// Example: Version vector implementation for edge consistency
package edge

type VersionVector map[string]uint64

type EdgeObject struct {
    ID        string
    Data      []byte
    Vector    VersionVector
    Timestamp int64
}

func (o *EdgeObject) Merge(incoming EdgeObject) (conflict bool) {
    for node, version := range incoming.Vector {
        if localVersion, exists := o.Vector[node]; exists {
            if version > localVersion {
                // Conflict detection: concurrent modifications
                if version-localVersion > 1 && o.Timestamp < incoming.Timestamp-5000 {
                    return true
                }
                o.Vector[node] = version
            }
        } else {
            o.Vector[node] = version
        }
    }
    return false
}

# Sloppy quorum implementation for edge storage
class EdgeStorageQuorum:
    def __init__(self, nodes, replication_factor=3):
        self.nodes = nodes
        self.R = replication_factor  # Read quorum
        self.W = replication_factor  # Write quorum
        self.N = len(nodes)

    async def write_with_quorum(self, key, value, preferred_nodes=None):
        """Write with dynamic node selection based on network conditions"""
        healthy_nodes = await self._detect_healthy_nodes()

        if len(healthy_nodes) < self.W:
            # Implement hinted handoff
            hinted_nodes = self._select_hinted_handoff_nodes(healthy_nodes)
            write_results = await self._parallel_write(key, value, hinted_nodes)
            self._log_hinted_handoff(key, hinted_nodes)
        else:
            write_results = await self._parallel_write(key, value, 
                                                      healthy_nodes[:self.W])

        return sum(write_results) >= self.W

    def _select_hinted_handoff_nodes(self, unavailable_nodes):
        """Select alternative nodes when primary nodes are partitioned"""
        # Implementation of consistent hashing with fallback
        pass

// Store-level inventory aggregation with WebRTC for peer communication
class StoreEdgeInventory {
    constructor(storeId) {
        this.storeId = storeId;
        this.localInventory = new Map();
        this.peerConnections = new Map(); // For cross-store synchronization
    }

    async processShelfDetection(detection) {
        // Local inference with TensorFlow.js
        const results = await this.localModel.predict(detection.image);

        // Update local inventory with monotonic consistency
        await this.updateInventory(results.items, detection.timestamp);

        // Compress and batch upload to regional every 5 minutes
        if (Date.now() - this.lastUpload > 300000) {
            await this.uploadToRegional(this.compressInventoryDelta());
        }

        // Immediate alert for critical low stock
        if (this.checkCriticalStock(results.items)) {
            await this.sendPriorityAlert(results);
        }
    }

    compressInventoryDelta() {
        // Protocol Buffers for efficient serialization
        const deltas = this.getInventoryChangesSince(this.lastUpload);
        return protobuf.serialize(InventoryDelta, deltas);
    }
}

# AWS Greengrass + EKS Anywhere deployment
module "edge_cluster" {
  source = "terraform-aws-modules/eks/aws"

  cluster_name = "${var.store_id}-edge"
  cluster_version = "1.24"

  vpc_id = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets

  # Edge-optimized configuration
  node_groups = {
    edge_core = {
      desired_size = 3
      max_size = 5
      min_size = 2

      instance_types = ["c6g.2xlarge"] # Graviton for cost efficiency
      capacity_type = "ON_DEMAND"

      # Edge-specific kubelet configuration
      kubelet_extra_args = {
        "max-pods" = "50"
        "node-labels" = "location=store,zone=${var.region}"
      }
    }
  }

  # Local storage for edge persistence
  enable_irsa = true
  cluster_addons = {
    aws-ebs-csi-driver = {
      most_recent = true
    }
  }
}

python
# Custom EdgeOperator for location-aware scheduling
class EdgeAwareScheduler:
    def __init__(self, k8s_client):
        self.client = k8s_client
        self.edge_nodes = self._discover_edge_nodes()

    def schedule_workload(self, workload_spec, constraints):
        """Schedule based on edge constraints: latency, data locality, cost"""

        # Filter nodes by geographic constraints
        candidate_nodes = self._filter_by_location(
            self.edge_nodes, 
            constraints['max_latency'],
            constraints['data_affinity']
        )

        # Apply resource-aware scoring
        scores = self._score_nodes(candidate_nodes, workload_spec)

        # Select optimal node with fallback strategy
        selected = self._select_with_fallback(scores, constraints)

        return self._deploy_with_affinity(workload_spec, selected)

    def _score_nodes(self, nodes, workload):
        """Multi-criteria scoring: latency, resources, cost, reliability"""
        scores = {}
        for node in nodes:
            latency_score = self._

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