Communication and Processing of Sensor Network Data

The course “Communication and Processing of Sensor Network Data” introduces students to the fundamental principles of design, operation, and optimization of Sensor Networks (SNs). It focuses on communication, energy management, access and routing protocols, and data processing techniques that enhance efficiency and reliability. Protocols such as LEACH and AODV are presented, along with energy management strategies (duty cycling, clustering, recharging) and the application of Artificial Intelligence for adaptive operation and extended network lifetime. The course also involves the use of simulation tools (Python frameworks, OMNeT++) and hands-on laboratory projects, enabling students to develop practical skills in designing and optimizing sensor networks.

Upon successful completion of the course, students will become familiar with:

• The fundamental principles and architecture of Sensor Networks (SNs), including communication models, node architectures, and key operational challenges.

• The core functions of SNs, such as data collection, processing, transmission, and routing, as well as the parameters affecting their performance.

• The Medium Access Control (MAC) layer in SNs, with emphasis on energy-efficient protocols and collision management techniques.

• The network layer and routing techniques, including Flooding, Gossiping, LEACH, and AODV.

• Energy management strategies and energy consumption minimization methods, such as adaptive duty cycling, clustering, and node recharging.

• The application of Artificial Intelligence (AI) techniques in SNs for routing optimization, network lifetime enhancement, and autonomous adaptation to environmental conditions.

• The use of simulators and sensor network analysis tools, such as Python-based frameworks and OMNeT++, for evaluating protocol and technique performance.

• The development of practical skills in designing and optimizing sensor networks through laboratory exercises and projects.

1. Fundamental Concepts and Characteristics of Sensor Networks

• Applications in IoT, smart cities, environmental monitoring, industry

• Key challenges: energy efficiency, connectivity, reliability

• Comparison with traditional networks

2. Structure and Architecture of Sensor Networks

• Types of nodes: sensors, aggregators, sink nodes

• Communication structures: centralized vs. distributed

• Topologies: mesh, tree, flat

3. Data Collection, Processing, and Transmission

• Data storage and synchronization

• Aggregation strategies

4. Medium Access Control (MAC) Protocols

• Comparison of TDMA, CSMA, S-MAC, T-MAC, B-MAC

• Energy efficiency in MAC protocols

5. Routing in Sensor Networks

• Flooding, Gossiping, clustering-based routing

• Path selection criteria: distance, latency, energy consumption

• Introduction to dynamic routing protocols

6. Energy Management in Sensor Networks

• Factors affecting energy consumption

• Duty cycling and adaptive wake-up mechanisms

• Clustering and energy-aware routing

• Energy harvesting (solar, RF, kinetic)

7. LEACH Protocol

• Architecture and operation

• Cluster heads and dynamic role assignment

• Comparison with other clustering techniques

8. Dynamic Routing Protocols

• On-demand path discovery

• AODV, DSR, OLSR

• Energy consumption in AODV

9. Recharging Techniques in Sensor Networks

• Solar, wireless, and kinetic charging

• Mobile charging stations and UAVs

• Combining energy efficiency with AI

10. AI Applications in Sensor Networks

• Routing optimization

• Anomaly detection and energy prediction

• Reinforcement learning for energy-efficient communication

• AI-driven data management

11. Sensor Data Analysis and Processing

• Statistical methods and ML-based analytics

• Edge computing for near-source processing

• Federated Learning in sensor networks

12. Simulation and Performance Evaluation

• Use of simulators

• Evaluation metrics: latency, energy consumption, packet delivery ratio

• Protocol comparison in different scenarios

13. Review and Research Perspectives

• Future trends and research directions