May 1, 2024
Updated November 11, 2026
26 minute read
Edge Computing: A Comprehensive Guide to a Transformative Technology
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Find a path to becoming a Edge Computing. Learn more at:
OpenCourser.com/topic/keg4z4/edge
Reading list
We've selected 17 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Edge Computing.
Bridging constrained sensor nodes, industrial gateways, and cloud backends, Perry Lea provides a comprehensive architectural blueprint for edge computing systems. Chapters cover fog topology design, Kubernetes at the edge (K3s/KubeEdge), hardware accelerators, and low-latency stream analytics.
Running machine learning inference directly on milliwatt microcontrollers eliminates network round-trip latency and preserves user privacy at the extreme edge. Pete Warden and Daniel Situnayake demonstrate how to quantize, deploy, and optimize TensorFlow Lite models on embedded sensors.
Ultra-reliable low-latency 5G networks and Multi-access Edge Computing (MEC) form the backbone of autonomous vehicles, smart cities, and mobile AR. Mengwei Xu, Xiao Ma, and Qing Li analyze task offloading, edge container orchestration, and video analytics across cellular edge nodes.
Coauthored by Edge Impulse founders Daniel Situnayake and Jenny Plunkett, this engineering guide walks through the full lifecycle of deploying embedded AI on edge devices. Readers master sensor dataset curation, digital signal processing, model compression, and fleet monitoring in production.
Distributing compute nodes outside physically hardened data centers exposes edge infrastructure to side-channel tampering, rogue gateways, and DDoS amplification. Mohiuddin Ahmed and Paul Haskell-Dowland detail zero-trust authentication, intrusion detection, and lightweight cryptography for edge deployments.
Extending cloud workloads closer to end users demands seamless orchestration across AWS Wavelength, Local Zones, Outposts, and IoT Greengrass. Sean Howard walks engineers through designing resilient hybrid edge topologies, securing disconnected device fleets, and optimizing telemetry pipelines.
Deploying autonomous AI agents across hybrid cloud and edge environments requires orchestrating small language models locally while delegating complex reasoning to cloud clusters. Brian Pitman explores latency-aware routing, edge inference optimization, and resilient multi-agent coordination.
Edited by distributed systems pioneer Rajkumar Buyya and Satish Narayana Srirama, this foundational volume establishes the taxonomy, resource scheduling algorithms, and storage hierarchies of fog and edge computing. Case studies evaluate smart healthcare, connected transit, and tactile internet testbeds.
Orchestrating containerized microservices on resource-constrained edge gateways requires lightweight Kubernetes distributions with minimal memory overhead. Karthikeyan Shanmugam demonstrates how to deploy, secure, and scale production MicroK8s clusters across ARM IoT devices using Kubeflow and telemetry add-ons.
Bringing deep neural networks onto constrained field hardware hinges on balancing model accuracy against strict power, memory, and thermal budgets. Pethuru Ray surveys federated edge learning, vision accelerators, and industrial automation case studies across smart manufacturing and healthcare.
Covering hardware platforms, virtualization layers, and edge-to-cloud middleware, K. Anitha Kumari, G. Sudha Sadasivam, and D. Dharani offer a structured introduction to edge computing fundamentals. Readers examine container offloading, latency modeling, and smart-grid deployment architectures.
Processing sensitive video, biometric, and location telemetry on local edge servers reduces raw data exposure but introduces differential privacy and federated learning challenges. Bruce Gu, Longxiang Gao, and Tom H. Luan formulate mathematical privacy guarantees for multi-tenant edge networks.
Pioneered by edge computing visionary Weisong Shi alongside Quan Zhang and Jie Cao, a concise architectural taxonomy distinguishes edge from cloud and fog paradigms. Readers explore computation offloading, latency-critical smart home gateways, and foundational open research challenges.
Decentralized edge nodes operating across untrusted administrative domains need tamper-evident audit trails and lightweight consensus mechanisms. Mubashir Husain Rehmani and coauthors investigate how permissioned blockchains and smart contracts secure resource trading and trust management at the network edge.
Human-centric mobile devices and vehicular sensors form dynamic social-physical networks at the edge of the internet. Daniel Zhang and Dong Wang combine game theory, crowdsensing, and edge intelligence to solve trustworthy data fusion under severe bandwidth and energy constraints.
Every distributed edge architecture must contend with network partitions, eventual consistency, conflict-free replicated data types, and local state synchronization. Martin Kleppmann's rigorous analysis of replication and stream processing equips edge engineers to handle disconnected operation gracefully.
Long before modern edge computing had a name, Amber Case articulated why smart devices should process context locally so products remain functional when cloud connectivity fails. Her principles offer a compelling human-centered philosophy for resilient, low-latency edge product design.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/keg4z4/edge
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