# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 3 Related Work
Within the smallest block, a neighbor of a failed node is chosen as the candidate node (CN) to replace the failed node. If more than one neighbor node is part of the smallest block, the neighbor nearest the failed actor is chosen to manage the block movement. The RNF aims to control existing route discovery activities in the network and enforce no extra pre-failure communication overhead. However, RNF consumes an additional power. Moreover, large number of nodes are involved in the recovery process which leads to more topology changes; thus, network is subject to extended failure causing a cascade recovery. Grid-based approaches are introduced in many WSANs. As one of the interesting grid-based approaches, an actor-supported repositioning approach supports the single static actor. Each grid consists of single static actor, in addition to several sensor nodes that sense the events. Static actors are responsible to obtain the information of sensors location along with grid information. Hence, the grid information collects the region information. Once an event occurs, the sensors nodes forward sensed data to the static actor node. The overhead of the approach increases when using single static actor node for multiple grid region monitoring [46]. This problem surges when multiple reporting regions instantaneously report to the same static node [14; 30; 47; 48; 49]. The distributed prioritized connectivity restoration algorithm (DPCRA) [11] is introduced to cover the partitions and reinstate the node connectivity by using small number of nodes. The algorithm aims to identify the negative effect of the actor on the partitions. Repairing processes are done locally while storing minimum information in each node. The main focus of the work is to use multiple backup nodes for the partitioned recovery. Nevertheless, the algorithm fails to address backup node selection criteria which leads that those nodes may have higher probability of failure. Thus, this can affect the overall network performance, especially energy consumption, which leads to higher probability of node failure throughout the network. The actor critical recovery (ACR) algorithm is proposed for efficient resource utilization [7]. The algorithm aims to minimize the delay and determine the primary backup node to satisfy application requirement. In this study, [PERSON] et al. proposed the Distributed Partition Detection and Recovery algorithm (DPDR) to handle cut-vertex node failure recovery. The main objective of the work is to minimize node movement distance during the recovery process. Cut-vertex node determination is done using Depth First Search (DFS). DPDR assigns a failure handler (FH) node for each cut-vertex. FH is responsible for the network recovery when the cut-vertex failure occur. FH chooses to replace the failed node with the node which has the closet distance to the failed node. The main drawbacks of DPDR are the involved communication and calculation overheads, and FH recovery assignment criteria. The Advanced-self-healing Connectivity Recovery Algorithm (ACRA) is introduced to recover failed actors. The ACRA determines the nature of the actor whether it is a cut vertex or node-connectivity actor [13]. This approach applies a depth-first search algorithm to determine the nature of the actor. For handling suddenly failed nodes, the minimal block backup nodes are used until the network is restored. The actor node with high transmission power and higher coverage area is selected and connectivity is recovered. This type of process consumes more energy since cluster head and actor node has to be selected in case of their absence in the network. The algorithm is based on two point crossover Genetic Algorithm (GA) to reconnect the partitioned network. Sensor and actor nodes are scattered randomly in the area of interest and form clusters. In this network, all the nodes are equipped with a failure detection system and are able to detect the failure of cut-vertex actor nodes by using the shared stored information of their 1-hop neighbors.