Wireless sensor networks (WSNs) are becoming widely used worldwide. Wireless Sensor and Actor Networks (WSANs) represent a special category of WSNs wherein actors and sensors collaborate to perform specific tasks. WSANs have become one of the most preeminent emerging type of WSNs. Sensors with nodes having limited power resources are responsible for sensing and transmitting events to actor nodes. Actors are high-performance nodes equipped with rich resources that have the ability to collect, process, transmit data and perform various actions. WSANs have a unique architecture that distinguishes them from WSNs. Due to the characteristics of WSANs, numerous challenges arise. Determining the importance of factors usually depends on the application requirements. The actor nodes are the spine of WSANs that collaborate to perform the specific tasks in an unsubstantiated and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power fatigue of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. It is essential to keep inter-actor connectivity in order to insure network connectivity. Thus, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). For network recovery process from actor node failure, optimal re-localization and coordination techniques should take place. In this work, we propose an efficient actor recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balances the network performance. The packet is handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets (Either from actor or sensor). This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, we compare the performance of our proposed work with state-of the art localization algorithms. Our experimental results show superior performance in regards to network life, residual energy, reliability, sensor/ actor recovery time and data recovery
