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Indoor localization is one of the key techniques for location-based services (LBSs), which play a significant role in applications in confined spaces, such as tunnels and mines. To achieve indoor localization in confined spaces, the channel state information (CSI) of WiFi can be selected as a feature to distinguish locations due to its fine-grained characteristics compared with the received signal strength (RSS). In this paper, two indoor localization approaches based on CSI fingerprinting were designed: amplitude-of-CSI-based indoor fingerprinting localization (AmpFi) and full-dimensional CSI-based indoor fingerprinting localization (FuFi). AmpFi adopts the amplitude of the CSI as the localization fingerprint in the offline phase, and in the online phase, the improved weighted K-nearest neighbor (IWKNN) is proposed to estimate the unknown locations. Based on AmpFi, FuFi is proposed, which considers all of the subcarriers in the MIMO system as the independent features and adopts the normalized amplitudes of the full-dimensional subcarriers as the fingerprint. AmpFi and FuFi were implemented on a commercial network interface card (NIC), where FuFi outperformed several other typical fingerprinting-based indoor localization approaches. [ABSTRACT FROM AUTHOR]

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. [ABSTRACT FROM AUTHOR]

Recently radio frequency identification (RFID) is promoting the rapid development of Internet of Things and has been widely applied in numerous industrial applications such as inventory management, object tracking and smart logistics, etc. Tags in some RFID applications can be classified into multiple categories according to the types of objects they are attached to. One of the important functionalities for the multi-category RFID systems is the missing tag cardinality estimation of each category. This paper focuses on solving the missing tag iceberg query problem for multi-category RFID systems, i.e., to determine a set of categories, whose missing tags are more than a threshold, with a required reliability. We firstly propose two basic missing tag i ceberg query schemes called MAC-SZE and MAC-HZE, which adopt the singleton-zero estimator and homogeneous-zero estimator, respectively. To further improve the query efficiency, we then propose two segmented enhanced schemes called MAC-SSZE and MAC-SHZE, which eliminate the unnecessary slots in each frame. We conduct theoretical analysis to guarantee the required reliability is satisfied. The simulations are finally conducted and the results illustrate that the proposed missing tag iceberg query schemes greatly outperform other existing one. [ABSTRACT FROM AUTHOR]

In many radio-frequency identification (RFID) applications, one of the essential systematic functionalities is to quickly detect missing-tag event in case of misplacement or other incorrect operations. In this paper, we focus on probabilistically detecting the missing tags for the anonymous multicategory RFID systems without revealing the tag privacy. The main objective is to minimize the detection time while satisfying the required detection reliability of each category. First, we propose to use a multihash technique to sequentially detect the missing tags category-by-category, called segmented sequential detection approach, in which the frame segmentation is adopted to reduce the detection time. Then, we propose an enhanced segmented sequential detection approach to further improve the detection efficiency by deactivating the identified existing tags. We conduct extensive simulations to illustrate the effectiveness of our proposed two missing tag detection approaches. [ABSTRACT FROM AUTHOR]

Delay-tolerant networks (DTNs) are an emergent communication paradigm characterized by intermittent connectivity. The nodes in DTNs can take advantage of their contact opportunities to forward messages. However, in noncooperative DTNs, the nodes may be selfish and reluctant to cooperate with each other in message forwarding. In such DTNs, stimulating cooperation among the nodes will be indispensable. Recently, many incentive mechanisms have been proposed to motivate nodes to cooperate in message forwarding. However, most of them cannot guarantee systematic security. To resolve the drawback of the previous incentive mechanisms, we first propose a credit-based rewarding scheme called the earliest path singular rewarding (EPSR) scheme to motivate the nodes to truthfully forward the messages during every contact opportunity. Then, we propose another credit-based rewarding scheme called the earliest path cumulative rewarding (EPCR) scheme by further considering that a node may get more contact information on others. We prove that both the EPSR and EPCR schemes are incentive compatible, and the payment for each delivered message is upper bounded. Furthermore, the proposed schemes can prevent selfish nodes having malicious behaviors. We have conducted real-trace-based simulations to illustrate the effectiveness of the proposed EPSR and EPCR schemes. [ABSTRACT FROM AUTHOR]

The acquisition and analysis of large amounts of mobile trajectory data promote the rapid development of mobile recommender systems, which can essentially recommend a series of positions to the mobile users. In this paper, we concentrate on the mobile sequential recommendation problem by considering both the distance and time factors, the objective of which is to recommend an optimal driving route consisting of a sequence of pick-up locations for the drivers. Firstly, we formulate this problem and develop a potential travel cost function to comprehensively evaluate each candidate route. Then we propose an efficient and effective R oute recommendation based on T emporal- S patial metric scheme called RTS, which adopts the simulated annealing algorithm and can achieve real-time route recommendation without severely sacrificing the recommendation performance. The extensive experiments based on both the real-world and synthetic data are conducted, the results of which demonstrate the efficiency and effectiveness of the proposed RTS scheme. [ABSTRACT FROM AUTHOR]

The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users' sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the object's information in hundreds of spectral bands. However, how to make full advantage of spatial and spectral information from a large number of spectral bands to improve the performance of HSI classification remains an open question. Many HSI classification works have recently been reported by employing multi-view learning (MVL) algorithms and have achieved promising results. Generally, MVL based HSI classification can be divided into three steps, i.e. (1) multi-view construction, (2) interactivity enhanced, and (3) multi-view fusion. This paper presents a review of MVL methods in HSI classification based on the general steps of MVL. Specifically, multi-view construction builds various representations from the raw HSI data as different views to adapt to an MVL setup. Secondly, interactivity enhanced aims to interact with different view features, so that the current view contains information from other views and to achieve a pre-fusion effect. Finally, multi-view fusion uses different fusion methods to combine multiple views and classify HSIs using complementary information between the views. In addition, we analyzed and discussed separately representative approaches in each step and their characteristics, and introduced some of the most advanced work. Overall, this survey aims to provide an insightful overview of developments in MVL in HSI classification and help researchers identify its future trends. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) has now become an effective paradigm to collect massive data for various sensing applications. However, the interactions between mobile users and the platform, and the data release to third parties, pose severe challenges of privacy leakage for MCS systems, such as the leakage of users' identities and locations. Although several works on MCS have explored the privacy issues in task allocation, incentive, and data reporting, there is still a lack of a comprehensive privacy preserving framework for MCS to protect the privacy of users throughout users' involvement in crowdsensing tasks. In this article, we divide the life cycle of each crowdsensing task in MCS into four phases: task allocation, incentive, data collection, and data publishing, and design a privacy-preserving framework for MCS to protect users' privacy in the whole life cycle of MCS. [ABSTRACT FROM AUTHOR]

With the ubiquitous usage of mobile devices, we are witnessing the emergence of commercial crowdsourcing applications that leverage the power of the crowd (workers) to collect massive data. However, the participation unbalance problem commonly occurs in existing location-dependent mobile crowdsourcing applications as workers tend to select nearby tasks while far away tasks are ignored. In this article, we propose a novel task bundling based incentive mechanism that dynamically bundles tasks with different popularity together to solve the participation unbalance problem. We consider the continuous sensing scenarios and categorize tasks into high-popularity (hot) tasks and low-popularity (cold) tasks at each round according to the real-time participation situation of tasks at the last round. We then formulate the task bundling problem as a multi-objective optimization problem, and propose a dynamic task bundling algorithm that dynamically bundles cold tasks with hot tasks at each round. The experimental results demonstrate that the bundling incentive mechanism has a more balanced participation for location-dependent tasks in mobile crowdsourcing systems. [ABSTRACT FROM AUTHOR]

Abstract: Cryogenic air separation is a high‐energy consumption process and contributes significantly to greenhouse gas emissions. Reduction potentials of the CO2 emissions for both the full‐tower heat‐integrated air separation columns (F‐HIASC) and the top‐tower heat‐integrated air separation columns (T‐HIASC) are investigated. The optimization models of the minimum CO2 emissions are further proposed. The unit CO2 emissions of F‐HIASC and T‐HIASC can be decreased significantly compared to the conventional air separation columns (CASC). Furthermore, the capital cost and total annual cost are analyzed so as to evaluate properly the economic feasibility of HIASC. The results demonstrate that the total annual costs of F‐HIASC and T‐HIASC are much lower than that of CASC. [ABSTRACT FROM AUTHOR]

In the field of Social Internet of Things (SIoT), the widespread adoption of Internet of Things (IoT) devices and the rise of social media have resulted in a significant amount of social IoT data, including textual data. However, the mining and analysis of these data have been a challenging task. To address this issue, we leverage semantic text exchange techniques from the field of Natural Language Processing (NLP) to generate text that preserves the original style while modifying its semantic information. this task can be used for a wide range of NLP tasks, including data augmentation, adversarial attacks on text, and conversational systems. However, the present methods of semantic text exchange have some shortcomings, such as the generated text is not natural, the content is not rich, and the cost of model training is high. This paper presents a semantically independent sentence generation method, which can generate fluent and diverse semantically independent sentences. We combine three word substitution patterns with a pre-trained text infilling model to generate text. The essential advantage of the proposed method is that it does not require training the model for text infilling on specific datasets, which greatly reduces the cost of text generation. Experimental results show that our method generates more fluent and more diverse sentences than baselines. And it has a good performance in sentiment preservation of the original sentence and semantic similarity between the replacement words and the generated sentences. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of a large number of static or mobile, low-cost, and low-power sensor nodes. And energy is one of the most important factors that should be considered. In this paper, we propose clustering-based routing protocol for dynamic networks (CRPD) to reduce energy consumption and improve energy efficiency through clustering and routing algorithms. The basic idea is to periodically update the network topology and select the node with larger degree and high residual energy as the cluster head to be responsible for data aggregation and transmission. With the nodes moving, joining, and choosing the optimal clustering radius, the energy load of the whole network can be evenly distributed to each sensor node, which can significantly prolong the network lifetime. Extensive simulations show that CRPD is more energy-efficient than the existing protocols. [ABSTRACT FROM AUTHOR]

Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex structures and require more attention in recovery processing. This paper proposes a saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details. More specifically, we exploit the salient maps of images to guide the recovery in two aspects: On the one hand, the saliency detection network in SG-GAN learns more high-resolution saliency maps to provide additional structure priors. On the other hand, the well-designed saliency loss imposes a second-order restriction on the super-resolution process, which helps SG-GAN concentrate more on the salient objects of remote sensing images. Experimental results show that SG-GAN achieves competitive PSNR and SSIM compared with the advanced super-resolution methods. Visual results demonstrate our superiority in restoring structures while generating remote sensing super-resolution images. [ABSTRACT FROM AUTHOR]

Anonymous tracking technology of network watermarking is limited by the deployment of tracking devices in traditional network structure, resulting in poor scalability and reusability. Software Defined Network (SDN) boasts more freedom thanks to its separation of the control plane from the data plane. In this paper, a new anonymous communication tracking model SDN-based Anonymous Communication Tracking (SACT) is proposed, which introduces network watermarking into SDN and combines IP time hidden channel and symbol expansion technology. In addition, we introduce a hopping protection mechanism to improve the anti detection ability of the watermark as well. The experimental results show that in a variety of simulated network environments, SACT achieves excellent detection rate and bit error rate, thus it is sufficient to determine the communication relationship between the two parties. Meanwhile, SACT solves the deployment problem of anonymous tracking and improves the availability and scalability of covert communication. [ABSTRACT FROM AUTHOR]

• A partially accurate model is proposed for tracking the dynamics of the HIDiC. • A relative distribution index is combined with PCA to estimate the parameters. • A reduced-order extended GMC controller for HIDiC is proposed. The heat integrated distillation column (HIDiC) has huge potential on energy saving. A partially accurate model is proposed to describe this system with a reduced form. The new model improves the tracking accuracy of the dynamic behaviors at specific stages related to the distillation products so as to serve for an efficient nonlinear model-based control scheme. Estimates of the unmeasured model parameters are generated based on a limited number of temperature measurements, and the locations of these measurements are determined strategically by the combination of a weighted PCA (principal component analysis) method and a relative distribution index. A nonlinear control scheme is then formulated by combining the partially accurate model and the structure of an extended generic model control. The relative order of the HIDiC is reduced by the proposed model, and hence the number of the controller parameters are decreased. The proposed reduced-order control scheme is compared to two control schemes, namely a normal extended generic model control strategy and a classical PI controller, to evaluate the validity of the nonlinear model-based control for the HIDiC. [ABSTRACT FROM AUTHOR]