Internet of Things (IoT) networks dependent on cloud services usually fail in supporting real-time applications as there is no response time guarantees. The fog computing paradigm has been used to alleviate this problem by executing tasks at the edge of the network, where it is possible to provide time bounds. One of the challenging topics in a fog-assisted architecture is to task placement on edge devices in order to obtain a good performance. The process of task mapping into computational devices is known as Service Placement Problem (SPP). In this paper, we present a heuristic algorithm to solve SPP, dubbed as clustering of fog devices and requirement-sensitive service first (SCATTER). We provide simulations using iFogSim toolkit and experimental evaluations using real hardware to verify the feasibility of the SCATTER algorithm by considering a smart home application. We compared the SCATTER with two existing works: edge-ward and cloud-only approaches, in terms of Quality of Service (QoS) metrics. Our experimental results have demonstrated that SCATTER approach has better performance compared with the edge-ward and cloud-only, 42.1% and 60.2% less application response times, 22% and 27.8% less network usage, 45% and 65.7% less average application loop delays, and 2.33% and 3.2% less energy consumption.