Applications of wireless sensor networks for urban areas

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

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Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Background/Objectives: Air pollution due to vehicular and industrial emission has become menace to the living beings Due to this menace both indoor and outdoor air quality monitoring in real time has become mandatory Methods/ Statistical Analysis: The evolution of Internet of Things (IoT) and Single Board Computers (SBC) has made real time remote monitoring as a ubiquitous process Remote monitoring was facilitiated using classical motes in the past, which has some pitfalls like limited memory, processsing speed and complex programming strategies This paper portrays the usage of SBC for integration of IoT with WSN for Air Quality Monitoring System (AQMS), where SBC are capable of performing even complex task with enhanced speed and reduced complexity The integration of cloud services with SBC makes alerting process smart and realtime Findings: With the review and realizing of immense literature in the field of WSN for air quality management, the design of sensor web node becomes essential The evolution of SBC adds merit towards monitoring and measuring of the critical factors in centralized Air Quality Monitoring System (AQMS) in any plant Sensor web node is proposed with commercial gas sensors for detecting the gases like CO, CO 2 , NH 3 and NOx to monitor both indoor and outdoor air quality The observed results are properly evaluated using ThingSpeak open source IoT platform The integration of open source cloud services for SBC in this proposed prototype model confirms low cost, comfort, convenience and rapid prototyping for flexible AQMS Applications/Improvements: This prototype can be easily adapted to any monitoring systems with minor changes and can be made scalable for tomorrow

IoT Enabled Air Quality Monitoring System (AQMS) using Raspberry Pi

Driven by the core technologies, i.e., sensor-based autonomous data acquisition and the cloud-based big data analysis, IoT automates the actuation of data-driven intelligent actions on the connected objects. This automation enables numerous useful real-life use-cases, such as smart transport, smart living, smart cities, and so on. However, recent industry surveys reflect that data-related challenges are responsible for slower growth of IoT in recent years. For this reason, this article presents a systematic and comprehensive survey on IoT Big Data (IoTBD) with the aim to identify the uncharted challenges for IoTBD. This article analyzes the state-of-the-art academic works in IoT and big data management across various domains and proposes a taxonomy for IoTBD management. Then, the survey explores the IoT portfolio of major cloud vendors and provides a classification of vendor services for the integration of IoT and IoTBD on their cloud platforms. After that, the survey identifies the IoTBD challenges in terms of 13 V’s challenges and envisions IoTBD as “Big Data 2.0.” Then the survey provides comprehensive analysis of recent works that address IoTBD challenges by highlighting their strengths and weaknesses to assess the recent trends and future research directions. Finally, the survey concludes with discussion on open research issues for IoTBD.

A Survey on IoT Big Data: Current Status, 13 V’s Challenges, and Future Directions

A spatiotemporal database model and query language

Environmental monitoring is required to understand the effects of various kinds of phenomena such as a flood, a typhoon, or a forest fire. To detect the environmental conditions in remote places, monitoring applications employ the sensor networks to detect conditions, context models to understand phenomena, and computing technology to process the large volumes of data. In this paper, we present an air pollution monitoring system to provide alarm messages about potentially dangerous areas with sensor data analysis. We design the data analysis steps to understand the detected air pollution regions and levels. The analyzed data is used to track the pollution and to give an alarm. This implemented monitoring system is used to mitigate the damages caused by air pollution.

Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

For efficiently managing of moving object trajectories, several indexing methods have been proposed such as 3DR-tree, STR-tree, TB-tree. These indexing methods have been focused on only improving query performance. However, due to property of continuously changing its position, moving objects generate massive data and have to update its information frequently. Therefore, in this paper, we proposed an indexing method for managing current and past trajectories of moving objects efficiently. The proposed method employed auxiliary cache for efficient updating performance. We also proposed the method which can be reduced the index size by removing redundant information for expression of moving object trajectory. We showed that the proposed method has not only the better performance for update and much smaller size of index size, but also almost same performance for the query compared to previous indexing method.

Indexing for Efficient Managing Current and Past Trajectory of Moving Object

Indexing for efficient managing current and past trajectory of moving object

The Spatiotemporal Relationship Operator

In the 21st century of information systems, the amount of information resources are ever increasing and the role of information searching system is becoming criticalto easily acquire required information from the web. Generally, it requires the user to have enough pre-knowledge and superior capabilities to identify keywords of information to effectively search the web. However, most of the users undertake searching of the information without holding enough pre-knowledge and spend a lot of time associating key words which are related to their required information. Furthermore, many search engines support the keywords searching system but this only provides collection of similar words, and do not provide the user with exact relational search information with the keywords. Therefore this research report proposes a method of offering expanded user relationship search keywords by analyzing user query patterns to provide the user a system, which conveniently support their searching of the information.

Kwang Woo Nam

Papers

Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

Indexing for Efficient Managing Current and Past Trajectory of Moving Object

Indexing for efficient managing current and past trajectory of moving object

The Spatiotemporal Relationship Operator

Pattern Analysis-Based Query Expansion for Enhancing Search Convenience