Hybrid connectivity restoration in wireless sensor and actor networks
 Ke Yan^{1},
 Guangchun Luo^{1}Email authorView ORCID ID profile,
 Ling Tian^{1},
 Qi Jia^{1} and
 Chengzong Peng^{1}
https://doi.org/10.1186/s1363801709214
© The Author(s) 2017
Received: 12 May 2017
Accepted: 25 July 2017
Published: 8 August 2017
Abstract
Wireless sensor and actor networks are becoming more and more popular in the recent years. Each WSAN consists of numerous sensors and a few actors working collaboratively to carry out specific tasks. Unfortunately, actors are prone to failure due to harsh deployment environments and constrained power, which may break network connectivity resulting in disjoint components. Thus, maintaining the connectivity among actors is especially important. This paper proposes hybrid connectivity restoration (HCR), which integrates proactive selection and reactive motion. An actor protectively selects a backup node through its onehop neighbor table and informs the backup node to supervise its stage. Once it fails, the backup node moves to the best position to restore the connectivity of the failed node’s neighbors reactively. This triggers a local recovery process at the backup node, which is repeated until network connectivity is restored. In order to minimize travel distance, HCR selects the backup node which moves the shortest distance to restore connectivity. Furthermore, HCR opts to reduce the number of messages by just informing the failure to its backup node. The correctness and effectiveness of HCR are validated through both theoretical analysis and simulations.
Keywords
1 Introduction
Wireless sensor networks (WSNs) are indispensable components of Internet of Things [5–7, 9–11, 15, 19, 23–25, 32, 37, 38]. A wireless sensor and actor network (WSAN) is a special kind of WSN, which has motivated lots of research works [28]. In the corresponding applications such as environmental monitoring, battlefield surveillance, border protection, target searching and tracking, a number of sensors and actors work cooperatively to monitor a specific area and track a target of interest. Sensors are responsible for collecting data, and actors are responsible for processing data and bridging the sensors and the control center. An actor and the sensors connected to it form a selforganized subnetwork. All the subnetworks collaborate with each other to carry out tasks. It is desired that all the actors in a WSAN are connected at any time.
Unfortunately, due to harsh deployed environments and limited battery power, actors may deplete energy fast. A sudden loss of a node may break network connectivity resulting in disjoint network components. Therefore, it is important to detect node failures and restore network connectivity as early as possible. Since WSANs are usually deployed far away from the control center and are operated autonomously and unattended, it is difficult and inefficient to control the restoring process in a centralized manner. Connectivity restoration therefore should be a distributed, localized, and selfhealing process. In addition, a rapid connectivity restoration is desired in order to reduce the baneful influence of node failures. Moreover, the overhead such as the total travel distance and the total number of messages should be minimized considering the limited energy supply. The average travel distance should be considered as well because one node traveling too far will consume too much energy and may cause another network disconnection. A node failure disrupting network connectivity is called a cut vertex, which is difficult to identify in largescale WSANs centralized and timely. Though there have been many distributed cutvertex detection algorithms, they are timeconsuming and resourceintensive. As a result, it is very challenging to restore network connectivity in a distributed, localized, and efficient manner.
This paper proposes hybrid connectivity restoration (HCR) considering singlenode failures, which integrates proactive selection and reactive motion. The selection of a backup node for a failure node is a proactive process. Each node identifies a backup node and is then monitored by its backup node. Once a node fails, its backup node moves to the best position that connects all the failed node’s onehop neighbors. It triggers the restoration of the backup node and the restoration is a reactive process. HCR is a distributed and localized scheme, where each node just maintains its onehop neighborhood information. Since a failed node only affects its direct neighbors’ connectivity, the main idea of the restoration is to move one of the failed node’s neighbors to a new position so that all the failed node’s direct neighbors can be reconnected. The node motion may trigger another disconnection on the moving node, so the restoration is a recursive process, the whole network is connected only when the motion node’s directed neighbors are connected. HCR opts to efficiently restore network connectivity through selecting the most proper backup node and moving it to the best position instead of the failed node’s position. The less distance the node moves, the less influence on the network connection. As aforementioned, only if a node failure breaks its directed neighborhood connectivity, it may further break network connectivity. The node whose failure breaks its directed neighborhood connectivity is called a critical node. On the opposite, the node is called an uncritical node. It should be noted that the uncritical node cannot be a cut vertex while the critical node may be a cut vertex. Though a critical node’s failure may not necessarily break network connectivity, it may bring unnecessary restoration. It is much more efficient and cheaper to identify a critical node and restore its direct neighbors’ connectivity than to identify a cut vertex and restore the network connectivity. To identify a cut vertex requires global information, which is impossible and inefficient in WSANs. At the same time, to identify a critical node just needs onehop neighborhood information, and the identification is done on the node itself. Moreover, compared with moving a backup node to the failed node’s position in DCR [17], it is better to move the backup node to the position which connects all the failed node’s directed neighbors. Only when all the neighbors are on the boundary of the communication range, the backup node needs to move to the failed node’s position.

. We proposed a hybrid connectivity restoration in WSAN, which integrates proactive selection and reactive motion. The proactive selection of a backup node can shorten a recovery process, the HCR offers effectiveness and timeliness.

. Different from moving a backup node to the position of failed node, HCR moves the backup node to the best position with the shortest travel distance to reconnect the failed node’s onehop neighbors. This can not only reduce a motion cost but also the total overhead, as the shorter a node travels, the fewer nodes are influenced.

. HCR opts to reduce the number of messages by just informing the failure to its backup node.

. The efficiency of HCR is mathematically analyzed and validated through simulations. The bounds on the incurred overhead are derived. HCR outperforms RIM and DCR in terms of the number of relocated nodes, total travel distance, average travel distance, and number of messages for both dense and sparse networks.
The remainder of the paper is organized as follows. Section 2 describes the system model and formulates the problem. Section 3 reviews the related works. Section 4 provides the detailed description of the proposed HCR, and the theoretical analysis is illustrated in Section 5. The simulation results are presented in Section 6. Finally, the conclusions and future work are presented in Section 7.
2 System model and problem formulation
2.1 System model
In WSANs, each actor has limited communication radius R _{ c }. Actors can send and receive messages within the communication range to discover other actors. Each actor maintains a onehop neighborhood table recording its neighbors’ positions and other information. Twohop neighborhood information or multihop neighborhood information can be obtained by exchanging onehop neighborhood table with neighbors. The more information it gets, the more communication and storage it incurs. However, in most WSAN applications, actors are battery powered and have limited energy. Thus, it is more efficient to maintain less information. In this paper, each actor i just maintains a onehop neighborhood table denoted as NT(i). NT(i) is a twodimensional table where each row contains onehop neighbor information such as unique node ID (ID), local position (POSITION), and critical character (CRITICAL). The critical character is defined as follows.
Critical character: It indicates whether a node failure breaks neighbor connectivity. CRITICAL=1 when the node failure breaks connectivity. Otherwise, CRITICAL=0.
In order to restore connectivity rapidly, each actor also has backup node information. The selection of a backup node is introduced in Section 4.1. The onehop neighborhood table and backup node information of each actor are maintained and updated during the process of connectivity restoration.
2.2 Problem formulation
Actors are prone to failures due to tough environments or energy depletion. The loss of an actor affects not only the sensors connected to it but also the neighboring actors. The latter case is even worse, so this paper focuses on the latter case. It is assumed that actors are movable, it is possible to restore network connectivity by relocating actors.
Though reconnecting two sibling nodes just needs to move one node for less than Rc, it may trigger another failure, which causes cascading failures. Even if the motion does not trigger other node failures, it may consume a lot of energy. Hence, load balancing should also be taken into consideration. In addition, a node motion may generate some other critical nodes, which increases the risk of node failure in the future. Considering all of these factors, we formulate the following problem.
Given a connected WSAN G=(V,R _{ c }). V is the set of actors, and R _{ c } is the actors’s communication radius. Each actor V _{ i } has ID and Positions. All the actors are homogeneous with the same R _{ c }. Each node just knows its onehop neighborhood information. When a single actor i fails, relocate the rest nodes so that (1) network connectivity is restored, (2) the total travel distance is minimized, (3) the average travel distance is minimized, and (4) the total number of messages is minimized.
HCR is proposed in Section 4 to solve this problem efficiently. It is assumed that no two or more actors fail simultaneously, and no node fails during restoration.
3 Related works
Connectivity restoration considering singlenode failures has attracted much attention, and there are a lot of surveys [22, 27, 34, 36] focusing on connectivity restoration. The existing schemes can be classified into two categories: proactive restoration and reactive restoration.
Proactive restoration schemes make use of redundant resources including nodes and paths to increase the robustness of a WSAN. When a node fails, it requires no connectivity restoration because there are redundant resources maintaining connectivity. Since the directional connection between a pair of actors is determined by the communication radius, it can only result in more redundant relay nodes to build Kdisjoint paths [30]. In this case, there are Kdisjoint paths between any pair of actors in a WSAN. Even K1 paths fail, there is still a path connecting them. Consider a twoconnected WSAN where there are at least two paths between any pair of nodes. CRAFT [18] establishes a biconnected interpartition topology while minimizing the longest path length and the number of deployed relay nodes. It strives to form the largest inner simple cycle or Backbone Polygon (BP) around the center of the damaged area where no partition lies inside, and deploys relay nodes to connect each outer partition to the BP through two nonoverlapping paths. The advantage of proactive restoration schemes is that it does not disturb a network when node fails, but it requires many redundant resources. The stronger fault tolerance is, the more resources are required. Moreover, it is very difficult to place relay nodes optically, as it needs the global network information. The time complexity is very high in largescale networks. In [20], it is proven that just listing a set of feasible sites for the relays is already at least APXhard. Though many heuristic algorithms have been proposed, such as Genetic Algorithm [12, 21], Artificial Bee Colony Algorithm [14], and Concentric Fermat Points [31], it is still extraordinarily timeconsuming.
Different from the proactive restoration schemes, the reactive restoration schemes are passive and a recovery process is triggered when a node failure is detected. They do not require reserving redundant resources. The basic idea is to reconnect the failed node’s neighbors. There are two kinds of approaches: cooperative communication and relocating nodes. Cooperative communication is first proposed in [8]. It allows a node to send message beyond its communication radius with the help of its neighbors. Two nodes are able to communicate if and only if the received average signaltonoise ratio (SNR) is no lees than the fixed threshold. Signal strength diminishes with the increase of transmission distance and overlays at the destination. CSFR [33] adopts cooperative communication to restore connectivity. Taking advantage of neighbors to transport data does not increase the neighbor’s energy consumption a lot. Though it has low overhead from the current perspective, it is still a longterm process which costs a lot of energy in the long run. In addition, it is unacceptable and very timeconsuming to select the help nodes.
Currently, most reactive restoration schemes reconnect a network by replacing a failure node with a proper backup node through movement which is a recursive process that may relocate the rest of the nodes. Therefore, which node moves and where to move is nontrivial. Ramezani proposed a distribute method to restore connectivity by using a centralized genetic algorithm [26] at the basic station. It strives to minimize the number of mobile nodes and the average length of all nodes’ paths. It is a heuristic algorithm. As mentioned before, only a cut vertex may break network connectivity. Many approaches decide whether a node is a cut vertex firstly and deal with cut vertex failure only, such as DARA [1], PDARA [2], PCR [16], and NNN [13]. DARA identifies a cut vertex through twohop neighborhood information. Once a failure happens, the failed node’s neighbors select the most proper backup node considering node degree and distance and inform its sibling nodes. In fact, the process of identifying a cut vertex is not introduced in details in DARA. In a latter improved approach PDARA, it forms a connected dominating set (CDS). PDARA informs a particular node in advance whether a partition occurs in case of failure. They both strive to localize the scope of the recovery process and minimize the movement overhead imposed on the involved actors. In nearest noncritical neighbor (NNN), each actor periodically determines its criticality (i.e., cut vertex or not). In addition, they both maintain twohop neighborhood information. In order to minimize the message overhead, DCR [17] identifies the critical nodes with onehop neighborhood information, and the restoration is similar to DARA’s.
Since cut vertex identification incurs significant overhead in terms of messaging and state maintenance, RIM [35] does not distinguish the importance of nodes. All the onehop neighbors move towards the position of the failed node till the distance is “ R _{ c }/2”. Other nodes perform a cascade inward movement to connect to the connected network when they cannot communicate with the moved nodes. RIM is simple and efficient, especially for sparse networks. But the performance degrades for dense networks. RIM involves too many unnecessary motions, especially the first step. As all the failed node’s neighbors do not know each other, they all move to the position which is “ R _{ c }/2” far from the failed node to maintain neighbor connectivity even when they are within half of the communication radius. These may incur outward motions. In addition, the message overhead is very high.
The above methods mainly focus on the moving distance and message overhead. In fact, network lifetime is the most important factor which depends on energy efficiency and load balancing. Abdelmalek [4] proposed a twophase restoration algorithm. It searches the redundant nodes using the cluster heads, then restores connectivity, and energy consumption is taken into consideration. CoRF [3] is another connectivity restoration algorithm that strives to increase network lifetime. It selects a backup node according to the fuzzy logic rules. In addition, there are some realistic connectivity restoration methods [29] that take obstacles or terrain elevation into consideration. The direct path movement may be impossible, or is not optimally energyefficient. In summary, energy efficiency and load balancing are the important evaluation metrics for the connectivity restoration algorithms.
4 Hybrid connectivity restoration algorithm
In this section, a hybrid connectivity restoration (HCR) algorithm is proposed to restore connectivity in WSANs. HCR is a distributed, localized, and efficient approach aiming at minimizing the cost of moving nodes. Instead of identifying a cut vertex, HCR just identifies the critical nodes. Each node maintains a onehop neighborhood table including unique node ID (ID), local position (POSITION) and critical character (CRITICAL). HCR combines proactive backup node selection and reactive cascade node motion.
4.1 Proactive backup node selection
In order to minimize the number of messages and shorten restoration time, each node will select a backup node from its neighbors before a node failure occurs. During the initialization of a onehop neighborhood table, each node sends a broadcast message containing its (ID) and (POSITION). All the nodes in its communication range will receive the message.
After a round of information exchanging, each node will determine whether it is critical through its onehop neighborhood table NT. A node is an uncritical node if and only if all its onehop neighbors form a connected network. In Fig. 2, node A3 has three onehop neighbors A1, A2, and A4. They form two disjoint components {A1, A2} and {A4}. So A3 is a critical node. Similarly, A4, A5, A6, and A7 are critical nodes. While node A8 is an uncritical node for all its onehop neighbors A5, A7, A9 form a connected network {A5, A7, A9}, so as node A9. Node A10 is a leaf node and just has one onehop neighbor A7, so it is also an uncritical node. It is worth mentioning that a critical node’s failure will not necessarily divide a network, e.g., {A5, A6, A7}. However, an uncritical node’s failure must not break network connectivity. Therefore, a backup node needs to be selected only for a critical node.
Theorem 1
BestPosition for node i must be the intersection of communication circle of any two nodes in NT(i) or the intersection of the line ij and communication circle of node j.
Proof
First, the intersection of a pair of nodes’ communication boundary in NT(i) must connect these pair nodes. If this intersection is within the rest sibling nodes’ communication range, this node can replace the failure node to connect all the failure node’s neighbors. But the intersection may not be the optimal solution. Actually, the intersection is optimal only when the moved node is beyond the communication range of the pair of nodes. As shown in Fig. 4 b, node C is out of the communication range of A and B. The intersection C _{ new } should be the optimal position. But in Fig. 4 a, node C is within the communication range of A. Node I is the intersection of A’s and B’s communication boundary. Node I can connect A and B, node C _{ new } can also connect A and B, and \(d_{C,C_{new}} < d_{C,I}\). \(d_{C,C_{new}} + d_{C_{new},B} < d_{C,I} +\) d _{ I,B } for Line Axiom, where \(d_{C_{new},B} = d_{I,B} = R_{c}\). Thus I is not the optimal position, but node C _{ new } is. □
Lemma 1
The node failure cost is no more than the nearestneighbor distance in HCR.
Proof
By Theorem 1, BestPosition must be the intersection of any pair of nodes’ communication boundary in NT(i) or the intersection of the two nodes connection line and one node’s communication boundary. As shown in Fig. 4, node C _{ new } is BestPosition. In Fig. 4 a, ∠ CC _{ new } F must be an obtuse angle; otherwise, FC _{ new } will be the tangent line of circle B, and node F will be out of the circle. As ∠ CC _{ new } F is an obtuse angle, \(d_{C,C_{NEW}} \leq d_{C,F}\), so as Fig. 4 b. Since each node’s best position moving cost is less than its distance to the failure node, the node failure cost is no more than the nearestneighbor distance. □
The previous approaches such as DARA and DCR all move the backup node to the position of the failure node, then the failure cost is equal to the nearestneighbor distance in the best case. So HCR outperforms DARA and DCR in terms of travel distance in a motion. The shorter distance it moves, the smaller impacts on its neighbors’ connectivity.
If there are two or more neighbor nodes with the same moving costs to restore the network connectivity, the one with the smallest failure cost will be selected as the backup node for the next restoration loop with the smallest moving cost. After selecting the backup node and obtaining BestPosition, it will send a broadcast message containing the information of the backup node and BestPosition. After a round of broadcasting, each node will update its backup node and BestPosition again and inform the changes to its new backup node only. By now, proactive backup node selection is finished. Then the node starts to send heartbeat messages to the backup node periodically to declare that it is functional. Once the backup node does not receive the heartbeat message within a period, it will start reactive cascade node motion to restore connectivity.
4.2 Reactive cascade node motion
As mentioned before, only a critical node will break network connectivity, so when a critical node i fails, its backup node j will detect the failure the first time and trigger reactive cascading node motion. Before the backup node j moves to BestPosition j ^{′}, it will update its onehop neighborhood table NT(j). It replaces the failure node’s position by j ^{′}, and checks whether its onehop neighbors’ connectivity is broken. If broken, node j will be the failure node and the restoration will be triggered. Node j will update its backup node k and BestPosition k ^{′}. Node k is selected from its rest neighbors except for the failed node i in order to avoid falling into an infinite loop. The backup node k will move to BestPosition k ^{′} to connect all its neighbors in NT(j) and j ^{′}. After selecting the backup node k and BestPosition k ^{′}, node j will send a message to backup node k about BestPosition k ^{′} it should move to and this will trigger a new round motion of node k. Node j will move to j ^{′} to replace the failure node i. This process will be repeated recursively until the failure node’s neighbors are connected.
The pseudocode for HCR is shown in Algorithm 1. HCR is a hybrid method that selects a backup node for each actor before it fails. At the beginning of network construction, each node will broadcast a message to notify its position within its communication range, and records its neighbors’ ID and Position to build onehop neighborhood table NEIGH_TABLE (line 3). Then each node will identify whether it is critical through its NEIGH_TABLE. For an uncritical node, its failure will not break connectivity, so failed_cost=0 (line 8). While for a critical node, it will select a backup node and BestPosition (line 6). The algorithm for backup selection is detailed in Algorithm 2. When a node just has two neighbors, move any one towards another has the same travel distance to restore connectivity. For this case, the one with smaller failed_cost is chosen (line 1116), this is to insure the next restoration has a smaller moving cost. By now, the initialization and proactive backup selection are done. Every actor sends a message to notify its backup node to monitor its status and BestPosition. When a node detects a failure of its neighbors, it will trigger the cascading node motion (line 1825). The backup node will add BestPosition into its NEIGH_TABLE to tell whether it is critical. If so, it will select its new backup node before moving to replace the failed node (line 21). Otherwise, network connectivity is restored.
The backup node selection is the key part of HCR and the pseudocode is presented in Algorithm 2. It is worth mentioning that all the selection of backup node is done on the failed node or moved node and it just explores NEIGH_TABLE. During this process, it does not need to send message to other nodes. This reduces the message overhead. According to Theorem 1, BestPosition must be the intersection of any pair of nodes’ communication boundary or the intersection of the two nodes connection line and one node’s communication boundary. Firstly, for each node i in NEIGH_TABLE, compute its Candidate_Set (line 613). Then remove the position that is out of the rest nodes’ communication range (line 1421). Afterwards, choose the closest point in Candidate_Set as BestPosition for node i. Finally, return the node’s ID and BestPosition which has the minimum travel_dist in NEIGH_TABLE.
5 Algorithm analysis
HCR combines proactive and reactive methods to handle network connectivity restoration from a singlenode failure in WSANs. The selection of a backup node is proactive, while the restoration is reactive. This scheme shortens the restoration process and reduces the overhead including distance cost and message cost. Next, the performance of HCR is analyzed.
First and foremost, network connectivity after a singlenode failure is restored. It is assumed that no other node fails during the restoration process and any two nodes can communicate with each other directly if they are R _{ c } apart or closer. Then network connectivity is not weakened and no new critical node is introduced during the restoration. In addition, load balancing is taken into consideration and no node travels too far while others too close. The overhead of communication and the complexity of computing are also analyzed. We introduce the following theorems.
Theorem 2
HCR restores network connectivity after a singlenode failure.
Proof
Uncritical node failure will not break network connectivity since all its neighbors are connected when it is removed from the network. HCR identifies a critical node at the initialization time and selects a backup node and decides BestPosition. When a critical node fails, it will trigger the restoration, and its backup node moves to BestPosition to reconnect all its onehop neighbors. In the following cascading node motion, the moved node will reconnect its sibling nodes until all its siblings are connected. In order to avoid an endless loop, each node can only move once during the restoration, and a moved node will not be selected as a backup node in the future. This can guarantee that HCR terminates in limited number of steps. □
Theorem 3
The total number of critical nodes does not increase.
Proof
Theorem 4
The maximum distance a node travels in HCR is the communication range R _{ c }.
Proof
In HCR, a backup node is one of the failed node’s neighbors, and it moves to BestPosition to reconnect all its siblings. In the worst case, a backup node moves to the position of the failed node, and it must connect all its siblings. That is the relocated scheme in DCR [17]. It has been proven that the maximum distance a node travels in DCR is the communication range R _{ c }. Lemma 1 proves that HCR outperforms DCR in terms of travel distance in one motion.
Theorem 5
The shorter distance a node travels, the fewer nodes are affected. The probability of a node affected by its moving neighbor is (\(1\frac {2\theta \sin {2\theta }}{\pi } \)), where θ= arccosd/2R _{ c }, d is the travel distance, and R _{ c } is the communication range. It approximately equals 0.62 times of d/R _{ c }.
Proof
Theorem 6
The time complexity of the backup node and BestPosition selection is O(n ^{3}), where n is the number of failed node’s onehop neighbors.
Proof
It has been proven that BestPosition must be the intersection of any pair of nodes’ communication boundary in NT(i) or the intersection of two nodes connection line and one node’s communication boundary in Theorem 1. For each node, the intersection of any pair of its sibling nodes’ circle is computed firstly, and the time complexity is O(n ^{2}). Then the intersection of a line that connects this node to its sibling node and the sibling node circle is obtained, and the time complexity is O(n). Finally, check whether the intersection is within the communication range of all the sibling nodes and find the minimum travel distance. The time complexity is also O(n ^{2}). Therefore, the time complexity of the best position selection for each node is O(n ^{2}+n+n ^{2}), that is O(n ^{2}). Then the node with the minimum traveling distance will be selected as the backup node, and its best position will be BestPosition. Hence, the time complexity of the backup node and BestPosition selection is O(n ^{3}). □
Theorem 7
The total message complexity of HCR is O(N), where N is the number of actors.
Proof
The selection of a backup node and BestPosition is done at the failed or moved nodes in HCR. It just maintains onehop neighborhood table for each node. In addition, the failed node only sends a message to its backup node about its movement. In the worst case, there are N−2 nodes moving. Each moved node sends a movement message to its backup node. Therefore, the total message complexity of HCR is O(N), where N is the number of actors. It is worth noting that the exchange with neighbors at a new position does not count in HCR, and it is considered as a part of status update for maintaining onehop neighborhood table. □
Theorem 8
The time it takes HCR to restore network connectivity is proportional to N and R _{ c }, where N is the number of actors and R _{ c } is the communication range.
Proof
Firstly, HCR proactively selects a backup node and BestPosition before a node failure, so when a node fails, the backup node will move to BestPosition. This will trigger cascading motion. For each moved node, it will select its new backup node and BestPosition before its moving and send a message to notify its backup node to move to BestPosition. According to Theorem 7, the time complexity is O(n ^{3}) where n is the number of its neighbors. Usually, n is very small compared with the total number of nodes N, so the computing time can be ignored. In the worst case, there are N−2 nodes moving, and each node moves at most R _{ c }, so the total time it takes HCR to restore network connectivity is (N−2)×R _{ c }, which does not exceed (N×R _{ c }). □
6 Simulation results
Extensive simulations have been conducted to evaluate the performance of the proposed HCR compared with the previous algorithms RIM and DCR. The simulation settings and performance metrics are introduced in Section 6.1, and the detailed results and analysis are presented in Section 6.2.
6.1 Simulation settings and performance metrics

Number of relocated nodes: It reports the average number of relocated nodes during a singlenode failure restoration. This metric assesses the scope of connectivity restoration within a network.

Total distance moved: It reports the total distance that the involved nodes move during the restoration. This metric assesses the efficiency of the restoration methods from the standpoint of a network.

Average travel distance: It depicts the average distance that the involved nodes travel during the restoration. This metric assesses the efficiency standing in the perspective of a node.

Number of messages: It captures the total number of messages sent among the nodes during the restoration. This metric assesses the communication overhead of the restoration methods.

Number of deployed nodes (DN): It reports the number of the deployed nodes in an area. Since the area is 1000 m ×1000 m, this parameter actually represents the density of a WSAN. The larger the DN, the bigger the node density, indicating a stronger network connectivity. In a rather highly connected WSAN, a node has many onehop neighbors, and it is easier to choose a backup node and BestPosition. It will increase the time of selecting at the same time.

Communication range (R _{ c }): It is assumed that all the actors are homogeneous with the same communication range and a pair of nodes can communicate with each other when they are within each other’s communication range. R _{ c } also affects network density DN. Small R _{ c } will generate a rather sparse network, while large R _{ c } will increase network connectivity. This will also increase the travel distance of the involved nodes during the restoration under HCR.
6.2 The overall results
Overall simulation results
DN  RC  Number of relocated nodes  Total moved distance  Number of messages  Average travel distance  

RIM  DCR  HCR  RIM  DCR  HCR  RIM  DCR  HCR  RIM  DCR  HCR  
10  20  2.5000  0.8667  0.7667  9.4481  11.7579  4.0453  11.2667  0.8000  0.6000  3.7173  6.4224  2.3910 
40  2.5333  1.1667  1.2000  22.3878  34.1477  14.3315  10.3333  1.1333  1.2000  8.5230  16.8824  6.6039  
60  2.7000  1.2667  1.2000  34.3570  54.9098  20.9049  11.3333  1.2667  1.1333  11.8734  27.7736  11.4697  
80  3.1667  1.2000  1.1000  54.5616  68.3552  25.6540  15.0000  1.2667  1.0667  16.9988  30.8971  12.3777  
100  2.8667  0.5667  0.8667  66.9697  41.0099  26.1766  13.0000  0.2000  0.8000  21.3045  33.2997  13.1408  
120  2.7333  0.9333  1.0333  71.1571  80.9414  35.6322  12.6000  0.8000  1.0000  27.5052  44.9467  17.3013  
20  20  2.6667  1.1500  1.0167  10.1203  15.8547  5.7045  12.9334  1.1333  0.8667  3.6459  8.0768  3.2332 
40  2.9500  2.2167  1.2000  24.3734  64.1113  14.8084  13.3000  3.0000  0.9667  8.0881  20.7230  8.5739  
60  3.0333  1.8667  1.2000  38.7232  78.6362  22.8659  14.4000  2.4000  1.0667  11.9809  27.1874  12.7959  
80  3.6500  1.0000  0.8500  60.9370  57.9764  19.7107  20.6000  0.9000  0.6000  15.7861  31.1569  13.0692  
100  3.7833  0.6833  0.8000  80.4082  47.1660  22.1815  21.8000  0.4000  0.6333  21.5298  33.2313  13.5711  
120  3.7667  0.8833  0.8000  101.2945  72.8401  21.3106  22.7333  0.7333  0.5667  25.8660  42.7639  13.5360  
40  20  3.0615  2.7385  1.1308  11.9039  37.9415  6.3112  15.3077  4.0923  0.8769  3.7377  9.4159  3.6941 
40  3.2154  2.2077  1.1615  25.6699  61.7150  11.2814  16.4154  3.0308  0.9385  7.7238  19.2907  6.6423  
60  3.7000  1.3154  1.0923  49.2846  58.5885  20.8707  20.4000  1.4154  0.9692  13.1892  26.9827  10.9955  
80  4.0923  0.8077  0.9154  69.0521  42.6712  18.4933  25.7077  0.5077  0.7231  16.7857  28.9037  10.8412  
100  4.3077  0.9231  0.9000  91.6229  61.6710  19.9848  27.9231  0.7846  0.7385  20.0957  33.9756  11.3504  
120  4.6385  0.5615  0.6385  113.5523  45.2215  20.8300  35.2000  0.3385  0.4923  24.4107  30.6194  12.0742  
60  20  3.4300  3.4300  1.4600  13.7143  47.4912  8.3110  17.1500  5.1700  1.2300  3.9234  11.2197  4.6324 
40  3.7250  2.2900  1.2100  30.3220  66.0921  12.9398  20.1000  3.1700  1.0100  7.9336  20.0784  7.1632  
60  4.5600  1.3700  1.1050  59.5532  59.2711  17.5455  28.9300  1.5200  0.9900  12.7306  25.7413  9.1642  
80  4.6250  1.2200  1.0150  76.5166  67.7799  21.1872  30.6400  1.2200  0.8100  16.7286  32.0698  12.1174  
100  5.7300  0.5450  0.6150  117.2685  34.8617  14.5538  44.9600  0.2800  0.4200  20.5859  24.7201  9.4990  
120  6.8150  0.4950  0.5450  167.6632  39.0700  16.8272  67.3100  0.2500  0.3500  24.6528  28.8812  11.4855  
80  20  3.0808  4.1846  1.3654  11.8660  59.3107  7.6236  15.0539  6.8077  1.1692  3.8286  10.6448  4.0656 
40  3.7692  1.3269  1.1769  31.2300  37.8081  13.7661  20.8461  1.3538  1.0538  8.0497  18.1350  7.2869  
60  4.3308  0.9231  0.8731  53.2154  36.9789  13.5749  29.1923  0.8000  0.7000  12.1273  20.0345  7.9371  
80  5.1346  0.6885  0.8231  86.9678  36.2184  15.8676  38.2769  0.3308  0.6000  17.1423  26.8037  9.7161  
100  5.7808  0.6192  0.7385  118.1022  41.5090  15.8088  46.8615  0.3385  0.5769  20.1898  29.5200  9.1799  
120  6.3769  0.5077  0.5846  155.5264  38.9712  14.9357  58.1000  0.2923  0.4462  23.9728  27.1466  9.5740  
100  20  3.3333  2.4485  1.2576  13.0345  33.3660  6.4384  17.0909  3.3879  1.0061  3.7086  10.0852  3.6431 
40  3.9000  1.4879  1.0394  32.7427  41.4879  11.4844  22.4606  1.7030  0.8061  8.2931  17.3802  6.9119  
60  5.0667  0.8970  0.8152  63.4889  37.4344  13.0444  35.6909  0.8000  0.6364  12.2978  20.0826  7.8285  
80  5.8394  0.6909  0.7545  98.9894  36.4659  14.7784  48.1454  0.4121  0.5394  16.8274  24.5749  9.2227  
100  6.8515  0.6061  0.6970  140.9332  39.3920  15.3282  66.2667  0.3697  0.5515  20.7653  26.3370  8.7655  
120  8.4636  0.4000  0.5182  201.1593  29.5398  13.5253  92.2545  0.1333  0.3697  23.6561  23.8265  8.8305 
6.2.1 Number of relocated nodes
Moreover, Fig. 9 a shows that HCR outperforms DCR when the communication range is less than 72, and has the similar results when the communication range grows. This is because of the candidate selection. HCR will choose a node to move the least distance to BestPosition and to reconnect the failed node’s neighbors, while DCR chooses the backup node by critical, degree, and distance. Furthermore, DCR moves the backup node to the failed node, and the failed node is not at BestPosition most of the time. When R _{ c } is small, the network is sparse, and the choice of a backup node is too limited, so HCD outperforms DCR. When R _{ c } is large, the network is dense, and it is likely to choose an uncritical node to restore network connectivity, so DCR can get the same result with HCR. Figure 9 b shows that DN has little impact on HCR and DCR in terms of relocated nodes when R _{ c }=80. At the moment, the WSAN is dense for the communication range. Considering Fig. 9 a, b, we can see that R _{ c } is more influential than DN on the number of relocated nodes.
6.2.2 Total moved distance
In Fig. 10 a, the curve of HCR rises slowly when R _{ c }<60, then it remains stable. It decreases with the increase of DN in Fig. 10 b. When the communication range is small, the network is sparse and the failed node has limited neighbors to select, so HCR needs many nodes to move to restore network connectivity. Since each node just needs to move a little, the growth is slow and small. While when the communication range increases, many choices make a rapid convergence. It is the same for DCR. However, DCR moves the backup node to the failed node, while HCR moves the backup node to BestPosition. It has been proven in Lemma 1 and the simulation results also verify that HCR outperforms DCR in terms of total moved distance. Figure 10 b shows a decreasing of the total moved distance for HCR when increasing DN. This is because when R _{ c } is determined, the growing of DN will increase the choices, and moving a shorter distance is enough to restore network connectivity.
6.2.3 Average travel distance
As aforementioned, node motion is a high energyconsuming operation, and nodes are prone to be out of work due to energy depletion. A node failure will incur cascading motion and more energy consumptions creating a vicious spiral. So the average travel distance of the involved nodes is of great importance in assessing the connectivity restoration algorithms.
Figure 11 b shows very different results from Fig. 11 a. The average travel distance for each involved node almost remains unchanged with varied network size when R _{ c } is 80. This indicates that network size does not influence the average travel distance because each node moves at most R _{ c }/2 in RIM, while R _{ c } is fixed to be 80. Both the curves of HCR and DCR decrease when increasing the deployed nodes with R _{ c }=80. The WSAN is dense, thus the failed node has more neighbors which increases the probability of choosing the best replacement position of nodes. Due to moving the backup node to the position of the failed node, HCR moves less than DCR.
6.2.4 Number of messages
Figure 12 a, b indicates that HCR and DCR achieve similar message overhead when the network is dense. This is because the decision of restoration is made by the failed node. The communication is maintained just between the failed node and its backup node. Figure 12 b also shows that the number of deployed nodes will influence the messages when the communication range equals 80. It is also true for other communication range, and the detailed results are shown in Table 1. It should be noted that HCR outperforms DCR when the communication range is small. In Fig. 12 a, the curve of HCR is below that of DCR when the communication range is less than 60. That means HCR outperforms DCR when the network is sparse. It contributes to the motion strategy. HCR moves less distance than DCR so that it incurs less motion in cascading moving. In sparse networks, the backup node’s traveling distance has a great impact on the cascading moving. Therefore, HCR outperforms DCR since it moves less during a node’s motion.
In conclusion, HCR outperforms RIM and HCR in terms of the four evaluation metrics on all the aspects whether the network is sparse or dense because of its proactive backup node selection and reactive cascading node motion. Though the proactive backup node selection is time consuming, it is carried out before the node failure. This will improve the response time. During the reactive cascading node motion, the computing of BestPosition is complex and timeconsuming. While HCR is a distributed and localized method, the number of each node’s neighbors is very small in all kinds of applications, so the selection of BestPosition will not spend so much time, and the connectivity restoration process will be fast.
7 Conclusions
There is a growing interest in the applications of WSANs in the recent years. Due to the harsh employed environment and limited energy supply, WSAN is prone to be out of work, which may break network connectivity. In this paper, we investigate the problem of restoring network connectivity when a single node fails. A hybrid distributed, localized, and efficient connectivity restoration algorithm HCR is proposed to solve this problem through moving the backup node to BestPosition. Compared with the previous schemes, HCR performs a localized network analysis to identify critical nodes, and only a critical node’s failure triggers the restoration process. It is a compromised proposal between the cut vertex identification and nonidentification. It is effective and has low complexity.
The performance of HCR is analyzed mathematically and validated through simulations. The simulation results have confirmed the effectiveness of HCR in terms of all the evaluation metrics. More importantly, HCR is applicable to various network topologies, sparse or dense. The performance of HCR remains stable when varying network topology. Though a comprehensive network will increase the complexity of the selection of BestPosition, it is acceptable.
Though HCR is designed for restoring network connectivity after a singlenode failure, that means it can only deal with a singlenode failure at a time and handle the sequential node failures. It can be extended to hand multinode failures at a time by adding one more constraint that no two nodes share the same backup node. In addition, the investigated WSANs are twodimensional, and we plan to study threedimensional WSANs in the future. At the same time, coverage is another factor that can be taken into consideration in connectivity restoration, which is also our future research interest.
Declarations
Acknowledgements
This research work was partly supported by the Special Project on Youth Science and Technology Innovation Research Team of Sichuan Province, under grant No.(2015TD0002) and Science and Technology Innovation Seed Project of Sichuan Province, under grant No.(2017RZ0008). We give thanks for the insightful discussion and help of Aiguo Chen. Finally, we give great thanks to the anonymous reviewers for the their suggestions to improve the quality of this paper.
Funding
This research work was partly supported by the Special Project on Youth Science and Technology Innovation Research Team of Sichuan Province, under grant No. (2015TD0002) and Science and Technology Innovation Seed Project of Sichuan
Authors’ contributions
All authors contributed equally to this work. KY and GL contributed to the conception and design of the study. KY and LT contributed to the algorithms and simulation. KY, QJ and CP contributed to the analysis and interpretation of simulation data. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
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