WikiSensing: A collaborative sensor management system with trust assessment for big data
Sensor data is the output of a device that detects and responds to some type of input from the physical environment. The output may be used to provide information or input to another system or to guide a process. Like a template for data.
In the early days of sensors, a pattern would have to be recognized in order to cause the sensor to activate a process. For instance an elevator door that opens immediately as soon as it senses an object in the way of the closing door.. An oil light in your car when you are low on oil is another type of sensor. With amazing elements, it’s the future.
With nanotechnology comes smaller sensors. Smarter sensors. Sensors that are able to collect data on just about everything. So not only will a heart sensor let a doctor know when the heart is beating, but data collected from sensors can help predict when it may stop.
Just like the elevator door that stops closing because its sensors detected a person while the doors were closing, that same sensor can now measure how many people rode the elevator, wirelessly diagnose itself of trouble and send data to the elevator maintenance company to schedule service.
How many sensors does a person deal with on an average day? Think about it.
Your smartphone is a sensor network. Depending on what apps you have installed on your smartphone, that phone is seeing, hearing, and reporting on its surroundings. But to whom?
We know a big search engine company that uses data from its phones to gain semantic language skills. Did you ever use Google Voice? When someone left a voicemail, Google would attempt to transcribe it into a text. When the user played the voice mail, Google would highlight the words in real time to indicate that it did not understand. So you corrected it and taught Google how to learn the human language in different tones, timbers and accents.
The little sensor in your phone called a microphone got what it needed.
Sensors have helped us get out of jams in the past.
But what do we do with this data? Throw it back in the cloud and use it only when we needed it? There are many uses for this data including but limited to health and surveillance.
In 2012 Dilshan Silva proposed a place where sensor analytics could be stored and studied collection of data from sensors and also a place to collaborate, share and even make new friends.
The drive toward smart cities alongside the rising adoption of personal sensors is
leading to a torrent of sensor data. While systems exist for storing and managing sensor
data, the real value of such data is the insight which can be generated from it. However there
is currently no platform which enables sensor data to be taken from collection, through use
in models to produce useful data products. The architecture of such a platform is a current
research question in the field of Big Data and Smart Cities
Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks. This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon.
This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data.
An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model. Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.
sensor data management; online collaboration; collaborative systems; aggregate queries; virtual sensors