Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Video surveillance has become ubiquitous due to the increasing security requirements in every sphere of life. The next generation video surveillance system (VSS) possesses great challenges in various applications, such as intelligent urban surveillance systems and smart cities. In these applications, we need to deal with the fast-growing number of surveillance nodes which introduce several constraints, e.g., high latency, high bandwidth, high energy consumption, and CPU and memory usage. To address these issues, the Internet of Video Things (IoVT), which is considered to be a part of the Internet of Things (IoT), can be a solution. The IoVT is composed of visual sensors (i.e., cameras) connected to the Internet. Unlike conventional systems, the VSS under an IoVT framework provides multiple layers (i.e., edge, fog, and cloud) of communication and decision making by capturing and analyzing rich contextual and behavioral information. Since an appropriate application layer protocol (ALP) can help in alleviating the challenges of future VSSs, the selection of ALPs is important for IoVT-based systems. Therefore, this paper presents a generic architecture of an IoVT-based VSS and a comparative analysis of several ALPs, such as MQTT, AMQP, HTTP, XMPP, CoAP, and DDS, with real-time experimentation. This analysis will assist the users to choose the appropriate ALPs in various surveillance applications and determine their suitability at different nodes of the IoVT framework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it