RSSI-Based Distributed Self-Localization for Wireless Sensor Networks\n used in Precision Agriculture
Bibliographic record
Abstract
Node localization algorithms that can be easily integrated into deployed\nwireless sensor networks (WSNs) and which run seamlessly with proprietary lower\nlayer communication protocols running on off-the-shelf modules can help\noperators of large farms and orchards avoid the difficulty, cost and/or time\ninvolved with manual or satellite-based node localization techniques. Even\nthough the state-of-the-art node localization algorithms can achieve low error\nrates using distributed techniques such as belief propagation (BP), they are\nnot well suited to WSNs deployed for precision agriculture applications with\nlarge number of nodes, few number of landmarks and lack real time update\ncapability. The algorithm proposed here is designed for applications such as\npest control and irrigation in large farms and orchards where greater power\nefficiency and scalability are required but location accuracy requirements are\nless demanding. Our algorithm uses received signal strength indicator (RSSI)\nvalues to estimate the distribution of distance between nodes then updates the\nlocation probability mass function (pmf) of nodes in a distributed manner. At\nevery time step, the most recently communicated path loss samples and location\nprior pmf received from neighbouring nodes is sufficient for nodes with unknown\nlocation to update their location pmf. This renders the algorithm recursive,\nhence results in lower computational complexity at each time step. We propose a\nparticular realization of the method in which only one node multicasts at each\ntime step and neighbouring nodes update their location pmf conditioned on all\ncommunicated samples over previous time steps. This is highly compatible with\nrealistic WSN deployments, e.g., ZigBee which are based upon the ad hoc\non-demand distance vector (AODV) where nodes flood route request (RREQ) and\nroute reply (RREP) packets in the network.\n
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".