The document is a seminar report submitted by Vintesh Patel to partially fulfill the requirements for a Master's degree in Computer Engineering. The report discusses integrating wireless sensor networks with cloud computing. It provides background on wireless sensor networks and cloud computing. It then describes the general architecture of a sensor-cloud, including its service lifecycle model and layered structure. Finally, it discusses current approaches to sensor-cloud infrastructures and provides a detailed study of data-centric and service-oriented sensor-cloud frameworks. The report aims to present an open, flexible, and reconfigurable platform for various monitoring and control applications using an integrated wireless sensor network and cloud computing infrastructure.
A Cloud Computing design with Wireless Sensor Networks For Agricultural Appli...Editor IJMTER
1. The document proposes a design for using wireless sensor networks and cloud computing together for agricultural applications. It describes how sensor nodes can collect environmental data and send it to the cloud for storage, analysis and decision making.
2. The proposed system has three main components - a sensing cluster with various sensors to collect data, a cloud service cluster to process and analyze the data, and a mechanism cluster with actuator nodes that can take actions based on the cloud's decisions.
3. Some potential applications discussed are image processing of unhealthy plants, predicting crop diseases based on sensor readings, and automatically controlling the cultivation environment through actuators. The system is aimed to help farmers optimize resources and increase productivity.
A survey on architectures applications and issues of sensor cloudIAEME Publication
The document provides an overview of sensor-cloud architectures, applications, and issues. It discusses three main sensor-cloud architectures: (1) WSN-cloud computing platform that connects wireless sensor networks to cloud infrastructure, (2) a sensor-cloud architecture with virtualization that decouples real sensors from virtual sensors in the cloud, and (3) a data-centric sensor-cloud infrastructure framework. It also outlines the key entities and components involved in sensor-cloud integration like sensor owners, administrators, users, portal servers, and monitoring servers. Finally, it examines applications of sensor-cloud like healthcare and issues around security, energy efficiency, and virtualization.
A survey on sensor cloud architecture, applications, and approachesNgoc Thanh Dinh
This document provides an overview of sensor-cloud infrastructure, which integrates wireless sensor networks with cloud computing. It discusses how sensor-cloud can address limitations of wireless sensor networks like limited storage, processing and scalability by leveraging cloud computing. The document outlines the definition and architecture of sensor-cloud, its applications in areas like healthcare, environment and advantages over traditional wireless sensor network approaches. It also discusses research challenges and approaches in sensor-cloud infrastructure.
Integrating Wireless Sensor Network into Cloud Services for Real-time Data Co...Mokpo National University
This document summarizes a presentation given by Rajeev Piyare on integrating wireless sensor networks with cloud services for real-time data collection. Piyare proposed an architecture with three layers - a sensor layer to collect data, a coordinator layer to manage data, and a supervision layer in the cloud to store data and provide interfaces. He demonstrated collecting temperature and voltage readings and accessing the data through RESTful web services. The system alerts users when sensor values exceed thresholds, with average notification times of 11 seconds. Experiments showed the impact of packet size and sleep cycles on battery lifetime for battery-powered sensors. The presentation concluded the architecture provides a flexible way to integrate sensor networks with cloud computing.
The document discusses sensor cloud, which integrates wireless sensor networks with cloud computing. It allows for the powerful analysis of sensor data through massive cloud infrastructure. The key benefits of sensor cloud include scalability, increased data storage and processing power, dynamic provisioning of services, and automation. Some challenges are implementation costs and maintaining continuous connectivity between sensors and the cloud. The document outlines the general architecture and components of a sensor cloud system and provides examples of applications in transportation monitoring, military use, weather forecasting, and healthcare.
IRJET- Integrating Wireless Sensor Networks with Cloud Computing and Emerging...IRJET Journal
This document discusses integrating wireless sensor networks with cloud computing through the use of middleware services. It proposes a model that combines wireless sensor networks and cloud computing, allowing for easy management of remotely connected sensor nodes and the data they generate. The model uses middleware as an intermediary layer between the wireless sensor networks and cloud to provide data compatibility, bandwidth management, security, and connectivity. It describes how sensor data can be collected via heterogeneous wireless networks, additional computational capabilities provided through cloud services, and information delivered to different types of end users through a networked control system. Load balancing of the cloud computing environment is achieved using a honey bee foraging strategy algorithm.
IRJET- Survey on Flood Management SystemIRJET Journal
1. The document discusses flood management systems that use data mining algorithms and IoT technologies for flood prediction and detection. It analyzes algorithms like kNN and SVM that can be used for flood prediction by training models on historical data.
2. For flood detection, the document proposes using IoT nodes with sensors to collect water level data, microcontrollers to process the data, and cloud services/GSM modules to send alerts if water levels exceed thresholds.
3. Several existing works that implement flood monitoring systems using techniques like IoT, sensors, microcontrollers and cloud services are reviewed and compared. The goal is to develop a system that can predict flood severity and detect floods early to minimize damage.
Desing on wireless intelligent seneor network on cloud computing system for s...csandit
Sensors on (or attached to) mobile phones can enable attractive sensing applications in
different domains such as environmental monitoring, social networking, healthcare, etc. In this
paper we propose a cloud computing system dedicated on smart home applications. We design
the proposed wireless vision sensor network (WVSN) with its algorithm and hardware
implementation. In WVSN, The partial-vision camera strategy is applied to allocate the
computation task between the sensor node and the central server. Then we propose a high
performance segmentation algorithm. Meanwhile, an efficient binary data compression method
is proposed to cope with the result on labeling information. The proposed algorithm can provide
high precision rate for the smart home applications such as the gesture recognition and
humanoid tracking. To realize the physical system, we implement it on the embedded platform
and the central server with their transmission work
A Cloud Computing design with Wireless Sensor Networks For Agricultural Appli...Editor IJMTER
1. The document proposes a design for using wireless sensor networks and cloud computing together for agricultural applications. It describes how sensor nodes can collect environmental data and send it to the cloud for storage, analysis and decision making.
2. The proposed system has three main components - a sensing cluster with various sensors to collect data, a cloud service cluster to process and analyze the data, and a mechanism cluster with actuator nodes that can take actions based on the cloud's decisions.
3. Some potential applications discussed are image processing of unhealthy plants, predicting crop diseases based on sensor readings, and automatically controlling the cultivation environment through actuators. The system is aimed to help farmers optimize resources and increase productivity.
A survey on architectures applications and issues of sensor cloudIAEME Publication
The document provides an overview of sensor-cloud architectures, applications, and issues. It discusses three main sensor-cloud architectures: (1) WSN-cloud computing platform that connects wireless sensor networks to cloud infrastructure, (2) a sensor-cloud architecture with virtualization that decouples real sensors from virtual sensors in the cloud, and (3) a data-centric sensor-cloud infrastructure framework. It also outlines the key entities and components involved in sensor-cloud integration like sensor owners, administrators, users, portal servers, and monitoring servers. Finally, it examines applications of sensor-cloud like healthcare and issues around security, energy efficiency, and virtualization.
A survey on sensor cloud architecture, applications, and approachesNgoc Thanh Dinh
This document provides an overview of sensor-cloud infrastructure, which integrates wireless sensor networks with cloud computing. It discusses how sensor-cloud can address limitations of wireless sensor networks like limited storage, processing and scalability by leveraging cloud computing. The document outlines the definition and architecture of sensor-cloud, its applications in areas like healthcare, environment and advantages over traditional wireless sensor network approaches. It also discusses research challenges and approaches in sensor-cloud infrastructure.
Integrating Wireless Sensor Network into Cloud Services for Real-time Data Co...Mokpo National University
This document summarizes a presentation given by Rajeev Piyare on integrating wireless sensor networks with cloud services for real-time data collection. Piyare proposed an architecture with three layers - a sensor layer to collect data, a coordinator layer to manage data, and a supervision layer in the cloud to store data and provide interfaces. He demonstrated collecting temperature and voltage readings and accessing the data through RESTful web services. The system alerts users when sensor values exceed thresholds, with average notification times of 11 seconds. Experiments showed the impact of packet size and sleep cycles on battery lifetime for battery-powered sensors. The presentation concluded the architecture provides a flexible way to integrate sensor networks with cloud computing.
The document discusses sensor cloud, which integrates wireless sensor networks with cloud computing. It allows for the powerful analysis of sensor data through massive cloud infrastructure. The key benefits of sensor cloud include scalability, increased data storage and processing power, dynamic provisioning of services, and automation. Some challenges are implementation costs and maintaining continuous connectivity between sensors and the cloud. The document outlines the general architecture and components of a sensor cloud system and provides examples of applications in transportation monitoring, military use, weather forecasting, and healthcare.
IRJET- Integrating Wireless Sensor Networks with Cloud Computing and Emerging...IRJET Journal
This document discusses integrating wireless sensor networks with cloud computing through the use of middleware services. It proposes a model that combines wireless sensor networks and cloud computing, allowing for easy management of remotely connected sensor nodes and the data they generate. The model uses middleware as an intermediary layer between the wireless sensor networks and cloud to provide data compatibility, bandwidth management, security, and connectivity. It describes how sensor data can be collected via heterogeneous wireless networks, additional computational capabilities provided through cloud services, and information delivered to different types of end users through a networked control system. Load balancing of the cloud computing environment is achieved using a honey bee foraging strategy algorithm.
IRJET- Survey on Flood Management SystemIRJET Journal
1. The document discusses flood management systems that use data mining algorithms and IoT technologies for flood prediction and detection. It analyzes algorithms like kNN and SVM that can be used for flood prediction by training models on historical data.
2. For flood detection, the document proposes using IoT nodes with sensors to collect water level data, microcontrollers to process the data, and cloud services/GSM modules to send alerts if water levels exceed thresholds.
3. Several existing works that implement flood monitoring systems using techniques like IoT, sensors, microcontrollers and cloud services are reviewed and compared. The goal is to develop a system that can predict flood severity and detect floods early to minimize damage.
Desing on wireless intelligent seneor network on cloud computing system for s...csandit
Sensors on (or attached to) mobile phones can enable attractive sensing applications in
different domains such as environmental monitoring, social networking, healthcare, etc. In this
paper we propose a cloud computing system dedicated on smart home applications. We design
the proposed wireless vision sensor network (WVSN) with its algorithm and hardware
implementation. In WVSN, The partial-vision camera strategy is applied to allocate the
computation task between the sensor node and the central server. Then we propose a high
performance segmentation algorithm. Meanwhile, an efficient binary data compression method
is proposed to cope with the result on labeling information. The proposed algorithm can provide
high precision rate for the smart home applications such as the gesture recognition and
humanoid tracking. To realize the physical system, we implement it on the embedded platform
and the central server with their transmission work
Wireless sensor network plays vital role in today’s life, it is a collection of sensors that are scattered in different directions which are further used to control and measure the physical conditions of environment as well as to organize to the data somewhere at centre location. As in context of greenhouse we can measure various parameters such as temperature, humidity, water level, insect monitoring and light intensity.
This document discusses energy efficiency in wireless sensor networks. It begins by introducing wireless sensor networks and some of their key applications. It then discusses several clustering-based energy efficiency protocols, including LEACH, HEED, TEEN, and EBC. These protocols aim to reduce energy consumption by organizing sensor nodes into clusters, with cluster heads responsible for aggregating and transmitting data from cluster members. The document also reviews related work on clustering algorithms and energy efficiency in wireless sensor networks. It discusses the goals of maximizing network lifetime while minimizing energy consumption.
IRJET- Fire Detector using Deep Neural NetworkIRJET Journal
This document summarizes a research paper that proposes using a deep neural network for real-time fire detection from CCTV surveillance videos. Specifically, it uses the SqueezeNet architecture, which requires fewer parameters and memory than other networks. The proposed system analyzes frames from surveillance videos and compares images to a trained dataset of fire and non-fire images using SqueezeNet. If a fire is detected, an alert message is immediately sent to the fire station. The system aims to provide early detection of fires from existing CCTV infrastructure to reduce accidents.
Iaetsd extending sensor networks into the cloud using tpss and lbssIaetsd Iaetsd
The document proposes two schemes, TPSS and LBSS, to improve the usefulness of sensory data and reliability of wireless sensor networks (WSNs) integrated with mobile cloud computing (MCC). TPSS uses a time and priority-based approach to selectively transmit sensor data to the cloud based on user requests. LBSS schedules sensor sleep states based on user location histories to optimize energy efficiency while maintaining reliable data collection. The schemes aim to balance useful data collection from WSNs with reliable delivery to mobile cloud users.
IRJET-E-Waste Management using RoboticsIRJET Journal
This document describes a proposed air quality monitoring system for cities using IoT technology. The system would use sensors to measure pollutants, temperature, humidity and air quality index in various locations. The sensor data would be wirelessly transmitted via Wi-Fi modules to a server hosting a website. The website would display the sensor readings in tabular form and provide alerts, news, and surveys about air pollution levels to raise public awareness. The proposed system was intended to be implemented using Arduino boards connected to sensors and ESP8266 Wi-Fi modules to transmit data to a cloud-based server and website.
This document summarizes a study on existing wireless sensor networks that can be used for structural health monitoring. It discusses three main wireless sensor network platforms: Sensor Andrew Architecture, a structural health monitoring system using smart sensors, and Snowfort, a new wireless sensor network platform designed for infrastructure monitoring. The document outlines the key components, advantages, and limitations of each wireless sensor network platform for structural health monitoring applications.
The emerge of the Internet of Things (IoT) data as a commodity to optimize public services such
as Fishing Locator has made sensor-cloud an important object. When sensors that are members of
multiple IoT gateways can inter-operate at the same time for more than one application, it will reduce cost
to deploy IoT infrastructure. However, reliability has also developed as the most important aspect for
real-time data collection that should be streamed constantly. Due to uncertainty factors sensors failure is
potentially occurred, then an adaptive approach should be addressed into this as to guarantee the flow of
streaming data. This paper proposed an adaptive sensor-cloud mechanism to manage the reliability by
using a runtime model approach where a transition model and dynamic software product line engineering
will take place to weaving the system. Our technique is comparable to other approaches and can be
implemented in many types of Cloud-based services.
This document discusses constrained passive tracking using wireless sensor networks. It begins with an introduction to wireless sensor networks and target tracking. It then describes the proposed system for passive tracking using a wireless sensor network. The system includes initializing the network, forming clusters using K-medoids clustering, creating an object to track, determining the sensor node nearest to the object, gathering information from sensors to the base station, and analyzing the results. It discusses the K-medoids clustering and Kalman filtering algorithms used for clustering and tracking, respectively. The document provides an example of applying the K-medoids algorithm to cluster a sample dataset.
Depiction of Body area network in Cloud EnvironmentIdris Ahmed
This document discusses combining body area networks (BANs) with cloud computing for medical applications. BANs use sensors placed on the body to monitor health metrics and transmit the data wirelessly. Combining BANs with cloud computing could address issues with BANs like limited storage and security by storing data remotely in the cloud. The document outlines the components of BANs, challenges of integrating them with cloud computing like security and power consumption, and potential solutions and applications of this approach like remote healthcare monitoring and increased data access.
Reliable Data Aggregation Protocol (RDAT) uses functional reputation to improve data reliability in wireless sensor networks. It assigns separate reputation values for sensing, routing, and aggregation actions. Nodes monitor neighbors and exchange reputation tables. Before transmitting data, nodes evaluate aggregators' aggregation reputation to detect compromised ones. Aggregators run the Reliable Data Aggregation algorithm to further ensure integrity by using routing and sensing reputation to identify false reports. Simulation results show RDAT significantly improves data reliability over attacked networks compared to existing trust systems.
Emergence Detection And Rescue Using Wireless Sensor NetworksIRJET Journal
This document describes a system that uses wireless sensor networks to help guide people to safety during emergencies. When an emergency occurs, sensors detect hazardous conditions and notify people in the area through their mobile devices. The system then provides navigation instructions to safely guide people out of the hazardous area while avoiding congestion. It considers alternative routes and ways to temporarily replace parts of routes. The proposed system aims to more efficiently evacuate an area during an emergency compared to existing systems that only focus on finding the safest individual path and do not account for potential congestion issues.
ABSTRACT The success of the cloud computing paradigm is due to its on-demand, self-service, and pay-by-use nature. Public key encryption with keyword search applies only to the certain circumstances that keyword cipher text can only be retrieved by a specific user and only supports single-keyword matching. In the existing searchable encryption schemes, either the communication mode is one-to-one, or only single-keyword search is supported. This paper proposes a searchable encryption that is based on attributes and supports multi-keyword search. Searchable encryption is a primitive, which not only protects data privacy of data owners but also enables data users to search over the encrypted data. Most existing searchable encryption schemes are in the single-user setting. There are only few schemes in the multiple data users setting, i.e., encrypted data sharing. Among these schemes, most of the early techniques depend on a trusted third party with interactive search protocols or need cumbersome key management. To remedy the defects, the most recent approaches borrow ideas from attribute-based encryption to enable attribute-based keyword search (ABKS
IRJET- Security in Ad-Hoc Network using Encrypted Data Transmission and S...IRJET Journal
This document discusses security techniques for data transmission in ad-hoc networks, specifically encrypted data transmission and steganography. It proposes a system that enables encrypted data transmission between nodes and uses steganography to hide encrypted data in cover files like images, audio, and video during transmission for additional security. The system architecture includes modules for user interface, embedding secret data in cover files, extracting secret data, sending files between nodes, and receiving files. It aims to securely transmit data in ad-hoc networks using both encryption and steganography to protect confidentiality and integrity of transmitted data.
DISTRIBUTED SYSTEM FOR 3D REMOTE MONITORING USING KINECT DEPTH CAMERAScscpconf
This article describes the design and development ofa system for remote indoor 3D monitoring
using an undetermined number of Microsoft® Kinect sensors. In the proposed client-server
system, the Kinect cameras can be connected to different computers, addressing this way the
hardware limitation of one sensor per USB controller. The reason behind this limitation is the
high bandwidth needed by the sensor, which becomes also an issue for the distributed system
TCP/IP communications. Since traffic volume is too high, 3D data has to be compressed before
it can be sent over the network. The solution consists in self-coding the Kinect data into RGB
images and then using a standard multimedia codec to compress color maps. Information from
different sources is collected into a central client computer, where point clouds are transformed
to reconstruct the scene in 3D. An algorithm is proposed to conveniently merge the skeletons
detected locally by each Kinect, so that monitoring of people is robust to self and inter-user
occlusions. Final skeletons are labeled and trajectories of every joint can be saved for event
reconstruction or further analysis.
IRJET-Experimental Investigation on the Effect of TiO2 Particles on MortarsIRJET Journal
This document describes a proposed system to monitor patient body temperature using IoT and a microservices architecture. An Arduino device with temperature sensor would collect body temperature data and send it over WiFi to a cloud system. The cloud system would use a microservices architecture with three main services: 1) an API to receive temperature data, 2) a queue to store incoming data, and 3) a computation service to analyze data for anomalies and send alerts. The system is designed to be horizontally scalable and fault tolerant to reliably process large amounts of patient monitoring data.
Abstract A wireless sensor network (WSN) consists of sensors which are densely distributed to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. The sensor data is transmitted to network coordinator which is heart of the wireless personal area network. In the modern scenario wireless networks contains sensors as well as actuators. ZigBee is newly developed technology that works on IEEE standard 802.15.4, which can be used in the wireless sensor network (WSN). The low data rates, low power consumption, low cost are main features of ZigBee. WSN is composed of ZigBee coordinator (network coordinator), ZigBee router and ZigBee end device. The sensor nodes information in the network will be sent to the coordinator, the coordinator collects sensor data, stores the data in memory, process the data, and route the data to appropriate node. Index Terms: WSN, ZigBee.
Environmental Monitoring using Wireless Sensor Networks (WSN) based on IOT.IRJET Journal
This document describes a wireless sensor network system for environmental monitoring using Internet of Things (IoT) technology. Key points:
- Sensor nodes collect data from sensors and send it wirelessly to a Raspberry Pi base station using a Zigbee protocol. The base station sends the data to a cloud server.
- The system monitors parameters like temperature, humidity, CO2, and vibration. Real-time sensor data is fetched by a web server and displayed to users via the Internet.
- The Raspberry Pi acts as the base station, connecting to multiple sensor nodes. It contains a database and web server to store and display the sensor data remotely.
- Common sensor nodes used include MQ2 and
An enhanced method for human life rescue systemeSAT Journals
Abstract
In the Modern World, Traffic is one of the main problem in our daily life. During Emergency conditions Vehicles like Ambulances
and Fire Controllers are delayed to reach their destination due to the Traffic Jams. Every one minute there will be an accident in
India, which causes loss of lives. This is a wireless module system which generates information from the accident zone to the
ambulance facility very quickly. Traffic blocking and management of free movement of vehicles were recognized as major
problem in modern urban areas. The accidents in the city have been increased due to negligible of rules and rise in motor
vehicles leads to the loss of life. The main idea behind this system is to endow with a flow for the rescue vehicles to reach the
hospitals in moment. The novelty in this paper is to manage the traffic signals in the alleyway of the rescue vehicle. This scheme is
fully programmed, thus it finds the accident point, to be in command of traffic lights, helps to reach the hospital in instance.
Keywords: SMAC, HLRS, etc
Development in building fire detection and evacuation system-a comprehensive ...IJECEIAES
Fire is both beneficial to man and his environment as well as destructive and deadly among all the natural disasters. A fire Accident occurs very rarely, but once it crops up its consequences will be devastating. The early detection of fire will help to avoid further consequences and saves the life of people. During the fire accidents, it is also important to guide people within the building to exit safely. Because of this, the paper gives a review of literature related to recent advancements in building fire detection and emergency evacuation system. It is intended to provide details about fire simulation tools with features, suitable hardware, communication methods, and effective user interface.
The document discusses groundwater contamination and depletion in the state of Gujarat and cities like Kanpur in India. It provides details on the status of groundwater in various districts in Gujarat, including those that are overexploited, critical or semi-critical. It notes the major groundwater quality issues in different districts. It also discusses how factors like excessive pumping, unregulated waste disposal and lack of rainwater harvesting are leading to a lowering of the water table in many areas in India.
The document is a seminar report on 4D ultrasound images submitted for a bachelor's degree. It discusses the history and development of ultrasound technology. 3D scans show still images of a baby in three dimensions, while 4D scans show moving 3D images, adding the dimension of time. 3D and 4D scans allow viewing a baby's skin rather than internal organs. They are as safe as 2D scans and used more for bonding than diagnosing problems. The best time for a 3D/4D scan is between 26-30 weeks of pregnancy.
The research team designed and built a proof-of-concept mesh network to collect environmental data from two sensors - a light sensor and a piezoelectric sensor - and display it to a base station laptop. The network connects the sensors to Arduino platforms which communicate wirelessly via XBee radios. A Python program formats and displays the sensor data for the user. While the network was able to successfully transmit sensor data, further work is needed to improve sensor reliability and network scalability for practical home implementation.
Wireless sensor network plays vital role in today’s life, it is a collection of sensors that are scattered in different directions which are further used to control and measure the physical conditions of environment as well as to organize to the data somewhere at centre location. As in context of greenhouse we can measure various parameters such as temperature, humidity, water level, insect monitoring and light intensity.
This document discusses energy efficiency in wireless sensor networks. It begins by introducing wireless sensor networks and some of their key applications. It then discusses several clustering-based energy efficiency protocols, including LEACH, HEED, TEEN, and EBC. These protocols aim to reduce energy consumption by organizing sensor nodes into clusters, with cluster heads responsible for aggregating and transmitting data from cluster members. The document also reviews related work on clustering algorithms and energy efficiency in wireless sensor networks. It discusses the goals of maximizing network lifetime while minimizing energy consumption.
IRJET- Fire Detector using Deep Neural NetworkIRJET Journal
This document summarizes a research paper that proposes using a deep neural network for real-time fire detection from CCTV surveillance videos. Specifically, it uses the SqueezeNet architecture, which requires fewer parameters and memory than other networks. The proposed system analyzes frames from surveillance videos and compares images to a trained dataset of fire and non-fire images using SqueezeNet. If a fire is detected, an alert message is immediately sent to the fire station. The system aims to provide early detection of fires from existing CCTV infrastructure to reduce accidents.
Iaetsd extending sensor networks into the cloud using tpss and lbssIaetsd Iaetsd
The document proposes two schemes, TPSS and LBSS, to improve the usefulness of sensory data and reliability of wireless sensor networks (WSNs) integrated with mobile cloud computing (MCC). TPSS uses a time and priority-based approach to selectively transmit sensor data to the cloud based on user requests. LBSS schedules sensor sleep states based on user location histories to optimize energy efficiency while maintaining reliable data collection. The schemes aim to balance useful data collection from WSNs with reliable delivery to mobile cloud users.
IRJET-E-Waste Management using RoboticsIRJET Journal
This document describes a proposed air quality monitoring system for cities using IoT technology. The system would use sensors to measure pollutants, temperature, humidity and air quality index in various locations. The sensor data would be wirelessly transmitted via Wi-Fi modules to a server hosting a website. The website would display the sensor readings in tabular form and provide alerts, news, and surveys about air pollution levels to raise public awareness. The proposed system was intended to be implemented using Arduino boards connected to sensors and ESP8266 Wi-Fi modules to transmit data to a cloud-based server and website.
This document summarizes a study on existing wireless sensor networks that can be used for structural health monitoring. It discusses three main wireless sensor network platforms: Sensor Andrew Architecture, a structural health monitoring system using smart sensors, and Snowfort, a new wireless sensor network platform designed for infrastructure monitoring. The document outlines the key components, advantages, and limitations of each wireless sensor network platform for structural health monitoring applications.
The emerge of the Internet of Things (IoT) data as a commodity to optimize public services such
as Fishing Locator has made sensor-cloud an important object. When sensors that are members of
multiple IoT gateways can inter-operate at the same time for more than one application, it will reduce cost
to deploy IoT infrastructure. However, reliability has also developed as the most important aspect for
real-time data collection that should be streamed constantly. Due to uncertainty factors sensors failure is
potentially occurred, then an adaptive approach should be addressed into this as to guarantee the flow of
streaming data. This paper proposed an adaptive sensor-cloud mechanism to manage the reliability by
using a runtime model approach where a transition model and dynamic software product line engineering
will take place to weaving the system. Our technique is comparable to other approaches and can be
implemented in many types of Cloud-based services.
This document discusses constrained passive tracking using wireless sensor networks. It begins with an introduction to wireless sensor networks and target tracking. It then describes the proposed system for passive tracking using a wireless sensor network. The system includes initializing the network, forming clusters using K-medoids clustering, creating an object to track, determining the sensor node nearest to the object, gathering information from sensors to the base station, and analyzing the results. It discusses the K-medoids clustering and Kalman filtering algorithms used for clustering and tracking, respectively. The document provides an example of applying the K-medoids algorithm to cluster a sample dataset.
Depiction of Body area network in Cloud EnvironmentIdris Ahmed
This document discusses combining body area networks (BANs) with cloud computing for medical applications. BANs use sensors placed on the body to monitor health metrics and transmit the data wirelessly. Combining BANs with cloud computing could address issues with BANs like limited storage and security by storing data remotely in the cloud. The document outlines the components of BANs, challenges of integrating them with cloud computing like security and power consumption, and potential solutions and applications of this approach like remote healthcare monitoring and increased data access.
Reliable Data Aggregation Protocol (RDAT) uses functional reputation to improve data reliability in wireless sensor networks. It assigns separate reputation values for sensing, routing, and aggregation actions. Nodes monitor neighbors and exchange reputation tables. Before transmitting data, nodes evaluate aggregators' aggregation reputation to detect compromised ones. Aggregators run the Reliable Data Aggregation algorithm to further ensure integrity by using routing and sensing reputation to identify false reports. Simulation results show RDAT significantly improves data reliability over attacked networks compared to existing trust systems.
Emergence Detection And Rescue Using Wireless Sensor NetworksIRJET Journal
This document describes a system that uses wireless sensor networks to help guide people to safety during emergencies. When an emergency occurs, sensors detect hazardous conditions and notify people in the area through their mobile devices. The system then provides navigation instructions to safely guide people out of the hazardous area while avoiding congestion. It considers alternative routes and ways to temporarily replace parts of routes. The proposed system aims to more efficiently evacuate an area during an emergency compared to existing systems that only focus on finding the safest individual path and do not account for potential congestion issues.
ABSTRACT The success of the cloud computing paradigm is due to its on-demand, self-service, and pay-by-use nature. Public key encryption with keyword search applies only to the certain circumstances that keyword cipher text can only be retrieved by a specific user and only supports single-keyword matching. In the existing searchable encryption schemes, either the communication mode is one-to-one, or only single-keyword search is supported. This paper proposes a searchable encryption that is based on attributes and supports multi-keyword search. Searchable encryption is a primitive, which not only protects data privacy of data owners but also enables data users to search over the encrypted data. Most existing searchable encryption schemes are in the single-user setting. There are only few schemes in the multiple data users setting, i.e., encrypted data sharing. Among these schemes, most of the early techniques depend on a trusted third party with interactive search protocols or need cumbersome key management. To remedy the defects, the most recent approaches borrow ideas from attribute-based encryption to enable attribute-based keyword search (ABKS
IRJET- Security in Ad-Hoc Network using Encrypted Data Transmission and S...IRJET Journal
This document discusses security techniques for data transmission in ad-hoc networks, specifically encrypted data transmission and steganography. It proposes a system that enables encrypted data transmission between nodes and uses steganography to hide encrypted data in cover files like images, audio, and video during transmission for additional security. The system architecture includes modules for user interface, embedding secret data in cover files, extracting secret data, sending files between nodes, and receiving files. It aims to securely transmit data in ad-hoc networks using both encryption and steganography to protect confidentiality and integrity of transmitted data.
DISTRIBUTED SYSTEM FOR 3D REMOTE MONITORING USING KINECT DEPTH CAMERAScscpconf
This article describes the design and development ofa system for remote indoor 3D monitoring
using an undetermined number of Microsoft® Kinect sensors. In the proposed client-server
system, the Kinect cameras can be connected to different computers, addressing this way the
hardware limitation of one sensor per USB controller. The reason behind this limitation is the
high bandwidth needed by the sensor, which becomes also an issue for the distributed system
TCP/IP communications. Since traffic volume is too high, 3D data has to be compressed before
it can be sent over the network. The solution consists in self-coding the Kinect data into RGB
images and then using a standard multimedia codec to compress color maps. Information from
different sources is collected into a central client computer, where point clouds are transformed
to reconstruct the scene in 3D. An algorithm is proposed to conveniently merge the skeletons
detected locally by each Kinect, so that monitoring of people is robust to self and inter-user
occlusions. Final skeletons are labeled and trajectories of every joint can be saved for event
reconstruction or further analysis.
IRJET-Experimental Investigation on the Effect of TiO2 Particles on MortarsIRJET Journal
This document describes a proposed system to monitor patient body temperature using IoT and a microservices architecture. An Arduino device with temperature sensor would collect body temperature data and send it over WiFi to a cloud system. The cloud system would use a microservices architecture with three main services: 1) an API to receive temperature data, 2) a queue to store incoming data, and 3) a computation service to analyze data for anomalies and send alerts. The system is designed to be horizontally scalable and fault tolerant to reliably process large amounts of patient monitoring data.
Abstract A wireless sensor network (WSN) consists of sensors which are densely distributed to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. The sensor data is transmitted to network coordinator which is heart of the wireless personal area network. In the modern scenario wireless networks contains sensors as well as actuators. ZigBee is newly developed technology that works on IEEE standard 802.15.4, which can be used in the wireless sensor network (WSN). The low data rates, low power consumption, low cost are main features of ZigBee. WSN is composed of ZigBee coordinator (network coordinator), ZigBee router and ZigBee end device. The sensor nodes information in the network will be sent to the coordinator, the coordinator collects sensor data, stores the data in memory, process the data, and route the data to appropriate node. Index Terms: WSN, ZigBee.
Environmental Monitoring using Wireless Sensor Networks (WSN) based on IOT.IRJET Journal
This document describes a wireless sensor network system for environmental monitoring using Internet of Things (IoT) technology. Key points:
- Sensor nodes collect data from sensors and send it wirelessly to a Raspberry Pi base station using a Zigbee protocol. The base station sends the data to a cloud server.
- The system monitors parameters like temperature, humidity, CO2, and vibration. Real-time sensor data is fetched by a web server and displayed to users via the Internet.
- The Raspberry Pi acts as the base station, connecting to multiple sensor nodes. It contains a database and web server to store and display the sensor data remotely.
- Common sensor nodes used include MQ2 and
An enhanced method for human life rescue systemeSAT Journals
Abstract
In the Modern World, Traffic is one of the main problem in our daily life. During Emergency conditions Vehicles like Ambulances
and Fire Controllers are delayed to reach their destination due to the Traffic Jams. Every one minute there will be an accident in
India, which causes loss of lives. This is a wireless module system which generates information from the accident zone to the
ambulance facility very quickly. Traffic blocking and management of free movement of vehicles were recognized as major
problem in modern urban areas. The accidents in the city have been increased due to negligible of rules and rise in motor
vehicles leads to the loss of life. The main idea behind this system is to endow with a flow for the rescue vehicles to reach the
hospitals in moment. The novelty in this paper is to manage the traffic signals in the alleyway of the rescue vehicle. This scheme is
fully programmed, thus it finds the accident point, to be in command of traffic lights, helps to reach the hospital in instance.
Keywords: SMAC, HLRS, etc
Development in building fire detection and evacuation system-a comprehensive ...IJECEIAES
Fire is both beneficial to man and his environment as well as destructive and deadly among all the natural disasters. A fire Accident occurs very rarely, but once it crops up its consequences will be devastating. The early detection of fire will help to avoid further consequences and saves the life of people. During the fire accidents, it is also important to guide people within the building to exit safely. Because of this, the paper gives a review of literature related to recent advancements in building fire detection and emergency evacuation system. It is intended to provide details about fire simulation tools with features, suitable hardware, communication methods, and effective user interface.
The document discusses groundwater contamination and depletion in the state of Gujarat and cities like Kanpur in India. It provides details on the status of groundwater in various districts in Gujarat, including those that are overexploited, critical or semi-critical. It notes the major groundwater quality issues in different districts. It also discusses how factors like excessive pumping, unregulated waste disposal and lack of rainwater harvesting are leading to a lowering of the water table in many areas in India.
The document is a seminar report on 4D ultrasound images submitted for a bachelor's degree. It discusses the history and development of ultrasound technology. 3D scans show still images of a baby in three dimensions, while 4D scans show moving 3D images, adding the dimension of time. 3D and 4D scans allow viewing a baby's skin rather than internal organs. They are as safe as 2D scans and used more for bonding than diagnosing problems. The best time for a 3D/4D scan is between 26-30 weeks of pregnancy.
The research team designed and built a proof-of-concept mesh network to collect environmental data from two sensors - a light sensor and a piezoelectric sensor - and display it to a base station laptop. The network connects the sensors to Arduino platforms which communicate wirelessly via XBee radios. A Python program formats and displays the sensor data for the user. While the network was able to successfully transmit sensor data, further work is needed to improve sensor reliability and network scalability for practical home implementation.
Review Paper on Smart Sensor Network for Air Quality MonitoringAM Publications
Green and clean environment across the globe is very much essential for the health of the nature. Unfortunately different kinds of pollutions are affecting the quality of the environment around us. This review paper is mainly dealing with “Air pollution”, which is a very sensitive issue in developing and developed countries and is directly affecting the human health and disturbs the biological balance of mother earth. Here our aim is to develop a system which will detect maximum air pollutants and which is highly responsive, accurate and low cost and low power consuming.
This document discusses cloud computing and outlines its key features and research challenges. It defines cloud computing according to NIST, describes common cloud service models (IaaS, PaaS, SaaS), and summarizes the state-of-the-art implementation including distributed file systems like HDFS and frameworks like MapReduce. However, it notes that current technologies are not developed enough and there are many open research challenges around automated provisioning, virtual machine migration, server consolidation, traffic management, data security, software frameworks, storage, and novel cloud architectures.
The document discusses the Global Positioning System (GPS). GPS is a worldwide radio-navigation system consisting of 24 satellites and their ground stations. It allows users to determine their precise location by calculating the time it takes signals from GPS satellites to reach their receiver. The signals contain information about the satellite's location and timing, allowing the receiver to use triangulation to determine the user's position. While there are some sources of error, such as atmospheric delays, most errors can be corrected through mathematics and modeling or with differential GPS.
The document discusses how the Internet of Things (IoT) can enable health and wellness. IoT involves connecting devices to securely capture, share, and analyze vital data through cloud-based services. The global IoT market is expected to be worth $373 billion by 2020. IoT in healthcare allows for remote patient monitoring through connected devices that transmit vital signs to doctors' dashboards. Examples described include a necklace that monitors various health metrics and smart trackers, forks, toothbrushes, and insoles that track activities and provide health feedback. Challenges to IoT-based systems include security, connectivity, power consumption, and lack of common standards.
Seminar Report on RFID Based Trackin SystemShahrikh Khan
The document is a seminar report submitted by Shahrukh Ayaz Khan on RFID based tracking system privacy control. It discusses RFID technology, how RFID works, applications of RFID, privacy and security issues related to RFID, and approaches to address these issues. The report contains an abstract, introduction discussing background and objectives of the report, literature review on related work and existing technologies, methodology covering RFID components and functioning, discussion on RFID security and privacy issues and solutions, analysis of advantages and disadvantages of RFID, and conclusion.
This document discusses sensors in the Android operating system. It describes the hardware and software architecture of the Android sensor framework. The hardware includes sensors like accelerometers and magnetometers. The software includes Android classes for accessing sensor data and interfaces for sensor event listeners. It also provides details on implementing the sensor library and callback functions to interface between the Android framework and low-level Linux drivers.
This document presents on PhoneGap, an open-source mobile development framework. PhoneGap allows developers to build mobile apps using HTML, CSS, and JavaScript and deploy them across various platforms. It bridges the gap between web technologies and native mobile development. Key features of PhoneGap discussed include writing once and deploying to multiple platforms, accessing device hardware, and using standards-based web technologies. Advantages are cross-platform development and leveraging native features, while limitations include not having latest features and relying on community support.
The document discusses various sensors and gestures used in smartphones. It describes common motion sensors like accelerometers, gyroscopes, and magnetometers that detect movement and orientation. Other sensors measure light, pressure, temperature and fingerprints. Gestures allow natural interaction through computer vision and touch inputs like taps, swipes and pinches. Sensors provide context awareness while gestures offer intuitive control, but accuracy can vary by distance and ambient conditions. Sensor networks extend basic functions but also introduce disadvantages around cost, speed and radiation exposure.
This document discusses testing sensors on Android devices and provides code examples. It explains how to create a project that checks which sensors are supported on a device by populating a list with sensor names and statuses. The document tests several sensors like accelerometer, light, and orientation on an emulator and real device. It also provides an example of using the accelerometer sensor to move an image based on device movement.
This document discusses the need for green data centers and provides strategies for making data centers more energy efficient. It notes that while many organizations say they are green, few have specific targets or programs to reduce their carbon footprint. As data center electricity consumption and costs rise, running out of power capacity, cooling capacity, and physical space are major concerns. The document then provides questions to assess a data center's energy efficiency in terms of facilities, IT equipment, and utilization rates. It recommends strategies like optimizing infrastructure utilization and choosing more efficient hardware and cooling options. The goal is to improve the data center infrastructure efficiency metric and lower costs by reducing redundant, underutilized resources.
This document discusses sensors and the sensor manager in Android. It begins by introducing the sensor manager and how to get sensor instances through the sensor manager. It then describes the sensor event listener interface that processes sensor events. Finally, it lists some of the different types of sensors available in Android like accelerometers and gyroscopes.
The document discusses the Internet of Things (IoT), which allows machines to communicate with each other through sensors and connectivity to share data and take actions. It describes IoT as a network of physical objects that can interact using technologies like RFID, sensors, wireless communication, energy harvesting, and cloud computing. The document outlines the architecture of IoT including sensor, gateway/network, management service, and application layers. It discusses current and future applications of IoT in areas like smart cities, healthcare, agriculture, and transportation. Major challenges of IoT include big data explosion, security/privacy, and power efficiency. The future of IoT is presented as increasingly connected smart homes, grids, cities, and factories.
Talk on Industrial Internet of Things @ Intelligent systems tech forum 2014Ahmed Mahmoud
The Industrial Internet can be thought of as Intelligent Industrial Systems. A subset of Intelligent Systems per IDC’s taxonomy, these systems have extremely high value not just in terms of product and process optimizations, efficiency and cost savings but in the enablement of new business models such as mass customization in manufacturing. This session will focus on the state of Industrial Internet today, the efforts underway to make the Industrial Internet a reality, leading companies, technologies and products in the space, efforts at standardization, case studies of the Industrial Internet in action, and opportunities in the space.
The document discusses wireless sensor networks and describes their key characteristics. It notes that wireless sensor networks consist of low-power smart sensor nodes distributed over a large field to enable wireless sensing and data networking. The sensor nodes contain sensors, processors, memory, and radios. Wireless sensor networks can be either unstructured with dense node distribution or structured with few scattered nodes.
This document declares that the project report titled "INVENTORY MANAGEMENT SYSTEM" submitted by the candidate Y.Srinivas to Regency Institute of Technology, Yanam is for the partial fulfillment of the requirements for a B.Tech degree in Computer Science. The candidate declares that the work reported in the project was carried out under the guidance of Mr. CH. RAJA RAMESH and has not been submitted elsewhere for another degree or diploma.
The document provides tips for a five part interview process: 1) Prepare before the interview by researching the company and dressing professionally. 2) Greet the interviewer positively. 3) Maintain good posture and provide thorough, honest answers to questions. 4) Ask relevant questions and follow up appropriately. 5) Send a thank you note after and follow up respectfully about the hiring decision. Key advice includes arriving early, making eye contact, having questions prepared, and following up to show continued interest in the position.
This document discusses the history and development of onboard vehicle diagnostics (OBD) standards. It describes the Clean Air Act and Air Quality Act which led to the establishment of emissions standards. The first OBD standard was introduced to help ensure reliable emissions control systems. OBD-II was later enhanced standard made mandatory for all vehicles from 1996 onward. It established a standardized way for technicians to access diagnostic information from a vehicle to help with repairs. The document then outlines the objectives and approach of a project to design a low-cost OBD-II scanner.
This document describes a project report for a GSM based e-notice board. It includes an introduction that provides an overview of the project, describing information transfer and system components. It also includes sections on literature survey, problem definition, system requirements specification, system modelling and design, implementation, testing, and conclusion. The project involved interfacing a GSM modem, LCD display, and microcontroller to allow notices to be sent via SMS and displayed on the board. It was implemented at the institute level as a proposal.
Seminar Report - Managing the Cloud with Open Source ToolsNakul Ezhuthupally
This document discusses managing the cloud with open source tools. It provides an overview of cloud computing, including its key characteristics like elasticity and pay-per-use model. It also covers open source philosophy and the importance of open source tools for cloud management. The document evaluates several popular open source provisioning, configuration, automation and monitoring tools used for cloud management. It concludes that while cloud computing provides benefits, effective management is still needed and open source tools can help organizations manage their cloud resources.
This document is a project report submitted by four students - Apeksha A. Jain, Rohit M. Kulkarni, Soham C. Wadekar, and Kedar D. Wagholikar - for their Bachelor of Engineering degree. The report details a project on dynamic routing of packets in wireless sensor networks conducted under the guidance of Prof. G.R. Pathak. The project aims to implement clustering in a wireless sensor network and analyze the effects of increasing cluster size on cluster head energy. It further aims to implement an energy efficient dynamic algorithm to re-elect cluster heads periodically in order to save energy. The report presents the background, problem statement, project planning, analysis, design,
This document summarizes a dissertation report on designing and implementing a SCADA system using wireless sensors to control fire effects in a refinery. The report acknowledges those who helped with the project and states the research aims to understand how to design interactive systems that are useful and save lives. It then discusses requirements for the hardware and software, technical issues considered, and designing the network topology.
M.tech Term paper report | Cognitive Radio Network Shashank Narayan
This document is a term paper report submitted by Shashank Narayan for the partial fulfillment of the requirements for a Master of Technology degree in Digital Communication. The paper is titled "Artificial Intelligence Based Cognitive Routing for Cognitive Radio Networks" and is supervised by Dr. Aarti Jain. The paper provides declarations, certificates, acknowledgements and explores topics related to cognitive radio, machine learning, decision making techniques, and the implementation of cognitive routing in cognitive radio networks.
Implementing Saas as Cloud controllers using Mobile Agent based technology wi...Sunil Rajput
Setup your own cloud for Software as a Service (SaaS) over the existing LAN in your laboratory. In this assignment you have to write your own code for cloud controllers using open source technologies without HDFS. Implementing the basic operations may be like uploading and downloading files on/from cloud in encrypted form.
This document describes a project to integrate satellite substations in Colombo City, Sri Lanka into the existing SCADA system of the Ceylon Electricity Board. The project aims to connect more satellite substations and automate them in a cost-effective way. It also aims to provide CEB engineers mobile access to alarm data through a virtual private network. The project is divided into four phases: 1) SCADA system integration using Viola M2M gateways and RTUs; 2) Design and implementation of a secondary server for packet inspection and analysis; 3) Development of Android and web applications; 4) Experimenting with wireless communication to bypass power transformers. Challenges addressed include interoperability issues and
This document presents a project on an accident detection and reporting system developed by Solomon Mutwiri and William Ateka. The system uses sensors to detect vehicle vibrations during an accident and then sends SMS and voice call alerts to emergency responders using a GSM module. The project aims to reduce response times during emergencies by notifying responders as quickly as possible after an accident occurs. It discusses the design and components of the system including an Arduino board, vibration sensor, LCD display, GSM module and power supply. The document outlines the methodology, testing and results of the prototype system, finding it was able to successfully detect accidents and transmit alerts. It concludes the system could help save lives by facilitating faster emergency response
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Web performance prediction using geo statistical methodeSAT Journals
This document discusses using geostatistical methods like the Turning Bands method to predict web performance in distributed systems and networks. It proposes applying this method to forecast spatial-temporal web performance across nodes. The method considers relationships between locations to interpolate performance at unmonitored locations. It also describes using a vibration sensor and GPS to detect vehicle accidents, locate the accident site, and route information to the nearest hospital to dispatch an ambulance.
This project report describes the development of a system using near field technologies like NFC for monitoring and surveillance purposes for the Government of India. The system would allow tracking of various entities and individuals using NFC tags and geographic location data. NFC cards would be encrypted and distributed to specified people or objects. When read using a mobile device or reader, location and timestamp data would be sent to a server. The system was designed with modules for authorization, chart rendering, map rendering, and NFC functionality. It would integrate with existing applications to provide useful real-time data and information to the government. The project involved designing the system architecture, developing the required services, and testing the functionality and performance.
This document describes an Android application called AMIZONER that was created to allow students to easily check their attendance records from Amity University's student portal. The application logs into the student portal using HTTPS, parses the attendance details, stores them locally in an SQLite database. It then displays the computed attendance information to users in a user-friendly way. The application was created using technologies like HTTPS POST/GET, HTML parsing, SQLite database, and the Android platform. It also includes features for server-client communication using Google App Engine and monitoring application usage with Google Analytics and monetization with advertisements.
Accelerated Prototyping of Cyber Physical Systems in an Incubator ContextSreyas Sriram
The document summarizes the prototyping history of a microscope prototype developed by a startup incubated at a technology business incubator. It describes the evolution from initial prototypes (V1 and V2) composed primarily of 3D printed parts to later prototypes (V3, static prototype, skeleton BOM, mechatronics BOM) composed mainly of commercial off-the-shelf components. This shift led to an increase in the total number of components from around 40 to over 200. The increased use of standardized, replaceable parts like fasteners contributed to making the design more modular. Analysis of contributions by student designers found that the prototyping process, which began in 2017, involved over 700 days of combined work until completion
This seminar report discusses digital twins, which create living digital simulation models of physical assets. A digital twin continuously learns and updates itself from multiple real-time data sources to represent the current status of its corresponding physical twin. The report provides definitions of different types of digital twins and discusses how GE uses digital twins for applications like asset management, operations optimization, and advanced controls. It also covers the characteristics and advantages of digital twins, as well as examples of their use in various industrial sectors.
This document is a dissertation presented by Kurt Portelli for the degree of Master of Science at the University of Glasgow. It discusses distributed statistical learning and knowledge diffusion in IoT environments. Specifically, it proposes a system that allows each sensor to locally gather knowledge through statistical learning and distribute it efficiently to minimize power consumption and transmission errors. It investigates how increasing the allowed error affects query accuracy on the system. The document outlines the contributions of the work, including the use of an ensemble learning approach and a "reliability" variable to select the best acquired statistical knowledge for different input spaces.
The document provides a seminar report on cloud storage. It discusses the evolution of cloud computing, cloud architecture including delivery and deployment models, and security challenges in cloud computing. Specifically, it outlines key security concerns around outsourcing data and computations to the cloud including loss of control, privacy violations, and ensuring only authorized access. The report was prepared by a student for their BTech degree and approved by their college.
E secure transaction project report (Design and implementation of e-secure t...AJIT Singh
The report is on the design and implementation of the e-secure transaction the formatting of the report is based on IIT
This is the project report of the Design and implementation of e-secure transaction system that is my college days.
the formatting of this report is based on the IIT formate so you can copy the formate
This document is Roshan Singh's internship report submitted in partial fulfillment of a Bachelor's degree in Information Technology from Tribhuvan University. The report details his internship at Subisu Cablenet Pvt. Ltd., an Internet service provider in Kathmandu, Nepal. During his internship, Roshan gained experience configuring routers, modems, and ONU devices for customers, troubleshooting internet issues, and replacing equipment. He learned about ISP network operations and improved his technical and communication skills. The report includes worksheets documenting the tasks he completed during the internship period.
This document is a dissertation submitted by Avinash Singh Yadav to the Malaviya National Institute of Technology in Jaipur, India to fulfill the requirements for a Master of Technology degree in Embedded Systems. The dissertation proposes an intelligent traffic light system using deep learning. It describes certificates from the department certifying the work, a declaration by the author, and acknowledgments. It also includes an abstract that summarizes the proposed use of deep learning and a raspberry pi to detect vehicle numbers with a camera and change the traffic light pattern accordingly to reduce congestion based on vehicle counts. The table of contents and list of figures and abbreviations are also included.
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1. Seminar Report
Titled
Integration of Wireless Sensor Network
with Cloud Computing
Submitted in Partial Fulfilment of the Requirement for
the Award of Degree of Master of Engineering in
Computer Engineering
Submitted by
Vintesh Patel
120420702017, 3rd Sem
Under the Guidance of
Prof. Bintu Kadhiwala, CO Deptt. SCET, Surat
October, 2013-14
Computer Engineering Department
Sarvajanik College of Engineering & Technology
Surat-395001, India
2. Department of Computer Engineering
SARVAJANIK COLLEGE OF ENGINEERING AND TECHNOLOGY,
SURAT
(2013-14)
Declaration
I hereby declare that the work being presented in this Seminar Report
entitled “Integration of Wireless Sensor Network with Cloud Computing” by
VINTESH PATEL, of 3rd Semester, ME (Computer Engineering) bearing
Enrollment No: 120420702017 submitted to the Computer Engineering
Department at Sarvajanik College of Engineering and Technology, Surat; is an
authentic record of my own work carried out during the period of odd semester
2013 under the supervision of Prof. Bintu Kadhiwala.
Neither the source code there in, nor the content of the seminar report
have been copied or downloaded from any other source. I understand that my
result grades would be revoked if later it is found to be so.
I also declare that I have read all the instructions given below.
_____________
Vintesh Patel
i
3. Department of Computer Engineering
SARVAJANIK COLLEGE OF ENGINEERING AND TECHNOLOGY,
SURAT
(2013-14)
Certificate
This is to certify that seminar entitled ‘Integration of Wireless Sensor Network
with Cloud Computing’ is delivered and report is submitted by Vintesh Patel
of 3rd Sem for partial fulfilment of requirement for the degree of MASTER OF
ENGINEERING (Computer Engineering) of Sarvajanik College of Engineering
and Technology, Surat during the academic year 2013-2014.
Prof. Bintu Kadhiwala
Examiners
Head, Department of Computer Engineering
Date: ___________
Place: ______________
ii
4. Acknowledgements
I consider myself fortunate to have been associated with Prof. Bintu Kadhiwala,
Supervisor(Guide), Department of Computer Engineering, Sarvajanik College of Engineering
& Technology College, Surat. I am deeply indented him, for his esteemed guidance and
continuous encouragement during my seminar work. I take this opportunity to thank him
heartily for keen interest, creative suggestions, constant guidance and kind cooperation given
by him during the entire preparation of this seminar.
I am most thankful to all staff members of Computer Engineering Department,
Sarvajanik College of Engineering & Technology College, Surat, for carrying out this seminar
work at the college duration.
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5. Abstract
A typical WSN contains spatially distributed sensors that can cooperatively monitor the
environmental conditions, like sound, temperature, pressure, motion, vibration, pollution, and
so forth. WSN applications have been used in several important areas, such as healthcare,
military, critical infrastructure monitoring, environment monitoring, and manufacturing. At
the same time, WSN have some issues like memory, energy, computation, communication, and
scalability, efficient management. So, there is a need for a powerful and scalable highperformance computing and massive storage infrastructure for real-time processing and
storing of the WSN data as well as analysis (online and offline) of the processed information
to extract events of interest.
In this scenario, cloud computing is becoming a promising technology to provide a
flexible stack of massive computing, storage, and software services in a scalable and
virtualized manner at low cost.
Therefore, Sensor-Cloud (i.e. an integrated version of WSN & Cloud Computing)
infrastructure is becoming popular nowadays that can provide an open, flexible, and
reconfigurable platform for several monitoring and controlling applications.
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6. Table of Contents
Declaration ........................................................................................................................... i
Certificate ............................................................................................................................ ii
Acknowledgements ............................................................................................................. iii
Abstract .............................................................................................................................. iv
Table of Contents ................................................................................................................ v
List of Acronyms ............................................................................................................... vii
List of Figures................................................................................................................... viii
1.
Introduction ........................................................................................................... 1
1.1
Motivation ............................................................................................................... 1
1.2
What is Sensor-Cloud Infrastructure? ...................................................................... 1
2.
Theoretical Background & Literature survey ...................................................... 3
1.1
Wireless Sensor Networks ....................................................................................... 3
1.2
Cloud Computing .................................................................................................... 4
3.
Sensor-Cloud Architecture.................................................................................... 7
3.1
General Architecture ............................................................................................... 7
3.3
Sensor-Cloud Service Life-Cycle Model and Its Layered Structure.......................... 9
3.3.1
Service Life Cycle Model of Sensor-Cloud ...................................................... 9
3.3.2
Layered Structure of Sensor-Cloud. ............................................................... 10
3.4
4.
Current Approaches ............................................................................................... 11
Detailed Study of Sensor-Cloud Infrastructures ................................................ 14
4.1
Sensor-Cloud Infrastructure ................................................................................... 14
4.1.1
Design Considerations ................................................................................... 14
4.1.2
Entities involved ............................................................................................ 16
4.1.3
System Architecture ....................................................................................... 17
4.1.4
Components in Architecture ........................................................................... 17
4.2
Data Centric – Sensor-Cloud Infrastructure Framework ......................................... 18
4.2.1
4.3
Service Oriented – Sensor-Cloud Infrastructure Framework .................................. 21
4.3.1
5.
Components for Message Exchange in Architecture ....................................... 20
Service Lifecycle ........................................................................................... 22
Pros & Cons of Sensor-Cloud Infrastructure ..................................................... 24
5.1
Pros of Sensor-Cloud Infrastructure ....................................................................... 24
5.2
Cons of Sensor-Cloud Infrastructure ...................................................................... 25
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7. 6.
Conclusion and Future Work ............................................................................. 26
References.......................................................................................................................... 27
vi
8. List of Acronyms
ACDU
ACEU
DPU
DR
IaaS
IAMU
PaaS
SaaS
SML
SOAP
SI
VS
WSN
Access Control Decision Unit
Access Control Enforcement Unit
Data Processing Unit
Data Repository
Infrastructure as a Service
Identity & Access Management Unit
Platform as a Service
Software as a Service
Sensor Markup Language
Simple Object Access Protocol
Service Instance
Virtual Sensor
Wireless Sensor Network
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9. List of Figures
Figure 2-1 Wireless Sensor Network – Architecture ............................................................. 3
Figure 2-2 Cloud Computing – Overview ............................................................................. 5
Figure 3-1 Sensor-Cloud Architecture .................................................................................. 8
Figure 3-2 Life Cycle of Sensor-Cloud Infrastructure ........................................................... 9
Figure 3-3 Layered Structured of Sensor-Cloud Model ....................................................... 11
Figure 4-1 Sensor Cloud Infrastructure - Overview ............................................................ 14
Figure 4-2 Relationship among Virtual Sensors, and Physical Sensors ............................... 15
Figure 4-3 Proposed System Architecture ........................................................................... 17
Figure 4-4 Proposed Architecture of Sensor-Cloud ............................................................. 19
Figure 4-5 Communication Flow in Proposed Architecture ................................................ 20
Figure 4-6 Service Module ................................................................................................ 21
Figure 4-7 Service Providing Phase ................................................................................... 22
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1. Introduction
1.1
Motivation
A typical sensor network may consist of a number of sensor nodes acting upon together to
monitor a region and fetch data about the surroundings. A typical WSN contains self-regulated
sensors that can cooperatively monitor the environmental conditions, like sound, temperature,
pressure, motion, vibration, pollution, fire like, and other application dependent events. Each
node in a sensor network is loaded with a radio transceiver or some other wireless
communication device, a small microcontroller, and an energy source most often cells/battery.
WSNs have some of the limitations like in terms of memory, energy, computation,
communication and scalability, efficient management of the large number of WSNs data.
Cloud computing allows the systems and users to use Platform as a Service (PaaS), for
example, Operating Systems (OSs), Infrastructure as a Service (IaaS), for example, storages
and servers, and Software as a Service (SaaS), for example, application level programs, and so
forth at a very low cost which are being provided by several cloud providers (e.g., Amazon,
Google, and Microsoft) on the basis of pay per use services [6]. Cloud computing platform
dynamically available, configures, and updates the servers as and when needed by end users.
The limitations of WSNs are the plus points in the Cloud Computing.
This is the reason why the integrations of Cloud Computing & WSNs will lead to
greater benefits & efficiency.
1.2
What is Sensor-Cloud Infrastructure?
Sensor-Cloud Infrastructure i.e. integrated version of Wireless Sensor Networks and Cloud
Computing is powerful and scalable high-performance computing and massive storage
infrastructure for real-time processing and storing of the WSN data (online as well as
previously collected offline) as well as analysis of the processed information to extract events
of interest.
Some of the definition of Sensor Cloud Architecture are (on the next page),
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An infrastructure that allows truly pervasive computation using sensors as an interface
between physical and cyber worlds, the data-compute clusters as the cyber backbone and the
internet as the communication medium [7, 8].
It is a unique sensor data storage, visualization and remote management platform that leverage
[sic] powerful cloud computing technologies to provide excellent data scalability, rapid
visualization, and user programmable analysis. It is originally designed to support long-term
deployments of MicroStrain wireless sensors, Sensor-Cloud now supports any web-connected
third party device, sensor, or sensor network through a simple OpenData API [9].
When WSN is integrated with cloud computing environment, several shortfalls of WSN
like storage capacity of the data collected on sensor nodes and processing of these data together
would become much easier. Since cloud computing provides a vast storage capacity and
processing capabilities, it enables collecting the huge amount of sensor data by linking the
WSN and cloud through the gateways on both sides, that is, sensor gateway and cloud gateway.
Sensor gateway collects information from the sensor nodes of WSN, compresses it, and
transmits it back to the cloud gateway which in turn decompresses it and stores it in the cloud
storage server, which is sufficiently large [10]. Sensor-Cloud is a new paradigm for cloud
computing that uses the physical sensors to accumulate its data and transmit all sensor data into
a cloud computing infrastructure [5]. Sensor-Cloud infrastructure is used that enables the
sensors to be utilized on a digital infrastructure by virtualizing the physical sensor on a cloud
computing platform according to the need of the user and application. These virtualized sensors
on a cloud computing platform are dynamic in nature and hence facilitate automatic
provisioning of its services as and when required by users.
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2. Theoretical Background & Literature survey
1.1
Wireless Sensor Networks
A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to
monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and
to cooperatively pass their data through the network to a main location [1]. Wireless Sensor
Network applications have been used in important areas, like healthcare, military, critical
infrastructure monitoring, environment monitoring, and manufacturing.
Figure 2-1 Wireless Sensor Network – Architecture [7]
WSN Applications are listed in [2] shows the usefulness in respective areas,
Military
Logistics in urban warfare
Battlefield surveillance
Military situation awareness
Computing, intelligence, surveillance, reconnaissance, and targeting systems
Mobile wireless low-rate networks for precision location
Including industrial, retail, hospital, residential, and office environments, while
maintaining low-rate data communications for monitoring, messaging and
control
Airports
Smart badges and tags
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Wireless luggage tags
Passive mobility (e.g., attached to a moving object not under the control of the
sensor node)
Medical/Health
Sensors for: blood flow, respiratory rate, ECG Electrocardiogram
Monitoring people's locations and health conditions
Monitor patients and assist disabled patients
Ocean/Weather Monitoring
Monitoring Fish, Water Displacements/NOAA Data
Even if WSN have the vast area of application but it also have limitation which restricts
its application and/or area. WSNs have to face many limitations due to its architecture
regarding their communications (like short communication range, security and privacy,
reliability, mobility, etc.) and resources (like power considerations, storage capacity,
processing capabilities, bandwidth availability, etc.). Besides, WSN has its own resource and
design constraints. Design constraints are application specific and dependent on monitored
environment. Based on the monitored environment, network size in WSN varies.
For
monitoring large environment, there is limited communication between nodes due to
obstructions into the environment, which in turn affects the overall network topology (or
connectivity) [3].
The Sensor-Cloud Architecture i.e. Integration of WSN with Cloud Computing focuses
on overcoming the limitations of the WSN by using the advantages of Cloud Services.
1.2
Cloud Computing
Cloud Computing can be defined by many definitions/ways, some of the definition is –
Cloud computing is a model for enabling convenient, on demand network access to a shared
pool of configurable computing resources (e.g., networks, servers, storage, applications, and
services) that can be rapidly provisioned and released with minimal management effort or
service provider interaction [4].
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Cloud is a new consumption and deliver model for many IT-based services, in which the user
sees only the service, and has no need to know anything about the technology or
implementation" [2].
Cloud computing is a model for enabling convenient, on demand network access to shared pool
of configurable computing resources [2].
Figure 2-2 Cloud Computing – Overview [2]
Cloud Computing provides services as – IaaS, PaaS, SaaS. SaaS provides board market
solutions where the vendor provides access to hardware and software products through portal
interface [2]. PaaS allows the consumer to run the specified application on the platform. In
these type of services, consumer have no control over the infrastructure as well as the on the
installed applications [2]. IaaS provides consumers with the benefit to consume the
infrastructure that includes processing power, data storage, and network etc. The consumer can
run multiple applications without worrying about maintenance of underlying infrastructure [2].
Cloud computing platform dynamically provisions, configures, and reconfigures the
servers as and when needed by end users. These servers can be in the form of virtual machines
or physical machines in the cloud. Cloud computing renders the two major trends in IT: (1)
efficiency, which is achieved through the highly scalable hardware and software resources, and
(2) agility, which is achieved through parallel batch processing, using computer-intensive
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business analytics and real-time mobile interactive applications that respond to user
requirements. Cloud Architecture have benefits that the end users need not to worry about the
exact location of servers and switch to their application by connecting to the server on cloud
and start working without any hassle [5].
The limitations of Wireless sensor network are the major plus points of Cloud
Computing. The Sensor Cloud Architecture takes the advantages of this points and provide
better system.
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3. Sensor-Cloud Architecture
3.1
General Architecture
When a user requests, the service instances (e.g., virtual sensors) generated by cloud computing
services are automatically provisioned to them [11, 12]. Some previous studies on physical
sensors focused on routing, clock synchronization, data processing, power management, OS,
localization, and programming [5]. There are very less work have been done which concentrate
on physical sensor management because these physical sensors are bound closely to their
specific application as well as to its tangible users directly. However, users, other than their
relevant sensor services, cannot use these physical sensors directly when needed. Therefore,
these physical sensors should be supervised by some special sensor-management schemes. The
Sensor-Cloud infrastructure would subsidize the sensor system management, which ensures
that the data-management usability of sensor resources would be fairly improved.
There exists no application that can make use of every kind of physical sensors at all
times; instead, each application required pertinent physical sensors for its fulfilment. To realize
the concept, publish/subscription mechanism is being employed for choosing the appropriate
physical sensor [5]. Server-Cloud infrastructure provides the facility to user to create the
template/virtual special group of sensor nodes whose data will be collaborated for the specific
applications. These service templates/virtual groups are reconfigurable according to the user
needs. Once service instances become useless, they can then be deleted quickly by users to
avoid the utilization charges for these resources. Every sensor node, application program senses
the application and sends the sensor data back to the gateway in the cloud directly through of
the base station. Sensor-Cloud infrastructure provides service instances (virtual sensors)
automatically to the end users as and when requested, in such a way that these virtual sensors
are part of their IT resources (like disk storage, CPU, memory, etc.) [13]. These template
services/virtual groups’ instances and their associated appropriate sensor data can be used by
the end users via a user interface through the web crawlers as described in Figure 3-1.
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Figure 3-1 Sensor-Cloud Architecture [5]
The physical sensors are ranked on a basis of their sensor readings as well as on their
actual distance from an event. The authors of [14] proposed a technique (FIND) to locate
physical sensors having data faults by assuming a mismatch between the distance rank and
sensor data rank. However, the study led by FIND aims at the assessment of physical sensors
faults, and there is a close relation between the virtual and physical sensors and hence a virtual
sensor will provide incorrect results if their relevant physical sensors are faulty.
Since the cloud computing enables the physical sensors to be virtualized by creating
templates or virtual grouping, the users of the Sensor-Cloud Infrastructure need not to worry
about the status of their connected physical sensors.
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3.3
Sensor-Cloud Service Life-Cycle Model and Its Layered Structure
The service life-cycle model and the layered structure of Sensor-Cloud infrastructures are
illustrated as follows –
3.3.1 Service Life Cycle Model of Sensor-Cloud
Before creating the service instances within Sensor-Cloud infrastructure, preparation phase
[17] is needed, and this includes the following –
Preparing the IT resources (processors, storage, disk, memory etc.)
Preparing the physical sensor devices
Preparing the service templates
Figure 3-2 Life Cycle of Sensor-Cloud Infrastructure [16]
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Figure 3-2 depicts the Sensor-Cloud service life cycle. The users of the sensors can
select the appropriate service template and request the required service instances. These service
instances are provided automatically and freely to the users, which can then be deleted quickly
when they become useless. From a single service template, multiple numbers of service
instances can be created. Service provider regulates the service templates and can add new
service templates as and when required by a different number of users [18].
3.3.2 Layered Structure of Sensor-Cloud.
Figure 3-2 depicts the layered architecture of the Sensor-Cloud platform, which is divided
mainly into three layers:
1. User and application layers,
2. Sensor-Cloud and virtualization layers,
3. Template creation and tangible sensors layers [18].
Layer 1: This layer deals with the users and their relevant applications. Several users want to
access the valuable sensor data from independent from the platform for a variety of
applications. This structure allows users of different platforms to access and utilize the sensor
data without facing any problem because of the high availability of cloud infrastructure and
storage.
Layer 2: This layer deals with virtualization of the physical sensors and resources in the cloud.
The virtualization enables the provisioning of cloud-based sensor services and other IT
resources remotely to the end-user without being worried about the sensors exact locations.
The virtualized sensors are created by using the service templates automatically. Service
templates are prepared by the service providers as service catalog, and this catalog enables the
creation of service instances automatically that are accessed by multiple users [18, 12].
Layer 3: This is the last layer which deals with the service template creation and service catalog
definition layers in forming catalog menu. Physical sensors are located and retrieved from this
layer. Since each physical sensor has its own control and data collection mechanism, standard
mechanisms are defined and used to access sensors without concerning the differences among
various physical sensors [17]. Standard functions are defined to access the virtual sensors by
the users.
Physical sensors are XML encoded that enable the services provided through these
sensors to be utilized on various platforms without being worried to convert them onto several
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platforms [18]. Sensor-Cloud provides a web-based aggregation platform for sensor data that
is flexible enough to help in developing user-based applications. It allows users quick
development and deployment of data processing applications and gives programming language
flexibility in accordance to their needs.
Figure 3-3 Layered Structured of Sensor-Cloud Model [5]
3.4
Current Approaches
In just few years Sensor-Cloud Infrastructure gaining the popularity & some of the
architectures are introduced. Some of them are as follows,
A New Model of Accelerating Service Innovation with Sensor-Cloud Infrastructure
[18]: A novel approach to integrate WSN with Cloud Computing for cost effective
applications and Service Provisioning in WSN. Introduced the third party cloud
application by inter-cloud connectivity. Authors also proposed Research Agenda.
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Sensor-Cloud Infrastructure – Physical Sensor Management with Virtualized Sensors
on Cloud Computing [19]: The most basic and latest when proposed, architecture
proposed by authors who proposed the making of virtual group AKA Service Template
by aggregating data from the different types of physical sensors. Then those ST/Virtual
groups can be re – used, configurable and sharable.
Integration of Wireless Sensor Network with Cloud [20]: Introduced the framework
which focuses on reliability, availability and extensibility. Combining SOA Roles in
proposed Architecture – in which sensor nodes are data/service providers & sinks are
consumers & clients make request to Integration Controller by SOAP Clients.
Integrating Wireless Sensor Networks with Cloud Computing [21]: Authors proposed
the framework in which WSN transfers data to the Cloud System, which will be
available to blogs, virtual communities, and social networking applications and many
more.
A Novel Architecture Based on Cloud Computing for Wireless Sensor Network [16]:
The framework proposed represents cloud as virtual sink node for collecting the data
from the WSN and simulation of the framework prove the improvement in
performance.
A New Framework to Integrate Wireless Sensor Networks with Cloud Computing [2]:
Proposed framework uses the Integration Controller for integrating WSN with Cloud
Computing. Integration Controller collects data from WSNs & passes to Cloud
Application which provides the service to the users depending upon the need of
individual.
These all frameworks available approaches follows one common base architecture of
making user to subscribe for different/same types of physical sensor and analyse the data
collected by the same. For that it creates the ST or virtual group
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These all frameworks are presented with respect to some specific application. It may
possible that one framework is not a best for the different situation/application. So one can
choose its application based on application area.
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4. Detailed Study of Sensor-Cloud Infrastructures
4.1
Sensor-Cloud Infrastructure
Authors M. Yuiyama, T. Kushida [11] proposed a novel methodology for integrating WSN
with Cloud Computing. The Figure 4-1 shows the overview of the architecture of the SensorCloud Integration proposed by authors. Various sensors with different owners can join SensorCloud infrastructure. Each owner registers or deletes its physical sensors. User can create
ST/Service Template or Virtual Groups by subscribing data for particular physical sensors.
User can easily add, remove or share the configurations of template as well as Sensors as per
the need.
Figure 4-1 Sensor Cloud Infrastructure - Overview [11]
4.1.1 Design Considerations
Virtualization: In the real scenario there are different kind of sensors are scattered over
the spatial area. We propose virtual sensor and virtual sensor group in order for the
users to be able to use sensors without worrying about the locations and the
specifications of physical sensors. Figure 4-1 describes the relationship among virtual
sensor groups, virtual sensors, and physical sensors. Each virtual sensor is created from
one or more physical sensors which is dependent on the user application area. A virtual
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sensor group is created from one or more virtual sensors. Users can create virtual sensor
groups and freely use the virtual sensors included the groups as if they owned sensors.
For example, they can activate or inactivate their virtual sensors, check their status, and
set the frequency of data collection from them. If multiple users freely control the
physical sensors, some inconsistent commands may be issued. The users can freely
control their own virtual sensors by virtualizing the physical sensors as virtual sensors.
Figure 4-2 Relationship among Virtual Sensors, and Physical Sensors [11]
Standardization & Automation: Different kinds of physical sensors have different
functions in terms of sensing the environment. Each physical sensor provides its own
functions for control and data collection. Standard like Sensor Markup Language/SML
[5] mechanism enables users to access sensors without concern for the differences
among the physical sensors. Sensor-Cloud infrastructure translates the standard
functions for the virtual sensors into specific functions for the different kinds of
physical sensors. Automation (in terms of response of data), improves the service
delivery time and reduces the cost. Sensor-Cloud infrastructure prepares templates for
the specifications of various types of physical sensors. When users select the template
of a virtual sensor or virtual sensor group, Sensor-Cloud infrastructure dynamically and
automatically provisions the virtual sensors in that virtual sensor group from the
templates. Sensor-Cloud infrastructure is an on demand service delivery and supports
the full lifecycle of service delivery from the registration of physical sensors through
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creating templates, requesting of virtual sensors, provisioning, starting and finishing to
use virtual sensors, and deleting the physical sensors.
Monitoring: Because the application has troubles if it cannot use the sensor data from
the virtual sensors, the application owner should check whether or not the virtual
sensors are available and monitor their status for sustaining the quality of the service.
The users can check the status and the availability of the virtual sensors by the
monitoring mechanism of Sensor-Cloud infrastructure.
Grouping: Although there are many kinds of physical sensors, each application does
not have to use all of them. Each application uses some types of sensors or when the
sensors which match certain constrains (such as a location). Sensor-Cloud
infrastructure can provide virtual sensors as virtual sensor groups. Users can control
each virtual sensor and virtual sensor groups. For example, a user can set the access
control and the frequency of data collection for virtual sensor groups. Sensor-Cloud
infrastructure prepares typical virtual sensor groups and users can create new virtual
sensor groups by selecting virtual sensors.
4.1.2 Entities involved
Sensor Owner: A sensor owner is a person who owns has physical sensors which are
deployed over the area of interest. One of the possible advantages for sensor owner
could be rental fees for using the physical sensors. The fees reflects the actual usage of
the physical sensors. A sensor owner registers the physical sensors with their properties
to Sensor-Cloud infrastructure. The owner deletes the registration of them when s/he
quits sharing them.
Sensor-Cloud Administrator: The Sensor-Cloud Administrator is the actor who
manages the Sensor-Cloud Infrastructure service. The administrator manages the IT
resources for the virtual sensors, monitoring, and the user interfaces. The administrator
also prepares the templates for the virtual sensors and for some typical virtual sensor
groups. The administrator can charge for the delivery of the Sensor-Cloud infrastructure
service.
End User: An end user is an actor with one or more applications or services that use the
sensor data. An end user requests the use of virtual sensors or virtual sensor groups that
satisfy the requirements from the templates. These templates are easily configurable,
sharable, and removable and easily can be created.
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4.1.3 System Architecture
Client: Users can access the user interface of Sensor-Cloud infrastructure using their
Web browsers.
Portal: Portal provides the user interface for Sensor-Cloud infrastructure.
Provisioning: Provisioning provides automatic provisioning of virtual sensor groups
including virtual sensors.
Resource Management: Sensor-Cloud infrastructure uses IT resources for the virtual
sensors and the templates for provisioning.
Monitoring: Sensor-Cloud infrastructure provides monitoring mechanisms.
Virtual Sensor Group: Sensor-Cloud infrastructure provisions virtual sensor groups for
end users.
Sensors: Sensors are used in Sensor-Cloud infrastructure.
Figure 4-3 Proposed System Architecture [11]
4.1.4
Components in Architecture
Portal server: When a user logs into the portal from a Web browser, the user’s role (end
user, sensor owner or Sensor-Cloud administrator) determinates the available
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operations. The portal server shows the end users the menus for logging in, logging out,
requesting for provisioning or destroying virtual sensor groups, monitoring their virtual
sensors, controlling them, creating templates of virtual sensor groups and checking their
usage-related charges. The portal server gives sensor owners the menus for logging in,
logging out, registering or deleting physical sensors, and checking the usage-related
rental fees. One of the menus or Sensor-Cloud administrators is for creating, modifying,
and deleting the templates for virtual sensors or virtual sensor groups.
Provisioning Server: The provisioning server provisions the virtual sensor groups for
the requests from the portal server. It contains a workflow engine and predefined
workflows. It executes the workflows in the proper order. First, it checks and reserves
the IT resource pool when it receives a request for provisioning. It retrieves the
templates of virtual sensors and virtual sensor groups, and then provisions the virtual
sensor groups including virtual sensors on the existing or a new virtual server. After
provisioning, the provisioning server updates the definitions of the virtual sensor
groups. The virtual servers are provisioned with the agents for monitoring.
Virtual Sensor Group: A virtual sensor group is automatically provisioned on a virtual
server by the provisioning server. Each virtual sensor group is owned by end user and
has one or more virtual sensors. The end user can control the virtual sensors. For
example, they can activate or inactivate their virtual sensors, set the frequency of data
collection from them, and check their status. The virtual sensor groups are controlled
directly or form a Web browser.
Monitoring Server: The monitoring server receives the data about virtual sensors from
the agents in the virtual servers and the servers. It stores the received data in a database.
The monitoring information for the virtual sensors is available using a Web browser.
The Sensor-Cloud administrators are also able to monitor the status of the servers.
4.2
Data Centric – Sensor-Cloud Infrastructure Framework
The authors [8] proposed a data centric Sensor-Cloud Infrastructure in which the data always
will be available for users from Cloud. The proposed architecture is integration of Cloud
Computing with Wireless Sensor Networks through Internet & Service Oriented
Architecture/SOA. The major components of the framework are Data Processing Unit/DPU,
Publisher/Subscriber Broker, Request Subscriber, Identity and Access Management
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Unit/IAMU and Data Repository/DR. The data gathered from WSN is passed through gateway
to DPU, which process data and add it to DR. In order to access the stored data from cloud
services, user connects through secured IAMU; on successful connection establishment user
will be given the access according to the account policies. User data request is forwarded to
RS which creates a request subscription and forward the subscription to the Pub/Sub Broker.
When DPU receives the data from gateway, it forwards the data to Pub/Sub Broker.
The user can access the data from any location in the world. The sensor networks are
ideally considered to be energy efficient and it's the major criticality of the network that must
be answered. In addition to that, short range hop communications is preferred in order to
communicate with a long range destination. Therefore, the information from source is
distributed across intermediate nodes in the path towards destination node.
Figure 4-4 Proposed Architecture of Sensor-Cloud [8]
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4.2.1 Components for Message Exchange in Architecture
Access Control Enforcement Unit: ACEU is used to authenticate the user and it is
consists of EN and three servers i.e. Authenticate Server/AS, Ticket Granting
Server/TGS and SS. The request received by EN is sent to AS. EN implements
Kerberos in order to authenticate the user with AS.
Access Control Decision Unit: ACDU is used to enforce the policy rules. It consists of
RBAC processor and policy storage. It communicates with ACEU through SS. After
successful authentication; user is given the access to the resources as constrained by the
access policies.
Communication flow between User and IAMU: The following Diagram shows the
overall communication in the system.
Figure 4-5 Communication Flow in Proposed Architecture [8]
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4.3
Service Oriented – Sensor-Cloud Infrastructure Framework
Authors of [18] proposed the extended version of [11] which focuses on the service
availability for end users with the help of Cloud Services. Proposed system model supports the
automation like most other frameworks of Sensor-Cloud Infrastructure by creating Service
Instances automatically whenever requested. The service providers have provided services
with the different configurations by each service requester’s requirements before cloud
computing service. The service providers prepare service templates as service catalog. The
service requesters select the service template menu from the predefined service catalog and
request a service instance. This cloud computing service model is giving big impacts to the
service because service requester can use necessary service instance quickly when service
requester would like to start new service or to need additional resources for extending existing
service.
Figure 4-6 Service Module [18]
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Existing sensor services use sensors directly or sensor data provided by other sensor
services. Sensor services know the details of sensors such as specification, configuration and
location. Cloud computing service’s advantage is the cloud computing service provider
optimizes everything below the service boundary i.e. all code/implementation complexity is
hidden from the user & users just have to use the service templates which is ultimately the
results or analysis of data.
4.3.1 Service Lifecycle
Service Catalog Definition Phase: The service providers have created the service
instances with the different configurations by each service requester’s requirements
before cloud computing service. Service providers prepare service templates as service
catalog. The service catalog includes the menus describing service’s specifications
including grouped sensors. For example, a menu describes Linux (OS), database
software (middleware), traffic analysis service (service), the sensors in Tokyo (grouped
sensors). The service providers prepare the service templates and define them as the
service catalog menus. The service instances are created by using service templates
automatically. Preparing service templates and defining service catalog enable to create
service instances automatically and to duplicate the same specification’s service
instances for various users.
Figure 4-7 Service Providing Phase [18]
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Service Providing Phase: The service requesters select the service menu from the
prepared service catalog for their purposes and requirements. They can request their
service instances. They can usually configure the setting of service instances. For
example, they can register other users to access their service instances. The service
requesters and other users who are allowed to access their service instances receive the
notification from cloud computing service. They can use their service instances freely.
They can start new service by using service instances as sensor service provider. When
the service instances become unnecessary, the service requesters can release their
service instances from the portal. They check the accounting report and pay the cost for
their usage. Because service requesters request the service instances by selecting the
menu from the service catalog, service providers can know the number of requests for
each menu. If service providers analyse the requests, they can modify or remove the
unpopular menus. They can also add the new menu by upgrading the popular menus.
They should manage the service catalog for improving the cloud computing service.
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5. Pros & Cons of Sensor-Cloud Infrastructure
5.1
Pros of Sensor-Cloud Infrastructure
Analysis: The integration of huge accumulated sensor data from several sensor
networks and the cloud computing model make it attractive for various kinds of
analyses required by users through provisioning of the scalable processing power [13].
Scalability: Sensor-Cloud enables the earlier sensor networks to scale on very large size
because of the large routing architecture of cloud [8]. It means that as the need for
resources increases, organizations can scale or add the extra services from cloud
computing vendors without having actually own it.
Collaboration: Sensor-Cloud enables the huge sensor data to be shared by different
groups of consumers through collaboration of various physical sensor networks [8]. As
it works by creating the template or virtual group for specific application so user can
use all available data of different kind of physical sensors available.
Visualization: Sensor-Cloud platform provide a visualization API to be used for
representing the diagrams with the stored and retrieved sensor data from several device
assets. Through the visualization tools, users can predict the possible future trends that
have to be incurred [15].
Free Provisioning of Increased Data storage and Processing Power: It provides free
data storage and organizations may put their data rather than putting onto private
computer systems without hassle. It provides enormous processing facility and storage
resources to handle data of large-scale applications [8].
Dynamic Provisioning of Services: Users of Sensor-Cloud can access their relevant
information from wherever they want and whenever they need rather than being stick
to their desks. This feature inherited from the Cloud Computing.
Multitenancy: The number of services from several service providers can be integrated
easily through cloud and Internet for numerous service innovations to meet user’s
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34. Integration of Wireless Sensor Network with Cloud Computing
demand [7]. Sensor-Cloud allows the accessibility to several numbers of data centres
placed anywhere on the network world (again the feature inherited from the Cloud
Computing).
Automation: Automation played a vital role in provisioning of Sensor-Cloud computing
services. Automation of services improved the delivery time to a great extent [12].
Flexibility: Sensor-Cloud provides more flexibility to its users than the past computing
methods. It provides flexibility to use random applications in any number of times and
allows sharing of sensor resources under flexible usage environment [8].
Quick Response Time: The integration of WSN’s with cloud provides a very quick
response to the user, that is, in real-time due to the large routing architecture of Cloud
Computing. The quick response time of data feeds from several sensor networks or
devices allows users to make critical decisions in near real time.
Resource Optimization: Sensor-Cloud infrastructure enables the resource optimization
by allowing the sharing of resources for several number of applications [15]. The
integration of sensors with cloud enables gradual reduction of resource cost and
achieves higher gains of services. With Sensor-Cloud, both the small and midsized
organizations can benefit from an enormous resource infrastructure without having to
involve and administer it directly by Cloud Computing.
5.2
Cons of Sensor-Cloud Infrastructure
Implementation Cost & Maintenance: Sensor-Cloud Infrastructure should prepare IT
resource [11] & there is the cost for managing the same.
Overload of creating the Template or Virtual Sensor Groups: Sometimes, according
to users’ need administrator have to prepare Template or Virtual Sensor Group [11].
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35. Integration of Wireless Sensor Network with Cloud Computing
6. Conclusion
Typical WSN have very large domain of applications but it still legging because of its
limitations like limited energy, storage, processing power, bandwidth, range, etc. There is the
way to overcome its limitation by Cloud Computing architecture which is the best fit to
overcome all limitations.
So, the integrated version of Cloud Computing & WSN, i.e. Sensor-Cloud
Infrastructure will provide the good way to enhance the area of applications of WSN.
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