## 4 Experimental Design
### Experiment Description  
Our simulation scenario uses the IEEE 802.11b standard for Wireless Local Area Networks, and considers mobile nodes forming a MANET. The simulated network allows for retransmission of lost packets due to collision or propagation loss [37]. The chosen propagation loss model is the maximal range, in which the propagation loss is determined based on the distance between transmitter and receiver (_i.e._, the communication threshold). Any transmission within this threshold is received at maximum transmission power, while transmissions beyond that threshold are received at -1000 dBm (effectively zero), and are therefore dropped. The simulations observe thresholds of 30, 50, and 80 m. In order to allow for more variation, we also use the random propagation delay model, using a time threshold of 0.7 s. If the random delay is over that threshold, the packet is dropped by the receiver. Toavoid flooding the network with requests, we only allow communication between nodes at a distance of one hop. Regarding the nodes, pedestrians represent people on foot, with a random direction and speed in the ranges of \([0,2\pi]\) and \([1.0,4.0]\) m/s, respectively, and movement based on the random walk mobility model [33]. Vehicle nodes represent people riding bicycles, cars, motorbikes, or similar land transportation vehicles. These nodes use the random waypoint mobility model [33], and have a speed in the range of \([5.0,20.0]\) m/s. Both vehicle and pedestrian nodes move for a time \(t\) or a distance \(d\), and then choose a new random direction and speed. Stationary nodes, on the other hand, represent devices that are immobile or doing micro-mobility, such as parked vehicles, people sitting around, or working in newstands or hotdog carts. These nodes use the constant position mobility model, in which they have an effective speed equal to zero, although their direction can still change. Nodes that begin the experiment as stationary never move during the simulation process, but pedestrians and vehicles can randomly become stationary for certain time periods. Each node has a role, depending on its positioning capabilities. They can either be be beacons (also known as anchors), or non-beacons. Beacon nodes have access to at least one positioning strategy (e.g., GPS). Non-beacon nodes, on the other hand, have no positioning capabilities whatsoever. The simulation scenario consists of an area of 500 \(\times\) 500 m\({}^{2}\), representing a park or a university campus, and is populated by the three types of nodes, in the following proportions: 65% pedestrians, 25% vehicles, and 10% stationary. All nodes are randomly positioned within the simulated scenario, and each node has a random initial direction and speed. The number of nodes we have considered is 100 for each simulated scenario. As for the roles, three different configurations of beacons can be assigned to a given scenario: 10%, 25%, and 50% of (beacon) nodes, respectively. In order to observe the impact of the nodes' speed during the positioning process, we first assigned the beacon role randomly to any type of node, then only to pedestrian nodes, and finally only to vehicles. Since we have only 100 nodes for each scenario, we had to increase the number of vehicles for the third scenario, reducing the other types of nodes proportionally in order to ensure that the results are not biased. All nodes begin with 100% energy, and they do not know their position at the beginning of the simulation. We assume that all participating devices are using the proposed context-aware positioning model. This means all nodes can potentially sense their environment and collaborate with their neighbors. The communication and navigation capabilities, represented in the simulation by the devices feature vector, are assigned randomly to all nodes.