Cyber-Physical Systems (CPS) represent a new generation of intelligent systems that integrate physical and digital technologies to achieve more efficient automation, control, and decision-making across various applications. CPS incorporates sensors, processors, actuators, communication networks, and cloud computing to create systems capable of sensing, analyzing, and responding to changes in their physical environments in real-time. Beyond merely integrating physical and digital technologies, CPS relies on advanced algorithms and software to analyze vast amounts of data in real-time, make predictions and decisions based on this data, and optimize their behavior to achieve specific goals [1].

CPS supported by Wireless Sensor Networks (WSN) have attracted widespread attention due to their ability to integrate physical and computational elements for real-time data collection and analysis. WSN plays a vital role in CPS, facilitating the collection of sensory data, enabling inter-connectivity, and supporting seamless communication between physical and virtual components [2].

In WSN, data aggregation is crucial for reducing the amount of transmitted data. Large-scale industrial applications are primarily based on wireless sensor nodes. In a real-time physical environment, sensors operate with minimal power, encompassing sensing, communication, and data processing. Data aggregation is the process of summarizing and combining sensor data to reduce the amount of data transmitted across the network. Sensor nodes are energy-constrained devices, and energy consumption is often correlated with the amount of data collected, as communication is typically the most energy-intensive activity. This paper investigates the relationship between energy and the data aggregation process and proposes the Energy-Conserving Data Aggregation Algorithm (ECDA).

CPS is the foundation of modern smart factories. The CPS can explore new information and communication technologies to improve production, service, and energy management. According to information and communication technology, these factories can monitor all parts of production in the time they are connected in the cloud [3]. Assembly lines play an important role in industrial production. Assembly lines not only reduce the working time of workers but also reduces the human losses. Besides, assemble lines can also improve the efficiency and success rate of overall production and manufacturing. However, assembly lines require sensors to meet the requirements of high precision and accuracy.

With the advancement of application technologies and the improvement of hardware performance, the number of sensor devices cannot meet the demand for information collection [4]. To achieve more accurate information sensing, analysis, and decision-making, CPS requires more sensors to support the entire system. Due to the large number of nodes, high distribution density, and certain dynamic characteristics of sensor networks, data redundancy and resource waste are prone to occur in receiving data. This requires a decision on whether to use or discard the obtained information [5]. Mukhopadhyay et al. [6] mentions that sensors are an indispensable part of an automated chemical plant. However, a lot of sensing devices transmitting to a single receiving device at the same time will easily cause data loss. Working on an assembly line requires to satisfy the six conditions in Fig. 1. Especially in the environment of intelligent factories, the completeness and regularity of the received information are the most important during the process of assembly line.

To reduce energy consumption and avoid incompleteness and irregularity of data transmission, this paper studies the information processing model in CPS-WSN. It mainly has four contributions:

(1) We propose the Information processing model to enhance the efficiency of data transmission and reception.

(2) We investigate the relationship between energy and data aggregation in sensor networks and propose an Energy-efficient Data Aggregation Algorithm (ECDA).

(3) We develop an Efficient Message Reception Algorithm (EMRA), including PMRA, STMRA, and SCMRA. These three algorithms are respectively applicable to message reception at any time, the shortest time, and under specific conditions.

(4) We evaluate the comprehensive performance of EMRA using the Analytic Hierarchy Process (AHP). AHP represents an innovative method to simplify data transmission.

Overall, the rest of the article is divided into five sections. The rest of this article is organized as follows. Section 2 represents related works. Then, Section 3 is the problem description. And the system methods are introduced in Section 4. Furthermore, Section 5 compares and analyzes the performance of the proposed algorithms. Finally, the conclusion is in Section 6.

The IoT can receive and transmit information through devices such as infrared sensors, radio frequency identification (RFID) [7], and global positioning systems. The IoT will realize efficient information interaction, information exchange, and communication in the network. It’s a network which can realize intelligent identification, positioning, tracking, monitoring, and management.

IoT sensors play an important role in improving operational efficiency, reducing production costs, enhancing safety of worker, and providing early warning to prevent crashes of system. Karunanithy et al. [8] propose a data collection system that utilizes IoT and WSN for continuous monitoring of wild species in the area. An intelligent health detection system is designed for data collection with the help of a wireless sensor network [9]. Belfkih et al. [10] focuse on sensor database methods used in WSN. Sutiarittham et al. [11] develop a system that uses artificial intelligence to predict student attendance. In addition, it performs optimal room allocation for courses to minimize space waste.

Reference [12] defines Industry 4.0 as the fourth industrial revolution, which goes digital through automated data exchange in manufacturing technology. To compare with the characteristics of traditional production lines of factory, smart factory enables accurate information tracking, which improves resource utilization. Besides, sensors connect various devices, which enable multiple machines to communicate and track each device. Therefore, integrating sensors into industrial environments can improve automation and sustainability. Time series data have become ever-present within manufacturing systems with the proliferation of affordable and robust sensors available in the market. Hence, time series analytics have experienced significant progress in Industry 4.0 [13].

Virtual sensing, in combination with physical sensors, has great potential in establishing a reliable and informative sensing environment. Assembly lines heavily rely on the sensor network to collect operational data. Virtual sensing, in combination with physical sensors, has great potential in establishing a reliable and informative sensing environment. However, virtual sensors are subject to their model uncertainties arising from input physical sensor errors and changes in system operations [14]. Although sensor networks are widely used in industry, real-time monitoring, and decision-making face great challenges for heterogeneous and large-scale data. This is particularly evident in time-sensitive industrial environments. Therefore, data and time are particularly important for the development of IoT. The delay is the time it takes for the packet to traverse the network from the source to the target. Connected to both the amount of energy consumption and the longevity of the network, packet delay can increase energy consumption in the network and reduce life cycle of network.

Reference [15] proposes a system which integrates various data processing techniques. Continuous casting steel lines integrates technology on the same Team Center platform. The results show that the solution can be well used in real-world industrial environments. However, the problem of data loss will occur in an environment with too much sensor data in a specific area. This issue requires further study and clarification. Fortunately, when the technology of IoT is introduced into the production process, the status of information management is gradually increasing. For example, even if the transmission frequency of each sensor is different, there will be no loss of information and data.

Currently, there is an issue of data loss in information processing. This paper proposes an Efficient Message Reception Algorithm for Sensors (EMRA), considering the message reception scenarios of different time series. The time series considered in algorithm include series under specific conditions, arbitrary time series, and the shortest time series.

AHP is a comprehensive evaluation method of systems analysis and decision-making founded by Saaty [16]. Statistics involving data collected over decades show that the most used method worldwide is AHP [17]. It presents for a full study of human thought processes. Also, AHP is a quantitative processing process to solve qualitative problems. This paper summarizes the relevant paper in recent years, as shown in Table 1. AHP is widely used in the following fields as an effective comprehensive evaluation method for experimental result analysis. This paper will use AHP to evaluate the experimental results of the three proposed transmission algorithms.

Due to the inherent redundancy in raw data collected by sensor nodes, within-network aggregation can usually reduce the communication cost by eliminating redundancy and forwarding only smaller aggregated information. In a sensor network, the benefit of data aggregation increases if the intermediate sensor node performs data aggregation incrementally when the data is forwarded to the base station [23].

Currently, WSN is facing four major issues namely power management, routing, localization and deployment techniques. Out of these in power management, energy conservation is the major constraint due to irreplaceable and limited power sources of the sensor nodes. Therefore, efficient cluster formation is very challenging in WSN by considering the energy consumption of cluster heads. The WSN area is divided into multiple clusters, with one node designated as the Cluster Head (CH). As shown in Fig. 2, CH collects data from all cluster nodes, performs aggregation, and routes the aggregated data to BS through direct transmission or with the help of adjacent CH nodes.

The biologically inspired selection of cluster heads (CH) has always been an important topic in the research of all clustering-based data aggregation methods. The choice of CH is usually based on the following parameters, including the proximity to BS, the hop distance of cluster nodes, the residual energy of the considered nodes, the computational and communication loads, and the cluster density [24].

However, the challenge lies in determining the metrics for CH selection and deriving the optimal mix to select the most suitable CH for a given application. This leads to a multi-objective optimization problem. In IoT applications where the nodes are mobile, the transmission energies required by a node varies widely. Therefore, estimation of residual energies of nodes and their hop-count to the BS is nondeterministic.

Hierarchical protocols play a crucial role in improving energy efficiency and prolonging network lifetime within Wireless Sensor Networks (WSNs) [25]. The LEACH is a popular clustering protocol for WSNs that helps in reducing energy consumption by allowing nodes to form clusters and electing cluster heads. However, some downsides of LEACH include the potential for CHs with low energy reservation to deplete quickly, the lack of scalability in large networks, and the use of random cluster head selection. They may not always result in optimal choices. To address this issue, Reference [26] proposes a solution where hybrid particle swarm optimization (HPSO) is paired with improved low-energy adaptive clustering hierarchy (HPSO-ILEACH) for CH selection in cases of data aggregation to increase energy efficiency and maximize the network stability of the WSN. Reference [27] proposes an Energy-Aware Routing protocol for Homogeneous and Heterogeneous WSNs called MAX LEACH. The proposed protocol improves the total network lifetime. PEGASIS follows greedy algorithm to organize the nodes in the form of a chain. The simulation results show that PEGASIS protocol is less effective when the long chain of sensor nodes is formed. More energy is consumed to transmit data over the long chain and introduces lot of delay. Reference [28] proposes E-PEGASIS protocol for enhanced energy-efficient data transmission based on PEGASIS protocol.

Sensor nodes consume a significant amount of energy during data transmission in network operations, emphasizing the importance of energy efficiency and network longevity [25]. The above article has made various improvements in reducing energy consumption. This paper further studies the two cluster-based data aggregation algorithms, LEACH and PEGASIS, as well as the recent improvements of the two algorithms through simulation experiments.

Based on the characteristics of assembly line and sensor devices, the sensor group is divided into multiple segments to achieve the integrity and regularity of information reception. Each segment does not interfere with each other. They can work simultaneously. There is one sensor task group A within each segment. Each sensor task group contains multiple sensors that send information in parallel. The specific values of sensors are input by the user. The sensor task group transmits information to the receiving device in a regular and continuous manner. The direction is from left to right. The receiver remains in a receiving state. Sensor task group A can be represented by Formula (1). Table 2 shows related symbols with corresponding meanings.

(1)A={A1=A,n=1A2=B,n=2A3=C,n=3…A10=J,n=10

Information transmission frequency (Ri). Sensor group A sends a signal to the receiver every Rj seconds. The number of sensor group A has limited range. Each sensors have different models. Therefore, this article specifies the value of Ai in the experiment, as shown in Formula (2). ASCII (Ai) represents the ASCII code of every sensor. The value of k represents the sensor information parameter that is related to the experimental settings. (2)Ri=2ASCII(Ai)−k−1

Signal repetition period (MaxST). The sensor group has a unified MaxST. As shown in Formula (3). (3)MaxST=2N−1+1

Time consumption (RecvT). Formula (5) represents the time it takes for the receiver group to receive information from the sensor group in this environment. Formula (4) indicates that the time of receiving the sensor i information is equal to the sum of the frequency of the sensor i and the delay in the actual production. (4)Tinow=Ri+ε (5)RecvT=TN={Ri,i=1(Ti−1_sum/Ti_now)∗Ri+Ti_now,i>1

Current minimum time consumption (MinRecvT). Formula (6) represents the shortest time for the receiver group to receive information from the sensor group in this environment. The experimental verification shows that this time is generated by the first sequential sequence of all sensor combination sequences. (6)MinRecvT=2N−1

Based on the background of the above issues and the characteristics of sensors, it is particularly important to verify the integrity of information, improve reception efficiency, and conform to different reception requirements. There are two corresponding problems as below. (1) Verify whether the receiver can receive all the signals sent by the group sensor within the MaxST. (2) Arrange the receiving order for the group sensor and find a specific receiving order scheme, so that the receiver can receive the signals sent by the group sensor according to the receiving scheme. In addition, it satisfies the minRecvT.

The experiments are divided into two parts: message transmission algorithm and data aggregation algorithm. For the message transmission algorithm, the parameters considered in this paper include running time and CPU usage. In addition, this paper also uses AHP to evaluate the overall performance of the message transmission algorithm. For the data aggregation algorithm, this paper mainly considers the situation of energy consumption.

This paper explores the issue of factory sensor data acquisition and transmission in CPS based on WSN and proposes the information processing model. Firstly, this paper proposes Energy-Conserving Data Aggregation Algorithm (ECDA). According to simulation results, the proposed methodology efficiently reduces energy consumption and increasing the transmission reliability. Secondly, this paper developed Efficient Message Reception Algorithm for Sensors (EMRA), which includes PMRA, STMRA, and SCMRA. Experiments show that EMRA helps in receiving information from the sensor regularly and completely. However, Due to resource constraints, WSN is easily targeted by Sinkhole, Sybil, wormhole, interference, and eavesdropping attacks. Therefore, providing Quality of Service (QoS) related to network security in terms of data confidentiality and privacy has become a major challenge for secure data communication. In the future, this article can further investigate an intelligent factory environment for bidirectional information detection and analysis.