N151-047 TITLE: Innovative Data Compression Algorithms to Increase System Throughput Efficiency on Navy Ship-to-Shore Communication Networks

TECHNOLOGY AREAS: Information Systems

ACQUISITION PROGRAM: PMS505, Littoral Combat Ship Fleet Introduction and Sustainment Program Off

OBJECTIVE: Develop an innovative system for increasing data transmission compression of installed shipboard hardware to shore communications networks.

DESCRIPTION: An innovative technology is sought for throughput management and data compression to facilitate faster transmission of data over existing ship-to-shore communication networks. This topic seeks a transmission technique for ship and system maintenance related data generated and available on board the LCS to shore sites because there are not enough sailors onboard to actually monitor and maintain the equipment on board LCS. The data paths and links available on LCS are monopolized by higher priority communications requirements. This topic is to define and prove a method to get this information off the ship to shore for monitoring thereby allowing for reduced manning onboard LCS. Off-board post-mission analysis and distance support require tremendously large amounts of data.

Post-mission analysis and distance support requires a tremendously large amount of data to be transmitted to shore based activities. LCS platforms rely on the Navy Information Application Product Suite (NIAPS) system to transmit much of this data using limited bandwidth capacity. Bandwidth limitations coupled with NIAPS replication times do not support the speed of delivery required for performance of shore based functions critical to support the minimally manned crew concept that is integral to the Littoral Combat Ship Concept of Operations. The limitation in shipboard data transmission capacity is the current choke point to the amount of distance support and post mission analysis that can be conducted off ship.

A solution is sought that will combine the latest technologies of data compression and data replication while minimizing resource consumption across existing systems and any new design infrastructure. This solution should help manage transmission bandwidth while transferring data as it is gathered by searching for open bandwidth between higher priority data traffic instead of holding it for periodic transmissions ashore in larger bulk transfers. The major design goal is for the latest data compression technologies and data replication conventions to be coupled with an infrastructure that minimizes information technology (IT) resource consumption by defining suitable connections between systems, minimizing infrastructure changes and disruption of services among existing systems, and obtaining acceptable performance for multisystem data transfers ashore. Origination of the data and formats to be processed will be accomplished by the same hardware and software resources currently used in local system environments, however, recommendations for current system modifications should be made with performance considerations that minimize additional IT resource demands and configuration change.

Data compression is an enabling technology that will allow for greater data transmission across networks without significant increase in resources. There are several data compression algorithms which are available to compress files of different formats such as Shannon-Fano Coding, Huffman coding, Adaptive Huffman coding, Run Length Encoding and Arithmetic coding (Ref 1). Additionally, academic researchers have improved network bandwidth utilization by an order of magnitude using algebra to eliminate the network-clogging task of resending dropped packets of data. (Ref 2). Since LCS networks also carry many different types of services with varying digital formats and priorities, local mechanisms that allow these services to co-exist are complex and compete for off-ship bandwidth. Research on dynamic bandwidth allocation can prove beneficial towards balancing data (packet) delay through use of a traffic load-based allocation approach, which includes data weight and queue priority calculations (Ref 3). These approaches are examples of innovative techniques that, when coupled in development of a single solution, may satisfy the objectives of this topic.

PHASE I: The company will determine technical feasibility and develop a concept for an improved data compression algorithm for increasing voice and data transmission throughput and that meets the requirements stated in the topic description. The company will demonstrate the feasibility of the data compression algorithm concept and will establish that the concept can be feasibly developed into a useful product for the Navy. Feasibility will be established by analytical modeling.

PHASE II: Based on the results of Phase I, the small business will develop a data compression algorithm prototype for evaluation. The prototype will be evaluated to determine its capability in meeting Navy requirements to increase voice and data transmission throughput on Navy ship-to-shore communication systems. System performance will be demonstrated through prototype evaluation and operational demonstration on targeted communication systems. Evaluation results will be used to refine the prototype into a design that will meet Navy requirements. The company will prepare a Phase III development plan to transition the technology to Navy use.

PHASE III: The company will support the Navy in transitioning the data compression algorithm technology for Navy use on targeted ship-to-shore communication networks. The company will develop the data compression algorithm to increase voice and data transmission throughput, and will perform operational testing to evaluate effectiveness in an operationally relevant environment. The company will support the Navy in transitioning the data compression algorithm to its intended platform.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Compression algorithms that aid in the transmission of large amounts of data have both commercial and military benefits. Communication systems that are limited by the available spectrum or power output may benefit from this technology. Compression algorithms can mitigate the adverse effects on communications equipment with design constraints, such as portability.

REFERENCES:
1. Shanmugasundaram, Senthil. "A Comparative Study of Text Compression Algorithms" International Journal of Wisdom Based Computing," Vol. 1 (3), December 2011. 68-76.

2. Talbot, David. "A Bandwidth Breakthrough," MIT Technology Review. October 23, 2012.

KEYWORDS: Increasing transmission throughput; data compression algorithm; digital communication network; dropped data packets; statistical compression techniques; lossless data compression

** TOPIC AUTHOR (TPOC) **

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