## 3 Related Works
### Swarm Intelligence (SI)-Based Approaches
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**IoT-Based Technique/Approach** & **Applications** & **Feature/Focus of Work** & **Ref.** \\ \hline A stochastic optimization scheme to improve waste recovery and collection processes & Health, entertainment, education, and urban living & Reduction of operational costs, energy dissipation, transportation and pollution emissions & [23] \\ \hline A novel algorithm providing effective and scalable solutions for dynamic waste collection & Health, education, urban living, gas stations, and factories & Optimizing routing functionalities for several trucks & [24] \\ \hline Application of data envelopment analysis (DEA) to quantify and benchmark waste generation intensity & Machine, energy, internet networks, logistics, mobile devices, and monitoring & Real-time benchmarking analysis of waste monitoring to realize waste reduction potentials on the floor of a manufacturing shop & [25] \\ \hline IoT-based technologies (Android application, cloud, GSM/GPRS, sensors, etc.) & Workplace, GSM, logistics, sensors, surveillance, GPS, RFID, and urban living & Providing efficient waste collection solutions and economic feasibility and obtaining real-time information about the status of dustbins & [26; 27; 28; 29; 30] \\ \hline A cloud-based advanced decision support system (DSSS) for dynamic scheduling and routing models & Automation, RFID, monitoring, sensors, and surveillance cameras & Efficient waste collection and dynamic route optimization in SCs & [31; 32; 33; 34] \\ \hline Simulation-based approach using load cell sensors & Load cell sensors, GPS, GSM, and Web applications & Determining the maximum waste load capacity inside waste bins & [35] \\ \hline A robust counter model of an open capacitated vehicle routing problem & Workplace & Determining the optimal routes for garbage transportation & [36] \\ \hline IoT-based approach for real-time data collection for waste management in an SC & Urban living, GPRS, RFID tags, and volumetric ultrasonic sensor & Optimizing waste collection routes and methods to minimize environmental hazards & [37] \\ \hline IoT-based automated machine learning approach & Raspberry Pi, GPRS, ultrasonic sensors, and urban environments & Real-time waste monitoring and collection and optimization of time costs and operational efficiency & [38; 39; 40; 41] \\ \hline Application of big data analysis to vehicle routing using GPS data & GPS and big data & Optimizing vehicle routing and waste collection & [42] \\ \hline A smart dual dustbin model using ultrasonic \& Itlarsonic sensors, IR obstacle sensors, \& IcHSVM, Servo, and Arduino & Efficient waste mgt. with advanced dynamic routing and optimized routes & [43] \\ \hline A Smart (IoT-Based) dustbin model using IR sensors, GSM, and monitoring systems & IR sensors, GSM, and monitoring systems & Garbage monitoring and control & [44] \\ \hline IoT-based smart garbage monitoring and clearance alert system using RGB LED, ultrasonic sensors, GSM, GPS, and Wi-Fi & LED lights, sensors, GSM, Arduino, GPS, and Wi-Fi & Garbage-level monitoring for disposal, clearance, routing optimization; detecting the max. capacity level of garbage & [45; 46] \\ \hline Application of a lab-scale landfill model & Sensors and LM35 temp. sensors & Monitoring and processing of food waste & [47] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous works on IoT-based strategies for waste management in an SC. SI algorithms to handle optimization problems. A comprehensive survey of nature-inspired and SI-based algorithms for wireless sensor networks is provided in [52; 53]. The authors of [54] presented a review of SI-based algorithms for IoT-based solutions. Their study aimed to introduce a new level of intelligence to IoT-based systems, providing an understanding of some technical aspect of SI algorithms and their potential applications in IoT.