Course

Unit I

Introduction to Sensor Networks- Introduction to sensor networks: definitions, applications, and characteristics; Sensor network architecture and components: sensors, microcontrollers, communication modules, and power sources; Communication protocols and standards for sensor networks: IEEE 802.15.4, ZigBee, and LoRaWAN; Energy-efficient design principles for sensor networks: power management, duty cycling, and sleep/wake scheduling: Data collection and processing in sensor networks: data aggregation, compression, and filtering.

Unit II

Sensor Network Design and Implementation – Sensor network topology and deployment: star, mesh, and tree topologies; Localization and tracking in sensor networks: triangulation, trilateration, and fingerprinting; Security and privacy in sensor networks: encryption, authentication, and key management; Programming and development tools for sensor networks: Arduino, Contiki, and TinyOS; Hands-on lab sessions: designing and implementing a sensor network using wireless sensor nodes and microcontrollers.

Unit III

Advanced Topics in Sensor Networks- Emerging trends and applications in sensor networks: smart cities, precision agriculture, and healthcare; Big data analytics and machine learning for sensor networks: data mining, classification, and prediction; Cloud-based sensor networks: architecture, services, and platforms; Integration of sensor networks with other systems and technologies: Internet of Things (IoT), Cyber-Physical Systems (CPS), and Wireless Sensor-Actuator Networks (WSANs); Final project: developing a sensor network application for a specific domain or problem.

Learning Objectives

LO1: To understand the architecture, components, and protocols used in sensor networks.

LO2: To explore energy-efficient design, data management, and deployment strategies in
           sensor networks.

LO3: To implement and program real-world sensor network applications using suitable
           platforms and tools.

LO4: To analyze advanced trends, including integration with IoT/CPS and applications in
           smart domains.

Course Outcomes

CO1: Describe the fundamental architecture, components, and protocols used in wireless sensor
           networks.

CO2: Analyze sensor network topologies, localization techniques, and energy-efficient
           communication strategies.

CO3: Design and implement secure and functional sensor networks using platforms such as
          Arduino, Contiki, or TinyOS.

CO4: Apply data analytics and machine learning techniques for processing sensor network
          data.

CO5: Integrate sensor networks with cloud platforms and other technologies like IoT and
          WSANs for domain-specific applications.

CO-PO Mapping

Textbook(s)

1. Feng Zhao and Leonidas Guibas, “Wireless Sensor Networks: An Information Processing Approach,
2. N. Sastry and S. Shakkottai, “Building Wireless Sensor Networks: Theoretical and Practical Perspective,
3. Chiara Buratti, Marco Stango, and Roberto Verdone “Sensor Networks with IEEE 802.15.4 Systems: Distributed Processing, MAC, and Connectivity”

Reference(s)

1. Wenbo Mao, Wei Li, and Sushil Jajodia, “Security in wireless sensor networks”
2. Ali H. Al-Bayatti, Azween Abdullah, and Mazin Abed Mohammed, “Machine learning for wireless sensor networks: A comprehensive survey”