Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
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Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep Space Station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network (DSN) are summarized: tracking complexes, ground communications, and network operations control capabilities are described.
Amorose, R. J.↗
Rate-based congestion control in networks with smart links, revision
The author uses a network simulator to explore rate-based congestion control in networks with smart links that can feed back information to tell senders to adjust their transmission rates. This method differs in a very important way from congestion control in which a congested network component just drops packets - the most commonly used method. It is clearly advantageous for the links in the network to communicate with the end users about the network capacity, rather than the users unilaterally picking a transmission rate. The components in the middle of the network, not the end users, have information about the capacity and traffic in the network. The author experiments with three different algorithms for calculating the control rate to feed back to the users. All of the algorithms exhibit problems in the form of large queues when simulated with a configuration modeling the dynamics of a packet-voice system. However, the problems are not with the algorithms themselves, but with the fact that feedback takes time. If the network steady-state utilization is low enough that it can absorb transients in the traffic through it, then the large queues disappear. If the users are modified to start sending slowly, to allow the network to adapt to a new flow without causing congestion, a greater portion of the network's bandwidth can be used.
Heybey, Andrew Tyrrell↗
Making tomorrow's mistakes today: Evolutionary prototyping for risk reduction and shorter development time
In the early days of JPL's solar system exploration, each spacecraft mission required its own dedicated data system with all software applications written in the mainframe's native assembly language. Although these early telemetry processing systems were a triumph of engineering in their day, since that time the computer industry has advanced to the point where it is now advantageous to replace these systems with more modern technology. The Space Flight Operations Center (SFOC) Prototype group was established in 1985 as a workstation and software laboratory. The charter of the lab was to determine if it was possible to construct a multimission telemetry processing system using commercial, off-the-shelf computers that communicated via networks. The staff of the lab mirrored that of a typical skunk works operation -- a small, multi-disciplinary team with a great deal of autonomy that could get complex tasks done quickly. In an effort to determine which approaches would be useful, the prototype group experimented with all types of operating systems, inter-process communication mechanisms, network protocols, packet size parameters. Out of that pioneering work came the confidence that a multi-mission telemetry processing system could be built using high-level languages running in a heterogeneous, networked workstation environment. Experience revealed that the operating systems on all nodes should be similar (i.e., all VMS or all PC-DOS or all UNIX), and that a unique Data Transport Subsystem tool needed to be built to address the incompatibilities of network standards, byte ordering, and socket buffering. The advantages of building a telemetry processing system based on emerging industry standards were numerous: by employing these standards, we would no longer be locked into a single vendor. When new technology came to market which offered ten times the performance at one eighth the cost, it would be possible to attach the new machine to the network, re-compile the application code, and run. In addition, we would no longer be plagued with lack of manufacturer support when we encountered obscure bugs. And maybe, hopefully, the eternal elusive goal of software portability across different vendors' platforms would finally be available. Some highlights of our prototyping efforts are described.
Friedman, Gary↗
Advances Made in the Next Generation of Satellite Networks
Because of the unique networking characteristics of communications satellites, global satellite networks are moving to the forefront in enhancing national and global information infrastructures. Simultaneously, broadband data services, which are emerging as the major market driver for future satellite and terrestrial networks, are being widely acknowledged as the foundation for an efficient global information infrastructure. In the past 2 years, various task forces and working groups around the globe have identified pivotal topics and key issues to address if we are to realize such networks in a timely fashion. In response, industry, government, and academia undertook efforts to address these topics and issues. A workshop was organized to provide a forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. The Satellite Networks: Architectures, Applications, and Technologies Workshop was hosted by the Space Communication Program at the NASA Lewis Research Center in Cleveland, Ohio. Nearly 300 executives and technical experts from academia, industry, and government, representing the United States and eight other countries, attended the event (June 2 to 4, 1998). The program included seven panels and invited sessions and nine breakout sessions in which 42 speakers presented on technical topics. The proceedings covers a wide range of topics: access technology and protocols, architectures and network simulations, asynchronous transfer mode (ATM) over satellite networks, Internet over satellite networks, interoperability experiments and applications, multicasting, NASA interoperability experiment programs, NASA mission applications, and Transmission Control Protocol/Internet Protocol (TCP/IP) over satellite: issues, relevance, and experience.
Bhasin, Kul B.↗
Optimal Sensor Mobility Design for Target Tracking with Distributed Sensing, Communication and Computing Infrastructure
The paper presents an airborne target tracking approach with data fusion from distributed stationary and mobile sensor network transmitted through a wireless/wired communication network and using a distributed computing infrastructure. To obtain high quality measurements, sensor flight platforms' positions and orientations are optimized with respect to the available target's position and velocity estimates, and a formation control strategy is applied to derive desired trajectories for the flight platforms. As sensors move, the communication network topology switching is determined using the worst-case approach to handle uncertainties in the target's and sensors' positions, which alters communication delays for sensors' data transmission to computing centers. An optimal data migration algorithm is periodically applied to determine these communication delays for each sensor and the optimal location with minimum end-to-end latency for the target tracking algorithm execution, which is based on adaptive information fusion from multi-modal mobile and stationary sensors inside an Extended Kalman Filter framework. The approach is validated in a desktop simulation environment using synthetic sensor data generated for a simulated target's flight.
Distributed sensing↗