The explosion of wireless devices has given rise to numerous data-sharing applications in smart-cities' Pervasive Sensing (PS) paradigms. This vision has been further expanded in the Internet of Things (IoT) era to embrace multipurpose resources within a smart city setup such as public sensors on roads/vehicles, cameras, RFID tags, and readers. The realization of such a prophecy is significantly challenged in terms of connectivity disruption, resource management, and data gathering under mobile conditions. In this paper, we present a hybrid pervasive sensing framework for data gathering in IoT-enabled smart-cities' paradigm. This framework satisfies service-oriented applications in smart cities where data is provided via data access points (APs) of various resources. Moreover, public vehicles are used in this work as Data Couriers (DCs) that read these APs data packets and relay it back to a base-station in the city. Accordingly, we propose a hybrid fitness function for a genetic-based DCs selection approach. Our function considers resource limitations in terms of count, storage capacity and energy consumption as well as the targeted application characteristics. Extensive simulations are performed and the effectiveness of the proposed approach has been confirmed against other heuristic approaches with respect to total travelled distances and overall data-delivery cost. (C) 2017 Elsevier B.V. All rights reserved.