# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 2 Related Works  
Nowadays, as new applications of wireless sensor network need data processing with temporal constraints in their tasks, one of the new challenges for wireless sensor networks is the handling of data access [11; 12]. There are three research aspects in wireless sensor network data access: data storage [13; 14; 15; 16], data aggregation [17; 18; 19; 20] and data query [21; 22; 23; 24]. These prior studies have looked at maximizing the energy efficiency in order to increase the lifetime of wireless sensor network, or focus on the time performance on data access of the wireless sensor network from a single aspect. As we acknowledge, the energy efficiency and time performance of wireless sensor networks are the result of a compromise between the data storage schemes, data aggregation methods, and data query strategies. Our study systematically investigates the data storage schemes and data query strategies through balancing the performance between access time and energy consumption of the underwater wireless sensor network. Data storage is the first step in data processing in the wireless sensor network. According to the different data storage strategies, storage can be divided into centralized storage, local storage, and distributed storage [9; 25; 26]. As for the analytical modeling of storage schemes and query strategies to increase the lifetime of the network and decrease time delays with respect to data access, there are two interesting categories studied: tree-based and grid-based protocols. The tree-basedprotocols include the method shown by [PERSON] of [27], as well as SDS [25], Scoop [28], DISAGREE [29], and so on. Grid-based protocols include the schemes shown by the authors of [14], [30; 31] and [16], etc. However, these studies did not consider real-time events and non-real-time data existing in the network simultaneously for data storage and querying. We focus on the access mechanisms for emergency real-time events and data acquisitions. Furthermore, our schemes can be used in non-real-time data acquisition with attractive time performance and energy consumption in the wireless sensor network. As we know, when comparing underwater acoustics and radio, there are great differences in communication bandwidth, delay and bit error rate. Hence, data storage schemes and query strategies of wireless sensor networks based on radio communication cannot be used in UWSNs directly [32; 33]. Recently, many methods for data storage, data aggregation and collection, data query, and acquisition in UWSNs have been proposed [20; 21; 22; 34; 35; 36; 37; 38; 39; 40]. For the data query, [21] proposed a sub-region query processing mechanism called QPM. In QPM, a routing tree is constructed where a sensor node corresponds to a tree node, and sensor node acknowledges whether it lies within the sub-region of interest or not, therefore it can complement the querying data function in the sub-region. For the data and event discovery and acquisition, work [36] provided a mechanism to gather and route sensory data to a sink node (SN) in order to detect events and determine event sources. For SDA, when an event occurs, sensory data of multiple neighboring sensor nodes are gathered by a sensor node (called a relay node) and are aggregated into a data packet for routing to a SN. Data storage, data discovery and aggregation, data query and acquisition of wireless sensor networks must be based on suitable network routing protocols [27; 41]. Many researchers have focused on this theme. Reference [42] presented a routing algorithm named Prolong-SEP (P-SEP). Reference [43] proposed an efficient routing algorithm for preserving k-coverage and the reliability of data with logical fault tolerance. Otherwise, for the wireless sensor networks or underwater wireless sensor networks, some researchers have focused on distributed database management [44], data gathering [45; 46], etc.