Performance Evaluation of an IEEE 802.15.4-Based Thread Network for Efficient Internet of Things Communications in Smart Cities

Abstract

The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, among which the Thread protocol has emerged as a promising option. Thread is IEEE 802.15.4 based and has advanced capabilities like mesh networking, IPv6 support, and multiple gateways providing no single point of failure. This paper presents the design and evaluation of a low-cost mesh network using Raspberry Pi, nRF52840 dongle, and OpenThread 1.2 (i.e., an open-source software implementation of the Thread protocol stack). The research elaborates on the hardware and software solutions used, as well as the network topologies adopted. To evaluate the performance of the developed system, extensive real-time tests are performed, considering parameters, such as jitter, packet loss, and round trip time. These tests effectively demonstrate the effectiveness of the Thread network. Furthermore, the impact of varying payload size and bitrate on the network is analyzed to understand its influence. The behavior of the multi-hop network is also examined under link failure scenarios, providing insights into the network’s robustness. Our findings provide valuable insights for researchers interested in designing low-cost and efficient mesh networks for various IoT applications, including home automation, building/campus monitoring systems, distributed industrial IoT applications, and smart city infrastructure.

Generic IoT for Smart Buildings and Field-Level Automation—Challenges, Threats, Approaches, and Solutions

“…However, distributed systems are also becoming more and more popular in this sector. They are usually based on simple SoC modules with radio communication (Wi-Fi, Bluetooth, Z-Wave, ZigBee, and others), with dedicated applications for mobile devices or with support from dedicated server applications and websites [2,23,26,54,55]. Therefore, this study focuses on the analysis of the development of distributed automation networks, the architecture of which naturally fits into the concept of IoT technology applications and the cloud, with distributed tools and services for data processing.…”

“…The rapid development of Internet-of-Things (IoT) technology over the last dozen or so years has contributed to a significant increase in the diversity and heterogeneity of datatransmission networks, as well as the emergence of numerous standards, communication protocols, and approaches in network organization. However, technological progress and development in this area are inevitable, and the IoT paradigm is constantly gaining new application areas, in particular in the field of smart solutions, both industrial, commercial, and utility, dedicated directly to customers [1,2]. This diversity of applications generates the need to consider the various requirements and expectations of users and applications.…”

“…This diversity of applications generates the need to consider the various requirements and expectations of users and applications. For example, the implementation of IoT devices (smart nodes) in the control and monitoring network of a production line with industrial robots poses completely different challenges to network installers and integrators than in the case of identifying products and a purchasing application based on smart IoT labels or operating IoT modules with a smartphone in smart-home or smart-city installations [2][3][4]. Therefore, it is a difficult task to define and sanction a uniform universal generic IoT framework for all its potential application areas, although many research and engineering teams are making efforts.…”

Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices

“…Researchers have developed several CNN-based models for various purposes, including applications in photovoltaics [49], crowd estimation [50], deep learning in big data [51], classification and detection [2,52,53], medical data/healthcare [54,55], renewable energy [56], energy consumption [57], IoT-based smart cities [58][59][60], and fire recognition [61][62][63][64]. Examples of these architectures include AlexNet, SqueezeNet, GoogleNet, Mo-bileNet, etc.…”

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

“…With its mesh topology, Thread scales effectively, accommodating a wide range of devices and increasing scalability. However, Thread provides a robust solution for multi-hop IoT networks with features such as mesh topology, IPv6 addressing, security, low-power operation, interoperability, dynamic routing, and scalability [77].…”