Random sketch learning for deep neural networks in edge computing

Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dimensional space pre-training and post-compression, present challenges when covering the resources deficit, making tiny artificial intelligence hard to be implemented. Here we report an architecture named random sketch learning, or Rosler, for computationally efficient tiny artificial intelligence. We build a universal compressing-while-training framework that directly learns a compact model and, most importantly, enables computationally efficient on-device learning. As validated on different models and datasets, it attains substantial memory reduction of ~50–90× (16-bits quantization), compared with fully connected DNNs. We demonstrate it on low-cost hardware, whereby the computation is accelerated by >180× and the energy consumption is reduced by ~10×. Our method paves the way for deploying tiny artificial intelligence in many scientific and industrial applications

Wear: A balanced, fault-tolerant, energy-aware routing protocol for wireless sensor networks

As more and more real Wireless Sensor Network’s (WSN) applications have been tested and deployed over the last five years, the research community of WSN realises that several issues need to be revisited from practical angles, such as reliability and security. In this paper, we address the reliability issue by designing a general energy-efficient, load balanced, fault-tolerant and scalable routing protocol. We first abstract four fundamental requirements of any practical routing protocol based on the intrinsic nature of WSN and argue that none of previous proposed routing protocols satisfies all of them at the same time. A novel general routing protocol called WEAR is then proposed to fill the gap by taking into consideration four factors that affect the routing policy, namely the distance to the destination, the energy level of the sensor, the global location information and the local hole information. Furthermore, to handle holes, which are a large space without active sensors caused by fault sensors, we propose a scalable, hole sizeoblivious hole identification and maintenance protocol. Finally, our comprehensive simulation shows that, WEAR performs much better in comparing with GEAR and GPSR in terms of eight proposed performance metrics; especially, it extends the Lifetime of the Sensor Network (LSN) about 15 % longer than that of GPSR

Asymmetry-aware link quality services in wireless sensor networks.

Abstract. Recent studies in wireless sensor networks (WSN) have observed that the irregular link quality is a common phenomenon, rather than an anomaly. The irregular link quality, especially link asymmetry, has significant impacts on the design of WSN protocols. In this paper, we propose two asymmetry-aware link quality services: the neighborhood link quality service (NLQS) and the link relay service (LRS). The novelty of the NLQS service is taking the link asymmetry into consideration to provide timeliness link quality and distinguishing the inbound and outbound neighbors with the support of LRS, which builds a relay framework to alleviate the effects of link asymmetry. To demonstrate the proposed link quality services, we design and implement two example applications, the shortest hops routing tree (SHRT) and the best path reliability routing tree (BRRT), on the TinyOS platform. We found that the performance of two example applications is improved substantially. More than 40% of nodes identify more outbound neighbors and the percentage of increased outbound neighbors is between 14% and 100%. In SHRT, more than 15% of nodes reduce hops of the routing tree and the percentage of reduced hops is between 14% and 100%. In BRRT, more than 16% of nodes improve the path reliability of the routing tree and the percentage of the improved path reliability is between 2% to 50%