## 3 Experiments, Comparative Investigations and Results
This work produced cross-layered packet analysis procedures, priority evaluation procedures, channel listening activities and flexible routing principles according to channel quality metrics. On this basis, this scheme used AODV  
Figure 4: E2-heuristic. protocol and QoS-MAC principles under a single point of concern. According to the system design, this work embedded a packet priority field in WMAC frames. This field was pointed with four priority levels, as shown in Figure 5.  
In this concern, E5 [50] and E6 [57] proposed load balanced sequence scheduling and a routing protocol, respectively, for reducing the sensor node's energy consumptions. In E5, each sensor node adaptively used the internal buffer to process the data on demand. In multi-hop communication, each sensor node is activated through aggressive scheduling-based MAC models to hold the time division multiple access (TDMA) slots. The even distribution of load among sensor nodes shall forward the channel data using minimal requirement margins. At the same time, these TDMA slots were occupied by multiple sensor data streams. Similarly, E5 proposed an energy-efficient collection tree protocol for low-powered WSNs. The protocol functions executed in each sensor node collected and distributed the data streams based on energy-sensitive tree-like paths. In particular, WSNs are categorized under a low-powered wireless personal area network standard due to their resource-limited environment. In this regard, both works suggested restricted channel allocation policies and low-powered routing mechanisms in WSNs. Notably, E6 compared energy-efficient collection tree protocol with lossy routing protocols and conventional routing protocols. Figure 6 illustrates the functions of E5. In addition, E7 [75] and E8 [79] produced notable solutions on energy-aware wireless routing protocols. Particularly, E7 generated trust value computation techniques using an average packet delivery ratio, route reply ratio, residual energy rate and number of retransmissions on the channel. In this scheme, local trust cost and global trust cost were computed for each sensor node. Local trust values of the sensor nodes were computed in the node itself. At the same time, global trust values were computed at border router points to optimize the data transmission rate. Under this trust evaluat  
Figure 5: E4-QoS and MAC and data priority analysis. Figure 3.0 E5-Aggressive TDMA scheduling and buffer management. sensor node formed a channel to avoid any discrepancies during the data transmission period. This practice reduced the number of retransmissions and packet drops on the channel. Thus, the cooperative routing methodology manages all active sensor nodes under trusted communities (Figure 7). At the end, E8 discussed the particulars of routing protocol issues, connectivity problems and energy control mechanisms in detail. In the experimental sections, this work compared the AODV routing protocol and stateless real-time routing protocol for ensuring energy optimization qualities. According to the technical observations, WMAC customization and channel allocation strategies [28], sleep and wake strategies [30], cross-layered implementations [37], priority-based channel allocation [39], balanced WMAC/routing solutions [50; 57] and adaptive routing models [75; 79] are observed for effective comparison practices. The diversified nature of the restricted choices from existing energy-saving policies provides the best understandings under experimental testbed conditions. On the execution of experimental study practices, this work was implemented using tools such as Network Simulator (NS-3.35) and the tool command platform. The scenario of the WSN is created with a maximum of 300 sensor nodes around the geographical area (1000 m \(\times\) 1000 m). Generally, NS-3.0 supports deploying more than 300 sensor nodes. At any rate, this experimental survey sets an assumption of 300 sensor nodes around a 1000 m\({}^{2}\) area, which is significant in terms of network population.