A group of interconnected sensor nodes through acoustic channel form a underwater wireless sensor network (UWSN). The collaborative behaviour of sensing devices in the network enables: monitoring of remote locations, physical environment, temperature, humidity, battlefield, oceans, volcanoes and many more [1
], whereas sensors are the key component of UWSN, which are randomly deployed over the specified network volume, to monitor, sense, gather and transmit the information of interest. In UWSN, sensor nodes have limited battery, which is key consideration while designing a routing strategy. Also the sustainable deployment of sensor system is required to reduce the deployment and operational cost to prolong the network operational time [3
In order to ensure successful communication among the nodes in acoustic network, the necessary factors are required to be considered in the design of a routing algorithm. For instance, the major factors associated with underwater channel need to be analyzed e.g., high delay of acoustic signal propagation because sound can propagate in acoustic environment with speed of 1500 m/s [4
], high bit error rate because of noise and dynamic nature of acoustic medium, limited bandwidth, multi-path fading, etc. [5
]. Therefore, an efficient routing strategy for acoustic channel is desired which balances energy dissipation to optimize the network lifespan [6
]. For minimal energy consumption, geographic routing is widely accepted because of its scalable and simple implementation methodology [6
]. Moreover, the stateless nature of geographic routing allows it to communicate without establishing entire path from source to destination. This algorithm only computes one eligible neighbor which acts as a potential forwarder to relay the data packet. Additionally, this routing mechanism is highly effective when node density is high because it follows greedy forwarding mechanism to transmit the data in multi-hop manner [5
]. While in sparse deployment, due to the greedy approach, nodes select an optimal route in terms of distance which results in immutable selection of the same node resulting in sudden depletion of the node battery [8
]. This death of the node creates energy hole which results in the breakage of the data route because of which downstream nodes cannot deliver their sensed information to the base station.
The aforementioned limitation is avoided through opportunistic routing (OR) paradigm, in which the selection of the forwarder set enures successful data delivery towards the destination node even if one node from the set fails, still, the data is delivered [9
]. However, the delivery of redundant packets at base station degrade the performance of OR. To avoid the transmission of duplicate packets, control message exchange or holding time mechanism is used in opportunistic routing strategy. In the former approach, node with minimum distance and shorter route from the destination compared to nominated neighbors of the sender, is elected to deliver the data by acknowledging with control message that data is delivered successfully. In the later one, holding time is computed for each neighbor node to assign the priority in order to communicate on the acoustic channel. Incase of high priority node failure, node with second high priority in the set, transmits the data packet after its holding time expires. Still, in receiver based communication, duplicate packets from the hidden terminal regions are not suppressed. The hidden terminal is a region, where nodes lie in the transmission range of source node, but these nodes are unable to receive transmission or failure acknowledgement from the high priority node and ultimately transmit data packet towards the destination.
Due to duplicate transmissions from the hidden terminal volume, unnecessary energy dissipates resulting in short network lifespan. To mitigate the aforementioned constraint, a paradigm known as geo-opportunistic routing emerges, in which geographic routing is adopted for greedy forwarding by using geographic location of the set of forwarder nodes [9
]. However, in multi-hop data delivery, nodes positioned nearby base station are overburden with traffic which dissipates the node energy very quickly. Due to the quick dissipation of node battery near the sink, nodes placed away from destination are unable to transmit data due to the unavailability of forwarders.
To reduce the data load at intermediate nodes and recover data from the void regions, mobile sinks are mounted over the ships, vehicle, etc. to gather the information of interest from the region of interest. The availability of mobile sinks enables new horizon of applications including but not limited to seabed survey, the detection of minerals from the oceans which are humanly not possible to monitor [11
]. Hence, the mobility provided ease to directly retrieve the information from the communication void. With the incorporation of sink mobility, the network topology and delay in the network increases with the passage of time. To reduce the aforementioned constraints in geo-graphic, opportunistic, geo-opportunistic and mobility of sinks, we have made the following contributions:
Contributions: We have proposed two routing algorithms; geo-spatial division based geo-opportunistic routing scheme for interference avoidance (GDGOR-IA) and geographic routing for maximum coverage with sink mobility (GRMC-SM). The distinctive features of our work are list as follows:
The distribution of the network field into small cubes is performed to make local routing decisions for efficient energy consumption.
The distributive geo-opportunistic routing in geo-spatial network field avoids the interference by restricting number of nodes.
In order to minimize traffic load on intermediate nodes, mobile sinks gather data directly from underwater nodes and also use to recover data from void hole.
An optimal holding time is formulated to ensure that successful transmission acknowledgement receives before the time expires of an individual node.
This paper is organized as: a comprehensive overview of existing underwater routing schemes is stated in Section 2
. While, Section 3
presents the pre-requisites of the network which are network model, energy model and control messages. Geo-opportunistic routing without sink mobility is discussed in Section 4
and Section 5
illustrates geo-opportunistic routing with sink mobility. In Section 6
, a detailed linear programming based mathematical problem formulation subjected to attain optimal network lifetime and packet delivery ratio (PDR). Section 7
presents a detailed discussion of simulation results regarding network lifetime, PDR and data traffic load. Finally, Section 8
concludes our proposed work based on the analysis made in Section 7
with compared existing literature. The symbols and notations used in the manuscript are listed in Table 1
number of mobile sinks
to retrieve information directly from nodes. Figure 2
illustrates multi-sink architecture which is also discussed in Section 3.1
sinks are replaced with mobile sinks
. The updated network model is depicted is Figure 4
. As illustrated in Figure 4
, all sinks are deployed uniformly within the network region, where nodes communicate with the nodes of neighbor cube in their transmission range to handover the data packet to the closest
. In case of coverage hole, sinks change their coordinates in order to gather data packet from the node directly. The sink movement is governed with the intent to minimize the total travelled distance which directly minimize the delay. Though, there is a particular cost associated with the mechanical movement of sinks but mobile sinks come to the water surface to deliver data and also get recharged, thus, sinks have no constraint of energy to perform network operations.
5.1. Data Forwarding and Routing in GRMC-SM
In GRMC-SM, all nodes forward their packets to one-hop neighbors or in-range sinks placed at shorter the distance from the surface than the node itself. The deployment of mobile sinks is uniform in the field to cover maximum volume of the network. If, a node is unable to find sink(s) in its transmission range then nodes relay data packet via multi-hop mechanism towards the destination by following the greedy approach. Algorithm 3 presents the data forwarding (DFM) and routing mechanisms in GRMC-SM.
In case, a node is trapped in a coverage hole and does not find a potential neighbor node or nearby sink. This node broadcasts a void-node-declaration message to its neighbors in the CC and to the NCs to avoid the data loss and transmission trap. This declaration saves node battery and allows the network nodes to operate for maximal time period. This information is further spread to the nearby mobile sink, which aid the void node to deliver its sensed and received information to the base station for further processing. Once
receives the void-declaration message, the movement of the closest mobile sink is triggered to change its course to provide to the void node at top priority. When mobile sink
disseminates the changed co-ordinates in its transmission range, the void node forwards its data to S
’. From there onward, mobile sink relays composite data to the sinks placed at the surface. As a last step, a set of surface sinks transmits data via radio link to monitoring centre on the surface.
|Algorithm 3: Data forwarding mechanism (DFM).|
5.2. Recovery Mode via Sink Mobility
Several methods of void hole recovery have been proposed e.g., physically replacing the dead nodes or recharging the sensor node battery; mechanical movement of the sensor nodes to adjust the depth [5
] and usage of relay nodes to perform particular function of relaying data in case of void occurrence.
We have incorporated the sink mobility in GDGOR-IA scheme to analyze the effect of controlled sink mobility when void hole occurs. During the operation of forwarding, when a node traps in the void region and finds no alternate route to proceed the network communication even after examining its neighbor information. To resume the greedy forwarding, void node recovery mechanism operates. To inform the low depth neighbors, void-node-declaration packet is disseminated to inform the mobile sink. If neighbor node receives this declaration message and is not a void node itself, it replies the void-node-declaration-reply message with its location and neighbor information. This step is basically a message-based recovery for sender void node.
In other case, if the downstream node is also in the void node, then scenario leads towards local maxima trap with couple of void nodes in it. Thus, all data packets will be dropped because potential forwarders are not available to relay the transmitted data packet. To overcome earlier said scenario, uniform mobile sink deployment is performed in GDGOR-IA scheme and evaluated the performance of the proposed GDGOR-SM. Deployment of sinks in three dimensional network field is intended to reduce and recover data from the void regions. In mountain like trapped region, nodes look for nearby sink using two hop information. When a sink receives void-node-declaration message disseminated by node having coordinates (X, Y, Z), it calculates its new depth based on location information of the void node. In worst scenarios, depth adjustment of sink node is not progressive towards the destination. However, data discarded due to communication void is forwarded to the sink.