Future healthcare systems, smart homes, and similar will involve a large number of smart inter-connected wireless devices (such as wireless sensor nodes). One of the major challenges to securing these systems presents loading initial cryptographic credentials into a relatively large number of wireless devices. Furthermore, many of these technologies involve low-cost and highly interface constrained devices (lacking usual wired interfaces, displays, keypads, and alike). We propose two novel multichannel key deployment schemes for wireless networks that only require a presence of a light source device, such as a multi-touch screen (tablet or smartphone device). The first key deployment scheme is based on secret key cryptography and is suitable for interface/resource-constrained wireless devices. The second scheme assumes a strong attacker and requires the use of public key cryptography. In both our solutions, we use one-way visible light channel of multi-touch screens (flashing displays) to initialize devices in a secure, usable, and scalable way. From the user's perspective, this boils down to placing the devices on the multitouch screen after which the remaining process is fully automatized. We showed through the experiments with 48 users that our solution is user-friendly and scales linearly with the number of nodes.
With the everyday growth of the Internet of Things (IoT), the number of connected sensor devices increases as well, where each sensor consumes energy while being constantly online. During that time, they collect large amounts of data in short intervals leading to the collection of redundant and perhaps irrelevant data. Moreover, being commonly battery powered, sensor batteries need to be frequently replaced or recharged. The former requires smarter and less frequent data collection, while the latter being complementary to the former requires putting them to sleep while not being used in order to save energy. The focus of this article is low-cost gas sensors as they need to preheat for several minutes to reliably collect gas concentration. However, instead of waiting for a sensor to heat up, a transient, i.e., a data trend that the sensor collects while heating up is analyzed. It is shown that long short-term memory (LSTM) neural network can be used to learn and later predict the actual gas level from a part of the transient. This way, instead of being constantly online or fully preheating, the sensor needs to be turned on for only 20 s and then sleep for 120 s. With high accuracy, our approach decreases energy consumption by up to 85% compared to a system where sensors are constantly online, and more than 50% compared to a system where a sensor collects actual values instead of a part of the transient.
Many applications from the Internet of Things (IoT) domain used in healthcare, smart homes, and cities involve a large number of interconnected wireless devices. To ensure privacy, confidentiality, and integrity of the information, devices should be initialized prior to any communication. In this paper, we present a secure initialization method for constrained IoT devices such as wireless sensors devices and/or actuators. The solution uses visible light communication (VLC) for the initial configuration of the IoT devices. The VLC system consists of a modulated light source such as a smartphone screen and a very simple photodetector. We analyze known coding and modulation techniques used for the VLC and propose BlinkComm, a differential coding technique that achieves threefold increase in transmission speed compared to existing solutions. We showed through experiments with 32 participants that the proposed solution achieves fast completion times and low error rates as well as high user satisfaction levels.
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