Cooperative Computing System for Heavy-Computation and Low-Latency Processing in Wireless Sensor Networks

Abstract

Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today’s devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices and simulations. Using HeaLow, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.

Deep Reinforcement Learning-Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks

“…The value of the node sensing radius is related to node density, which is discussed in the simulation results. The parameters used in the training environment are shown in Table 1 , in which some parameter settings are referred to in the literature [ 3 , 33 , 37 ]. The hidden layer of DDPG’s actor networks and critic networks is a three-layer structure, and the number of neurons is 256, 128, 64, respectively.…”

“…built a one-to-one cooperative computing model and proposed a new method to minimize the energy consumption of a pair of nodes under delay constraint [ 3 ]. In [ 37 ], a collaborative computing framework for urgent task processing in WSNs was proposed and implemented on real devices. For high-density WSNs, Jiang et al.…”

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

“…With respect to the general architecture of IoMT, Wireless Body Area Networks (WBANs) [38,50,51] are installed where various types of sensors are used, most likely activity sensors (e.g., accelerometer), physiological sensors (e.g., heart rate, ECG and body temperature) and environmental sensors (e.g., humidity and air pressure) (see Figure 1). Various types of applications are recognized with enhanced sensing and communication capability, such as biomedical and wearable solutions for health monitoring, human activities control, organ implantation monitoring and remote surgical interventions.…”

Cooperative computing schemes in wireless sensor networks

“…The goal is to optimise the total energy consumption of processing application and to meet certain completion deadline requirements. Under the influence of [22], for the scenario of audio applications, a cooperative scheme aimed at improving the timeliness and reducing delay is proposed in [23], without considering the energy efficiency of the system. For video stream information, Shu et al .…”