Enhanced Low-Power, Low-Cost, High Accuracy Smart Parking Solution for Urban Areas

United Nations projects that 70% of the global population will be living in urban areas by 2050. This will further exacerbate the already challenging issue of urban parking, where it is currently estimated that 45% of total traffic congestion is due to drivers looking for parking. In our prior work, we have proposed a cross-sensor-based urban parking solution consisting of a smart parking server (SPS) and smart parking units (SPU). Each SPU utilizes a magnetometer sensor and an LPWA connectivity module. This was accomplished by configuring multiple thresholds in each of the SPUs, such as occupancy threshold, adjacency threshold, opposite threshold, etc. corresponding to various automobile makes/models. While these thresholds helped significantly in the accurate determination of parking spot occupancy, to be able to support various automobile makes and models including electrical vehicles (EVs) that tend to have lower ferrous content in their chassis, based on our further analysis we have come to realize that these configuring various thresholds accurately is challenging. The configuration of appropriate thresholds for occupancy and adjacent thresholds is critical in achieving high accuracy. To reduce the complexity of being able to determine and configure appropriate thresholds that could be sensitive to various automobile makes and models, and to be able to handle all practical parking events - we have developed an enhanced framework that demonstrated higher accuracy while dramatically reducing the sensitivity to different automobile makes and models incl. EVs. In this enhanced approach, each SPU is configured with only a single threshold T. T is chosen to be less than the change that would be caused by a vehicle with the lowest ferrous content being parked in an adjacent spot. The interference from a car parking in an adjacent spot is much greater than most environmental fluctuations, allowing for T to be between these two values, just high enough to capture anything that could correspond to a parking event. The built-in redundancy of the system will enable the other SPUs around to “correct” it as they would not detect any change greater than T. Whenever an SPU's reading changes by some Δx > T within a specified duration Δt, it sends Δx to the SPS. All computation and deduction can be done server-side, as will be illustrated with various caseworks, enabling each SPU to be extremely simple. Because no additional processing is needed at the SPU, thereby reducing power consumption and lowering the cost, the proposed approach is much more efficient than current methods involving onboard filtering, processing, and sensor-level determination. The built-in redundancy of the system can help lessen the effects of an SPU malfunctioning. This enhanced framework also enables the magnetometer to be extremely low power because it only requires a minimal sampling rate to check every Δt seconds. Rather than analyzing a complex function for each parking event, it only needs to look at the overall change (displacement) in magnitude over duration Δt. This greatly reduces the power required and helps prevent false readings from possible momentary spikes in magnetic flux.