Cellular Sidelink Enabled Decentralized Pedestrian Sensing

Abstract

Pedestrian based mobile sensing enables a large number of urban-centric use cases in the areas of intelligent mobility, smart city and crowd management. With increasing standardization in Vehicle-to-Everything (V2X) communication to increase localized environmental awareness, i.e. cooperative perception (CP), a technological basis is already heavily discussed. Work in this area is usually directed towards road safety use cases. However, the same technologies could also be applied to pedestriancentric applications in urban areas. Use cases like spatio-temporal density maps of pedestrians for public transportation optimization or urban route planing are such examples. This paper introduces an opportunistic decentralized mobile crowd sensing (MCS) approach where arbitrary measurement quantities are collected, aggregated and disseminated in decentralized pedestrian measurement maps (DPMM). The sensing, dissemination, and aggregation are driven by mobile devices, without the need for centralized aggregation and dissemination infrastructure. By utilizing cellular sidelink communication (i.e. via the PC5 interface in 5G/6G systems) and node local aggregation, the perception of the environment can be directly shared with neighboring nodes. The described DPMM approach is evaluated using CrowNet, an open-source simulation framework based on OMNeT++/INET. The proposed approach is evaluated by several detailed simulation studies: first, using a synthetic measurement quantity with a linear change rate behavior and second, a real use case concerning decentralized pedestrian density measurements. The results indicate that DPMM can provide spatio-temporal maps of the local area with high level of detail and low delay -close to the optimum achievable in a specific mobility situation -while only requiring a moderate amount of cellular bandwidth.

Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework

“…That means the missing part must usually be generated based on some available statistical data or over-simplified to completely hide the given aspect’s representation. One outstanding exception is the framework CrowNet used for a crowd-sensing strategies study [ 56 ]. This integrated framework consists of several open-source projects that also have an important role in our solution (see later in Section 4 ).…”