# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 2 Contribution and Paper Organization  
The main difference between WSAN and WSN is the existence of actor nodes. Actor nodes add major enhancements and robustness to the sensor network. In WSNs detection, processing and handling are usually done using sensor nodes which make those node subject to failure and degrades the network performance, while in WSANs, sensor nodes detect events and forward their information to actor nodes. Then, the actor nodes handle the processing of information, decision making and event handling. Thus, the actor node is a major component of WSAN. The failure of an actor node can degrade the overall network performance. Furthermore, the failure of an actor node may result in a partitioning of the WSAN and may limit event detection and handling. In this paper, a new method is proposed for efficient actor recovery paradigm (EAR) which guarantees the contention-free traffic-forwarding capacity. Unlike previous studies, EAR aims to provide efficient failure detection and recovery mechanism while maintaining the Quality of service. Thus, the proposed paradigm contributes three novel algorithms and optimized low latency deterministic model based on RSSI work as follows:  
* Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the nodes types and distinguish critical nodes. The NMCND algorithm checks the entire network to determine the critical node during the network life time and pre-assign a backup node for each critical node; so in case the failure of critical node, this node takes place in order to improve and balances the network performance.
* Optimized RSSI model is introduced that selects the different power strengths for each beacon in order to ensure the proper delivery of the beacon to each node. This aims to reduce the latency and estimating the prediction of the node energy-level. As a result, QoS provisioning is maintained and extended the network lifetime.
* Network Integration and Message Forwarding (NIMF) improves the QoS by handling the packet forwarding process. NIMF works to reduce packet forwarding through critical nodes and enhances network lifetime. Moreover, NIMF has the capability to decide the source of the forwarded packet which enhances the packet forwarding flow. Thus, accurate packet forwarding process reduces the latency and bandwidth consumption.
* Priority-Based Routing for Node Failure Avoidance algorithm (PRNFA) handles the routing process. PRNFA analyzes and evaluates the information of the packet in order to route it to the next node. It determines the priority of the forwarded packets. In addition, PRNFA eliminates redundant data prior to routing the packets.  
The remainder of the paper is organized as follows: Section 3 presents a comprehensive review of relevant literature. Section 4 formulates the problem and provides details of the optimized deterministic actor recovery system model. Section 5 provides the simulation setup and presents the analysis of our findings. Finally, the summary of the paper is provided by the conclusions in Section 5.