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"IEEE Sensors Alert" is a pilot project of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Environment mapping is a key component in assisted and autonomous driving. This paper introduces a method to generate 2D occupancy maps using light detection and ranging (LiDAR) and radar data, leveraging their clustered, sparse nature. It presents a linear sensor measurement model and pattern-coupled sparse Bayesian learning approach for occupancy map estimation. Tested with real-world data highlighting the methods, it shows superior performance in detecting obstacles.
The Internet of Things (IoT) connects different objects embedded with sensors, software and state-of-the-art technologies through the internet, enabling them to communicate in real-time through the wired or wireless communication system. Its application requires validation before actual implementation to reduce the risk involved, especially in security and privacy. The development of experimental environment (testbeds) provides an efficient platform for conducting practical IoT experiments under real conditions and precise testing techniques for wireless sensor networks (WSN) and IoT applications before implementation.
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