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Efficient Crowd Sensing Task Distribution Through Context-Aware NDN-Based Geocast

The An Binh Nguyen; Pratyush Agnihotri; Christian Meurisch; Manisha Luthra; Rahul Dwarakanath; Jeremias Blendin; Doreen Böhnstedt; Michael Zink; Ralf Steinmetz
In: 2017 IEEE 42nd Conference on Local Computer Networks (LCN). IEEE Conference on Local Computer Networks (LCN-2017), 42nd Conference on Local Computer Networks, Pages 52-60, IEEE, 10/2017.


Crowd sensing exploits users' smart devices and human mobility to collect information on a large scale. To realize a crowd sensing campaign, sensing tasks with spatio-temporal requirements are distributed to the devices that can provide the requested information. Typically, the distribution of sensing tasks relies on a centralized communication infrastructure such as cloud servers. However, such an approach will be unsuitable if access to communication infrastructure is restricted, for example in disaster relief scenarios. To fill this gap, we propose a distributed context-aware framework for disseminating sensing tasks, based on the Named Data Networking (NDN) paradigm. By adding context attributes to Interest packets, we allow a device to utilize this information to make forwarding decisions autonomously, thus guiding the sensing tasks towards the suitable sensing devices. Through intensive evaluation, we show that our framework achieves a timely delivery of sensing tasks, while keeping the communication overhead to a minimum compared to pure geo forwarding and flooding approaches.