Constraint Optimization Model for Dynamic Parking Space Allocation.

Managing car parking systems is a complex process because multiple constraints must be considered; these include organizational and operational constraints. In this paper, a constraint optimization model for dynamic parking space allocation is introduced. An ad hoc algorithm is proposed, presented, and explained to achieve the goal of our proposed model. This paper makes research contributions by providing an intelligent prioritization mechanism, considering user schedule shifts and parking constraints, and assigning suitable parking slots based on a dynamic distribution. The proposed model is implemented to demonstrate the applicability of our approach. A benchmark is constructed based on well-defined metrics to validate our proposed model and the results achieved.

An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases.

Objective: Chronic lung-related diseases, with asthma being the most prominent example, characterized by diverse symptoms and triggers, present significant challenges in disease management and prediction of exacerbations across patients. This research aimed to devise a practical solution by introducing a personalized alert system tailored to individual lung function and environmental conditions, offering a holistic approach for the management of a range of chronic respiratory conditions. Methods: In response to these challenges, we developed a personalized alert system based on individual lung function tests conducted in diverse environmental conditions, as determined by air-quality sensors. Our research was substantiated through an observational pilot study involving twelve healthy participants. These participants were exposed to varying air quality, temperature, and humidity conditions, and their lung function, as indicated by peak expiratory flow (PEF) values, was monitored. Results: The study revealed pronounced variability in pulmonary responses across different environments. Leveraging these findings, we proposed a design of a personalized alarm system that monitors air quality in real-time and issues alerts under potentially unfavorable environmental conditions. Additionally, we investigated the use of basic machine learning techniques to predict PEF values in these varied environmental settings. Discussion: The proposed system offers a proactive approach for individuals, particularly those with asthma, to actively manage their respiratory health. By providing real-time monitoring and personalized alerts, it aims to minimize exposure to potential asthma triggers. Ultimately, our system seeks to empower individuals with the tools for timely intervention, potentially reducing discomfort and enhancing management of asthma symptoms.

A Smart Real-Time Parking Control and Monitoring System.

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle's license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.

Source Anonymity in WSNs against Global Adversary Utilizing Low Transmission Rates with Delay Constraints.

Wireless sensor networks (WSN) are deployed for many applications such as tracking and monitoring of endangered species, military applications, etc. which require anonymity of the origin, known as Source Location Privacy (SLP). The aim in SLP is to prevent unauthorized observers from tracing the source of a real event by analyzing the traffic in the network. Previous approaches to SLP such as Fortified Anonymous Communication Protocol (FACP) employ transmission of real or fake packets in every time slot, which is inefficient. To overcome this shortcoming, we developed three different techniques presented in this paper. Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD) were developed to overcome the anonymity problem against a global adversary (which has the capability of analyzing and monitoring the entire network). Most of the current techniques try to prevent the adversary from perceiving the location and time of the real event whereas our proposed techniques confuse the adversary about the existence of the real event by introducing low rate fake messages, which subsequently lead to location and time privacy. Simulation results demonstrate that the proposed techniques provide reasonable delivery ratio, delay, and overhead of a real event's packets while keeping a high level of anonymity. Three different analysis models are conducted to verify the performance of our techniques. A visualization of the simulation data is performed to confirm anonymity. Further, neural network models are developed to ensure that the introduced techniques preserve SLP. Finally, a steganography model based on probability is implemented to prove the anonymity of the techniques.