Wireless Sensor Systems

Overview

The module covers key building blocks and essentials in wireless sensor systems. The lecture notes cover mainly protocols at layer 2 (MAC layer) and layer 3 (routing) as well as key technologies for enabling IoT (ZigBee, 6LoWPAN, LoRaWAN, 802.11ah). Power management in WSN, synchronization and synchronization protocols are also covered.
The coursework covers another two key aspects: sensor technology (CW1) and analysing and forecasting sensor data (CW2 and CW3).

Coursework:
1. CW1- Sensor Technology (Semester 1)
2. CW2. Sensor Statistics (Semester 1)
3. CW3- Data Analytics and Forecasting (Semester 2)

Learning Objectives

• Apply knowledge of mathematics for: Throughput and delay calculations. Time synchronization. Power consumption calculations.
• Apply knowledge of statistics to a broadly define problem (sensor data sets and time series from pollutant data in Coursework 2)
• Analysing the suitability of existing wireless technologies (which may include ZigBee, WiFi, IEEE 802.11ah, LoRaWAN) to support Internet of Things. Limitations of each technology. Appreciation of new developments in IoT (systems and platforms).
• Analyse broadly defined problems reaching conclusions. Conclusions on data patterns, data trends and forecasting in Coursework 3.
• Selecting and applying techniques such as regression for forecasting in Coursework 3 using appropriate software. Recognising limitations on forecasting and measuring performance.
• Select and evaluate technical literature for assessing, comparing, and choosing a specific sensor in the marketplace as part of Coursework 1.
• Understanding sensors to monitor air pollution and how data analysis and forecasting can aid in predicting pollutant concentrations.
• Understanding of different roles in a collaborative project in coursework 3. Initiative and personal responsibility for their individual role.
• Apply an integrated system approach in wireless sensor systems throughout the lectures. Understanding different sub-systems and interfaces in a sensor systems and being able to understand how these are put together for different applications.
• Understanding of telecommunications protocols used for communication in the system. Understanding enabling technologies for the Internet of Things. Recent standardization activity on these new technologies.
• Data Analytics Skills as part of Coursework 3. Use of statistics, forecasting and appropriate software.
• Select and apply appropriate sensors in Coursework 1 and engineering technologies for enabling Sensor networks and IoT throughout the lectures.
• Project management in teams for coursework 3 (data analytics project), commercial context in coursework 1 (researching and choosing a commercial sensor to measure air pollution).

Skills

The ability to critically assess and design modern wireless communications systems and in particular wireless sensor networks and systems.
The ability to understand existing sensors, system architectures, communication protocols and standards in such a context.
Use software, statistics and mathematical techniques for sensor data analysis and forecasting.

Assessment

None