Improved Cooperation in Underwater Wireless Sensor Networks

The WSNs (Wireless Sensor Networks) lead to great opportunities to explore it scientifically. In this network different numbers of SN (Sensor Nodes) are deployed in a specific area to gather information. The UWSNs (Underwater Wireless Sensor Networks) is a highly distributed network of sensor nodes deployed underwater to gather environmental information. Hence, acquirement of real-time data at enhanced data rate and to reduce power consumption is a key concern while designing routing protocol for UWSNs. In this paper, a cooperation based solution is suggested. The solution proposed here uses the DF (Decode and Forward) strategy for relying the information from the source to the destination using a relay node. The signals coming towards the destination are weighted and combined on the basis of their SNRC (Signal to Noise Ratio Combing). The simulation results verify enhancement in different factors, required for evaluation of a UWSN. After implementation of the proposed solution the stability of the network is increased which maximize the PDR (Packet Delivery Ratio). In our proposed solution the transmission is based on channel estimation, an estimate is made for higher reliable channel, which reduces retransmission of packets. Hence, sink receive the packets with lesser delay and as a result E2E (End-to-End) delay is decreased. Data is forwarded using data forwarding by neighbor nodes. It improves average energy consumption of the system. Hence the overall performance and lifetime of a UWSN is increased.

categorized different protocols on the bases of localization. Ahsan et. al. [5] has discussed different types of protocols for UWSNs, they grouped them into three categories i.e. Clustering-based routing, Localization-free routing and Cooperation-based routing.
Different types of protocols of said categories have also discussed. Jafri et. al. [6] suggested an AMCTD (Adaptive Mobility of Courier Nodes in Threshold-Optimized Depth-Based) routing protocol. In AMCTD, the network lifetime was enhanced by implementation of optimal weight functions. This proposal consists of 3 stages; weight updating, depth threshold variation and adaptive mobility of courier nodes.
Sheeraz et. al. [7] and Li et. al. [8] proposed Co-UWSN (Cooperative UWSN). In these solutions relays are nominated on the basis of their distance and SNR calculations of the channel conditions. Sheeraz et. al. [9] proposed ARCUN (Analytical Approach towards

COOPERATION BASED MODELING
We consider a situation of WSN that contains "Ni" SNs randomly deployed in a particular area underwater. In this model every SN has two roles, either it works as "source" to send its data or to work as "relay" to assist other nodes by sending their data to the destination. Let "S i " is ith number of source nodes and "R i " is ith number of relay nodes. Let "A" be the area under sea where these SNs are randomly deployed. Consider "L" be the total number of links creating a path between different sensor nodes.

Cooperation Model
This transmission mechanism ensures a distinct transmission between source node and relay. Fig. 1 represents a 3 nodes cooperation model. we use multi hop mechanism for transmission of data, each source node uses less power to transfer its data towards it neighbor.
Through this data forwarding technique using neighbors, the data is send towards destination by consuming less power as that of an individual node respectively. This mechanism works in two-steps.
In the 1 st step the S (Source) directs data towards the D (Destination) and the R (Relay) simultaneously, and in the 2 nd step "R" sends the received data to "D". Total distance between source and destination is "d 1 + d 2 "where, "d 1 " is separation between source and relay pair while the relay and destination are separated by the distance "d 2 ".
The mathematical model for first step of transmission is given in Equation (1-2): Here the Y SR is the data symbol received from the source to relay and Y SD is the data symbol received from source to destination node respectively. The X S is the forwarded symbol, SR and  SD are the channel noises from the source to the relay and from the relay to the destination respectively. The h SR and h SD are path coefficients from the source to the relay and the source to the destination node correspondingly. The h SR and h SD are stated in Gaussian random variable CN(0, 2) where "2" is the variance and "0" is its arithmetic mean.
In the second step, the relay implements DF processing technique on received data and then re-transmits it to the destination. The mathematical equation of the second step is given in Equation (3): The f (ySR) represents the processing function on the received symbol. The  RD is the noise of channel between relay and destination.

Network Model
We consider a "K-hop" cooperative channel which is designed by using communication techniques. In this model every SN has two roles; either it works as "source" to send its data or work as "relay" to assist other node by transmission of their data. Hence, this transmission mechanism allows a non-overlapping transmission of source node and relay. The bandwidth of around 10-32 The probability of overlapping is low and because of that reason non-overlapping channel have been assumed [15].
For a group of receivers r i R k and transmitters tT k the channel is in series with l-number of links (l 1 ,... … … l i )) that are cooperative. It links the i-sources to the r- The Equation (4) may be stated as:

Channel Model
We assume a "t i " node, forwarding its data "x i " to the receiving node "r i " which is accepting data "y j " in direct phase or broadcasting. The "x i " has the unity power while "t i " transmitter has the ability to withstand its power P b,i to P max . Data received at r j can be denoted by Equation (6).
Where, the N j is the noise of underwater channel, and the r j represent interference in the UW channel. The "d" represents the direct transmission at superscript. Where the "d ij ", is separation between "r j " and the node "t i ". The "α" is path loss and it normally varies from 1-3. The h ij is the complex channel gain among "t i " and "r j ", it can also be formulated as Equation (7) Here P nj is the noise power at the receiver "r j .Ash ij  2 =1, so Equation (9) can be written as: Here the B is used for the bandwidth for the x j transmitted signal having the power P xj .

Channel's Reliability
The technological advancement and inventions of many useful techniques has decrease the data loss due to outage of the channel. We also focused on link reliability to achieve diversity. The results of different combining strategies may apply to achieve diversity and reliability.
Many links join together to make hops, then these channels make a sequence of nodes that communicate with each other, the whole process complete a channel between the S and D. The transmission will consider as successful when entire data is transferred from the source to the destination. The probability of end to end reliability can be represented as "!, it may be formulated as Equation (12)(13): Where  is representing a function that established on separation between two nodes, their depth in water and channel's state.
From Equation (13), the total reliability can be calculated for a complete end to end channel: For the path that has reduced distance or minimum distance and decrease this sum will have maximum reliability.

Relay Strategy
In the existing scheme [5]

Improved Cooperation in Underwater Wireless Sensor Networks
By combining the values of Equation (16) Here, x a , y a and  a (for a = 1,2,3 … N-1) are the signs being transmitted and received at relay.

Combining Strategy
The combining strategies used to combine a signal that is coming from multiple sources. As the destination node received packets from multiple paths, so these packets have to recombine at the destination. We used Signal to SNRC for the signals coming from the source and the relays.
The SNR is parameter that is used to analyze the quality of the channel. This can also be utilized to weight the received signals. This is represented by Equations

SIMULATION RESULTS
The MATLAB simulation has been done to analyze the performance of the IM-COUWSN. For this purpose, we simulate an underwater environment that covers an area of 500 square meters. The BW is around10 Kb on each medium. We deployed 225 nodes that are followed by 10 sinks on surface. There can be three or more than three nodes between source node and sink nodes, however the delay is around 0.05 seconds. This pattern has been randomly varied for 5000 rounds of simulation by considering multiple sink models. We assume 100 meters as range of transmission for sensor node that also

Network Lifetime v/s End-to-End delay
It is the time required for a packet to travel through the network from its source towards destination. It is another KPI for UWSNs. The IM-COUWS improves the E2E delay which is achieved by minimizing the forwarding distance between SN. The Fig. 3 shows the result of three simulation rounds.
For the CO-UWSN this E2E delay is higher for startup conditions because of larger the distance between SNs.
The E2E delay than decreases as the system complete initial 500 rounds, because at that time network become sparse.

FIG. 2. NETWORK LIFETIME V/S STABILITY PERIOD
In the IM-COUWSN the E2E delay is better than the CO-UWSN, because of load balancing.
The CO-UWSN transfer packets with least number of hops but due to low quality channel, the packet loss increases at the destination. Hence, the packets need to be transmitted again. This retransmission of lost packet also causes E2E packet delay [5]. In IM-COUWSN transmission is based on channel estimation, an estimate is made for higher reliable channel, which reduce retransmission of packets. Because of this estimation in the IM-COUWSN the sink received packet with lesser delay.

Network Lifetime v/s Energy Consumption
It is the energy consumed by network from the initialization state to the state where last SN dies. This represents total energy consumed by network for its operations. The total energy consumption is always a key concern while designing routing protocols for the

Network Lifetime v/s Path Losses
The path losses are the loss of channel through which a packet is travelling it is also known as path attenuation. In the CO-UWSN, the transmission loss

Ratio
It is a fraction of the number of packets received to the number of packets transmitted. It is an important parameter to examine the performance of any routing protocol designed for UWSN. In Fig. 6, a relationship has shown between the IM-COUWSN and the CO-UWSN.
It shows that the PDR is comparatively better in the IM-