## 2 Related Work  
In this section, we introduce studies on cloud-assisted WSNs, task offloading in edge computing, and cooperative computing. In addition, we introduce some of the recent studies of acoustic sensing based applications. After that, we indicate limitations of related work and differences between our research and them. In general, nodes in WSNs have relatively limited resources, so there have been many studies to overcome the limitation by using powerful resources of cloud servers. Firstly, [PERSON] et al. focused on energy-optimal application execution in their cloud-assisted platform [30]. To reduce the energy consumption of sensor nodes, they formulated strategies to configure the clock frequency and schedule data transmissions optimally. According to their numerical results, a large amount of energy can be saved by offloading task to the cloud clone. Similar to [PERSON] [30], [PERSON] et al. utilized resources of servers to alleviate the limitations of devices, but they utilized multiple servers together based on their task partitioning algorithm [31]. Their evaluation results show that the proposed partitioning algorithm outperformed existing algorithms. Unlike [PERSON] [30] and [PERSON] [31], [PERSON] et al. focused on the insufficient storage of sensor nodes [32]. In their system, sensor nodes store sensed data into servers, and the system provides access to authorized users for retrieval of sensor information stored in the servers. Including the above work, many studies indicated that utilizing cloud servers is an effective method to alleviate the resource limitation of sensor nodes. However, utilizing cloud servers is improper for latency-sensitive applications or services required to frequently transmit large-sized data due to the long distances between cloud servers and devices, according to [PERSON] [16]. Moreover, existing cloud computing models were mainly designed for traditional web applications, so those are not suitable for future Internet applications running on diverse mobile and sensor nodes, including work done by [PERSON] et al. [19]. In recent years, devices for sensor nodes in WSNs have progressed remarkably, so various processes can be done in sensor nodes without cloud servers. Thus, many researchers such as [PERSON] et al. focused on edge computing and conducted studies on task offloading which is an important process in edge computing [33]. Firstly, [PERSON] et al. proposed a method for optimal partition among nodes to minimize the time to search for a pattern of interest in a large data [34]. They conducted a simulation evaluation, and the simulation result shows that the expected time of processing was reduced by partitioning tasks among nodes. [PERSON] et al. designed a distributed computation offloading algorithm and formulated the offloading decision making problem [35]. They conducted numerical studies, and the results of the studies show that their algorithm improves the performance of computation offloading and scales well depending on the number of nodes. Similar to [PERSON] [35], [PERSON] et al. proposed a dynamic computation offloading algorithm which decides the offloading decision, the CPU-cycle frequencies, and the transmission power jointly [36]. The major advantage of this algorithm is that the decisions can be made without requiring other distribution information. The proposed algorithm was evaluated through simulations. In addition to edge computing, some studies employed the concept of cooperative computing to improve the processing performance and evenly consume nodes' energy. [PERSON] et al. proposed a new approach to minimize energy consumption of processing [19]. They evaluated the proposed solution through simulations, and the results of simulations show that the total energy consumption was significantly reduced by distributing workloads among sensor nodes. By using cooperative computing, it is possible for nodes to perform applications which require large computations, such as applications using video or audio data.