Sensor Web Research Articles

IOT BASED COLOR AND SIZE WISE SORTING MACHINE USING CAMERA

The process of arranging items systematically is named sorting. Manual sorting of Object is preferred at the wholesale market and industries supported different parameters like size, shape, quality, etc. But it's a time consuming, less efficient, and inconsistent method. The existing systems in the market can sort single Object with single or multiple parameters. To replace this traditional sorting way, the proposed system presents an automatic Object sorting mechanism with an image processing technique. It recognizes and classifies two different Object with two different Color analysis method. Key Words: Image Processing, Python, Arduino Uno, Color Detection, IR Sensor Web Camera, Conveyor Belt.

Integrated Node Infrastructure for Future Smart City Sensing and Response

Emerging smart cities and digital twins are currently built from heterogenous cutting-edge low-power remote sensing systems limited by diverse inefficient communication and information technologies. Future smart cities delivering time-critical services and responses must transition towards utilizing massive numbers of sensors and more efficient integrated systems that rapidly communicate intelligent self-adaptation for collaborative operations. Here, we propose a critical futuristic integrated communication element named City Sensing Base Station (CSBS), inspired by base stations for cell phones that address similar concerns. A CSBS is designed to handle massive volumes of heterogeneous observation data that currently need to be upgraded by middleware or registered. It also provides predictive and interpolation modelling for the control of sensors and response units such as emergency services and drones. A prototype of CSBS demonstrated that it could unify readily available heterogeneous sensing devices, including surveillance video, unmanned aerial vehicles, and ground sensor webs. Collaborative observation capability was also realized by integrating different object detection sources using advanced computer-vision technologies. Experiments with a traffic accident and water pipeline emergency showed sensing and intelligent analyses were greatly improved. CSBS also significantly reduced redundant Internet connections while maintaining high efficiency. This innovation successfully integrates high-density, high-diversity, and high-precision sensing in a distributed way for the future digital twin of cities.

Approach to Urban Geospatial Monitoring Combining Sensor Web and High-performance Computing Infrastructure

Approach to Urban Geospatial Monitoring Combining Sensor Web and High-performance Computing Infrastructure

Modeling of spatiotemporal data about the environment in GIS

A large number of sensors are used to monitor the environment, and a large volume of spatio-temporal data on epizootic risks and the environment is processed in real time. To date, GIS data models are represented by static models and more modern time models. However, many of the epizootological and environmental data management systems do not meet the requirements of real-time data management. The purpose of the work is to propose, based on the analysis of foreign literature sources, a modern method for managing epizootological and environmental data based on a new GIS model in real time in comparison with the Sensor web service model.Two experiments were conducted in the urban environment and on the territories of livestock farms with different epizootic situations for potential risk management in zoonoses. Real-time monitoring of air quality and real-time monitoring of soil moisture was carried out in Wuhan (China). The circulation of pathogens of zoonoses and sapronoses in the environment, including in the soil, and their preservation in the form of spores and hard-to-cultivate forms, determines the ecological component of emergent epizootics and epidemics with the coverage of new areas. Experimental results have shown that the use of the proposed GIS data model on the Sensor web service platform for managing epizootological/epidemiological and environmental data in real time is reliable and effective.

Integration of Wireless Sensor Networks in Environmental Monitoring Cyber Infrastructure

Abstract: Wireless sensor networks (WSNs) have great potential to revolutionize many science and engineering domains. We present a novel environmental monitoring system with a focus on overall system architecture for seamless integration of wired and wireless sensors for long-term, remote, and near-real-time monitoring. We also present a unified framework for sensor data collection, management, visualization, dissemination, and exchange, conforming to the new Sensor Web Enablement standard. Some initial field testing results are also presented. The monitoring system is being integrated into the Texas Environmental Observatory infrastructure for long-term operation. As part of the integrated system, a new WSN-based soil moisture monitoring system is developed and deployed to support hydrologic monitoring and modeling research. This work represents a significant contribution to the empirical study of the emerging WSN technology. We address many practical issues in real-world application scenarios that are often neglected in the existing WSN research

A Heterogeneous Access Metamodel for Efficient IoT Remote Sensing Observation Management: Taking Precision Agriculture as an Example

Standard remote sensing observation (RSO) access and formulization is essential to Internet of Things (IoT) data management, such as in precision agriculture (PA). Because of the heterogeneous characteristics and the petabyte data size of RSO, massive remote sensing processing in RSO management has been hampered. Here, we present a heterogeneous access metamodel for efficient RSO management (HAMERM) and verify it in PA. The structure of basic metadata components is defined. A five-tuple metadata structure based on the metaobject facility is designed. HAMERM consists of identification, platform, observation, product, and access, which represent the five aspects of RSO metadata information. In addition, the flatMap/reduceByKey algorithms and the table structure have been proposed under Sensor Web and Geographic Information Science (GIS) techniques. Intensive experiments in Guangdong Province, China are conducted to test the proposed method. Two RSO metadata formulization instances were conducted to examine the ability of sheltering the differences of multisource and heterogeneous RSO. Experiments containing data storage and data soil moisture (SM) mapping were performed. The results suggest that the HAMERM method achieved a performance 30.1 times higher than that of Hadoop and three times higher than that of Spark (stand-alone). Consequently, the proposed HAMERM can be applied to achieve efficient SM mapping within PA, which is helpful for efficient RSO management for the IoT.

A distributed geospatial publish/subscribe system on Apache Spark

Publish/subscribe is a messaging pattern where message producers, called publishers, publish messages which they want to be distributed to message consumers, called subscribers. Subscribers are required to subscribe to messages of interest in advance to be able to receive them upon the publishing. In this paper, we discuss a special type of publish/subscribe systems, namely geospatial publish/subscribe systems (GeoPS systems), in which both published messages (i.e., publications) and subscriptions include a geospatial object. Such an object is used to express both the location information of a publication and the location of interest of a subscription. We argue that there is great potential for using GeoPS systems for the Internet of Things and Sensor Web applications. However, existing GeoPS systems are not applicable for this purpose since they are centralized and cannot cope with multiple highly frequent incoming geospatial data streams containing publications. To overcome this limitation, we present a distributed GeoPS system in the cluster which efficiently matches incoming publications in real-time with a set of stored subscriptions. Additionally, we propose four different (distributed) replication and partitioning strategies for managing subscriptions in our distributed GeoPS system. Finally, we present results of an extensive experimental evaluation in which we compare the throughput, latency and memory consumption of these strategies. These results clearly show that they are both efficient and scalable to larger clusters. The comparison with centralized state-of-the-art approaches shows that the additional processing overhead of our distributed strategies introduced by the Apache Spark is almost negligible.

A Method of the Active and Passive Event Service Based on the Sensor Web

The intelligent information system constructed by the sensor web can monitor all kinds of sudden abnormal events, improve the ability of event discovery and rapid disposal, and promote the development of smart city and the construction of the Internet of Things (IoT). In this paper, we consider the problem of complex integration and poor expansibility in the large-scale full-network operation and maintenance system construction and propose an active and passive event service (APES) method based on the sensor web. In the APES, a system framework with the perception layer, data service layer, event service layer, and user layer is firstly defined and constructed. Secondly, a middleware with the ability to active and passive event service (APES) is designed and implemented based on the system framework. Finally, taking abnormal weather and fire warning as examples, the performance of the proposed event service middleware is tested, respectively. Experimental results show that the proposed APES model in this paper has the advantages of high precision, stable operation, and strong practicability and solves “Information Island” and low reusability in the whole network operation and maintenance system. This is an attempt at the structural design of a similar intelligent information system.

Exploitation and Incursion Finding Approach in Various WSW

Incursion finding is a process of identifying and responding malicious activity. Wireless sensor webs consisting bulk of ‘sensors’ are useful to integrate data in variety of environment. The basic sensors are simple and have limited power supplies. Heterogeneous wireless sensor webs are better scalable and lower overall cost than homogeneous sensor webs. In this paper, we are improving the lifetime of wireless web and we present a survey of various energy efficient techniques in a various wireless sensor web. It is important to improving wireless web because sensor nodes in wireless webs are constrained by limited energy.

Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization

In recent years, innovative positioning and mobile communication techniques have been developing to achieve Location-Based Services (LBSs). With the help of sensors, LBS is able to detect and sense the information from the outside world to provide location-related services. To implement the intelligent LBS, it is necessary to develop the Semantic Sensor Web (SSW), which makes use of the sensor ontologies to implement the sensor data interoperability, information sharing, and knowledge fusion among intelligence systems. Due to the subjectivity of sensor ontology engineers, the heterogeneity problem is introduced, which hampers the communications among these sensor ontologies. To address this problem, sensor ontology matching is introduced to establish the corresponding relationship between different sensor terms. Among all ontology matching technologies, Particle Swarm Optimization (PSO) can represent a contributing method to deal with the low-quality ontology alignment problem. For the purpose of further enhancing the quality of matching results, in our work, sensor ontology matching is modeled as the meta-matching problem firstly, and then based on this model, aiming at various similarity measures, a Simulated Annealing PSO (SAPSO) is proposed to optimize their aggregation weights and the threshold. In particular, the approximate evaluation metrics for evaluating quality of alignment without reference are proposed, and a Simulated Annealing (SA) strategy is applied to PSO’s evolving process, which is able to help the algorithm avoid the local optima and enhance the quality of solution. The well-known Ontology Alignment Evaluation Initiative’s benchmark (OAEI’s benchmark) and three real sensor ontologies are used to verify the effectiveness of SAPSO. The experimental results show that SAPSO is able to effectively match the sensor ontologies.

Advanced hybrid secure multipath optimized routing in Internet of Things (IoT)‐based WSN

SummaryExploration has been done on Internet of Things (IoT) with accentuation on the security like remote system security, correspondence security, sensor information security, respectability of physical signals, and impelling gadgets. The procedure for current security is not reasonable for IoT applications as gadgets that are included at the ground level have some of the restricted assets, low multifaceted nature, and vitality requirements. IoT being a system of systems permits billions of people and machines to connect with each other. IoT offers us a chance to build compelling organizations, applications for assembling, lifesaving arrangements, appropriate development, and the sky is the limit from there. The manuscript proposes a broad review of the IoT innovation and its differed applications in life sparing, brilliant urban communities, rural, mechanical by looking into the ongoing examination work. Existing strategies manage static remote sensor web to reason the interruptions in which the sensor hubs are conveyed in a uniform way to hold the consistency. Since the sensor hubs are referred to through various transmission locales having a determination of sensor checking hubs has become a difficult activity in ongoing examination. The enemies are additionally moving starting with one area then onto the next to investigate its particular tasks around the system. Hence, to given adaptable security, a protected routing along with the observing convention with multivariation tuples utilizing two‐fish symmetric key (TFSK) way to deal with the foes in the worldwide sensor organize. The proposed approach is the advanced hybrid secure multipath optimized routing in Internet of Things (IoT)‐based WSN. The two methodologies that are involved making a better routing protocol is the enhanced‐multipath optimized link state routing (E‐MP‐OLSR) and secure and efficient ad hoc on‐demand multipath distance vector (SE‐AOMDV) protocols. Besides, the proposed directing system is tough to different versatile foes; and henceforth, it guarantees multipath conveyance.

Next-Generation Soil Moisture Sensor Web: High-Density In Situ Observation Over NB-IoT

Soil moisture is an essential variable both in environmental monitoring research and application. With the requirement of high-precision soil moisture data, it is highly necessary to construct in-situ soil moisture sensors Web in high density, in which the economics, complexity, and low-power consumption of sensor Webs should be essentially considered. However, most current existing soil moisture monitoring networks have limitations due to high power consumption, complex architecture, and expensive equipment. These issues are not conducive to high-density soil moisture observation. In view of the existing problems in the current soil moisture in-situ sites, an effective resolution for high-density soil moisture observation is needed. For the first time, we are bringing Narrow-Band Internet of Things (NB-IoT), a low-power Internet-of-Things technology, into geospatial sensor Web for soil moisture observation. Moreover, we built a high-density in-situ soil moisture sensor Web via NB-IoT and compared it with ZigBee at the Baoxie experimental zone in Wuhan, China, there about 1 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and we acquired data for up to 12 months. We analyze the acquisition record of battery status, signals, and soil moisture. We analyzed the battery life, the impact of the signal on the battery performance, the signal quality, and data acquisition clearly and intuitively. This unprecedented study and application, we discovered and concluded that the low-power sensor Web of the NB-IoT communication protocol could be suitable for high-density soil moisture observation.

The Design and Implementation of a Dashboard Web-Based Video Surveillance in OpenStack Swift

Abstract The purpose aims at deploying a fully functional OpenStack cluster with different settings and operational environments, followed by an in-depth analysis of its network performance and explanation of what happens behind the scene. Where OpenStack Virtual Machines perform these services for data storage files are stored in an authenticated cloud storage service center. The methodology used presents various scenarios using OpenStack Virtual machines and cloud-based to improve network performance video surveillance backend system architecture is developed with the Sensor web Enablement framework and cloud-based data storage. Data is obtained through simulators on camera/edge devices, file and metadata sharing, and fast cloud base storage. This study aims to compare the performance load resulting from large-scale video surveillance with small ones to show satisfactory performance, scalability, and good reliability from cloud-based storage systems for smart MCS. The most important feature is storage is adjusted dynamically, and there won’t be any worry about space being inadequate or wasted. This paper presents a solution for deploying web applications for CCTV video surveillance, an object cloud storage service system based on the open-source cloud operating system OpenStack Swift. It’s working in the OpenStack swift on IaaS and allocates resources for storage original data by multiple camera surveillance (MCS).

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards-Case Study: Workplace Reopening.

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

GEOSPATIAL SENSOR WEB ADAPTOR FOR INTEGRATING DIVERSE INTERNET OF THINGS PROTOCOLS WITHIN SMART CITY

Abstract. In recent years, the multi-scale comprehensive perception is central to smart city development. We propose an "adaptor" for geospatial sensor web as an integrated sensory system that can integrate access to geodetic equipment based on the Internet of Things technology with multiple platforms and protocols. At the same time, the acquisition, fusion, and processing of sensory resources can perform. The geospatial adaptor can access and process sensors of different IoT protocols to different conditions simultaneously. Grace to this geospatial adaptor, a considerable number of the sensor based on IoT in the community, can achieve distributed access, ensuring the better robustness of the geospatial sensor web. This paper describes the system architecture of the geospatial sensor web adapter. Furthermore, from the perspective of protocol access, it introduces the access capabilities of geospatial sensor web adapter to the standard IoT interface protocols. By comparing the geospatial sensor web adapter with traditional observation methods by experiments and acquisition of test data. The results show that the geospatial sensor web adapter can achieve powerful access capabilities and network stability, and it is a better solution for heterogeneous sensing platform access in smart cities.

A Novel Hybrid Routing Algorithm with Two Fish Approach in Wireless Sensor Networks

Over the years, Internet of Things has advanced greatly advanced with technological inventions, enabling the development of smart networks globally. Network edge is introduced into the architectures in order to provide better user experience and quality of service But the issue with this these systems should be able to resist the attacks from external sources. There are many existing methods which are concerned with detecting the intrusion over static wireless sensor web. Due to various transmission regions being involved when using the sensor nodes, it is challenging task to choose the right guard nodes and sensor monitoring nodes. In order to address this issue, we propose a safe monitoring and routing protocol that incorporates Artificial intelligence on the basis of Authentication and Encryption Model. Analysis of the proposed work shows that it proves to be more efficient when compared to other routing and monitoring methods.

Toward a Standardized Encoding of Remote Sensing Geo-Positioning Sensor Models

Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain.

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm.

Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.

An Intelligent Transportation System: the Quito City Case Study

Managing traffic in a large city has become a topic of great interest in both politics and science. The costs of poor traffic management have been quantified as losses equal to millions of dollars, not counting the unquantifiable value of the time that a person loses in traffic jams. Intelligent transport systems (ITS) offer a set of innovative solutions specific to the management of different modes of transport. This article focuses on the development of an ITS for the city of Quito that allows smart decision-making to direct heavy haul transporters that want to enter the city via one of its main access routes. Technologies such as Sensor Web Enablement (SWE), in association with the Message Queuing Telemetry Transport (MQTT) communication protocol, facilitate the development of a vehicular management platform/system capable of sending notifications in real-time and issuing instructions to drivers regarding traffic delays along routes, average speeds, etc. The system supports a network of heterogeneous sensors accessible through the web. It can integrate any device that uses HTTP protocol. Time interval and location range testing have been undertaken to refine the accuracy of the system and make it adaptable to any geographic situation. The system allows communicate with the server through MQTT or through web services, using technologies such as: MongoDB and GeoJSON. One of the most relevant results is that the degree of accuracy of the system is within appropriate ranges when compared to commercial applications such as Google Maps and Waze.

The Spatial and Spectral Resolution of ASTER Infrared Image Data: A Paradigm Shift in Volcanological Remote Sensing

During the past two decades, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra satellite has acquired nearly 320,000 scenes of the world’s volcanoes. This is ~10% of the data in the global ASTER archive. Many of these scenes captured volcanic activity at never before seen spatial and spectral scales, particularly in the thermal infrared (TIR) region. Despite this large archive of data, the temporal resolution of ASTER is simply not adequate to understand ongoing eruptions and assess the hazards to local populations in near real time. However, programs designed to integrate ASTER into a volcanic data sensor web have greatly improved the cadence of the data (in some cases, to as many as 3 scenes in 48 h). This frequency can inform our understanding of what is possible with future systems collecting similar data on the daily or hourly time scales. Here, we present the history of ASTER’s contributions to volcanology, highlighting unique aspects of the instrument and its data. The ASTER archive was mined to provide statistics including the number of observations with volcanic activity, its type, and the average cloud cover. These were noted for more than 2000 scenes over periods of 1, 5 and 20 years.