Chapter-4-SYNCHRONIZATION-AND-REPLICATION-IN-IOT-AND-EMBEDDED-SYSTEMS.pptx

UNIT 4: SYNCHRONIZATION AND REPLICATION IN IOT AND EMBEDDED SYSTEMS  Introduction Goal: Understanding how synchronization and replication are applied in IoT and embedded systems, which consist of multiple interconnected devices communicating i...

UNIT 4: SYNCHRONIZATION AND REPLICATION IN IOT AND EMBEDDED SYSTEMS  Introduction Goal: Understanding how synchronization and replication are applied in IoT and embedded systems, which consist of multiple interconnected devices communicating in real-time. Key Concepts: Embedded systems are at the core of IoT devices, often operating in a distributed environment (e.g., smart homes, autonomous vehicles). Synchronization ensures that these devices work in harmony and communicate consistently. Replication guarantees reliability and fault tolerance by maintaining redundant systems or data, which is crucial in real-time IoT environments where failure isn't an option.  Overview of Distributed Synchronization Definition: Distributed synchronization in IoT ensures that devices and sensors communicate and operate in a coordinated manner. Importance in IoT:Devices in IoT often work autonomously, but their actions need to be coordinated. For instance, in a smart home, lights, security cameras, and thermostats must synchronize to provide seamless user experience. Lack of synchronization can lead to inconsistent data or unpredictable behavior.  Overview of Distributed Synchronization Example 1 (IoT): A smart irrigation system that uses soil moisture sensors to water plants. Synchronization ensures that all sensors report moisture levels at the same time, so the watering system works correctly based on accurate, current data. Example 2 (Embedded Systems): In a smart car, embedded sensors (like LiDAR, cameras, GPS) must be synchronized to provide accurate environmental data, preventing accidents by ensuring that braking and steering decisions are made based on consistent inputs  Overview of Distributed Synchronization Time Synchronization Protocols: IoT devices often rely on synchronized clocks (e.g., Network Time Protocol (NTP)) to ensure accurate event timing. Resource Coordination: Techniques like mutexes and semaphores can prevent two IoT devices from accessing the same resource (e.g., a shared database) simultaneously, avoiding inconsistencies.  Coordination and Agreement Definition: Achieving consensus across distributed IoT devices is essential, especially when devices need to agree on a shared state or action, such as security decisions or system updates. Importance in IoT:IoT systems often operate in dynamic, unreliable environments where devices may fail or lose connectivity. Coordination ensures that these systems remain robust and functional despite these challenges.  Coordination and Agreement Example 1 (IoT): In a smart grid, various smart meters at different locations must agree on the electricity usage data reported to the central system. If one node reports an anomaly, the system must coordinate a consensus among all meters to ensure accuracy Example 2 (Embedded Systems): In industrial IoT (IIoT), machines on a factory floor may need to coordinate to agree on production parameters. For instance, if one machine malfunctions, others need to adjust their behavior in real-time to maintain smooth operations.  Coordination and Agreement Consensus Algorithms: IoT devices often use protocols like Paxos or Raft to ensure agreement even if some nodes fail or behave unpredictably. Fault Tolerance and Leader Election: When nodes fail, leader election algorithms (e.g., Bully Algorithm) can be used to appoint a new leader device that coordinates the activities of other devices in the network.  Distributed Deadlock Definition: Deadlock occurs when devices or processes in an IoT system are waiting on each other, causing the system to freeze. This is a critical issue in real-time embedded systems like smart cities or autonomous vehicles. Importance in IoT:In distributed IoT environments, where devices frequently interact with each other (e.g., smart traffic lights), deadlock prevention is necessary to ensure the system continues to function without interruptions.  Distributed Deadlock Example 1 (IoT): In a smart city, traffic control systems embedded at various intersections must coordinate. Deadlock could occur if two intersections wait for each other to release control of a traffic lane, causing traffic jams. Example 2 (Embedded Systems): In a fleet of autonomous delivery drones, deadlock could occur if two drones wait for each other to vacate an airspace, leading to delays in deliveries.  Distributed Deadlock Deadlock Detection and Prevention: Algorithms like Wait-Die or Wound-Wait can help detect or prevent deadlock in distributed IoT systems by managing resource allocation and process priorities. Deadlock Recovery: In IoT, deadlocks can sometimes be resolved by forcibly rolling back one device’s operation to allow the system to continue functioning.  Replication in Distributed Systems Definition: Replication involves creating and maintaining multiple copies of data or processes across IoT devices to ensure system resilience, reliability, and fault tolerance. Importance in IoT:IoT systems, especially those in critical industries like healthcare or transportation, require high availability. Replication ensures that if one device or node fails, other devices can continue providing service without disruption.  Replication in Distributed Systems Example 1 (IoT): In a healthcare IoT system, patient data is replicated across multiple embedded devices (e.g., patient monitors, hospital databases). If one device fails, the replicated data on another device ensures continuous monitoring and care. Example 2 (Embedded Systems): In a smart farm, data from embedded sensors tracking soil conditions and weather are replicated across several servers. If the primary server fails, the farm can continue to operate based on the data replicated on backup servers.  Replication in Distributed Systems Types of Replication: Primary-Backup Replication: In an IoT surveillance system, one camera acts as the primary, and others act as backups. If the primary camera fails, the backup camera takes over seamlessly. Active-Active Replication: Multiple devices process data simultaneously, such as in an IoT temperature control system for a data center where multiple sensors actively report the temperature to ensure system stability. Consistency Models: Strong Consistency: All IoT devices see the same data simultaneously (suitable for critical systems like autonomous driving, where real-time data is essential). Eventual Consistency: Devices might temporarily have different data views, but they eventually converge. This is useful for large-scale IoT deployments like environmental monitoring, where real-time accuracy isn't always critical.  Additional IoT and Embedded Systems Examples: Smart Home Automation: Synchronization: Devices like lights, door locks, and cameras must be synchronized to ensure they respond correctly to user commands. For instance, when the user leaves home, all systems need to turn off in sync. Replication: Data about energy consumption, security alerts, and user preferences are replicated across multiple home automation hubs to ensure that a failure in one hub doesn’t affect system operation. Autonomous Vehicles: Synchronization: In a fleet of autonomous vehicles, each vehicle must synchronize its actions (braking, lane changes) with the surrounding vehicles to prevent accidents. Replication: Critical driving data (such as sensor readings and vehicle positions) is replicated across multiple onboard and cloud-based systems to ensure safety, even if the primary control system fails.  Conclusion IoT and embedded systems heavily rely on synchronization and replication to ensure the correctness, reliability, and fault tolerance of distributed devices. By implementing these principles effectively, IoT systems can handle dynamic, unpredictable environments while ensuring continuous, safe, and efficient operation.