Self-Supervised Temporal Pattern Mining for wildfire evacuation logistics networks for low-power autonomous deployments

Self-Supervised Temporal Pattern Mining for wildfire evacuation logistics networks for low-power autonomous deployments

Introduction: The Learning Journey That Sparked This Research

It was during the devastating 2023 wildfire season, while watching real-time evacuation chaos unfold through drone footage and sensor networks, that I had my research epiphany. I was experimenting with self-supervised learning for time-series anomaly detection in industrial IoT systems when I realized the same techniques could be radically adapted for something far more critical: wildfire evacuation logistics. My exploration began with a simple question: Could autonomous systems deployed in remote, low-power environments learn evacuation patterns without labeled data, adapting to the chaotic temporal dynamics of spreading wildfires?

Through my investigation of self-supervised learning papers, particularly contrastive learning approaches for temporal data, I discovered that most research focused on controlled environments with abundant power and connectivity. The real challenge emerged when I started testing these algorithms on edge devices with severe power constraints. One interesting finding from my experimentation with temporal pattern mining was that traditional supervised approaches failed catastastically when fire behavior deviated from historical patterns—which, as climate change accelerates, happens with increasing frequency.

Technical Background: The Convergence of Multiple Disciplines

The Core Problem Space

Wildfire evacuation logistics present a unique temporal mining challenge characterized by:

• Non-stationary patterns that evolve as fires spread
• Multi-scale temporal dependencies from minutes to hours
• Sparse, noisy sensor data from edge deployments
• Extreme power constraints requiring algorithmic efficiency
• Missing data during network disruptions

While studying temporal graph neural networks, I realized that evacuation networks aren't just static graphs—they're temporal hypergraphs where relationships (road capacities, shelter availability, vehicle positions) change minute by minute. My exploration of recent papers on temporal point processes revealed that most approaches assume continuous power availability, which simply doesn't hold in wildfire scenarios where communication towers and power infrastructure are often compromised.

Self-Supervised Learning Paradigm Shift

Through my research of self-supervised learning for temporal data, I came to understand that the key innovation lies in creating meaningful pretext tasks that force the model to learn robust temporal representations. Traditional approaches use reconstruction or forecasting tasks, but I discovered during my experimentation that these often fail for evacuation scenarios because they assume stationarity.

One breakthrough in my learning journey came when I combined temporal contrastive learning with sparse attention mechanisms. By creating positive pairs through temporal augmentations (time warping, segment sampling, noise injection) and negative pairs from different fire events, the model learned to distinguish between normal evacuation flow and crisis patterns without any labeled data.

Implementation Details: Building the Core System

Architecture Overview

The system I developed consists of three main components:

1. Temporal Encoder: Lightweight transformer with sparse attention
2. Contrastive Learning Module: Manages positive/negative pairs
3. Pattern Mining Engine: Extracts actionable evacuation patterns

Here's the core architecture implementation I developed during my experimentation:

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple
import numpy as np

class SparseTemporalEncoder(nn.Module):
    """Lightweight encoder for temporal patterns on edge devices"""

    def __init__(self, input_dim: int = 8, hidden_dim: int = 64,
                 num_heads: int = 4, num_layers: int = 3):
        super().__init__()

        # Learned positional encoding for temporal sequences
        self.pos_encoder = nn.Parameter(torch.randn(1, 1000, hidden_dim))

        # Sparse attention blocks for computational efficiency
        self.attention_blocks = nn.ModuleList([
            SparseAttentionBlock(hidden_dim, num_heads, sparsity=0.3)
            for _ in range(num_layers)
        ])

        # Adaptive pooling for variable-length sequences
        self.adaptive_pool = nn.AdaptiveAvgPool1d(1)

    def forward(self, x: torch.Tensor, mask: torch.Tensor = None):
        # x shape: (batch, sequence_length, features)
        batch_size, seq_len, _ = x.shape

        # Project input to hidden dimension
        x = self.input_projection(x)

        # Add positional encoding (truncated to sequence length)
        x = x + self.pos_encoder[:, :seq_len, :]

        # Apply sparse attention blocks
        for block in self.attention_blocks:
            x = block(x, mask)

        # Global temporal pooling
        x = x.transpose(1, 2)  # (batch, hidden, seq_len)
        x = self.adaptive_pool(x).squeeze(-1)

        return x

class TemporalContrastiveLearner:
    """Self-supervised learning module for temporal patterns"""

    def __init__(self, temperature: float = 0.1):
        self.temperature = temperature

    def create_temporal_augmentations(self, sequence: np.ndarray):
        """Create positive pairs through temporal augmentations"""
        augmentations = []

        # Time warping (variable speed)
        warp_factor = np.random.uniform(0.8, 1.2)
        warped = self.time_warp(sequence, warp_factor)
        augmentations.append(warped)

        # Random segment sampling
        if len(sequence) > 10:
            start = np.random.randint(0, len(sequence) - 5)
            sampled = sequence[start:start + 5]
            augmentations.append(sampled)

        # Gaussian noise injection (simulating sensor noise)
        noisy = sequence + np.random.normal(0, 0.05, sequence.shape)
        augmentations.append(noisy)

        return augmentations

    def compute_contrastive_loss(self, anchor: torch.Tensor,
                                 positive: torch.Tensor,
                                 negatives: List[torch.Tensor]):
        """NT-Xent loss for temporal representations"""
        # Normalize embeddings
        anchor = F.normalize(anchor, dim=-1)
        positive = F.normalize(positive, dim=-1)
        negatives = [F.normalize(neg, dim=-1) for neg in negatives]

        # Positive similarity
        pos_sim = torch.exp(torch.sum(anchor * positive, dim=-1) / self.temperature)

        # Negative similarities
        neg_sims = torch.stack([
            torch.exp(torch.sum(anchor * neg, dim=-1) / self.temperature)
            for neg in negatives
        ])

        # Contrastive loss
        loss = -torch.log(pos_sim / (pos_sim + torch.sum(neg_sims)))
        return loss.mean()

Low-Power Optimization Techniques

During my investigation of edge deployment constraints, I discovered several critical optimizations:

class PowerAwareInference:
    """Dynamic computation scaling based on power budget"""

    def __init__(self, base_model, power_budget_mw: float = 100):
        self.model = base_model
        self.power_budget = power_budget_mw
        self.computation_modes = {
            'ultra_low': {'sparsity': 0.8, 'precision': 'int8'},
            'low': {'sparsity': 0.5, 'precision': 'float16'},
            'normal': {'sparsity': 0.3, 'precision': 'float32'}
        }

    def adaptive_inference(self, input_data: torch.Tensor,
                          battery_level: float) -> dict:
        """Dynamically adjust computation based on power constraints"""

        # Estimate remaining operation time
        time_remaining = self.estimate_operation_time(battery_level)

        # Select computation mode
        if time_remaining < 1.0:  # Less than 1 hour
            mode = 'ultra_low'
        elif time_remaining < 6.0:
            mode = 'low'
        else:
            mode = 'normal'

        config = self.computation_modes[mode]

        # Apply sparsity
        if config['sparsity'] < 1.0:
            input_data = self.apply_sparsity(input_data, config['sparsity'])

        # Apply precision
        if config['precision'] == 'int8':
            input_data = input_data.to(torch.int8)
        elif config['precision'] == 'float16':
            input_data = input_data.to(torch.float16)

        # Run inference
        with torch.no_grad():
            output = self.model(input_data)

        return {
            'prediction': output,
            'computation_mode': mode,
            'estimated_power_used': self.estimate_power_usage(mode, input_data.shape)
        }

    def apply_sparsity(self, tensor: torch.Tensor, sparsity: float):
        """Apply structured sparsity to reduce computations"""
        mask = torch.rand_like(tensor) > sparsity
        return tensor * mask

Temporal Pattern Mining Algorithm

The core pattern mining algorithm I developed through extensive experimentation:

class TemporalPatternMiner:
    """Mines evacuation patterns from learned representations"""

    def __init__(self, min_support: float = 0.1, max_gap: int = 30):
        self.min_support = min_support
        self.max_gap = max_gap  # Maximum time gap between events (minutes)

    def mine_evacuation_patterns(self, temporal_sequences: List[np.ndarray],
                                embeddings: np.ndarray):
        """Discover frequent temporal patterns in evacuation data"""

        patterns = []

        # Convert embeddings to discrete symbols for pattern mining
        symbols = self.quantize_embeddings(embeddings)

        # Mine sequential patterns using modified PrefixSpan algorithm
        for seq_idx, symbol_seq in enumerate(symbols):
            candidate_patterns = self.generate_candidates(symbol_seq)

            for pattern in candidate_patterns:
                support = self.calculate_support(pattern, symbols)

                if support >= self.min_support:
                    # Calculate temporal statistics
                    stats = self.calculate_pattern_stats(pattern, temporal_sequences[seq_idx])

                    patterns.append({
                        'pattern': pattern,
                        'support': support,
                        'mean_duration': stats['mean_duration'],
                        'variance': stats['variance'],
                        'predictive_power': self.calculate_predictive_power(pattern, symbols)
                    })

        # Filter and rank patterns
        ranked_patterns = self.rank_patterns(patterns)
        return ranked_patterns

    def generate_candidates(self, sequence: List[int], max_length: int = 5):
        """Generate candidate temporal patterns"""
        candidates = []

        for length in range(2, min(max_length, len(sequence)) + 1):
            for start in range(len(sequence) - length + 1):
                candidate = sequence[start:start + length]

                # Check temporal constraints
                if self.validate_temporal_constraints(candidate):
                    candidates.append(candidate)

        return candidates

Real-World Applications: From Theory to Life-Saving Systems

Autonomous Deployment Architecture

Through my experimentation with edge AI systems, I developed a deployment architecture that addresses the unique challenges of wildfire scenarios:

class AutonomousEvacuationCoordinator:
    """Coordinates low-power autonomous nodes in wildfire scenarios"""

    def __init__(self, node_network: Dict[str, dict]):
        self.nodes = node_network
        self.pattern_miner = TemporalPatternMiner()
        self.shared_knowledge = {}

    async def distributed_pattern_mining(self):
        """Collaborative pattern mining across low-power nodes"""

        # Phase 1: Local pattern discovery
        local_patterns = {}
        for node_id, node in self.nodes.items():
            if node['battery'] > 0.1:  # Only if sufficient power
                patterns = await self.collect_local_patterns(node_id)
                local_patterns[node_id] = patterns

        # Phase 2: Pattern aggregation via gossip protocol
        aggregated = await self.gossip_aggregate(local_patterns)

        # Phase 3: Consensus on critical patterns
        consensus_patterns = await self.reach_consensus(aggregated)

        # Phase 4: Update all nodes with new knowledge
        await self.distribute_knowledge(consensus_patterns)

        return consensus_patterns

    async def adaptive_communication(self, urgency_level: float):
        """Dynamically adjust communication based on urgency and power"""

        if urgency_level > 0.8:  # Critical situation
            # Use all available communication methods
            protocols = ['lorawan', 'mesh', 'satellite']
            power_allocation = {'lorawan': 0.4, 'mesh': 0.4, 'satellite': 0.2}

        elif urgency_level > 0.5:
            # Balanced approach
            protocols = ['lorawan', 'mesh']
            power_allocation = {'lorawan': 0.6, 'mesh': 0.4}

        else:
            # Power-saving mode
            protocols = ['lorawan']
            power_allocation = {'lorawan': 1.0}

        return protocols, power_allocation

Quantum-Inspired Optimization

While exploring quantum annealing for optimization problems, I discovered that certain quantum-inspired algorithms could be adapted for classical low-power devices:

class QuantumInspiredOptimizer:
    """Quantum-inspired optimization for evacuation routing"""

    def __init__(self, num_qubits: int = 10):
        self.num_qubits = num_qubits

    def solve_evacuation_routing(self, road_network: dict,
                                population_nodes: list,
                                shelter_capacities: dict):
        """Solve evacuation routing using quantum-inspired algorithms"""

        # Formulate as QUBO (Quadratic Unconstrained Binary Optimization)
        qubo_matrix = self.formulate_evacuation_qubo(
            road_network, population_nodes, shelter_capacities
        )

        # Solve using simulated quantum annealing
        solution = self.simulated_quantum_annealing(
            qubo_matrix,
            num_sweeps=1000,
            beta_range=(0.1, 10.0)
        )

        # Decode solution to evacuation routes
        routes = self.decode_solution(solution, road_network)

        return routes

    def formulate_evacuation_qubo(self, road_network, population_nodes, shelters):
        """Formulate evacuation as optimization problem"""

        # Objective: Minimize evacuation time + maximize people saved
        # Constraints: Road capacities, shelter capacities, time windows

        n_variables = len(population_nodes) * len(shelters)
        Q = np.zeros((n_variables, n_variables))

        # Linear terms: evacuation time for each route
        for i, (source, target) in enumerate(self.generate_routes(population_nodes, shelters)):
            travel_time = self.calculate_travel_time(source, target, road_network)
            Q[i, i] = travel_time

        # Quadratic terms: capacity constraints
        for i in range(n_variables):
            for j in range(i + 1, n_variables):
                if self.share_resources(i, j, road_network, shelters):
                    # Penalize simultaneous use of constrained resources
                    Q[i, j] = self.capacity_penalty

        return Q

Challenges and Solutions: Lessons from the Field

Power Management Challenges

During my field testing with solar-powered edge devices, I encountered several unexpected challenges:

1. Intermittent Power Failures: Solar panels covered in ash, battery degradation in high temperatures
2. Communication Blackouts: Fire-damaged infrastructure, atmospheric interference
3. Sensor Drift: Temperature and smoke sensors losing calibration under extreme conditions

One solution I developed through experimentation was a hierarchical power management system:

class HierarchicalPowerManager:
    """Manages power across computation, communication, and sensing"""

    def __init__(self, total_budget_mw: float = 500):
        self.total_budget = total_budget_mw
        self.components = {
            'computation': {'priority': 2, 'current_usage': 0},
            'communication': {'priority': 1, 'current_usage': 0},
            'sensing': {'priority': 3, 'current_usage': 0},
            'storage': {'priority': 4, 'current_usage': 0}
        }

    def allocate_power(self, emergency_level: float):
        """Dynamic power allocation based on situation severity"""

        available_power = self.estimate_available_power()

        if emergency_level > 0.8:
            # Crisis mode: Maximize communication and computation
            allocation = {
                'computation': available_power * 0.4,
                'communication': available_power * 0.5,
                'sensing': available_power * 0.1,
                'storage': 0
            }
        elif emergency_level > 0.3:
            # Alert mode: Balanced allocation
            allocation = {
                'computation': available_power * 0.3,
                'communication': available_power * 0.3,
                'sensing': available_power * 0.3,
                'storage': available_power * 0.1
            }
        else:
            # Monitoring mode: Maximize sensing
            allocation = {
                'computation': available_power * 0.1,
                'communication': available_power * 0.2,
                'sensing': available_power * 0.6,
                'storage': available_power * 0.1
            }

        return allocation

Data Sparsity and Quality Issues

Through studying real wildfire datasets, I found that missing data wasn't random—it correlated with fire intensity. This required developing specialized imputation techniques:

python
class FireAwareImputation:
    """Context-aware data imputation for wildfire scenarios"""

    def impute_missing_sensor_data(self, sensor_readings: np.ndarray,
                                  fire_proximity: np.ndarray,
                                  time_since_last: np.nd