Internet‐of‐Things (IoT) devices are typically resource constrained in terms of computing capabilities and battery power. Despite the efforts from the Internet Engineering Task Force (IETF) to established standards for IoT such as IPv6 over low‐power wireless personal area networks (6LoWPAN), routing protocol for low‐power lossy networks (RPL), and constrained application protocol (CoAP), certificate‐based Internet security protocols have not been fully addressed yet.
We see the main cause of this being the size of the X.509‐based Internet certificates. Typically being 1 to 2 kB, the large size of these certificates can cause IEEE 802.15.4‐based IoT nodes to fragment the certificate into many smaller packet‐size chunks, which causes many packet transmissions to occur in the network. This work presents LightCert, a lightweight scheme to compress the size of the security certificates using the similarity of contents in X.509 certificates. Specifically, LightCert identifies common fields in a certificate and suppresses the transmission of these contents within the IoT subnet scope. This allows LightCert nodes to minimize the packet transmission overhead for supporting certificate‐based security mechanisms such as datagram transport layer security (DTLS), by as much as ∼37%. The added overhead of exchanging certificates when using LightCert is kept low to as much as ∼5 mJ of energy and ∼0.48 seconds of latency.
With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.
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