Back in the 1990s, when the U.S. military still relied primarily on line-of-sight rather than satellites for command and control and other communications, the Office of Naval Research developed and tested a breakthrough approach—a self-organizing mesh network for Navy line-of-sight communications.

With this network, a ship, for example, can send radar data far beyond the horizon, using ships, planes, and Navy stations in a series of line-of-sight relays. Algorithms chart the most efficient path from one line-of-sight platform to the next. Data might travel half a dozen or more “hops” before reaching its ultimate destination.

As innovative as the research was, the mesh network was never put into operation. Satellite communications were quickly coming on their own in the Navy and the other services, and there was no longer a pressing demand for line-of-sight relays to go beyond the horizon.

There may be a need for such a mesh network again. In the event of a conflict in the Pacific, satellite communications could be degraded or denied, undermining the effectiveness of Joint All-Domain Operations. If that were to happen, the Department of Defense (DOD) would need to rely on line-of-site networks for sensor, command-and-control, and other data. Unfortunately, current approaches to line-of-sight networks have significant limitations—such networks tend to be inefficient and unstable over long distances.

However, by bringing back the mesh relay network developed by the Navy in the 1990s—and updating it with AI and infrastructure improvements—DOD can strengthen its ability to maintain all-domain operations in a satellite-denied environment.

One of the weaknesses of current line-of-sight networks is that they try to create a global topology, or map, that shows all the connections between various platforms, as well as the most efficient communications routes. Satellite networks can create such global topologies because every platform can “see” the satellites. However, it’s much more difficult for line-of-sight networks to create fully comprehensive maps.

Line-of-sight communications must be conducted at relatively low power to avoid revealing the platforms’ locations to adversaries. But lower power means lower bandwidth, or capacity. And when line-of-sight networks try to create a global topology, they often end up using most of the available bandwidth just to maintain the map. Each time there’s a change in connectivity—with a ship or plane moving in or out of line of sight—the routers and algorithms on the network’s platforms have to completely update the global topology. 

This intensive router-to-router traffic between platforms not only crowds out intelligence information—sometimes there’s not even enough bandwidth for the router traffic itself. This can be a particular issue for U.S. forces in the Pacific, where airborne and seaborn platforms are constantly moving in and out of sight of one another. A global topology is typically not sustainable in a frequently changing line-of-sight environment.

Instead of trying to create a global topology, the mesh network developed by the Navy in the 1990s uses an innovative relay system that moves data from one line-of-sight hop at a time.

Here’s how it works: For example, say an unmanned aerial vehicle (UAV) needs to send radar data to a number of ships, planes, and bases beyond the horizon in the Pacific. With the mesh network, the UAV and all the platforms within its line of sight are using their routers and algorithms to communicate with one another. In essence, they’re creating a highly localized network map.

It wouldn’t be practical for the UAV to send its data to all of its line-of-sight neighbors—that would create too much network traffic. Instead, the UAV determines which neighbors have the most line-of-sight connections of their own and sends its data only to them. In the next step, the platforms that get the UAV’s data relay it to their own line-of-sight neighbors that have the most connections. This process is repeated, from one group of line-of-sight platforms to the next, until the UAV’s data reaches its ultimate destination.

A major advantage of this approach is that data moves throughout the network with the minimum number of platform-to-platform relays. This makes the most efficient use of line of sight’s limited bandwidth, freeing up capacity for intelligence data. And because the fewest possible platforms are relaying the data from one hop to the next, it lowers the risk of detection by adversaries. There’s another benefit: Unlike line-of-sight networks that try to create global topologies, the mesh network is self-healing—it seamlessly incorporates constant changes in connectivity.

The latest advances in artificial intelligence (AI) have the ability to make the mesh network far more powerful than Navy researchers envisioned in the 1990s. In particular, AI can help maximize routing and network efficiency. It can determine which platforms, and which data transmissions, have the highest priority based on the operational mission and the commander’s intent.

Mesh networks alone, however, are not enough. In order for them to operate efficiently—even with AI—they need to be part of an infrastructure that is geared toward line-of-sight communications, not just satellites. For example, in recent years sensors have been increasingly designed to stream data through satellite communications. However, it is difficult for lower bandwidth, line-of-sight communications to manage and consume streamed data. Too much data from too many sensors will bog down a line-of-sight network.

This means that sensors will need to operate differently in a satellite degraded or denied environment. Instead of streaming oceans of data, they will only be able to send the most relevant bits of information. Here again AI can help—by selecting the most relevant sensor data based on mission, evaluating network conditions, and determining how much data can be sent at a given time.

In addition, sensors will need to be specifically designed to accommodate line-of-sight communications. This is being done now. One example: With some small UAVs, the resolution on the cameras is intentionally lower, and the frame rates are intentionally slower, so the video can be processed more easily through line-of-sight communications.

A line-of-sight infrastructure also calls for changes to the routers and algorithms that communicate with one another to form a mesh network. DOD now largely relies on commercial, proprietary routers and algorithms that are specifically designed for global topologies. With open operating systems and other open approaches, DOD can develop routers and algorithms tailored to line-of-sight communications.

U.S. forces in the Pacific may someday need to transition from satellite to line-of-sight communications to maintain all-domain operations. By leveraging the mesh relay network the Navy developed in the 1990s, updating it with the latest AI, and developing a mature line-of-sight communications infrastructure, DOD can meet that challenge.