Using Smartphone as the “Sensing Device” for Crowdsensing Applications

In early March 2013, I have the opportunity to present a paper entitled “Performance Evaluation of Binary Spray & Wait OppNet Protocol in the Context of Emergency Scenario” (see presentation slides) at the post-conference Workshop PerNEM 2013 (Third International Workshop on Pervasive Networks on Emergency Management), San Diego, USA, which highlights several new research areas of utilizing network for emergency cases. This paper gives different insights to the audience of how to use smartphones in such cases. It raises many interesting points of discussions regarding the usage of smartphones and the mobility patterns of the people during an emergency.

In fact, it opens up new areas of research, especially how to model the mobility behaviour of people during emergency whether in building or outside in open areas. It is critical to determine the performance of the network.

One main topic that kept appearing in many of the papers presented and the demos showcased is how to leverage the smartphone as a “Sensing Device” for many applications. An emerging category of devices at the edge of the Internet is consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. We believe these devices will drive a plethora of IoT applications that elaborate our knowledge of the physical world. These applications can be broadly classified into two categories, personal and community sensing, based on the type of phenomena being monitored.

In particular sensing applications, the events are about an individual. For example, the monitoring of movement patterns (e.g. running, walking, exercising) of a person for personal record-keeping or healthcare reasons. Another example of personal sensing is one that monitors the transportation modes of an individual to determine his or her carbon footprint.

2.pngOn the other hand, community sensing pertains to the monitoring of large-scale phenomena that cannot be easily measured by a single individual. For example, intelligent transportation systems may require traffic congestion monitoring and air pollution level control. These phenomena can be measured accurately only when many individual- also provide speed and air quality information from their daily commutes, which are then aggregated spatiotemporally to determine congestion and pollution levels in cities.

 

 

 

About the Author: 

Mazlan-Abbas-e1468839685939Dr. Mazlan is ranked No. 20th Thought Leader in IoT by Onalytics Report – “The Internet of Things – Top 100 Thought Leaders” and ranked Top 100 in Smart Cities Top Experts by Agilience Authority Index May 2016. He is currently the CEO of REDtone IOT and is a public speaker at leading IoT events. You can get in touch with him on LinkedInFacebook, and Twitter.

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