Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment

Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment

Abstract

This article surveys Cognitive Edge Computing as a practical and methodical pathway for deploying reasoning-capable Large Language Models and autonomous AI agents on resource-constrained devices at the network edge. We present a unified, cognition-preserving framework spanning: one, model optimization (quantization, sparsity, low-rank adaptation, distillation) aimed at retaining multi-step reasoning under tight memory/compute budgets; two, system architecture (on-device inference, elastic offloading, cloud-edge collaboration) that trades off latency, energy, privacy, and capacity; and three, adaptive intelligence (context compression, dynamic routing, federated personalization) that tailors computation to task difficulty and device constraints. We synthesize advances in efficient Transformer design, multimodal integration, hardware-aware compilation, privacy-preserving learning, and agentic tool use, and map them to edge-specific operating envelopes. We further outline a standardized evaluation protocol covering latency, throughput, energy per token, accuracy, robustness, privacy, and sustainability, with explicit measurement assumptions to enhance comparability. Remaining challenges include modality-aware reasoning benchmarks, transparent and reproducible energy reporting, edge-oriented safety/alignment evaluation, and multi-agent testbeds. We conclude with practitioner guidelines for cross-layer co-design of algorithms, runtime, and hardware to deliver reliable, efficient, and privacy-preserving cognitive capabilities on edge devices.

One Introduction

The convergence of Large Language Models and AI agents with edge computing heralds the emergence of Cognitive Edge Computing-a revolutionary paradigm that brings sophisticated cognitive capabilities directly to resource-constrained devices at the network periphery. Unlike traditional edge computing that focuses primarily on data processing and basic analytics, Cognitive Edge Computing represents a fundamental shift toward deploying advanced AI systems that can understand context, reason autonomously, and make intelligent decisions in real-time, all while operating within the severe constraints of edge environments.