MobEyes Simulation Results

We evaluated MobEyes protocols via extensive ns-2 simulations. This page shows the most important results, with the goal of investigating MobEyes performance from the following perspectives:

MobEyes Harvesting Delay for 1 Agent and 1 Hop diffusion: we simulate MobEyes protocols for summary collection on regular nodes as well as for agent harvesting;
Effect of k-hop Relay and Multiple Agents : we examine how MobEyes effectiveness can be increased by leveraging k-hop passive diffusion and the deployment of multiple agents;
Summary Diffusion Overhead : we investigate the trade-off between the delay in the harvesting process and the load imposed on the communication channel;
Stability Check: we verify that the system is stable, even in the worst case of a single harvesting agent and of highest summary generation rate expected for MobEyes scenarios;
Tracking Application: we prove MobEyes effectiveness in supporting a tracking application, where trajectories of regular nodes are locally reconstructed by a police agent based on harvested summaries;
Border Effect and Turn Over: we tested MobEyes with a novel mobility model, which takes node turn over effects into account.

Simulation Settings

We consider vehicles moving in a fixed region of size 2400m x 2400m. The default mobility model is Real-Track (RT). RT permits to model realistic vehicle motion in urban environments. In RT nodes move following virtual tracks, representing real accessible streets on an arbitrary loaded roadmap. For this set of experiments, we used a map of the Westwood area in the vicinity of the UCLA campus, as obtained by the US Census Bureau data for street-level maps.

At any intersection, each node randomly selects the next track it will run through; speed is periodically allowed to change (increase or decrease) of a quantity uniformly distributed in the interval [0, Δs]. To evaluate the impact of the mobility model on MobEyes performance, we tested two additional well-known models, namely Manhattan (MAN) and Random WayPoint (RWP). Similarly to RT, MAN builds node trajectories following urban roads; however, in MAN roads are deployed according to a regular grid, thus allowing a more uniform node deployment. In our simulation, we adopted a 10 x 10 grid. RWP instead does not constrain node positions to follow actual road tracks, but moves nodes toward randomly selected destinations with random speeds. When a node reaches its destination, it remains still for a fixed period (which we set equal to 0 by homogeneity with the other models), and then selects a new destination. Surprisingly, RWP is considered "a good approximation for simulating the motion of vehicles on a road", generally producing limited distortion on protocol performance. Let us remark that MobEyes agents do not exploit any special trajectory or controlled mobility pattern, but move conforming with regular nodes.

Our simulations consider number of nodes N=100,200,300. Vehicles move with average speed v=5,15,25; to obtain these values, we carefully tuned maximum and minimum speeds depending on the mobility model. The summary advertisement period of regular nodes and the harvesting request period are kept constant and equal to 3s through all the simulations. We note that if the value of this parameter is too large, MobEyes effectiveness is reduced since it is possible that two nodes do not exchange messages, even if they occasionally enter in each other transmission range; this effect is magnified, as node speed v increases. The chosen value has been experimentally determined to balance the effectiveness of our protocol and the message overhead, even in the worst case, i.e., v=25. A deeper and more formal investigation of the optimal value of the advertisement period is object of future work.

Finally, we modeled communications as follows: MAC protocol IEEE 802.11, transmission band 2.4 GHz, bandwidth 11Mbps, nominal radio range equal to 250m, and Two-ray Ground propagation model. Where not differently stated, reported results are average values out of 35 repetitions.