1. Introduction
With the development of microelectronics, wireless communication technology, and embedded systems, the cost and size of sensors have gradually decreased, and their performance has also improved significantly, making it possible to deploy sensors in various environments. The wide application of sensors has also led to the establishment of sensor networks. In addition, the rapid development of the Internet of Things, which connects various devices and systems through the Internet for intelligent management and control, has also increased the use of sensor networks. In most current sensor networks, the increase in power consumption is becoming a major drawback due to the increase in the number and type of sensors and the type and size of transmitted data. In addition, we also noticed some shortcomings of the current ZigBee network, such as its overly simple and single topology, limited transmission performance, insufficient network flexibility, and lack of scalability. Therefore, this paper designs a wireless sensor network with a hybrid topology based on the ZigBee protocol and the CC2530 chip. The results can be used in engineering applications such as multi-scenario environmental monitoring. As shown in Figure 1, the network node consists of multiple parts, the most prominent of which is the hybrid topology consisting of a coordinator and terminals. Through the onboard antenna, a large amount of data can be transmitted and processed at low power consumption while data processing is performed. The combination of wireless communication technology and data processing technology provides new solutions for sensor network data.
ZigBee wireless communication technology is an important development. It mainly operates in the 2.4 GHz band, with a transmission rate of 20–250 Kb/s and a transmission distance of less than 100 m. It uses IEEE 802.15.4 [1] as the wireless transmission technology standard to achieve wireless sensor data transmission and communication, and plays an important role in short-distance transmission. The ZigBee protocol has many advantages in wireless sensor networks, such as low power consumption, low cost, and suitability for large-scale deployment. However, the ZigBee protocol also has some potential disadvantages, which we will also discuss in order to fully evaluate its suitability in wireless sensor networks.
First, the ZigBee protocol has a relatively low transmission rate, and compared to other wireless communication protocols (such as Wi-Fi or Bluetooth), its data transmission speed and bandwidth are limited, which may become a limitation in applications that require high data rates or large data volume transmission. Second, ZigBee also has certain limitations in terms of network topology and communication distance. In this paper, we will improve the limitations of its network topology by using a hybrid topology. In addition, the ZigBee protocol is relatively weak in terms of security. Although it provides certain encryption measures, its ability to resist complex attacks and security guarantees are not as strong as some modern protocols, which may be a potential problem in some scenarios that require high security.
However, despite these drawbacks, ZigBee still has its unique advantages, making it the preferred protocol in some specific scenarios. For example, ZigBee’s low-power feature makes it very suitable for battery-powered sensor networks, especially in environments that require long-term operation. It is suitable for scenarios where the data transmission rate is not demanding, such as smart homes, industrial automation, environmental monitoring, and other applications. In these applications, ZigBee’s low power consumption and low-cost advantages can effectively reduce overall energy consumption and hardware costs, thereby improving the feasibility and economy of the system.
Therefore, this paper uses the ZigBee protocol, taking into account its advantages in specific wireless sensor network scenarios. In particular, for applications with low power consumption, low data transmission requirements, and large-scale node deployment, the ZigBee protocol provides a cost-effective and feasible solution. The article further elaborates on its scope of application and how to compensate for its shortcomings by designing wireless sensor networks with hybrid topologies, thereby providing a more comprehensive reference for the application of wireless sensor networks.
Sensor networks are widely used in many fields [2]. For example, in environmental monitoring, sensor networks can collect real-time data such as air quality, temperature and humidity, gas concentration, etc., to help monitor environmental changes and provide timely warnings. In the construction of smart cities, sensor networks are used in traffic flow monitoring, intelligent lighting systems, public safety monitoring, etc., effectively improving the efficiency of urban management. In addition, sensor networks are also widely used in industrial automation, agricultural monitoring, health care, and other fields to provide intelligent and automated data support and a decision-making basis for various industries. In recent years, many scholars have studied wireless sensor communication technology. For example, Ragnoli et al. [3] designed a construction site monitoring system using LoRa, and in the article proposed the feasibility of using LoRa communication systems for data collection, as well as the idea of using LoRa technology for environmental data collection. However, LoRa’s communication system has obvious limitations, with a slow transmission rate that cannot meet the high-speed transmission characteristics of sensor networks. At the same time, if we need to build a network that transmits a large amount of environmental data, the LoRa network is also not suitable for big data transmission and therefore needs to be improved. Mabon et al. [4] proposed the concept of sensor network nodes in their paper, which is consistent with our emphasis on the construction of sensor networks. However, the sensor node structure they used is too simple and has many shortcomings. Tang et al. [5] proposed an improved ZigBee protocol in “Agriculture” for WSN design in agricultural greenhouses. However, their sensor network structure is still insufficient to support the flexibility and accuracy required by modern sensor networks.
In real life, multi-node and multi-scenario measurements are often required to improve the efficiency of information transmission. The Z-Stack protocol, which is based on the ZigBee protocol, plays an important role in short-range wireless transmission [6]. This wireless communication transmission is characterized by a clear protocol layer, secure and fast information transmission, support for bus and star network topologies, large capacity, low power consumption, and significant advantages as a wireless sensor system with a hybrid topology.
Traditional communication methods, i.e., point-to-point information transmission, are inefficient and require a large number of physical connections and infrastructure, which makes them spatially demanding and challenging to deploy in compact environments. Currently, topologies such as star, bus, and tree have many disadvantages, such as a simplistic structure, limited functionality, oversimplified topology, limited transmission performance, and a lack of flexibility and network scalability. Therefore, we designed a wireless sensor network with a hybrid topology to more effectively observe the data obtained by the device. Based on ZigBee technology, we used the CC2530 chip manufactured by TI to design a hybrid topology WSN based on the ZigBee protocol. With this new hybrid topology network, we found that in a variety of scenarios, environmental gas concentrations, light intensity, temperature, and humidity can be read more quickly, transmitted over short distances, and controlled by thresholds. During operation, the hybrid topology network allows the coordinator to simultaneously obtain data transmitted by multiple terminals. When the router is damaged, the terminal can be converted to temporarily replace the router’s functions. At the same time, it has high scalability, which can more effectively obtain data and adapt to different demand scenarios, thereby improving communication efficiency. The novel hybrid topology network structure proposed in this paper can be applied to enterprise networks, large-scale distributed systems, etc.
This paper is organized into the following sections: Materials and Methods, Results and Discussion, and Conclusions. It aims to verify the superiority of the hybrid topology ZigBee wireless sensor network we created through analysis, comparison, and simulation.
2. Materials and Methods
2.1. Overall Flowchart of the System with Hybrid Topology
García et al. [7] reported a tree topology wireless sensing network to improve the performance of a WSN-enabled environmental monitoring system. This system utilizes data processing and thresholding techniques to monitor and process environmental information to ensure efficient processing of environmental information measurements. However, while this tree topology has the advantage of being easily scalable, damage to one of its root nodes will cause its entire sensor network to be paralyzed, which also results in its poor stability. In this paper, we set up a hybrid topology sensor network that can be set up by the user to make its topology, thus making it both stable and easily scalable.
The D2D communication method proposed by Alaa et al. [8] has many applications in 5G technology. However, this communication method is complex and not relevant to the scenarios we have set up, which is not in line with the aim of simple and effective environmental measurements. It is not easy to build a more effective sensing network.
Rao et al. [9] proposed a topology based on the ZigBee module star, which has the advantage of being simple to build, but it has non-negligible drawbacks such as a single point of failure, higher wiring costs, and limited expansion. These are the drawbacks that need to be corrected in the hybrid topology of the ZigBee wireless transmission network that we have built.
Although Zigbee routers are already widely used in existing networks, the hybrid topology proposed in this paper is innovative in several ways. First, unlike traditional Zigbee networks, this topology is dynamically self-healing. When a router fails, the end device can automatically switch to the role of router to ensure the continued operation of the network. This feature significantly improves the reliability and stability of the network, especially in complex or harsh deployment environments.
Second, the hybrid topology described in this paper breaks new ground in terms of flexibility. Unlike traditional fixed star, tree, or mesh topologies, this design can dynamically combine the advantages of multiple topologies according to specific application scenarios.
In addition, the hybrid topology introduces a multi-node collaboration mechanism to reduce data transmission latency and energy consumption by optimizing communication paths. This design not only improves network efficiency but also provides stronger support for network scalability. Compared with the use cases of existing Zigbee routers, the topology in this paper is more adaptable and practical and can provide a more flexible and reliable solution for large-scale deployment.
Figure 2 illustrates a flowchart of the ZigBee sensor network with a hybrid topology structure that we have constructed. We have constructed a sensor network with a hybrid structure independently by allowing users to freely choose a combination of topology structures after hardware initialization, and then replace or retain the topology structures according to the transmitted data and requirements, and by changing the router and terminal functions. For example, when building a large-scale ecological environment parameter measurement network, the topology can be switched manually when additional nodes are required, making it easier to expand the scale of the sensor network. The ZigBee module is highly adaptable [10]. After a hybrid topology is established, the end node and coordinator can build a sensor network to facilitate data transmission and processing, which is then recorded in the user-built router.
2.2. Hardware and Software Design
The wireless sensor network with a hybrid topology consists of sensor modules, a ZigBee coordinator, multiple ZigBee routers, and nodes, actuators, an OLED display module, and a buzzer module. The sensors [11] include a DHT11 temperature and humidity sensor, an MQ2 smoke sensor, an MQ3 ethanol sensor, an MQ7 carbon monoxide sensor, and a light sensor. The actuators consist of relays and various actuators.
The core of the software component is supported by the driver provided by the CH340 chip. The corresponding software was written using the IAR Embedded Workbench 8.32.4 development environment. The program is implemented in C and runs on the Z-Stack protocol stack. Z-Stack is a semi-open source protocol stack that includes the OSAL (operating system abstraction layer) operating system. In order to establish a connection between the ZigBee coordinator and the end node and to collect data, the status of the ZigBee coordinator and end node must first be determined, the serial port and ZigBee protocol stack initialized, and the address, operating mode, and communication parameters of the device configured to ensure successful interconnection between the devices.
In this system, the ADC (Analog to Digital Converter) module plays a crucial role. Since the MQ series gas sensors output analog signals, and the ZigBee protocol and its communication stack usually only support digital signal transmission, it is necessary to convert analog signals into digital signals in order to effectively transmit them through the ZigBee network. At this point, the ADC module plays the role of a bridge, which converts the continuous analog voltage signal output from the sensor into discrete digital data for subsequent processing and transmission.
Specifically, the gas concentration detected by the MQ series gas sensor changes its resistance value, which in turn affects the output voltage value. This voltage signal is continuous and cannot be directly transmitted to a digital system. Therefore, the ADC module built into the terminal node samples this analog signal according to a certain sampling frequency and converts the voltage value at each sampling point into the corresponding digital value. In this way, the converted digital signal can be transmitted to the coordinator through the ZigBee network for further data processing and analysis.
The accuracy of the ADC module directly affects the accuracy and reliability of the sensor data. The ADC usually has a certain resolution (e.g., 8-bit, 10-bit, or 12-bit, etc.), and the higher the resolution, the more accurate the details of the analog signal represented by the converted digital signal. In addition, the sampling rate of the ADC also affects the real-time data. The higher the sampling rate, the faster the sensor can be obtained to change the information, allowing more accurate environmental monitoring.
In the user display interface, the sensor data are displayed through the OLED display module. A button can be used to switch working mode, i.e., to switch between the data displayed via terminal 1 or terminal 2. In addition, an alarm system was added, which can be set to switch the working state of the buzzer when a parameter exceeds a certain threshold.
2.3. Wireless Sensor Networks
The system uses the CC2530 chip designed by Texas Instruments (TI), which is well-known for its low-power SoC (system-on-chip) design that effectively integrates the radio frequency components and the ZigBee communication protocol stack. This advanced chip not only has significant advantages such as low power consumption, low cost, stable performance, and high flexibility, but also integrates the semi-open source Z-Stack protocol stack, which greatly simplifies the system development and maintenance cycle, and is therefore widely used in many fields such as sensor networks [12]. The system provides a new and efficient solution for environmental monitoring through the integration of the low-power SoC CC2530 chip and a modular design. This design not only improves the stability and reliability of data transmission but also enables the effective management of large-scale sensor networks. By providing real-time data collection and analysis capabilities, the system can significantly improve the accuracy and timeliness of environmental monitoring, promote scientific research and applications in fields such as smart cities, agriculture, and public safety, and expand the application scenarios of sensor networks.
The system’s innovation is reflected in several key aspects. We must first understand the hybrid topology structure proposed in this article.
In this hybrid topology, the coordinator, as the core node of the network, is responsible for starting the network, assigning addresses, and managing communication. It communicates directly with the surrounding terminal devices or routers to form the central part of the structure. This design enables the coordinator to handle data exchange with nearby nodes quickly and efficiently, making it suitable for scenarios that require low latency and high communication rates. The hybrid topology further extends the network’s reach by introducing router nodes. Each router can connect to multiple end devices or other routers, expanding communication capabilities level by level and enabling the network to cover a larger physical area. This hierarchical structure is particularly suitable for large-scale applications that require distributed node monitoring and data collection.
In addition, it provides reliability and redundancy for the network. With multipath connections between routers, data packets can take multiple paths to reach the destination node, effectively avoiding communication failures caused by the failure or interruption of a certain path. This multipath design not only improves the fault tolerance of the network, but also balances the network load, reduces the communication pressure on a single router, and ensures the stable operation of the entire network. ZigBee hybrid topology can meet the diverse application requirements in complex network environments, providing an efficient and flexible solution for wireless communication.
In order to achieve synchronous data acquisition from multiple end devices by the coordinator, the hybrid topology uses, multi-channel communication. The ZigBee protocol supports multiple communication channels, and the coordinator establishes independent communication links with different terminals or relay nodes through the channel segmentation technique to achieve parallel data reception. Meanwhile, it has a Time Division Multiple Access (TDMA) mechanism in multi-hop communication, the terminal device sends data through the nearest router or routing node, and the coordinator allocates time slots for different terminals to avoid channel conflicts. Through TDMA, multiple terminals can efficiently share communication resources while ensuring orderly data transmission. In addition, the processing power of the coordinator is enhanced by distributed computing or hardware parallelism, which is able to process data streams from multiple channels at the same time. This design demonstrates a high data throughput rate in simulation experiments.
Ultimately, as shown in Figure 3, the system uses a CC2530 chip in combination with a ZigBee communication module [13] to achieve efficient and stable wireless communication. The low-power design of the CC2530 chip effectively extends the battery life of the sensor node, making it suitable for long-term operation of the sensor network. Its built-in high-performance 8051 microcontroller and IEEE 802.15.4 radio frequency transceiver make the module more reliable and able to withstand interference during data transmission. Finally, the system effectively transmits and receives data via the RF antenna, ensuring signal stability and coverage, especially in multi-device and environmental conditions, outperforming existing solutions.
The system effectively overcomes the main challenges of traditional sensor networks in terms of connection stability, transmission distance, topological network expansion, and node failure [14,15]. Many existing sensor networks often experience unstable connections and data loss when faced with environmental changes and an increase in the number of devices. The system uses a hybrid topology sensor network design to enhance the adaptability and robustness of the network and ensure efficient communication performance in different usage scenarios.
2.4. Sensor Module Design
We used sensor modules as shown in Figure 4 in order to build the sensor network and thus measure the results of the whole sensor network.
The DHT11 Digital Temperature and Humidity Sensor is a compliance sensor with calibrated digital signal output, using an 8-bit MCU to control a resistive humidity sensing element and an NTC thermistor element. The DHT11 module uses a single-bus communication mode, and after connecting and warming up for a certain period of time, it is able to measure the temperature and humidity of the environment automatically, as well as configure and adjust them. It is capable of outputting ambient temperature and humidity data simultaneously and the results are presented in digital form.
It is worth mentioning that the DHT11 sensor fully embodies the concept of open source when used in conjunction with open-source hardware platforms such as Arduino. The open-source nature of Arduino allows developers easy access to hardware and software resources, while the simple interface and broad compatibility of the DHT11 further lower the threshold of development and promote the sensor’s wider use in open-source projects. Such features make the DHT11 a popular choice for many applications.
A light sensor senses the intensity of light and converts it into an electrical output. A light resistor is a sensor in which the resistance of a material varies with the intensity of light. When the light shines on the surface of the light resistor, the resistance value changes; thus, the light intensity can be measured indirectly by measuring the resistance value.
This system uses MQ2, MQ3, and MQ7 as gas sensors. MQ2 is a smoke gas sensor that uses a tin dioxide semiconductor as a gas-sensitive material. It is a surface ion-type N-type semiconductor. When it comes into contact with smoke, the smoke changes the barrier at the grain boundary, resulting in a change in the surface conductivity. The sensor uses this to obtain information about the presence of smoke. The higher the smoke concentration, the greater the conductivity, the lower the output resistance, and the larger the output analog signal. MQ3 is an alcohol ethanol sensor module. When alcohol gas molecules are present in the environment, the gas-sensitive material in the sensor reacts with them to reduce the conductivity. MQ7 is a sensor that monitors carbon monoxide levels. It functions because the electrochemical properties of semiconductor materials change under the action of gas. In addition, it has a heater inside that is heated by an electric current, thus accelerating the movement of gas molecules. At a specific temperature, the metal oxide on the surface of the semiconductor gas sensor undergoes a redox reaction with carbon monoxide gas in the air, resulting in a change in its resistance value.
2.5. Code Display
In order to illustrate in detail the workflow of the whole sensor network as well as the experimental results in this paper, we will show a partial code diagram as shown in Figure 5 to demonstrate our work. We use the IAR Embedded Workbench 8.32.4 software for the relevant programming, so as to program the CC2530 chip and the ZigBee module accordingly, thus obtaining a ZigBee sensor network with a hybrid topology that can be set by the user to set the relevant topology to adapt to the user’s needs in different scenarios.
3. Results and Discussion
As shown in Figure 6, we constructed a hybrid topology wireless sensor system which is a major innovation in sensor network design. The system consists of two core components, the coordinator and the terminals, and makes full use of modern wireless communication and sensing technologies. We will describe in this section the experiments we have conducted and compare them with some existing technologies to prove the superiority of this technology.
This hybrid topology ZigBee wireless sensing network shows enhanced performance and benefits over most of the existing wireless sensor networks. The entire sensor network can be manually modified to meet the user’s needs depending on the required environment. For example, we set up a complete sensor network node but found that the sensor network did not cover the entire environment. We therefore needed to add more sensor nodes and then adjust the topology. Another example is if the terminal is damaged, a single topology of the wireless sensor network may cause the whole network to be paralyzed.
In this case, the end devices are equipped with advanced sensors that can effectively collect environmental information. Compared with conventional sensor systems, these terminals transmit data through high-performance on-board antennas, ensuring reliable communication even in complex environments. This design provides greater flexibility for real-time monitoring.
The coordinator plays a key role in the system by receiving and integrating information from each terminal and visualizing the data on an OLED screen. This interface enables users to quickly understand and respond to sensor data. The user-friendly interface enhances the visualization of data and improves the overall user experience.
In addition, the interaction mechanism in the system allows users to design different terminal and router structures with simple keystroke operations, thus building hybrid topologies of sensor networks for different scenarios and needs. This innovation greatly improves the communication efficiency of sensor networks. Users can obtain the required information with the highest efficiency by changing the diverse topologies of sensor networks in different scenarios, which improves the shortcomings of most sensor networks with a single topology, and thus improves the timeliness and accuracy of decision-making.
To verify the performance of the system, we first used simulation scenarios such as combustible smoke bombs and alcohol sprays, and conducted multiple tests over several days to test the system’s performance under various environmental conditions. Then, in order to compare the advantages of this hybrid topology sensor network over traditional single sensor networks, we designed a physical experiment and a modeling simulation experiment to further demonstrate the superiority of the network through comparison. Finally, a series of simulations were conducted to evaluate its scalability. These tests not only evaluated the robustness and adaptability of the system in practical applications but also demonstrated the reliability of the system in the face of different challenges, proving that this wireless sensor network is superior to other existing solutions. The strategy of optimizing the system design through actual operational feedback fully demonstrates the system’s innovative capabilities in the field of applied technology.
In short, the hybrid topology of this wireless sensor network not only outperforms traditional technologies in terms of flexible hybrid topologies but also provides new ideas and solutions for modern sensor network applications through efficient coordination and flexible topology setting mechanisms. These innovations open up new avenues for research and development in related fields and show great potential in intelligent monitoring and environmental monitoring.
First, we need to explain why we use the ZigBee protocol to build a hybrid topology network. We have two main communication protocols to choose from: ZigBee and LoRa. These two communication protocols make it relatively easy to build a sensor network, but we chose ZigBee as the basis for building a hybrid topology network because LoRa is deficient in some key aspects that make it difficult to meet the needs of the sensor network in this paper. First, LoRa has a low transmission rate, ranging from only 0.3 kbps to 50 kbps, which is much lower than ZigBee’s 20–250 kbps. This makes LoRa unsuitable for scenarios that require high frequency and real-time transmission of large amounts of data. Second, although LoRa’s energy consumption has advantages in long-distance communication, it is not conducive to the long-term operation of battery-powered devices in short-distance, high-frequency transmission applications. In addition, the networking complexity of LoRa is also an important limiting factor. Unlike ZigBee, which supports automatic networking and dynamic node joining, LoRa nodes require manual configuration of frequency and communication parameters, which increases the difficulty of deployment and maintenance. We have created a bar chart, as shown in Figure 7, to more clearly compare the two situations.
We then set up a simple ZigBee wireless sensor network to specifically measure the feasibility of its transmission. Figure 8 shows the installation of the system coordinator and terminals, which stipulate the wireless communication range and the accuracy of data collection, including the measurement accuracy of temperature, humidity, gas concentration, and light intensity. These data are presented in the form of graphs and charts, which are used to evaluate the accuracy of the system’s measurement of each parameter.
When testing the communication range of a mixed topology ZigBee wireless sensor network, we focused on the importance of this indicator for the performance of the ZigBee wireless communication network. The communication range is affected by a variety of factors, including environmental conditions and the transmission power of the ZigBee module. In this experiment, the effects of transmission power and environmental factors can be ignored because they are relatively stable.
We then carried out the test using an ethanol spray. First, the system was installed with the MQ3 ethanol sensor connected to the end node, and the end node and coordinator placed in the playground and laboratory, about 60 m apart, powered by a 3.3 V battery. Then, the test environment was prepared. In the laboratory, it was ensured that no other interfering signals affected the processing and transmission of the ZigBee signals. Start the terminals and coordinator and pair them to ensure that they can communicate successfully. Then, spray the terminals with ethanol spray every 5 min and continuously collect ethanol concentration data via the ZigBee node. These data are sent to the coordinator via the wireless sensor network and recorded in a data format that includes ethanol concentration values, temperature, humidity, carbon monoxide concentration, smoke concentration, and light intensity. Finally, the collected data are analyzed graphically. The measured data are shown in Table 1.
The experiment verified its innovation and reliability in terms of signal transmission. Through systematic testing, we gained an in-depth understanding of the performance characteristics of ZigBee sensor networks, which provided new ideas for scientific research on sensor networks, namely the application of hybrid topologies in sensor networks, and laid a foundation for subsequent experiments.
In order to study the long-term operation of ZigBee wireless sensor networks with mixed topologies, we conducted a one-week test, and the physical test map is shown in Figure 9. The upper part of Figure 9 shows the wireless sensor network terminal located in the corner by the library, and the lower part shows it in the corner of the playground. In this experiment, we made extensive use of multifunctional sensors and sensor networks. We used the sensor network structure shown in Figure 8. The coordinator node and end nodes were placed in the laboratory and playground, respectively, with a distance of about 60 m between them. For safety reasons, the experiment was conducted in an open and well-ventilated playground, as smoke bombs would have generated a large amount of particulate smoke and carbon monoxide. In the experiment, the end nodes were connected to MQ series sensors and temperature and humidity sensors. Light sensors were also used for measurements, and the entire sensor network was powered by a 3.3 V battery. In addition, in the experiment, we will randomly turn off terminal 1 or 2 to simulate its damage, and add more coordination nodes during operation. Finally, we will change the topology of this wireless sensor network many times to verify the superiority of this hybrid topology sensor network compared with the traditional sensor network.
To avoid interference signals affecting the results of the experiment, stable transmission and processing of the signals must be ensured in the laboratory. The end nodes and coordinator nodes automatically pair up after activation, which facilitates subsequent signal processing and data recording. In this experiment, we will observe and record the changes over a period of one week. During this week, smoke bombs will be detonated at regular intervals of 4 min each day, and the data will be collected and recorded in real-time via the ZigBee wireless sensor network.
During the experiment, the sensors monitored the environmental data on the playground in real-time, including smoke concentration, carbon monoxide concentration, temperature and humidity, and light intensity. During the week of continuous monitoring, all data was collected and transmitted to the coordinator for further analysis of the trends in these data. In addition, since we will randomly interfere with the wireless sensor network, we can obtain a more comprehensive understanding of the operating requirements of the sensors in terms of long-term continuous measurement conditions and various changing conditions, as well as the changing characteristics of various environmental parameters after the smoke bomb is detonated, through the transmitted data.
The collected experimental data set is shown in the table, which contains detailed data on all environmental parameters recorded by the sensor over a period of one week, including particulate matter concentration, carbon monoxide concentration, temperature and humidity, and light intensity. These data were collected and transmitted by a ZigBee wireless sensor network with a hybrid topology, which was able to continuously record and form a complete data set in real-time, providing strong support for subsequent analysis. Using these data, it is possible to conduct an in-depth analysis of the changing characteristics of environmental parameters over time, assess the impact of smoke bombs, and verify the long-term stability and response performance of the sensor network. These detailed data will provide a reliable basis for further understanding of the interaction between smoke and the environment, and also provide an important reference for optimizing the design and deployment of sensor networks.
Some preliminary conclusions can be drawn from the observation and analysis of the seven days of data, which we have plotted in Figure 10. In terms of temperature, there was an increase due to the proximity of the heating smoke bomb, with some fluctuations from day to day, with the lowest temperature on individual days (such as day 4). Overall, the temperature changes were relatively stable, usually within a small range, but occasionally there were larger fluctuations, which were related to the weather conditions (rainy or cloudy) on that day. Humidity fluctuated more widely from day to day, with particularly high levels on the first and second days, which is consistent with rainy or cloudy weather conditions. Light intensity also fluctuated greatly over the seven days. In particular, light conditions were weaker on the first and second days, which may have been caused by cloudy or rainy weather. In contrast, light intensity was relatively even on the fourth and seventh days, indicating relatively clear and stable weather on these days.
During the experiment, we considered the arrangement of the sensor array as shown in Figure 11. We first discussed and analyzed the impact of height on the sensor, and plotted the results according to the experiment. As shown in Figure 12, we used this ZigBee wireless sensor network to conduct experiments at 10 cm, 30 cm, 80 cm, and 100 cm, respectively. The measured results are shown in Figure 13. From the figure, we can see that the measured results are more accurate at lower distances, which may be because the sensor is more susceptible to environmental influences at higher heights. Therefore, in the following text, we choose to test at a height of about 20 cm.
The concentration of smoke increased significantly as a result of the smoke bomb we released, which allowed us to verify the transmission performance of the wireless sensor network we had established. At the same time, the combustion of the smoke released substances containing carbon monoxide, causing the concentration of carbon monoxide to rise.
Through long-term monitoring and data collection for a week, the experiment verified the ZigBee hybrid topology wireless sensor network, as well as the network’s environmental parameter monitoring, data transmission stability, and adaptability in the face of random interference and network topology changes. The main purpose of the experiment was to study the performance of the ZigBee hybrid topology under long-term operation and compare it with that of traditional sensor networks.
In the experiment, we randomly shut down the end nodes to simulate node damage and observe the network’s performance under this fault condition. The results show that the ZigBee hybrid topology can automatically adjust the network path through a self-healing mechanism to ensure the continuity of data transmission. This feature greatly improves the robustness of the network, especially in the event of a node failure. It can automatically switch to an alternative path to ensure that data are not lost, which further verifies the adaptability of the ZigBee protocol in dynamic environments.
In addition, we tested the flexibility and scalability of the ZigBee hybrid topology by repeatedly adding router nodes and changing the network topology in our experiments. In this way, the topology can be adjusted so that the network can automatically adapt to different scales and environmental conditions as needed, further demonstrating its advantages over traditional wireless sensor networks in terms of scalability, flexibility, and fault tolerance. In particular, the transmission stability and data reliability of the ZigBee network can be ensured even in the presence of signal interference and changes in network topology.
Experimental results show that ZigBee wireless sensor networks with hybrid topologies exhibit excellent stability, scalability, and adaptability under various challenges such as long-term monitoring, environmental changes, and random faults. The accuracy and integrity of real-time experimental data show that wireless sensor networks can effectively cope with issues such as network topology adjustments and node faults during the experiment to ensure the accuracy and integrity of experimental data. Compared with traditional sensor networks, ZigBee hybrid topologies can provide a more reliable solution when facing complex environments and uncertainties, especially in application scenarios that require long-term operation and high reliability.
To more comprehensively compare the traditional ZigBee module network with the innovative ZigBee wireless transmission network using a hybrid topology, we simulated a fire scenario and then conducted experiments for practical applications to test and verify the performance of the two networks in the event of node damage. The central objective of the experiment was to assess the reliability and resilience of the two network structures in complex environments.
In the experimental setup of the traditional topology, the coordinator was arranged inside the laboratory, while the two router nodes and one terminal node were arranged in the bushes 50 m away from the laboratory, simulating a typical scenario of a distributed sensor network. Then, in order to simulate the impact of the fire scene on the network, we used smoke bombs to create a smoky environment, and further artificially destroyed the router nodes in the network to simulate the direct damage that a fire may cause to the sensor network. The experimental results show that when a key node (such as a router) in a traditional topology is damaged, the network cannot recover because it relies on a fixed role assignment for the central nodes. As a result, other nodes cannot effectively take over its functions, which leads to a complete disruption of communication in the entire sensor network.
We then switched to a wireless sensor network configuration with a hybrid topology for the experiment. In this configuration, we maintained the same physical layout as shown in Figure 14. We placed the coordinator in the laboratory as usual, and the two router nodes and one end node were set up in the bushes 50 m from the laboratory. After simulating a fire scene with the same smoke bomb, we again deliberately damaged the router node to test the fault tolerance of the network. As shown in Figure 15, one of the router nodes will be damaged at this time, and then the wireless sensor network with a hybrid topology will self-repair, as shown in Figure 16. In this process, the hybrid topology has shown significant advantages. When the router node fails, the network can rely on its dynamic role adjustment mechanism, and other nodes can quickly take over the functions of the failed router, ensuring that the communication capability of the sensor network can be quickly restored. This mechanism not only effectively reduces the impact of single-point failures on the entire network, but also significantly improves the overall reliability and redundancy of the network.
Subsequently, based on the above simulation results, we built an experimental platform in a real environment for physical testing to further verify the performance of the hybrid topology in a real fire scenario. As shown in Figure 17, we set up the same ZigBee coordinator, router, and terminal devices in the laboratory and its surrounding area, which is consistent with the simulation environment.
During the simulation of the fire scenario, we used the same smoke bombs as in the simulation phase to create a smoke environment and deliberately damaged one of the router nodes to observe the response capability when a critical node is damaged. By continuously collecting data during the test, we found that the hybrid topology network can quickly detect router failures and trigger its dynamic role-switching mechanism. In contrast, traditional topology networks often lead to complete communication disruptions due to damage to critical nodes under the same destruction conditions, and it is difficult to recover in a short period of time.
Through the above comparative experiments, we have come to the following conclusion: the ZigBee wireless transmission network with a hybrid topology structure demonstrates excellent performance in complex environments. Its wide coverage, excellent fault tolerance mechanism, and ability to dynamically adjust roles give the network a clear advantage in application scenarios requiring multi-node collaboration and high reliability.
In order to more comprehensively illustrate the performance advantages of ZigBee wireless sensor networks with a mixed topology over a traditional ZigBee topology, we designed two models and conducted simulation experiments in MATLAB R2023b. The experiments focused on coverage, energy consumption, propagation delay, and fault-tolerant recovery capabilities.
In an environment with a 2.4 GHz band, factors such as walls, metal partitions, and human activities can cause significant signal attenuation. Each meter or obstacle may cause a certain degree of loss. Given that ZigBee (IEEE 802.15.4) is commonly used in indoor scenarios in the 2.4 GHz band, we have selected 0.9 as a relatively conservative and simplified “unit attenuation coefficient” to uniformly calculate the process of signal attenuation with distance, based on the results of our own laboratory tests in actual scenarios.
The load factor is commonly used to describe the proportion of time during which data are being sent/received in the network. For most low-power wireless sensor networks, their nodes transmit data intermittently or event-driven, which makes the proportion of time when the overall network is actively communicating relatively small. Therefore, we selected a value of 0.2 to simulate the network load level, which not only reflects the low-power working characteristics of ZigBee but also facilitates the observation of the impact of different traffic volumes on performance metrics in experiments or simulations.
In the experiment, we considered the effects of environmental signal attenuation (attenuation factor of 0.9) and communication load (load factor of 0.2). To simulate energy consumption, we assumed that energy consumption is proportional to the distance from the node to the coordinator or router.
First, using MATLAB R2023b, we generated the locations of the devices in the two network structures based on the model we built and the parameters we set. As shown in Figure 18, in the traditional topology, all devices are directly distributed in a relatively close range with a radius of about 50 m centered on the coordinator. In the hybrid topology, the devices are distributed in a wider range with a radius of about 200 m centered on the coordinator, and routers are randomly placed in this area to simulate the relay function of multi-hop communication.
When calculating propagation times in a hybrid topology, a distributed multi-hop mechanism is used: the end device first communicates with the nearest router, which then communicates with the coordinator, and the time is calculated based on the transmission rate and the distance traveled. As shown in Figure 19, in most cases, the hybrid topology can significantly reduce power consumption over a larger coverage area.
It should be noted that if the distance from some terminal devices to the nearest router is greater than the distance from these devices to the coordinator, these devices will still first pass through the nearest router and then be forwarded to the coordinator, which may lead to a reduction in transmission efficiency in local scenarios. However, overall, hybrid topologies have better scalability and coverage performance in large-scale and multi-node networks, and can provide a more flexible and reliable solution for actual deployment.
Energy consumption simulations show that the energy consumption of the star topology increases linearly with the distance from the device to the coordinator. However, the hybrid topology significantly reduces the overall energy consumption by introducing a multi-hop mechanism that greatly reduces the distance from the device to the router. Experimental data show that the hybrid topology has a more even energy consumption distribution and has a significant advantage in terms of energy consumption, especially for devices that are farther away.
In the fault tolerance and recovery test, a fault was simulated on a randomly selected router in the hybrid topology, and the propagation time of the shortest path from the remaining routers to the coordinator was calculated. As shown in Figure 20, when a router in the star topology fails, the entire network will be completely disrupted and cannot be recovered. However, the hybrid topology can quickly take over the communication tasks of other faulty routers through its dynamic adjustment mechanism, and the recovery time is significantly lower than the response time of a single point of failure in the star topology.
Finally, we studied the scalability of this hybrid-topology ZigBee wireless sensor network. The study of the scalability of the ZigBee hybrid-topology wireless sensor network was realized through MATLAB simulation, aiming to deeply analyze the network’s ability to transmit information in different scenarios, as well as the reliability and adaptability of the system. In the simulation, we constructed a wireless sensor network topology containing one coordinator, three routers, and 20 end nodes, as shown in Figure 21, where the red nodes are routers, the green nodes are coordinators, and the blue nodes are end nodes. This hybrid topology was modeled using MATLAB tools, and the node deployment in an actual scenario was simulated by randomly generating the node positions.
Simulation evaluates the transmission performance of the network from multiple perspectives, including throughput, latency, and packet loss rate analysis. During the simulation process, we defined a communication distance threshold of 50 m and constructed an adjacency matrix between nodes to form a communication topology map of the network. By drawing the overall network topology map, we can visually observe the connections between end nodes and coordinator nodes, and make a preliminary assessment of the overall network coverage and density.
The key features that differentiate ZigBee from other wireless protocols are its tree-like logic and power consumption model. First, ZigBee clearly defines three roles at the protocol level: Coordinator, Router, and End Device, which can automatically generate and maintain routes using tree or mesh logic. This standardized hierarchical topology is not common in most WSN protocols [16]. Some protocols either only have a star topology or require the implementation of a mesh function. ZigBee’s built-in tree-based address assignment and tree routing mechanism can automatically expand the network based on network layer parameters, which is more concise and efficient for scenarios with limited resources and small to medium sizes.
In terms of power consumption, ZigBee, combined with the low-duty cycle MAC layer mechanism of IEEE 802.15.4, allows terminal devices to remain in sleep mode for long periods of time, only waking up briefly to send or receive data, significantly extending battery life. This sleep mode, combined with Beacon Mode, further reduces the energy consumption caused by idle listening. At the same time, coordinators and routers usually have a more stable power supply or a higher power budget to maintain network management and routing functions.
In the model construction process, we combined the ZigBee power consumption model and further defined its characteristic function. Specifically, we adopted the tree routing logic in the ZigBee protocol. Through this logic, we ensured that the wireless sensor network simulated was unique, that it could meet the needs of a specific application scenario, and that it demonstrated the advantages of ZigBee networks in terms of energy consumption and routing strategies.
During the information transmission process, a terminal node is randomly selected as the source node of the data packet, and the coordinator node serves as the receiving node. The shortest path algorithm is used to determine the transmission path of the data packet.
Latency assumptions are a common tool in network performance evaluation and emulation studies and have been widely adopted in various tools. For example, common network simulation tools (e.g., NS-2) typically use 1–10 ms as the default per-hop delay assumption to reflect typical levels of transmission and processing times in real networks.
We simulated the transmission process and calculated the transmission delay of the data packet by counting the number of hops in each transmission process, assuming that each hop introduces a delay of 5 ms. In addition, if no feasible path is found between the source node and the destination node, the data packet is considered to be lost during transmission, and the number of lost data packets is calculated. This process helps us evaluate the accessibility and reliability of the network under complex topologies.
We conducted multiple simulation tests on the ZigBee hybrid-topology wireless sensor network in the experiment to quantify its transmission performance and reliability. Specifically, we conducted 100 experiments and obtained the following key performance indicators: the average transmission delay was 13.0168 ms, and the packet loss rate was 0.105%. These results will be analyzed in detail below.
First, the estimated average transmission delay of 13.0168 ms reflects the timeliness of packet transmission between nodes. That is to say, in a hybrid topology, data can be successfully transmitted from the source node to the destination node in most cases within about 13 ms. This basically meets the real-time requirements of typical sensor network applications such as environmental monitoring and home automation. From the statistical average of 100 experiments, the fluctuation range of the delay is relatively small, which indicates that the transmission performance of the network has good stability and consistency in different experiments, ensuring that the data can be transmitted within a reasonable time.
Second, the packet loss rate was 0.105%, which means that on average 1 in 1000 packets may be lost during transmission. Such a low packet loss rate indicates that although the nodes in the network are distributed randomly, the network can still maintain a high connectivity and transmission success rate. This means that with a reasonable number of nodes and network size, the mixed topology design can ensure that most packets successfully reach the destination node, thus reflecting high transmission reliability. This is particularly important for practical application scenarios that require data integrity, such as industrial monitoring and medical sensing, to ensure data accuracy and reliability.
Third, we studied the impact of external environmental factors on transmission delay and plotted the distribution of group transmission delay considering the effect of temperature. Under the influence of the default ambient temperature, the average transmission delay is very small, as shown in Figure 22. In addition, we added the influence of abnormal temperature to this ZigBee wireless sensor network model. We chose a temperature of 60 degrees Celsius, and the transmission delay of the sensor quickly increased to more than 500 ms. The simulation results are shown in Figure 23. Within a certain range, the impact of temperature changes on the overall network performance is relatively limited. However, this does not mean that external environmental factors can be completely ignored. In particular, it should be noted that under extreme temperature conditions, the performance of node devices may be seriously affected.
In the simulated fire experiment shown in Figure 17, in order to more comprehensively analyze the impact of high temperatures on transmission performance, we designed additional experiments focusing on the dual impact of temperature changes and smoke on node transmission rates in a fire environment. The experimental scenario simulated a typical fire area, in which a group of nodes were deliberately placed close to the point of smoke bomb release to simulate the dual interference conditions of high temperatures and high concentrations of smoke.
By monitoring the transmission rate of these nodes in real-time, we found that as the temperature continued to rise, the transmission rate showed a clear downward trend, and the results are shown in Figure 24. This decline is highly correlated with the ambient temperature of the node. This shows that high temperatures not only have a significant impact on transmission delays but also have a noticeable negative effect on the hardware performance and communication efficiency of the node.
In addition, through statistical analysis of the packet transmission delay distribution, we found that most delays are concentrated in a relatively stable range, which indicates that the network is stable and has a certain degree of robustness under different experimental conditions. Although in some cases network path congestion or node failure may cause an increase in delay or even packet loss, overall, the hybrid topology is better able to cope with the dynamic changes of wireless sensor networks and maintain stable transmission performance.
We compare the results of multiple experiments with those of many existing wireless sensor networks [17,18,19,20]. The result comparison is shown in Table 2. We can clearly see the advantages of the hybrid wireless sensor network we created. The results show that the ZigBee hybrid topology wireless sensor network has good transmission performance and stability under complex conditions, and can effectively cope with the challenges posed by the random distribution of nodes. In practical applications, this topology is very suitable for data transmission tasks that require high reliability and low latency. For example, in smart agriculture, sensor nodes can transmit environmental data to a central controller in real-time, and the low packet loss rate ensures data integrity, which helps to make accurate decisions in a timely manner. Similarly, in smart homes, industrial automation, and other applications, the latency and packet loss rate performance also meet the requirements of communication networks in these fields.
Through this experiment, we have gained a deeper understanding of the performance of the ZigBee hybrid topology in different scenarios and verified that it can provide stable, low-latency, and highly reliable communication services in most application scenarios. These findings provide important data support for the future optimal design and practical deployment of the network, and point out the details that need attention and improvement under specific environmental conditions, so as to further improve the performance and adaptability of the network.
The simulation results show that the average transmission delay of the hybrid topology can effectively quantify the transmission speed of information in the network, while the packet loss rate can measure the stability of the network under the condition of random node distribution.
4. Conclusions
A hybrid ZigBee topology, the core feature of which is flexibility and adjustability, is innovatively introduced into the system. Users can dynamically adjust the overall structure of the sensor network according to actual needs through simple operations so that it can quickly adapt to different application scenarios. The hybrid topology structure organically combines multiple basic network forms, integrating their advantages. Not only does it break through the limitations of traditional single topologies but it also gives the network the ability to adjust on demand, providing stable and efficient communication support in a changing environment and providing a new solution for the design and application of wireless sensor networks.
The design and implementation of wireless sensor networks with hybrid topologies have made significant contributions to sensor networks, especially in terms of comparison with state-of-the-art technologies, innovation, and overcoming current challenges. Hybrid topologies overcome the shortcomings of traditional wireless sensor networks, which are difficult to expand and vulnerable, and are an advancement in sensor networks.
In Experiment 1, we set up a ZigBee wireless sensor network based on a hybrid topology structure to specifically test its transmission feasibility in practical applications. The measurement accuracy of the system in various parameters was evaluated in the form of charts. The system test showed that the ZigBee sensor network has good communication performance and stability in a hybrid topology structure, which provides new ideas for subsequent research on sensor networks and lays a foundation for subsequent experiments.
In Experiment 2, we further tested the performance of ZigBee hybrid wireless sensor networks in more complex application scenarios for long periods of time, especially in high-density node deployment and large-scale environmental monitoring tasks. In this experiment, we first conducted a physical experiment to compare the working status of sensors at different heights, and obtained a more suitable arrangement of sensors, thus laying the foundation for the entire experiment. The size of the sensor network was expanded by adding more end nodes to simulate more complex real-world applications, such as sensor networks in smart city environmental monitoring and industrial automation. The experimental results show that the ZigBee wireless sensor network can maintain high stability and reliability under conditions such as large-scale node deployment, signal interference, and node failure. The hybrid topology has better network flexibility and scalability, can quickly recover from node failures, and can still guarantee network performance when facing high-density data streams.
In Experiment 3, we compared the performance of a conventional ZigBee module network with an innovative ZigBee wireless transmission network in a simulated fire scenario. We used both simulation and physical verification, with the main objective of assessing the reliability and resilience of the two network structures when nodes are damaged in a complex environment. In the simulation, we found that the ZigBee hybrid topology model we built has good resilience, enabling it to quickly rebuild the entire network without damage when it is disturbed. In the physical experiment with the traditional topology, we placed a coordinator in the laboratory along with two router nodes and one end node in the bushes 50 m away to simulate a distributed sensor network. The researchers used smoke bombs to simulate a fire scenario and deliberately damaged the router nodes. The results showed that the traditional topology, which relies on a fixed role assignment, was unable to recover from the damage to the critical nodes, resulting in a complete disruption of communication. Under the same conditions, the hybrid topology network showed significant advantages. When a router node is damaged, the network uses its dynamic role adjustment mechanism to allow other nodes to quickly take over the functions of the failed router. This ensures the rapid recovery of communication and highlights the self-healing ability of the hybrid topology, thereby reducing the impact of single points of failure and improving overall reliability and redundancy.
In Experiment 4, the performance of a traditional ZigBee topology and a hybrid topology network was compared using a simulation model in MATLAB R2023b. The evaluation focused on coverage, energy consumption, propagation delay, and fault tolerance and resilience, taking into account environmental signal attenuation and communication load. In terms of network coverage, the traditional star topology distributes devices within a 50-meter radius centered on the coordinator. In contrast, the hybrid topology extends coverage to a larger radius by incorporating multi-hop communication routers, enabling a wider and more decentralized network layout. In terms of propagation delay, the traditional topology relies on direct communication between devices and the coordinator, with the delay being proportional to the distance between the device and the coordinator. In the hybrid topology, devices first communicate with the nearest router, which then forwards the information to the coordinator. By shortening the distance between devices and routers, the hybrid topology achieves a more even distribution of energy consumption, resulting in a significant reduction in energy consumption, especially for devices at a long distance. In fault tolerance and recovery tests, when the coordinator fails, the traditional topology will experience a complete network failure and there is no recovery mechanism. The hybrid topology shows strong resilience through a dynamic role adjustment mechanism. When a router fails, the other routers will quickly take over its tasks, so that communication can be maintained. The hybrid topology outperforms the traditional topology in terms of coverage, energy efficiency, propagation delay, and fault tolerance.
In Experiment 5, we will study the performance of the ZigBee hybrid topology wireless sensor network under large-scale high-density node deployment and dynamic environmental changes. The main goal of the experiment is to evaluate the scalability, reliability, and adaptability of the network in real-world application scenarios, especially in the face of node failures, communication interference, and network topology adjustments in complex environments. Experiment 5 verifies the superior performance of ZigBee hybrid topology wireless sensor networks in large-scale high-density node deployment and dynamic environments. Compared with the traditional single topology structure, ZigBee hybrid topology has obvious advantages in many aspects.
The study comprehensively explores the design, implementation, and performance evaluation of ZigBee hybrid topology wireless sensor networks, and verifies the reliability, flexibility, and scalability of the network in different application scenarios through a series of system experiments. The experimental results show that compared with the traditional single topology, the ZigBee hybrid topology network has significant advantages in terms of scalability, node fault recovery, and dynamic environmental adaptability. In particular, it can maintain stable performance and high data transmission efficiency when dealing with challenges such as high-density node deployment, large-scale environmental monitoring tasks, and node faults and communication interference.
The system’s design fully demonstrates its competitive advantage over existing technologies. Not only has it achieved breakthroughs in improving network performance and resource utilization efficiency, but it has also enhanced the applicability of wireless sensor networks in various practical applications. By optimizing the topology and network protocols, the system improves the flexibility and reliability of the network. It can quickly adjust the network topology in the face of complex environments and dynamic changes to ensure the efficient transmission and integrity of data. These innovations provide an important reference for theoretical research and practical applications in the field of sensor networks, and lay a solid foundation for the widespread application of smart technologies in the future.
In addition, the ZigBee hybrid topology proposed by the Institute has strong advantages in terms of performance [21] and provides more efficient and reliable network support for smart applications in many fields such as smart cities, environmental monitoring, and industrial automation. This solution not only improves the overall performance of sensor networks but also opens up new development paths for the in-depth application of smart technology in many fields. In particular, driven by cutting-edge technologies such as the Internet of Things, smart homes, and smart healthcare, it has greater application potential and broader market prospects. Through these innovations and optimizations, the ZigBee hybrid topology network provides strong support for the future development of smart technology and promotes the depth and breadth of sensor network applications in the fields of smart technology and automated control.
Conceptualization, H.L. and L.L.; methodology, H.L.; software, L.L.; validation, H.L., L.L., and J.L.; formal analysis, H.L.; investigation, J.L.; resources, L.L.; data curation, L.L.; writing—original draft preparation, Y.S.; writing—review and editing, H.L.; visualization, J.L.; supervision, H.L.; project administration, Y.X.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.
Data are contained within the article.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 8. Three-dimensional model of the communication protocol comparison experiment.
Enlarge this image.Figure 22. Packet transmission delay distribution taking temperature effects into account.
Ethanol test experimental data sheet.
| Temp (°C) | Hum (%rh) | Light (lux) | Smoke (ppm) | Ethanol (ppm) | Co (ppm) | |
|---|---|---|---|---|---|---|
| 1 | 16 | 76 | 56 | 119 | 237 | 18 |
| 2 | 17 | 78 | 59 | 151 | 362 | 13 |
| 3 | 18 | 81 | 49 | 102 | 287 | 19 |
| 4 | 17 | 70 | 58 | 112 | 398 | 22 |
| 5 | 17 | 74 | 56 | 125 | 298 | 24 |
| AVG | 17.0 | 75.8 | 55.6 | 121.8 | 316.4 | 19.2 |
| Actual | 17 | 73 | 52 | 119 | 326 | 23 |
Comparison table with other wireless sensor networks.
| Feature/Criteria | Paper 17 (Zheng et al.) [ | Paper 18 (Orfanos et al.) [ | Paper 19 (Ghafouri et al.) [ | Paper 20 (Gong et al.) [ | This Paper |
|---|---|---|---|---|---|
| Main Focus | Poultry farming management system based on IoT and cloud | Review of IoT technologies for smart home applications | Secure three-factor authentication for IoT environments | Lightweight cross-domain IoT authentication | Design of hybrid topology WSN nodes based on ZigBee protocol |
| Topology Design | Limited to specific use in poultry environments | Focus on general IoT connectivity without topology details | No emphasis on topology | Focuses on cross-domain secure communication | Hybrid topology with enhanced stability and scalability |
| Energy Efficiency | Energy concerns addressed through cloud-based methods | Limited focus on energy-efficient technology | Discusses lightweight cryptography for IoT | Symmetric polynomial for lower computational cost | Hybrid topology reduces energy consumption while maintaining stability in complex environments |
| Scalability | Focused on limited scalability for poultry farms | Examines scalability but lacks experimental verification | No focus on scalability | Scalability discussed within secure communication | Demonstrates scalable and stable hybrid WSN topology in various environmental scenarios |
| Experimental Validation | Prototype tested for efficiency | Mostly theoretical; lacks specific experimental results | Security analysis through the AVISPA tool | Theoretical validation of cross-domain security | Comprehensive simulations to validate performance in environmental monitoring and multi-node applications |
| Transmission Performance and Stability | Transmission performance cannot be guaranteed | Performance and stability cannot be guaranteed. | Performance is mentioned, but the relationship between performance and topology is weak | Focuses on security during authentication, but does not consider transmission performance and is not flexible. | It has good transmission performance and stability under complex conditions, and can effectively meet the challenges posed by the random distribution of nodes. |
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