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The emergence of sensor-rich smartphones has expanded the growth of Mobile Crowd Sensing (MCS). Presence of the required context is vital for mobile crowd sensing. This research focuses on the development of a distributed architecture for executing mobile crowd sensing applications in real-time. Emerging MCS applications such as tracking a perpetrator in real time,  surveillance and monitoring disaster or an event have analytical components that can be processed on edge devices as the event is in progress. The ContextAiDe architecture is a middleware that enables easy development of real-time MCS applications. Important aspects of ContextAiDe are: a.  Design considerations to obtain contextual data,  b.  Optimization of operational overheads involved in complex analytical processing tasks executed on edge devices, c. Proactive strategies to reduce task failures. 

System model shows user devices in the edge acquire data through various sensors. This data is processed on edge devices, fog servers or cloud.
ContextAiDe can support MCS app such as perpetrator tracking which recruits mobile users as the perpetrator moves through series of location.


  1. Pore, M., Sadeghi, K., Chakati, V., Banerjee, A., & Gupta, S. K. (2015, September). Enabling real-time collaborative brain-mobile interactive applications on volunteer mobile  devices. In Proceedings of the 2nd International Workshop on Hot Topics in Wireless (pp. 46-50). ACM. (pdf)
  2.   Pore, M., Chakati, V., Banerjee, A., & Gupta, S. K. (2016, October). ContextMete: Context Optimized Peer Offload Architecture for Distributed Mobile Applications. In 2016 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 105-106). IEEE. (pdf)

Researchers:   Madhurima Pore, Vinaya Chakati, Dr. Ayan Banerjee, Dr. Sandeep K.S. Gupta