Towards Optimized Machine-to-Machine Communications in Tactical Wireless Networks
Abstract
The proposed research is broken into three tasks:Task 1: Machine-to-Machine (M2M) Resource Management in Tactical Networks - Develop algorithms and approaches for optimal and stable resource configurations; self-organizing learning with finite memory; arbitration messages and utility functions; and resource management with a massive number of Machine Type Devices (MTDs).Task 2: Data Aggregation and In-Device Processing - Develop distributed clustering algorithms; scheduling with aggregators; in-device processing; and mobile data collection approaches.Thrust 3: M2M-Aware Network Planning and Dimensioning - Develop modeling for M2M traffic; network planning and dimensioning algorithms; and backbone planning approaches.The proposed research will provide significant new capabilities for current and future Naval tactical wireless networks. The proposed mathematical framework will provide the first holistic approach to providing optimized resource management solutions that enable a tactical wireless network to support a massive number of MTDs and M2M communication links. Machine-type communication is prevalent in all military networks and will range from autonomous vehicles to surveillance apparatus, as well as wearable sensors that are used by soldiers in the battlefield for collecting a variety of data that ranges from vitals to battlefield information.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 25, 2017
- Source ID
- N000141512709
Entities
People
- Walid Saad
Organizations
- Office of Naval Research
- United States Navy
- Virginia Tech