Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this article propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale.

This paper presents a group-based dynamic stuck-at fault diagnosis scheme intended for resistive random-access memory (ReRAM). Traditional static random-access memory, dynamic random-access memory, NAND, and NOR flash memory are limited by their scalability, power, package density, and so forth. Next-generation memory types like ReRAMs are considered to have various advantages such as high package density, non-volatility, scalability, and low power consumption, but cell reliability has been a problem. Unreliable memory operation is caused by permanent stuck-at faults due to extensive use of write- or memory-intensive workloads. An increased number of stuck-at faults also prematurely limit chip lifetime. Therefore, a cellular automaton (CA) based dynamic stuck-at fault-tolerant design is proposed here to combat unreliable cell functioning and variable cell lifetime issues. A scalable, block-level fault diagnosis and recovery scheme is introduced to ensure readable data despite multi-bit stuck-at faults. The scheme is a novel approach because its goal is to remove all the restrictions on the number and nature of stuck-at faults in general fault conditions. The proposed scheme is based on Wolfram’s null boundary and periodic boundary CA theory. Various special classes of CAs are introduced for 100% fault tolerance: single-length-cycle single-attractor cellular automata (SACAs), single-length-cycle two-attractor cellular automata (TACAs), and single-length-cycle multiple-attractor cellular automata (MACAs). The target micro-architectural unit is designed with optimal space overhead.

Existing street light monitoring systems use vehicle-borne sensor platforms, LiDAR etc. which are obtrusive for in-the-wild deployments. In this paper, we propose BikeL; a crowd sensed system to monitor street lighting conditions in a novel approach using smartphone sensors during Bike navigation. We identify the underlying issues and challenges from pilot experiments to make the system phone-invariant, robust, and user-friendly. We used regression models and unsupervised clustering to resolve these issues. We have carried out extensive experiments under various road type illumination scenarios and phones type covering more than 400 km. Over 80 night trips collecting 10,000 functional light pole samples to tune the system parameters. Results show that the overall system successfully detects both functioning and non-functioning light poles with good accuracy (F1 score > 0.85) and can produce uniformly calibrated illumination levels. This viable, economical, and easy to deploy solution can work effectively for under-developed regions of low and middle-economy countries.

This work presents a software-centric visible light communication (VLC) system in full duplex mode with CSMA/CA and RTS-CTS in mobile environment. We focus on channel access mechanism problems in the same environment. To solve these we modify Distributed Coordination function (DCF) by adding ambient light measurement slot. The result shows modified DCF can handle the problems.