# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 5 Simulation Setup and Experimental Results  
There are two processes running throughout the network's deployment and monitoring, the underlying process obtains individual node properties while the second monitors the network consistency. Our goal is to prolong the network lifetime while maintaining the minimum overhead and determining the nodes' failure causes. We have implemented and simulated efficient actor recovery protocol over wireless sensors and actor networks. The simulation is conducted on OMNET++ simulator. The size of the network is 1400 \(\times\) 1400 square meters. Nodes are deployed randomly in the network. The main objective of simulation is to determine the performance of the proposed EAR algorithm in order to ensure the effectiveness of the protocol in presence of QoS parameters, energy efficiency when incident of node failure occurs. In addition, the performance of proposed EAR algorithm is compared with known similar type of schemes such as RNF, DPCRA, ACR, and ACRA. RNF, DPCRA, ACR, and ACRA are state-of-the-art actor failure recovery algorithms. Detailed description was provided in Section 3. The proposed algorithms manages cut-vertex actor failure and recovery while they differ in their selection and objective obtained while recovery as given in Table 1. The similar parameters including properties have been used for testing purposes. The simulation scenario consists of 400 nodes including 27-54 actor nodes and 173-356 sensor nodes with a transmission range of 70 m. The sensor/actor nodes are arbitrarily deployed in a mesh fashion. The initial energy of the actor nodes is set 20 J and sensor nodes have 4 J. The bandwidth of the actor node is 4 Mbps, and maximum power consumption of the sensor/actor node for receiving and transmitting the data is set to 13.5 Mw and 15.0 Mw respectively. Sensing and idle modes have 12.4 mW and 0.60 mW, respectively. The total simulation time is 36 min that is enough to determine the effectiveness of the proposed versus stat-of-the-art schemes. However, the simulation time could also be minimized or maximized, and the pause time is 20 s set to warm up the nodes before beginning of the simulation. The results demonstrate presented here are the average of 10 simulation runs. The simulation parameters are summed up in Table 2.  
The simulation consists of three simulation scenarios that replicate the real wireless sensors and actor wireless sensor network environment. The obtained simulation results are equitably significant and indistinguishable to realistic tentative results. * Scenario-I: Sensors-to-actor communications. In this scenario, the source nodes are set as the sensors, while the destination nodes are set as actors. The multiple connections are setup with one actor. Thus, the actor node acts as the sink of the communication. There is 86.5%:13.5% ration of sensor-to-actor, and 20% mobile sensor nodes are set. In this scenario, we used different sizes of the network; 1000 \(\times\) 1000 m\({}^{2}\), 1200 \(\times\) 1200 m\({}^{2}\) and 1400 \(\times\) 1400 m\({}^{2}\). * Scenario-II: Actor-to-actor communications. In this scenario, the distance between the two actors is 300 m. The distance is covered by less than 4 hops. This scenario involves multi-hop communication among the actors. In this scenario, a maximum 54 actors are used. * Scenario-III: Actor-to-sensor communications. In this scenario, communication is done between actors and sensor. The distance between actor and sensor is set to 250 m. The number of hops are 5 and mobility of the nodes is 20% in this communication. There is 13.5%:86.5% ration of sensor-to-actor, and 20% mobile sensor nodes are set. In this scenario, we used different sizes of the network; 1000 \(\times\) 1000 m\({}^{2}\), 1200 \(\times\) 1200 m\({}^{2}\) and 1400 \(\times\) 1400 m\({}^{2}\). 15-70 connections are set up among the nodes. The connections start working randomly during the warm up time. The source and destination nodes are randomly chosen in each scenario.