## 1 Introduction  
The complexity of indoor scenario characterization concerns signal propagation, but also interferences caused by the coexistence of different communication systems working in the same frequency band or by the congestion due to abundant deployment of wireless devices. A seasoned designer of wireless sensor networks (WSN) usually quite easily identifies which are the most suitable sites to locate the nodes of the network, but a subsequent experimental validation is required in order to ensure that the decision is correct and that the network operates in accordance with the requirements set by the customer. For such reason, simulation, modeling and analysis of WSNs are required. As it is well known, WSNs need to minimize the energy consumption of their nodes to ensure the longevity of the networks, and maximize service performance. For such a reason, an inefficient network design may cause high energy consumption due to an inappropriate selection of the topology of the network and an unsuitable location of the nodes. The different existing proposals for routing in WSNs focus on the use of clusters, chains or hybrid solutions. LEACH [1] is the reference proposal on cluster-based WSNs, a cluster-head collects data from all other sensors in the cluster, aggregates the data, and then transmits the whole information to a base station, also called sink. LEACH rotates the cluster-head duty in order to evenly distribute the energy consumption. PEGASIS [2; 3] forms, by means of a greedy algorithm, a chain covering all the nodes in the network, ensuring that each node only communicates with its neighbors. The transmission of the aggregated information is performed by a node which is randomly selected each round to uniformly distribute the energy consumption. Cluster- based topologies ensure fault tolerance, but they do so at the expense of a higher energy consumption due to collisions and retransmissions [4]. Chain-based topologies follow a space filling curve that ensures a minimal consumption of battery nodes due to transmission and reception [5]. In this case nodes minimize the number of messages exchanged and then battery consumption decreases in both transmission and reception modes. Other authors propose hybrid methods trying to maximize the advantages of both clusters as chains (fault tolerance, minimal energy consumption), while minimizing their disadvantages (latency, cost of chain regeneration, high amount of messages exchanged) as [6] do achieving the maximum reliability in a multi-hop network by finding the best place for the cluster-head and the proper shape/size of the clusters without requiring any error controlling minimizing computation and communication overheads. [PERSON] _et al._[7] proposed the formation of sensor nodes chains in each cluster rotating the cluster-head locally inside the cluster without re-clustering. [PERSON] _et al._[8] proposed a distributed algorithm in order to avoid centralized information about energy consumption when agreeing the cluster-head. Those proposals [6; 7; 8; 9] seek to improve the energy consumption and to increase the lifetime of the WSN compared to other classical hierarchical routing schemes such as LEACH and PEGASIS, but significantly increase the complexity of the algorithms. Indoor scenarios can be covered using chain-based routing protocols, where an adequate radio planning ensures that the chain should be rarely redone. This is the case addressed on this paper, where the whole process of designing and deploying a chain-based WSN by means of in-house 3D ray launching simulation is illustrated. The paper describes the results obtained by simulation and compares them with those obtained by real measurements, proving that an in-house 3D ray launching simulation is an efficient method for reducing the time and effort required to design and develop WSNs on such scenarios.