Bubble Sensing - Seminar Reports|PPT|PDF|DOC|Presentation


A sensor (also called detector) is a converter that measures a physical quantity and converts it into a signal which can be read by an observer or by an (today mostly electronic) instrument.

Sensors are used in everyday objects such as touch-sensitive elevator buttons (tactile sensor) and lamps which dim or brighten by touching the base. There are also innumerable applications for sensors of which most people are never aware. Applications include cars, machines, aerospace, medicine, manufacturing and robotics.

The tremendous growth of sensor technology in Smartphone increases day by day and will experience fabulously over the next few years. Success of smart phones is leading to an increasing amount of MEMS & sensors in mobile phones to provide new features/ services to end-users, to reduce cost through more integration or to improve hardware performance.


Area monitoring
Area monitoring is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. A military example is the use of sensors to detect enemy intrusion; a civilian example is the geo-fencing of gas or oil pipelines.

Bubble Sensing PPT Topics

When the sensors detect the event being monitored (heat, pressure), the event is reported to one of the base stations, which then takes appropriate action (e.g., send a message on the internet or to a satellite). Similarly, wireless sensor networks can use a range of sensors to detect the presence of vehicles ranging from motorcycles to train cars.

System Integration

Use of the mobile phone as a sensor in the bubble-sensing system should not interfere with the normal usage of the mobile phone. Our bubble-sensing software implementation is light weight, so users can easily switch it to background, and use their phone as usual. The software only accesses sensors on demand and release the resources immediately after use. An incoming or user-initiated voice call has high priority, and our software does not try to access the microphone when it detects a call connection. By adapting in this way, our implementation does not disrupt an ongoing call and also the bubble-sensing application will not get killed by an incoming call. We test the CPU and memory usage of our software in a Nokia Phone, using a bench mark application, CPUMonitor . The peak CPU usage is around 25%, which happens when sound clips are taken. Otherwise, the CPU usage is about 3%. The memory usage is below 5% of the free memory, including the overhead of the python virtual machine and all the external modules

Sensors In Smartphones

                   1            Microphone

                   2            Accelerometer

                   3            Ambient Light Sensor

                   4            Proximity Sensor

                   5            Gyroscope

                   6            GPS Module

Experiment Setup

Ten mobile phones are carried by people who move around three floors of the Dartmouth computer science building. The carriers stay mobile for the duration of the experiment, except for momentary pauses at the water cooler, printer, or desk (to check for important emails). No particular effort is made to orchestrate the mobility to maintain density in the sensing bubble or elsewhere. The participants are told to carry the cell phones as they normally would. Most of the time the mobile phones are put in the front or back pockets and sometime held in the hand (e.g., when making a call, checking the time, sensing a SMS message, etc). Static beacons are used to provide a WiFi localization service. In our experiments, the center of the task bubble is defined to be the Sensor Lab, which is a room on the middle of the three floors. The task is assumed to already be registered by the bubble creator. During the experiment, we play music in the bubble and the task is simply capturing sound clips in this room once every ten seconds. To emulate a heterogeneous network, we intentionally limit device capabilities (i.e., long range connectivity and localization) in some cases. We evaluate the following five different cases ..............

Online Sensing Task Optimization for Shared Sensors

Sensing systems now allow sensors to be shared among multiple users and applications . Open interfaces using the Internet protocol and web services have been prototyped to facilitate such shared access to sensors. Multiple applications can use such sensing infrastructures to provide new functionalities using live sensor data. Also, within a single application, multiple users can access different data based on their needs. As the numbers of applications and users within applications grow, the amount of data to be provided from the sensors and the amount of computation performed on that data go up. This increases the load on the sensing infrastructure, limiting the number of allowable concurrent application requests. “Hot” sensors, i.e., ones that contain events of interest to several users, are likely to become especially overloaded. Consider, as an example, the road traffic sensors deployed by the Department of Transportation on several roads, to measure the volume and average speed of traffic for the covered road segments. In a shared system, multiple sensing applications, such as driving directions computation, traffic characterization , congestion prediction, cab fleet management, or urban planning tools, may obtain data streams from these sensors.