ICACT20260340 Question.1
Questioner: pangb@dsp.ac.cn 2026-02-09 오전 4:27:07 |
ICACT20260340 Answer.1
Answer by Auhor shil@dsp.ac.cn 2026-02-09 오전 4:27:07
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| Could you please provide a detailed explanation of the meanings of the neighbor factor and the node factor, and clarify their respective impacts on the beacon period? In addition, what do the parameters a and b in the equation represent, and how should they be selected in practice? |
In the APID protocol, each underwater node dynamically adjusts how often it sends out beacon signals (its beacon period) based on two main factors: its neighbor factor and its node factor. The neighbor factor, described by Equation (1), considers the number of nearby nodes and how fast they are moving relative to itself. If a node has many neighbors or if those neighbors are moving very differently from it, this factor increases. The node factor, from Equation (2), reflects the nodes own condition—specifically how much battery energy it has left and how much its own speed has recently changed. More energy or a recent sharp change in speed increases this factor.
The combined value of these two factors directly controls the beacon timing. A higher total value means the network conditions around the node are changing rapidly or the node itself is very active and energy-rich. In such cases, the protocol shortens the beacon period to update neighbor information more frequently. Conversely, a lower value leads to a longer, more energy-saving beacon period.
To prevent the beacon period from becoming impractically too short or too long, the protocol uses two control parameters, a and b, in the final calculation (Equation 3). Parameter b sets an upper limit on the adjustment, ensuring beacons are never sent too frequently even in the most chaotic scenarios. Parameter a sets a lower limit, preventing beacons from becoming too infrequent even in very calm conditions. In practice, these parameters (a and b) are selected through simulation or testing to balance between having fresh routing information and conserving the limited energy of the UUVs, typically with a being a small positive number and b being a larger number greater than one. |
ICACT20260340 Question.10
Questioner: hejf@dsp.ac.cn 2026-02-11 오후 7:14:56 |
ICACT20260340 Answer.10
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:14:56
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| What is the main problem that the APID routing protocol aims to solve in multi-UUV underwater mobile swarm networks? |
The main problem that the APID routing protocol aims to solve in multi-UUV underwater mobile swarm networks is the challenge of achieving stable and energy-efficient communication under conditions of high mobility, energy constraints, and unstable acoustic links. Specifically, existing routing protocols suffer from excessive topology maintenance overhead, poor tracking of moving destination nodes, overly simplistic forwarder selection criteria, and inefficient handling of routing voids. APID addresses these issues through adaptive beaconing, self-decision location information diffusion, confidence and angle-based forwarding, and packet buffering, thereby improving packet delivery rate and reducing energy consumption in dynamic underwater environments. |
ICACT20260340 Question.11
Questioner: hejf@dsp.ac.cn 2026-02-11 오후 7:15:37 |
ICACT20260340 Answer.11
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:15:37
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| Can you explain the self-decision location information diffusion method and how it helps track moving destination nodes? |
The self-decision location information diffusion method in APID works as follows: when a UUV changes its motion, it broadcasts a location advertisement. Each receiving node independently decides whether to forward it based on its forwarding willingness (energy, timestamp freshness, duplicate count) and forwarding demand (how much its neighbors are affected by the source’s movement, measured by angular change). A node forwards only when the weighted combination of both meets a threshold.
This helps track moving destination nodes because location updates are selectively propagated toward areas where nodes are actually impacted by the movement, rather than flooded everywhere. Potential senders and forwarders along relevant paths thus receive fresh position information timely, reducing the chance of packets being sent to outdated locations. This targeted diffusion maintains accurate destination tracking with low overhead. |
ICACT20260340 Question.12
Questioner: hejf@dsp.ac.cn 2026-02-11 오후 7:16:11 |
ICACT20260340 Answer.12
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:16:11
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| What role does the packet buffering mechanism play in handling routing voids in sparse network areas? |
The packet buffering mechanism in APID handles routing voids by temporarily storing packets when no qualified forwarder is available. The node waits while gradually expanding its forwarding angle thresholds, and rechecks for suitable neighbors whenever a new node appears or a beacon is received. If a qualified forwarder eventually becomes available, the buffered packet is forwarded. This approach prevents packet dropping in sparse areas, exploits node mobility to create forwarding opportunities, and improves delivery success without resorting to inefficient long-distance detours or excessive retransmissions. |
ICACT20260340 Question.13
Questioner: md02anas02@gmail.com 2026-02-11 오후 7:18:05 |
ICACT20260340 Answer.13
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:18:05
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| Do you plan to test APID in real-world sea trials to validate the simulation-tuned parameters a and b? |
Yes, we do plan to conduct real-world sea trials to further validate and calibrate the APID protocol, including parameters a and b. Although such trials have not yet been carried out due to current experimental constraints, we are actively seeking appropriate opportunities and collaborations to test the protocol under realistic ocean conditions in the future. |
ICACT20260340 Question.14
Questioner: hominpark@etri.re.kr 2026-02-11 오후 7:19:48 |
ICACT20260340 Answer.14
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:19:48
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| In void scenarios, you buffer packets and eventually remove thresholds to force a selection. Given the high latency of underwater communications, doesnt this risk causing routing loops or compromising data validity due to stale information? |
You raise a valid concern. In high-latency underwater environments, forcing packet forwarding after timeout does carry risks of routing loops and stale data delivery. APID mitigates these issues by excluding the previous hop from selection, requiring geographic progress toward the destination, and discarding packets that exceed the maximum waiting time. These measures help prevent immediate loops and ensure that only still-relevant data is delivered. Nevertheless, we acknowledge that this is not a complete solution. In future work, we plan to further enhance the protocol’s robustness in void scenarios. |
ICACT20260340 Question.2
Questioner: pangb@dsp.ac.cn 2026-02-09 오전 4:28:30 |
ICACT20260340 Answer.2
Answer by Auhor shil@dsp.ac.cn 2026-02-09 오전 4:28:30
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| In the simulation experiments, what is the communication scenario used when calculating the PDR? Specifically, which nodes are transmitting data to which nodes? |
A network of 10 to 100 Unmanned Underwater Vehicle (UUV) nodes is randomly deployed within a three-dimensional underwater volume of 5 km × 5 km × 4 km. Some of the UUVs move randomly inside the area, while the rest remain stationary. The simulation aims to model general communication requirements in multi-UUV cooperative missions. Therefore, data communication occurs between multiple randomly selected source and destination nodes. Specifically, multiple concurrent peer-to-peer data flows are established in the network. Source nodes generate packets directed at specific destination nodes, which may be either mobile or stationary. This setup tests the protocols ability to track moving destinations and maintain stable routes under dynamic topological conditions. The overall packet delivery success rate across the entire network is aggregated to compute the PDR. Thus, the PDR reflects the protocol’s end-to-end reliability in a many-to-many, peer-to-peer communication scenario that simulates real multi-UUV cooperative operations. |
ICACT20260340 Question.3
Questioner: tanghr@dsp.ac.cn 2026-02-09 오전 4:29:37 |
ICACT20260340 Answer.3
Answer by Auhor shil@dsp.ac.cn 2026-02-09 오전 4:29:37
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| How is the confidence value for a neighbor node calculated in APID, and what factors (e.g., timestamp freshness, relative speed) influence this value? How does this confidence metric improve the stability of link selection compared to traditional geographic routing? |
In APID, the confidence value for a neighbor is calculated by combining the freshness of its location timestamp, the relative speed between it and the current node, and its remaining energy. A newer timestamp, lower relative speed, and higher residual energy all result in a higher confidence score.
Compared to traditional geographic routing, which usually picks the next hop only based on which neighbor is geographically closest to the destination, APID’s confidence metric improves link stability significantly. It does this by ensuring that the selected forwarder is not only well-positioned but also stable over time—meaning it is moving similarly to the current node, has enough energy to participate, and its location information is up-to-date. This integrated approach reduces the chance of link breaks caused by mobility or node failure, leading to more reliable packet delivery in dynamic underwater networks. |
ICACT20260340 Question.4
Questioner: yangyn@dsp.ac.cn 2026-02-09 오전 4:30:40 |
ICACT20260340 Answer.4
Answer by Auhor shil@dsp.ac.cn 2026-02-09 오전 4:30:40
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| Based on the results in Figure 3, how does APID maintain a high packet delivery rate (PDR) as node density increases, and what specific features contribute to its superiority over GPSR and OLSR? |
APID maintains a high packet delivery rate as node density increases by combining several adaptive features that traditional protocols lack. Unlike GPSR, which selects forwarders based only on geographic distance, APID chooses next-hop nodes using a confidence metric that considers link stability, energy level, and information freshness. This results in more reliable multi-hop paths even in crowded networks. Compared to OLSR, which floods control traffic and suffers from collisions in dense networks, APID uses an adaptive beaconing mechanism that reduces unnecessary overhead and avoids congestion. APID also propagates mobility updates efficiently through its self-decision diffusion method, keeping destination information accurate without excessive flooding. Finally, its packet buffering mechanism helps overcome temporary routing voids. Together, these features allow APID to scale better with node density while maintaining stable and energy-efficient routing. |
ICACT20260340 Question.5
Questioner: zhaoyb@dsp.ac.cn 2026-02-09 오전 4:31:58 |
ICACT20260340 Answer.5
Answer by Auhor shil@dsp.ac.cn 2026-02-09 오전 4:31:58
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| In the formula (3) used to calculate the adaptive beacon interval, parameters a and b define the upper and lower bounds of the period. Could you please clarify how these two parameters are determined? What specific impact do their values have on the overall routing performance? |
Parameters a and b in Equation (3) are preset design values chosen through simulation tuning rather than calculated in real-time. They define the range within which the adaptive beacon period can vary.
A smaller value of a allows the beacon interval to be longer when network conditions are calm, helping to save energy. A larger value of b prevents the beacon interval from becoming too short even when the network is highly active, which avoids excessive control overhead and packet collisions.
Together, properly tuned values of a and b enable APID to adjust its beaconing rate efficiently across different mobility and density conditions. This balance helps maintain fresh routing information without wasting energy, contributing directly to APID’s higher packet delivery rate and lower energy consumption compared to protocols with fixed beacon intervals like GPSR and OLSR. |
ICACT20260340 Question.6
Questioner: zhaoyb@dsp.ac.cn 2026-02-09 오전 4:33:40 |
ICACT20260340 Answer.6
Answer by Auhor shil@dsp.ac.cn 2026-02-09 오전 4:33:40
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| In Equation (7), parameters λ and μ are introduced to adjust the weights between forwarding willingness and forwarding demand. In a practical scenario, how would the protocol perform if a UUV has sufficient energy (high willingness) but is not located on a critical forwarding path (low demand)? What are the specific impacts of different weight combinations on the network performance? |
In a practical scenario where a UUV has high energy (high forwarding willingness) but is located in a non-critical area (low forwarding demand), the outcome depends on the weight combination of parameters λ and μ in Equation (7).
If λ (weight for willingness) is set significantly higher than μ (weight for demand), the UUV is more likely to forward the location advertisement even if its neighborhood has little need for the update. This could lead to unnecessary transmissions, increasing energy consumption and channel congestion without improving routing accuracy—especially if the node is far from the movement-affected area.
Conversely, if μ is weighted more heavily, the protocol prioritizes topological relevance. In this case, even a high-energy UUV in a low-demand region would suppress forwarding, conserving energy and reducing redundant traffic while still allowing the advertisement to propagate through more affected parts of the network.
The overall network performance is thus sensitive to the λ/μ balance:
A λ-heavy setting improves information spread and may help in highly uncertain or sparse networks, but at the cost of higher overhead.
A μ-heavy setting enhances energy efficiency and reduces congestion, but risks under-propagating updates if demand estimation is too conservative.
In APID’s design, these weights allow the protocol to be tuned according to mission priorities—whether to emphasize robustness and fresh topology awareness (favor λ) or to maximize energy and spectrum efficiency (favor μ). Proper tuning ensures that advertisements reach nodes that truly need them while avoiding wasteful broadcasts, contributing to the protocol’s high delivery rate and low energy consumption in dynamic multi-UUV swarms. |
ICACT20260340 Question.7
Questioner: hhl9438@chungbuk.ac.kr 2026-02-11 오후 7:12:20 |
ICACT20260340 Answer.7
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:12:20
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| You use packet buffering to handle routing voids. Given the limited memory of UUVs, what is your strategy for buffer management (e.g., drop policy) when the buffer is full during long waits? |
Given the limited memory of UUVs in practical implementation, a combination of first-in-first-out and timeout-based strategies is adopted. When the buffer is full, the earliest arriving packet is dropped to accommodate new incoming packets. Additionally, each buffered packet is assigned a maximum waiting time; if no suitable forwarder is found within this period, the packet is discarded. This approach prevents stale packets from occupying limited memory resources and ensures that the buffer remains available for more recent or deliverable packets. |
ICACT20260340 Question.8
Questioner: Lee19711936516@163.com 2026-02-11 오후 7:13:19 |
ICACT20260340 Answer.8
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:13:19
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| The adaptive beacon interval mechanism in the APID protocol calculates the dynamic beacon period through neighbor factors and node factors. The design of these two factors considers which network and node states are considered, and how they jointly affect the length of the beacon period? |
In the APID protocol, the adaptive beacon interval mechanism is designed to allow each UUV to adjust its beacon transmission frequency based on its current network environment and individual status. The neighbor factor primarily reflects the density and mobility dynamics of the surrounding nodes. Specifically, it considers how many neighbors a node currently has and how fast those neighbors are moving relative to itself. A higher neighbor factor indicates either a near-ideal neighbor density or significant relative motion in the vicinity, both of which suggest that topology information may become outdated quickly and thus more frequent beacons are needed. The node factor, on the other hand, captures the node’s own condition, including its remaining energy and the magnitude of its recent speed change. A node with higher residual energy or a sharp change in velocity is more capable and more necessary to update its status frequently. These two factors are combined to jointly determine the beacon period. When the sum of the neighbor factor and the node factor is large, indicating high dynamics or high forwarding suitability, the beacon period is shortened to improve neighbor synchronization. Conversely, when the sum is small, the beacon period is lengthened to conserve energy and reduce unnecessary overhead. This joint adaptation ensures that beaconing is both responsive to network changes and efficient in energy usage. |
ICACT20260340 Question.9
Questioner: Lee19711936516@163.com 2026-02-11 오후 7:14:30 |
ICACT20260340 Answer.9
Answer by Auhor shil@dsp.ac.cn 2026-02-11 오후 7:14:30
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| In the high mobility scenario of multi UUV networks, what are the core advantages of APID protocols self decision location information diffusion strategy compared to traditional flood based information diffusion in reducing routing overhead and improving location information timeliness? How does the coupling decision mechanism of forwarding willingness and forwarding demand achieve this effect? |
In high-mobility multi-UUV networks, the self-decision location information diffusion strategy of APID has two core advantages over traditional flooding: it significantly reduces routing overhead and improves the timeliness of location information.
Traditional flooding blindly broadcasts every advertisement to the entire network, wasting energy and bandwidth on nodes that neither need nor are affected by the information. APID avoids this through a coupling mechanism that combines forwarding willingness and forwarding demand. Forwarding willingness ensures that only nodes with sufficient energy and fresh information participate, while forwarding demand ensures that only nodes whose neighbors are truly affected by the movement will forward the advertisement. A node will only relay the update when both conditions are met.
This selective propagation ensures that location updates travel efficiently to the areas that actually need them, without redundant transmissions. As a result, destination position information remains fresher across the network while control overhead stays low, enabling more accurate forwarding decisions and higher delivery rates under continuous mobility. |
