Energy Efficient Location and Activity-aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds

Abstract

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data has become easier than ever.

However, large scale data management tasks still require significant amounts of

resources that can be expensive regardless of whether they are purchased or rented (e.g. pay-as-you-go infrastructure). Further, not everyone is interested in such large scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data.

Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called C- MOSDEN (Context-aware Mobile Sensor Data ENgine) for the IoT domain.

Literature Survey

Internet of things is the communication of anything with any other thing. The communication mainly consists of transferring useable data, for example output of a sensor in a room to monitor and control the temperature. It is estimated that in the near future there will be about 50 billion internet-enabled devices. This paper presents a security alarm system using low processing power chips using Internet of things which helps to monitor and get alarms when motion is detected and sends photos and videos to a cloud server.

Moreover, lnternet of Things ( IoT ) based application can be used remotely to view the activity and get notifications when motion is detected. The photos and videos are sent directly to a cloud server, when the cloud is not available then the data is stored locally on the Raspberry Pi and sent when the connection resumes. Hence, advantages like these make this application ideal for monitoring homes in one’s absence.

Fig 1.: The proposed platform can be installed on both mobile and static resource constrained devices. The platform provides easy way to connect sensors. Each of this platform instances act as worker nodes and able to carry out sensing tasks as directed by the cloud based IoT middleware.

Introduction

The Internet of Things (IoT) has become popular over the past decade. As part of the IoT infrastructure, sensors are expected to be deployed all around us, from everyday objects we use, to public infrastructure such as bridges and roads. As the prices of sensors diminish rapidly, we can soon expect to see very large numbers of objects comprising of sensors and actuators. In addition, the modern technology-savvy world is already full of devices comprising of sensors, actuators, and data processors. The concentration of computational resources will enable the sensing, capturing, collection and processing of real time data from billions of connected devices , and can be envisaged to serve many different applications including environmental monitoring, industrial applications, business and human-centric pervasive applications.

The Internet of Things allows people and things to be connected any time, any place, with anything and anyone,ideally using any path/network and any service. IoT is expected to generate large volumes of sensors data. Due to the latest innovations in the computer hardware sector and the reduction in hardware costs, large scale data processing is becoming increasingly economical. Specially, with the popularity of utility-based cloud computing that offers computational resources in a ’pay as you-go’ model, the tendency to collect a large amount of data has been increasing over the last fewyears.

Fig2.: Usage case Scenario 2. Wearable sensors are attached to patients body. Doctors and researchers are expected to collect data from the sensors based on context information.

Block Diagram

Use for fall detection wait for 5sec

Description

Many embedded systems have substantially different designs according to their functions and utilities. In this project design, structured modular design concept is adopted and the system is mainly composed of a single credit card sized computer (Raspberry PI module), VGA monitor, webcam and GSM. Input devices such as USB keyboard and mouse are plugged into the computer to allow user-friendly interface.

The ARM microcontroller on the Raspberry Pi module, at the centre of the block diagram, forms the control unit of the entire project. Embedded within the microcontroller is a program written in phython script that helps the microcontroller to take action based on the inputs provided by the output of the webcam.

When the entire unit is powered, the two accelerometers mounted on body, along with temperature sensor gives analog output. This analog output is given to an ADC chip. This chip then sends the processed signal, in digital form, to the microcontroller on Raspberry pi board.

This is then checked by the phyton script embedded within the microcontroller. While the information is transferred via GSM, simultaneously, information is also up-loaded onto the server through the help of Ethernet.

Heart Rate sensor measures the pulses of the human body and passes information to the microcontroller on board. USB keyboard and mouse are used for interfacing with the microcontroller on Raspberry pi board.

The normal key simulates the patient falling if the key is pressed. A buzzer is automatic activated after 5 second from the time the patient falls to the ground. The buzzer is used as an alert sound so that any individual in the next room can come to the patient’s assistance.

Note: VGA Monitor is used to display the data passed by sensors to the microcontroller.

Components List

Raspberry Pi Module

Accelerometer

Temperature Sensor

Heart Rate Sensor

ADC chip

Buzzer

Key

GSM

USB keyboard and mouse

VGA Monitor

Softwares Used

Phyton Script

Amazon Cloud Server (Web Page)

Advantages

Low in cost compared to other machines.

Can serve as a server for light traffic

Low power consumption. It can be powered by portable batteries which can act as UPS (uninterrupted power supply)

The options are unlimited for notification and storage of the files depending on the user's requirements

Disadvantages

The main limitation of Internet of things is that as the devices have limited computing power the security aspects come in question as the transmitted and received data cannot be encrypted and decrypted.

Future Scope

Going further, changes can be implemented to enhance security to higher levels in order to suit the needs relating to data security by using encryption and decryption algorithms.

References:

[I] C. Pfister, Getting Started with the Internet of Things. Sebastopol, CA:

O'Reilly Media Inc., 2011.

[2] M. Roelands et ai., "Enabling the masses to become creative in smart

spaces", in Architecting the Internet of Things, Berlin, Germany:

Springer-Verlag, 2011, pp 38-43.

[3] Zhuankun Wu. : Initial Study on lOT Security architecture. 1. Strategy

anddecision-making research (2010)