# On the Realistic Radio and Network Planning of IoT Sensor Networks
## 2 Related Work  
The design of complex IoT setups requires the support of large scale test-beds or the usage of scalable simulation tools [7].  
A number of such tools and experimental frameworks, as proposed by the research community, is presented in literature. In [8], the authors present a framework that supports the parallel and distributed simulation of large scale, complex wireless system models. Results show that the proposed simulation framework is able to transparently adapt to the execution system and model characteristics, by dynamically reducing communication overhead, thus reducing simulation time and increasing simulation scalability. In [9], authors suggest a model-driven simulation (based on the standard language SDL) to describe an IoT scenario. Starting from this, an automatic code generation transforms the description into an executable simulation model for the ns-3 network simulator. In [10], researchers presented DPWSim, a simulation toolkit designed to support the development of service-oriented and event-driven IoT applications on top of IoT devices with secure Web service capabilities and a seamless integration into existing World Wide Web infrastructure applications. DPWSim allows developers to prototype, develop, and test IoT applications using the DPWS (Devices Profile for Web Services) technology without the presence of physical devices.  
A notable work that reinforces the finding of our work is presented in [11] where authors measure the Received Signal Strength Indication (RSSI) of near-ground WSNs at 470 MHz under four different terrains (flat concrete road, flat grass, undulating grass and flat grass with obstacles) and obtained the corresponding path loss models. Through comprehensive analysis of the influence of different antenna heights and terrain factors, they showed the limit of existing theoretical models. Furthermore, they proposed a propagation model selection strategy to more accurately reflect the true characteristics of the near-ground wireless channels for WSNs. These models were implemented on Cooja simulator and showed that simplistic theoretical models would induce great inaccuracy of network connectivity estimation.  
In [12], the authors research the functionality of compression and routing protocols in an IoT environment. In particular, they study how the COOJA network simulator enables the emulation of different kinds of nodes and how the routing matrices are computed. COOJA simulator is used as a power visualizer.  
Finally, the authors of [13] present Cup Carbon, a multi-agent and discrete-event, WSN simulator. In this tool, networks can be designed in an ergonomic user-friendly interface using the Open Street Map (OSM) framework, where sensors are deployed directly on map and one can study the behaviour of a WSN network and its related cost. A more broad survey is presented in [3], where authors conclude that, despite the significant technological advances presented, the difficulties associated with the evaluation of IoT solutions under realistic conditions in real world experimental deployments, still hampers their maturation and significant roll out. Furthermore, in their work, they identify the requirements for the next generation of experimental IoT facilities. In particular, they compare 23 existing testbeds--simulations publicly accessible and with notable use, concluding that a new generation of IoT experimentation facilities is required.  
In the aforementioned research work, a common drawback in proposed simulation tools/frameworks is the utilization of simplified, inaccurate propagation models for the estimation of physical layer parameters. Ignoring or miscalculating the behaviour of the physical layer renders the realistic planning and placement of IoTs in suburban or urban environments infeasible.  
Similar issues also occur when utilizing built-in/default radio propagation models in typical WSNs simulators such as ns-2 [14], ns-3 [15], TOSSIM [16], OMNET++ [17], Cooja [6] and OPNET [18].