*Article* **A Monitoring System Based on NB-IoT and BDS/GPS Dual-Mode Positioning**

**Zhibo Xie \*, Ruihua Zhang, Juanni Fang and Liyuan Zheng**

Department of College of Information and Intelligence Engineering, Zhejiang Wanli University, Ningbo 315104, China

**\*** Correspondence: xiezhibo@zwu.edu.cn

**Abstract:** Monitoring system is widely used to detect the environment parameters such as temperature, humidity and position information in cold chain logistic, modern agriculture, hospital and so on. Poor position precision, high communication cost, high packet loss rate are the main problems in current monitoring system. To solve these problems, the paper presents a new monitoring system based on Narrow Band Internet of Things (NB-IoT) and BeiDou system/Global System Position (BDS/GPS) dual-mode positioning. Considering the position precision, a dual-mode positioning circuit based on at6558 is designed, and the calculation formula of the positioning information of the monitored target has been derived. Subsequently, a communication network based on wh-nb75-ba NB-IoT module is designed after compared with the LoRa technology. According to the characteristics of high time correlation of sensor data, an adaptive optimal zero suppression (AOZS) compression algorithm is proposed to improve the efficiency of data transmission. Experiments prove the feasibility and effectiveness of the system from the aspects of measurement accuracy, positioning accuracy and communication performance. The temperature and humidity error are less than 1 ◦C and 5% RH respectively with the selected sensor chips. The position error is decided by several factors, including the number of satellites used for positioning, the monitored target moving speed and NB-IoT module lifetime period. When the monitored target is stationary, the positioning error is about 2 m, which is less than that of the single GPS or BDS mode. When the monitored target moves, the position error will increase. But the error is still less than that of the single GPS or BDS mode. Then the AOZS compression algorithm is used in actually experiment. The compression ratio (CR) of it is about 10% when the data amount increasing. In addition, the packet loss rate test experiment proves the high reliability of the proposed system.

**Keywords:** NB-IoT; BDS/GPS dual-mode positioning; data compression algorithm

#### **1. Introduction**

Monitoring system can detect environmental parameters such as temperature, humidity and location information, and send these information to the monitoring center, which greatly reduce the workload of staff and enhanced management efficiency. Hence, it has gotten more and more recognition and application in cold chain logistic monitoring, modern agriculture arrangement, hospital monitoring and so on. Due to hardware technology, communication network and other reasons, the system still has the following deficiencies:

(i) Poor positioning precision, especially for the moving object. Currently, the positioning error is about 50 m for the stationary object, while that is more than 100 m for moving object. (ii) High cost, especially for the communication cost. The hardware cost is a one-time investment, but the communication cost needs to be paid for every day. Actually, the transmission data amount of a monitoring system is small, but it needs to communicate with the monitoring center through 3 g/4 g network, so it has to pay for the expensive network resources, and the cost of communication is very high. (iii) Unreliable network performance, poor stability and high packet loss rate. The packet loss rate of existing monitoring systems is about 10% when the distance is about 1 km. When the communication

**Citation:** Xie, Z.; Zhang, R.; Fang, J.; Zheng, L. A Monitoring System Based on NB-IoT and BDS/GPS Dual-Mode Positioning. *Electronics* **2022**, *11*, 2493. https://doi.org/ 10.3390/electronics11162493

Academic Editors: Antonio Cano-Ortega, Francisco Sánchez-Sutil and Aurora Gil-de-Castro

Received: 7 July 2022 Accepted: 7 August 2022 Published: 10 August 2022

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network becomes worse, the packet loss rate will be higher. Therefore, it is essential to develop a monitoring system with high reliability and low cost.

Generally, the monitoring system mainly includes the three parts: communication network design, position function design and realization, and data transmission. The data transmission network of the monitoring system belongs to long-distance network. 4 g and low-power wide area network (LPWAN) are main long-distance communication. 4 g has high power consumption and high traffic cost, which is not suitable for non-real time communication. LoRa and NB-IoT are representative technologies of LPWAN [1]. LoRa is a physical layer technology which uses a proprietary spread spectrum technique to modulate signals in sub-GHz ISM bands. The bidirectional communication of LoRa is provided by the chirp spread spectrum (CSS) modulation that spreads a narrow-band signal over a wider channel bandwidth. The resulting signal has low noise levels, enabling high interference resilience and is difficult to detect or jam [1]. It provides long-range communication up to 10–40 km in rural areas and 1–5 km in urban areas and has very high energy efficiency [2–4]. Compared to LoRa, NB-IoT can use the current 3 g/4 g network to save the network cost and shortens the developing period with the license frequency band [5–7]. It uses a minimum system bandwidth of 180 kHz for downlink and uplink communication and can be deployed in three operating modes: (a) Stand-alone, (b) Guard band, and (c) In-band [8–10]. The physical channels and signals of NB-IoT are time-division multiplexed. The data rate for uplink is about 160 to 200 kHz and 160 to 250 kHz for downlink. The coverage is 18 km in cities and 25 km in suburbs. Because of the above advantages, NB-IoT has been widely used in many fields [11–13]. It is regarded to be a very important technology and a large step for 5 g IoT evolution [14,15]. Many famous companies have shown great interest in NB-IoT as part of 5 g systems, and spent lots of effort in the standardization of NB-IoT [16,17]. Shi proposed a smart parking system using NB-IoT communication technology, which can effectively improve the utilization rate of the existing parking facilities [18]. Anand presented a remote monitoring mechanism for the water level in a storage tank using NB-IoT [19]. Haibin studied NB-IoT in smart hospitals [20]. An infusion monitoring system was developed to monitor the real-time drop rate and the volume of remaining drug during the intravenous infusion. Srikanth put forward the utilization of onshore narrowband IoT infrastructure for tracking of containers transported by marine cargo vessels while operating near the coastline [21]. Xihai applied NB-IoT in an Information monitoring system to reduce the power consumption [22]. Cao applied the NB-IoT in intelligent traffic lights system for urban areas in Vietnan to reduce traffic congestion [23]. The above studies show that NB-IoT is oriented to applications that require high QoS and low latency and has strong links, high coverage, low power, and low cost [24]. Because of these, the paper proposed the monitoring system scheme based on NB-IoT.

BeiDou satellite System (BDS) is a global positioning system independently developed by China. Its space station consists of 5 geostationary orbit satellites and 30 non-geostationary orbit satellites, while the space station of Global Position System (GPS) consists of 24 satellites (21 working satellites and 3 standby satellites). The user terminal of BDS has doubledirection message communication, and the user can transmit short-message information of 40–60 Chinese characters per time [25]. GPS does not have the function of short-message communication. Unlike GPS which uses dual-frequency signals, BeiDou-3 uses triple-frequency signals, which can better eliminate the effect of the ionosphere and improve the positioning reliability and accuracy [26–28]. With the initial service provided by the BDS foundation strengthening system, it can provide meter-level, sub-meter-level, decimeter-level and even centimeter -level service. In addition, BeiDou-3 satellite network has laid an "inter satellite link" in space. Thus, all satellites in the constellation can be connected without global stations, and the satellites can continue to provide services even if they are disconnected from the ground. Because of these advantages, BDS begins to be widely used to measure height [29], vehicle position [30], anomaly detection [31], and train position [32], etc. Some scholars propose to combine BDS with other positioning technologies to produce higher cost performance [33–35]. However, for both BDS or GPS, the number of observation satellites

in a single satellite navigation system is limited, they will become extremely vulnerable in the case of severe environmental interference, and they cannot guarantee the positioning accuracy and availability of the receiver. Since GPS and BDS have common features in system design and positioning principle, the receiver can simultaneously receive the satellite signals of the two satellite navigation systems for dual-system integrated positioning to avoid the situation that a single satellite system cannot locate due to the lack of satellites. Therefore, in theory, BDS/GPS dual mode positioning can optimize the satellite position and improve the accuracy and availability of positioning results. This paper will use the BDS/GPS dual mode positioning system to improve the positioning accuracy.

In wireless sensor networks, there is a large amount of redundant information in the original data collected by sensor nodes, including the temporal redundancy collected by the same node at adjacent times and the spatial redundancy collected by adjacent nodes in geographical areas [36]. If the data carrying a large amount of redundant information is transmitted, the communication bandwidth will be wasted and increased network delay and node energy consumption, which will affect the stability and life of the whole sensor network system. Compressing redundant information before transmitting original data is a mechanism that can effectively reduce node energy consumption. In recent years, researchers have proposed many data compression algorithms for wireless sensor networks. The main algorithms for wireless sensor data are divided into compression based on time correlation and compression based on space correlation. Data compression algorithm based on time correlation is a kind of typical compression algorithm. It often focuses on mining data time correlation and removing data time redundancy with the help of some classical coding technologies, such as Huffman [37], LZW [38], S-LZW [39], LEC [40], RLE [41]. The data compression algorithm based on spatial correlation is also a typical compression algorithm, which is often combined with clustering mechanism [42–44], and strives to fully mine the spatial correlation of data and reduce and balance the energy consumption of each node of the network. Data compression algorithms based on temporal and spatial correlation have attracted more and more attention. For example, the algorithm proposed by Ciancio and Donoho [45,46] not only involves removing the temporal redundancy of data, but also discusses how to establish an optimal path, so that the spatial redundancy can be removed to the greatest extent when the data is transmitted along this path. Difference mechanism is often used in data compression [47–49]. The common point of the data compression algorithm based on the difference mechanism is that by selecting a reference data, a single sensor node only needs to transmit the difference between the original sensing data and the reference data, so as to remove the temporal redundancy, or the adjacent sensor nodes in the geographical region only need to transmit the difference between their original sensing data and the reference data, so as to remove the spatial redundancy. The difference between these algorithms is the choice of difference coding. Differential Code Compression Method (DCCM) is the typical algorithm. The disadvantages of DCCM algorithm are: (i) simply taking the average value of data as the reference value, which is lack of rationality; (ii) The correlation between data is not mined. So the paper will propose a new algorithm to compress the sensor data.

In view of the problems in the above literature, the paper proposes a monitoring system scheme with high positioning accuracy, low cost and high network reliability. Three main contributions of this paper can be summarized as follows:


packet loss rate through the experiment. The development period of the system is shorter and the cost is lower.

The remaining paper is organized as follows. Section 2 describes the monitoring system in detail, including hardware design and software design. Section 3 gives the results and test data. Section 4 presents the discussion and analysis. Finally, Section 5 presents conclusions.
