Integrating IOT-Commodity Cameras Through LoRaWAN: An Architectural Implementation

Integrating IOT-Commodity Cameras Through LoRaWAN: An Architectural Implementation

Rajiv Pandey, Shahnaz Fatima, Shubham Asthana, Ayush Kumar Rathore
DOI: 10.4018/978-1-5225-9574-8.ch009
(Individual Chapters)
No Current Special Offers


IoT devices and their applications are supporting humankind in almost all domains. This chapter explores LoRaWAN and proposes to integrate the commodity cameras installed at the security points of most of the gates to residential areas. LoRaWAN is a media access control (MAC) protocol for wide area networks and it has been opted for its functional and architectural scalability. The intelligent inputs are transferred from the commodity cameras to the data concentrators (DC), on edge-based computing, the DC can transfer this input to fog, cloud, remote servers for machine learning integrations. This chapter demonstrates the basic architectural framework of the said implementation. However, the detailed implementation and prototype is beyond the scope of this chapter. The chapter has however demonstrated the architecture. The features of the commodity cameras have been listed that can serve as the feed to the concentrators that shall enable alarm generations at the local and remote policing sites.
Chapter Preview


This chapter highlights the points related to integrating the small existing surveillance systems in ordered to obtain a unified and autonomous system and the steps involved in this process. The chapter also presents the use of python programming language as a tool for implementation of the same and its features shall also be highlighted. The Python programming language is a versatile programming language and is commonly used for application developments and data analytics and visualization purposes with active support of libraries like CV2 or OpenCV for image capture and analysis also for user friendly graphical environment developments.

Key Terms in this Chapter

Chirps: A chirp is a signal in which the frequency increases (up-chirp) or decreases (down-chirp) with time.

Face Recognition: Face Recognition is a recognition technique used to detect faces of individuals whose images saved in the data set.

Smart Home: In IoT-enabled Smart Home environment various things such as lighting, home appliances, computers, security camera etc. all are connected to the Internet and allowing user to monitor and control things regardless of time and location constraint.

QoS (quality of service): Quality of service (QoS) technologies used in the electronic or telephone networking business typically assists in optimizing network traffic management in order to improve the experience of network users.

Ubiquitous: Ubiquitous computing (also called pervasive computing) is the growing trend towards embedding microprocessors in everyday objects so they can communicate information and make intelligent actions possible.

2dfeature Framework: This subclass of openCV contains various functions that are common to all feature detection and descriptor extraction classes.

Machine-to-Machine: M2M describes the interaction of billions of devices and machines that are connected to the internet and to each other.

Complete Chapter List

Search this Book: