# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 3 Related Work
On the other hand, a study conducted by [PERSON] et al. [40], used 1-hop message exchange to identify the critical actors. This is performed by calculating the distance from the actor to its adjacent nodes. If the distance is less than the neighbor's communication range, the actor is defined as non-critical; otherwise, the actor is defined as critical. The Distributed Recovery from Network Partitioning in Movable (DRNPM) [40] and Actor Positioning with Minimal Movement (APMM) [7], in Wireless Sensor and Actor Networks approaches were introduced for node recovery. These studies applied pre-assigned backup procedures to recover the actor node failure. However, the approaches fall short in addressing the energy efficiency during backup node selection. In [35] a two-hop actor node failure recovery algorithm is introduced in which, an adjacent actor and best candidate are selected to handle the failure node. However, this approach suffers due to the overhead. [PERSON] et al. [36] introduced Nearest Non-critical Neighbor (NNN) algorithm that attempts to achieve inter-actor connectivity caused by network splitting. The proposed algorithm uses a localized and distributed approach. When the neighbors of a critical actor detect the failed actor, they initiate the reinstatement process. This process consists of the replacement of the critical node with the nearest non-critical actor to control any further splitting overhead. The overhead may occur when a critical actor movement is selected for node replacement. Distinguishing between critical and non-critical actor nodes favors the NNN procedure to have slighter recovery scope in comparison to [35]. However, the network is adversely affected due to spilt transposition overhead. A new energy efficient recovery method for recovery of lost connectivity (RLC) based on two-point crossover genetic algorithm (GA) is proposed to reconnect the partitioned network to discover the actor node failure process [41]. A study that handles the tolerating simultaneous failures (TSF) in WSANs is proposed by [42]. In this study, TSF is based on the ranking of the network nodes pertinent to a pre-assigned root actor. Ranking uses a tree that helps the coordination process among the nodes. The nodes are virtually grouped in order to minimize the recovery overhead. Cluster-based node failure algorithms were proposed by [43; 44] that are based on 1-hop node failure recovery process. Furthermore, the route duration improvement (RDI) algorithm based on decision tree is incorporated in the reactive routing protocol to support WSANs [45]. This aims to select the most long-lived routers. The decision tree supports the node mobility. The improved flooding control is used to improve the route performance and reduce the overheads, whereas, power consumption caused by control packets is not handled. Similarly, recovering from node failure (RNF) based on the Least-Disruptive Topology Repair (LeDiR) algorithm is used to handle actor failure/recovery. The RNF handles the autonomous transposition for the subset of the actor nodes to restore connectivity [10]. The LeDiR algorithm depends on the local view of the actor node to develop a recovery plan that rearranges the least number of nodes and confirms that no path between any pair of nodes is protracted. In addition, LeDiR attempts to detect and manage cut-vertex node failure and perform recovery using path discovery and routing information. In the case of node failure in LeDiR, neighboring nodes will re-compute their routing tables and drive their enrollment decisions for the recovery process. In response to the failure of a critical node, the neighbor containing the smallest block replaces this node. LeDiR assumes that each node calculates the shortest path to every other node and stores this information in its routing table. In case node failure occurs, the 1-hop neighbors identify if the node is critical or non-critical using the shortest path routing table. Then, the smallest block is identified.