Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications

Abstract

Regular citizens equipped with smart devices are being increasingly used as “sensors” by Smart Cities applications. Using contacts among users, data in the form of messages is obtained and shared. Contact-based messaging applications are based on establishing a short-range communication directly between mobile devices, and on storing the messages in these devices for subsequent delivery to cloud-based services. An effective way to increase the number of messages that can be shared is to increase the contact duration. We thus introduce the Friendly-Sharing diffusion approach, where, during a contact, the users are aware of the time needed to interchange the messages stored in their buffers, and they can thus decide to wait more time in order to increase the message sharing probability. The performance of this approach is anyway closely related to the size of the buffer in the device. We therefore compare various policies either for the message selection at forwarding times and for message dropping when the buffer is full. We evaluate our proposal with a modified version of the Opportunistic Networking Environment (ONE) simulator and using real human mobility traces.

FALCON: A new approach for the evaluation of opportunistic networks

“…So, it can start a transmission and, if the setup time plus the transmission time of the first item is greater than the contact duration, no items will be exchanged (that is, it can be considered as a wasted contact). Finally, the order in which the items are exchanged is based on their number (that is, messages with lower numbers are exchanged first); but other models can be used in order to improve efficiency (for example, items with lower sizes first, as described in [29]).…”

Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing

“…Actually, the model considers, instead of a given expiration time, a simple exit rate that models when nodes delete their local message (that is, as known in the epidemic process, a death rate). In our recent works [46,11], we show that it is important to implement certain mechanisms to improve buffer management in the context of epidemic diffusion, showing that the best results were obtained when local buffer management follows a combination of smallest message forwarding and largest message dropping. Social aspects can also be considered in the management of local buffers and in the message forwarding strategy.…”

Simulating Opportunistic Networks: Survey and Future Directions

“…A simple approach is to generate messages with message sizes and frequencies based on known application usage statistics. For example in [78], three message sizes and frequencies were considered: (1) a short text message (1 kB) every hour; (2) a photo (1 MB) every 18 hours; and (3) a video or high-resolution picture (10 MB) every 96 hours. These frequencies were based on the statistics of Whatsapp (Facebook, Inc.) message usage from [79], while sizes are approximations of the content produced by current mobile phone hardware.…”