Table 4 Data Forwarding Protocols’ selecting mechanism
Protocols | Advantages | Disadvantages |
|---|---|---|
VBF | VBF is Energy efficient, Scalable and robust protocol High Success data delivery rate due to multiple path selection to the sink nodes Self-adaption algorithm that reduces the number of nodes in the forwarding process. [4] Reduce the multiple copies of the data packet in the network that achieves energy efficiency | Energy holes due to nodes dying quickly in the vertical pipe which is caused by high data load (dead nodes). [5] Performance sensitivity to the number of nodes in the vertical pipe Performance sensitivity to the radius of the vertical pipe VBF lacks communication void algorithm. [52] |
HH-VBF | Minimal energy hole compared to VBF thanks to controlling the data forwarding load on the nodes. [5] Significantly high packet delivery ratio due to multiple vertical pipe paths from each forwarder node toward the sink node, especially in low network density compared to VBF protocol | High computational delay due to the necessity to recompute the virtual pipe for each forwarder node. [5] High energy cost in the dense network due to multiple paths for the source to the destination. [40] No mechanism to handle the communication holes. (not void aware) [40] The data forwarding performance can be influenced and affected by the Radius of the virtual pipe. [4] A hop-by-hop approach in the H-VBF protocol increases the exchange of messages which will create a signaling overhead and will impact the throughput of the overall network. [4] |
FBR | FBR has a high energy efficiency and low end-to-end delay FBR reduces the number of nodes in the forwarding process. [55] | FBR faces Low throughput when the network density is low, (nodes are far apart). [5] It utilizes a transmitting cone that covers only a portion of the underwater sensor node The necessity to rebroadcast and send every time RTS message when it cannot find a next forwarding node in its transmitting cone CTS message may easily collide in high dense networks because it lacks a collision handling mechanism Communication overhead due to the frequent use of RTS message that will affect the data packet delivery ratio in low network density. [73] |
DBR | Loosen the need for the 3D geographical location information of the sensor nodes. [5] High scalability and High throughput. [5] Algorithm used by this protocol is much simpler. [41] | Increasing the depth threshold result in decreasing the packet delivery ratio. [41] Low performance in low density network. [41] High end-to-end delay. [41] Significant energy consumption due to the transmission of multiple data packets. [5] High packets collision There is no mechanism for handling the void region (communication holes) |
HydroCast | High Energy Efficiency. [42] Provide a mechanism to handle void communication holes in the underwater network. [42] HydroCast uses a multiple sink system, thereby improves performance. [42] | Performance sensitivity to sparse areas. [42] High data forwarding load of shallower nodes (nodes closer to the water surface) due to opportunistic routing. [42] Shallower nodes (low depth nodes) rapidly die due to the high data forwarding load on them. [42] Energy metrics are not considered in forwarding nodes’ selection. [42] High communication overhead because of the needs of localization information in the two‐hop clustering technique. [42] High network overhead and High energy consumption due to repetitive use of the void‐handling algorithm used in this protocol. [52] High network load due to redundant copies of the same data packet being forwarded to the sink node. [4] |
HydroCast | High Energy Efficiency. [42] Provide a mechanism to handle void communication holes in the underwater network. [42] HydroCast uses a multiple sink system, thereby improves performance. [42] | Performance sensitivity to sparse areas. [42] High data forwarding load of shallower nodes (nodes closer to the water surface) due to opportunistic routing. [42] Shallower nodes (low depth nodes) rapidly die due to the high data forwarding load on them. [42] Energy metrics are not considered in forwarding nodes’ selection. [42] High communication overhead because of the needs of localization information in the two‐hop clustering technique. [42] High network overhead and High energy consumption due to repetitive use of the void‐handling algorithm used in this protocol. [52] High network load due to redundant copies of the same data packet being forwarded to the sink node. [4] |
VAPR | Provide a mechanism to avoid void communication holes in the network. [59] VAPR is a simple and robust soft-state protocol. [59] VAPR does not forward redundant copies of the same data packets | The VAPR protocol uses a much complex algorithm High network overhead and energy consumption due to sending periodical beacon messages in a dynamic topology in the UWSNs. [59] The VAPR protocol does not consider link quality in finding a new path. [52] Performance sensitivity to the network density. [59] Performance sensitivity to the number of buoys (sinks). [59] Significant end to end delay. [59] |
MPDF | High chance of reliable data delivery since MPDF has better coverage (communication range). [41] High Energy efficiency. [41] MPDF is scalable. [41] | Low Packet Delivery Ratio (PDR), due to collision which increases the packet loss rate. [41] Low Packet Delivery Ratio (PDR), with an increased number of source nodes, which results in an increased collision and hence a high packet loss rate. [41] High routing overhead with increased packet generation interval. [41] High routing overhead with an increased number of source nodes. [41] • Significant end-to-end delay due to the need for each forwarder to send and receive a control packet before selecting the next forwarder limited performance due to the lack of consideration of node movement. [41] |
Sidewinder | High packet delivery ratio and low latency. [68] Sidewinder utilizes geographic-based routing, that uses shorter path length. [68] | Relatively high energy consumption during prediction. [68] Significant overhead due to the calculation of the next hop forwarder and retransmission. [68] Performance sensitivity to the speed of mobile sink nodes that cause the increases in the number of hops, which causes a higher chance of packet collisions Sidewinder achieves a high delivery ratio due to long path length, a high number of retransmissions, and routing overhead Sidewinder does not suppose multiple mobile sink nodes Performance may change depending on the beaconing frequency in Sidewinder. [68] |
STE | High Energy efficiency High packet delivery ratio | High end-to-end delay Significant overhead due to the calculation of the next hop forwarder in space, time and energy Not suitable for real-time networks |
H2-DAB | High packet delivery ratio H2-DAB is robust and scalable | High end-to-end delay End-to-end delay is sensitive to sparseness Significant overhead due to the calculation of the next hop forwarder Communication overhead due to the frequent use of Request and Reply messages |
MPODF | MPODF has single copy of the data packet in the network that achieves energy efficiency MPODF has a high energy efficiency, scalable and low end-to-end delay MPODF provide a mechanism to avoid void communication holes in the network | Significant computational delay arises from the need to recompute the virtual pipe for each forwarder node The MPODF protocol employs a considerably intricate algorithm increased propagation delay and increased energy consumption result from the substantial computational overhead of MPODF |
QDTR | QDTR achieves the lowest number of transmissions, due to the accuracy of its prediction. [71] High delivery rate, because QDTR adapts more quickly to mobility changes. [71] Low average delay due to the significantly adaptive prediction mechanism especially in dynamic network. [71] | Restrictive communication pattern, which led to a limited application domain due to layered network structure. [73] QDTR presumes that the sink is always situated on the topmost layer. [73] |
OFAIM | OFAIM is appropriate for heterogeneous networks where sensor nodes have different movement patterns and various communication ranges. [72] OFAIM achieves a favorable data delivery ratio (67% higher than the worst case). [72] The number of redundant data copies forwarded at each time slot is limited to either two or three copies, therefore, the message cost is significantly reduced. [72] | OFAIM algorithm is much complex due to the recalculations of dynamic routes at each slot. [72] High propagation delay and high energy consumption since OFAIM has high computational costs. [72] Performance sensitivity to the number of forwarded copies. [72] |