Mohand Hamadouche
email: mohand.hamadouche@univ-brest.fr

MoTIe : Monitoring of mobile Things with Intelligent and embedded adaptations for secure services

In the context of maritime mission, ❖ Cooperation with a UAV or Multi-UAV system improves mission quality, productivity, and safety. ❖ The UAVs provide a complementary viewpoint and the maritime drones are used as a 'Mothership'. ❖ Aerial drones have to face hazards (malfunctioning of the system/sensors, cyber-attacks e.g. spoofing). ❖ Many challenges in multi-UAV systems: ➥ Distributed decision-making, taking into account conflicts; ➥ Failures and safety threats in multi-UAV systems.

II. Comparison of fundamental methods for mission planning [START_REF] Hamadouche | Comparison of value iteration, policy iteration and q-learning for solving decision-making problems[END_REF] ❖ Markov decision process (MDP) approach is the most common for modeling UAV missions. ❖ Fundamental methods for solving it: ➥ Value Iteration and Policy Iteration which are Dynamic programming (DP) methods.

➥ Q-Learning which is a temporal-difference (TD) method. Mission planning of a UAV: ➥ Sophisticated case with irregular states. ➥ Actions are non-similar in each state of the problem.

➥ It is more difficult to set up the Q-Learning method, and so, DP methods are more efficient.

III. Self adaptation based rewards tuning for mission planning [START_REF] Hamadouche | Reward tuning for self-adaptive policy in mdp based distributed decision-making to ensure a safe mission planning[END_REF][START_REF] Hamadouche | Online reward adaptation for mdp-based distributed missions[END_REF] ❖ Policy π is a (solution of an MDP) mapping from state space (S) to action space (A).

❖ Policy π is used to determine the optimal action given the current state. ❖ Change in action reward modifies the action associated with a state. ❖ Modification of the policy requires updating the action rewards. ❖ Method principle:

1. Definition of conflicts and constraints.

2. Definition of priorities (action/UAV).

Conflict management:

✺ Reward computation:

(a) UAV level: ➥ Executing simultaneous actions can lead to conflicts. ➥ Identification of conflicting states with antagonistic actions. ➥ Action-states with conflicts are adjusted by modifying the rewards of conflicting actions.

(b) Multi-UAV level: ➥ UAV decisions are unique and optimal at their level. ➥ Decisions can be contradictory and do not meet the mission's objectives of the mission. ➥ The UAV favors an alternative action to solve the conflict in the team. ➥ The method is efficient in terms of both latency (µ(time)=2ms) and energy consumption. ➥ Simulation of conflict resolution scenarios using the CoppeliaSim simulator.

➥ Proposal of a method for adapting the UAV operating mode for conflict resolution by using the Cloud or the local network.

  Execution time for size 4 × 4 to 55 × 55: (a) Version 1-a. (b) Version 1-b. (c) Version 2.

IV.

  Adaptation with cooperation through the cloud/the local network ❖ Cooperation within a UAV team can be necessary in case of UAV malfunctioning. ❖ Mode switching evaluated by BN: ➥ Local mode: local data (+ neighbor data) are sufficient; ➥ Hybrid mode: local data are insufficient or communications are compromised. ❖ Data used: ➥ QoS UAV ➥ QoS Local communication ➥ QoS Cloud communication V. Conclusion ❖ Contributions: ➥ Comparative study of methods for solving MDPs for mission planning. ➥ Proposal of a systematic method for resolving conflicts at UAV and Multi-UAV team level.