NeCoRPIA: Network Coding with Random Packet-Index Assignment for Mobile Crowdsensing
Abstract
The universal proliferation of mobiles devices, and specifically of smartphones with rich sensing capabilities, has given rise to a new fast-growing paradigm of sensing: mobile crowdsensing. Mobile crowdsensing (MCS) takes advantage of the ubiquity of the devices to process and collect information through voluntary sensing.This paper focuses on one specific issue: the challenge of decentralized data collection in MCS. While numerous techniques from managed networks can be adapted, one of the most efficient (from the energy and spectrum use perspective) is network coding. Network coding is well suited to networks with mobility and unreliability, however, practical network coding requires a precise identification of individual packets that have been mixed together. In a purely decentralized system, this requires either conveying identifiers in header along with coded information, or integrating a more complex protocol in order to efficiently identify the sources (participants) and their payloads.This paper presents a novel solution where packet indices in network coding headers are selected in a decentralized way, by simply choosing them randomly. Traditional network decoding techniques apply directly when all original packets have different indices. When this is not the case, i.e., in case of collisions of indices, specific decoding techniques are proposed. The proposed network decoding techniques are detailed and their performance evaluated on some examples.