Performance Analysis of ZigBee-Based Wireless Sensor Networks with Path-Constrained Mobile Sink(s)
Bibliographic record
Abstract
In the wireless sensor network (WSN) literature, the use of a mobile sink is commonly viewed as one of the most successful means of load balancing as well as an effective defense against the so-called hot-spot phenomenon. The aim of this paper is to investigate the real-world applicability of the known theoretical benefits associated with the use of mobile sink(s). In particular, we examine the pros and cons of deploying path-constrained mobile sink(s) in IEEE 802.15.4 / ZigBee-based WSNs.The main contributions of this paper are as follows: First, analytically and through simulation, we demonstrate that in idealistic (zero-overhead) networks the use of a mobile sink does result in a more even distribution of routing load and longer network lifetime, as earlier suggested in the WSN literature. Moreover, in small- to medium- size zero-overhead WSNs, the outer-peripheral and the diagonal-cross trajectory appear to be more effective than other types of mobile-sink trajectories. Unfortunately, real-world networks, including ZigBee WSNs, are not zero-overhead - these networks employ special mechanisms that generate additional (overhead) traffic in order to manage congestion and node mobility. The results of our OPNET-base simulation study demonstrate that in IEEE 802.15.4 / ZigBee WSNs, once all of the overhead traffic is accounted for, the theoretical advantage of deploying a mobile- vs. deploying a static- sink completely disappears. Hence, for anybody contemplating the use of a mobile sink in ZigBee sensor networks, the minimization of protocol overhead may have to be the first course of action. In the last part of the paper, we introduce two simple mechanisms for reduction of mobility-related overhead in ZigBee WSNs. The presented simulation results suggest that with these mechanisms in place the superiority of mobile- over static- sink deployment can be regained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".