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Nowadays, sensor-cloud systems have received wide attention from both academia and industry. Sensor-cloud system not only improves performances of wireless sensor networks (WSNs), but also combines different functional WSNs together to provide comprehensive services. However, a variety of malicious attacks threaten the sensor-cloud security, such as integrity, authenticity, availability and so on. Traditional available security mechanisms (e.g. cryptography and authentication) are still vulnerable. Although there are schemes to provide security by trust evaluation, the evaluation considers whether or not a sensor is credible only by checking the communication behaviors. Furthermore, when mobile sensor sinks are employed to collect sensing data, there appears a type of attacks called replicated sink attacks that are often ignored in the previous work. These attacks may bring serious vulnerability to trustworthy data collection in sensor-cloud systems. In this paper, we propose a comprehensive trustworthy data collection (CTDC) approach for sensor-cloud systems. Three kinds of trust, i.e., direct trust, indirect trust, and functional trust are defined to evaluate the trustworthiness of both sensors and mobile sinks. Except for resisting malicious attacks, the performances of sensor-cloud, such as energy, transmission distance and network throughput are also considered. We also conduct extensive simulations to evaluate the efficiency of CTDC. The simulation results show that CTDC correctly identifies malicious nodes and offers an improved performance in the data collection.

A three-dimensional (3D) FBG accelerometer was proposed by installing three FBGs in an integrated oscillator structure which consists of two pairs of flexible hinges, a thin cylinder mass block and a base. The theoretical model of the proposed 3D accelerometer was established, and the dynamic response characteristics of the 3D accelerometer with optimal structural parameters were simulated using finite element software. Lots of sensing tests were finished and the sensing data showed that the sensitivities of the proposed 3D accelerometer in X-, Y-, and Z-directions are 108.7 pm/g, 144.2 pm/g, and 27.9 pm/g respectively, the working frequency range in X- and Y- directions are 40 – 300 Hz, and the acceleration amplitude range is 1 – 10 g. The proposed 3D accelerometer has the advantages of small size (28 mm $\times38$ mm $\times38$ mm), light weight (42 g), and simultaneous measurement of multi-dimensional acceleration.

Crime has been a complex social problem worldwide, impacting numerous individuals in both property and psychology, and affecting public safety as well. To prevent and avoid crime is of great importance for urban authorities and citizens. Crime prediction using various urban sensing data provides a promising paradigm to cope with this challenging problem. However, previous studies on crime prediction mainly focus on coarse-grained prediction at the region level, which fail in fine-grained road-level crime inference due to data sparsity problems. This article proposes a road-level framework for fine-grained crime risk inference leveraging heterogeneous open data. We first extract a set of features from relevant external datasets and historical crime records, i.e., temporal, spatial, and recurrence crime features. We then establish a spatio-temporal crime pattern using spatio-temporal features, and further infer road-level crime risk incorporating with recurrence crime features. The framework not only considers a useful spatio-temporal pattern but also considers a unique crime near-repeat phenomenon, which helps address the data sparsity. We further present an application crime risk-aware route recommendation to demonstrate the effectiveness of our framework. This application cannot be solved well with coarse-grained (e.g., region-level) crime risk results since it is inconvenient for people to travel while avoiding large regions with potential crime risks. Experiments with the real-world datasets from New York City show that our framework accurately infers road-level crime risk and outperforms other baseline methods.

A smartphone-based dual-mode sensing system for accurate and reliable estimation of sulphate and chloride level in water is reported. The designed sensor is based on the detection of transmitted modulated signal (for spectrophotometric based study) and scattered signal intensities (for turbidimetric based study) from a sample. The ambient light sensor (ALS) of the phone has been used as a detector for receiving the modulated signal for both the sensing modes of studies. The applicability of the designed tool has been realized through monitoring of sulphate and chloride ions concentrations in water. A custom-designed application has been developed to convert the sensing system into a standalone tool that converts the sensing data into a readable format. The proposed sensing system could emerge as an alternative platform which can be utilized for monitoring of other parameters of water and other media as well.

In Internet of Everything (IoE), malicious attacks detection and mitigation are important issues. These issues can be resolved through an access control framework where two entities first authenticate each other prior to establishing any secret key for their secure communication. The sensing data of various smart devices in an IoE environment are processed securely at the nearby fog servers and at the same time legitimate users can also access the real-time data directly from designated smart devices through access control mechanism. We first discuss various attack trends in an IoE environment. After that, we discuss evolution of the blockchain technology in the IoE. An artificial intelligence based blockchain-envisioned access control framework for malicious attacks detection and mitigation has been suggested to secure the IoE environment. Finally, a blockchain-based implementation has been conducted on the proposed blockchain-envisioned access control framework for measuring the computational time needed for varying number of blocks mined in the blockchain and also for varying number of transactions per block.

As the 5G environment is established due to the development of network and information and network technologies, wireless sensor network is drawing attention as a technology that will lead the information technology industry in the future. The sensor-based service must collect and process data recognized from various sensors and deliver information to users to understand. However, there is no research to visually express information about sensor resources to make it easier for users to understand. Therefore, this research proposes an efficient visualization model that is both realistic and economical. Then, we designed a scenario applied to real life and built a webpage.

An arrayed host:guest fluorescence sensor system can discriminate among and classify multiple different noncanonical DNA structures by exploiting selective molecular recognition. The sensor is highly selective and can discriminate between folds as similar as native G-quadruplexes and those with bulges or vacancies. The host and guest can form heteroternary complexes with DNA strands, with the host acting as mediator between the DNA and dye, modulating the emission. By applying machine learning algorithms to the sensing data, prediction of the folding state of unknown DNA strands is possible with high fidelity.

This article considers a dual-hop Internet of Things communications network where sensor nodes transmit data to a gateway either directly or via other nodes using dual-hop communications. Each node employs separate transmission buffers to store its own sensing data and data received from other nodes. End-to-end delay quality-of-service constraints in terms of the maximum acceptable delay-outage probabilities are imposed. We investigate energy-efficient adaptive resource allocation problems (i.e., joint link scheduling, rate, and power allocation) to support minimum data rates of the nodes. A novel approach is proposed exploiting asymptotic delay analysis to first determine the achieved delay exponents of the queue length tail distributions to satisfy the delay-outage constraints. Next, the relation between the delay exponents and resource allocation variables are derived. Last, the solutions to the resulting constrained optimization problems are obtained using the Lagrangian approach and convex optimization. Illustrative examples demonstrate the effects of the rate requirements and delay constraint stringency on the power consumption and routing configuration.

With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face of this problem, there are many solutions to identify dead and sick chickens through sound and image, but they can not achieve the ideal effect. In this paper, a sensor detection method based on artificial intelligence is proposed. This method 1) The maximum displacement of chicken activity is measured by fastening a foot ring on each chicken, and the three-dimensional total variance is designed and calculated to represent the chicken activity intensity. 2) The detection terminal collects the sensing data of foot ring through ZigBee network. 3) The state of chicken (dead chicken and sick chicken) can be identified by machine learning algorithm. This method of artificial intelligence combined with sensor network not only has high recognition rate, but also can reduce the operation cost. The practical results show that the accuracy of the system to identify dead and sick chickens is 95.6%, and the cost of the system running for 4 years can be reduced by 25% compared with manual operation.

Applicability of the whispering gallery mode sensor in the form of the multiresonator imaging device for the atmospheric-relevant parameters monitoring has been demonstrated. The sensor design and the appropriate techniques for sensing data collection have been discussed. The sensor performance has been numerically and experimentally studied on examples of the temperature variation detection and the monitoring of small spherical microparticles ( $ ) that have been used to mimic the particulate contamination of the environment. The thermal sensitivity of the sensor on the level of 8.6 pm/°C has been experimentally demonstrated. The experimental results on the distinguishing of the microparticle concentrations (0.5% and 1%) via the wavelength sweeping technique and the results on the single particle detection under illumination at the fixed wavelength have been shown.

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, we study how to reuse building sensing data to predict traffic volume on nearby roads. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperforms all baseline methods with up to 65.3% accuracy improvement (e.g., 2.2% MAPE) in predicting nearby traffic volume., Comment: 17 pages; 17 figures. in IEEE Transactions on Mobile Computing

This paper investigates the malicious users (MUs) detection problem against restricted spectrum sensing data falsification (RSSDF) attack in cooperative spectrum sensing (CSS) network. We consider a CSS network with non-ideal reporting channels, in which there are two kinds of secondary users (SUs): interactive SUs (ISUs) and non-interactive SUs (NSUs). We simplify the interaction process among ISUs with proper assumptions and formulate the impact of non-ideal reporting channels on falsified data. Based on the traditional probability SSDF attack on M-ary quantized data, we generalize the attack model by considering the restriction on attack magnitude. To detect ISUs, we construct a detection architecture with ISUs partitioned into groups of two and perform distributed detection by proposed interaction-based detection algorithm. For the detection of NSUs and undetected ISUs, we further propose a semi-supervised fuzzy c-means-based (SSFCM-based) detection algorithm with potential interaction assistance. Simulation results demonstrate the validity of the proposed theorems. In addition, we illustrate the detection performance of the proposed strategy in different conditions and show the superiority of the proposed strategy over other detection algorithms.

The study of human activity–travel patterns for urban planning has evolved a long way in theories, methodologies, and applications. However, the scarcity of data has become a major barrier for the advancement of research in the field. Recently, the proliferation of urban sensing and location-based devices generates voluminous streams of spatio-temporal registered information. In this study, we propose an approach using the linear-chain Conditional Random Fields (CRFs) model to learn the spatio-temporal correspondences of different types of activities and the inter-dependencies among sequential activities from training dataset such as the household travel or time use surveys, and to infer the hidden activity types associated with urban sensing data. The performance of the CRFs model is compared against the Random Forest (RF) model, which has been used in a number of existing studies. The results show that the linear-chain CRFs models generally outperform the RF counterparts with respect to classification accuracy of activity types, in particular for those travelers having more outdoor daily activities. The proposed methodology is demonstrated by reconstructing the activity landscape of the surrounding area of a major Mass Rail Transit station in Singapore using the transit smart card transaction data. The inferred activities from the transit smart card data are expected to complement the ground surveys and improve our understanding of the interactions of different components of activities/travels as well as the relationship between urban space and human activities.

Calibration curves are essential constructs in analytical chemistry to determine parameters of sensing performance. In the classification of sensing data of complex samples without a clear dependen...

The Industrial Internet of Things (IIoT) is a critically important implementation of the Internet of Things (IoT), connecting IoT devices ubiquitously in an industrial environment. Based on the interconnection of IoT devices, IIoT applications can collect and analyze sensing data, which help operators to control and manage manufacturing systems, leading to significant performance improvements and enabling automation. IIoT systems are characterized by a variety of IIoT applications, which generate different computing tasks depending on their functionalities. Some tasks are time sensitive (TS), while others are not, and more importantly, some tasks are nonpreemptive in IIoT scenarios. Thus, processing the different IIoT applications efficiently in an IIoT environment is key to achieving automation. Since computing resources are limited in IIoT, how to rapidly process TS tasks is a critical issue. Although some existing scheduling schemes can deal with the latency requirements of TS tasks, they lack consideration for nonpreemptive tasks. To address this issue, in this article we consider a typical smart warehouse system as an example and propose a generic task scheduling scheme that reserves computing resources to wait for upcoming TS tasks in such an IIoT environment. In doing so, our proposed scheme is capable of minimizing the overall waiting time for TS tasks. To evaluate the proposed scheme, we have implemented a simulation platform for a smart warehouse and conducted extensive experiments. Our experimental results demonstrate the efficacy of our scheme, which can allocate computing resources so that the processing time for the TS tasks can be reduced. Additionally, we discuss some potential research directions toward improving performance in IIoT environments with respect to resource management, machine learning, and security and privacy.

Reservoir-infiltration uranium deposits («sandstone» type) today are the main active source of uranium mineral raw materials in the world and the only one in Kazakhstan. Their main advantage in the form of better environmental friendliness, productivity and minimum production costs creates prospects for their further detection in various parts of the world. It is important to simplify and improve multi-stage, expensive and difficult geological exploration works for the purpose of forecasting and searching them with affordable innovative solutions. The available multispectral satellite imagery has opened up new opportunities for the study of uranium ore provinces. Mapping of uranium ore provinces based on multispectral satellite imagery allows them to be compared with certain key ore-controlling data from geological and geophysical studies. The near-surface visible nature of geotectonic structures, climatic conditions and zonal anomalies are more easily and efficiently visualized using modern space technologies and computer solutions. The explanation of the derived correlations with the geotectonic and climatic conditions allows the use of multispectral images in order to simplify and improve the quality of forecasting, prospecting and exploration of reservoir-infiltration uranium deposits. More advanced aerial and space remote sensing methods make it possible to detect surface anomalies associated with this type of ore. The scientific explanation of the nature of these anomalies and their role in the geological and genetic model of ore formation creates a solid theoretical basis for improving the exploration methodology. The convergence of the results obtained, their theoretical explanation, simplicity and convincingness of the results make it possible to make new predictions of promising areas of reservoir-infiltration uranium regions for several of the key ore-controlling factors and use this methodology in conjunction with other data from regional and local studies at all stages of exploration.

Remote sensing using drones has the advantage of being able to quickly monitor large areas such as rivers, oceans, mountains, and urban areas. In the case of applications dealing with large sensing data, it is not possible to send data from a drone to the server online, so it must be copied to the server offline after the end of the flight. However, online transmission is essential for applications that require real-time data analysis. The existing computation offloading scheme enables online transmission by processing large amounts of data in a drone and transferring it to the server, but without consideration for real-time constraints. We propose a novel computation offloading scheme which considers real-time constraints while minimizing the energy consumption of drones. Experimental results showed that the proposed scheme satisfied real-time constraints compared to the existing computation offloading scheme. Furthermore, the proposed technique showed that real-time constraints were satisfied even in situations where delays occurred on the server due to the processing of requests from multiple drones.

This paper investigates the utility of information and communication technologies data for the measurement of visitation dynamics in third places across a large metropolitan university campus. A suite of seven metrics encompassing intensity, popularity, capacity, tempo, dwell, churn, and periodicity are employed to describe the visitation dynamics of each third place. Findings suggest that despite overt functional similarities, significant variability exists in the visitation dynamics of third places. Increasing pervasiveness of Wi-Fi networks in public spaces will improve the utility of social sensing data in recording the spatial and temporal dynamics of visitation.

Even though Internet of things (IoT) sensing services are introduced in a wide range of areas, it is not applicable to mission-critical services due to the lack of the trustworthiness of the IoT sensing data. To address this problem, we propose TruSense, a novel trusted sensing framework for the IoT environment that covers end-to-end implementation from an IoT device to a cloud service. The TruSense framework includes a small sensing board, a communication protocol, and a cloud service for the trusted sensing in the IoT environment. To show our framework's feasibility, we design an ARM TrustZone-based IoT sensing board and implement an application and a trusted sensing service running in the Secure world. We also implement a cloud service for the trusted sensing in Google app engine, which is one of the widely used cloud services.

Today's growth in big data, edge computing, high-performance computing, and machine learning has opened tremendous opportunities for advances in mobility. The 13 th International Workshop on Computational Transportation Science (IWCTS 2020) is particularly timely given the prominence of connected automated vehicles technologies in the global auto industry's near-term growth strategies, of big data analytics, unprecedented access to sensing data of mobility, and of integration of this analytics into the optimization of mobility and transport. These developments (as listed below) are deeply computational. • Transportation Planning & Modeling: travel behavioral analysis, modeling and simulation of population movements and freight transportation systems. • Transportation Operations: Connected and Autonomous (CAVs), Computational traffic flow models and control algorithms, role of transportation in community spread of a pandemic (COVID-19). • Infrastructure Sensing: Digital Twin, Data-driven approaches to transportation systems operations • Other Technologies: Urban sensing technologies, Geoinformatics and Regional Science