We consider a wireless network setup with devices uniformly distributed in the square region. We implement multiple sources scenarios to evaluate the performance of the proposed scheme under heavy traffic load, congestion, long data queues, and high time delays. Moreover, the random waypoint mobility model implementation on each node incorporates the effect of topological changes on the performance of the proposed scheme introduced due to link breaks, active route time outs, and route errors, which makes the experimental scenario more practical to test the performance of the proposed algorithm. The theoretical expressions are plotted using Matlab, while simulations are performed using Omnet++ simulation software. EDS is implemented by modifying the source code of AODV available in Omnet++ library.
4.2. Results and Discussion
In this section, we present and discuss the results of the proposed algorithm for various network parameters and analyze the performance by comparing other route search methods.
In
Figure 8, we show the number of nodes in the disk with the number of iterations for low, medium, and high-density networks. In this setup, 100 nodes are uniformly distributed in the network area, with the source node in the center and the destination is chosen randomly.
Figure 8 shows that the number of nodes in nearby disks to the source is high, and this number decreases in proceeding disks for low-density networks. For medium density networks, maximum nodes lie in two and three disks, whereas for high-density networks, the maximum nodes are accommodated in five to six disks.
Figure 8 further shows that a higher value of TTL is required to accommodate maximum nodes in the disk when node density increases.
In
Figure 9, we show the Commutative Distribution Function (CDF) of the probability to find the destination with respect to the number of iterations or number of attempts made by the source node. The graph shows that the probability of finding the destination increases with an increase in the number of iterations. With the increase in the number of iterations, the search area around the source node increases, and more nodes are accommodated in the disk, increasing the probability of finding the destination.
Figure 9 shows that the probability of finding the destination in the case of EDS increases at a higher rate as compared to ERS. For instance, ten iterations are needed to locate the destination in ERS, but EDS can locate the destination in eight iterations. Hence, locating the destination in fewer iterations saves waiting time as well as reduces cost.
In
Figure 10, we show the cost of a single search query in the destination search process with respect to the number of iterations.
Figure 10 shows that the cost of EDS is slightly higher than ERS. However, (
2) demonstrates that the average search cost of EDS is lower as compared to ERS due to the high probability of finding the destination.
Figure 11 further elaborates the point where we plot the ratio of probability to find the destination and cost of the search for ERS and EDS algorithms. As the slope of the line is greater than one, it implies the probability of finding the destination in case of EDS increases at a higher rate than the increase in cost.
Figure 10 further illustrates that the slope of the line decreases as node density in the network increases. This trend is observed because the exponential increase in cost overcomes the beneficial probability factor when node density is high. Therefore, the performance of EDS is almost the same as ERS if node density is high.
In
Figure 12, we show the expected time to find the destination with respect to the number of iterations. The x-axis represents the threshold value of TTL chosen by the source node while initializing the route discovery process. In other words, it represents the number of attempts made by the source node to search the destination node by expanding the search area before it decides to initiates a broadcast packet.
Figure 12 shows that the expected time to locate the destination in the case of EDS is lower than ERS. The proposed solution accommodates more nodes in the search area as compared to the ERS algorithm. As a result, the probability of finding the destination increases, reducing the expected time to find the destination. Furthermore, in
Figure 13, we show the expected time to find the destination for low, medium, and high-density networks.
Figure 13 shows that the expected time to locate the destination is short for low-density networks. When the number of nodes in the network increases, the average number of hops between the source and the destination increases, which increases the expected time to find the destination.
Figure 14 shows the expected broadcast cost with respect to the number of iterations or number of attempts before broadcast. It can be observed from the figure that the expected broadcast cost of EDS and ERS is almost the same at a low value of iterations. If the destination is not far away from the source node, the number of nodes in the ring and disks is almost the same; thus, broadcast cost is the same at a low value of iterations. However, when the distance between the source and the destination increases, the expected broadcast cost of EDS is much lower than ERS. The expected broadcast cost follows a decreasing trend up to five to six iterations. It then increases with an increase in iterations, which shows an optimum value of the number of attempts that provides the minimum cost.
Figure 14 also shows that the source node can achieve the lowest cost if it chooses a threshold value between four and six number of attempts. Increasing the threshold above six increases the cost of failure, which results in high expected broadcast cost.
Figure 15 shows a comparison of the expected time to locate the destination for three variants of the proposed EDS.
Figure 15 compares the expected time to locate the destination for EDS-S, EDS-L, and EDS-R where time latency of route discovery increases with the number of iterations.
Figure 15 further shows that the expected time to find the destination of EDS-S is lower than EDS-L and EDS-R for a high number of iterations. On the other hand, at a low number of iterations, the destination search time of EDS-R is less than the other two variants. The time latency of all the techniques is almost the same at low TTL values, but the performance of EDS-S is better than the other two when the distance between the source and the destination is relatively high.
In
Figure 16, we show the expected broadcast cost of EDS-S, EDS-L, and EDS-R. It is clear that the broadcast cost initially decreases up to some iteration then follows an increasing trend. Initially, when the disk size is small, the number of packets relayed in the network and the probability of finding the destination in the disk are not high enough. When the source node increases its TTL value or equivalently the disk area, the probability of finding the destination increases, which reduces the number of network-wide broadcasts, resulting in a decrease in the overall search cost. Furthermore, an increase in the number of iterations increases the cost of expanding search procedure, which sometimes exceeds the network-wide broadcast cost. Thus, there is an optimal value for the number of iterations between low and high values that provides minimum route search cost. Moreover,
Figure 15 and
Figure 16 show that there is a trade-off between expected broadcast cost and expected time cost for the three techniques. The expected time cost of EDS-S is lower than the other two, but its expected broadcast cost is high. Similarly, EDS-R performs well in terms of expected broadcast cost, but its expected time to locate the destination is higher than EDS-S. Therefore, we can conclude that EDS-S is a suitable choice when reduced time latency is a major requirement. Similarly, EDS-R is appropriate to implement in situations where nodes do not have enough energy resources, such as IoT networks in 5G.
Figure 17 shows the throughput comparison of ERS and EDS algorithms when implemented on the AODV routing protocol.
Figure 17 shows that the proposed EDS technique outperforms the conventional ERS scheme; when the mobility of nodes increases, throughput decreases. The rate of decrease in throughput is much lower when EDS is used as a destination search algorithm. The EDS algorithm searches the destination in a lower number of attempts, which reduces time delays and length of the queue at the nodes, resulting in high throughput. Furthermore,
Figure 18 shows the time to search the destination of ERS and EDS techniques with respect to node mobility.
Figure 18 shows that the time to find the destination of EDS is lower than the ERS algorithm. However, the time to find the destination increases with an increase in node mobility; when a node moves at high speed, the number of disconnections between nodes increases, increasing the time to find the destination. Nevertheless, EDS uses expanding disk-like pattern to search the destination, decreasing search time compared to ERS.
Table 5 summarizes performance comparison of the proposed method with Expanding Ring Search (ERS), Distribute Weighted Clustering Algorithm (DWCA), and Cluster Head based Modified-Blocking Expanding Ring Search (CMBERS+) algorithms. The comparison shows that the proposed search method outperforms previously proposed schemes in terms of high network throughput and less route latency.