The Internet of Things (IoT) is expanding and reaching the maturity level beyond initial deployments. An integrative and interoperable IoT platform proves to be a suitable execution environment for Smart City services because users simultaneously use multiple services, while an IoT platform enables cross-service data sharing. A large number of various IoT and mobile devices as well as the corresponding services can generate tremendous input load on an underlying IoT platform. Thus, it is crucial to analyze the overall input rate on Smart City services to ensure predefined quality of service (e.g., low latency required by some IoT services). An aggregate input rate which characterizes a real world deployment can be used to check if a platform is able to adequately support multiple services running in parallel and to evaluate its overall performance. In this paper we review IoT-based Smart City services to identify key applications characterizing the domain, e.g., smart mobility, smart utilities, and citizen-driven mobile crowd sensing services. Next, we analyze the potential load which such applications pose on IoT services that continuously process the generated data streams. The analysis is used to create a model estimating an aggregate load generated by Smart City applications. We simulate a number of characteristic application compositions to provide insight about the aggregate input load and its potential impact on the performance of Smart City services. The proposed model is a first step towards predicting the processing load of Smart City services to facilitate the assessment and planning of required resources for continuous processing of sensor data in the context of Smart City services.