# Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection
## 5 Results  
In this section, some of the results that will show the importance/strength of our proposed method of route selection for the garbage collection is presented. The typical value of each parameter has been used for simulations and is given as follows unless stated otherwise. The no. of bins is represented by \(n\), which is chosen from the set \(\{8,12,16,20\}\). The total number of point-to-point paths is represented by \(e\), which is kept at twice the value of \(n\) in the figure from Figures 3-6. The value of toxicity \(\
u\) ranges from 0-1, and the volume of the bin \(V\) ranges from 600-1200 litres. The distance of the point-to-point path  
Figure 2: Sample snippet from simulation with starting node 1 and ending node 7.  
ranges from 10 to 200 m, and the time elapsed \(t\) for garbage in the bin ranges from 1-100 h. The vehicle capacity for the waste collection is set to 5000 L.  
Figure 4: Accumulated waste time in bin before being collected vs. no. of bins. Figure 3: Accumulated toxicity level of collected waste vs. no. of bins. Figure 5: Accumulated volume of collected waste vs. no. of bins. Figure 6: Total distance traveled from start to the dump vs. no. of bins. It can be seen from Figure 3 that the proposed TOPSIS-based route selection method approaches the result of the algorithm that is solely based on toxicity. The volume- and the time-based algorithms perform worst in terms of accumulated toxicity value as they are aimed at maximizing the corresponding parameters. The proposed TOPSIS-based algorithm considers all parameters, enabling it to select a route that balances performance across the considered parameters. Figure 3 illustrates this consistent pattern across various bin values denoted by \(n\). Similar to Figure 3, it can be seen in Figure 4 that the proposed scheme performs better in terms of collecting the waste that has seen more time in the bin. The purple bar will be maximum, as it is an upper bound on the waste collected based on time. The time-based algorithm is designed to maximize the waste collected based on time, and hence, it will give a lower score in the other parameters, as can be seen from Figure 3. Similarly, the trend is followed for various values of \(n\). The data depicted in Figure 5 illustrate a consistent pattern across all algorithms, except for the volume-based algorithm, which attains the maximum threshold of 5000. This value represents the vehicle's capacity limit. The volume threshold is a factor in all algorithms, resulting in the proximity of all outcomes to the 5000 threshold. However, only the volume-based algorithm effectively reaches and approaches the 5000 value. Finally, as evident from Figure 6,Figure 8: Accumulated waste time in bin before being collected vs. total no. of one-to-one paths between the bins. Figure 7: Accumulated toxicity level of collected waste vs. total no. of one-to-one paths between the bins. A similar trend can be seen in Figure 9, where it can be seen that the proposed algorithm performs at par with the volume-based algorithm. and secondly, as the number of routes are increased, the overall combined volume is increased. Figure 10: Total distance traveled from start to the dump vs. total no. of one-to-one paths between the bins. Figure 9: Accumulated volume of collected waste vs. total no. of one-to-one paths between the bins. Figure 10 shows the distance traveled by each of the schemes for collecting waste according to their desired parameter, and it can be seen that the proposed TOPSIS-based algorithm performs better than all of the other schemes as it gives the lowest distance among all. This figure shows the power of the TOPSIS-based algorithm, similar to Figure 6, as it shows that in a shorter distance, it can collect waste that is better in terms of all the parameters i.e., volume, toxicity, and time.