┌─────────────────────────────────────────────────────────┐
│                    CLOUD TIER                          │
│  Global optimization, mission planning, data fusion    │
│  (Runs on Earth or high-orbit compute)                 │
└────────────────────┬────────────────────────────────────┘
                     │ Intermittent, high-latency link
                     │ (minutes to hours)
┌────────────────────▼────────────────────────────────────┐
│                    FOG TIER                            │
│  Regional coordination, local optimization             │
│  (Runs on landers, rovers, or orbital relays)          │
└────────────────────┬────────────────────────────────────┘
                     │ Low-latency, short-range link
                     │ (milliseconds to seconds)
┌────────────────────▼────────────────────────────────────┐
│                    EDGE TIER                           │
│  Individual agents, real-time sensing and actuation    │
│  (Runs on individual rovers/drones)                    │
└─────────────────────────────────────────────────────────┘

class SwarmCoordinator:
    def __init__(self, agents, communication_graph):
        self.agents = agents
        self.graph = communication_graph
        self.task_queue = PriorityQueue()
        self.consensus_rounds = 3  # Number of consensus iterations

    def coordinate_cycle(self, environment_state):
        """
        Execute one coordination cycle across the swarm.
        Returns updated agent directives.
        """
        # Phase 1: Local sensing and state estimation
        local_states = {}
        for agent in self.agents:
            local_states[agent.id] = agent.sense_environment()

        # Phase 2: Distributed consensus on task allocation
        task_assignments = self._run_consensus_auction(local_states)

        # Phase 3: Collision-free path planning
        trajectories = self._plan_collision_free_paths(task_assignments)

        # Phase 4: Edge-local execution with cloud sync
        directives = []
        for agent_id, trajectory in trajectories.items():
            directive = self._create_directive(
                agent_id,
                trajectory,
                sync_with_cloud=(self._is_sync_window_open())
            )
            directives.append(directive)

        return directives

    def _run_consensus_auction(self, local_states):
        """
        Distributed auction for task allocation.
        Uses gossip protocol for consensus.
        """
        bids = {}
        for agent_id, state in local_states.items():
            # Each agent bids on tasks it can reach
            for task in self.task_queue.peek_all():
                bid_value = self._calculate_bid(agent_id, task, state)
                bids[(agent_id, task.id)] = bid_value

        # Gossip-based consensus on bids
        for _ in range(self.consensus_rounds):
            self._gossip_bids(bids)

        # Assign tasks based on consensus bids
        assignments = {}
        for task in self.task_queue.peek_all():
            best_bidder = max(
                [a for a in self.agents if (a.id, task.id) in bids],
                key=lambda a: bids[(a.id, task.id)]
            )
            assignments[best_bidder.id] = task

        return assignments

class InverseSimulationVerifier:
    """
    Verifies swarm coordination by running inverse simulation.
    Given observed trajectories, reconstructs the inputs and
    checks consistency with the actual inputs provided.
    """

    def __init__(self, forward_simulator):
        self.forward_sim = forward_simulator
        self.epsilon = 0.01  # Tolerance for verification

    def verify_mission(self, initial_state, observed_trajectories):
        """
        Verify that observed trajectories are consistent with
        the coordination algorithm and physics model.
        """
        # Step 1: Reconstruct the expected coordination directives
        reconstructed_directives = []
        for t in range(len(observed_trajectories[0])):
            state_snapshot = self._extract_state_at_time(
                observed_trajectories, t
            )
            # Run the coordination algorithm in reverse
            directives = self._inverse_coordination(state_snapshot)
            reconstructed_directives.append(directives)

        # Step 2: Check consistency between forward and inverse
        verification_results = []
        for t, directives in enumerate(reconstructed_directives):
            # Simulate forward from reconstructed directives
            predicted_state = self.forward_sim.step(
                initial_state, directives
            )
            actual_state = self._extract_state_at_time(
                observed_trajectories, t+1
            )

            # Check if states match within tolerance
            error = self._calculate_state_error(
                predicted_state, actual_state
            )
            verification_results.append(
                error < self.epsilon
            )

        # Step 3: Aggregate results
        pass_rate = sum(verification_results) / len(verification_results)
        return {
            'pass_rate': pass_rate,
            'verification_results': verification_results,
            'failed_timesteps': [
                i for i, r in enumerate(verification_results) if not r
            ]
        }

    def _inverse_coordination(self, state_snapshot):
        """
        Inverse of the coordination algorithm.
        Given a state, determine what directives would have
        produced the observed transitions.
        """
        # This is the tricky part - requires the coordination
        # algorithm to be invertible, or at least have a
        # well-defined inverse
        directives = []
        for agent_state in state_snapshot['agents']:
            # Invert the physics model
            acceleration = self._invert_kinematics(
                agent_state['position'],
                agent_state['velocity'],
                agent_state['acceleration']
            )
            # Invert the coordination logic
            directive = self._invert_coordination_logic(
                agent_state['task_id'],
                acceleration
            )
            directives.append(directive)
        return directives

import numpy as np

class QuantumInspiredTaskAllocator:
    """
    Uses QUBO formulation for optimal task allocation.
    Can run on classical hardware or be adapted for
    quantum annealing systems.
    """

    def __init__(self, agents, tasks, cost_matrix):
        self.num_agents = len(agents)
        self.num_tasks = len(tasks)
        self.cost_matrix = cost_matrix

        # Build QUBO matrix
        self.Q = self._build_qubo_matrix()

    def _build_qubo_matrix(self):
        """
        Construct the QUBO matrix for task allocation.
        Binary variable x[i,j] = 1 if agent i takes task j.
        """
        n = self.num_agents * self.num_tasks
        Q = np.zeros((n, n))

        # Objective: minimize total cost
        for i in range(self.num_agents):
            for j in range(self.num_tasks):
                idx = i * self.num_tasks + j
                Q[idx, idx] += self.cost_matrix[i, j]

        # Constraint: each task assigned exactly once
        for j in range(self.num_tasks):
            for i1 in range(self.num_agents):
                for i2 in range(self.num_agents):
                    idx1 = i1 * self.num_tasks + j
                    idx2 = i2 * self.num_tasks + j
                    if i1 != i2:
                        Q[idx1, idx2] += 2.0  # Penalty
                    else:
                        Q[idx1, idx1] -= 1.0  # Linear term

        # Constraint: each agent handles at most one task
        for i in range(self.num_agents):
            for j1 in range(self.num_tasks):
                for j2 in range(self.num_tasks):
                    idx1 = i * self.num_tasks + j1
                    idx2 = i * self.num_tasks + j2
                    if j1 != j2:
                        Q[idx1, idx2] += 2.0  # Penalty
                    else:
                        Q[idx1, idx1] -= 1.0  # Linear term

        return Q

    def solve_with_simulated_annealing(self, iterations=1000):
        """
        Solve QUBO using simulated annealing.
        This mimics quantum annealing on classical hardware.
        """
        n = self.num_agents * self.num_tasks
        current_solution = np.random.randint(0, 2, n)
        current_energy = self._calculate_energy(current_solution)

        best_solution = current_solution.copy()
        best_energy = current_energy

        temperature = 10.0
        cooling_rate = 0.995

        for iteration in range(iterations):
            # Flip a random bit
            candidate = current_solution.copy()
            bit_to_flip = np.random.randint(0, n)
            candidate[bit_to_flip] = 1 - candidate[bit_to_flip]

            candidate_energy = self._calculate_energy(candidate)

            # Metropolis acceptance criterion
            delta_energy = candidate_energy - current_energy
            if delta_energy < 0 or np.random.random() < np.exp(-delta_energy / temperature):
                current_solution = candidate
                current_energy = candidate_energy

                if current_energy < best_energy:
                    best_solution = current_solution.copy()
                    best_energy = current_energy

            temperature *= cooling_rate

        return self._decode_solution(best_solution)

    def _calculate_energy(self, solution):
        """Calculate the QUBO energy for a given solution."""
        return solution @ self.Q @ solution

    def _decode_solution(self, solution):
        """Convert binary solution to task assignments."""
        assignments = {}
        for i in range(self.num_agents):
            for j in range(self.num_tasks):
                idx = i * self.num_tasks + j
                if solution[idx] == 1:
                    assignments[i] = j
        return assignments

python
class EdgeOptimizedCoordinator:
    """
    Lightweight coordination for resource-constrained edge devices.
    Uses fixed-point arithmetic and pre-computed lookup tables.
    """

    def __init__(self, config):
        self.config = config
        # Pre-compute collision avoidance lookup table
        self.collision_table = self._precompute_collision_table()
        # Pre-compute sensor coverage patterns
        self.coverage_patterns = self._precompute_coverage_patterns()

    def _precompute_collision_table(self):
        """
        Pre-compute collision avoidance responses for
        common relative positions. This avoids expensive
        real-time computations.
        """
        table = {}
        resolution = self.config['spatial_resolution']
        max_range = self.config['sensor_range']

        for dx in range(-max_range, max_range, resolution):
            for dy in range(-max_range, max_range, resolution):
                distance = np.sqrt(dx**2 + dy**2)
                if distance > 0 and distance < 2 * self.config['agent_radius']:
                    # Compute avoidance vector
                    avoidance = np.array([-dx/distance, -dy/distance])
                    table[(dx, dy)] = avoidance
        return table

    def coordinate_step(self, agent_states, tasks):
        """
        Execute one coordination step using only local information.
        Designed for real-time execution on edge hardware.
        """
        # Use integer arithmetic for speed
        directives = []
        for agent_state in agent_states:
            # Find nearest task
            nearest_task = self._find_nearest_task(
                agent_state['position'], tasks
            )

            # Compute movement direction
            direction = self._compute_movement_direction(
                agent_state['position'], nearest_task
            )

            # Check for collisions using lookup table
            for other_agent in agent_states:
                if other_agent['id'] != agent_state['id']:
                    relative_pos = (
                        other_agent['position'] - agent_state['position']
                    )
                    # Quantize for lookup
                    quantized = (
                        int(relative_pos[0] / self.config['spatial_resolution']),
                        int(relative