Study Guide Cisco 300-915 DEVIOT Developing Solutions using Cisco IoT and Edge Platforms Exam (eBook)

Anand Vemula (Autor)

eBook Download: EPUB

2025 | 1. Auflage
107 Seiten
Publishdrive (Verlag)
978-0-00-096414-4 (ISBN)

Infos

The Study Guide for Cisco 300-915 DEVIOT: Developing Solutions using Cisco IoT and Edge Platforms Exam offers a comprehensive and detailed roadmap for professionals preparing for the Cisco DEVIOT certification. This guide breaks down the complexities of IoT solution development using Cisco's edge computing platforms, including the Cisco IOx framework, Industrial Asset Vision, and Kinetic. Each section delves into critical topics such as IoT architecture, networking fundamentals, application lifecycle management, edge data analytics, and secure device onboarding.

Readers will explore the full spectrum of Cisco's IoT ecosystem, gaining insights into key technologies like Field Network Director, DevNet tools, and edge processing capabilities. It emphasizes real-world deployment scenarios, cloud platform integration, and robust security strategies like secure boot, network segmentation, and Zero Trust architecture. The guide also includes 150 curated multiple-choice questions to assess knowledge and reinforce key concepts. Hands-on labs and scenario-based exercises provide practical experience to help learners understand application deployment and troubleshooting.

Tailored for developers, network engineers, and IoT architects, this study guide not only prepares candidates for the 300-915 DEVIOT exam but also equips them with actionable skills needed in modern IoT deployments. It bridges theoretical knowledge and practical application, making it a vital resource for anyone aiming to build scalable, secure, and efficient IoT solutions using Cisco's advanced toolsets and platforms. Whether you are aiming for certification or enhancing your IoT skill set, this guide provides a structured and thorough learning path.

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2. IoT Fundamentals

IoT Architecture and Protocols

IoT (Internet of Things) architecture refers to the framework that supports the seamless connection, communication, and management of devices, networks, data, and applications. A robust IoT architecture allows devices (often resource-constrained) to communicate, process data, and interact with cloud or enterprise systems effectively.

The standard layered IoT architecture comprises the following major layers:

1. Perception Layer (Sensing Layer)

This is the physical layer where data originates. It includes sensors, actuators, RFID tags, GPS modules, and embedded systems. Devices in this layer are responsible for detecting and collecting data from the environment, such as temperature, humidity, motion, pressure, light, or location.

This layer faces challenges like low power, limited memory, and constrained processing, which influence the choice of protocols and processing capabilities.

2. Network Layer

The network layer transmits the data collected from the perception layer to processing units or cloud infrastructure. It ensures reliable and secure transmission using both short-range (Bluetooth, Zigbee, Wi-Fi) and long-range (LoRaWAN, LTE, 5G) communication technologies.

Network infrastructure may use IP-based protocols or specialized ones like 6LoWPAN to support low-power and lossy networks.

3. Edge/Fog Layer

This optional but increasingly critical layer is where edge computing devices process data close to the source. By running analytics at the edge, latency is reduced, and bandwidth is saved. Devices like Cisco IR/IE routers with IOx can run containerized applications to filter, analyze, and forward only meaningful data to the cloud.

Fog computing (coined by Cisco) refers to a distributed approach where computation is spread across network devices from edge to core, offering scalability and reduced latency for real-time applications.

4. Application Layer

This layer delivers services to end users or systems. It includes dashboards, control panels, business intelligence tools, and data integration platforms. Applications vary by industry—smart cities, industrial automation, healthcare monitoring, and transportation are common domains.

5. Business Layer

Often considered part of a more holistic architecture, this layer defines how data is used for decision-making, policy enforcement, monetization, and compliance.

IoT Communication Protocols

IoT devices utilize both standard internet protocols and purpose-built lightweight protocols. These protocols are optimized for low-power, unreliable, and high-latency environments common in IoT deployments.

1. MQTT (Message Queuing Telemetry Transport)

MQTT is a publish/subscribe protocol widely used in IoT for telemetry. It’s lightweight, runs over TCP/IP, and uses a broker to facilitate communication between clients.

• Advantages: Low overhead, ideal for constrained devices.
• Use Cases: Sensor data reporting, remote monitoring, real-time messaging.

MQTT has features like last will and testament (LWT), quality of service (QoS) levels (0, 1, 2), and persistent sessions.

2. CoAP (Constrained Application Protocol)

CoAP is a RESTful protocol that runs over UDP and is optimized for constrained devices and lossy networks.

• Advantages: Works well in lossy networks, supports multicast.
• Use Cases: Smart home automation, constrained wireless networks.

It mirrors HTTP methods (GET, POST, PUT, DELETE) but consumes less bandwidth and supports asynchronous message exchange.

3. HTTP/HTTPS

Traditional web protocols like HTTP and HTTPS are used in IoT for interoperability with web services and APIs. While not lightweight, HTTPS ensures secure transmission of data to cloud servers.

• Advantages: Compatibility with REST APIs, mature ecosystem.
• Disadvantages: Higher power and data consumption.

4. AMQP (Advanced Message Queuing Protocol)

AMQP is used for reliable message delivery in enterprise and cloud integration.

• Use Cases: Financial transactions, server-to-server communication.
• Disadvantages: Heavier compared to MQTT or CoAP.

5. DDS (Data Distribution Service)

DDS is a data-centric, real-time publish/subscribe protocol used in industrial systems and robotics.

• Advantages: High throughput, real-time performance.
• Use Cases: Autonomous vehicles, industrial control systems.

6. 6LoWPAN

6LoWPAN allows IPv6 packets to be sent and received over low-power wireless networks like IEEE 802.15.4.

• Advantages: Enables IP-based networking on constrained devices.
• Use Cases: Smart grid, home automation.

IoT Networking Concepts

Networking in IoT involves the interconnection of physical devices using a mix of wired and wireless technologies. Unlike traditional networks, IoT networks must support scalability, reliability, low power consumption, and heterogeneous protocols.

1. Network Topologies

Common topologies in IoT networks include:

• Star: All devices connect to a central gateway.
• Mesh: Devices relay messages through each other, increasing range and reliability.
• Hybrid: Combines multiple topologies to achieve resilience and efficiency.

2. Wireless Technologies

IoT relies heavily on wireless connectivity due to the remote and mobile nature of devices.

• Wi-Fi: High bandwidth but power-hungry. Useful for local networks with mains power.
• Bluetooth/Bluetooth Low Energy (BLE): Low power and short range. Ideal for wearables and indoor tracking.
• Zigbee: Mesh protocol for low-power sensors.
• LoRa/LoRaWAN: Long-range, low-bandwidth protocol used in LPWANs.
• NB-IoT/Cat-M1: Cellular-based LPWANs for wide-area coverage with deep indoor penetration.
• 5G: Offers massive machine-type communications (mMTC) with low latency and high density.

3. IP Addressing and IPv6

IPv6 adoption in IoT is driven by the exhaustion of IPv4 and the need for unique device addressing. Protocols like 6LoWPAN enable IPv6 over low-power networks.

• Stateless Address Autoconfiguration (SLAAC) is often used to assign IPv6 addresses without a DHCP server.

4. QoS and Latency Management

IoT applications have diverse QoS needs. Real-time applications (e.g., industrial automation) require ultra-low latency and high reliability, while telemetry data can tolerate delays.

• Differentiated Services Code Point (DSCP) and Traffic Engineering can help prioritize critical IoT traffic.
• Edge computing is used to reduce latency by processing data closer to the source.

5. NAT and Port Forwarding

Many IoT deployments operate behind NAT (Network Address Translation). This complicates inbound communication and is often resolved by using:

• Reverse proxies
• Cloud relays or message brokers
• Port forwarding on edge routers

6. Device Onboarding

Onboarding refers to the secure and automated process of registering a new device to the network. This involves:

• IP configuration (DHCP, static)
• Authentication (X.509 certificates, pre-shared keys)
• Authorization (assigning roles and policies)

Cisco’s Zero-Touch Provisioning (ZTP) and Plug and Play (PnP) capabilities simplify onboarding in enterprise environments.

Data Management and Security in IoT

IoT environments generate massive volumes of data that must be securely collected, stored, processed, and analyzed. Due to the scale and distribution of IoT deployments, managing data and security is both critical and complex.

1. Data Lifecycle in IoT

The data lifecycle includes:

• Data Generation: Sensors and devices generate structured and unstructured data.
• Data Collection: Gateways or edge devices gather data using pull/push mechanisms.
• Data Processing: May happen locally (at the edge) or in the cloud.
• Data Storage: In edge caches, cloud databases, or distributed storage.
• Data Analysis: Real-time (for alerts) or batch (for historical trends).
• Data Disposal: Secure deletion after the retention period.

2. Edge vs Cloud Processing

• Edge Processing reduces latency and bandwidth usage. Only meaningful or aggregated data is sent to the cloud.
• Cloud Processing allows deep analytics, AI/ML workloads, and integration with business systems.

Cisco’s Edge Intelligence enables policy-driven data filtering and routing between edge devices and cloud platforms.

3. Data Integrity and Confidentiality

Ensuring data integrity and confidentiality during transmission and storage is critical:

• TLS/SSL for encrypting data in transit.
• AES and other algorithms for encrypting data at rest.
• Hashing (SHA-2) for verifying data integrity.

Transport protocols like MQTT can be secured using MQTTS, which runs over TLS.

4. Identity and Access Management (IAM)

IAM mechanisms in IoT must cater to device, user, and application identities.

• X.509...

Produkt-Details

• Erscheint lt. Verlag: 13.5.2025
• Sprache: englisch
• Themenwelt: Sozialwissenschaften ► Pädagogik
• ISBN-10: 0-00-096414-X / 000096414X
• ISBN-13: 978-0-00-096414-4 / 9780000964144