Underwater acoustic sensor network (UASN) refers to a procedure that promotes a broad spectrum of aquatic applications. UASNs can be practically applied in seismic checking, ocean mine identification, resource exploration, pollution checking, and disaster avoidance. UASN confronts many difficulties and issues, such as low bandwidth, node movements, propagation delay, 3D arrangement, energy limitation, and high-cost production and arrangement costs caused by antagonistic underwater situations. Underwater wireless sensor networks (UWSNs) are considered a major issue being encountered in energy management because of the limited battery power of their nodes. Moreover, the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of service. Moreover, increased transmission power levels result in higher channel interference, thereby increasing packet loss. Considering the facts mentioned above, this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in UWSNs. The contributions of this technique is threefold. First, it uses the adaptive power control mechanism to utilize the sensor nodes’ battery and reduce channel interference effectively. Second, thresholds are defined to ensure successful communication. Third, clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference significantly. Additionally, mobile sinks are deployed to gather information locally to achieve the previously mentioned benefits. The suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWSN strategies. Findings show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio.
In addition to this study’s motivations, this section provides a brief overview and introduction to underwater wireless sensor networks (UWSNs) highlights the contributions of this study, and describes the structure of the paper.
Water can be found almost everywhere in the world. It covers approximately 70% of the earth. Underwater environments are incredibly essential for human existence. Research focuses on underwater elements because of the decrease in terrestrial methods. UWSNs remain as researchers’ interests because of their capability to screen underwater environments. UWSNs are broadly utilized in coastline observation and assurance, sea calamity anticipation, observing of underwater contamination, military protection, route assistance, checking of marine oceanic environment, and underwater asset investigation [
Moreover, UWSNs face other challenges, such as low bandwidths, that result in low data rates. The available underwater bandwidth is 40 kbps. The low propagation speed results in high delay. Underwater propagation speed is almost 1500 m/s or essentially 5 times less than the radio waves [ Most existing routing schemes employ sensor nodes’ peak transmission power while transmitting packets to neighboring nodes or sinks. This mechanism results in the rapid energy consumption of sensor nodes. Moreover, high transmission power utilization creates more channel interferences [ The unbalanced load distribution of forwarding data results in the rapid reduction of node energy and creates energy holes in the network. Owing to the occurrence of energy hole, various sensor nodes die prior to their rest. This energy hole problem results in increased energy consumption and decreased network lifetime [ The existing schemes cannot avoid additional load on nodes near the sink node. Thus, any node near the sink expends its energy faster than those positioned away from the sink node. Consuming energy rapidly also decreases the PDR [ A majority of the existing UWSN considers the use of one or multiple static sinks on the water’s surface. These sinks acquire data from sensor nodes using different routing mechanisms. This approach poses great problems in scalability when the network size is increased. Moreover, it creates a bottleneck and lowers the performance of the network. Additionally, the nodes closer to these sinks deplete their energy very rapidly and create energy holes that force the rest of the nodes into distant communication and results in rapid energy depletion. Thus, the overall network lifetime is reduced.
Most of the techniques do not apply the mechanism of adaptive transmission power control. Furthermore, they work well in most situations. However, in extreme underwater environment where a lot of disturbances, fading and noises, unwanted failures, interferences, and collisions occur, the use of maximum transmission power slows down the performance of the network. Thus, a routing scheme that utilizes minimum transmission power must be proposed to improve the lifetime, PDR, and interference of the network. The routing protocol that is being proposed in this work minimizes channel interference and reduces energy consumption in sparse areas using an adaptive communication power control mechanism. Furthermore, in dense regions, clustering is implemented to decrease repetitive transmissions. By contrast, mobile sinks (MSs) are used to gather information for minimum energy consumption.
Underwater wireless sensor networks (UWSN) elicit considerable attention in academic, research, and industrial fields. Reduction of energy depletion of sensor nodes improves the network life of UWSNs, which in turn decreases the overall network cost [ First, an unbalanced load for forwarding data results in the rapid reduction of node energy, known as the energy hole. Owing to the occurrence of energy hole, various sensor nodes die prior to their rest. This energy hole problem increased energy consumption and decreased network lifetime [ Second, these existing schemes cannot avoid additional load on nodes closer to the sink node. Nodes closer to the sink devour their energy rapidly than nodes positioned away from the sink node because of the large load. Therefore, consuming energy rapidly also decreases the PDR [ Third, these schemes use maximum transmission power to reduce network performance [
This research presents a controlled transmission power-based SEEC (CTPSEEC) protocol in UWSNs. The contributions of the proposed protocol is threefold. It uses the adaptive power control mechanism to utilize sensor nodes’ battery and reduce channel interference efficiently. Moreover, thresholds are defined to ensure a successful communication.
Furthermore, clustering is implemented in dense regions to decrease the repetitive transmissions that affect nodes and interference’s energy consumption ultimately. Additionally, MSs are deployed to gather information locally to achieve the previously mentioned benefits.
Most techniques do not use the mechanism of adaptive transmission power control. Furthermore, they work well in ultimate situations. However, in an extreme underwater environment, where a lot of disturbance, fading and noises, unwanted failures, interference, and collision occur, the use of maximum transmission power slows down the performance of the network. Therefore, a type of routing scheme that utilizes minimum transmission power must be proposed to improve the network lifetime, PDR, and interference. This routing protocol is being proposed to minimize channel interference and reduce energy consumption in sparse areas using an adaptive communication power control mechanism.
Furthermore, in dense regions, clustering is implemented to decrease repetitive transmissions. By contrast, MSs are used to gather minimum energy consumption information. The advantages of our proposed protocol can be summarized as follows:
Channel interference that ultimately increases the network lifetime reduces the interference and avoids the energy hole creation. The clustering technique is incorporated in dense regions to reduce the duplication of transmitted packets and balance the load of data packets that potentially reduces the interference and improves the battery usage of sensor nodes. Deployment of MSs to collect the packets for enhancing the network lifetime locally by reducing transmission distance and minimizing the interference. Moreover, it helps avoid energy holes and increases successful packet delivery.
The rest of this paper is organized as follows:
Section 2 presents the background and classification of the routing protocol of UWSNs. Section 3 discusses the state-of-the-art work related to this article’s focus area. Section 4 presents the proposed solution.
Section 5 explains the simulation setup. Section 6 presents the performance evaluation of the proposed scheme in terms of network lifetime, stability period, residual energy, packets received per round, and total packets received at the sink. The proposed protocol is evaluated on the basis of the following metrics: network lifetime and packets received per round residual energy. To verify the proposed scheme, rigorous comparison with current state-of-the-art schemes is conducted with depth-based routing protocol (DBR), energy-efficient DBR (EEDBR), and SEEC underwater routing schemes by using simulation parameters, as shown in
Simulation parameters | Values |
---|---|
Node numbers | 100 |
Initial energy of nodes | 5 J |
Data rate | 16 Kbps |
Bandwidth | 16 Kbps |
Center frequency | 30 KHz |
Packet size | 50 Bytes |
Packet reception power | 0.1 W |
Packet transmission power | 2 W |
Transmission range | 50 m |
Running rounds | 3500 |
Packet transmission power | 2 W |
In this section, some of the concepts aside from the existing routing approaches in UWSNs are discussed briefly. These concepts are grouped according to their characteristics and functions; the authors classified and discussed the routing protocols as non-localization, localization-aware, and clustering-based protocols (
This class contains strategies that do not need prior geographical knowledge of the network. The strategies implement processes without requiring the position information of other nodes. Prior geographical knowledge of the network in such strategies is not needed. Such strategies are mostly targeted toward flooding events. These strategies are viewed as having a fast packet delivery rate and minimum E2E delay.
In DBR [
Authors in [
This class of routing strategies commonly require the geographical updates of all nodes and sink positions. These strategies are supposed to be energy effective. However, most of the energy is wasted during the collection of geographical updates. The registrations are frequently modified because the nodes’ location may shift due to water flow. In localization-aware routing protocols, a node requests information from every node in the network and the sink. This framework requires former network data to function. In [
Similarly, in [
Furthermore, in [
A clustered-based routing protocol on node mobility is established to indicate a cluster’s structure. This technique improves the cluster head (CH) nodes and cluster-element nodes. CH nodes gather information from clustered element nodes and pass it to the sink nodes. The presented study defines each cluster-based routing protocol based on node mobility, as described below. In [
SEEC makes the network efficient by moving the sink in scattered areas and clustering in dense regions. SEEC also ensures network stabilization with the best clustering in densely populated areas, where each logically dense site represent a fixed set. SEEC reduces network power consumption by distributing load
where
The node with low depth and high energy level is chosen as CH. The authors proposed a “cooperative energy-efficient optimal relay selection protocol” for wireless underwater sensor networks in [
This method eliminates the necessity of synchronization among the source, relay, and sink nodes. Furthermore, the sink transfers the acknowledgement to the source node to receive or re-transmit the data. Hence, this scheme adds in a fewer amount of packet drops. However, the data load on the source, relay, and sink node may increase or reduce the nodes’ stability period. The authors in [
For instance, in [
This study focused on efficient energy expenditure by using sensor nodes at sparse areas with the free location of high mobility sensor nodes underwater. After reviewing several routing protocols and classifying them as location-free and location-aware protocols, DBR, EEDBR, and SEEC are proposed to reduce the energy consumed by the sensor nodes during data transmission. We compare the implementation among these energy-efficient location-free routing protocols in UWSN. We found that DBR and EEDBR work quite well in dense areas in the network. However, they do not work well in sparse areas.
In DBR [
In [
The authors in [
In [
The lack of uniform load distribution among sensor nodes causes high interference, thereby resulting in lower network performance concerning rapid energy consumption in sensor nodes. Moreover, sensor nodes use the maximum transmission power level during data transmission. Consequently, the probability of energy hole creation is also increased, thereby resulting in a decline in network lifetime. Considering the consequences of existing protocols, we have proposed controlled transmission power-based SEEC (CTP-SEEC) to enable UWSNs to overcome the challenges identified during simulations. Our objective is to enhance the overall network time by decreasing the rate at which energy is depleted, increasing the PDR, reducing the interference, and achieving a stable network in sparse regions. The proposed routing protocol is applied to minimize channel interference efficiently. It also utilizes the sensor nodes’ energy efficiently by reducing the amount of energy expended in sparse networks.
Moreover, it utilizes the adaptive power control technique, which is similar to the performance of SEEC in terms of dividing the network field into 10 equal-sized sub-areas, finding and specifying sparse and dense areas using network algorithms, and using two MS spreads in sparse areas. However, its distinctiveness from other routing protocols is used in incorporating the transmission power control technique. CTPSEEC function includes two stages: setup-stage and communication data stage.
During the setup-stage, cost fields are built up in each node’s routing table to determine the fastest route from the source to the sink. When the setup stage is accomplished, communication data are transmitted to the sink using the minimum cost route. A sender node transmits information to its adjacent node using the stored and adjusted TPL. The sender node will adjust its TPL adaptively upon receiving acknowledgement with RSSI value. The sender uses the condition of the upper and lower threshold values. It compares the receiving RSSI value with its RSSI lower and upper threshold values. If it crosses any of these values, then the sender node tunes its TPL accordingly (
In the proposed scheme, the network is composed of three types of sensor nodes, namely, anchored, relay, and sink nodes. Anchored nodes are responsible for data collection from the bottom of the water fixed in a certain location. By contrast, the relay nodes are placed at a different underwater positions that forward the received packets toward the sink [
We divide the site into 10 sub-areas of equal size to specify the dense and sparse areas. AE and AW refer to the right and left side areas, respectively. The start point coordinates considered a measure point for area creation. Start point coordinates are referred to as S(X0; Y0).
The following equations split the network site into five left side areas:
The following equations split the network site into five right side areas:
where B is the X-axis point of an area. Double B is used for the right side areas of
where L is calculated from the length of the network site. L is the Y-axis point of an area. The values of A
From
where N is the number of areas located either left or right. The coordinates for an N
Sparse and dense regions are determined through Algorithms 1 and 2, as mentioned by the SEEC protocol, respectively [
When sparse and dense areas are sought, the following stage is a grouping of nodes in dense areas. We utilized a grouping strategy for areas with the highest density to expend energy productivity and system lifespan. In SEEC, the nodes in a dense area cooperatively selects a node to be the CH and then transmit information to the elected CH. The head performs information conglomeration and transmits the compacted information to any of the closest sinks. The choice of the CH is determined by the bottom and remaining energy. CH selection: The process of determining CH in SEEC is not the same as those in different protocols. In SEEC, a node with low bottom and remaining high power is selected as a CH. The choice model of selecting a node as a CH in a particular area depends on the accompanying conditions:
where
where,
where A
In this phase of network arrangement, its field is distributed into n sections, and relay nodes are arbitrarily positioned in the network area (
Moreover, each receiving node sets a suitable transmission power level (TPL) with the sink based on the received signal strength indicator (RSSI). The cost values in the number of hops are forwarded after a delay1 to other CH in the adjustment dense regions. The receiving CH nodes store the cost value and set a suitable TPL based on RSSI. Each node determines its distance from the sink along these lines, sets a suitable TPL, and then broadcasts this information in its transmission range. Other nodes follow the same procedure. If a node receives a value greater than its current one, then it disposes the ping message; otherwise, it refreshes its distance information, sets a suitable TPL, and broadcasts the ping message again to nodes in its transmission range. This procedure continues until each sensor node sets a suitable TPL with the selected neighbors and obtains its distance from the sink. In brief, the control packet in the form of a ping message contains a cost field, which reflects the number of hops to reach the sink. In the setup phase, such control packets are propagated throughout the network. This approach establishes a minimum or shortest route from source to sink and achieves the TPL adjustment of each node with its selected neighboring nodes.
Sparse regions refer to the areas with the least number of nodes. Sparse regions are sought, and two portable sinks are located in these regions for information gathering in SEEC. The sinks are versatile. For instance, moving sink 1 (MS1) varies its situation every round from highest in sparsity to lowest meagre sparsity aside from the district of MS2. However, MS2 stays in the area with the highest sparsity until every sensor node in the region passes. All sensor nodes are kicked by MS2. At that point, it converts its situation into the area with the highest sparsity among the remaining sparse areas.
Meanwhile, MS1 varies its location as required. The MSs should be placed around the center of the region so that most of the nodes connect with it. Sparsity is the most reasonable way to deal with gathering information from the greatest sensor nodes of the system, to accomplish something great using the least energy of the whole system. Likewise, awareness of the sparsity approach is useful in discovering the dense areas of the system field, where the sensor hubs structure groups. Instead, the sensor hubs transmit information to the sinks through multi bouncing, and nodes transmit information to their district’s particular CH. In this manner, only the CH sends information to the sink. Due to these instances, SEEC is considered an energy effective protocol.
After splitting the site of the network into subareas logically (
Algorithm 1 or DSA is used to find dense regions in the network. Algorithm 2 or SSA is used to find sparse regions. These algorithms search for the number of sensor nodes in each zone. The zones are sorted in ascending array based on the number of nodes. Based on the nodes’ density in each logical area, the sparse and dense areas are specified. For example, [
Both of them move from maximum sparse to the minimum sparse area and change their regions after each round from most sparse to the least sparse region and the opposite correct. In this way, more areas are covered with a void energy hole creation. As a result, more PDR would be achieved. In a dense area, the clustering approach is used. CH is selected using the procedure discussed earlier. A CH node refers to a node with high remaining energy and low depth (
The MS nodes move in the sparse regions with very few nodes. The first MS moves within such regions while the second MS remains in such a region until all the nodes die.
When cost fields are built up through the network in the setup phase, a node that wants to direct information to the sink uses the minimum cost path. The node directs the information bundle to its minimum cost adjoining node while its adjusted TPL is stored in the routing table. After the reception of data packets, the sender node (source or relay) acknowledges the receiving node and the RSSI value. Suppose the RSSI crosses its predefined limit specified as either low or high. In that case, the sender node tunes its TPL accordingly, that is, it switches to the next power level if RSSI is below the limit. By contrast, if the RSSI is above the high limit, the power level declines to a level. The RSSI limits are fixed at the beginning of the data communication phase, depending on the channel condition. The proposed technique is depicted in
This section presents a simulation setup and parameter to evaluate the performance of our proposed protocol (CTPSEEC) in terms of network lifetime, stability period, residual energy, packets received per round, and total packets received at the sink. The simulation period consists of 3500 rounds to obtain accurate results using the same environmental parameters. The total number of 100 sensor nodes is deployed randomly in 100 m × 100 m underwater. Initially, each node contains 5 joules of energy. Each sensor node can transmit in the range of 50 m. The depth threshold is set to 15 m. The proposed protocol is evaluated on the basis of the following metrics:
This metric shows how much time the network nodes function. It refers to the total number of rounds when all network field nodes are alive and functional. This metric is important when considering the effectiveness of a scheme because energy is the primary concern in UWSNs. The ultimate purpose of most of the underwater environment mechanisms is to extend the network lifetime, which is dependent on energy consumption.
This analysis demonstrates the ratio of packets received at the sink in the particular round and their capability to accept the data packets. If the sending packets’ ratio exceeds the threshold, then the packet will be dropped.
This metric refers to the variance concerning each node’s initial energy and total energy after the transmission and reception of data packets.
This section presents the performance evaluation of the proposed scheme in terms of network lifetime, stability period, residual energy, packets received per round, and total packets received at the sink. To verify the proposed scheme, rigorous comparison with current state-of-the-art schemes is conducted. For this purpose, we have considered the DBR [
The network lifetime is improved after implementing the controlled transmission strategy in SEEC (
The random deployment of sensor nodes does not always provide the same number of dense and sparse regions. Suppose the number of dense regions is maximum. The nodes within the same dense regions communicate with the same transmission power. In that case, the stability period is compromised.
Furthermore, a controlled transmission power strategy can be adapted, and then the stability period is improved (
Thus, resources continue to be underutilized, thereby causing the lowest stability period for the protocol. The nodes in DBR without CTP are dead at round 2000 (
In SEEC, the factor of transmission power in the equation was constant. Regardless of the distance between two communicating nodes, almost the same amount of energy was consumed while transmitting a packet. In CTP-SEEC, the function varies its return value after sending the packet, thereby resulting in tremendous residual energy. In the proposed scheme, transmission power is adjusted on the basis of the range from the source to the destination node. The lesser the distance, the lesser the transmission power and vice versa. Less transmission power results in less energy tax, thereby resulting in higher network residual energy in CTP-SEEC. DBR only considers depth and fails to consider the residual energy. Thus, it has the minimum residual energy.
In EEDBR, selecting nodes by only considering residual energy cannot achieve a high PDR in the sparse region. The total remaining energy depletion of CTP-SEEC, SEEC, EEDBR, and DBR is illustrated in
This article investigated the existing methods of different energy-efficient location-free routing protocols of UWSN. Existing studies do not address multiple issues, including the failure to adjust sensor nodes’ transmission power during data transmission. Moreover, existing strategies utilize one or more static sinks at the water’s surface using different routing mechanisms during data transmission from source to sink. This phenomenon creates a scalability issue when the network size increases (