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Abstract

In underwater wireless sensor networks, stability and reliability of the network are of paramount importance. Stability of the network ensures persistent operation of the network that, in consequence, avoids data loss when nodes consume all the battery power and subject to death. Particularly, nodes bearing a low pressure of water die early in the usual routing approach due to being preferred choices for data routing. Reliability ensures minimization of the adverse channel effects on data packets so that the desired information is easily extracted from these packets. This article proposes two routing protocols for underwater wireless sensor networks: reliable and stability-aware routing and cooperative reliable and stability-aware routing. In reliable and stability-aware routing, energy assignment to a node is made on the basis of its depth. Sensor nodes having the lowest depth are assigned the highest amount of energy. This energy assignment is called the energy grade of a node and five energy grades are formed in the proposed network from top to bottom. The energy grade along with energy residing in a node battery and its depth decide its selection as a forwarder node. The reliable and stability-aware routing uses only a single link to forward packets. Such a link may not be reliable always. To overcome this issue, the cooperative reliable and stability-aware routing is proposed which introduces cooperative routing to reliable and stability-aware routing. Cooperative routing involves the reception of multiple copies of data symbols by destination. This minimizes the adverse channel effects on data packets and makes the information extraction convenient and less cumbersome at the final destination. Unlike the conventional approach, the proposed schemes do not take into account the coordinates of nodes for defining the routing trajectories, which is challenging in underwater medium. Simulation results reveal a better behavior of the proposed protocols than some competitive schemes in terms of providing stability to the network, packet transfer to the ultimate destination, and latency.

Keywords

Underwater wireless sensor networks reliable and stability-aware routing cooperative reliable and stability-aware routing

Introduction

Reliable communications and stability of the network are the most vital challenges in the underwater wireless sensor networks (UWSNs). These parameters are particularly important when using UWSNs for applications such as submarine detection and navigation, disaster management, and oil leakage detection,^1–4 because, in these situations, the network stability and correct data reception with minimum error at the destination are essential. In UWSNs, data reception with accuracy is one of the challenging tasks because the data loss probability is high due to the harshness of underwater scenario: multi-path fading, attenuation, noise, salinity, and presence of marine animals.³ Due to harshness of the underwater medium, acoustic waves are used for communications instead of radio waves,^5–7 because absorption and attenuation of the radio waves in underwater are greater than those of the acoustic waves.⁷ But the acoustic speed is five times slower in water than the radio speed, due to which the latency of the UWSNs is greater than that of the terrestrial networks.⁸ Moreover, the harshness makes an underwater network dynamic due to the flow of water and the positions of the nodes change with water currents. So finding the exact position of every node is difficult in underwater.⁹ Keeping in view the harshness of the underwater scenario, the stability of these networks is critically important because nodes are powered by batteries having limited energy. Also, recharging and changing the battery is difficult in underwater.⁴

Cooperative communications are used to reduce the channel effects and to minimize errors in the data for correct reception and data reliability. In cooperative communications, the overheard data by the relay nodes are utilized to achieve successful data reception at the destination and provide data reliability.¹⁰ The overheard data are either amplified or decoded by the relay before transmission to destination. The first one is termed as amplify and forward (AF) and the second one as decode and forward (DF). When a destination hears the data from the relay always, it is termed as fixed cooperation (FC).^11–13 In FC, the data are always forwarded by a relay to a destination which provides high reliability. Using a feedback for the cooperation of the relay to limit the transmission of the relaying data is termed as incremental cooperation (IC). In IC, the data are checked at the destination. If they are correct, then no more retransmission is required. The relay transmits data in case of incorrect reception of data at the destination. This approach brings small latency and less energy consumption as compared to FC.¹⁴ In cooperative communication, the reliability is achieved in respect of fewer packets being dropped, more packet reception, and packet acceptance ratio (PAR). However, the energy consumption and latency are high. Excessive use of energy leads the nodes to die soon and reduces the lifetime and stability of the network.

Non-cooperative schemes^15,16 do not guarantee a reliable delivery of the data because in these algorithms the data are delivered to the destination over a single channel and the chances of the errors occurring in the data are high. On the other hand, cooperative algorithms overcome this shortcoming of the non-cooperative routings which provide a reliable communication. But, in cooperative algorithms, the stability of the network is smaller than that of the non-cooperative algorithms because of the high energy consumption in the cooperative schemes.¹⁷

The routing protocols in Nasir et al.¹⁷ and Yan et al.¹⁸ utilize the depth information of the nodes instead of geographical information. In these protocols, all the traffic from the highest depth nodes comes to the water surface and increases the load on the uppermost nodes and causes the load imbalance on nodes. Due to the burden on the uppermost nodes, the probability of packet loss is high and uppermost nodes tend to die quickly than the other nodes. When the uppermost nodes die, holes are created and then the rest of the network becomes less effective because of no path to the sink on the water surface. In Nasir et al.,¹⁷ this problem is more critical because its energy consumption rate is greater because of cooperation. Also, nodes with the lowest depth are always selected for data routing, which creates data burden on the nodes near the water surface and makes the nodes die quickly. This reduces the stability of the network. On the other hand, protocols that require geographical information using the global positioning system (GPS) for location information of the nodes^19–21 are challenging as GPS signals are much attenuated in water.^22,23

This article presents two routing algorithms, namely, reliable and stability-aware routing (RSAR) and cooperative reliable and stability-aware routing (CoRSAR). In the RSAR algorithm, five energy grades are formed based on depth information. The lowest depth nodes have the highest grade and the most powerful batteries (initial energy). The motivation for making these energy grades and assignment of high initial energy to the nodes is that the nodes having the lowest depth have the highest data traffic on them and die quickly. Next, the selection of the forwarder candidate is made on a function. Calculation of the function for the next forwarder candidate requires the depth, residual energy, and energy grade value of the next forwarder candidate. Among all the neighbor nodes, the next forwarder node is selected which has the maximum value of the function. Selection of nodes merely on the lowest depth overburden low-depth nodes that severely affects the stability of the network. It is because low-depth nodes die quickly due to overburdening of data. Since there is no check on the reliable data delivery in RSAR, reliability can still be compromised in RSAR. It is because data are sent over a single link. To overcome this shortcoming, a CoRSAR protocol is proposed. In this algorithm, the data are cooperatively forwarded to the next forwarder candidate. The data overheard by the neighbor nodes are utilized for reliability. The data sent by a source are received by the destination. The relays also receive the same data due to data broadcasting by the source node. Amplification of these data is done by the relay node and then transmitted to the destination. The destination then combines data using the maximal ratio combining (MRC) technique. The destination checks the bit error rate (BER) and decides the quality of data.

In essence, our contribution is threefold as follows:

The RSAR protocol is proposed to provide stability to the network operation. Early death of sensor nodes is overcome by assigning initial energy to every node based on its energy grade. From top to bottom of the network, nodes are assigned energy in descending order. Relay selection is done based on residual energy, energy grade, and depth. Particularly, nodes bearing a low water pressure are retained to participate in the network that, in turn, results in stable operation of the network for data transfer.

Packet delivery in RSAR over a single link does not guarantee reliability in transferring packets to the sink at the water surface. It is because channel effects on a single link may change due to the harshness of the underwater channel. This is overcome by adding cooperative routing to RSAR that leads to the development of the CoRSAR protocol. Cooperative routing makes use of the broadcast nature of the data packets to overcome severe channel properties. The destination receives multiple copies of data to process and decides accordingly.

The proposed protocols are free of node localization, which requires extra computation.

Literature review

A cooperative routing algorithm which utilizes the AF technique is presented in Nasir et al.¹⁷ The data are forwarded by the sender as well as by the two relays to the destination. The next forwarder candidate is selected by the sender which has the minimum value of the depth. The relays forward their data and amplify as they receive regardless of any threshold. This approach achieves better PAR by utilizing a high amount of energy and suffers from high latency. The lowest depth nodes are used as forwarders, so the data traffic is high on the lowest depth nodes and the nodes die quickly, due to which the stability of the network is reduced.

In Khan et al.,²⁴ the decision making for the selection of a destination is made by depth information and the separation by which the node and the sink are far apart. The node nearest to the sink and having the smallest value of depth is selected as a destination. The relay is selected which is nearest to the destination. This approach improves delivery to the sink with the cost of high consumption of energy and high latency.

Routing in a cooperative and opportunistic manner is dealt with in Rahman et al.²⁵ The selection of neighbor nodes is made on the depth value. The sender finds its all neighbor nodes that have the lowest depth. When a sender has to send the data, the node that is selected from the neighbor nodes has the maximum energy as the next forwarder. When the next forwarder does not receive data successfully, then the relay forwards the same data. If this also fails, then a second relay is selected which forwards the data. This approach gives better PAR and greater network lifetime and consumes less amount of energy. However, updating the energy information is required for the routing. Also, the neighbor list is updated every time before data forwarding which brings delay in the packet delivery.

Pervaiz et al.²⁶ presented cooperative routing. The node’s leftover energy, depth, and link quality are used for the selection of relay and destination. The flooding of the data is controlled by varying the depth threshold value. The relay node cooperates with the destination to receive data reliably and utilizes the MRC technique. Better results are obtained in terms of consumed energy and PAR. However, the DF or the AF technique is not utilized so there is a chance of packet reception with error.

A void avoidance cooperation algorithm is proposed in Javaid et al.²⁷ The nodes forward the data inside a cylindrical shape defined by the sender toward the final destination. The node having a smaller value of depth than the sender and inside the cylindrical shape is considered as a forwarder. The other nodes outside the shape do not take part in data forwarding. The relay sends its data after receiving a request from the sender. The request is sent in case the BER is greater than the threshold. This algorithm improves the PAR, delay, and consumption of energy. However, the three-dimensional coordinates are required for data forwarding.

Another cooperative routing is presented in Hussain et al.²⁸ The depth and remaining energy are used as the selection parameters of the next node. Three nodes are selected: one as the destination and the other two as relays. The relay cooperates with the destination to achieve reliable communications. The cooperation is made when the destination receives data above the specific threshold of the BER. It attains a good PAR with high energy consumption and latency.

Wu and his colleagues present a reliable algorithm in Sun et al.²⁹ Using the location of the node, power residing in its battery, and previous retransmission history, the next forwarder is selected. The protocol is better than the other algorithms in reducing energy utilization and advancing packets to the water surface. However, node localization is not avoided.

Wei and Kim.³⁰ propose a reliable algorithm. The path is followed to the destination through which the data are delivered in the lowest time. The sender transmits a hello packet to all its neighbors. Every neighbor responds to the sender and calculates the time for every neighbor. The next hop is selected which has the smallest delivery time. A better PAR with a small consumption of energy and latency is achieved. However, the route that leads to delivering data with a small latency is also followed by the other nodes near to it. Hence, the traffic on this route is high due to which the packet loss probability is high. It also causes data congestion.

A reliable algorithm in Khasawneh et al.³¹ is presented which considers three metrics for the selection of the destination: depth, remaining energy, and link information. The performance of the algorithm is better in packet delivery, network lifetime, latency, and consumption of energy. Another reliable algorithm in Wahid et al.³² considers the remaining energy, link quality, and the distance between the node and the sink as the routing metrics. A node near the surface of the water with better link quality and greater remaining energy is considered as a forwarder. This scheme has better PAR, less consumption of energy, and less latency.

The proposed scheme

RSAR

Network setting

The network is a cube with a length of 500 m of a side/face. The energy is assigned to the nodes based on depth. This energy assignment is called the energy grades of the nodes. Five grades are made and a single grade size is 100 m as shown in Figure 1. The highest amount of energy is assigned to the nodes with energy grade 1. This energy assignment decreases with the increasing grade from top to bottom. The node’s energy and energy grade assignment is shown in Algorithm 1. The motivation for making these energy grades is that, as packets are routed to the top of the network, the load becomes high on low-depth nodes. Due to the high traffic, the uppermost nodes die soon which leads to instability of the network.

Figure 1.

The proposed network.

Algorithm 1. Algorithm for assigning energy to nodes.
1. N_i = ith node
2. N_i.d = node depth value
3. N_i.e = energy of the node
4. N_i.g = grade value of the node
5. E₀ = initial energy of the node
6. forN_i = 1:225 do
7. ifN_i.d ≤ 100 then
8. N_i.e = 2 × E₀;
9. N_i.g = 1;
10. else ifN_i.d > 100 and N_i.d ≤ 200 then
11. N_i.e = E₀ + 3/4 × E₀;
12. N_i.g = 2
13. else ifN_i.d > 200 and N_i.d ≤ 300 then
14. N_i.e = E₀ + 1/2 × E₀
15. N_i.g = 3;
16. else ifN_i.d > 300 and N_i.d ≤ 400 then
17. N_i.e = E₀ + 1/4 × E₀
18. N_i.g = 4;
19. else
20. N_i.e = E₀;
21. N_i.g = 5;

The randomly deployed nodes use acoustic modems for communications with each other. The perpendicular distance of the nodes from the ocean surface is considered as the depth. The sinks with a dual modem (acoustic and radio) are kept at the top of the ocean for the collection of data from the nodes. Interaction among sinks is performed through radio links and between a sink and a node through acoustic links.

Network initialization

After network setup, the network initialization takes place. In the initialization, information of the nodes and sinks is exchanged with each other for data routing. Every node broadcasts a hello message to identify its neighbors. When a sender broadcasts a hello message, the nodes within its transmission range respond to the sender. The hello message contains the ID, depth, remaining energy, and grade value of the node which represent the necessary information required for the selection of the suitable candidate. The structure of the hello signal is shown in Figure 2. When a neighbor receives a hello signal, then it replies to the sender. The neighbor embeds its own ID, depth, remaining energy, and grade value in the hello message and forwards it to the sender. When a sender receives the information of the neighbor, then it calculates the function $F_{n}$ for every neighbor. The $F_{n}$ for node $i$ is calculated as

$F_{n} (i) = \frac{R_{i}}{D_{i} + G_{i}}$ (1)

where $R_{i}$ is the remaining energy, $D_{i}$ is the depth, and $G_{i}$ is the grade value of node $i$ . The sender has the values of $F_{n}$ for every neighbor. The sender takes a decision on $F_{n}$ , and the neighbor that has the maximum value of $F_{n}$ is selected as a forwarder.

Figure 2.

Hello message format.

When a sender broadcasts a hello packet to all neighbor nodes, then these nodes respond to the hello packet and share the information with the sender. Then the sender extracts the information and decides the relay and destination which has the maximum value of equation (1).

The remaining energy of every node changes due to its involvement in exchange of information or data. So the energy of the nodes is not the same every time. Therefore, energy updating is required, which is done after regular intervals of time. The water currents change only the horizontal position of the node and the vertical movement is slightly small which is ignored.¹⁵ So the depth of the node does not change. But, for the best selection of the next forwarder, all the information of the nodes is updated after some intervals. Updating information causes communication overhead and burden, due to which the updating of the network is done after some intervals of time.

Data forwarding in RSAR

Parameters under measurement are converted to data by sensor nodes. For analyzing and processing these data, they are delivered to the data center/base station through the sinks. The node transmission range is not enough and not capable of directly transmitting the data to the sink. So multi hopping is used to deliver the data to the sink at the water surface. The sink then transmits the data to the base station. Direct transfer is done if the sink is within the range of the sender. Otherwise, the data are sent to the next forwarder. The next forwarder is selected from the neighbor list which has a maximum value of the function $F_{n}$ . The sender then puts two IDs in the data header: its own and that of the next node expected to forward data. All the neighbors get the data received. After reception of the data by the neighbors, every neighbor checks whether its ID is present in the embedded data. If it is not so, the data are discarded. Otherwise, the receiver node accepts the data and forwards them to the next forwarder in the same manner as discussed above. The flowchart in Figure 3 shows data forwarding of RSAR.

Figure 3.

Data forwarding flowchart of RSAR.

CoRSAR

In RSAR, data are delivered to the destination over a single link which does not guarantee a reliable data delivery. To overcome this shortcoming, the CoRSAR protocol is proposed. The network architecture and initialization are the same as those in RSAR. Finding relays and idea of the cooperation in CoRSAR are discussed in the following subsections in detail.

Finding relay

When a sender needs to send the data, first the sender selects the destination from the neighbor nodes which has the maximum value of the function $F_{n}$ given in equation (1) and two relays which are nearest to the destination. The sender broadcasts the data, and all the neighbors within its transmission range hear the data. All neighbors check their IDs in the data for checking whether they could be expected for further forwarding or not. If they do not find their IDs, the data are discarded. Otherwise, as the relays receive data, they are forwarded to the destination after amplification. Figure 4 shows the flowchart of the proposed CoRSAR.

Figure 4.

Data forwarding flowchart of CoRSAR.

When a sender has to transmit data, it embeds the relay and destination IDs. When a node hears a data, then it checks for its ID in the data packet. If the ID is present in the data packet, then the relay forwards the data packet, otherwise discards the data. The relay node extracts the ID of the destination and transmits the data as it receives from the sender.

Cooperation

The cooperation of the nodes is used to reduce the channel effects for the purpose of correct data reception at the sink. The basic idea of the cooperation is depicted in Figure 5. Data broadcasting is initiated by the sender $S$ . The destination $D$ and relay $R_{1}$ and relay $R_{2}$ receive the broadcasted data. The received data from $S$ to $D$ , $S$ to $R_{1}$ , and $S$ to $R_{2}$ are given by the following equations, respectively³³

$Y_{SD} = H_{SD} \times x + N_{SD}$ (2)

$Y_{S R_{1}} = H_{S R_{1}} \times x + N_{S R_{1}}$ (3)

$Y_{S R_{2}} = H_{S R_{2}} \times x + N_{S R_{2}}$ (4)

where $x$ is the original transmitted data. The terms $H_{SD}$ , $H_{S R_{1}}$ , and $H_{S R_{2}}$ are gains of the source–destination, source–first relay, and source–second relay links, respectively. The respective noise densities and received data symbols along the same links are $N_{SD}$ , $N_{S R_{1}}$ , and $N_{S R_{2}}$ and $Y_{SD}$ , $Y_{S R_{1}}$ , and $Y_{S R_{2}}$ , respectively.

Figure 5.

Basic idea of cooperation.

The received data by the two relays $R_{1}$ and $R_{2}$ are then transmitted to the destination $D$ after amplification by a factor $β$ . The received data symbols from the two relays at the destination are given by $Y_{D R_{1}}$ and $Y_{D R_{2}}$ as

$Y_{D R_{1}} = H_{D R_{1}} \times β Y_{D R_{1}} + N_{D R_{1}}$ (5)

$Y_{D R_{2}} = H_{D R_{2}} \times β Y_{D R_{2}} + N_{D R_{2}}$ (6)

where $H_{D R_{1}}$ and $H_{D R_{2}}$ are the channel gains between the destination and first relay and the destination and second relay, respectively; $N_{D R_{1}}$ and $N_{D R_{2}}$ are the channel noise from the first relay–destination and second relay–destination links, respectively. Expanding equations (5) and (6), the received data symbols at the destination through both relays are given as

$Y_{D R_{1}} = H_{D R_{1}} \times β (H_{S R_{1}} \times x + N_{S R_{1}}) + N_{D R_{1}}$ (7)

$Y_{D R_{2}} = H_{D R_{2}} \times β (H_{S R_{2}} \times x + N_{S R_{2}}) + N_{D R_{2}}$ (8)

The amplification factor $β$ for the transmitted power $P_{S}$ and the noise power $σ$ is given as³³

$β = \sqrt{\frac{1}{| H_{RD}^{2} | P_{S} + σ^{2}}}$ (9)

The destination receives multiple copies of the same data. All these data copies are independent of each other. These independent copies are combined at the destination using MRC which is given as³³

$Y_{D} = Y_{SD} + \frac{Y_{SR} \times Y_{RD}}{1 + Y_{SR} + Y_{RD}}$ (10)

The binary phase shift keying (BPSK) scheme is used for simplicity and the BER is calculated as

$BER = \frac{1}{2} (1 - \sqrt{\frac{E_{b} / N_{o}}{1 + E_{b} / N_{o}}})$ (11)

where $E_{b} / N_{0}$ is the ratio of the signal energy over noise.

As the relay nodes receive the data, they forward the data without any latency to the destination for data synchronization at the destination.

Simulation results

MATLAB simulates all the schemes. The developed schemes are compared with depth-based routing (DBR)¹⁸ and cooperative depth-based routing (CoDBR).¹⁷ It is because the depth value of the nodes is one of the common factors used for routing in all the schemes. The network is a cube with a length of 500 m of a side/face and consists of 225 nodes deployed randomly. The energy assigned to every node in the counterpart schemes is kept at 6000 J. For the proposed schemes, the energy of the nodes from grade 1 to grade 5 nodes is kept at 12,000, 10,500, 9000, 7500, and 6000 J, respectively. The reason for this energy assignment is that nodes close to the water surface suffer from more data traffic. Every node can sense or forward data of the other nodes. The nodes move with water and it is considered only in the horizontal direction in the simulation. It is because the water movements just change the horizontal position of the nodes. The vertical movement of the nodes is slightly small and hence ignored. The vertical distance of the node from the surface of water is considered as the depth value of the node. Four sinks 100 m apart from each other are deployed and kept fixed on the surface of water. The sinks are considered having an unlimited source of energy due to their ease of recharging. All the data from the nodes are collected by the sinks. The exchange of information and data among the nodes and sinks is done using the acoustic links, while, between sinks, the exchange of information and data is accomplished through the radio links. The base station receives data from the sink over a radio link.

The medium access control (MAC) protocol is used to address channel detection and avoid packet collision.¹⁹ The sender forwards the data when the channel is free; otherwise, after several attempts the data are discarded. When the destination receives the data, it acknowledges the sender about the delivery of the data. The IP65 standard is assumed for the protection of the nodes from the water. The parameters used in the simulation are kept the same as used by the modem designed for underwater communication such as LinkQuest UMW 2000.²⁷ The power consumed during the transmission of the packet is kept 2 W, in the receiving mode it is 0.8 W, and in the idle state it is 8 mW. The transmission range and depth threshold are kept 100 and 60 m, respectively. One packet is generated by each node in 1 s having the size of 1600 bits. The acoustic bandwidth is set to 30 kHz. For convenience, Table 1 shows the considered metrics.

Table 1.

List of considered metrics.

Parameters	Values
Network dimension	3D
Depth threshold	60 m
Idle mode power	8 mW
Modulation type	BPSK
Transmission mode power	2 W
Receiving mode power	0.8 W
Transmission range	100 m
Data packet size	1600 bits
Propagation speed	1500 m/s
Energy (counterpart schemes)	6000 J
Movement	Random walk
Network size	Each side is 500 m
Channel	AWGN Rayleigh fading
Grade size	Each grade having 100 m size
Grade	Five grades are in the network
Nodes	225 nodes are randomly deployed
Sink nodes	Four sinks are placed fixed at the surface
Packet generation	One packet is generated by every alive node in every round
Energy (proposed schemes)	12,000 J energy is assigned to grade 1, 10,500 J to grade 2, 9000 J to grade 3, 7000 J to grade 4, and 6000 J to grade 5

3D: three-dimensional; BPSK: binary phase shift keying; AWGN: additive white Gaussian noise.

Figure 6 shows the details of the alive nodes of all the schemes. The alive nodes in all the schemes tend to zero as the round number goes up because the nodes consume energy during data forwarding and reception. Conversely, the dead nodes become greater as the round number increases.

Figure 6.

Alive nodes.

Both the non-cooperative schemes (DBR and RSAR) have a greater number of alive nodes than the cooperative schemes because, in cooperative schemes, the energy is consumed at a greater rate than in the non-cooperative schemes. The RSAR has the greatest number of alive nodes. It is because of excessive energy assignment to the nodes in RSAR. Comparing the result with DBR, the RSAR has the higher number of alive nodes than DBR by consuming higher energy. RSAR utilizes more energy at the cost of lower alive node number due to the fact that the assigned energy of the proposed RSAR is greater than that of DBR. As a result, the stability of RSAR is greater and we have a greater number of alive nodes than DBR. At the 559,340th round, all the nodes die and the alive node number becomes zero in DBR, while the RSAR still has 49 alive nodes. This shows the stability of RSAR.

The CoDBR has the lowest number of alive nodes than all the schemes as it has the highest count of nodes involved in data advancement. Comparing CoRSAR with CoDBR, CoRSAR has a greater number of alive nodes than CoDBR. It is because excessive energy is assigned to nodes in CoRSAR. As a result, the stability of CoRSAR is greater and has a greater number of alive nodes than CoDBR. The alive node number becomes zero at the 559,340th round in CoDBR, while in CoRSAR 24 nodes are still alive identifying the stability of CoRSAR. Figure 7 shows the dead nodes of all the schemes, which is inverse to the behavior in which nodes remain alive in the schemes.

Figure 7.

Dead nodes.

The result of PAR is shown in Figure 8. Both the cooperative schemes have a better PAR than the non-cooperative schemes because fewer packets are dropped and greater delivery of packets is achieved due to cooperation. The proposed CoRSAR has the greatest PAR than all the other schemes. It is because of the cooperation due to which the same packet is received through different paths. At the beginning, in both cooperative protocols, all the nodes are alive and more nodes act as relays and cooperate, due to which fewer packets are dropped and more packets are received. So there is no such difference in the results of CoRSAR and CoDBR up to 50,000 rounds. As the round number increases, the PAR of CoRSAR becomes greater than that of CoDBR because in CoRSAR the alive node number is greater as compared with CoDBR. At the 5000th round, there are 185 nodes alive in the proposed cooperative scheme, while 161 nodes are alive in CoDBR which is less than that in CoRSAR. This shows that more relays are available to help the source to receive correct data which increases the PAR of the CoRSAR. At the 500th round, the PAR of CoDBR is $40 %$ and that of CoRSAR is $56 %$ . The CoDBR has a greater PAR than the non-cooperative schemes due to the cooperation because the same packet is forwarded to the destination through multiple paths.

Figure 8.

Packet acceptance ratio.

The RSAR has less PAR than DBR from the start up to 400,000 rounds. Because the DBR is a greedy forwarding algorithm in which every node that receives packets forwards it further. After 400,000 rounds, the PAR of the proposed RSAR becomes better than that of DBR. It is because at the 400,000th round, the numbers of dead nodes are 175 in DBR and 130 in RSAR. This shows that the stability of the DBR becomes less than that of RSAR and dead nodes near to the water surface are created because DBR prefers low-depth nodes for routing, due to which the probability of packet reception is less. On the other hand, the number of dead nodes is less and, therefore, more nodes are there for data transmission in RSAR. Therefore, RSAR has a higher PAR than DBR.

The result of consumed energy in all the schemes is shown in Figure 9. As the round number increases from the start, the consumption of energy in all the schemes increases because the energy consumed in the previous round is added to the consumption in the present round. The energy consumption values of the cooperative schemes are greater than those of the non-cooperative schemes due to the multiple forwarders used in the cooperative schemes. These forwarder nodes transmit the same packet to the destination as the source to the destination packet.

Figure 9.

Energy consumption.

The energy consumed in CoRSAR is the greatest of all the other schemes. Because of cooperation and the greatest number of nodes participating in routing, the rate of consumption of energy is the greatest among all the other schemes. The CoRSAR and CoDBR are both cooperative schemes, but the former has greater utilization of energy because in CoDBR the nodes die soon so few nodes are left to route the data, while its consumption in the non-cooperative schemes (DBR and RSAR) is higher because of cooperation. The RSAR energy consumption is less than that in the cooperative schemes up to 250,000 rounds because it is a non-cooperative scheme. After 250,000 rounds, CoDBR utilizes greater energy. This is because the energy of CoDBR almost vanishes and RSAR still has energy, while energy consumption of RSAR is greater than that of DBR because in DBR the nodes die soon and only few nodes forward the data. On the contrary, energy is kept greater in RSAR to prevent nodes from dying soon. This makes more nodes available for participation in data routing and consume more energy than DBR. Energy utilization is the greatest for DBR by virtue of the aforementioned reasons.

The result of the network delay is shown in Figure 10. The delay in the cooperative schemes is greater than that in the non-cooperative schemes. Cooperative schemes suffer from a greater delay than the non-cooperative schemes due to the involvement of relay nodes in addition to communication between source and destination. The CoRSAR has the greatest delay because the chance of the cooperation of relay(s) is greater due to more alive nodes in it than the CoDBR and the other non-cooperative schemes.

Figure 10.

Delay of the network.

DBR and RSAR have smaller delays than the cooperative schemes as only one path exists between the source and destination for communication. The delay of DBR is greater than that of RSAR. It is because in DBR the destination holds data for a specific time to avoid collision so its delay is greater than that of the proposed RSAR algorithm.

Conclusion and future work

In UWSNs, channel noise and attenuation affect reliable communications among nodes. Also, the stability of these networks is low because nodes have a limited energy and unbalanced data load during data routing. For reliable communication and stability of the networks, this article presents two algorithms: RSAR and CoRSAR. The network stability is achieved by making energy grades on the basis of the node depth information. The lowest depth nodes are assigned the highest amount of energy. To balance data traffic on nodes during routing, a function is used for the selection of a node as a destination. In the second algorithm, the reliability is achieved by making use of data broadcasting. The data received by the relays are also transmitted after amplification to the destination for reliable delivery. Two relays are used for more satisfactory data reception. The destination receives original data and two copies of the data through the relays. These packets are combined by MRC. Reliable communications between nodes are achieved by cooperative routing. The proposed protocols perform better than the existing algorithms DBR and CoDBR to reliably deliver data to the sink. Also, the proposed algorithms have more alive nodes and fewer dead nodes than the existing schemes.

In the future, further addition can be made to improve the routing performance. The nodes which are selected as the destination nodes and have maximum residual energy will vary their transmission range to deliver data directly to the sink. The direct communications with sinks will reduce the delay of the network. Moreover, to further increase the network reliability, the link/channel quality will be considered.

Footnotes

Handling Editor: V Vijayakumar

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Anwar Khan

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