Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.
Urban areas worldwide are in the race to become smarter, and the Kingdom of Saudi Arabia (KSA) is no exception. Many of these have envisaged a chance to establish devoted municipal access networks to assist all kinds of city administration and preserve services needing data connectivity. Organizations unanimously concentrate on sustainability issues with key features of general trends, particularly the combination of the 3Rs (reduce waste, reuse and recycle resources). This paper demonstrates how the incorporation of the Internet of Things (IoT) with data access networks, geographic information systems and combinatorial optimization can contribute to enhancing cities' administration systems. A waste-gathering approach based on supplying smart bins is introduced by using an IoT prototype embedded with sensors, which can read and convey bin volume data over the Internet. However, from another perspective, the population and residents' attitudes directly affect the control of the waste management system. The conventional waste collection system does not cover all areas in the city. It works based on a planned scheme that is implemented by the authorized organization focused on specific popular and formal areas. The conventional system cannot observe a real-time update of the bin status to recognize whether the waste level condition is 'full,' 'not full,' or 'empty.' This paper uses IoT in the container and trucks that secure the overflow and separation of waste. Waste source locations and population density influence the volume of waste generation, especially waste food, as it has the highest amount of waste generation. The open public area and the small space location problems are solved by proposing different truck sizes based on the waste type. Each container is used for one type of waste, such as food, plastic and others, and uses the optimization algorithm to calculate and find the optimal route toward the full waste container. In this work, the situations in KSA are evaluated, and relevant aspects are explored. Issues relating to the sustainability of organic waste management are conceptually analyzed. A genetic-based optimization algorithm for waste collection transportation enhances the performance of waste-gathering truck management. The selected routes based on the volume status and free spaces of the smart bins are the most effective through those obtainable towards the urgent smart bin targets. The proposed system outperforms other systems by reducing the number of locations and smart bins that have to be visited by 46% for all waste types, whereas the conventional and existing systems have to visit all locations every day, resulting in high cost and consumption time.