## 3 Experiments, Comparative Investigations and Results
Additionally, the network configuration sets a mobility model for the sensor nodes in the prescribed region. The real-time deployment for this type of sensor network (300 sensor nodes with mobility features around a 1000 m\({}^{2}\) area) provides enough challenges for data transmissions, energy harvesting schemes, channel management and route establishment tasks. NS-3.0 is a network scenario creator with simulated configurations of WSNs. The network scenario has been assumed with the heterogeneous nature of sensor nodes where the internal components of nodes vary in terms of energy, transmission range and mobility constraints. In this regard, node characteristics such as initial energy (joules), transmission energy (joules), receiving energy (joules) and node coverage abilities (meters) are differently configured for each sensor node in the network (Table 6). Table 6 illustrates the implementation details of WSNs in the NS-3.0 environment. Consequently, the supportive packages of Python and C++ were used to implement the existing techniques. The performance metrics for evaluating the exiting techniques illustrated in Table 4 (E1, E2, E3, E4, E5, E6, E7 and E8) are average energy consumption rate (joules), liveliness rate, successful data delivery rate, number of retransmissions (count), computational overhead (\(\%\)), routing delay (milliseconds), scheduling time (milliseconds), packet drops due to downtime (count) and energy optimization rate. The definition of each performance metric is given as follows:  
* Average energy consumption rate (AACR): The average amount of joules spent by a sensor node throughout data transmission, collection and idle listening modes. * Liveliness rate: The availability rate of active appearance (data transmission, collection and idle listening) made by each sensor node against expected lifetime. * Successful data delivery rate (SDDR): The ratio between the quantity of packets delivered successfully by a sensor node against the packets dropped by the node. Figure 3.1 E7-Cooperative feedback trust values for energy-efficient routing principles. * Number of retransmissions: Total number of packets retransmitted by a sensor node during a simulation cycle. * Computational overhead: The excessive amount of packets (control messages, retransmitted data and other recovery messages) processed against the average number of network packets processed. * Routing delay: The time taken by the routing protocol to find the optimal route and deliver the data to the destination. * Scheduling time: Time taken by WMAC scheduler to make the unique channel period for sending the data through the multiple access medium. * Packet drops due to downtime (PDD): Number of packets dropped by an inactive node due to failure. * Energy optimization rate (EOR): The ratio between the amount of joules spent using the energy optimization policies and the amount of joules spent without using optimization policies. The existing works are investigated under variable constraints as given in Figure 3.2 According to that base, the routing protocols are chosen as AODV and AODV-link-state (LS) models. Similarly, the traffic characteristics, mobility, coverage and energy levels of sensor nodes are configured as variable at different sensor nodes. In addition, the signaling models configured in this experiment are built with the functionalities of both electromagnetic and acoustic nature (underwater/underground). The experiment starts with the performance validations of the existing techniques (Table 4) using the metric AECR of each sensor node. In this experiment, the cross-layered techniques (WMAC and routing) consume minimal AECR compared with other single-layered solutions. In this concern, the observation has been conducted with the minimal AECR of E3, E4 and E5. The existing works E3, E4 and E5 consider the impactful factors of medium, power limitations and equilibrium in load distribution.