## 1 Introduction
Because of these advantages, many researchers have been interested in utilizing audio data in WSNs, and various applications using audio data have been proposed [22; 23; 24; 25; 26]. To process or analyze the audio data in such applications, Fast Fourier Transform (FFT) [27] is almost always the key component. Even cross correlation [28] or wavelet transformation [29] is essentially a composition of multiple FFT operations or a partial result from FFT. Because FFT requires large computational resources, operations for audio data processing require a large amount of computation. Furthermore, many of the applications have a requirement that the operations should be done by the deadline, which makes it difficult for a single node in WSNs to run relatively heavy applications by itself. To overcome the limitations, we propose a cooperative computing system, _HeaLow_, for heavy-computation and low-latency processing in WSNs. To finish the heavy-computation task by the deadline, _HeaLow_ conducts processing and offloading properly by considering various factors. Because the audio data processing (acoustic signal processing) is one of the most widely used operations for applications in WSNs, we consider the audio data processing as the target task in this paper. However, _HeaLow_ can be used for any applications which require large computations and processing to be completed within a tight deadline. The design goal of _HeaLow_ is to aid the incapable wireless sensor nodes to finish heavy computations within deadline. In addition, if sufficient resources are present in the multi-device system, utilizing free resources to improve the application performance is the secondary goal of _HeaLow_. As timeliness of heavy computation is a challenging task, the power efficiency issue is not considered in this paper. In this paper, taking advantage of the powerful resources of today's devices, we focus on utilizing a few more powerful nodes for less powerful devices and propose the system enabling the incapable nodes to finish heavy computations within deadline, which is novel and different from traditional offloading research, including ones on WSNs. The contributions of this paper are summarized as follows:  
* Using _HeaLow_, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement. * Unlike related studies which conduct evaluations using simulations only, we implemented _HeaLow_ on real devices. Thus, we evaluated _HeaLow_ through not only simulations but also several experiments using the devices. * We consider processing acoustic signals, which can be used for various services and applications in WSNs, as the target task in this paper. Thus, such services and applications are able to utilize _HeaLow_ easily. The remainder of the paper is organized as follows. Firstly, we introduce related work and describe _HeaLow_'s novelties and advantages compared to the related work in Section 2. Secondly, we explain the design of _HeaLow_, the implementation details, and the offloading decision process of _HeaLow_ in Section 3. After that, Section 4 explains experiments and simulations, and verifies the performance of _HeaLow_. Finally, Section 5 concludes this paper.