Computer Laboratory

Course pages 2017–18

Mobile and Sensor Systems

Principal lecturer: Prof Cecilia Mascolo
Taken by: Part II
Past exam questions

No. of lectures: 11
Suggested hours of supervisions: 3
Prerequisite courses: Operating Systems, Concurrent and Distributed Systems


This course will cover topics in the areas of mobile systems and communications, and sensor systems and sensor networking. It aims to help students develop and understand the additional complexity introduced by mobility and sensing, including energy constraints, communication in dynamic networks and handling measurement errors. The course will be using various applications to exemplify concepts.


  • Introduction to Mobile Systems. MAC Layer concepts. Examples of mobile systems, differences with non mobile systems. Introduction to MAC layer protocols of wireless and mobile systems.

  • Mobile Infrastructure Communication and Opportunistic Networking. Description of common communication architectures and protocols for mobile and introduction to models of opportunistic networking.

  • Introduction to Sensor Systems and MAC Layer concepts. Sensor systems challenges and applications. Concepts related to duty cycling and energy preservation protocols.

  • Sensor Systems Routing Protocols. Communication protocols, data aggregation and dissemination in sensor networks. Sensor Reprogramming and Management.

  • Mobile Sensing: Modelling and Inference Mobile and wearable sensing. Inference of activity. Modelling and machine learning for mobile devices.

  • Mobile Sensing: Systems Considerations Considerations of energy preservation. Local computation vs cloud computation.

  • Privacy in Mobile and Sensor Systems. Concepts of location privacy. Privacy and sensor based activity inference.

  • Localization Overview of techniques for localizing mobile entities indoors and outdoors. E.g. GNSS, proximity, lateration, angulation, radio fingerpriting, inertial and pedestrian dead reckoning systems.

  • Dealing with sensor errors Sources of errors. Bayesian estimation frameworks to mitigate errors and incorporate constraints (Kalman filter, particle filter). Examples using inertial and GNSS systems.

  • Internet of Things Emerging concepts and communications protocols for the Internet of Things.

  • Robots and Drones Concepts related to control, communication and coordination of robotic systems.


On completing the course, students should be able to

  • describe similarities and differences between standard distributed systems and mobile and sensor systems;

  • explain the fundamental tradeoffs related to energy limitations and communication needs in these systems;

  • argue for and against different mobile and sensor systems architectures and protocols.

  • Understand typical error sources for sensing and be aware of techniques to minimise them.

  • put concepts into context of current applications of mobile and sensor systems as described in the course.

Recommended reading

* Schiller, J. (2003). Mobile communications. Pearson (2nd ed.).
* Karl, H. & Willig, A. (2005). Protocols and architectures for wireless sensor networks. Wiley.
Agrawal, D. & Zheng, Q. (2006). Introduction to wireless and mobile systems. Thomson.