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Although deep learning has achieved remarkable successes over the past years,few reports have been published about applying deep neural networks to WirelessSensor Networks (WSNs) for image targets recognition where data, energy,computation resources are limited. In this work, a Cost-Effective DomainGeneralization (CEDG) algorithm has been proposed to train an efficient networkwith minimum labor requirements. CEDG transfers networks from a publiclyavailable source domain to an application-specific target domain through anautomatically allocated synthetic domain. The target domain is isolated fromparameters tuning and used for model selection and testing only. The targetdomain is significantly different from the source domain because it has newtarget categories and is consisted of low-quality images that are out of focus,low in resolution, low in illumination, low in photographing angle. The trainednetwork has about 7M (ResNet-20 is about 41M) multiplications per predictionthat is small enough to allow a digital signal processor chip to do real-timerecognitions in our WSN. The category-level averaged error on the unseen andunbalanced target domain has been decreased by 41.12%.

Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images