, Cloud/Edge Computing, Big Data, Artificial Intelligence, and Blockchain.

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

Recently, work with Distributed Ledger Technologies (DLTs) has focussed on leveraging the decentralised, immutable ledger for use outside of cryptocurrency. One industry poised to benefit from DLTs is the Industrial Internet of Things (IIoT); as the inherent cryptographic mechanisms and alternative trust model make DLTs an attractive solution for distributed networks. Existing DLTs are unsuitable for the IIoT, owing to the large computational and energy requirements for consensus operations and the slow throughput of validated blocks. With limited processing, energy and storage resources and a deadline sensitive operational environment, DLTs in their current state could serve to introduce intolerable latency into IIoT processes and deplete constrained, device resources. Designed for the IIoT context, and based off Delegated Proof of Stake, this work serves to introduce a new consensus mechanism called Delegated Proof of Proximity (DPoP). Using existing location discovery processes, nodes in close proximity to a sensor event are elected as delegates; whose role is to handle consensus and block generation. In using information already known to IIoT devices, DPoP aims to reduce wasted effort, improve throughput by limiting the number of nodes required for consensus operations and improve scalability and flexibility of DLT solutions as the IIoT network continues to grow.

Identifying non-line-of-sight (NLOS) conditions is important to discard, or improve, any location estimates that have been estimated with NLOS ranges. Typically, NLOS identification relies on channel statistics that have been collected for both LOS and NLOS channels. We investigate NLOS identification using distance residuals instead. The results show that distance residuals can be used to identify location estimates with NLOS ranges with very high accuracy, and that in some cases, individual NLOS ranges can also be identified.

Security is a key challenge for any IIoT network and more so for constrained IWSN deployments. Novel methods are thus required to enhance security, taking into account the lossy and low power nature of the IWSN. The use of ICMP ping packets is proposed and tested as a method to generate fingerprinting information for IWSN devices. The ICMP ping based method was evaluated on three different IWSN nodes in a star and multi-hop topology. The results showed that the effect of the physical layer can be averaged out of the measurement if enough samples are available. A linear relationship was found between hop count and round-trip time for a static network which can be used in the design phase of the IWSN network or alternatively as a method to fingerprint routing anomalies in real-time.

It is well known that location accuracy is low in non-line-of-sight (NLOS) scenarios. Several techniques have been proposed to mitigate ranging errors to improve location accuracy. These techniques play an important role in accurate localization for many applications which target safety and productivity in harsh industrial environments. In some cases, these mitigation techniques target specific NLOS scenarios, therefore such scenarios have to identified before mitigation can be applied. In this paper, we study identification of two types of NLOS scenarios that are common indoors: Through-the-Wall (TTW) and Around-the-Corner (ATC). We conduct a measurement campaign in various indoor TTW and ATC scenarios, and show that these two types of NLOS scenarios can be differentiated with high accuracy using channel statistics from ultra-wideband radios.

This Special Section on "Security, privacy, and trust for Industrial Internet of Things" of the IEEE Transactions on Industrial Informatics (TII) highlights the main research challenges in the industrial Internet of Things (IoT) security, privacy, and trust. The designated nine high-quality research articles cover a wide range of the special section theme, including innovative solutions and novel technologies. These articles are briefly summarized.

Physical-layer key agreement is used to generate a shared key between devices on demand. Such schemes utilize the characteristics of the wireless channel to generate the shared key from the device-to-device channel. As all characteristics are time-dependent and location-dependent, it is hard for eavesdroppers to get the key. However, most research works in this area use passive attack models whereas active attacks that aim at manipulating the channel and key are also possible. Physical-layer key agreement with User Introduced Randomness (PHYUIR) is a solution similar to the Diffie-Hellman protocol against such a kind of active attack. The users (devices) introduce their own randomness to help to prevent active attacks. In this paper, we analyze the possibility of launching a session hijacking attack on PHY-UIR to allow an attacker to control the shared key established. The session hijacking attack manipulates the key agreement through a man-in-the-middle interaction and forces legitimate devices to run the PHY-UIR protocol with the attacker. Our simulation and experiment results validate our attack and show the high performance of our attack on manipulating the generated key. We also propose PHY-UIR± where devices simultaneously exchange information about the established shared keys, which allows them to detect whether they have agreed to different keys with a third party.

Some recent survey statistics suggest that low power wide area networks (LPWANs) are fast becoming the most prevalent communication platform used in many applications of the Internet of Things (IoT). However, because most LPWANs are generally deployed in the presently congested industrial, scientific, and medical bands, they are invariably plagued by problems associated with spectral congestion, such as increased interference, reduced data rates, and spectra inefficiency. These problems are solvable by integrating cognitive radio (CR) technologies in LPWAN (termed CR-LPWAN), for which some pioneering solutions now exist in the literature. Consequently, this article takes an early look at some of these pioneering efforts pertaining to the development of CR-LPWAN systems. We discuss a general network architecture and a physical layer front-end model suitable for CR-LPWAN systems. Then, some notable state-of-the-art approaches for CR-LPWAN systems are discussed. Potential advantages of CR-LPWAN systems for IoT-based applications are also presented, and this article closes with a few research challenges and future research directions in this regard. This article aims to serve as a starting point for most budding researchers who may be interested in the development of effective and efficient CR-LPWAN systems for the enhancement of different IoT-based applications.

Accurate ranging and localization in non-line-of-sight (NLOS) conditions is an open research problem. In indoor environments, NLOS causes ranging and location errors due to reflections, refraction, and other propagation phenomena. This article proposes a method to mitigate ranging errors caused by through-the-wall (TTW) NLOS conditions. We develop a TTW ranging model that has less parameters than the conventional ranging model found in the literature and evaluate it using measurements from ultrawideband radios. The developed ranging model expresses the difference between NLOS and line-of-sight (LOS) ranges, i.e., bias, in terms of walls' relative permittivity and thickness. NLOS ranges expressed with the proposed TTW ranging model are used as input to a trilateration algorithm. The proposed method is evaluated via simulations, and it is shown that localization using the simplified ranging model as input to the trilateration algorithm effectively mitigates NLOS ranging errors, resulting in location estimates that are close to LOS location estimates.

The ability for the smart microgrid to allow for the independent generation and distribution of electrical energy makes it an attractive solution towards enabling universal access to electricity within developing economies. Distributed Ledger Technologies (DLTs) are being considered as an enabling technology for the secure energy trade market however the high processing, energy and data exchange requirements may make them unsuitable for the Industrial Internet of Things technologies used in the implementation of the microgrid and the limited connectivity infrastructure in developing technologies. This work serves to assess the suitability of DLTs for IIoT edge node operation and as a solution for the microgrid energy market by considering node transaction times, operating temperature, power consumption, processor and memory useage, in addition to mining effort and end user costs.

Relay attacks is a serious and practical threat to contactless systems that is becoming more attractive with the increased use and transaction value of contactless payments. Distance-bounding is a countermeasure widely studied in academic research but it has not transferred to commercial systems. Conventional communication channels cannot achieve the theoretical properties often required for the protocol to be secure and special channels might not be feasible to adopt. Our objective is to design `best-effort' distance-bounding channels suitable for real RFID systems. In this paper we discuss standard channel implementation using off-the-shelf emulators and readers, and discuss potential modifications to channels implementable on current devices that would results in more relay-resistant channel designs. We are present reference relay attacks for ISO 14443A and Felica type systems using our implemented devices.

This paper draws attention to techniques available for the minimization of control overhead in software-defined wireless sensor networks (SDWSNs). Software-defined networking (SDN) promises improved management flexibility and control for inherently resource-constrained and heterogeneous wireless sensor network (WSN) implementations. However, due to the in-band traffic channel available for data and control traffic in SDN-based WSNs, overhead control traffic has been viewed as a bottleneck affecting network performance and controller responsiveness. A discussion on the need for control message quenching (CMQ) and the various categories of CMQ implementations in SDWSN is made in this paper. Furthermore, a CMQ algorithm based on reducing duplicate flow request packets is discussed and demonstrated for implementation in an SDN-based WSN framework. Results show a significant reduction in control overhead traffic and noticeable improvement in energy efficiency. However, trade-offs in terms of packet delivery rate and packet delay are also observed as a result of the CMQ algorithm. A discussion on future work necessary to optimize CMQ algorithms in order to minimize the associated trade-offs is also made.

In this paper, we have discussed the integration of Cognitive Radio (CR) in Low Power Wide Area Network (LPWAN) based on a generic network architecture and a PHY layer front-end model. Essentially, since most existing LPWAN technologies are proprietary in nature, it is necessary to present insights that may spur newer developments to enhance many Internet of Things (IoT)-based applications, including Industrial IoT (IIoT) applications such as smart factories, smart metering, and smart city architectures. Generally, this paper will benefit researchers who may be seeking to develop CR-LPWAN systems towards enhancing IoT-based applications.

Accurate indoor positioning is challenging due to non-line-of-sight (NLOS) conditions which cause ranging and location errors. This paper proposes an iterative algorithm to improve location accuracy in NLOS conditions caused by through-the-wall (TTW) propagation. The proposed algorithm uses a floor plan with details of walls' locations and dimensions. The algorithm reduces the NLOS range between a tag and anchors iteratively, resulting in a final location that is closer to the tag's true location than the NLOS estimated location. It is shown that the algorithm can reduce the impact of NLOS conditions on location accuracy significantly.

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Software-defined wireless sensor networks (WSNs) are a new and emerging network paradigm that seeks to address the impending issues in WSNs. It is formed by applying software-defined networking to WSNs whose basic tenet is the centralization of control intelligence of the network. The centralization of the controller rouses many challenges such as security, reliability, scalability, and performance. A distributed control system is proposed in this paper to address issues arising from and pertaining to the centralized controller. Fragmentation is proposed as a method of distribution, which entails a two-level control structure consisting of local controllers closer to the infrastructure elements and a global controller, which has a global view of the entire network. A distributed controller system brings several advantages and the experiments carried out show that it performs better than a central controller. Furthermore, the results also show that fragmentation improves the performance and thus have a potential to have major impact in the Internet of things.

The tracking-by-detection framework receives growing attention through the integration with the convolutional neural networks (CNNs). Existing tracking-by-detection-based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. The extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against the state-of-the-art methods.

There is a need for robust positioning systems that can operate in noisy environments such as in hospitals. Although there have been several systems proposed in literature, most systems are aimed at conventional environments such as office buildings or shopping malls. In this paper, a robust ultrasound based positioning system aimed specifically at noisy environments is developed. The system consists of nodes equipped with RF transceivers and ultrasound transducers and estimates the location of mobile tags using time-of-arrival. It is shown that the accuracy attained is better than 35 cm at an update rate of 3 seconds in both line-of-sight and non-line-of-sight conditions.

This paper presents the development a smartphone user authentication system which takes advantage of the device's pre-existing hardware. The authentication was based on a smartphone user's gait pattern which is a biometric feature. If the authentication outcome is positive the authentication process continues uninterrupted in the background. If the authentication fails, the device's location information should be sent to a predetermined email address to notify the authorized user of the device's whereabouts. While the performance of the proposed scheme is promising, it does need to be improved in order for the system to become practically viable.

In this paper we present an overview of the various proximity based authentication mechanisms that can be used in the Industrial Internet of Things (IIoT). We seek to identify and highlight from a holistic point of view which mechanisms can enable proximity based authentication for the Industrial Internet of Things. In addition, we identify which upcoming proximity authentication mechanisms are most important for the proliferation of the Industrial Internet of Things, and highlight major obstacles that remain unsolved with regard to authentication. In answering this, we present seven mechanisms for proximity based authentication (i.e. wire, radio, acoustics, light, image, gesture and biometrics) and discuss each mechanism in perspective of their vulnerability to different kind of attacks (such as eavesdropping, impersonation and denial of service attacks etc.) and their usability (such as proximity range, hardware requirement and ease of use) in terms of the practicality in IIoT environment in the light of which we present two typical IIoT use cases that require proximity based authentication.

Cognitive radio and dynamic spectrum access can reform the way that radiofrequency spectrum is accessed. Problems of spectrum scarcity, coexistence, and unreliable wireless communication that affect industrial wireless networks can be addressed. In this paper, a game theoretic dynamic spectrum access algorithm that improves upon on a hedonic coalition formation algorithm for spectrum sensing and access is presented. The modified algorithm is tailored for faster convergence and scalability and makes use of a novel simultaneous multichannel sensing and access technique. Results to demonstrate the performance improvements of the adapted algorithm are presented and the use of different decision rules are investigated revealing that a conservative decision rule for exploiting spectrum opportunities performs better than an aggressive decision rule in most scenarios. The algorithm that was developed could be a key enabler for future cognitive radio networks.

Authentication in wireless communications often depends on the physical proximity to a location. Distance-bounding (DB) protocols are cross-layer authentication protocols that are based on the round-trip-time of challenge-response exchanges and can be employed to guarantee physical proximity and combat relay attacks. However, traditional DB protocols rely on the assumption that the prover (e.g., user) is in the communication range of the verifier (e.g., access point); something that might not be the case in multiple access control scenarios in ubiquitous computing environments as well as when we need to verify the proximity of our two-hop neighbour in an ad-hoc network. In this paper, we extend traditional DB protocols to a two-hop setting, i.e., when the prover is out of the communication range of the verifier and thus, they both need to rely on an untrusted in-between entity in order to verify proximity. We present a formal framework that captures the most representative classes of existing DB protocols and provide a general method to extend traditional DB protocols to the two-hop case (three participants). We analyze the security of two-hop DB protocols and identify connections with the security issues of the corresponding one-hop case. Finally, we demonstrate the correctness of our security analysis and the efficiency of our model by transforming five existing DB protocols to the two-hop setting and we evaluate their performance with simulated experiments.

For a wide range of applications in industry, it is sometimes necessary to perform acoustic source localization. In this paper, a passive sound localization and classification system is designed and implemented. Each sensor consists of a microphone array which is used to detect the direction-of-arrival (DoA) of an acoustic signal. Multiple DoA sensors can be combined to form a wireless sensor network. The system can detect the acoustic signature of power tools and the effectiveness of the system to be used as an early warning system to detect misuse of machinery is demonstrated. It is shown that the system can detect the DoA of an acoustic signal with an overall mean estimation error of 7° and can correctly classify the signal source with a classification rate of 71.5%.

Software Defined Networking (SDN) is a developing networking paradigm that advocates a complete overhaul of the conventional networking. SDN decouples the control logic from the data forwarding functionality; which traditionally are coupled on the network device. The coupling stifles innovation and evolution because the network often becomes rigid. Software Defined Wireless Sensor Networks (SDWSN) is also an emerging network paradigm that infuses the SDN model into Wireless Sensor Networks (WSNs). WSNs have inherent constraints such as energy, memory etc. which have been a major hindrance of their progress. The application of SDN model in WSN is set to cultivate the potential of WSNs in modern communication and to bring about the efficiency that the WSNs have not yet achieved due to their inherent constraints. SDN based networks are anchored on the central controller for functionality. As the network scale up, issues of scalability, reliability and congestion arises and for that a distributed controllers are proposed. This paper investigates the viability of a distributed control system for SDWSN. The test results conducted show that it is viable to deploy a distributed control system for SDWSN; however an improvement is needed on the efficiency.

Wireless localization has been the focus of research due to the wide range of applications it can enable, particularly in industrial hazardous areas where warning systems are crucial for safety. Although there are a number of different technologies available for indoor localization such as Wi-Fi and ultra-wideband, Bluetooth based localization is usually less expensive to deploy and all smartphones have the ability to receive Bluetooth signals. In this paper, a Bluetooth Low Energy (BLE) location system is proposed. The system consists of a number of beacons that serve as fixed references and a smartphone that serves as a mobile tag. Results show that the system can locate a mobile tag (i.e. smartphone) with an average location error of 1.8 m at an update rate higher than 0.5 seconds.

There are increasing preferences to employ wireless communication technologies for high mobility, high scalability and low-cost applications in smart city development. This paper gives a brief synopsis of typical wireless technologies in smart city applications and the comparison analysis between them. The trend for smart city wireless technology is also presented. Examples, for several key applications within smart city development (healthcare, smart grid, localization) are studied and current advanced solutions supporting these applications are summarized with futuristic trends and demands are presented.

Localization is important for a wide range of industries and applications. In underground mining, for instance, it is helpful to know the miners' locations, particularly in emergency situations. One way this can be achieved is by using ultrasonic based localization. This paper presents the design and implementation of a wireless sensor network which can be deployed in underground mines to perform time-difference-of-arrival based localization. It is shown that the implemented ultrasound receivers are resistant to noisy conditions that may arise in harsh underground environments.

Recent breakthroughs in wireless technologies have greatly spurred the emergence of industrial wireless sensor networks (IWSNs). To facilitate the adaptation of IWSNs to industrial applications, concerns about networks' full coverage and connectivity must be addressed to fulfill reliability and real-time requirements. Although connected target coverage (CTC) algorithms in general sensor networks have been extensively studied, little attention has been paid to reveal both the applicability and limitations of different coverage strategies from an industrial viewpoint. In this paper, we analyze characteristics of four recent energy-efficient coverage strategies by carefully choosing four representative connected coverage algorithms: 1) communication weighted greedy cover; 2) optimized connected coverage heuristic; 3) overlapped target and connected coverage; and 4) adjustable range set covers. Through a detailed comparison in terms of network lifetime, coverage time, average energy consumption, ratio of dead nodes, etc., characteristics of basic design ideas used to optimize coverage and network connectivity of IWSNs are embodied. Various network parameters are simulated in a noisy environment to obtain the optimal network coverage. The most appropriate industrial field for each algorithm is also described based on coverage properties. Our study aims to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications.

Due to the rise of wireless networked cameras, video surveillance is ubiquitously in many applications. A Wireless Ad-hoc NETwork (WANET)-based video surveillance system could allow clients to access and request video content from a group of cameras. Hence, efficient many-to-many (i.e. from multiple cameras to multiple clients) video streaming is a fundamental issue. Scalable Video Coding (SVC) encodes a video stream into layers (i.e., sub-streams) to support video scalability, which provides a promising solution for efficient video streaming. Existing works generally focused on the Quality-of-Service (QoS) in scalable video streaming, while energy efficiency was not sufficiently studied. Since wireless nodes are always battery powered with limited energy capacity, overusing these nodes may result in some video services stopping early. Therefore, in this paper, we investigate an Energy Efficient Scalable Video Manycast problem (E2SVM) in WANET, which is to achieve batter trade-off between video QoS and energy efficiency. In order to solve the problem, we propose a multitree routing scheme. Simulation results show that the proposed scheme can improve both video QoS and service time.

Monitoring systems are necessary in buildings to monitor the working environment. This paper describes the development of a wireless monitoring system which can be deployed in a building. The system measures carbon dioxide, carbon monoxide and temperature. The system developed in this paper can serve as the monitoring component of a HVAC control system and function as an indoor air quality monitor independently.

Accurate indoor localization requires non line-of-sight ranging error identification and mitigation. Current research has focused on methods that improve accuracy but are too complex to be executed in resource-constrained hardware in real-time. In this paper, we propose and evaluate a non line-of-sight mitigation technique using impulse radio ultra-wideband (IR-UWB) experimental ranging data. We show that using this technique, ranging non-line-of-sight (NLOS) errors can be significantly reduced in the location estimate. The method is less complex than other methods used in related work and can potentially be implemented on microcontrollers.

The concept of data distribution within cluster of sensor nodes to the source sink has resulted to intense research in Wireless Sensor Networks (WSNs). In this paper, in order to determine the scheduling length of packet distribution, a tree-based network topology is constructed indicating the distribution of various sensor nodes within a specific coverage area (CAi). To evaluate the performance of various channel assignment methods; Receiver-Based Channel Assignment (RBCA), Tree-Based Multichannel Protocol (TMCP), and Capacitated Minimal Spanning Tress (CMSTs), time slot assignment scheme is used in developing the various channel assignments. The performance of packet distribution based on the assignment schemes are evaluated using appropriate simulation tool for the tree-based network topology.

We propose a user-centric private key management framework that supports future personal sensing applications. This framework allows for a simple distributed private key system with embedded secure key storage and recovery in multiple parties. The user maintains control over implementation choices and the ability to consent to data being utilised or transmitted by the system. Our proposed framework could be used to secure user-driven data collection. We show our idea to be feasible by demonstrating that it can be implemented using existing key management and software tools.

In emergency situations such as fires in homes and offices, it is often the case where members of a response team (eg. firefighters) are not able to locate each other due to fire and smoke. It is desirable to have a system which is able to locate all personnel such that a team leader can monitor their location and to enable the firefighters themselves to know their proximity to other firefighters. This paper discusses the design and implementation of an impulse-radio ultra-wideband sensor network that can be easily deployed around the perimeter of a building and can be used to locate personnel indoors. It is found that the ranging accuracy of the sensor network meets the accuracy requirements but has limited range, especially through very thick walls.

The papers in this special section focus on advancements in healthcare technologies and services. THE demand and market of healthcare services are increasing exponentially. This is due to the problem of aging and the reformation of healthcare services. The aging problem is a serious global issue that after 30 years, the portion of the elderly who are aged at least 60 years old will be 20% of the world’s population. The human immune system becomes weaker with aging and the elderly are more likely to suffer various injuries and diseases such as cancer, cardiovascular diseases, diabetes, and respiratory infection. Medical services such as hospitals and clinics are facing critical challenges to the quality of services and the capability of handling patients. To reform modern healthcare services, one solution is to realize smart healthcare in the concept of the smart city. There are four key components in futuristic smart healthcare, which are smart sensors, healthcare network, anomaly detection algorithm, and robot-assisted medical services.

Camera sensor networks (CSNs) are gradually being used in a tele-intensive care unit (tele-ICU), providing useful patient information to remote intensivists. Intensivists wish to focus on different regions of interest (RoIs) containing their patients. We consider a situation where preinstalled camera sensors' locations remain static and they can change the fields of view only by rotating orientations, while the RoIs are dynamically changed in location and size because of changes in the number of patients and care unit configuration. Therefore, an important issue is how to enhance the coverage of these RoIs by controlling the camera sensors' orientations. Previous studies on coverage optimization either focus on single area coverage or point(s) coverage. However, ignoring those multiple RoIs or simply treating them as points can cause unwanted coverage, resulting in performance degradation. In this paper, we investigate a novel multiple RoI coverage (MRC) problem in a CSN-based tele-ICU, aiming to maximize the lowest coverage ratio of all RoIs. The MRC problem is nondeterministic polynomial-time hard, so we propose an efficient heuristic algorithm MRC-Priority to solve it. We have implemented a CSN testbed to evaluate the performance of our proposed algorithm. Experimental results show that our proposed algorithm can improve the lowest coverage ratio up to 200% as compared with existing solutions.

Impulse radio ultrawideband ranging has recently received significant attention due to the high accuracy it can achieve. Although most research efforts have focused on ranging in indoor and outdoor environments, other environments such as harsh industrial environments introduce unique challenges. This paper discusses the impact of propagation characteristics of harsh industrial environments on ranging accuracy, and also discusses principles and challenges of non-line-of-sight identification in industrial scenarios. To illustrate these challenges, a measurement campaign using 802.15.4a radios was conducted in a Heavy Machines Laboratory. The results show that the non-line-of-sight condition can be accurately identified if adequate models for such an environment are used.

Video surveillance is an essential tool for many security-related applications but video is also increasingly used in combination with computer vision technologies in other sensing applications. With the growth of installed camera infrastructure and development it would be beneficial to enable video surveillance systems to support many application simultaneously. Service-oriented Architecture (SoA) is a prospective solution to implement such a multi-application surveillance system, which can provide video data and processing methods to different applications based on their requirements. However, such a system presents several challenges, which needs to be addressed and considered along with the constrains on the system resources. In this paper, we propose a framework for a SoA based video surveillance system with efficient system resource management, including device, data and network management. Our contribution is to present the benefits of such an approach and highlight the research challenges that need to be addressed to realise such a system.

Automation systems are gaining popularity around the world. The use of these powerful technologies for home security has been proposed and some systems have been developed. Other implementations see the user taking a central role in providing and receiving updates to the system. We propose a system making use of an Android based smartphone as the user control point. Our Android application allows for dual factor (facial and secret pin) based authentication in order to protect the privacy of the user. The system successfully implements facial recognition on the limited resources of a smartphone by making use of the Eigenfaces algorithm. The system we created was designed for home automation but makes use of technologies that allow it to be applied within any environment. This opens the possibility for more research into dual factor authentication and the architecture of our system provides a blue print for the implementation of home based automation systems. This system with minimal modifications can be applied within an industrial application.

Impulse radio ultra-wideband (IR-UWB) ranging and localization have recently received significant attention due to the high accuracy and precision. This high accuracy can be exploited for many location aware applications in a wide variety of environments. Although most research efforts have focused on location-aware applications for indoor or outdoor environments, there are other environments, such as industrial, which introduce unique challenges. In an effort to understand how industrial environments affect IR-UWB based ranging and localization performance, this paper introduces the principles behind ranging in industrial environments and highlights practical challenges which have an impact on the ranging accuracy of IR-UWB. Results from field tests which illustrate the impact of harsh conditions on the ranging accuracy are presented.

This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram. Then, these discrete cosine transform coefficients are scaled down according to their frequency indices. These scaled discrete cosine transform coefficients of each electrocardiogram are employed as features for performing the discrimination. Second, the high order statistic moments of each feature of the training electrocardiograms corresponding to the same type of tachycardias are evaluated. These high order statistic moments of each feature corresponding to same type of tachycardias form a vector. Third, the high dimensional linear discriminant function is employed to minimize the intraclass separation and maximize the interclass separation of these statistic moment vectors. In particular, new vectors are formed by projecting these statistic moment vectors to the high dimensional linear discriminant function. Fourth, the principal component analysis is employed to reduce the dimension of the projected vectors. Finally, a bank of perceptrons with multi-piece domain activation functions having multi-level functional values is employed for performing the discrimination. By using this bank of perceptrons, the condition for general two class pattern recognition problems achieving the error free pattern recognition performance is guaranteed. Computer numerical simulation results show that our proposed method is robust and effective.

Authentication for resource-constrained devices is seen as one of the major challenges in current wireless communication networks. The HB+ protocol performs device authentication based on the learning parity with noise (LPN) problem and simple computational steps, that renders it suitable for resource-constrained devices such as radio frequency identification (RFID) tags. However, it has been shown that the HB+ protocol as well as many of its variants are vulnerable to a simple man-in-the-middle attack. We demonstrate that this attack could be mitigated using physical layer measures from distance-bounding and simple modifications to devices' radio receivers. Our hybrid solution (HB+DB) is shown to provide both effective distance-bounding using a lightweight HB+-based response function, and resistance against the man-in-the-middle attack to HB+. We provide experimental evaluation of our results as well as a brief discussion on practical requirements for secure implementation.

Industrial wireless sensor networks (IWSNs) have to contend with environments that are usually harsh and time-varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilized by many other wireless technologies. With wireless applications rapidly growing, it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and coexisting with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio (CR) methods offer a potential solution to improve resistance of IWSNs to interference. Integrating CR principles into the lower layers of IWSNs can enable devices to detect and avoid interference, and potentially opens the possibility of utilizing free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarize CR methods relevant to industrial applications, covering CR architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access (DSA), game theory, and CR network (CRN) security.

Wireless underground sensor networks (WUSNs) are a category of wireless sensor networks (WSNs) with buried nodes, which communicate wirelessly through soil with sensor nodes located aboveground. As the communication medium (i.e., soil) between traditional over-the-air WSNs and WUSNs differs, communication characteristics have to be fully characterized for WUSNs, specifically to enable development of efficient communication protocols. Characterization of link quality is a fundamental building block for various communication protocols. The aim of this paper is to experimentally investigate the link quality characteristics of the three communication channels available in WUSNs for underground pipeline monitoring to gain further insight into protocol development for WUSNs. To this end, received signal strength (RSS), link quality indicator (LQI), and packet reception ratio (PRR) are characterized for the three communication channels in WUSNs. The RSS and PRR results show that the underground-to-underground channel is highly symmetric and temporally stable, but its range is severely limited, and that the aboveground-to-underground/underground-to-underground channels are asymmetric and exhibit similar temporal properties to over-the-air communication channels. Interestingly, the results show that RSS is a better indicator of PRR than LQI for all three channels under consideration.

In this paper, a wireless sensor network based measurement system was proposed to conduct real-time measurement of energy consumption of household appliances and display this information on a graphical user interface on a computer. The system was also able to calculate an optimized schedule for the appliances to reduce energy cost in terms of time of use (TOU) tariffs. The sensor node and sink node has been developed in compliance with IEEE1451.2 and IEEE802.15.4 standards. And also the current and voltage sensors were also designed from first principals. The Zigbee series II RF transceivers employing the Zigbee communication protocol was used for communication and forming of the wireless sensor network. We have created a user interface able to display real time and historic energy consumption of appliances from IEEE1451.1 standard. We have created an optimization algorithm which scheduled appliance operation times within user set allowable operation times according to TOU tariffs.

In various industrial automation applications, positioning enables location awareness which can be exploited in applications such as robotic precision control, amongst others. This paper discusses positioning for industrial automation applications. Examples of applications from an infrastructure perspective are presented, and the integration of high precision positioning technology into existing network infrastructure for such applications is discussed, as well as challenges involved. An impulse-radio ultra-wideband 802.15.4a based system for high accuracy/precision positioning is presented, which shows promising results, therefore motivating the use of UWB as a high accuracy and precision technology for positioning applications in future industrial automation systems.

The use of commercial off-the-shelf components for implementing Internet of Things devices has become a common practice amongst researchers and solution providers. IOT solutions, based on the Raspberry Pi, BeagleBone and BeagleBone Black, offer cost effective, versatile and uncomplicated platforms for rapid application development. The devices are treated as black box devices and little work has been done to quantify the performance of these devices when the system architecture, software components or communication channels are varied. This paper introduces micro- and macro-benchmarking methods for these devices; quantifying the performance of each device for the varying hardware architectures. Micro-benchmarking was performed using lmbench - a cross platform benchmarking framework for UNIX devices. The macro-benchmarking was implemented using a custom developed CoAP benchmarking utility created using the libCoAP library. The results showed that the selection of the platform processor is a key design requirement and has the most potential to optimise CoAP server performance. The latency associated with the communication channels was found to be a dominating factor for round-trip times associated with CoAP requests.

The authors of this paper explore the use of IPv6 over Low power Wireless Personal Area Networks (6LoWPAN), IPv6 Routing Protocol for Low power and Lossy Networks (RPL) and Constrained Application Protocol (CoAP) as a possible solution for realising the Internet of Things (IOT) vision in Industrial Wireless Sensor Networks (IWSNs), The aim of this paper is to investigate the feasibility of using Internet Engineering Task Force (IETF) standards in industrial environments by identifying and quantifying several attributes of a 6LoWPAN, RPL and CoAP based IWSNs relating to bounded time interval communications. The paper identifies several possible causes of latency in IWSNs and can be used as a basis for deploying Internet Protocol (IP) based IWSNs requiring IOT connectivity.

The eight papers in this special section focus on industrial wireless sensor networks.

Dual Radio Streaming ZigBee Homecare Gateway (DRS-ZHG) was devised and implemented to support remote medical services. The novelty of DRS-ZHG is increases the transmission data rate of ZigBee. Consequently, the DRS-ZHG design furnishes low latency and highly accurate telehealth service at home. More important, Zero packet loss has been recorded during the functional testing of DRS-ZHG,

The need for implementing adequate security services in industrial applications is increasing. Verifying the physical proximity or location of a device has become an important security service in ad-hoc wireless environments. Distance-bounding is a prominent secure neighbor detection method that cryptographically determines an upper bound for the physical distance between two communicating parties based on the round-trip time of cryptographic challenge-response pairs. This paper gives a brief overview of distance-bounding protocols and discusses the possibility of implementing such protocols within industrial RFID and real-time location applications, which requires an emphasis on aspects such as reliability and real-time communication. The practical resource requirements and performance tradeoffs involved are illustrated using a sample of distance-bounding proposals, and some remaining research challenges with regards to practical implementation are discussed.

RFID systems often use near-field magnetic coupling to implement communication channels. The advertised operational range of these channels is less than 10 cm and therefore several implemented systems assume that the communication channel is location

Cryptographic distance-bounding protocols verify the proximity of two parties by timing a challenge-response exchange. Such protocols rely on the underlying communication channel for accurate and fraud-resistant round- trip-time measurements, therefore the channel's exact timing properties and low-level implementation details become security critical. We practically implement 'late-commit' attacks, against two commercial radio receivers used in RFID and sensor networks, that exploit the latency in the modulation and decoding stages. These allow the attacker to extend the distance to the verifier by several kilometers. We also discuss how 'overclocking' a receiver can make a prover respond early. We practically implement this attack against an ISO 14443A RFID token and manage to get a response 10 µs earlier than normal. We conclude that conventional RF channels can be problematic for secure distance-bounding implementations and discuss the merits and weaknesses of special distance-bounding channels that have been proposed for RFID applications.

The number of RFID devices used in everyday life has increased, along with concerns about their security and user privacy. This paper describes our initial findings on practical attacks that we implemented against 'proximity' (ISO 14443 A) type RFID tokens. Focusing mainly on the RF communication interface we discuss the results and implementation of eavesdropping, unauthorized scanning and relay attacks. Although most of these attack scenarios are regularly mentioned in literature little technical details have been published previously. We also present a short overview of mechanisms currently available to prevent these attacks

Radio-frequency identification tokens, such as contactless smartcards, are vulnerable to relay attacks if they are used for proximity authentication. Attackers can circumvent the limited range of the radio channel using transponders that forward exchanged signals over larger distances. Cryptographic distance-bounding protocols that measure accurately the round-trip delay of the radio signal provide a possible countermeasure. They infer an upper bound for the distance between the reader and the token from the fact that no information can propagate faster than at the speed of light. We propose a new distance-bounding protocol based on ultra-wideband pulse communication. Aimed at being implementable using only simple, asynchronous, low-power hardware in the token, it is particularly well suited for use in passive low-cost tokens, noisy environments and high-speed applications.