978-1-4799-0059-6/13/$31.00 ©2014 IEEE

Three Hops Reliability Model for Underwater Wireless Sensor Network

Abstract- Due to the instability of underwater environment, the reliable data transfers is more sensitive issue in Underwater Wireless Sensor Networks (UWSNs) as compare to land based sensor network. Underwater networks are mostly designed by acoustic sensor nodes and surface sinks that are connected to any onshore control center. Acoustic channel characteristics are create many problems like, low bandwidth; long propagation delays and high error channel rates that can cause to hamper the efficiency of UWSNs. With these constraints, it is very difficult task to design a routing protocol which has the ability to maximize the reliability of these networks. In this paper, we proposed a reliable model with the name of 3 Hop-Reliability Model (3H-RM), in which every sender node of each group of three layers will maintain the copy of same successful transferred data packets without creating extra burden on the networks. Simulation results shows that 3H-RM can achieve better delivery ratios as compare to 2H-ACK reliability model without using any additional resources and configurations.

Keywords- UWSN, Routing Protocol, 3H-RM, 2H-ACK, UWSN

I. INTRODUCTION A very accurate and continuous monitoring system that

works in real time is vital for a variety of applications. These applications include the monitoring of off-shore oil fields, detecting pollution, and collecting oceanographic information. Long term monitoring of the chosen areas is necessary for most of the applications when the collection of information is the goal. This is the reason why an Underwater Wireless Sensor Network (UWSN) can be used to solve issues regarding monitoring areas which are impractical for manned operations because of unstable underwater conditions as well as high water pressure. Networks such as these are usually made up of sensor nodes that are acoustic. As shown in Fig. 1, these networks are situated at different depth levels in the areas of interest, and sinks on the surface are utilized for data collection in real time [1]. An UWSN is made up of several sensor nodes that are dependent upon the deployment area. Any event taking place in the surrounding environment is sensed by theses nodes, and after the necessary processing is finished, they transmit the data that has been sensed on to a sink located on the surface. It is important to note that whilst in UWSNs, nodes may be resource constrained individually, however, as a group, they can span huge areas. They first

sense the data in a given area and then forward the useful information on to a sink on the surface. Moreover, this is accomplished with a level of accuracy that is acceptable. The sensed data in submarine detection and some other like applications can be time critical and, therefore, must be delivered within the proper time interval. Thus, this kind of applications need guaranteed data deliveries of packets on time. On the other hand, the sensed data is meant to be transferred within the network by these sensor nodes. This creates congestion in various areas at different periods of time because of their nature of being multi-hop [2]. The convergent nature of the network is one of the major reasons for this congestion. This is because the data packets for all of the sensor nodes are transmitted on towards either one surface sink or only a small number of sinks on the surface. When data packets begin to travel on towards the sinks, the level of congestion begins to rise. The nodes most seriously impacted on are those located around the sinks. Buffer space and other such available resources are constrained. Therefore, quite a bit of data packet loss can take place if the congestion is not detected and suitable methods of avoidance are not employed. When packets are lost, they need to be retransmitted and this results in a large amount of energy being lost. Moreover, it can cause significant end-to-end delays.

Fig. 1: Possible Underwater Communication Network [1]

Conventional wired and other land sensor networks are unlike the sensor nodes utilized for communication in underwater environments. These differences are found in various aspects. One of these aspects is that some important

Tariq Ali CIS DEPARTMENT

Universiti Teknologi PETRONAS Tronoh, Malaysia

tariq_G01713@utp.edu.my

Low Tang Jung CIS DEPARTMENT

Universiti Teknologi PETRONAS Tronoh, Malaysia

lowtanjung@petronas.com.my

Ibrahima Faye FAS DEPARTMENT

Universiti Teknologi PETRONAS Tronoh, Malaysia

ibrahima_faye@petronas.com.my

applications in underwater situations need huge amounts of data although only from time to time; this uses varying amounts of energy [3]. Another aspect is that rather than representing independent users, these UWSNs are most often applied to a common task. The maximization of throughput is the key aim of these networks rather than providing fairness among the nodes. Yet another one is that an important relation is present between the reliability, link distance and number of hops in these networks. Where energy is concerned, multiple short hops are preferred to deliver packets rather than using long links. On the other hand, routing data packets using an increased number of hops will degrade the function of the end-to-end reliability; this is even more so in environments underwater. The last aspect is that, most often, it is an individual organization that deploys these types of networks using inexpensive hardware [4]. Thus, there is no strict interoperability with the standards existing today necessary. These reasons are why the UWSNs allow for platforms supporting reviews of the present structure of typical protocols used for communication.

II. HOPE-BY-HOPE RELIABILTY Every type of network based on reliable data transfers

methods, but the UWSN need special attention. There are two approaches are available for reliability to increase the performance of network. First, end-to-end, these approaches are applicable only for the stable environments such as wired based network in which nodes remains static. This approach is not suitable for underwater environments due to some additional problems like, lone propagation delays, high error probability and continuous node movements and also can cause to waste the critical resources. Second, hop-by-hop, in the absence of any Internet, such as unique addressing mechanism for the UWSN and frequent topology change, it makes hop-by-hop reliable data transfer approach more attractive.

It is proved that hop-by-hop reliability models are more energy efficient as compared to end-to-end reliability models [5]. Hop-by-hop control methods react faster in such type of networks in which intermediate nodes are adjusted the transmission rates. It not only results in energy saving but also can help to detect the congestions with smaller end-to-end delays.

III. RELIABILITY CHALLENGES FOR UWSN It has been shown that Transmission Control Protocol

(TCP) and other congestion control mechanisms like this are highly problematic for wireless multi-hop networks [6-9] TCP is a connection oriented protocol that requires 3-way handshake between the sender and receiver before actual data packet transmission starts. In UWSNs, where actual data might be only a few bytes, the 3-way handshake process will definitely is a burden for such a small volume of data. Moreover, UWSNs are considered as multi-hop where each inter-hop link characterized by its pathetic and error prone acoustic channel. So the time required to establish a TCP connection between two end nodes that are a significant number of hops away from each other, might be very high. For reliability concerns, TCP required an end-to-end ACK and retransmission strategy, which can results in a poor throughput

and longer transmission time. Most of the available TCP implementations use a window-based mechanism for the flow control functionality, which requires high accuracy for round trip time, which itself is twice the end-to-end delay . Also TCP assumes that only congestion is responsible for the packet loss, so it focuses mainly on the congestion control mechanisms by decreasing the transmission rate. But for UWSNs, data packets can get lost due to many problems like, erroneous acoustic channel and failure of nodes. When packet losses are due to problematic channel, then there is no need to decrease the transmission rate to maintain throughput efficiency.

On the other hand, when we talk about UDP, then it does not offer any flow control and congestion control mechanisms. In the case of congestions, UDP simply drops the packets without providing any scope for recovering these lost packets. Besides, no ACKs are available when we choose UDP as a transport layer protocol as it relies on the lower or some upper layers in order to recover the lost packets. So, such UDP like approaches obviously does not work for UWSN. Finally, rate based transport protocols are also not reasonable for UWSNs [10]. Although, the protocols belong to this category, do not implement a window-based procedure, still their performance base on the feedback control messages sent by the destination. These feedbacks used to adjust the transmission rate during different conditions, which are not appropriate for these networks. In addition to unavailability of end-to-end paths, large delays and delay variations can create instability during the feedback control messages.

IV. PROBLEM BACKGROUND As previously discussed, it is infeasible to achieve end-to-

end reliability due to the frequent network partitioning of UWSNs. For this, it is necessary to focus on the hop-by-hop reliability to make it more responsible for these environments.

In normal hop-by-hop ACK (HbH-ACK) scheme; only two nodes are involved, as receiving node will reply the ACK when it successfully receives an error free packet. When the sending node receives the ACK, it can discard the current packet and continue to process the next available data packet. For stable environments such as wired networks, this HbH-ACK has no objection, but for the unstable environments like underwater as nodes can die or get lost due to many reasons, this traditional ACK method is not suitable. Here, the receiving node is the only node in the network, which has the current data packet now that the sending node will discard it after receiving the ACK [11]. For UWSNs, due to the continuous node movements and sparseness, it is possible that the receiving node cannot find the next hop for long intervals to reach the destination. During this, it can die due to limited power or any failure can occur due to fouling and corrosion problems. In such cases all the packets held by the current node will be permanently lost because none of the other node maintains the backup of these lost data packets.

In the case of 2H-ACK [12], two nodes are involves to make the copy of same data packets until first node receive the acknowledgement to increase the reliability of network, but still this technique have a problem of data packet loss. The ACK method can be interrupt when the intermediate node will die due to limited power or any failure can occur due to

fouling and corrosion problems. Due to this, the source node does not know, how long it have to keep the save copy of data packets and when it will delete the data packets. Therefore, this can be the reason for over burden on the network, more energy consumption and as well as the memory of that node can be lost.

V. PREVIOUS WORK In [13], the authors presented Layer by Layer Angle based

Flooding (L2-ABF) protocol. The angle-based flooding approach is used in this proposed routing protocol. This routing mechanism is not based on sensor node location information and has been designed for delay and power-efficient multi-layer communication in underwater acoustic networks. In this routing mechanism, there is no need for a sender node to know its own location or the location of the final destination (Sink) before transmitting the data packets. Anchor nodes flood the sensed data towards the surface sinks via the upper layer nodes. The forwarder node will define the flooding zone by using the initial angle Ө =90 ±10K. Here, K is a variable and has a finite set of values, K, (1, 2…..8). After defining the flooding zone, the node will send Hello Packets (HP) within the defined zone and wait for the Hello Reply (HR). If there is no HR received, the node will increase the value of K in the initial angle, to increase its flooding zone until the basic condition is met (0 < Ө < π). Here, it assumes that after the completion of one round by using the different values of the variable k and the node did not receive any Hello Reply, the nodes can send data packets directly to the sink nodes on the maximum power. Here, it is important to note that nodes can use random values of the variable K to increase the size of the flooding zone. The randomness of the K value is more helpful to control the End-to-End delays as well as the power consumption of the nodes. The selection of the random values for K depends on the movement of the nodes.

VI. 3 HOPE-RELIABILTY MODEL

This reliable model is used to increase the reliability for our designed routing scheme Layer by Layer Angle Based Flooding (L2-ABF) [14] which has many advantages such as not requiring any specialized hardware, no dimensional location information required and easiness in handling node movements without maintaining complex routing tables. For getting more precise results, it furthermore still requires some reliability mechanisms to handle the problem of node or packet loss. Following are the purposes to design this reliable model:

• Assure the delivery of data packets • To control the congestion in the network • To save the energy consumed.

In this model, three nodes will take the responsibility to keep the copy of same successful transferred data packet. Fig.2 describes the process to keep the copy of data packets and data packet forwarding by using the 3H-RM. Here it is important to note that every node will check the field “Step” value of the final message. If it is less than three, nodes will forward the data packets to next hop; otherwise node sends the final message to the previous forwarder when send the data

packets to next layer nodes. The node N21 has data packet and it ready to send towards the destination on water surface (Sink). Node N15 resides in the upper layer and inside the flooding zone which is calculated by node N21. Node N21 sends the Hello Packet inside the defined zone. Fig. 3 described the Hello Packet format. N15 is inside the flooding zone and send Hello Reply to node N21 along with the information as mention in Fig.4. After receiving the hello reply the node N21 send data packet to node N15 with keeping the save copy of data packets and adds 1 in the value of field “Step” of the final message. The final massage formats is shown in Fig.5. The node N15 will check if the value of “Step” field still is less than 3 and will follow the same process as N21 and send data packets to node N8 with the increment 1 into the current value of “Step” field. N8 will check if the value of “Step” field is still less than 3. So node N8 will send the data packets to node N5. When node N8 will receive the acknowledgement from N5 and the “Step” field value becomes 3, the node N8 send the final message to the lower layer nodes that have a save copy of these data packets by reducing the value of “Step” into zero. After receiving the final message to node N8, it will delete the save copy of data packets and rebroadcast the final message to previous forwarder nodes so they can delete the data packets from the memory. The three hope final message algorithm is described in Fig 6. Although, nodes delete the saved data packets when nodes will receive the final message but the deletion of data packets is not fully depend on the receiving of final message. To save the memory and remove the extra burden on the network, nodes can delete the save copy of data packets with the time limit which is 60s after sending the packets and did not receive the final message. This time can be varied but now we are using 60s in our simulations.

Fig. 2: Keeping the copy of the data packet between 3 nodes

Fig. 3: Hello Packet format

Node-ID Layer-ID Energy

Status Priority Buffer Size

Fig. 4: Hello Reply format

S-Node-ID Layer-ID

Fig. 5: Three step final Message format …………………………………………………………………

Algorithm: …………………………………………………………............

Data packets (DP) ready to send 1. Step=3 2. Define the flooding Zone 3. Send Hello Packet inside Zone // to find the next node 4. If ( Hello Reply received) yes 5. Send DP 6. Save the copy of DP into buffer 7. Wait for Three hope final meaasge 8. If ( Step < 3) 9. Add 1 in the value of Step 10. Else 11. Steps==0 12. Send the final message to previous three hope 13. If ( three hope Final message received) 14. Delete the save the copy of DP 15. Else 16. Go to step7 17. Else 18. Go to step 1 19. End ……………………………………………………………………

Fig. 6: Three hope final message process

A. RESPONSE TIME CALCULATION Forwarder node � will define the flooding zone and

transmit a data packet to the node inside zone. Then, if no ACK is received from the receiving node of the packet, it will wait with no further calculation of flooding zone for the same data packet. There will be no immediate reply by Node N15 to the ACK. It will first attempt to locate the next hop. As soon as the next hop becomes available, the node will then transmit the ACK to forwarder node �. Equation (1) can be used to calculate this response time for node �.

4 (1)

As can be seen in (1), the total response time , is dependent on three parameters. These parameters are as follows. The propagation time for each single hop is , the processing time is and the time that N15 can use to locate the next hop is β. Among these parameters, and , can be fixed, such as with the processing power of the node and the propagation speed (1500 m/sec) [15]. Therefore, both of these values are constant. On the other hand, β can be a value that varies in relation to conditions in the environment. Moreover, it is dependent on the speed of movement of the nodes, �, and the network density, �. Under particular conditions, the hello packet is transmitted by N15 which then must wait until the next hop in its flooding zone becomes available. The � and � effect can be represented by the following:

β ∝ 1/� (2)

β ∝ � (3)

When there is a decrease in the value of �, there will be an increase in the value of β and vice versa. The value of β will be zero, with average densities, if the requesting node is able to locate neighbor nodes in its flooding zone area. However, in less dense areas, if the node making the request is unable to locate any neighbor node, it must wait for another node to come into flooding zone. Otherwise, the requesting node can be moved into another node’s range by the movement of the water currents. The value of β is dependent on � in these types of situations.

Typically, a node’s movements in terms of direction are mostly irregular; however, it can move 1-3 m per second [16]. Because of the characteristics of these movements, nodes may enter and exit the coverage area almost simultaneously. The value of β depends only on � if it is assumed that � =0.

B. CONGESTION CONTROLL AND ENERGY CONSUMPTION As we know that, congestion is an important factor for any

type of networks and as well as for the transport protocols. We can achieve a significant reduction in buffer size requirements when we are using the layer-by-layer approaches instead of end-to-end or per hop with high probability of delays. As we have already mention that in L2-ABF uses layer-by-layer approach to forward the data packets. So it is clear that the source node or even the next hop node on the route will not decide the next forwarder on the available information in the routing table. In L2-ABF, the nodes are inside the calculated flooding zone that can only participate in the process of data packets forwarding. Rest of the nodes that are not part of the forwarding process in the network can save the energy to prolong the network life time. Every group of three nodes maintain the same copy of successful transferred data packets which provide the guaranty delivery of data packets on destination and also helpful to reduce the retransmission of loss data packets in the network. Calculated and guaranteed transmission of data packets can reduce the overall energy consumption of network.

VII. RESULTS AND COMAPRISIONS NS-2 was used to evaluate the performance of proposed

reliability model. In our simulation, 40 sensor nodes (grid network of 8 × 8) were deployed in the area of 400 × 400 meters. The transmission range for sensor nodes can be up to 100 meter, where depth and width of every layer are defined at 80m. Among these, 10 nodes are deployed in the bottom of defined area and consider these nodes remain fixed. Remaining 30 nodes will form 8 layers with distance of 50m between each layer. Multiple sinks are used at water surface and all sinks are static having a support of both (Radio, Acoustic) types of communication [17]. The horizontal movement was considered between the floating nodes due to different water current up to 1-3m/s at fixed motions. Due to these movements, random way point mobility model was used to handle the problem of slows down the movements of nodes. Every sensor nodes can hold 50 data packets in its buffer memory. The medium access control (MAC) protocol is used based on the IEEE 802.11 and traffic resources are constant bit rate (CBR) with 1000 Byte data packet size.

Source Node-ID

First Forwarder ID

Second Forwarder ID Step

Data delivery ratios with different number of nodes, time intervals and end-to-end delays were considered the metrics to evaluate the performance of our routing protocol. Packets losses and duplication are also compared to the existing technique. Packet and time intervals as generated by all sensor nodes in the network.

(a) Delivery Ratios with different number of nodes

(b) Delivery Ratios with time intervals

Fig 7: Comparisons between 3H-RM vs 2H-ACK

Here the results of our proposed model presented and compared these results with a well-known existing reliability scheme 2H-ACK. From the Fig. 7 (a), it is clear that the generated results of 3H-RM are better when the number of nodes in the network is starting to decrease. The delivery ratios with different number of nodes are decreasing with pace when we use 2H-ACK model. At the same it can be seen that delivery ratios are less affected in 3H-RM. The biggest difference in delivery ratios can be seen when around 20% nodes are available in the network, still 3H-RM can achieve around 90% packets delivery ratios. As UWSNs are error prone and nodes can die or leave the network any time which might be resulted to the sparseness of the network, so in this situation, the 3H-RM model is more results oriented with small densities of nodes.

Further, we compared the data delivery ratios with different number of time intervals. In Fig. 7 (b), it can be seen the difference in delivery ratios is smaller in the beginning and both techniques have almost the same results. With the passage of time when the generated numbers of packets are increasing, the data delivery ratios are starting to affected but 3H-RM is seem to be less affected than the 2H-ACK model.

Fig. 8: Data packet loss

To provide the reliability in 3H-RM, different nodes having the three copies of same data packets. Although, there is a possibility that multiple copy of same data packets can be received at the sink nodes, the probability of this happening is very low as compared to data packet losses. Fig. 8, described these observations by illustrating the comparison of data packet duplications with the average of data packet losses. In 3H-RM, very small number of duplicate of data packets are received at destination, while the packet losses are also very small. On the other hand, the result of 2H-ACK shows that the data packet losses are very high and duplication of data packets is also above the benchmark. These high data packet losses are due to node failure or sparseness of nodes in the both cases when nodes cannot communicate with each other and all the data packets reside in the buffer of nodes.

Fig. 9: End-to-End Delays

Fig.9, describes the comparison of end-to-end delay between 3H-RM and 2H-ACK. Here we can see that a delay is increasing when numbers of nodes are decreasing in the network. Delays are very less when 100% nodes are available in the network. 3H-RM are less affected as compared to 2H-ACK when numbers of nodes are decreasing. This is only due to congestions that are not properly handled in 2H-ACK when nodes floods data packets on the whole network. On the other hand, an angle based technique is used in 3H-RM to prevent the flooding on the whole network.

VIII. CONCLUSION In UWSNs, turbulent conditions of the environments

where the sensor nodes are deployed can cause the nodes to die. This causes a reduction in how the networks perform. Commonly, data packets that are lost can be recovered via the help of retransmissions and acknowledgements. However, an increased amount of traffic as well as quite a bit of end-to-end delays is often the result; this, at times, leads to nodes failing. Generally, for networks that are multi-hop, hop-by-hop is thought to be superior in regards to reliability. On the other hand, sensor nodes in UWSNs are more likely to die because of energy loss which causes a tremendous number of packets to be lost. We have proposed a Three Hopes Reliability Model (3H-RM) so that this issue can be dealt with. In our model, three nodes keep a copy of the same data packet. Data packets in underwater environments, no matter at which place in the network they are created, usually need at most 6 to 8 hops to get to their destination. Our proposed approach, under these conditions, can deal with the loss of packets and also aids in the reduction of the issue of congestion with no extra toll being put on the network. Our conclusions have been clarified and supported by simulation results that were achieved using a variety of parameters. The more power reduction in 3H-RM can be the future work.

ACKNOWLEDGMENT We would like to thank Universiti Teknologi PETRONAS

for supporting our research.

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