Identification of Intelligence Requirements of Military Surveillance for a WSN Framework and Design of a Situation Aware Selective Resource Use Algorithm
Bibliographic record
Abstract
Protecting or safeguarding the place we live in is a basic activity of life. This is also observable in animals. Identifying a suitable place to build a nest or create a cozy home is a primary requirement of carefree living. Once such a place is identified and selected, a lot of effort and planning goes into making it comfortable. From then onwards begins the constant task of safeguarding the place of domicile. Protecting a place involves keeping a constant lookout for disruptive elements, invasion or attacks. The term corresponding to this activity is surveillance. Surveillance when extended to a city, a state, a nation and then to continents will perform the same functionality of enabling protection against attacks but at a larger scale. Unlike in case of a bird, animal or a family unit, such surveillance requires dedicated infrastructure. Usually such a large-scale surveillance infrastructure is designed, implemented and maintained by a dedicated military. Increase in organized crime and acts of terrorism have made military surveillance evermore important and an indispensable requirement of safety. In addition to attacks by men or manmade agents, natural calamities and disasters also require surveillance on an equally large scale. Surveillance which was historically centralized in deployment and investigative in essence needs to change. Existing surveillance and sensor infrastructure can be further used to gather intelligence. Aim of this work is to identify intelligence requirements of military surveillance for a WSN framework. We have designed and implemented an algorithm to compute the area under attack, communicated nearest neighbor nodes to carry out surveillance under attack. The proposed algorithm achieves situation aware selective use of sensor infrastructure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".