# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 1 Introduction  
Wireless Sensor and Actor Networks collaborate to transmit and process information in order to perform specific tasks. WSANs comprise sensors, actors, and a base station [1] as shown in Figure 1. The sensors are responsible for sensing and transmitting the events to the actor node. In response, the actors collect, process, transmit and utilize the data, due to their high performance capability and power properties [2, 3]. The communication in WSANs can be classified as: (i) sensor-actor (SA) communication; (ii) actor-actor (AA) communication; (iii) actor-sink (AS) communication and (iv) sensor to sensor (SS) communication. WSANs can further be classified into semi-automated or automated WSANs. In semi-automated WSANs, sensor nodes sense the events and forward the data to the sink node, while the sink node forward the sensed data to the proper actor in-order perform the specific action. WSANs are potentially suitable for several applications including transportation, battlefield surveillance, industry, health, environmental monitoring, smart energy grids, cloud computing, and nuclear fields. WSANs performance is affected by several factors, such as energy efficiency, transmission media, scalability, and node density. The selection of the parametersfor optimization depends on the nature of the application. WSANs have an edge over the traditional Wireless Sensor Networks (WSNs) due to the high energy and low power consumption characteristics. In WSANs, the actor nodes possess sophisticated features that increase the power capability and network usage. Thus, maintaining the inter-actor connectivity is indispensable in WSANs. The failure of an actor may cause loss of communication or a network disconnect. Thus, actors must communicate with each other to guarantee the entire network coverage and to harmonize their actions for the best response. In case of actor failure, adjacent actors should restore the process or they may be replaced by a backup actor. This solution, however, could be costly and infeasible. Alternatively, the actor can be replaced by one of its neighbor actors. Distributed localization algorithms are used to handle these situations. In this case, \"hello\" messages are exchanged by the neighbor actors in distributed localization algorithms despite the limitation of the network resources. Furthermore, the nodes can compute their distances and positions by exchanging the information carried in these messages. The distributed localization techniques are used for calculating the distance between the nodes, identifying the physical locations of actor nodes, reallocating the locations, and salvage of sensor or actor node failure. Received Signal Strength Indicator (RSSI) is a range base localization technique that is applicable for determining the received signal strength including the distance between the sender and the receiver nodes. At the receiver side, RSSI technique is used to measure the signal strength [4]. Higher levels of RSSI indicate stronger signals. Some localization approaches utilize the RSSI for nodes distance measurement. In WSANs, most of the node localization and routing approaches depend on the hop count information rather than table-based routing protocols. With these motivations, this study introduces a mathematical model for determining actor forwarding capacity in WSAN using RSSI message information. The model aims at guaranteeing contention-free forwarding capacity. The paper also contributes to the literature by providing the best RSSI value to improve the traffic forwarding process. State-of-the-art research is used to provide the best node failure recovery process to extend the network lifetime. A tradeoff between the QoS improvement and reduction in the power consumption is considered. The paper introduces three novel algorithms and optimized low latency deterministic model based on RSSI.