This paper presents an AI-driven approach to monitor and improve air quality at urban traffic signals. The Urbanbreathe system integrates low cost gas sensors cloud connectivity and machine learning to detect pollution hotspots and automatically trigger corrective actions the system uses the esp32 microcontroller with integrated wi-fi for seamless cloud connectivity for the communication between the hardware and the software modules sensor data is streamed to firebase then analyzed by a decision tree classifier and visualized via a web dashboard experimental evaluation with combined government datasets and local sensor readings shows that the system attains reliable classification performance and fast automated response offering a scalable low-cost solution for smart-city air.

In this paper, a sensor cloud data intrusion detection framework is proposed. The framework uses parallel discrete optimization techniques for feature refining and incorporates machine learning principles to improve sensing cloud security. Firstly, a set of optimal feature evaluation criteria is established, and a parallel discrete optimization feature extraction system is built to reduce the data dimension and strengthen the stability of feature processing. Then, a widely used discrete optimization algorithm is developed, proving its global convergence. The optimal feature set is obtained through parallel screening feature subsets. Finally, using these features and distributed fuzzy cluster analysis, the intrusion behavior of the sensing cloud is accurately detected. This method incorporates the concept of intelligent iterative evolution and self-regulating clustering strategy, which not only overcomes the local optimal trap that the conventional fuzzy clustering algorithm may encounter but also realizes the automatic adjustment of the number of clusters. The experimental data show that the intrusion detection algorithm performs excellently in providing accurate intrusion determination results. Compared with other detection algorithms, the accuracy of anomaly detection and the reduction of missing detection rate is significantly improved. In addition, the algorithm shows anti-interference solid ability and can maintain stable performance in noisy environments.

In air traffic control (ATC) sensor–cloud networks, legitimate fluctuations in radar returns, ADS-B/MLAT reports, weather feeds, and controller–pilot communications often resemble abnormal phenomena from fragility or interference, so fixed rules or static thresholds misfire in dynamic, interference-prone settings. We propose an edge-enabled framework that unifies lightweight models at sensor gateways with large-scale cloud models: edge models provide fast, privacy-preserving screening with calibrated uncertainty, and an uncertainty-aware gate selectively offloads ambiguous or high-risk states to the cloud for deeper, cross-sensor fusion. The pipeline: 1) quantifies correlation strength between normal and abnormal patterns across multi-source ATC streams to reveal weak yet actionable dependencies; 2) identifies anomalies using a dynamic support threshold bound to sliding observation windows to retain rare but critical events such as spoofed trajectories or abnormal handoffs; and 3) extracts robust multimodal features that emphasize intrinsic vulnerability cues while suppressing benign diurnal or weather-induced variations. An indicator system, detection efficiency, connectivity health across the ATC network, and post-decision impact on sector load drive adaptive thresholds and an interpretable decision policy. The resulting model combines fitted and gradient parameters with indicator-guided control, enabling privacy-preserving edge–cloud co-inference and periodic knowledge distillation back to devices without sharing raw surveillance data. Comprehensive evaluations across vision, inertial, and acoustic benchmarks demonstrate that the proposed correlation-based pre-screening and indicator-driven offloading framework, reinforced by continual cloud-to-edge knowledge distillation, reduces latency by up to 41%, halves offload traffic, and improves detection accuracy by 6.8% over state-of-the-art baselines, delivering an interpretable and efficient solution for large-scale ATC sensor–cloud systems.

In order to solve the problem of accurate and low-cost location of substation personnel in digital cities, a substation personnel location system based on wireless sensor cloud computing network is proposed. ZigBee wireless sensor cloud computing network is introduced into the substation to improve the direct location algorithm of substation personnel, and a fuzzy reasoning algorithm is proposed. The algorithm takes the signal strength received by each reference node and the relative distance between the reference nodes as input. After fuzzy, fuzzy reasoning and deblurring, the reliability of the received signal strength of each reference node is obtained, and then three reference nodes with high reliability are selected for trilateral positioning calculation. The experimental results show that the positioning error after improvement is more stable than that before improvement, and the maximum error before improvement is 1.4115m. Practice has proved that the algorithm can significantly improve the positioning accuracy of substation personnel without adding any hardware and using fewer nodes.

Dynamic Sensor Scheduling for Data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Dynamic authentication for intelligent sensor clouds in the Internet of Things

In today's interconnected world, diverse sensor types are critical for powering various applications and services.The limited energy resources of these sensors present a significant challenge in managing sensor networks efficiently.To address this, we propose an energy-saving sensor cloud that utilizes a data prediction technique.Typically, a sensor node in a Wireless Sensor Network (WSN) gathers and transmits data to the cloud every 10 minutes, consuming substantial energy.In contrast, our proposed method requires sensor nodes to communicate with the cloud every 110 minutes, as the cloud system's forecasting method is capable of predicting ten steps ahead, thus reducing transmission frequency.We have applied Wavelet-based Forecasting (WBF), Auto-Regressive Integrated Moving Average (ARIMA), and a hybrid ARIMA-WBF for these predictions.The ARIMA model demonstrates superior performance compared to the other techniques when dealing with linear sensor data.Our method results in a power consumption that is approximately 90.9% lower than that of traditional methods within the sensor cloud, owing to reduced data transmission frequency.Additionally, our approach yields notably lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) in predictions.

CITE: A content based trust evaluation scheme for data collection with Internet of Everything

<p>Star sensors are an important means of autonomous navigation and access to space information for satellites. They have been widely deployed in the aerospace field. To satisfy the requirements for high resolution, timeliness, and confidentiality of star images, we propose an edge computing algorithm based on the star sensor cloud. Multiple sensors cooperate with each other to form a sensor cloud, which in turn extends the performance of a single sensor. The research on the data obtained by the star sensor has very important research and application values. First, a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition, which can reduce the amount of data computation. Then, a mapping model between content and space is constructed to achieve low-rank image representation and efficient computation. Finally, the data collected by the wireless sensor is delivered to the edge server, and a different method is used to achieve privacy protection. Only a small amount of core data is stored in edge servers and local servers, and other data is transmitted to the cloud. Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage, and has strong privacy.</p>

The concept of sensor clouds has been populated for utilizing data from massive amount of IoT devices. In the sensor cloud, a large number of sensors and users are connected and sensor data are traded among them. A number of market frameworks for such data ecosystems have been proposed so far, most of which assumes multiple stakeholders and coordinates their interests using techniques such as the traditional economic theory and game theory. However, because of the duplicability of IoT data, designing a natural pricing scheme based directly on market principles, such as the balance between seller competition and consumer demands, is still a challenge. In this paper, we propose a new pricing scheme for IoT stream data, where prices are determined by the balance between seller competition and consumer demand. Unlike conventional methods, our method is based on simulation. By simulating the market and sellers’ pricing behaviors on the broker’s platform, fair pricing is achieved without causing undesirable phenomena such as price wars. The evaluation results show that the proposed pricing method has desirable characteristics for an IoT data market.

A cloud is a new paradigm for IoT-based WSN that overcomes several limitations of traditional WSN and decouples the owners of the physical sensors from the network users. This paper proposes a cloud-based Internet of Medical Devices (IoMD), a novel architecture for the healthcare system to validate the efficiency of sensor-cloud virtualisation technique. IoT, cloud computing and fog are the three key technologies that make up the framework outlined in this paper. IoT and medical devices are integrated into our cloud-based architecture, and deep learning algorithms are used to process the collected data. A deep learning neural network method called Generative Adversarial Network (GAN) model that runs in both fog and cloud platforms and is capable of processing massive data in a fast and efficient manner. The suggested GAN is trained on a real-data set from the UCI Machine Learning Repository. Even yet, the results show that the GAN classifier can correctly categorise the medical data activities with a 99.16% accuracy rate. The proposed architecture for validation case study will ensure to benefit the sensor-cloud virtualisation paradigm for developing innovative applications in different sectors of the IoT system.

A cloud is a new paradigm for IoT-based WSN that overcomes several limitations of traditional WSN and decouples the owners of the physical sensors from the network users. This paper proposes a cloud-based Internet of Medical Devices (IoMD), a novel architecture for the healthcare system to validate the efficiency of sensor-cloud virtualisation technique. IoT, cloud computing and fog are the three key technologies that make up the framework outlined in this paper. IoT and medical devices are integrated into our cloud-based architecture, and deep learning algorithms are used to process the collected data. A deep learning neural network method called Generative Adversarial Network (GAN) model that runs in both fog and cloud platforms and is capable of processing massive data in a fast and efficient manner. The suggested GAN is trained on a real-data set from the UCI Machine Learning Repository. Even yet, the results show that the GAN classifier can correctly categorise the medical data activities with a 99.16% accuracy rate. The proposed architecture for validation case study will ensure to benefit the sensor-cloud virtualisation paradigm for developing innovative applications in different sectors of the IoT system.

This paper is a comparative study on the performance of the fog-enabled sensor cloud (FSC) and traditional cloud computing and fog computing modes in a smart logistics park. Based on our previous work, we describe the physical sensor virtualization scheme and framework of the proposed FSC, construct the network model, and mathematically describe the parameters of the FSC. To assess the performance of the proposed platform, we take a large logistics enterprise in China as an example and illustrate the network setup of the proposed platform in a real logistics scenario. The experiment proves that the FSC for smart logistics parks has a practical advantage over the traditional cloud computing and fog computing modes in terms of bandwidth consumption and service latency.

Abstract Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns because of the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth, defined as a visible cloud optical depth multiplied by (1 − asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multisensor-combined cloud profiles significantly reduce positive TOA SW BB biases, relative to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 μm), the 1.04–1.90-μm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable. Significance Statement The purpose of this study is to improve shortwave irradiance computations at the top of the atmosphere by using combined cloud properties from active and passive sensor measurements. Relative to the simulation results with passive sensor cloud measurements only, the combined cloud profiles provide more accurate shortwave simulation results. This is achieved by more realistic profiles of cloud extinction coefficient and cloud particle effective radius. The benefit is pronounced for optically thick clouds composed of large ice particles.

Machine learning-based proactive social-sensor service for mental health monitoring using twitter data

Sensor Cloud is an integration of sensor networks with cloud where sensed data is stored and processed in the cloud. The applications of sensor cloud can be seen in forest fire monitoring, healthcare system, and other Internet-of-Things systems. Outliers may present within this data due to malicious activities, low-quality sensors, or node deployment in harsh environments. Such outliers must be detected timely for effective decision making. Many clustering-based machine learning schemes for outlier detection have been devised. However, accuracy of these techniques can be further improved. This paper proposes a density-based machine learning scheme (DBS) for outlier detection which is implemented in Python and executed on the two datasets of different forest fire monitoring networks. DBS makes density-based clusters of all data points where outliers lie in low-density region. The use of a density-based model in the proposed approach improves precision, throughput, and accuracy. DBS outperforms the existing Mean Shift and K Means based clustering schemes with maximum accuracy 98.40%.

Small and medium-sized enterprises (SMEs) are an indispensable part of the development of the market economy, and they occupy a major position in the national economic system. Nowadays, the information construction of SMEs is becoming more and more important. Having an informationized accounting system can speed up the economic development of SMEs. So, this article designs a new type of accounting system, mainly for SMEs. Therefore, this article is based on sensor monitoring and cloud computing to optimize the informatization construction of the accounting system of SMEs. This paper proposes a cloud computing SOA architecture to build a cloud computing-based accounting system, and then combines the wireless sensor network routing protocol in the wireless sensor network system and the method of measuring the distance of the sensor monitoring node, and the wireless sensor network is applied to the cloud computing-based accounting system. Then, designed the enterprise information construction investigation experiment to formulate the rules applicable to SMEs, and then tested the data detection ability of the new accounting system by testing the performance of the sensor network protocol. Finally, the data obtained from the analysis of the weight of the enterprise cloud service is used to optimize the new accounting system, and the performance of the final optimized accounting system is compared with the traditional system. Experiments show that the accuracy of data monitoring by an accounting system based on sensor monitoring and cloud computing has increased by 13.84% compared to traditional accounting systems; compared with the traditional accounting system, the data processing efficiency of the accounting system based on sensor monitoring and cloud computing has increased by 14.63%.

Due to the sharp increase in global industrial production, as well as the over-exploitation of land and sea resources, the quality of drinking water has deteriorated considerably. Furthermore, nowadays, many water supply systems serving growing human populations suffer from shortages since many rivers, lakes, and aquifers are drying up because of global climate change. To cope with these serious threats, smart water management systems are in great demand to ensure vigorous control of the quality and quantity of drinking water. Indeed, water monitoring is essential today since it allows to ensure the real-time control of water quality indicators and the appropriate management of resources in cities to provide an adequate water supply to citizens. In this context, a novel IoT-based framework is proposed to support smart water monitoring and management. The proposed framework, named SmartWater, combines cutting-edge technologies in the field of sensor clouds, deep learning, knowledge reasoning, and data processing and analytics. First, knowledge graphs are exploited to model the water network in a semantic and multi-relational manner. Then, incremental network embedding is performed to learn rich representations of water entities, in particular the affected water zones. Finally, a decision mechanism is defined to generate a water management plan depending on the water zones’ current states. A real-world dataset has been used in this study to experimentally validate the major features of the proposed smart water monitoring framework.

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real‐time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology‐enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES‐ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC‐PSO, and PSO‐COGENT algorithms, the created HES‐ACO algorithm was simulated at CloudSim and found to optimize all parameters.