Abstract: With the development of Internet and wireless communication technologies, embedded mobile database technology has become a new research branch in the database field. This paper analyzes the architecture of embedded mobile databases, systematically elaborates on the key technologies of embedded mobile databases, and improves the corresponding solutions. Keywords: Embedded system; Architecture; Mobile database 1 Introduction With the rapid development of embedded systems and wireless communication network technologies, new mobile service concepts such as mobile office and mobile communication have emerged. People have put forward more and more demands on the occasions, times, methods, and approaches for obtaining and using information. Under the background of this application demand, embedded mobile databases have emerged and become an important branch of database development in recent years. This paper conducts in-depth research on embedded mobile database systems, improves and solves the key technologies of embedded mobile database systems. 2 System Model of Embedded Mobile Databases In traditional distributed computing systems, each computing node is connected through a fixed network and maintains the technical connectivity of the network. Mobile computing systems change this assumption. A mobile computing system is a distributed computing system composed of fixed nodes and mobile nodes. The network environment of mobile computing has distinct characteristics: mobility, disconnection, bandwidth diversity, scalability, weak reliability, asymmetry of network communication, and limited power supply capacity. Distributed databases in mobile environments are mobile databases. It is an extension of traditional distributed database systems and can be regarded as a distributed system with dynamic connection between clients and fixed server nodes. The model of the mobile database system [2][6] is shown in Figure 1. Among them, the mobile client MC (Mobile Client) includes portable computers, PDAs, etc.; the MSS (Mobile Support Station) supports fixed nodes for mobile computing and has a wireless communication interface; the FH (Fixed Host) does not have a wireless communication interface and is equipped with a database and database management system. 3 Key technologies of embedded mobile databases The theories and technologies involved in mobile databases cover the latest achievements in communication and computer development. Among them, how to manage data in a mobile environment is the key to realizing mobile databases. In the design of mobile database systems, many issues that do not need to be considered in traditional distributed database systems need to be considered, such as client mobility, frequent disconnection between clients and the network, diverse network conditions, asymmetric network communication, limited power supply capacity of mobile computing components, low reliability, high scalability, inconsistency between client and server data, and mobile data query. In order to solve the above problems, research on data replication/caching technology, data broadcasting technology, location-related query optimization and other technologies is of particular importance in mobile databases. These technologies are key technologies to solve a series of problems caused by client mobility. The following is a detailed explanation of several key technologies involved in mobile databases. 3.1 Data replication/caching technology This technology is the key technology to solve the disconnection problem of mobile databases. Traditional replication/caching technologies assume that the client and server are constantly connected and maintain consistency based on this premise. This is not applicable in mobile computing. At present, people have proposed a variety of mobile replication algorithms. The most typical algorithm is: Three-Tier Replication Architecture (TTR structure). We will take TTR as an example to introduce replication. Three-Tier Replication Architecture [3], as shown in Figure 2. The first layer of replication refers to the replication between servers in a fixed high-speed network using traditional replication technology, which is called "server-level replication". In order to support the mobile computing environment, a weakly consistent server-level replication mechanism (WCSR) is generally adopted. This strategy allows each replication server to support query and update operations and permits temporary inconsistencies between replications. Therefore, a user accessing the database only needs to access one replication server, and can perform both query and update transactions. To reduce communication overhead and improve reliability, WCSR employs a periodic pairwise synchronization method. Each server periodically selects another server, and the two servers exchange their temporary transaction logs. After a limited number of pairwise synchronization processes, all database states eventually reach consistency. Layer 2 replication refers to the server utilizing the inherent broadcast capability of the wireless network to organize frequently accessed public hotspot data from the database and broadcast it to all MCs within the wireless network unit via the MSS. This is essentially data replication over the wireless broadcast channel, known as "over-the-air replication," which fully leverages the asymmetric nature of wireless networks. First, because the broadcast capability unique to wireless networks differs significantly from that of ordinary networks, it can support a large number of mobile clients (MCs) receiving data simultaneously. Furthermore, regardless of the number of receiving clients, the broadcast cost of the mobile service segment (MSS) remains unchanged. This allows a large number of mobile users to simultaneously access broadcast hotspot data, greatly improving system scalability. Second, since MCs can replicate frequently used hotspot data over the air, the frequency of their access requests to the server is significantly reduced, and they may even no longer need to connect to the server. This not only allows MCs to utilize the uplink more efficiently or avoid costly wireless communication, but also reduces the overhead of the server processing each online MC, thus allowing the server to receive access from more online MCs simultaneously. Therefore, over-the-air replication is a technology with low overhead but significant practical applications. The third layer of replication is to support disconnection operations for mobile users. MCs utilize their processing and storage capabilities to cache a portion of the database data, known as "client caching." Since the storage capacity of MCs cannot compare to that of database servers, and ordinary users do not need to access the entire database system during disconnection, a subset MC caching mechanism, called MCC (Mobile Client Caching), is generally used in TTR systems. The core of the MCC caching mechanism is the cache manager, which has three different working states under different network connection conditions: online state, offline state, and integrated state. In the online state, the cache manager transfers the transactions of MC users to the server for execution and is responsible for establishing and maintaining the MC cache. In the offline state, the cache manager simulates the function of the server and records the user update transactions in the local offline transaction log. In the integrated state, the cache manager merges with the server and returns to the online state. 3.2 Data broadcasting In layman's terms, data broadcasting [4] refers to sending data to the client in the form of periodic broadcasting in the mobile computing environment by taking advantage of the asymmetry of communication between the client and the server. Its biggest advantage is that the broadcasting overhead does not change with the number of mobile users. With the help of data broadcasting, the disconnection problem of the mobile database system can be solved to a certain extent. The key issue in the research of data broadcasting technology is the data broadcasting scheduling problem. The key to data broadcasting scheduling is what kind of scheduling algorithm to use and how to determine the broadcast period. Current research on data broadcast scheduling mainly focuses on optimizing access time and tuning time, but it has significant limitations. For example, it only considers optimizing access time or tuning time, failing to effectively combine the two. Furthermore, existing data broadcast scheduling mechanisms largely lack operability and do not support practical applications in mobile database systems. To address these shortcomings, it is necessary to theoretically analyze the optimal values ​​of access time and tuning time in data broadcast technology research. Guided by theoretical analysis, a data broadcast scheduling algorithm should be proposed that can optimize tuning time while maintaining a low access time. Data broadcast scheduling can be viewed as a bandwidth allocation problem: given the probability distribution of all client access to data items, the server attempts to determine the optimal proportion of each data item in the broadcast bandwidth and then generates broadcast schedules based on this proportion. The simplest scheduling method is to simply group all data items to be broadcast together, ensuring that each data item appears exactly once in each broadcast cycle, and that the average access time for each data item is the same (i.e., half the broadcast cycle). This type of scheduling is called flat scheduling. If, in a broadcast schedule, the number of times each data item appears is not necessarily 1, i.e., the proportion of bandwidth occupied is not necessarily the same, then the scheduling is called non-flat scheduling. However, simply determining the bandwidth ratio of each data item is insufficient. If the interval between data items (i.e., the time difference between two occurrences) is uneven within a broadcast cycle, non-flat scheduling will not produce good results. This can be illustrated with a simple example. As shown in Figure 3, a data broadcast containing three data items can have three different scheduling methods: program a is a flat scheduling; while programs b and c are non-flat scheduling, where data item d1 occurs twice in a broadcast cycle, while data items d2 and d3 occur only once. Program b is a skewed scheduling because the two occurrences of data item d1 are consecutive within a cycle, making the broadcast interval of d1 uneven. Program a, on the other hand, is a uniform scheduling, where the interval time occupied by each data item in the broadcast bandwidth is uniform within the broadcast cycle. Thus, data item d1 appears to reside on a disk that is twice as fast as d2 and d3. This type of scheduling is called multi-disk scheduling. Assume that the arrival of mobile client data item access requests is completely random, i.e., it may fall at any time within the data broadcast period. Table 1 lists the average access time of the three schedulers under different data item access probability distributions. [align=center] [/align] Table 1 illustrates three issues: First, when the data item access probability is uniformly distributed (i.e., 1/3), the average access time of flat scheduling is the lowest. This shows that multi-disk scheduling technology cannot be used arbitrarily, and may be counterproductive in some cases. Second, when the data item access probability distribution tends to be skewed, the performance of non-flat scheduling will be better than that of flat scheduling. Finally, in both non-flat scheduling methods, the performance of multi-disk scheduling will be better than that of skewed scheduling, which shows that uniform non-flat scheduling can achieve better performance. The process of constructing a broadcast scheduler by multi-disk scheduling algorithm is as follows: (1) Sort all data objects in descending order of access probability. (2) Divide these data objects into K adjacent rents in sequence, called "disks". Define the capacity Ci of disk Bi as the number of disk objects. (3) Determine the relative broadcast frequency fi of each disk, i.e., the ratio of the bandwidth occupied by each disk object in the broadcast. fi must be coprime integers, i = 1, 2, ..., k. (4) Generate a broadcast scheduler. ● Divide each disk into several blocks. First, find the least common multiple of the broadcast frequencies of all disks, LCM; then, divide each disk Bi into num_chunks(i) = LCM/fi blocks of the same size, denoted as Cij, j = 1, ..., num_chunks(i). If Bi cannot be divided evenly, fill the unfilled blocks with free data. ● Interleave the blocks of each disk according to the following procedure to generate a broadcast scheduler. For (i=0; i For (j=1; j=k; j++) Broadcast block C[sub]j(i mod num_chunks(j))[/sub]; 3.3 Location-related query optimization In mobile databases, there are queries and updates related to location. Queries are usually location-related, and even for the same question, the query results will be different in different places, such as "Where is the nearest hospital?". Mobile query optimization technology refers to the use of various methods to eliminate the impact of bandwidth diversity, disconnection and other factors on the basis of traditional distributed database query optimization technology, so that the query engine can take appropriate optimization strategies according to the current available network conditions; at the same time, for the limited power capacity of mobile computers, the operation of local database management, remote database access and other power-consuming operations is reasonably organized to achieve energy saving and extend the availability time of key data. When adopting the segmentation-based address update strategy, the mobile user object maintained by the location server contains the following data members and methods: Segmentation set - records the segmentation of MSS, such as {Cell1,Cell2}, {Cell3,Cell4,Cell5}; LOC—Records the address (radio unit ID) most recently reported by the mobile user, such as Cell1; ERR—The segment where the mobile user is currently located, for example, if LOC=Cell1, then ERR={Cell1,Cell2}; loc()—A method used to return the user's actual address, i.e., the address lookup process described above. In mobile query applications, there are various address-related queries, such as "Please find a doctor near the campus," "Find X, Y, and Z, these three people are all on the same road, and Y is between X and Z," etc. Generally, this type of address-related query can be represented as: SELECT x1,x2,…,xn FROM Users WHERE (x1.loc=11∧…∧xn.loc=1n)∧C(11,…,1n)∧W(x1,…,xn) Where C(11,…,1n) are n-ary constraints on addresses 11,…,1n, and W(x1,…,xn) are n-ary constraints on the non-address attributes of objects x1,x2,…,xn, and Users is the set of all mobile users. This type of location-related query requires checking whether the actual addresses of each object satisfy the constraints, and these actual addresses must be obtained through address lookups because the location server only provides addresses assigned to a single level with imprecise precision. Therefore, to obtain the final answer to a location-related query, sufficient precise address information must first be retrieved. Appropriate optimization of this type of query can significantly reduce the communication overhead required to retrieve address information. 4 Conclusion Embedded mobile databases will develop rapidly with the development of various mobile devices, intelligent computing devices, and embedded devices. It will play an increasingly important role in the fields of military, aviation, land resource management, and mobile healthcare in the future. Embedded mobile databases are a brand-new research topic, and there are still many problems to be solved. When mobile database technology gradually matures, it will generate huge commercial and social value. The innovation of this paper: In view of the asymmetry of client-server communication and the disconnection problem of data transmission in the mobile computing environment, the author proposed a three-layer replication architecture and a multi-disk scheduling algorithm, and explored the preliminary solutions to some problems. 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