Smart Environments

Course objectives

Goal of this course is to provide an overview of the large world of wireless and wired technologies that are will be used for the Smart Environments. These technologies will be able to provide infrastructures of networks and digital information used in the urban spaces and smart environments to build advanced applications. Recent advances in areas like pervasive computing, machine learning, wireless and sensor networking enable various smart environment applications in everyday life. The main goal of this course is to present and discuss recent advances in the area of the Internet of Things, in particular on technologies, architectures, algorithms and protocols for smart environments with emphasis on real smart environment applications. The course will present the communication and networking aspects as well as the processing of data to be used for the application design. The course will propose two cases studies in the field of smart environments: Vehicular Traffic monitoring for ITS applications and Network cartography. In both cases instruments, models and methodologies for the design of smart environments applications will be provided.

Channel 1
STEFANIA COLONNESE Lecturers' profile

Program - Frequency - Exams

Course program
Topics: Enhanced Services by Smart Devices Data Acquisition, Coding, and Aggregation in Smart Environments Device Communication, Networking Practical examples of Data Processing for Smart Environments
Prerequisites
Knowledge acquired in Data Science courses regarding communications, networking and signal processing are appreciated.
Books
1. Cook, Diane J., and Sajal K. Das. "How smart are our environments? An updated look at the state of the art." Pervasive and mobile computing 3.2 (2007): 53-73. 2. M. R. Palattella et al., "Internet of Things in the 5G Era: Enablers, Architecture, and Business Models," in IEEE Journal on Selected Areas in Communications, vol. 34, no. 3, pp. 510-527, March 2016. doi: 10.1109/JSAC.2016.2525418 3. Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) Wiley-Interscience ©2004 "http://onlinelibrary.wiley.com.ezproxy.uniroma1.it/book/10.1002/047168659X" 4. Gupta, Akhil, and Rakesh Kumar Jha. "A survey of 5G network: Architecture and emerging technologies." IEEE access 3 (2015): 1206-1232. 5. Lora alliance. [Online]. Available: https://www.lora-alliance.org/ 6. Leduc, Guillaume. "Road traffic data: Collection methods and applications." Working Papers on Energy, Transport and Climate Change 1.55 (2008). 7. J. Zhang, F.-Y. Wang, K. Wang, W.-H. Lin, X. Xu, and C. Chen, Data-driven intelligent transportation systems: A survey," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624{1639, 2011.
Frequency
Attending classes is strongly encouraged.
Exam mode
EXAM Standard way: • 2 open questions (written answers are required) approx 2/3 evaluation • face-to-face interview approx 1/3 evaluation Homework/Project-based: • 2 Homeworks/Projects approx 2/3 evaluation • face-to-face interview approx 1/3 evaluation or 1 Homework approx 1/3 evaluation face-to-face interview approx 1/3 evaluation 1 open question (written answer is required) approx 1/3 evaluation face-to-face interview approx 1/3 evaluation
Lesson mode
Lecture and exercises
STEFANIA COLONNESE Lecturers' profile

Program - Frequency - Exams

Course program
Topics: Enhanced Services by Smart Devices Data Acquisition, Coding, and Aggregation in Smart Environments Device Communication, Networking Practical examples of Data Processing for Smart Environments
Prerequisites
Knowledge acquired in Data Science courses regarding communications, networking and signal processing are appreciated.
Books
1. Cook, Diane J., and Sajal K. Das. "How smart are our environments? An updated look at the state of the art." Pervasive and mobile computing 3.2 (2007): 53-73. 2. M. R. Palattella et al., "Internet of Things in the 5G Era: Enablers, Architecture, and Business Models," in IEEE Journal on Selected Areas in Communications, vol. 34, no. 3, pp. 510-527, March 2016. doi: 10.1109/JSAC.2016.2525418 3. Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) Wiley-Interscience ©2004 "http://onlinelibrary.wiley.com.ezproxy.uniroma1.it/book/10.1002/047168659X" 4. Gupta, Akhil, and Rakesh Kumar Jha. "A survey of 5G network: Architecture and emerging technologies." IEEE access 3 (2015): 1206-1232. 5. Lora alliance. [Online]. Available: https://www.lora-alliance.org/ 6. Leduc, Guillaume. "Road traffic data: Collection methods and applications." Working Papers on Energy, Transport and Climate Change 1.55 (2008). 7. J. Zhang, F.-Y. Wang, K. Wang, W.-H. Lin, X. Xu, and C. Chen, Data-driven intelligent transportation systems: A survey," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624{1639, 2011.
Frequency
Attending classes is strongly encouraged.
Exam mode
EXAM Standard way: • 2 open questions (written answers are required) approx 2/3 evaluation • face-to-face interview approx 1/3 evaluation Homework/Project-based: • 2 Homeworks/Projects approx 2/3 evaluation • face-to-face interview approx 1/3 evaluation or 1 Homework approx 1/3 evaluation face-to-face interview approx 1/3 evaluation 1 open question (written answer is required) approx 1/3 evaluation face-to-face interview approx 1/3 evaluation
Lesson mode
Lecture and exercises
PIETRO SPADACCINO Lecturers' profile
PIETRO SPADACCINO Lecturers' profile
  • Lesson code1056023
  • Academic year2025/2026
  • CourseTelecommunication Engineering
  • CurriculumIngegneria delle Comunicazioni (percorso valido anche ai fini del rilascio del doppio titolo italo-francese o italo-statunitense )
  • Year2nd year
  • Semester2nd semester
  • SSDING-INF/03
  • CFU6