Karthik Dantu Bryan Kate Jason Waterman Peter Bailis Matt Welsh Presentors: Yuxuan Dai, Long Ma

Advantages Extremely small Large number Can be applied to enclosed, close-quarter areas Can perform tasks that challenging for larger platforms Provide robustness to failure in the field deployed to achieve a task massively

Restrictions Limited resources Complex coordination swarm size

Combine MAV behaviors with a swarm-level goal Eliminate low-level MAV coordination from users Simplify MAVs’ behavior uniformly

hive-drone model drones cannot communicate with each other drones operate without precise knowledge of location restriction assumption programming model (X, Y)

Introduction Hive-Drone Model Karma Implementation Evaluation Our Thoughts

Coordination complexity drones cannot communicate with each other restriction Programmer cannot explicitly coordinate MAV’s behavior restriction

1.Simiply MAV programming 2.Better decision making Introduce a information delay in the system

Sortie Behavior Application Disease!

Spatial Decomposition Disease! 1. Make it easier to reason about MAV allocation. 2. It is unlikely that the MAVs can access to high-resolution location services in the field 3. Using a Cartesian coordinate system is not necessary

Data Model Updates to this data structure are asynchronous, occurring when drones return from a sortie The hive maintains a key-value repository called the Datastore. T,R,N The Datastore can be queried both temporally and spatially

Programming Model 1. Activation Predicate 2. Progress Function Every behavior produces some type of information under normal execution Based on the information in the Datastore !

Scheduling Problem The hive-drone, programming model transform the problem of executing an application on a MAV swarm into a problem of scheduling behaviors on drones. 1. Use the shortest time to Complete application Drone 2 run behaviors B in R6 (t2) Drone 4 run behaviors A in R3 (t4) Drone 1 run behaviors A in R1 (t1) Drone 3 run behaviors A in R7 (t3) Advocate scheduling all available drones greedily System executes behaviors that are concurrently activated in sequence

Interleaves allocations for A and B If behavior A and B are activated concurrently (greedily schedule all available drones) If behavior A and B are activated concurrently (greedily schedule all available drones) Schedule all drones for A then for B Drone 1 run behaviors B in R1 (t2) Drone N run behaviors A in Rx (tn) Drone 1 run behaviors A in R1 (t1) Drone n run behaviors B in Rx (tn) No distinction

Scheduling Problem 1. Use the shortest time to Complete application 2. Achieve fairness between behaviors Minimize the difference in progress between any two activated behaviors output

Introduction Hive-Drone Model Karma Implementation Evaluation Our Thoughts

Karma Implementation Karma Controller Scheduler Dispatcher Datastore

Process Total Workload Remaining Workload Service Level Allocated Drones A B

Two kinds of Dispatch policies: a. Continuous dispatch policy Provide a constant presence of drones in the field Minimize the information latency b. Greedy dispatch policy Dispatch the drones opportunistically

Introduction Hive-Drone Model Karma Implementation Evaluation Our Thoughts

Evaluate the effectiveness from three aspects: Execution time Energy cost Information latency

Karma: a. greedy dispatching b. continuous dispatching Oracle: with foreknowledge of all activities, hence it can give a lower bound of requirements.

Resilience to Failure fail-->cannot update Datastore-->Dispatcher detects that-->Scheduler re-arranges in next allocation cycle. Can be mitigated by reserving drones or increasing the swarm size.

Adaptability introduce a constant wind over the bottom third of the field. In that field, the round trip time reduces 32% For same amount of work, 12% more drones. Equally, 7% higher energy cost

 Hive-drone paradigm can be used to continuously measure time varying phenomena  Example: a chemical plume tracking application Interesting feature: static VS active

Sliding window Active Static T:5m

 The current system is limited by the flight time of drones. If the drones had a longer flight time, they can operate different tasks per sortie which makes the system more efficient.  The assumption in the design is that the drones cannot communicate with each other. If the communication could be achieved, the allocation policy might be changed and information latency might also be reduced.  Inspired by idea of communication between two drones, communication between two hives may improve the coverage of application.