EcoDefender: Eco Protection & Monitoring

• EcoDefender is a comprehensive system combining computational analysis, sensing, and machine learning to safeguard ecosystems and monitor wildlife in real time.
• It employs hybrid anomaly detection, autonomous UAV surveillance, and edge AI to deliver energy-efficient, adaptive defense solutions for ecological and IoT infrastructures.
• The framework integrates simulation, security games, and NLP-based policy tracking to optimize environmental strategies and sustain green IoT operations.

EcoDefender is an umbrella term for a suite of computational, sensing, and machine learning systems designed for ecological protection, wildlife monitoring, anomaly detection in green IoT infrastructure, and real-time policy impact assessment. Spanning edge-deployable anomaly detection frameworks, autonomous aerial and sensor-based wildlife patrols, grid-based simulation platforms for ecological optimization, and transformer-based NLP for environmental policy and social discourse, EcoDefender exemplifies the integration of formal methods, robust optimization, and sustainability-conscious computation in environmental monitoring and defense.

1. Hybrid Anomaly Detection for Green IoT Edge Gateways

EcoDefender, in the context of green IoT edge gateways, is defined as a lightweight, sustainable hybrid anomaly detection framework combining Autoencoder (AE)-based representation learning with Isolation Forest (IF) for unsupervised anomaly scoring (Jamshidi et al., 23 Nov 2025). The methodology is characterized by:

• Composite Feature Normalization:

$$x'_{ij} = \frac{x_{ij} - \mu_j}{\sigma_j + \epsilon} + \delta\log(1 + \frac{|x_{ij} - \mu_j|}{\sigma_j + \epsilon}) + \eta\frac{x_{ij}-\mathrm{median}(x_j)}{\mathrm{IQR}(x_j)}$$

ensuring Lipschitz continuity and numerical stability for AE input.

• Autoencoder Architecture:

$$z = \sigma(W_\mathrm{enc} x + b_\mathrm{enc}), \qquad \hat{x} = \sigma(W_\mathrm{dec} z + b_\mathrm{dec})$$

with composite loss consisting of mean squared error, weight decay, Jacobian smoothing, KL-divergence (latent isotropy), and trace alignment regularization.

• Isolation Forest Scoring:

$$s(x) = \exp\left(-\frac{E[h(x)]}{c(n)}\right), \qquad c(n) = 2H(n-1) - \frac{2(n-1)}{n}$$

where $E[h(x)]$ is the path-length-based anomaly indicator.

• Convex Fusion and Thresholding:

$$F(x; \alpha) = \alpha e(x) + (1-\alpha) s(x) + \mu \log\frac{p(F|y=1)}{p(F|y=0)} + \rho \operatorname{Var}[z]$$

with adaptive $\alpha^*$ for fidelity-stability trade-off and dynamic threshold $\tau$ for F1 optimization.

• Theoretical Guarantees: Almost sure convergence, global Lipschitz stability, certified adversarial robustness radius, monotone-descent learning bounds, and an explicit energy–complexity coupling

$E_{\text{total}} \approx \kappa [NdL + mn\log n]$

directly linking inference cost to carbon emissions.

Empirical Results: On Bot-IoT, EcoDefender achieves detection accuracy of 94% (F1=0.92, ROC-AUC=0.963), mean CPU usage 22%, 27 ms latency, and 30% lower energy consumption over AE-only models, directly supporting SDG 9 and SDG 13 (Jamshidi et al., 23 Nov 2025).

2. Autonomous Sensing and Wildlife Protection Systems

EcoDefender also refers to advanced field-deployable platforms for species detection, poacher deterrence, and agricultural crop protection using multispectral and AI-driven sensing:

2.1 Astro-Ecological Drones and TIR Sensing

Thermal-infrared pipeline combines:

• FLIR Tau 640 (7.5–13 µm, 640×512 px), flown on DJI F550 hexacopters at 70–100 m AGL (Burke et al., 2018).
• “Astrophysics-inspired” radiometric corrections (dark-frame subtraction, non-uniformity correction, sigma-clipping).
• Adaptive T₉₉ thresholding, dual-morphological filtering, connected component analysis, and Kalman filter tracking in image space.
• Environmental error analysis: field tests in Tanzania revealed 100% detection in low-ambient, low-vegetation, but only 13% under hot, cluttered ground. Vegetation, atmospheric absorption, and spurious IR sources are limiting factors.
• Solution roadmap: dynamic spatio-temporal background subtraction, atmospheric τ estimation, embedded CNN classification, multi-modal sensor fusion, gimbal stabilization, edge inference (NVIDIA Jetson-class), and bandwidth-aware alerting.

2.2 Conditional Geographical Species Monitoring

On-device models using MobileViTV2-0.5 with Mixture-of-Experts (MoE) Transformer blocks, where GPS encoding conditions the sparse routing mechanism (Mensah et al., 11 Apr 2025). Locality-driven, unsupervised pruning yields compact, geographically-adaptive detectors.

Practical performance: At τ=90%, parameters/FLOPs reduced by 34–39% with sub-2% accuracy loss; pruned model fits in <6 MB FP32, enabling deployment on ARM cortex-A53 devices (Raspberry Pi/Jetson Nano) with <40 ms/image and <130 mJ/image inference. OTA expert pruning enables fast, energy-optimal adaptation as new sites or species arise.

2.3 Autonomous UAV Deterrence and Coverage (GUARD)

Integrated UAV system (PX4, Orin Nano, RTK GPS/ArUco, charging station) executes:

• Real-time YOLOv5 deer detection ($\text{[email protected]} = 0.693$, recall $=0.86$, F1 $=0.69$, FPR $=0.00$) with TensorRT FP16 (Temesgen et al., 16 May 2025).
• Energy-optimal coverage via Ant Colony Optimization with explicit edge cost $$E(c_{ij}|prev=h)=\lambda d_{ij} + \gamma \theta_{hij}$$.
• RL supervisor integration for multi-agent coordination and autonomous charge scheduling.
• Demonstrated 92% detection accuracy, 100% path coverage, and 15% energy reduction over boustrophedon baseline.

Potential extensions include multi-sensor fusion (thermal, acoustic), distributed fleet coordination, and geofenced hot zone defense.

3. Simulation and Ecological Optimization Platforms

EcoDefender is also an event-based grid simulation platform inspired by plant-herbivore interaction modeling (Dietrich et al., 19 Sep 2025).

• Discrete Event Grid Architecture: $W \times H$ grid; plants and “predator clusters” acting according to event-driven scheduling (priority queues or time-step iteration).
• Defense Modeling: Constitutive (shape, camouflage) and induced (toxins, predator-attractants, signaling) with explicit per-step energy accounting for defenses.
• Herbivore Behavior: Solitary, group, or swarm movement/attack modeled with stochastic pathfinding and feeding dynamics.
• Signaling and Diffusion: Symbiotic network (Von Neumann) and airborne (Moore/ball) signaling, propagating substances that pre-activate defenses.
• Optimization Game: Multi-objective (survival, total offspring, energy used, damage), Nash equilibrium for inter-species strategy calibration.
• Sensor Network Analogy: Direct mapping to cyber-physical security—plants as sensors, predators as intruders, toxins as active defenses, signaling as alarms. Spatial placement and defense switching correspond to real sensor node deployment and energy-aware security.

Key applicability: experimental modeling, adversarial robustness testing, and the generation of formal benchmarks for edge-embedded ecosecurity protocols.

4. Policy Tracking and Social Discourse Monitoring

EcoDefender integrates scalable NLP pipelines for legislative and social media analysis, yielding near-real-time environmental policy impact surveillance.

4.1 Governmental Policy Tracker

Automated scraping and NLP on daily XML from the Brazilian Federal Official Gazette (BFOG), using:

• Rule-based pre-tagging and expert-in-the-loop curation.
• Cleaned, jointly-annotated 1,181-document corpus (12-class to 3-class schema, highly imbalanced).
• Four classifiers: Multinomial NB, BiLSTM, BERT1 (fine-tuned BERTimbau), BERT2 (domain-adaptive pre-trained + fine-tune). BERT2 yields F1 = $0.714 \pm 0.031$, MCC = $0.538 \pm 0.046$ (Cação et al., 2022).
• Error sources primarily in Regulation/Neutral ambiguity and Deregulation paraphrase confounds. Expansion to metadata-aware or hierarchical ratings, as well as active learning, are effective remediation.
• Blueprint for porting: modular ingestion, model serving, and periodic retraining; international generalization by seeding with local jurisdiction data.

4.2 Social Discourse on Ecological Impact (EcoVerse)

Three-level transformer-based schema:

1. Eco-Relevance: binary (eco vs. non-eco); DistilRoBERTa achieves 89.4% micro-accuracy.
2. Environmental Impact: tri-class (positive/negative/neutral); ClimateBerts yield 78.6% accuracy, though all models underperform on nuanced neutral cases.
3. Stance Detection: tri-class (supportive/neutral/skeptical); RoBERTa/DistilRoBERTa ≈81.3% accuracy, high F1 for supportive/overt classes (Grasso et al., 8 Apr 2024).

Strong annotation consistency ($$\kappa_{eco} = 0.94,\ \kappa_{impact} = 0.82,\ \kappa_{stance} = 0.86$$) and precise error source analysis inform best practices: multi-task learning, environmental ontology enrichment, and active learning for ambiguous instances.

5. Security Games and Patrol Scheduling with Signaling

EcoDefender algorithms include formalized Green Security Games with Signaling (SGS) solved via Evolutionary Algorithm for SGS (EASGS), developed to coordinate multi-resource defender strategies under sensor/detection noise (Żychowski et al., 2022).

• Game Representation: Undirected graph $G=(V,E)$, assignment of $k$ patrollers, $l$ drones/sensors. Defender pure strategy $e \in \mathcal{E}$ specifies patrol/sensor allocation and signaling vectors $(\Psi, \Phi)$.
• Signaling Model: Sensors emit noisy strong or weak signals ($\sigma_1, \sigma_0$) with detection error $\gamma$ and transmission matrix $\Pi$.
• Payoff Calculation: Marginal coverage probabilities per target, Bayesian belief updates, best-response attacker, and defender-expected payoff aggregation.
• EASGS Implementation: Chromosome encoding of strategy supports, recombination, specialized mutation (prob shift, allocation/signaling tweak, local coverage repair), elite preservation, and population refresh.
• Empirical performance: On large benchmarks (up to 342 game instances, up to $\mathcal{N}=100$), EASGS is $5\times$–$10\times$ faster and uses $\approx$100 MB RAM versus 20 GB for MILP, outperforming RL and MILP baselines on dense graphs.

Recommended: periodic re-solving with sensor performance and dynamic wildlife density updates, multi-objective cost optimization, hybrid RL/Evo initialization, and bounded rational attacker extensions.

6. AI-Based Ecosystem Simulators

Integration with AI ecosystem simulators (e.g., “Ecotwin”) provides realistic evaluation and sensitivity analysis for EcoDefender platform interventions (Strannegård et al., 2023).

• 3D terrain and agent-based modeling: Terrain mesh from elevation and land-cover grids; Unity-based simulation of animal agents (hares/foxes) with deep RL controllers and continuous perception/action cycles.
• Curriculum learning for agent behaviors: Stagewise RL improvement, leading to adaptive, realistic foraging, predation, and avoidance behaviors.
• Scenario analysis: Systematic interventions for land-cover conversion, hunting pressure, roadkill, pollution, invasive species, and sea-level rise, with quantitative tracking of carrying capacity, Shannon diversity, and population crashes.
• Key findings: Nonlinear thresholds for collapse, the necessity of safe corridors, and robustness of RL-driven adaptation only in sufficiently resource-rich scenarios.

7. Synthesis: Architecture and Deployment Guidelines

EcoDefender, across its instantiations, emphasizes:

• Modular, theoretically grounded ML components deployable from resource-limited edge nodes to UAV swarms and simulation clusters.
• Hybridization of unsupervised, deep, and evolutionary algorithms for anomaly detection, patrol optimization, and adversarial resilience.
• Data-driven, active-learning pipelines for robust, low-latency policy and environmental awareness.
• Explicit coupling of energy efficiency, computational complexity, and sustainability metrics into all system design and performance evaluation.
• Generalization to cross-domain ecological defense: from wildlife and crop protection, to IoT/cloud security, to legislative and social media monitoring.

Performance Table: EcoDefender Instantiations

Application Area	Key Metric(s)	Best Reported Result
Edge IoT Anomaly	F1, latency, energy	F1=0.92, 27ms, 8.5 J/inference, 30% less energy than AE-only (Jamshidi et al., 23 Nov 2025)
Field TIR Patrol	Human/animal recall	100% (cool); 13–77% (hot/occluded) (Burke et al., 2018)
On-device Vision	Params, FLOPs, accuracy drop	39% param reduction at <2% accuracy loss (Global to region-pruned) (Mensah et al., 11 Apr 2025)
UAV Deterrence	Detection acc., coverage, duration	92% accuracy, 100% field coverage, 18min sorties (Temesgen et al., 16 May 2025)
Policy NLP	F1-score (BERT2, GAT dataset)	F1=0.714 ± 0.031 (Cação et al., 2022)
Twitter EcoNLP	Eco-relevance acc. (DistilRoBERTa)	89.4%; Impact (ClimateBerts) 78.6%; Stance (RoBERTa) 81.3% (Grasso et al., 8 Apr 2024)
SGS/Evo Patrol	Defender payoff, memory, time	Best payoff on 200/342 games, constant memory up to N=100 (Żychowski et al., 2022)

EcoDefender represents the current apex of sustainable, scalable, and adaptive computational defense for complex ecological and environmental monitoring tasks.