## 6 Simulation Results and Discussions
The reason behind this decreased energy consumption with increasing time is that nodes fail to find optimal relay nodes because of reduction in network density. As a result, chances of cooperative routing being performed by any source node are reduced which reduces energy consumption and most of the data is directly transmitted to the MSs or next hop node only. The proposed scheme achieves a much better energy management in spite of performing cooperative routing which involves high energy consumption. Figure 12 shows throughput of proposed and compared protocols. Throughput of DBR and EEDBR is almost constant after 100 s. This constant value is due to quick fall in network density. As network density decreases, nodes fail to find optimal forwarders for data forwarding due to which throughput attains a constant value. This behavior is unsuitable for both delay-tolerant and delay-sensitive applications. CoDBR attains high throughput due to cooperatively forwarding the data packets. Throughput of iAMCTD, DEADS-SRC and DEADS-MRC demonstrate the amount of threshold-optimized data obtained from reactive networks. In these protocols, unnecessary data transmission is avoided. In iAMCTD, throughput is high because of the selection of most optimal  
forwarder. This optimal forwarder is the one that possesses highest residual energy, least depth and depth difference (between source and next-hop node) and best link state at any particular time instant. Hence probability of data packet being corrupted by link impairments are reduced and high throughput is achieved. The proposed model for data routing attains optimal results with linear sink mobility as compared to elliptical sink mobility. Although, in DEADS-SRC and DEADS-MRC, only forwarder node's depth and residual energy are considered as selection parameters represented by Equations (11) and (15); however, improved results are achieved as compared to iAMCTD due to implementation of cooperative routing. If data received via direct route (source to \(DS\) node) is corrupted, it can be received successfully via an alternate route (source to \(CC\) to \(DS\) node). Along with this, implementation of MRC (diversity combining technique) at \(DS\) nodes further improves SNR of received data which results in high throughput. The highest throughput is achieved DEADS-SRC and DEADS-MRC with linear mobility pattern of MSs. Hence, this particular mathematical model performs the best with linear mobility pattern of MSs. Figure 13 show packet drop. High packet drop of DBR, EEDBR and iAMCTD is due to the availability of single link with poor link state most of the time. In CoDBR, packet drop is lesser because of cooperative routing. Although, iAMCTD considers link state of potential forwarder node before their selection, even then, the presence of redundant links as well as implementation of MRC (diversity combining technique) gives improved results and makes DEADS-SRC and DEADS-MRC more reliable. In CoDBR, DEADS-SRC and DEADS-MRC, packets are dropped only when no link (direct link to the \(DS\) node or via \(CC\) to \(DS\) node) is available for data forwarding or SNR of combined signal received at \(DS\) node (source to \(DS\) node, source to \(CC\) to \(DS\) node) is below an acceptable limit. In CoDBR, multiple transmissions of same data packet are performed before it is received by static sink. Effect of link impairments is high because of these multiple transmissions and as a result packet drop is high. Packet drop is higher in DEADS-SRC and DEADS-MRC with elliptical mobility pattern as compared to linear mobility pattern of MSs. This is because the proposed model is not optimized for elliptical mobility pattern. Whenever the condition(s) specified by \(H_{th}\) and \(S_{th}\) is met, packets are generated by nodes. Sink only accepts those packets that possess an acceptable SNR value and drops the remaining packets.