# On the Realistic Radio and Network Planning of IoT Sensor Networks
## 1 Introduction  
The main radio propagation mechanisms taking place in outdoor and/or indoor suburban or urban areas include reflections, transmissions, diffraction and in certain occasions scattering. Empirical or semi-empirical radio propagation modelling techniques can be limited in nature, can be used only in similar environments and conditions like the ones used to develop the models and tend to suffer from high standard deviations around the mean received signal strength (RSS) value. These type of models do have their usefulness when one is not interested in highly accurate predictions but instead is interested in fast predictions. Still, when the wireless system performance is characterised by a bimodal behaviour, i.e., in wireless sensor or IoT networks based on Zigbee or 802.15.4 technologies, accurate prediction results at the edge of coverage are of paramount importance since miscalculations can lead to non-functional Wireless Sensor Networks (WSNs) or IoT Network deployments. Instead, in order to avoid such pitfalls, the wireless network designer can utilise deterministic radio planning tools that can increase simulation accuracy [1; 2], since significant differences exist between simplified theoretical performance and actual deployments [3]. This fact imposes significant challenges indesigning and developing smart cities, where the efficient operation of large scale, heterogeneous Wireless Sensor Networks is mandatory [4]. Existing wireless network design tools/simulators may fail in providing functional deployed networks due to their inability to simulate realistically at the physical layer the performance of complex wireless network setups, i.e., their RSS coverage or any interference conditions. Existing wireless network layer simulators tend to implement and use simplistic propagation models, such as free space loss, plane earth loss, or utilize statistical or semi-empirical models, which are inadequate to extract realistic coverage results [3]. Such simplified approaches lead to miscalculation of radio coverage parameters and erroneous predictions of radio propagation characteristics which subsequently influence prediction performance at the network layer, resulting in erroneous network performance predictions. In this work, we present the result analysis of the proposed simulation methodology that combines results from two simulators that operate at different OSI layers for the purpose of increasing simulation accuracy. The first simulator, TruNET wireless [5], is a realistic 3D polarimetric physical layer simulator capable of accurately simulating radio propagation effects in urban and suburban scenarios based on advanced ray tracing techniques. The second simulator, Cooja [6], is an open source network simulator for the Contiki open source operating system especially designed for WSN or Internet of Things networks. With Cooja, large and small networks of Contiki nodes can be simulated. Nodes can be also emulated at the hardware level, which is slower, but allows precise inspection of the system behavior, or at a less detailed level, which is faster and allows simulation of larger networks. Finally, Cooja has the unique advantage that the simulated source code can be downloaded and run into real nodes. By combining the two simulators, realistic physical predictions from realistic simulated environments in TruNET can be passed to Cooja, hence achieving accurate network performance estimations, which is critical for developing reliable WSN or IoT networks. The proposed simulation methodology aims to create a realistic and functional cross layer wireless sensor network design platform, focusing on the deployment of WSNs and IoT networks, in smart city environments. The interconnection of the two aforementioned simulators offers the following advantages:  
1.