Maharashtra Autonomous Surveillance & Logistics Network
AASIOM's flagship project — a fully sovereign autonomous aerial surveillance system planned for Maharashtra's expressway corridors. Currently in active planning and concept development.
MH-ASLN is AASIOM's planned flagship project — a network of sovereign autonomous drones, edge AI inference nodes, and encrypted mesh communications across Maharashtra's expressway corridors. The system is designed to provide continuous surveillance, rapid incident detection, and real-time command visibility with zero foreign technology dependencies.
Maharashtra operates some of India's most critical road infrastructure — yet surveillance, incident detection, and emergency response remain dangerously inadequate, with systems that depend heavily on imported hardware and human reporting.
MH-ASLN is designed for Maharashtra's two highest-priority expressway corridors, covering the most critical stretches of national economic and humanitarian importance.
The arterial expressway connecting India's financial capital to Pune — one of the highest-traffic and highest-risk corridors in Maharashtra, spanning challenging Western Ghats terrain.
Maharashtra's landmark 701 km expressway connecting Nagpur to Mumbai — the longest expressway in India and a critical artery for economic development across Vidarbha, Marathwada, and Konkan regions.
MH-ASLN is designed as four interconnected sovereign systems — each addressing a distinct layer of the surveillance challenge, collectively forming a complete autonomous surveillance and response network. These are currently in the concept and planning stage.
Autonomous Drone Fleet Management
Sky-Hive is the planned autonomous multi-drone coordination system within MH-ASLN. The concept: a distributed fleet of VTOL drones that patrols designated expressway sections autonomously — self-dispatching on incident triggers, self-docking for recharge, and operating in continuous shifts without manual flight intervention.
Edge AI Vision & Inference Engine
VAYU-Net is the planned on-device intelligence layer of MH-ASLN. The design goal: all AI processing — accident detection, vehicle classification, debris identification, fire/smoke detection — runs entirely on the drone. No cloud latency. No data sovereignty risk. No external connectivity required for real-time decisions.
Encrypted Resilient Communication Network
MeshLink is the planned communication backbone of MH-ASLN — an encrypted mesh protocol designed to enable drone-to-drone, drone-to-ground, and ground-to-command-centre communication across the entire corridor. Designed for resilience: if any node drops, the mesh self-heals. End-to-end encryption using indigenous cryptographic design.
Unified Situational Awareness & Command Centre
Sky-Command is the planned operator-facing command and control interface — a unified dashboard designed to consolidate feeds from all active drones, AI-detected incidents, corridor status, and fleet telemetry. Built with MSRDC and highway authority operators as the intended users — single-screen situational awareness across the full corridor.
MH-ASLN follows a structured five-phase execution — from foundational R&D through pilot deployment to full-scale rollout — with clear government milestones at each stage.
Company incorporation, founding team assembly, concept development, and project planning.
Build functional prototypes of all 4 systems. Lab testing. Edge AI model training. Drone platform integration.
10 km live demonstration on a designated corridor section. MSRDC evaluation. System performance certification.
Formal partnership agreement. Full technical specification handover. Procurement and contract finalisation with MSRDC/NHAI.
Complete rollout across Mumbai-Pune and Samruddhi corridors. Operator training. 24/7 live operations handover.
These are the target performance specifications AASIOM is designing MH-ASLN to achieve — based on Maharashtra expressway incident data and infrastructure requirements.
For MSRDC, NHAI, defence bodies, and strategic investors — we are ready to present a full technical demonstration and partnership proposal.