Should the camp interior use the YN300 or 1.4G Mesh?

The judging standard is whether there is a 'no-network, long-distance mountain-crossing section' ahead. Field no-network long-distance sections use 1.4G Mesh (P1+P2+P3) for multi-hop remote backhaul; camp/substation/project-office interiors use the YN300 roaming client for hotspot-area mobile roaming, at lower cost.

Do the devices withstand low/high temperatures? Can they run in extreme cold or scorching heat?

Absolutely. The P1 operates from -40C to +75C with an IP67 rating, verified in Mohe in the northeast (-48C), the Flaming Mountains in Xinjiang (+47C), and other extreme environments. It is fitted with surge protective devices with grounding resistance <=4 ohms.

Remote-Site Major Project Supervision: 1.4G Mesh Multi-Hop Networking for Public-Network-Free Construction Camps

Blog 2026-08-20

Remote-Site Major Project Supervision: 1.4G Mesh Multi-Hop Ad-Hoc Networking for Public-Network-Free Construction Camps

Q: If the construction camp has no public network, can 1.4G Mesh backhaul multiple HD video streams?

Yes. The 1.4G Mesh single-hop physical-layer rate is up to 120Mbps@40MHz with effective throughput of about 80-100Mbps, supporting 10-15 simultaneous 1080P video streams. Bandwidth scales linearly after multi-hop aggregation.

Q: Is the 1.4GHz band legal? Does it require an application?

1.4GHz (1350-1450MHz) is a dedicated wireless band allocated by the state for public safety, emergency communication, and private-network communication. Compared with civil bands it has less interference and a higher permitted transmit power. Use must follow local radio-management regulations and requires registration.

Q: Can solar power on remote nodes survive consecutive overcast-and-rainy days?

Yes. A properly sized solar-power system supports 7-14 consecutive overcast-and-rainy days. The P1 built-in lithium battery works independently for 6-12 hours. Smart power management reduces draw by 30% at night.

Q: Does multi-hop Mesh latency affect real-time command?

No. Single-hop latency is <=10ms, 5-hop <=50ms, 10-hop <=100ms, and route convergence <=500ms. The human-ear perception threshold is about 200ms, and system latency stays within both thresholds, fully meeting real-time command requirements.

Q: How many nodes are appropriate, and how is it calculated?

Extrapolate from bandwidth demand rather than spacing by distance. Steps: 1) count monitoring points and bandwidth demand (1080P estimated at 6Mbps); 2) set max per-hop carried bandwidth =-70dBm.

Q: Compared with an ordinary wireless bridge, where is Mesh stronger?

The core difference is multi-hop self-healing and networking flexibility: a wireless bridge is point-to-point/point-to-multipoint and a single fault breaks the whole link; Mesh supports multi-hop ad-hoc networking and auto-routes around single-point faults. It scales to 64 nodes (bridges usually <=8) and supports seamless roaming.

Q: Can a system be reused after a project ends? How long is its life cycle?

It can absolutely be reused. The P1 radio has a design life of >=10 years and can relocate with the project or become an operations-phase monitoring network. In power scenarios it can be reused as an inspection communication network, and in mining scenarios as a production-monitoring network. One investment, years of benefit.

Core Overview

Intended audience: IT officers, project managers, and safety directors of infrastructure enterprises in power, oil, mining, and water resources; construction-site monitoring system integrators and emergency-communication service providers in the energy and infrastructure sectors.

Core pain point: Construction camps for power lines, substations, mines, tunnels, and hydraulic dams are frequently located in remote areas with no public network. This creates three loss-of-control situations by which surveillance video cannot reach the cloud, on-site command relies on shouting, and safety data is delayed by hours, leaving safety and schedule without essential “visibility”.

Technical conclusion: With a 1.4GHz-band multi-hop ad-hoc (Mesh) network as the transport backbone, build a four-layer architecture of “sensing layer sensors → transport layer Mesh backbone → platform layer edge computing → application layer AI supervision”. Use backpack-mount radios (P1) for multi-hop networking along the project right-of-way, a command platform (P3) to aggregate and schedule, and UAV-carried relays (P2) to fill coverage gaps, then uplink to the cloud via satellite/public-network interfaces, delivering “connected in the no-man’s land, construction stays visible, and data is backhauled”.

Solution depth: This article covers the 1.4G Mesh routing protocol, multi-hop networking mechanism, edge-computing deployment, solar-powered redundancy design, dedicated plans for tunnel/mining/power-line scenarios, and the standard networking direction in the power industry.

Keywords: Mesh Ad-Hoc Networking for Unmanned Zones, Construction-Camp Wireless Monitoring, Field-Project Supervision, Power-Grid Construction Remote Supervision, Public-Network-Free Construction-Site Networking, 1.4G Ad-Hoc Networking Technology, Power-Construction Wireless Backhaul

Where exactly is the “supervision blind spot” on remote construction sites, and what is the core need?

Core contradiction: The safety and efficiency of infrastructure construction is fundamentally a “visibility” problem. What a remote site lacks is not cameras; it is the autonomous broadband network that connects those cameras and sensors and carries them out. That is the root of the supervision blind spot.

Construction sites for power, oil, mining, and water-resources projects often sit deep in mountains, barren deserts, and dense canyons: no public network, no practical dedicated lines, and harsh environments (sandstorms, rain and snow, extreme high/low temperatures, lightning). According to State Grid’s 2025 construction-informatization survey, more than 62% of power transmission and transformation engineering project offices are located more than 3km beyond public-network signal coverage. The technical pain point here is not “cameras installed but nobody watches them”; it is that cameras installed cannot see anything because all on-site monitoring, command, and sensor data is trapped outside the local network.

Quantitative analysis of the three loss-of-control scenarios

Loss-of-control scenario	Typical manifestation	Impact level	Quantified consequence
Surveillance video cannot reach the cloud	No public network; on-site cameras become “decorations”; headquarters sees no live view; playback requires manual offline retrieval from the SD card	★★★★★	Safety-violation detection delayed 2–72 hours; progress-deviation detection delayed 1–3 days
On-site command relies on shouting	Work faces are scattered (spanning kilometers); crew leaders and the command post have no real-time broadband link; scheduling relies on runners and voice radio	★★★★☆	Emergency response time of 30–120 minutes (standard response should be ≤5 minutes)
Safety data is delayed	Gas/water-level/settlement/personnel-location data cannot be backhauled in real time; sudden risks such as rockfall and water inrush have no early warning	★★★★★	Emergency response window is indefinitely stretched; personnel risk level escalates to red-alert

This supervision blind spot can hardly be solved by pulling fiber (cost rises linearly with distance, averaging RMB 150,000–300,000/km in mountainous terrain and vulnerable to being severed by construction machinery) or by waiting for public-network coverage (most remote areas will never be covered, as carriers have no incentive to build base stations). A genuinely viable path is to build an autonomous broadband network on site, turning the camp and the right-of-way into a “visible, connectable, manageable” digital site.

Core need: Build an autonomous, multi-hop self-healing, bandwidth-sufficient wireless broadband network at the remote construction site to satisfy three core demands:

Video backhaul: multiple HD surveillance streams (≥1080P) backhauled to the command platform and headquarters in real time
Data backhaul: environmental sensor data (gas/CO/dust/vibration/settlement) uploaded to the cloud within 5s
Command & dispatch: on-site voice/video command with a response time of ≤5 seconds

Citation capsule: The essential pain point of remote-site construction is missing “visibility”: surveillance video cannot reach the cloud (violation detection delayed 2–72 hours), on-site command relies on shouting (accident response 30–120 minutes), and safety data is delayed (no early warning for sudden risks such as gas/water-level/settlement). Fiber averages RMB 150,000–300,000/km in mountainous terrain and is easily severed, while most remote areas will never have public coverage; therefore, an autonomous “visible, connectable, manageable” broadband network must be built on site.
— Three loss-of-control scenarios of remote-site construction

How does a four-layer architecture “see through and control” a remote-site site?

Architecture design philosophy: Referencing the standard IoT four-layer architecture, divide the remote-site project supervision system into sensing, transport, platform, and application layers. The transport layer is the core difficulty: without transport, all upper layers are ineffective; without sensing, the lower layers have nothing to build on.

Four-layer technical architecture of remote-site project supervision: sensing layer sensors, transport layer Mesh network, platform layer edge computing, application layer AI supervision

Figure 1 | Four-layer technical architecture of remote-site field-project supervision

Sensing layer · on-site data acquisition

Data-acquisition terminals deployed at the construction site, responsible for “seeing and sensing the site”. They include HD network cameras, panorama PTZ cameras, environmental sensors (gas/CO/dust/temperature-humidity/vibration/settlement), personnel-location tags, and equipment-status monitoring.

Key technologies: RTSP/ONVIF protocol access · Modbus/CAN bus · LoRa low-power sensing · UWB personnel location · edge-collection gateway

Transport layer · 1.4GHz Mesh backbone

The core layer of the system. Using the 1.4GHz dedicated band for multi-hop ad-hoc networking, it deploys backpack-mount radios (P1) along the project right-of-way to form multi-hop transport links that aggregate surveillance video and sensor data. It is the only autonomous scheme able to deliver multi-kilometer to tens-of-kilometers broadband backhaul in a remote site.

Key technologies: 1.4GHz band · multi-hop ad-hoc networking (Mesh) · AODV routing protocol · 120Mbps@40MHz · 5–50km ground single hop · 10km+ air-ground · 2W legal high power

Platform layer · edge computing and data processing

The edge-computing node deployed at the camp command platform (P3) performs local pre-processing, protocol conversion, and initial AI analysis on the aggregated video and sensor data, then uplinks to the cloud via satellite/public-network interfaces. Edge computing substantially reduces backhaul bandwidth pressure and enables low-latency response.

Key technologies: edge-computing gateway · video structuring · AI object detection/violation recognition · time-series data compression · edge-cloud collaboration · store-and-forward on link outage

Application layer · supervision command and AI analysis

A supervision and command platform for project offices, branch companies, and headquarters, offering live video wall, electronic map, multi-screen dispatch, AI alerts, SOP workflow management, and data reporting.

Key technologies: B/S command and dispatch platform · GIS electronic map · AI behavior analysis (smoking/non-hard-hat/trespass) · multi-screen video wall · mobile App

Citation capsule: Remote-site project supervision adopts a four-layer architecture of “sensing layer → transport layer → platform layer → application layer”: the sensing layer acquires on-site data (cameras, environmental sensors, personnel location), the transport layer uses 1.4GHz Mesh as a multi-hop backbone (the only autonomous scheme delivering multi-kilometer to tens-of-kilometers broadband backhaul in a remote site), the platform layer performs edge computing at P3, and the application layer provides command & dispatch and AI analysis. The transport layer is the core difficulty; without it, all upper layers are ineffective.
— Four-layer technical architecture of remote-site supervision

How should the sensing layer be deployed so the site is “visible and sensed”?

Design principle: The design of the sensing layer directly determines “what is visible and what is sensed”. Appropriate sensing devices must be chosen according to the construction-scenario difference (tunnel/open-air/heights/deep excavation), and data must be reliably handed into the transport layer.

Video-surveillance equipment

Device type	Specifications	Applicable scenario	Access method
HD network camera	4MP, H.265, IP67, infrared 30m	Fixed monitoring of work faces, checkpoints	PoE into P1 radio
Panorama PTZ camera	2MP, 360° rotation, 30x optical zoom, smart tracking	Camp vantage points, tower tops	Ethernet into P1 radio
Thermal-imaging camera	IR resolution 640×512, temperature accuracy ±2℃	Tunnel working face, power equipment overheating detection	Into edge gateway
UAV payload camera	4K aerial, 10x optical zoom, thermal-imaging dual-light	Line inspection, emergency repair, aerial evidence	Backhaul via P2 UAV-mounted radio
Panorama VR camera	360° surround, 8K resolution	Full-view construction progress capture, remote VR viewing	WiFi into YN300 roaming

Environmental and safety sensors

Sensor type	Monitored parameter	Range/accuracy	Alert threshold	Communication method
Gas detector	CH₄, CO, H₂S, CO₂, O₂	0–100% LEL, ±2% FS	CH₄≥1%, CO≥50ppm	LoRa → edge gateway
Dust/PM monitoring	PM2.5, PM10, TSP	0–1000μg/m³, ±10%	TSP≥500μg/m³	RS485 → edge gateway
Noise monitoring	Equivalent sound level Laeq	25–140dB, ±1dB	Laeq≥85dB	LoRa → edge gateway
Vibration/settlement monitoring	Structural vibration, surface settlement	0.001mm accuracy	Settlement rate ≥5mm/day	Modbus → edge gateway
Temperature/humidity monitoring	Temperature, humidity	−40~85℃, ±0.5℃	Configurable on demand	LoRa → edge gateway
Personnel location	Personnel position, vital signs (heart rate/SpO₂)	UWB accuracy ≤0.5m	Trespass/timeout-stay alert	UWB → edge gateway

Edge-collection gateway

The edge-collection gateway is the bridge between the sensing layer and the transport layer: it unifies multiple sensor protocols and hands the aggregated data into the Mesh backbone. It supports multi-protocol access including RS485/Modbus, LoRa, UWB, and Ethernet, and performs local data parsing, packing, and caching locally; on link outage, local storage supports 72 hours of data buffering with breakpoint-resume transfer.

Edge-gateway specifications: ARM Cortex-A7 quad-core · 2GB DDR · 8GB eMMC · 4×RS485 · 2×Ethernet · LoRa 868/915MHz · operating temperature −40℃~+75℃ · IP65

Why can 1.4G Mesh cross mountains, and how does the multi-hop mechanism work?

Why 1.4GHz? The 1.4GHz band is a dedicated wireless band allocated by the state (1350–1450MHz) with a wavelength of about 21cm, combining three physical characteristics: strong diffraction, good penetration, and superior non-line-of-sight (NLOS) performance. These determine its ability to “transmit across mountains and connect across valleys” rather than requiring a visible path. Transmit-power and usage boundaries of license-exempt/dedicated bands must comply with [FCC 47 CFR Part 15].

Physical characteristics of the 1.4GHz band

Characteristic	1.4GHz	2.4GHz (WiFi)	5.8GHz	Comparison conclusion
Wavelength	≈21cm	≈12.5cm	≈5.2cm	The longer the wavelength, the stronger the diffraction
Diffraction/penetration	Strong: crosses mountains and dense forest	Weak: drops out in ravines and dense forest	Very weak: line-of-sight dominant	1.4G is the only suitable choice for mountains/canyons
Single-hop distance (ground)	5–50km (open)	0.5–1km	200–500m	1.4G single-hop distance is 5–50× that of 2.4G
Single-hop distance (air-ground)	10km+	2km	1km	Significant UAV-relay advantage
Maximum transmit power	2W (legal dedicated band)	100mW (regulatory limit)	200mW	1.4G legal power is 10–20× that of WiFi
Bandwidth	10/20/40MHz adjustable	20/40MHz	20/40/80MHz	Dedicated bandwidth is clean and interference-free
Anti-interference	Strong: dedicated band, no civil congestion	Weak: 2.4G band is congested	Medium: 5.8G relatively clean	Dedicated band carries no interference risk

Core mechanism of multi-hop ad-hoc (Mesh) networking

Mesh ad-hoc networking differs fundamentally from traditional WiFi/bridge in multi-hop relay and self-healing. In a traditional point-to-point/point-to-multipoint network, a single broken link takes the whole network down; in a Mesh network, every node acts as both router and relay, and data can travel over any available path, so a single-point failure does not affect the entire network. Its self-healing and dynamic route-selection mechanism is consistent with the wireless-Mesh mesh topology and nine classes of Mesh frame exchange defined by [IEEE 802.11s].

Citation capsule: 1.4GHz multi-hop ad-hoc (Mesh) networking is essentially a mesh structure in which “every node can both transmit/receive and forward”, breaking the “one site down, whole network down” limitation of traditional point-to-point/point-to-multipoint bridges. Single-point failures route around automatically, with route recomputation completed in <500ms. The Mesh multi-hop and self-healing mechanism corresponds to the standard topology concept of a wireless mesh network, covering tens of kilometers of public-network-free construction area.
— IEEE 802.11s · Wireless Mesh Networking

Multi-hop networking topology diagram

Work face A

→

P1 Node 1

→

P1 Node 2

→

P1 Node 3

→

P3 Command Platform

Data is aggregated hop by hop; any node failure auto-routes around via other paths

Core technical parameters

Operating band: 1350–1450MHz (1.4GHz dedicated network)
Channel bandwidth: 10 / 20 / 40 MHz adjustable
Modulation: OFDM, supporting adaptive BPSK/QPSK/16QAM/64QAM/256QAM switching
Physical-layer rate: up to 120Mbps @ 40MHz
Single-hop distance: 5–50km ground (open), 1–5km (mountain shadowing); 10km+ air-ground
Network scale: a single Mesh network supports ≤64 nodes
Route convergence: route recomputation completed in <500ms after topology change
End-to-end latency: ≤10ms single hop, ≤100ms over 10 hops
Transmit power: ≤2W (33dBm), configurable
Receiver sensitivity: ≤−95dBm
Protection rating: IP67 ([IEC 60529] dust/waterproof standard)
Operating temperature: −40℃ ~ +75℃
Power supply: external AC/DC, solar + storage battery, or built-in lithium battery (6–12h)

Three device roles and positioning

Role	Device model	Positioning	Core responsibility
Backbone node	P1 backpack-mount radio	Fixed deployment along right-of-way, long-term operation	Multi-hop relay, aggregation access, link maintenance
Airborne relay	P2 UAV-mounted radio	Temporary/emergency-repair air node	Temporary relay across canyons/ridges, rapid coverage-gap fill
Aggregation platform	P3 portable command platform	Camp core node, gateway node	Whole-network aggregation, edge computing, cloud egress
Roaming client	YN300 roaming module	Camp/station hotspot access	2.4G/5.8G seamless roaming, short-range mobile access

Citation capsule: A remote-site construction network is composed of three core device roles: P1 backpack-mount radios as multi-hop backbone nodes along the right-of-way, P2 UAV-mounted radios as temporary airborne relays across canyons/ridges to fill coverage gaps, and a P3 portable command platform for whole-network aggregation and cloud egress, plus the YN300 for camp hotspot roaming access. The role division determines whether the network can cover the “multi-kilometer to tens-of-kilometers” deep construction area.
— YNWMicro Mesh device role system

How do the communication protocol stack and routing algorithm guarantee reliable long-distance multi-hop operation?

Protocol-design core: The protocol stack of a remote-site Mesh network must simultaneously satisfy four requirements: long-distance transport, multi-hop self-healing, low latency, and high reliability. Adding a Mesh routing protocol on top of the WiFi MAC layer is the key to ad-hoc capability.

Full layered protocol stack

Layer	Protocol/technology	Function
Application layer	RTSP, ONVIF, HTTP, MQTT, Modbus	Video-stream transport, device management, sensor-data acquisition
Transport layer	TCP/UDP, RTP	Reliable transport and real-time streaming
Network layer	IP (IPv4), ICMP	IP addressing and network diagnostics
Mesh routing layer	Enhanced AODV (Ad-hoc On-demand Distance Vector)	On-demand route discovery, multi-hop route selection, topology maintenance
MAC layer	CSMA/CA + TDMA hybrid scheduling	Medium-access control, time-slot allocation, collision avoidance
Physical layer	OFDM, 1.4GHz, 10/20/40MHz	Orthogonal frequency-division multiplexing modulation (basis [ETSI EN 300 744] for fixed broadband wireless systems), adaptive rate switching

AODV routing algorithm workflow

AODV is an on-demand routing protocol designed specifically for mobile ad-hoc networks: route discovery is initiated only when needed, and node maintenance overhead stays low, which suits remote-site Mesh networks well. Its workflow is as follows:

1 Route Discovery

When a source node needs to communicate with a destination but has no valid route, it broadcasts an RREQ (Route Request) packet. The RREQ is forwarded hop by hop, and each intermediate node records the reverse path.

2 Route Reply

On receiving the RREQ, the destination returns an RREP (Route Reply) along the reverse path. The source confirms the route is usable after receiving the RREP and establishes the forward path.

3 Route Maintenance

Nodes periodically probe neighbor reachability with Hello packets. On link break, the upstream node broadcasts an RERR (Route Error) to notify all affected nodes to rediscover routes.

4 Route caching and switching

Each node maintains a route table that stores destination, next hop, hop count, and expiry time. Route recomputation completes in <500ms on topology change.

Field performance validation: In a 20-node, 5-hop Mesh network field test, route reconvergence time after link break was ≤480ms, data packet-loss rate ≤0.3%, and end-to-end video latency ≤95ms, fully meeting construction command and safety-monitoring requirements.

Citation capsule: The remote-site Mesh protocol stack uses AODV on-demand routing plus hybrid CSMA/CA and TDMA scheduling: control signaling uses contention-based transmission while high-volume video uses fixed time slots, balancing channel utilization and collision avoidance. Field tests on a 20-node, 5-hop network show link-break reconvergence ≤480ms, packet loss ≤0.3%, and video latency ≤95ms, meeting long-distance construction command and safety-monitoring requirements.
— AODV on-demand routing for mobile ad-hoc networks · OFDM physical layer

Hybrid CSMA/CA + TDMA scheduling

At the MAC layer, traditional CSMA/CA suffers from the “hidden-terminal” problem, where two distant nodes may transmit simultaneously and collide. To address this, the system adopts a hybrid CSMA/CA + TDMA scheduling mechanism: control signaling (route maintenance, link detection) uses CSMA/CA contention transmission, while high-volume traffic (video streams) uses fixed TDMA time slots, both preserving channel utilization and avoiding collisions.

Time-slot allocation: control slot of 2ms/frame (guaranteeing route maintenance), data slots allocated on demand (video streams occupy exclusive slots, sensor data shares slots). Each 100ms frame can accommodate time-slot allocation for ≤64 nodes.

How does platform-layer edge computing save more than 80% of bandwidth?

Why edge computing: Bandwidth in a remote site is precious, and uploading all data to the cloud wastes bandwidth and adds latency. Edge computing performs real-time processing near the data source (the camp P3 platform) and uploads only high-value results (alerts, structured data), saving bandwidth by more than 80%.

Edge-computing node architecture

Edge-computing architecture diagram: sensor data flows to the edge gateway for processing and then uploads to the cloud

Figure 2 | Edge-computing data-flow architecture diagram

Edge-computing node hardware specifications (P3 command platform)

Component	Specification	Purpose
CPU	Intel i7-12th Gen, 8-core/16-thread	Multi-protocol conversion, data processing
Memory	16GB DDR4 (32GB optional)	Real-time data buffering
Storage	512GB NVMe SSD + 2TB HDD	Local video storage (7–30 days)
Display	15.6" HD screen, supports 4 external displays	Command & dispatch video wall
AI acceleration	Intel UHD/Iris Xe GPU · NPU ≥5 TOPS	AI object detection, behavior analysis
Network	4× Gigabit Ethernet · 4G/5G module · satellite interface	Mesh backbone access + cloud egress
Power	Built-in 2h lithium battery, external DC supply	Uninterrupted field operation
OS	Windows 10 LTSC Industrial	Stable, reliable, long-term supported

Data-processing pipeline

Data acquisition

→

Protocol conversion

→

Data cleaning

→

AI processing

→

Tiered storage

→

Cloud upload

1 Data acquisition and aggregation

Aggregate video streams, sensor data, and device status backhauled from all P1 nodes over the Mesh backbone. The edge gateway supports concurrent access to ≤32 video streams and ≤256 sensor-data channels.

2 Protocol conversion and data cleaning

Transcode RTSP/H.265 video to H.264/AV1 to suit different bandwidths; parse Modbus/LoRa sensor data into standard JSON; clean outliers, deduplicate, and calibrate timestamps.

3 Edge AI real-time analysis

Run lightweight AI models (YOLOv8-nano, SSD-MobileNet) on the edge node to detect personnel, equipment, and violations in real time. The analysis results (alert events, structured labels) trigger alert workflows immediately.

4 Tiered storage and cloud upload

Raw video is stored locally for 7–30 days; AI structured data for 1 year; alerts and high-value data upload to the cloud in real time; on link outage, data is cached locally and auto-resumed once the link recovers.

Store-and-forward on link outage and edge–cloud collaboration

Key mechanism: When the Mesh backbone or the cloud egress fails, the edge-computing node automatically switches to local autonomous mode:
· all video storage, sensor-data recording, AI analysis, and alerting are completed locally
· on link recovery, missing data is auto backfilled by time window
· zero data loss is guaranteed under any conditions

Citation capsule: Edge computing is deployed at the camp P3 platform, performing video structuring, sensor-protocol conversion, and initial AI screening near the data source, and uploading only alerts and high-value structured results to the cloud, saving bandwidth by more than 80%. On link outage it switches to local autonomous mode, stores raw video locally for 7–30 days, and auto-resumes breakpoint transfer on recovery, guaranteeing zero data loss.
— Edge computing · edge–cloud collaboration

What quantifiable early-warning improvements can AI analytics deliver?

AI value: Traditional monitoring can “see but not manage”—a single operator can watch at most 4 screens, so 50 video channels would require 12 operators on shifts. The core value of AI is turning “human eyes” into “AI automatic vision”, delivering 7×24 real-time supervision and proactive early warning.

Edge AI detection capability matrix

AI capability	Detection target	Applicable scenario	Response requirement	Model accuracy
Person intrusion detection	Intrusion by people into hazard zones	Excavations, blasting zones, high-voltage areas	Real time (≤2s)	Recall ≥95%
Safety-PPE recognition	Hard hat, hi-vis vest, safety harness	Whole construction area	Real time (≤2s)	Accuracy ≥92%
Person behavior analysis	Smoking, phone use, climbing, running	Whole construction area	Real time (≤3s)	Accuracy ≥88%
Vehicle recognition and speed	Vehicle type, plate, speed, wrong-way	Haul roads, loading/unloading areas	Real time (≤2s)	Accuracy ≥90%
Equipment-status monitoring	Excavator, crane, loader operating status	Work-face monitoring	Real time (≤5s)	Accuracy ≥85%
Fire/smoke detection	Flame, smoke anomalies	Whole construction area	Real time (≤1s)	Recall ≥98%
Person gathering/stay	Regional personnel density, timeout stay	Confined spaces, tunnel entrances	Near real time (≤10s)	Accuracy ≥90%

Alert and response workflow (SOP)

1 Real-time detection

Edge AI performs frame-level real-time detection on each video stream at a processing rate of ≥15 FPS per channel.

2 Alert trigger

Upon detecting a violation/hazard, it immediately triggers a three-tier alert: Tier-1 (instant) pop-up + audible alert; Tier-2 (within 5s) SMS + voice notification to the crew leader; Tier-3 (within 30s) escalation to the project office and headquarters.

3 Event recording

Automatically capture the 10s video clip around the alert and generate an event report (time, location, type, detection confidence, associated personnel), stored for the record.

4 Response closure loop

Duty operators confirm/handle the alert on the command platform and record the result and responsible person; unresolved alerts auto-escalate to the next level of management.

5 Data analysis

A daily safety report is generated and a weekly violation-trend analysis, providing data support for management decisions.

Quantified results: After deploying edge AI, the average time to detect a safety violation dropped from 45 minutes to under 3 seconds; the annual safety-event detection rate rose from 23% to 96%; and manual inspection workload fell by 70%.

Citation capsule: Edge AI upgrades “human eyes” to “AI automatic vision”, enabling 7×24 proactive supervision. After deployment, the average time to detect a safety violation dropped from about 45 minutes to under 3 seconds, the annual safety-event detection rate rose from about 23% to 96%, and manual inspection workload fell by about 70%, shifting remote-site operations from “after-the-fact accountability” to “proactive early warning”.
— YNWMicro field-tested edge AI supervision

How should node deployment, power supply, and redundancy be designed for maximum stability?

Deployment principle: Node deployment is not about “uniform spacing by distance” but about extrapolating node density from bandwidth demand. The position of each node should be planned against three indicators: the monitoring points to cover, per-hop bandwidth demand, and link-redundancy requirements.

Typical node configuration plans

Scenario	Monitoring points	P1 nodes	P3 platforms	Power scheme	Avg. single-hop distance
30km power-line construction	48 video streams	12	1	Solar + storage battery	2.5km
Open-pit mine working area	32 video streams	8	1	Solar + storage battery	1.5km
5km tunnel construction	24 video streams + 16 sensors	10	1	External power + battery	500m (in-tunnel shadowing)
Large hydraulic hub	64 video streams + 48 sensors	16	2	Hybrid power	2km
Temporary repair/emergency	Deployed on demand	3–5	1	Battery + solar	On demand

Three-step node deployment

1 Bandwidth-demand calculation

Calculate point by point for the monitoring points:
· 1080P live video: 4–8Mbps/channel (H.265)
· 720P live video: 2–4Mbps/channel
· sensor data: ≤100kbps/channel (negligible)
· per-hop aggregated bandwidth ≤80Mbps (to avoid bottlenecks)

2 Topology planning and link budget

Use GIS tools to plan node positions, ensuring:
· each monitoring point has at least 1 direct link to a backbone node
· each backbone node has at least 2 paths to P3 (redundancy requirement)
· link RSSI ≥−70dBm (to guarantee signal-to-noise ratio)
· link budget: transmit power 33dBm − path loss + antenna gain ≥ −70dBm (line-of-sight attenuation per the [ITU-R P.530] terrestrial propagation model)

3 Physical deployment and tuning

Install P1 nodes on site (tower/high pole/cliff top), with antenna height ≥3m above ground and azimuth aligned. After deployment, use MESHCOM network-management software to test signal strength, throughput, and packet loss link by link, adjusting antenna angle and node position to optimum.

Network redundancy and self-healing design

Redundancy design principle: If a remote-site network fails, the site may be unreachable for days. Redundancy must therefore ensure that even if any 2 nodes fail simultaneously, the whole network stays connected.

Redundancy type	Implementation	Recovery time	Guarantee objective
Link redundancy	Each node has at least 2 links to the aggregation point	≤500ms (route reconvergence)	Single link failure has no impact
Node redundancy	Dual-node hot standby at critical points	≤1s (primary/backup switch)	Single node failure has no impact
Power redundancy	Three-tier solar + storage battery + built-in battery	Seamless switching	No link loss over 7 consecutive overcast-and-rainy days
Cloud-upload redundancy	4G/5G + satellite dual backhaul	≤10s (link switching)	Single backhaul-link failure has no impact
Edge redundancy	P3 edge-node local autonomy + store-and-forward	Instant switching	Zero data loss on cloud-link outage

Citation capsule: Node deployment follows “extrapolate node density from bandwidth demand” rather than uniform spacing: first calculate the bandwidth of each monitoring point (about 4–8Mbps per 1080P channel), then use GIS to plan topology so each backbone node has at least 2 redundant paths, and finally verify with a link budget that RSSI ≥−70dBm. The redundancy design guarantees that even any 2 nodes failing simultaneously keep the whole network connected, and that power supports 7 consecutive overcast-and-rainy days without link loss.
— Three-step node deployment · five-fold redundancy model

On-site deployment illustration

Remote highland construction site in western China: an excavator and workers operating, with steel pylons and P1 Mesh relay nodes mounted on a clifftop, layered mountain background

Figure 3 | Remote highland construction site in western China: P1 backpack-mount relay nodes are mounted on high poles and cliff tops, relaying site footage back to the camp across mountains

How are the tunnel/mining/power-line scenario topologies built?

Scenario-classification approach: Network-topology requirements differ substantially across construction scenarios: tunnels need “penetration coverage + emergency escape”, open-pit mines need “wide-area coverage + high mobility”, and power lines need “narrow-and-deep + UAV inspection”. Each is analyzed below.

Scenario 1: Tunnel construction (deep chain + penetration coverage)

Dimension	Design approach
Scenario characteristics	Narrow and long (2–20km), enclosed, dusty, poor visibility, with rockfall/water-inrush risk
Network topology	Chain multi-hop: portal P3 → multi-stage P1 relays in-tunnel → working-face collection point, with a P1 node every 500m
Band selection	1.4GHz (strong NLOS, works even around in-tunnel bends), supplemented by 2.4G WiFi (in-tunnel hotspot coverage)
Power scheme	In-tunnel 380V external power (stable wired supply), with the P1 built-in 6h battery for emergency
Special requirements	UWB personnel location (accuracy ≤1m), emergency broadcast (wired broadcast on outage), real-time toxic-gas monitoring
Typical configuration	5km tunnel: 10 P1 + 1 P3 + 30 video streams + 20 sensor nodes

Scenario 2: Open-pit mine (area mesh + high mobility)

Dimension	Design approach
Scenario characteristics	Wide area (several sq km), many machines (excavators/haul trucks), high mobility, multiple parallel work faces
Network topology	Mesh multi-hop: P1 master nodes on mine vantage points → P1 slave nodes at each work face → grid-shaped coverage, auto-routing around any node failure
Mobile access	Haul truck/excavator-mounted YN300 roaming clients (5.8G) switch seamlessly within P1 coverage; inspection staff connect via handheld PDA
Power scheme	Fixed P1 nodes use solar + storage battery (50W solar panel, 100Ah battery); truck/vehicle-mount equipment draws from vehicle power
Special requirements	Vehicle dispatch (GPS track + video), overload detection, blasting-zone guard, dust monitoring
Typical configuration	2km² mine: 8 P1 + 1 P3 + 40 video streams + 15 vehicle-mount roaming clients

Scenario 3: Power line (narrow-and-deep chain + aerial inspection)

Dimension	Design approach
Scenario characteristics	Narrow and long (tens to hundreds of km), no public network along the way, complex terrain (mountains/canyons/desert), dispersed tower bases
Network topology	Chain multi-hop along towers: one P1 node every 3–5 towers (mounted on tower tops), forming a deep backhaul link
Aerial relay	P2 UAV-mounted radios act as mobile relays, temporarily lifting over canyon/ridge shadowed sections to fill gaps, or providing rapid coverage during repairs
Power scheme	Tower P1 nodes use solar + storage battery (80W solar panel, 200Ah battery, supporting 14 consecutive overcast-and-rainy days)
Special requirements	Real-time UAV-inspection video backhaul, tower-base settlement monitoring, line-temperature monitoring, emergency-repair dispatch
Typical configuration	100km line: 25 P1 + 1 P3 + 2 P2 UAVs + 60 video streams + 40 sensor nodes

Citation capsule: The three construction scenarios correspond to three different Mesh topologies: tunnel construction uses a “deep chain + penetration coverage” (one node every 500m, 1.4GHz NLOS turns corners); open-pit mining uses an “area mesh + high mobility” (vantage-point master nodes + vehicle-mount roaming clients with seamless switching); power lines use a “narrow-and-deep chain + aerial inspection” (node placement along towers, P2 UAVs temporarily lifting to fill gaps). Topology differentiation determines node density and band selection.
— Tunnel · mine · power-line scenario topologies

How should solar power be sized for long-term unattended operation?

Power design philosophy: The core requirement for remote-site nodes is long-term unattended operation. Solar + storage battery is the only economically viable scheme, and the key lies in correctly sizing the solar-panel wattage and battery capacity for local insolation conditions.

Power-sizing calculation formulas

Solar-panel wattage calculation:
P_panel = (P_device × T_overcast × 1.2) / (H_insolation × η_system)
where: P_device = average device power draw (W); T_overcast = required consecutive overcast-rainy days (days); H_insolation = local average effective daily sunshine hours (h/day); η_system = system efficiency (0.75–0.85)

Battery-capacity calculation:
C_battery = (P_device × T_overcast × 1.2) / (U_battery × η_discharge)
where: U_battery = battery voltage (V); η_discharge = discharge efficiency (0.8–0.85)

Typical power-sizing table

Device type	Average power draw	Solar panel	Storage battery	Overcast-rain endurance
P1 fixed node (incl. camera)	15W	50W (monocrystalline)	12V 100Ah LiFePO₄	≥7 days
P1 fixed node (incl. 2 cameras + gateway)	30W	80W (monocrystalline)	12V 200Ah LiFePO₄	≥14 days
Edge-collection gateway	8W	30W (monocrystalline)	12V 60Ah LiFePO₄	≥5 days
P2 UAV-mounted radio	12W	Onboard battery (3000mAh)	Li-ion pack	Onboard endurance 4–6h
P3 command platform	65W	100W (optional)	Built-in 2h Li-ion + external DC	Uninterrupted mains

Smart power-management system

Each P1 node incorporates a smart power-management module delivering the following functions:

Solar MPPT charging: maximum-power-point tracking with charging efficiency ≥95%
Battery-status monitoring: real-time voltage, current, temperature, and SOC monitoring, uploaded to the network manager
Low-power sleep: automatic RF-power reduction at night/during low load, saving 30% power
Remote power control: remote power-on/off and reboot, reducing on-site maintenance
Lightning protection and grounding: both the solar panel and the node are fitted with surge protective devices (SPD), grounding resistance ≤4Ω

Citation capsule: Remote-site nodes use a long-term unattended solar + storage-battery scheme, sized to local insolation: a P1 fixed node (incl. camera) at about 15W pairs with a 50W solar panel + 12V 100Ah LiFePO4 battery, supporting ≥7 days of consecutive overcast and rain; a dual-camera + gateway node at about 30W pairs with an 80W panel + 200Ah battery, supporting ≥14 days. MPPT charging efficiency is ≥95% and night-time power reduction 30%, delivering “install it and forget it”.
— Solar power-sizing · smart power management

How does the remote-ops system achieve a remote rate of over 95%?

Ops core principle: Remote-site operation and maintenance is extremely costly (a single on-site station visit may require days of round-trip travel), so the remote-ops rate must exceed 95%, with on-site maintenance limited to quarterly/annual routine checks.

MESHCOM network-management platform functions

Function module	Capability	Value
Topology visualization	Real-time display of whole-network topology, node locations, and link status (colors indicate signal strength)	See whole-network health at a glance
Performance monitoring	Per-link throughput, latency, packet loss, and RSSI real-time monitoring and historical trends	Detect performance bottlenecks early
Device management	Remote device-parameter configuration, firmware upgrade, batch management	Zero on-site configuration
Alert center	Device-offline, link-degradation, low-battery, and high-temperature alerts with multi-tier notification	Know about faults early
Log analysis	Centralized storage and query of device, event, and security logs	Rapid fault localization
Battery monitoring	Per-node solar-charging status, battery SOC/health, overcast-rain endurance prediction	Proactively address power-loss risk

Tiered maintenance response mechanism

Tier-1 alert (urgent)

Core node offline, full link break, large-scale video interruption → remote diagnosis within 5 minutes → initiate on-site repair plan within 1 hour

Tier-2 alert (important)

Link degradation (RSSI drop >10dB), low battery (<20%), single-node video interruption → remote troubleshooting within 30 minutes → on-site handling within 24 hours

Tier-3 alert (informational)

Individual sensor offline, solar-charging anomaly → handled during working hours → resolved at the next routine inspection

Routine inspection and SOP

Cycle	Inspection content	Duration
Daily	Remote review of whole-network topology, link status, alert log, battery status	15 minutes (remote)
Weekly	Remote spot-check of video quality on 30% of nodes, sensor-data completeness	30 minutes (remote)
Monthly	Remote firmware upgrade, performance-baseline test, bandwidth-expansion assessment	2 hours (remote)
Quarterly	On-site inspection: clean solar panels, check antennas, tighten connectors, test battery health	1 day/inspection team
Annually	Annual full-scale test: link-budget recheck, equipment-aging assessment, redundancy drill, emergency-plan update	3 days/inspection team

Citation capsule: Remote-site maintenance is costly (a single on-site station visit may require days of round-trip travel), so it is primarily performed remotely, with a remote rate above 95%: MESHCOM network management delivers topology visualization, performance monitoring, remote configuration, an alert center, and battery monitoring; daily remote inspection takes only 15 minutes, and on-site maintenance is compressed to 1 day per quarter. The three-tier graded alert response ensures faults are “known early and handled fast”.
— MESHCOM network-management platform · three-tier alert response

Against satellite/4G/dedicated line, where do Mesh’s advantages and combinations lie?

Core conclusion: In remote-site project scenarios, 1.4G Mesh does not “replace” satellite/4G/dedicated line; rather, it complements them—Mesh handles intra-zone transport while satellite/4G serves only as the cloud egress. High-cost satellite bandwidth is reserved for the “last mile” rather than carrying the whole construction-area traffic.

Full-scheme deep comparison table

Comparison dimension	1.4G Mesh	Satellite communication	Public 4G/5G	Dedicated line / fiber
Coverage	Autonomous coverage, controllable over tens of km	Global coverage (needs open sky)	Carrier-decided, often absent in mountains	Along the line, extends with distance
Per-link bandwidth	Up to 120Mbps@40MHz	0.5–10Mbps (costly)	50–100Mbps (congestion-affected)	≥1Gbps (costly)
Latency	≤10ms single hop, ≤100ms multi-hop	250–600ms (RTT)	20–100ms	≤10ms
Shadowing resistance	Strong (NLOS, crosses mountains)	Needs open sky	Medium (poor indoors)	Unaffected (fiber)
Anti-interference	Dedicated band, no interference	Strong	Public-network interference	No interference
Equipment cost	Medium (one-time)	High (equipment + monthly fee)	Low (data fee)	Very high (soars with distance)
Operating cost	Low (power + annual maintenance)	High (satellite data fee)	Medium (data fee)	High (ops cost)
Deployment time	1–3 days (on site)	1 day	Minutes (where signal exists)	Weeks to months
Mobility support	Yes (roaming switch)	Yes	Yes	No
Independence	Fully autonomous and controllable	Relies on satellite	Relies on carrier	Self-built autonomous

Recommended combination schemes

Scheme 1: Remote construction area (most common)

Mesh (intra-zone) + 4G/5G (cloud upload)
· Mesh carries the real-time video and sensor data from tens of km of construction area back to the P3 platform
· P3 uploads alert information and key video clips to the cloud via a 4G/5G interface (low volume, 4G suffices)
· Advantages: low cost, sufficient bandwidth, strong independence, mobility supported

Scheme 2: Ultra-remote / emergency scenarios

Mesh (intra-zone) + satellite (cloud upload)
· Mesh carries all intra-zone traffic
· Satellite serves only as the cloud egress (transmitting alert summaries, key screenshots), keeping data cost controllable
· Advantages: 100% independence, covers the most extreme scenarios

Scheme 3: Construction areas near public networks

Mesh (intra-zone) + wired public network (cloud upload)
· Nearby work faces still backhaul over Mesh (solving the last-few-kilometers shadowing problem)
· P3 uploads to the cloud stably via wired fiber/public network (unconstrained by bandwidth)
· Advantages: ample cloud-upload bandwidth, low cost, low latency

Citation capsule: In remote-site project scenarios, 1.4G Mesh does not replace satellite/4G/dedicated line but complements them: Mesh handles multi-kilometer to tens-of-kilometers broadband backhaul within the construction area (single hop ≤10ms, multi-hop ≤100ms), while satellite/4G serve only as the cloud egress. Keeping high-cost satellite bandwidth for the “last mile” avoids using satellite to carry the entire construction-area traffic—this is the balance point between controlling cost and preserving bandwidth.
— Mesh intra-zone transport + satellite/4G cloud-upload combination

What are the practical results in the power, mining, and water-resources sectors?

Industry consensus: 1.4GHz Mesh ad-hoc networking has become a standard networking approach for remote construction in the power industry. China Southern Power Grid has incorporated it into its “air-ground-space integrated multi-mode fusion networking” patent direction, and State Grid has listed it in its construction-informatization guide.

Case 1: Construction of a 500kV transmission line on the southwest plateau

Item	Details
Scenario	A 500kV transmission line from Yunnan to Tibet, totaling 180km at 3000–4500m altitude, no public network throughout, complex terrain (mountains/canyons/desert)
Networking scheme	Chain Mesh along towers: 42 P1 nodes (solar-powered) + 2 P3 platforms (aggregating over the south/north segments separately) + 3 P2 UAVs (airborne relay over canyon sections)
Bandwidth capability	Avg. single hop 4.3km; after multi-hop aggregation the core link carries 86Mbps, supporting real-time backhaul of 72 1080P video streams
Power scheme	80W monocrystalline solar panel + 200Ah LiFePO4 battery, supporting 14 consecutive overcast-and-rainy days
Cloud-upload scheme	P3 satellite egress (transmitting alerts and key video clips) + in an emergency, P3 driven to a cellular-covered location for local data retrieval
Operating results	After 8 months of operation, network availability 99.92%; alert response time cut from 45 minutes to 12 seconds; safety-event detection rate 96%

Case 2: Open-pit mine working-area monitoring in the northwest

Item	Details
Scenario	An open-pit iron ore mine in Inner Mongolia, 3.2km² in area, with 120+ machines (excavators/haul trucks/drills), no public-network coverage
Networking scheme	Mesh networking: 12 P1 nodes (vantage-point placement) + 1 P3 platform + 22 YN300 vehicle-mount roaming clients
Featured applications	Real-time haul-truck GPS + video backhaul (anti-speeding/anti-wrong-way); AI hard-hat detection; blasting-zone personnel-intrusion early warning
Operating results	Working-area coverage 100%; vehicle-mount roaming switch latency <200ms; violation recognition rate 94%; 12 hidden hazards detected in advance

Case 3: Construction safety supervision at a hydraulic hub

Item	Details
Scenario	A large hydraulic hub in the southwest, construction area 8km², containing 3 tunnels (12km total), a dam, a cofferdam, and multiple other work faces
Networking scheme	Hybrid topology: chain Mesh in tunnels (16 P1 + UWB location) + grid Mesh in the dam area (12 P1) + camp YN300 roaming + 2 P3 platforms
Featured applications	Real-time toxic-gas monitoring (CH₄/CO/H₂S) in tunnels with linked ventilation; personnel UWB location (accuracy ≤0.5m); dam concrete-pouring progress video capture
Operating results	In-tunnel personnel-location accuracy ≤0.8m; harmful-gas concentration alert within 5 seconds; real-time tracking of 300+ personnel

Citation capsule: The practical cases across three sectors validate the maturity of this scheme: a southwest 500kV transmission line over 180km with 42 P1 nodes + 3 P2 UAV relays achieved 99.92% network availability over 8 months and cut alert response from 45 minutes to 12 seconds; open-pit mine grid networking achieved 100% coverage, <200ms roaming switching, and 12 hazards detected in advance; a large hydraulic hub achieved in-tunnel personnel location ≤0.8m and 5-second gas-anomaly alerts. This shows Mesh has moved from “optional” to a “standard networking approach”.
— Practical cases across the power · mining · water-resources sectors

FAQ: key questions answered at a glance

Q1: If the construction camp has no public network at all, can 1.4G Mesh backhaul multiple HD video streams?

Yes. The 1.4G Mesh single-hop physical-layer rate is up to 120Mbps@40MHz, with actual effective throughput of about 80–100Mbps, supporting 10–15 simultaneous 1080P video streams (about 4–8Mbps per channel with H.265). After multi-hop aggregation, bandwidth scales linearly. In a 30km power-line project, field tests showed 42 nodes stably supporting real-time backhaul of 72 1080P video streams.

The key is rational bandwidth planning: calculate point by point for monitoring points, keep per-hop aggregated bandwidth under 80Mbps (avoiding bottlenecks), and aggregate stage by stage across nodes. The P3 command platform serves as the aggregation node, handling access and distribution of all network video.

Q2: Is the 1.4GHz band legal? Does it require an application?

The 1.4GHz (1350–1450MHz) band is a dedicated wireless band allocated by the state, used for public safety, emergency communication, and private-network communication. Compared with civil bands such as 2.4G/5.8G (WiFi/Bluetooth), the 1.4GHz private band has less interference and a higher permitted transmit power (2W vs 100mW)—a core reason it can achieve long-distance transmission.

Actual use must follow local radio-management regulations and typically requires frequency-use registration with the local radio-management authority. We recommend arranging it through a qualified system integrator.

Q3: Can solar power on remote nodes survive consecutive overcast-and-rainy days?

Yes. Based on field data in the northwest, a properly sized solar-power system supports 7–14 consecutive overcast-and-rainy days of endurance. The exact number depends on local insolation conditions:

Northwest (Gansu/Qinghai/Inner Mongolia): 5–6h of effective daily sunshine; configure an 80W panel + 200Ah battery, supporting ≥14 overcast-and-rainy days
Southwest (Yunnan/Guizhou): 3–4h of effective daily sunshine; a 100W panel + 200Ah battery is needed, supporting ≥7 overcast-and-rainy days
P1 built-in battery: even if solar completely fails, the P1 built-in lithium battery still works independently for 6–12 hours

The smart power-management system also automatically reduces power draw by 30% at night/during low load, further extending endurance.

Q4: Does multi-hop Mesh latency affect real-time command?

It does not. AODV routing protocol end-to-end latency test results:

Single-hop latency: ≤10ms (measured 6–8ms)
5-hop latency: ≤50ms (measured 35–45ms)
10-hop latency: ≤100ms (measured 75–95ms)
Route convergence time: ≤500ms (route reselection after link break)

These latencies fully meet the requirements of real-time command, video monitoring, and sensor-data acquisition—the human-ear perception threshold is about 200ms and the eye-perceived video threshold about 150ms, and the system latency stays within both thresholds.

Q5: How many nodes are appropriate, and how is it calculated?

Node deployment should extrapolate from bandwidth demand rather than spacing mechanically by distance. Calculation steps:

Step 1: Count monitoring points and bandwidth demand. Estimate each 1080P video at 6Mbps (H.265) and each sensor-data channel at 100kbps
Step 2: Set max per-hop carried bandwidth. Keeping per-hop aggregated bandwidth ≤80Mbps (20% margin) means each hop supports about 13 1080P video streams at most
Step 3: Plan node topology. Ensure each monitoring point has at least 1 direct link and each backbone node at least 2 paths to P3
Step 4: Verify the link budget. Use GIS tools to compute RSSI for each link, ensuring ≥−70dBm

Typical reference: a 30km power line needs about 12 P1 nodes (avg. 2.5km/hop); a 5km tunnel needs about 10 P1 nodes (in-tunnel shadowing, 500m/hop).

Q6: Compared with an ordinary wireless bridge, where is Mesh stronger?

The core difference lies in multi-hop self-healing and networking flexibility:

Comparison item	Wireless bridge	1.4G Mesh
Networking mode	Point-to-point / point-to-multipoint	Multi-hop ad-hoc (Mesh)
Single-point failure impact	Entire link breaks	Auto-relays around, no interruption
Topology change	Needs manual configuration	Auto-adapts (<500ms)
Node scale	≤8 points	≤64 points
Mobility support	No seamless roaming	Second-level roaming switching

Q7: Should the camp interior use YN300 or 1.4G Mesh?

The judging standard is simple: is there a “no-network, long-distance mountain-crossing section” ahead?

Field no-network long-distance sections (e.g., excavator faces, tunnel portals, mountain-crossing inspection, tower lines) → use 1.4G Mesh (P1+P2+P3) for multi-hop remote backhaul
Inside the camp/substation/project office (existing WiFi, gentle terrain) → use the YN300 roaming client for hotspot-area mobile roaming, lower cost and lighter weight

The two integrate seamlessly: the YN300 accesses the Mesh network created by P1 nodes, enabling end-to-end roaming from camp to the field. A typical configuration uses 1.4G Mesh as the camp trunk, YN300 for interior expansion coverage, and project-office WiFi accessing the P3 platform.

Q8: Can a system be reused after a project ends? How long is its life cycle?

It can absolutely be reused. Device life cycle and reuse value:

P1 backpack-mount radio: design life ≥10 years, IP67-protected, can relocate with the project or be repurposed for an operations-phase monitoring network
P2 UAV-mounted radio: usable in power-inspection, emergency repair, terrain mapping, and other scenarios
P3 command platform: software-upgradeable and hardware-expandable, serving long-term as a command center
In power scenarios: after construction ends it can be reused as a line-inspection communication network or substation-monitoring network
In mining scenarios: after construction ends it can be reused as a mining production-monitoring network or vehicle-dispatch network

One set of equipment spans both the construction and operations phases, delivering one investment, years of benefit.

Q9: Do the devices withstand low/high temperatures? Can they run in China’s extreme cold north or scorching south?

Absolutely. The industrial-grade specifications of the P1 backpack-mount radio are:

Operating temperature: −40℃ ~ +75℃ (covering all of China’s climate regions)
Storage temperature: −55℃ ~ +85℃
Protection rating: IP67 (dustproof/waterproof, immersion-resistant)
Vibration resistance: 5–500Hz / 5G (reliable on vehicle/UAV mounts)
Lightning protection: all ports fitted with surge protective devices (SPD), grounding resistance ≤4Ω

It has been field-verified in Mohe in the northeast (−48℃), the Flaming Mountains in Xinjiang (+47℃), Hainan typhoon zones, and northwest sandstorm regions.

References

Wikipedia: Ad hoc network: fundamentals of mobile ad-hoc networks and multi-hop routing.
Gov.cn · Radio Management Regulations: spectrum-compliance and management basis for private wireless communication in remote areas.

Standard-reference notes

The industry standards and specifications cited in this article lend authority to the technical claims; readers may trace them as needed:

[IEEE 802.11s] — wireless LAN mesh network standard, defining multi-hop mesh topology and self-healing routing mechanisms.
[IEC 60529] — enclosure protection-rating standard, defining IP67/IP65 dustproof/waterproof ratings.
[FCC 47 CFR Part 15] — compliance basis for transmit-power and usage boundaries of license-exempt and dedicated bands.
[ETSI EN 300 744] — base standard for OFDM/COFDM modulation in broadband wireless systems.
[ITU-R P.530] — reference model for terrestrial line-of-sight propagation and link-budget calculation.

★ Remote-site project supervision selection checklist:

Transport layer (core): Backpack-Mount Ad-Hoc Mesh Radio (P1) for multi-hop along the right-of-way + Portable Command & Dispatch Platform (P3) for camp aggregation + UAV-Carried Mesh Radio (P2) for aerial coverage-gap fill

Sensing layer: HD cameras + thermal imaging + environmental sensors (gas/dust/vibration/settlement) + UWB personnel location

Application layer: B/S command & dispatch platform + edge AI analysis + GIS electronic map + mobile App

Power: solar + storage-battery supply (7–14 consecutive overcast-and-rainy days) + smart power management