## 1 Introduction  
Recently, the integration between Internet of Things (IoT) with the cloud (IoT-Cloud or sensor-cloud) has received significant interest from both academia and industry [1; 2; 3; 4]. The integration is motivated by taking advantages of powerful processing and storage abilities of cloud computing for sensing data. By enabling such an integration, sensing-as-a-service (SSaaS) enables the cloud to provide sensing data to multiple applications at the same time. In addition, constrained sensor nodes can transfer processing tasks to the cloud to save energy. Some initial studies have been conducted toward detailed design for IoT-cloud as discussed in detail in the next section. For example, in [1; 2; 5; 6; 7], the authors present architectural design for IoT-cloud. Sensor virtualization and pricing model are discussed in [8; 9], while the studies [10; 11; 12] propose various approaches to optimize data delivery from physical wireless sensor networks to the IoT-cloud. Although the above studies discuss how to integrate sensors with the cloud and how to distribute sensing data efficiently, few works are investigating how the sensor-cloud integration can help improve energy efficiency for resource constrained sensor nodes. In the previous work [3], we design a framework to enable the IoT-cloud to minimize the number of requests sent to shared physical sensorsin the scenarios of multiple applications so that energy consumption of sensors are optimized while their sensing services still meet requirements of applications. Although some methods like request aggregation [3] or data caching [12] may help reduce data transmission, sensors are still required to transmit their data regularly. For example, in the conventional periodic sensing data collection model [3, 13], sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. Such a continuous sensing report without awareness of sensing demands leads to unnecessary energy loss due to redundant transmission. In many mobile cloud computing applications such as networked robots, vehicular safety, e-health, and personal cyber-physical systems, location of mobile users are normally known by the cloud (i.e., by tracking using GPS or various location tracking schemes [14, 15, 16]). Therefore, when the mobile users request for sensing services, the cloud can know their location of interest. As a result, the cloud has knowledge for which location sensing services are required and in which locations sensing services are not required. For such a promising application field, the location-based on-demand interactive model between IoT and cloud can be applied, and thus need to be modeled. This paper presents a location-based interactive model for IoT-cloud in which sensing data collection of sensors is triggered only on-demand based on mobile users' location of interest. The model enables on-demand interaction between cloud and IoT, and exploits the cloud's capabilities as a coordinator to save energy for resource-constrained sensors through controlling scheduling of sensors. The on-demand sensing services controlled by the cloud offer three main benefits: (1) sensors only work when needed; (2) sensing data is gathered on demand based on applications' interest, so data redundant is reduced; and (3) sensing service quality (i.e., latency, sensing interval) can be set on-demand based on requirements of application users. We provide a comprehensive analysis of the model in comparison with the periodic sensing model under various parameters to explore when the location-based model performs better than the periodic model. In summary, this paper makes the following contributions:  
* We model a location-based interactive approach for IoT-cloud to served mobile cloud computing applications;
* We present an on-demand scheduling scheme for WSNs on the top of the model.