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Next-generation smart city applications, attributed by the power of Internetof Things (IoT) and Cyber-Physical Systems (CPS), significantly rely on thequality of sensing data. With an exponential increase in intelligentapplications for urban development and enterprises offering sensing-as-aservicethese days, it is imperative to provision for a shared sensing infrastructurefor better utilization of resources. However, a shared sensing infrastructurethat leverages low-cost sensing devices for a cost effective solution, stillremains an unexplored territory. A significant research effort is still neededto make edge based data shaping solutions, more reliable, feature-rich andcosteffective while addressing the associated challenges in sharing the sensinginfrastructure among multiple collocated services with diverse Quality ofService (QoS) requirements. Towards this, we propose a novel edge based datapre-processing solution, named UniPreCIS that accounts for the inherentcharacteristics of lowcost ambient sensors and the exhibited measurementdynamics with respect to application-specific QoS. UniPreCIS aims to identifyand select quality data sources by performing sensor ranking and selectionfollowed by multimodal data pre-processing in order to meet heterogeneousapplication QoS and at the same time reducing the resource consumptionfootprint for the resource constrained network edge. As observed, theprocessing time and memory utilization has been reduced in the proposedapproach while achieving upto 90% accuracy which is arguably significant ascompared to state-of-the-art techniques for sensing. The effectiveness ofUniPreCIS has been evaluated on a testbed for a specific use case of indooroccupancy estimation that proves its effectiveness.

UniPreCIS : A data pre-processing solution for collocated services on shared IoT