ACM MSWiM DIVANET

1. SAPFANET: Spatially Adaptive
Positioning for FANETs
Joshua Rentrope and Dr. Mustafa Ilhan Akbas
Computer Science
Florida Polytechnic University
Design and Analysis of Intelligent Vehicular Networks and
Applications (DIVANET)
November 21, 2017

2. Table of Contents
1. Introduction
2. Problem Definition
3. Previous System
4. Optimizations
5. Simulation Results
6. Conclusions

3. Introduction
● UAV and UAV Swarm technologies have advanced
● Increased capabilities for UAVs
○ Better optical and auditory sensors
○ More reliable communications
○ Flexible controller interfaces
● Current research is effective swarm applications and autonomy

4. Problem Requirements
● UAV Swarm that can fill irregular environments (Odd spatial
domains, Obstacles in environment)
● A formulation that can adjust UAV behavior to its environment
● Applications of this protocol to hazardous tasks (search and
rescue, surveillance)

5. Problem Specification
● Already existing 3d Swarms do not give enough weight to the
environmental constraints
● There may need to be a centralized system
● Objective
○ Create optimizations that consider the environment and can dynamically
reposition UAVs into better configurations

6. Previous Systems
● Virtual Forces
○ Adjusts drone positions based on nearby drones and their relations
○ Good in theory, but it is expensive in terms of communication
○ This approach also ignores environmental macro-behaviours
● VSEPR Drone Positioning
○ Calculates the equilibrium for Virtual Forces based on the geometry of the atoms
○ Optimizes the communication needed by the swarm by updating key positions
and pair numbers

7. VSEPR Positioning
● VSEPR provides the optimal
positions from Virtual Forces
● Can be a hierarchical organization
and decentralized from a main
controller
● VSEPR does not take into account the
environment. Less optimal as
Environment changes

8. APAWSAN: VSEPR-based Positioning
Actor positioning for aerial wireless sensor and actor networks
● APAWSAN uses precalculated VSEPR positions for drone configurations
● A leader drone will broadcast its position to the follower drones
● Each follower drone will move to a its labeled position based on the formula
This works great for unbounded spaces, each drone will have optimal angles. However,
this solution is not generalized for corridor-like spaces and with obstacles

9. SAPFANET: An Optimization
● Spatially Adaptive Positioning for
FANETs merges the spatial
constraints of the environment to
generate drone positions
● SAPFANET assigns spatial
sub-domains to drone positions and
attempts to equalize them as much as
possible.

10. ● First it calculates the optimal
subdomains.
○ Each subdomain is defined by a set
boundary planes.
○ Some boundary planes are the
constraining planes of the environment
○ Boundary planes we can manipulate are
planes that part between two drones
○ A configuration, which keeps each volume
of the set as close to the average as
possible, is estimated
SAPFANET

11. SAPFANET Results
When compared to APAWSAN, the pure
VSEPR configuration, SAPFANET displayed
favorable results in terms of space
The time of convergence was slightly
higher, but can be reduced through better
communication protocols

12. Conclusion
● Can adapt VSEPR to dynamically constrained environments
● SAPFANET is mainly suited for static environment, and needs further adaptation
for high mobility systems
● Future work
○ More tests with obstacles, discontinuities in environment
○ Using more realistic antenna models for communication
○ Currently designing a simulation for Search and Rescue using SAPFANET
configurations and belief networks