1. Introduction
The Mine Internet of Things (MIoT) has become a widely used technique that achieves the construction of smart mines by employing IoT technologies in industrial mining scenarios [
1]. Generally, the system architecture of the MIoT is composed of three layers, i.e., a perception layer, a network layer, and an application layer, as shown in
Figure 1. The perception layer is the foundation of MIoT applications, consisting of various intelligent terminals and devices equipped with sensors that are responsible for collecting physical information, such as data regarding personnel, equipment, and the environment in the mine. The information collected by the perception layer is transmitted to the application layer via the network layer. At present, there are many wireless communication technologies available for data transmission in underground mines, such as4G/5G, Wi-Fi, and LoRa [
2]. In the application layer, the collected information can be processed and analyzed, and appropriate decisions are made to meet different production requirements. The MIoT system has been utilized to monitor the status of an entire mine, thereby improving production safety and the level of accident prevention in the mine [
3].
In the actual process of coal mining, disasters such as gas explosions, roof shedding, and water penetration can easily occur due to the harsh environmental conditions [
4]. These disasters usually cause roadway blockages or personal injuries, etc., preventing underground personnel from actively evacuating the accident area and constraining them to wait for rescue in the mine roadway. However, rescuers entering the accident area after the disaster are prone to secondary accidents due to unknown information about the mine’s environment, triggering even more casualties. In order to access specific information about the disaster roadway, environment detection should be carried out for the affected area before the rescue [
5]. In fact, some communication facilities may be damaged in accidents, leading to the interruption of original communication links and the loss of on-site information in post-disaster accident areas [
6]. Consequently, restoring normal communication between the accident area and the ground command center as soon as possible after the disaster has become an urgent issue.
In general, post-disaster communications can be restored by laying cables or placing auxiliary communication devices in accident tunnels. However, this approach is costly to deploy and cannot be achieved in a short time underground in the mine [
7]. To ensure timely and efficient underground communications, the surviving facilities can be utilized to quickly reconfigure the post-disaster network. In addition, post-disaster communications should be capable of perceiving environmental and personnel information and promptly transmit the site information from the incident area to the rescue center. Therefore, reconstruction of the Mine Internet of Things (MIoT) using wireless networks is an effective solution.
Considering a scenario in which the Mine Internet of Things (MIoT) is rescued in the context of a fully mechanized mining face accident, a large number of surviving nodes are randomly scattered in the accident area, and the planar topology is managed via self-organization to re-establish the post-disaster MIoT network based on a wireless multi-hop routing sensor network. These nodes have the ability to perceive and transmit data information. When the source node collects data information regarding the mine disaster, it uses a multi-hop routing data transmission method to forward the data packet to the sink node, thus completing the effective monitoring of the fully mechanized mining face. Unlike normal mining scenarios, the surviving nodes in post-disaster MIoT networks have extremely limited amounts of energy and cannot be replenished in a timely manner. In addition, frequent data transmission after disasters can cause high levels of communication energy consumption, thereby shortening the entire network’s lifetime [
8]. Due to the collapse and dispersion of objects such as coal blocks, rock masses, and electrical devices, network nodes in the roadway can be damaged or displaced, which causes the topology to become sparse [
9]. In addition, the affected roadway environment is highly complex, with a non-uniform, layered geological structure that conducts a large amount of signal loss and multipath attenuation. As a result, the high rate of packet loss will lead to the failure of the post-disaster MIoT to ensure the reliable transmission of data. Due to the aforementioned problems, existing wireless network routing protocols cannot be applied well to post-disaster MIoT data transmission. Therefore, an efficient and reliable routing protocol is urgently required for post-disaster MIoT.
Opportunity routing (OR) can improve the reliability of data transmission in wireless networks by fully utilizing the broadcasting characteristics of wireless channels and collaboration between forwarding nodes [
10]. Initially, OR was designed to meet the communication requirements of ad hoc networks in sparse mobile scenarios. Nowadays, it has become an important technique for data collection and sharing in wireless multi-hop networks. Considering various Quality-of-Service (QoS) requirements for WSNs, a fuzzy-based load-balanced opportunity routing protocol is proposed [
11]. In contrast to traditional deterministic wireless network routing protocols, OR does not require the sender to select a specific relay node before data forwarding, but only needs to maintain the candidate forwarding set (CFS). Moreover, OR can effectively address the problem of link unreliability caused by unstable wireless channels, thus reducing packet retransmissions and improving network throughput. In [
12], a virtual-range-forwarding-based opportunistic routing is proposed to overcome the unfavorable characteristics of wireless channels. Hence, the OR method can effectively cope with the vulnerability and the intermittent connectivity of the post-disaster MIoT networks.
Nevertheless, there are some drawbacks to traditional OR research studies. On the one hand, the selection of routing metrics is relatively single, without considering various attributes of the network comprehensively. On the other hand, these protocols are designed to improve packet delivery success rates by using a large number of duplicate packets while ignoring the high latency problem in the network. In reality, high latency in MIoT scenarios can seriously affect the efficiency of accident rescue work. Different from the terrestrial routing method, there exist the following challenges for opportunistic routing in the post-disaster Mine Internet of Things (MIoT). First, the surviving nodes after the disaster are constrained by the limited power resources. If there are too many nodes participating in packet forwarding, high communication energy consumption will be generated in the network, and nodes will fail quickly due to energy depletion, thereby aggravating the intermittent connectivity of the post-disaster MIoT. Conversely, if there are few nodes for packet forwarding, the packet delivery success rate of the post-disaster MIoT cannot be guaranteed. Therefore, the size of the candidate forwarding set becomes a crucial factor affecting the network performance. Second, the MIoT network data transmission in affected regions tends to be directional, and critical network nodes close to the sink will undertake more transmission tasks, resulting in the routing hotspot problem, which severely reduces the energy utilization of surviving nodes. Third, the harsh environment in post-disaster underground mines makes surviving nodes prone to physical damage, causing unstable network topology and void routing, which severely degrade the reliability of transmission links.
To the best of our knowledge, there are fewer studies on OR methods in the post-disaster Mine Internet of Things (MIoT). Therefore, the focus of our work is to address the aforementioned problems of opportunistic routing in the post-disaster MIoT. In this paper, we propose a Directional-area-forwarding-based Energy-efficient Opportunistic Routing (DEOR) algorithm to improve the reliability and robustness of data transmission in post-disaster MIoT networks.
The main contributions and innovations of this paper are summarized as follows.
In order to restore post-disaster data transmission of MIoT in the planar accident area rescue scenarios, such as a fully mechanized coal face, a post-disaster flat network architecture of MIoT based on the multi-hop routing of surviving nodes is established, which consists of a sink node and multiple sensor nodes. This network architecture achieves the purpose of comprehensive perception and effective transmission of environmental information in the planar accident mine after disasters;
We propose a directional-area-forwarding-based candidate forwarding set construction strategy. In the network initialization phase, according to the deployment density and communication radius of nodes in the accident roadway, a forwarding zone (FZ) is designed for each node to route packets toward the sink. Then, the candidate forwarding set (CFS) is constructed by the nodes within the FZ that satisfy the energy constraint and the neighboring node degree constraint. By restricting the number of duplicated packets in the network, DEOR improves the energy utilization of the surviving nodes;
We propose a relay node selection method based on routing quality evaluation. In the data transmission phase, we take multiple attributes of the nodes into account, such as direction angle, transmission distance, and residual energy. Next, nodes in the CFS are prioritized based on the routing quality, and the forwarding node with the highest priority is selected as the relay node to forward packets. Other nodes in the CFS discard packets after listening for a successful transmission message. By utilizing the collaboration between forwarders, DEOR addresses the hotspot problem and balances the traffic load between nodes in the post-disaster network;
We design a recovery mechanism for void nodes. When packets encounter the routing void during forwarding, a recovery mechanism is triggered. By employing the modified routing quality evaluation function, packets can bypass the void routing region and select available relay nodes to continue forwarding. DEOR overcomes the void routing node problem and improves the robustness of the whole post-disaster network.
Extensive simulation tests proved that the proposed DEOR algorithm obviously improves the performance of post-disaster MIoT data transmission in a planar accident mine. Moreover, our work provides a novel data transmission method for emergency communication in coal mines, which is of great practical significance for improving the efficiency of post-disaster rescue.
The rest of this paper is organized as follows.
Section 2 discusses the related works. In
Section 3, we describe the system model and problem description.
Section 4 explains the proposed DEOR algorithm.
Section 5 elaborates on the simulation setup and simulation results. Finally,
Section 6 concludes this paper.
4. Proposed DEOR Algorithm
To solve the routing problem described in
Section 3.3, a Directional-area-forwarding-based Energy-efficient Opportunistic Routing (DEOR) for the post-disaster MIoT network is proposed, aiming to improve the energy efficiency and the data transmission reliability of the post-disaster MIoT network in the planar accident mine. The proposed DEOR algorithm mainly consists of three parts: candidate forwarding set construction, relay node selection, and void routing node recovery. Firstly, a directional-area-forwarding-based candidate forwarding set construction strategy is designed. In the initialization phase, according to the deployment density and communication radius of nodes in the accident roadway, a forwarding zone (FZ) is designed for each node to route packets toward the sink. Then, the CFS is constructed by the nodes within the FZ that satisfy the energy constraint and the neighboring node degree constraint. Subsequently, a relay node selection method based on routing quality evaluation is proposed. In the data transmission phase, we take multiple attributes of nodes into account, such as direction angle, transmission distance, and residual energy. All nodes in the CFS are prioritized based on the routing quality evaluation, and the node with the highest priority is selected as the relay to forward packets. Finally, a recovery mechanism for void nodes is designed. When packets encounter the routing void during forwarding, a recovery mechanism is triggered. By employing the modified routing quality evaluation function, packets can bypass the void region and select available relay nodes to continue forwarding. Details are described below.
4.1. Construction of Candidate Forwarding Set
In this section, we mainly provide a detailed presentation of the construction of the candidate forwarding set (CFS). OR utilizes multiple neighbors of the sending node to simultaneously receive and forward packets, thus improving the packet forwarding efficiency. Therefore, the construction criterion of the CFS is particularly important in opportunity routing design.
In general, the size of the CFS can affect the post-disaster network performance. The larger the CFS, the more packet copies are generated and the higher the packet transfer success rate; however, the waiting time of the sender will also be longer, leading to higher energy consumption and end-to-end latency. On the contrary, the smaller the CFS, the sparser the network topology, and the lower the packet transfer success rate, resulting in unreliable data transmission after the disaster. To address the above issue, this paper proposes a directional-area-forwarding-based candidate forwarding set construction strategy. Here, we take the packet forwarding process from the source node
to the sink in the post-disaster network as an example to build a schematic diagram of the CFS selection of node
ni, as shown in
Figure 3.
Figure 3 shows the selection of the CFS in the proposed DEOR algorithm. It can be seen that when the accident area is certain, the size of the CFS is related to the network density
. The higher the network density, the greater the probability of packets being overheard and duplicated. In addition, the direction of data transmission in the accident mine is upward to the sink. In order to reduce transmission energy consumption, nodes closer to the sink should be selected as much as possible when constructing the CFS. Therefore, to restrict the number of forwarders and reduce the energy consumption, we defined a Forwarding Zone (FZ) for each node
, denoted by
, so that the packets will be routed upwards to the sink within the
. The size of the FZ is determined by the network density
; larger network density means a smaller FZ and fewer forwarders, and vice versa, such that the number of forwarders can be dynamically adjusted. Here, we denote the network density
as
where the first term reflects the deployment density in the target field, and the second term reflects the degree of connections between the sender and other nodes. Note that,
is the sum area of
nodes,
is the size of post-disaster network,
is the communication radius of nodes,
is the area of target field, and
is the number of neighbors of
.
In
Figure 3, it can be seen that FZ is a rectangular shape with a size of
defined by four points
, where the length of the FZ is the Euclidean distance from the node to the sink, i.e.,
. Obviously, the width of the FZ
is related to the communication range
and the network density
. According to the model studied in [
37], the maximum width of the FZ satisfies
. Consequently, the
is expressed as
According to Formula (4), there is a positive correlation between the width of the FZ and the communication radius r of the sender , and a negative correlation between the width and the network density . When the communication radius of node is fixed, the larger the network density is, the smaller the width of the sender . This is because a larger network density means more candidate forwarding nodes of , leading to an increase in packet replicas in the post-disaster network. To reduce the number of neighbors participating in packet forwarding, the width of the FZ should be reduced. Conversely, the smaller the network density , the larger the width of the sender . This is because the smaller the network density , the fewer candidate forwarding nodes of there are, leading to a lower packet success delivery rate. Therefore, under the condition of a fixed sensing region, the width of FZ should be increased to improve the probability of packet forwarding by relay routing nodes.
The location of the sink is denoted by
. In the network initialization phase, each node
computes the location of the four points of
using Formula (5). These four points
are attached to the header of packets. Obviously, the number of forwarders can be limited according to the FZ.
We define the neighbor set of
within the forwarding zone as
. In
Figure 3, the candidate zone (CZ) of each node
is defined as the intersection area between the forwarding zone
and the communication range of
. Furthermore, the set of nodes within the CZ is described as
In practice, due to the harsh underground mine environment after the disaster, there still exists the problem of node failure at any time. Therefore, in order to avoid encountering void routing during packet forwarding, candidate nodes with more neighbor nodes should be selected so that the packet transmission success rate can be improved. In addition, candidate nodes with relatively high residual energy should be selected to balance the node load and extend the network lifetime. Consequently, the node
in the CFS of node
should satisfy Formulas (7) and (8).
where
and
represent the number of neighbor nodes of
and
, respectively, while
and
represent the residual energy of
and
, respectively.
As a result, the nodes satisfying Formulas (6)–(8) constitute the candidate forwarding set
of
as follows:
In this article, the constructed candidate forwarding set
restricts the number of forwarders, which contributes to reducing the waiting time of the sender. Moreover, the nodes in
have the characteristics of high energy and more neighboring nodes, which is conducive to improving the network lifetime and data transmission reliability of post-disaster MIoT. The pseudocode of the selection of the candidate forwarding set is shown in Algorithm 1.
Algorithm 1: Construct the Candidate Forwarding Set |
Input: Output: The candidate forwarding set 1: for each node do 2: Define the Forwarding Zone using Equation (5) 3: Get the subset using Equation (6) 4: end for 5: for each node do 6: Get the subset using Equation (7) 7: Get the subset using Equation (8) 8: if 9: then add 10: end if 11: end for 12: if 13: then 14: switch to Algorithm 3 15: else 16: switch to Algorithm 2 17: end if |
4.2. Selection of Relay Node
In this section, the proposed relay node selection method based on routing quality is described in detail. In opportunistic routing, the relay node is the ultimate node responsible for packet forwarding, so the selection of relay nodes will directly affect the performance of data transmission in the post-disaster Mine Internet of Things (MIoT). After the candidate forwarding set is determined, we need to further optimize the forwarding strategy with the goal of selecting the optimal forwarder as the next-hop relay node. Based on the system model shown in
Figure 2, aiming to reduce the energy consumption for data transmission in the post-disaster MIoT, an energy-efficient routing path should be selected from the source to the sink [
38]. Here, we design a routing quality evaluation function in the proposed DEOR algorithm for forwarders that considers three factors, including direction angle, transmission distance, and residual energy of nodes. The smaller the directional angle attribute value of the current forwarder, the closer the forwarder is to the sink and the lower the transmission energy consumption of sensor nodes. The larger the relative distance attribute value between the sender and forwarder, the farther the transmission distance of the current forwarder is within the same communication range, which shortens the total routing path for packet forwarding and thus reduces the transmission energy consumption of the post-disaster network. In this article, the schematic diagram of the directional angle and transmission distance between the sender
and forwarder
is shown in
Figure 4. Then, nodes in the CFS are prioritized based on the routing quality value. After the forwarder’s coordination, the node with the highest priority is selected as the relay to forward packets, and other nodes in the CFS will drop packets after listening for a successful transmission message.
As shown in
Figure 4a, the directional angle attribute of the node is considered in this paper to give higher priority to forwarders closer to the sink. The direction angle
between the sender
and the neighbor
towards the sink is expressed by Formula (10), where
and
.
Figure 4a shows that the smaller the direction angle
between the sender
and its neighbor
is, the closer the neighbor
is to the sink, where it can provide lower energy consumption for packet forwarding. To avoid the situation where
, the
is normalized into
by Formula (11), where
. Then, the distribution of the direction angle
is obtained by using the mass function expressed as Formula (12).
where
is the number of nodes in
,
, and
is the control parameter of the direction angle factor. Note that a larger
indicates the greater probability distribution for forwarders that are closer to the sink to be selected as relay nodes.
As shown in
Figure 4b, the transmission distance attribute of nodes is considered in our work to give higher priority to forwarders that are further away from the sender. This is because when the communication radius
of nodes is fixed, the larger the distance between the sender
and its neighbor
is, the shorter the routing path toward the sink becomes, thereby reducing the energy consumption for routing packets. The transmission distance
between
and
is represented by Formula (13). The variable
is normalized to
by Formula (14), where
. Then, the distribution of transmission distance
is obtained by using the mass function expressed as Formula (15).
where
is the control parameter of the transmission distance factor. Note that a larger
indicates the greater probability distribution for forwarders that are closer to the sink to be selected as relay nodes.
Furthermore, some network nodes can deplete energy earlier than other nodes due to undertaking more packet forwarding in data transmission. In order to balance the load of network nodes, the energy attribute of nodes is also considered in our work to give higher priority to forwarders with greater residual energy. For a forwarder
of the sender
, the residual energy
is normalized to
by Formula (16). Then, the distribution of remaining energy
is obtained by using the mass function expressed as Formula (17).
where
is the initial energy of
and
is the control parameter of the energy factor. Note that a larger
indicates the greater probability distribution for forwarders that have greater remaining energy to be selected as relay nodes.
Based on the above analysis, we define the routing quality of
’s forwarder
as the product of the directional angle factor
, the transmission distance factor
, and the residual energy factor
, which is expressed as
in Formula (18). Then, it is normalized to
by Formula (19).
According to Formula (19), we can deduce that the forwarder with higher routing quality has a higher chance of being selected as a relay node. Note that these three factors are controlled by three control parameters , , and , respectively, such that increasing the value of any control parameter will enhance the impact of the corresponding indicator. Normally, the control parameters are set to .
Once the CFS of the sender is determined, the number of packet replicas needs to be limited by the collaboration of candidate forwarders, ensuring that only one forwarder is selected as the relay node to forward packets. In this article, the proposed relay node selection method based on routing quality evaluation determines the optimal relay node for packet forwarding, so that an energy-efficient routing path between the sender and the sink can be achieved. By utilizing the local metrics of forwarders to make routing decisions, DEOR reduces routing overhead and extends network lifetime. The schematic diagram of relay node selection is shown in
Figure 5.
According to the routing quality
value, we sort the forwarders of
in descending order, denoted as
. As we can see in
Figure 5, for the current sender
carrying packets,
is the candidate forwarding sets of
, where
. When
needs to find the next-hop routing node, it will send a request message to all nodes in
. After receiving this message, these forwarders send a reply message about their own information to
. Then, according to Formulas (12), (15) and (17),
calculates the three attribute values of nodes
,
, and
, i.e.,
,
, and
. Next,
calculates the routing quality values of three forwarders based on Formula (19), i.e.,
,
, and
. Finally, the three routing quality values are compared. Assuming that
, the forwarders’ priority sorting set of the sender
can be denoted as
. The sender
selects the candidate with the largest
value as the next hop, i.e.,
. The best relay node
forwards the packets, and if the transmission is successful, the other nodes
and
discard the packet copies. Since
is not the destination node, it becomes the new sender and continues to select the next hop through the above process until the packet is forwarded to the sink. Finally, a complete routing path is formed in the network, denoted as
in
Figure 5. The pseudocode of the selection of relay nodes is shown in Algorithm 2.
Algorithm 2: Select the Best Relay Nodes |
Output: The ID of the best relay nodes 1: for each node do 2: node receives the packets sent by node 3: Get the using Equation (12) 4: Get the using Equation (15) 5: Get the using Equation (17) 6: Calculate using Equation (19) 7: sort in descending order to 8: end for 9: select the node from the highest— 10: if forwards the packet successfully 11: then other nodes in drop the packet 12: else 13: set the node where has lower— 14: end if 15: until the timer expired 16: if packet is not delivered to Sink 17: then 18: switch to Algorithm 1 19: end if |
4.3. Recovery Mechanism
In this section, we describe in detail a recovery mechanism in the proposed DEOR algorithm. The collapse of loose coal in mines after disasters and the depletion of node energy are common phenomena that can increase the probability of void nodes being selected as relay nodes [
39]. However, although the above methods reduce the probability of selecting void nodes, they cannot completely avoid the problem of void routing. According to
Figure 2, we assume that the routing node
of the current sender
is an invalid node; the routing recovery process of void nodes in the post-disaster MIoT network is shown in
Figure 6. By adopting the proposed recovery mechanism, the current void node
can quickly find the optimal relay recovery node
on the reverse routing path towards the sink, thereby bypassing the void area and improving the packet delivery rate of post-disaster network data transmission.
In
Figure 6, if the relay node
of the sender
is a void node, no neighbors in the upward path to the sink can forward packets, i.e., the candidate forwarder set
obtained according to Algorithm 1 is empty. Here, we denote the set of neighbor nodes of the void node
as
, and its subset of neighbors
in the downward path can be defined as
where
is the neighbor of void node
and
represents the Euclidean distance from
to the sink.
Similar to Formula (8), in order to ensure load balancing and energy conservation in the post-disaster network, nodes with higher residual energy should be selected. Hence, the subset
of
nj’s neighbors that satisfy energy constraints is denoted as
Combining Formula (20) and Formula (21), the candidate recovery node set
of void node
can be defined as
Unlike the routing method described in
Section 4.2, when an invalid node
selects a recovery relay node in a downward path to the sink, it will consume more energy to forward packets to nodes at greater distances. Therefore, the neighbor node with a smaller transmission distance difference from the void node should be selected as the recovery relay node. Consequently, in the recovery mechanism, Formulas (15) and (19) should be rewritten as
where
is the number of candidate forwarders of void node
.
Once the void node forwards packets to a normal routing node, it exits the recovery mechanism and continues to route packets to the sink, as described in
Section 4.1 and
Section 4.2. In the recovery mechanism, nodes will record the ID of the previous hop node, and these nodes will not be repeatedly selected when selecting the recovery relay node, thus avoiding routing loops. By utilizing the updated routing quality assessment, void nodes can select appropriate recovery relay nodes downward in the planar accident mine, thereby effectively restoring the transmission path. The pseudocode of the recovery mechanism is shown in Algorithm 3.
Algorithm 3: Recovery Mechanism of Void Nodes |
Input: Output: The candidate recovery forwarding set 1: for each node do 2: Get the subset using Equation (20) 3: Get the subset using Equation (21) 4: if 5: then add 6: 7: end if 8: end for 9: switch to Algorithm 2 |
4.4. Analysis and Flowchart of DEOR
According to the pseudocode of the three sub-algorithms, e.g., Algorithms 1–3, the DEOR algorithm mainly consists of a cycle in the calculation process, so the computational complexity of the proposed DEOR in this paper is , where is the number of nodes in the network. The energy consumption in DEOR entirely depends on how many nodes are in the sender’s forwarding zone. For the post-disaster MIoT network, this complexity is usually within the computing capacity of nodes, and the proposed DEOR is an energy-saving routing strategy. Therefore, the surviving nodes have the ability to execute the DEOR algorithm, which restricts the number of duplicate packets generated in the network and avoids the routing void problem during data transmission.
The proposed DEOR is an opportunistic routing algorithm that considers both global and local information of the network. Nodes make routing decisions based on network density and multiple attributes of neighboring nodes. The flowchart of DEOR is shown in
Figure 7. Firstly, during the network initialization phase, each surviving node defines a forwarding zone (FZ) according to Formulas (3)–(5). It is assumed that the source node
needs to send the perceived data information to the sink; that is, node
is the current sender. Then, the sender
constructs a candidate forwarding set (CFS)
according to the forwarding zone constraint, energy constraint, and neighboring node degree constraint, i.e., Formulas (7)–(9). Next, the nodes within the
calculate their routing quality
values and are sorted in descending order to
. In addition, if the set
is empty, the recovery mechanism is activated. The sender
reconstructs a new forwarder set
according to Formulas (20)–(22). Then, the nodes within the
calculate their routing quality
values and are sorted in descending order to
. The nodes in
are selected in sequence as relay nodes to forward the packets before the timer expires; otherwise, the data transmission fails. Finally, if any node in
successfully forwards the packet, the other nodes will discard the packet copies and loop the above process until the packet is routed to the sink.
6. Conclusions
In this paper, we propose a directional-area-forwarding-based energy-efficient opportunistic routing (shorted as DEOR) algorithm for the post-disaster MIoT network. Firstly, in order to restore post-disaster data transmission of MIoT in the planar accident area rescue scenarios, such as for a fully mechanized coal face, we design a multi-hop opportunity routing network architecture composed of one sink node and several survival sensor nodes. Then, according to the forwarding area constraint, energy constraint, and neighbor node degree constraint, DEOR constructs the candidate forwarding sets to restrict the number of duplicate packets and improve the energy utilization of nodes. Moreover, to meet multiple quality-of-service requirements of the post-disaster MIoT, a routing quality function is designed by considering the directional angle, transmission distance, and residual energy attributes of nodes. DEOR selects a relay node to forward packets based on the priority of the nodes in the CFS, which ensures the network load balancing. Finally, we propose a recovery mechanism aimed at bypassing the void area and forwarding the packets continuously, which can reduce retransmissions and improve network connectivity. The simulation results demonstrated the proposed DEOR algorithm achieves better performance compared to the ORR, OBRN, and ECSOR in terms of energy consumption, average hop count, packet delivery ratio, and network lifetime.
In our future work, we intend to investigate a more reasonable method to obtain the location information of sensor nodes for post-disaster mining scenarios. Moreover, how a realistic post-disaster mine roadway simulation experimental platform can be constructed is also a meaningful research topic.