## 2 Related Work
Thus, cooperative computing can be an effective solution for such applications in WSNs. There are some studies on using cooperative computing to improve the performance of video processing in WSNs. Firstly, [PERSON] et al. proposed a solution for optimizing the distribution of tasks among nodes to minimize the time needed to complete the distributed visual analysis in a visual sensor network [37]. Similarly, [PERSON] et al. designed a cooperative video processing scheme to maximize the quality of video processing result, and they conducted simulation studies using MATLAB [21]. In addition to video data, audio data are also among the most widely used data types, and there are a lot of studies utilizing audio data in WSNs. Firstly, [PERSON] et al. developed an application which uses Doppler effect to track the user's motion in real time [38]. Similarly, [PERSON] et al. designed a system to detect human encounters based on Doppler profiles of sound signals [39]. In addition, [PERSON] et al. proposed a system to perceive surroundings using acoustic reflections [40]. [PERSON] et al. developed a range-free localization technique for room recognition using signal reflections of the surrounding walls and furniture [41]. Recently, there are attempts to adopt acoustic based signals into drone field, which is one of the fastest emerging fields, including [PERSON]'s work for indoor localization [42]. The provided is a recent selection of studies using acoustics, and there are more. While processing acoustic signals, various operations such as FFT and cross correlation are performed essentially. Among the operations, FFT is very frequently used in studies and applications utilizing acoustic signals. Actually, including the above studies, most such studies and applications include the FFT process, and acoustic signals have been one of the primary sources to obtain information in various fields including WSNs, which is well presented in [PERSON]'s work [43]. As acoustic signals can provide information that is not available otherwise, there is an increasing interest in acoustic signals. Acoustic signals are being used in various ways in WSNs: localization, human contact detection, location tagging, guidance, etc. However, most studies using acoustic signals use either specialized devices or smartphones. Many applications including acoustic signal processing require large computations and processing to be completed within a tight deadline. The above requirements are the biggest setbacks to adopt complicated acoustic systems to nodes in WSNs where most nodes only have limited capabilities. To overcome the aforementioned limitation, we propose the cooperative computing system, _HeaLow_, which enables nodes in WSNs to perform heavy-computation processing with meeting the deadline. Compared to the related work, _HeaLow_ has novelties and advantages in several perspectives. First, many related studies focused on processing heavy-computational but delay-tolerant operations. In other words, such studies did not place a lot of emphasis on the low-latency requirement, which means that they cannot be applied to applications requiring both large computations and processing to be completed within a tight deadline. _HeaLow_ is designed to perform heavy-computation process while satisfying a completion time requirement, so _HeaLow_ can be applied to more diverse applications in various fields. Secondly, including the aforementioned studies, most studies focusing on cooperative computing and offloading in WSNs conducted performance evaluations through simulations without implementation on real devices and experiments using the devices. On the contrary, we implemented _HeaLow_ on the real devices and conducted several experiments using the devices to verify the effectiveness of _HeaLow_. Moreover, in this paper, we consider processing acoustic signals, which is one of the most widely used operations in WSNs, as the target task. Thus, many services and applications needing acoustic signal processing are able to utilize _HeaLow_ easily.