Module Descriptors
INTERNET OF THINGS: ADVANCED SMART SENSOR NETWORKS
COCS71181
Key Facts
Faculty of Computing, Engineering and Sciences
Level 7
15 credits
Contact
Leader: Mohamed Sedky
Hours of Study
Scheduled Learning and Teaching Activities: 36
Independent Study Hours: 114
Total Learning Hours: 150
Assessment
  • ASSIGNMENT weighted at 100%
Module Details
INDICATIVE CONTENT
This module will cover:
• Advanced Digital Signal Processing (DSP) concepts, and their implementation in software, e.g. Matlab & embedded C as well as formal system design.
• Information theory, including Entropy coding, channel capacity, design methodologies, network protocols and algorithms, quality of service management, coverage optimization and time synchronization.
• Regulations surrounding Safety Critical Systems Software, including characterisation of safety, safety culture, risk and its management, cost of failure, categorisation and impact of formal approaches, role of proof, model checking, standards and safety life-cycle, fault tolerance as well as the implications of safety within a smart sensor network environment.
INDICATIVE CONTENT
Assignment (100%) that consists of:-

- a programming task (60%) including code and a report of about 2000 words assessing learning outcomes 3, 4 and 5.
- a presentation (40%) on the programming task, 30 minutes which discusses the code and the choice of data sets and its characteristics, and how these impacted the approach and tools to digital signal processing and representation. All illustrated by the undertaken assignment, assessing learning outcomes 1 and 2.
LEARNING STRATEGIES
12 hours of lectures and 24 hours of practical based teaching taught in a 3 hour block period.

This module will have 1 hour Lecture and 2 hours Practical each week. The direction and key elements of the module will be covered in lectures. You will be required and encouraged to investigate topics on your own or in small groups in independent study time. The practical side of the course will involve you in developing models addressing aspects of the theory taught in the module, again in independent study time. Software tools and hardware elements will be provided, as appropriate, for the practical work.
TEXTS
Andreas A., 2005, Digital Signal Processing: Signals, Systems and Filters, McGraw-Hill, ISBN: 9780071454247

Ingle, V., & Proakis, J. Digital signal processing using MATLAB. Cengage Learning, 2011.

Formal object-orientated specification using Object 2, Roger Duke and Gordon Rose, 2000, Macmillan, ISBN: 0-333-50123-7

Embedded Systems Design, 2nd Edition, Steve Heath, Newnes, 2002, ISBN: 0-7506-5546-1

Practical Design of Safety Critical Computer Systems, William Dunn, Reliability Press, 2002, ISBN: 0971752702

Functional Safety. David J. Smith & Kenneth G. L. Simpson. Elsevier, 2004. ISBN 0-7506-6269-7
RESOURCES
Embedded systems laboratory
Development boards
PC's running 68HC11/ARM Cross-Compiler/Assembler and Visual studio C/C++
Laboratory exercise sheets provided by course instructor
Laboratory containing LabView, C and National Instruments Data Acquisition cards and MATLAB
Lecture Theatre containing C and Labview.
SPECIAL ADMISSIONS REQUIREMENTS
Prior study of COCS71180 Embedded Programming (IoT) or equivalent
LEARNING OUTCOMES
1. Demonstrate a systematic knowledge and understanding of concepts of digital signal processing techniques, devices and architectures and of data communication techniques and systems. (KNOWLEDGE AND UNDERSTANDING, APPLICATION)
2. Demonstrate a systematic understanding of information theory. (KNOWLEDGE AND UNDERSTANDING, LEARNING)
3. Design and implement elements of a smart engine based on digital signal processing. (ANALYSIS, APPLICATION)
4. Explain the problems and critically evaluate solutions involved in a safety critical system, including any relevant legislation. (ENQUIRY, LEARNING)
5. Evaluate critically the appropriate use of a range of internet of things DSP structures and algorithms using formal methods and static testing processes. (APPLICATION, PROBLEM SOLVING, REFLECTION)