Addressing Limitations of Tactical Networks by Estimating and Exploiting Information Quality and Information Value

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Geraldine Conley
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1 Addressing Limitations of Tactical Networks by Estimating and Exploiting Information Quality and Information Value Niranjan Suri Senior Research Scientist Florida Institute for Human & Machine Cognition (IHMC) Visiting Scientist US Army Research Laboratory

2 Motivation Old Adage Getting the Right Information to the Right People at the Right Time Or put another way Getting Actionable Information to Soldiers at the Right Time Objective has not changed What has changed? Increased deployment of sensors (with increasing resolution, coverage, persistence) Increased access to open-source and social media data What has (unfortunately) not changed? Tactical networking environments still limited in terms of bandwidth, latency, reliability, stability, and connectivity

3 New Measures and Metrics Need to move away from the (mostly) current paradigm: Gather everything we can Shove what we gather into the network Live with what we get out of the network Quality of Information (QoI) and Value of Information (VoI) promise to be new metrics that can help QoI and VoI can be used to filter and prioritize transmitted information For example Do not transmit low quality information (exceptions?) Do not transmit information with little or no value Another benefit: filtering unnecessary information can reduce the cognitive workload on the Soldier

4 Panel Objectives Attempt to define QoI and VoI as metrics Some disagreement among researchers (which is healthy!) Discuss approaches to determine QoI and VoI Consider different types of information video, pictures, audio, sensor reports, tracks, documents, maps, etc. Discuss applications of these concepts to the DoD missions Consider different perspectives Analyst, Commander, Operator, others? How would we experiment with these concepts and measure improvements? Are there other metrics / measures worth including? For example Quality of Experience (QoE)?

5 Panelists (1) Lance Kaplan is a Scientist in the Networked Sensing and Fusion Branch, Sensors and Electron Devices Directorate (SEDD), at the US Army Research Laboratory (ARL). At ARL, Dr. Kaplan is serving as the Government Technical Area Lead for the Information Network Academic Research Center, which is a multi-institutional basic research program aimed at advancing fundamental understanding of information networks in conjunction with their interactions with social/cognitive and communication networks. Dr. Kaplan has published over 180 technical articles. His current research interests include signal and image processing, information/data fusion, resource management, and network science.

6 Panelists (2) Michael Kolodny has over 50 years of R&D engineering and management experience at The Army Research Laboratory (ARL) / Harry Diamond Laboratories. He retired in May 2009 and is currently serving as a Senior Technology Consultant for ARL. He has been very proactive in autonomous networked sensors and unattended ground sensor (UGS) activities within DoD. He founded and chaired the RDECOM UGS IPT and has been working actively with DoD in defining technology programs to support the Army and other DoD organization s autonomous sensing needs in various programs. Mr. Kolodny s most recent efforts have been focused on new technology concepts including interoperability of ISR assets. He founded the initial UGS Standards Working Group that has now been chartered by USD(I) and is chaired by DIA. Mr. Kolodny established and chairs the Conference on Ground/Air Multi-sensor Interoperability, Integration & Networking for Persistent ISR which is part of the annual SPIE DSS Symposium.

7 Panelists (3) Mark Linderman is a Principal Researcher in Information Management Technologies in the Air Force Research Laboratory Information Directorate where he leads the Information Management technology area. From , Dr. Linderman was the Technical Lead of the AFRL Information Directorate Joint Battlespace Infosphere (JBI) Program. The JBI program addresses a broad spectrum of information management challenges from access control, the role of metadata, dissemination, persistence and destruction of information. Dr. Linderman joined AFRL in 1994 after completing his M.Eng. and Ph.D. in electrical engineering from Cornell University. He holds a BSEE degree from the University of Delaware.

8 Panelists (4) Derya Cansever is the Chief Engineer of the Communication Networks and Networking Division at US Army CERDEC, where he conducts research in Tactical, Mission Aware and Software Defined Networks. Prior to CERDEC, Dr. Cansever worked at Johns Hopkins University Applied Physics Laboratory, AT&T Bell Labs, and GTE Laboratory. He taught courses on Data Communications and Network Security at Boston University and University of Massachusetts. He has a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana Champaign.

9 Panelists (5) Sastry Kompella received the Ph.D. degree in electrical and computer engineering from the Virginia Tech, Blacksburg, VA, in Currently, he is the Head of the Wireless Networks Research Section under the Information Technology Division, U.S. Naval Research Laboratory, Washington, DC. His research interests include various aspects of wireless networks, from mobile ad hoc to underwater acoustic networks, with specific focus toward the development of cognitive, cooperative network optimization techniques for efficient spectrum and other wireless resource allocations.

10 Panel Format Begin with each panelist presenting opening remarks (10-15 minutes) Open the floor for discussion Audience please make note of questions to ask the panel! Otherwise, I have to resort to my canned questions Potential Next Step (after Panel) Edited Book Provided there is sufficient interest! Combination of invited and peer-reviewed chapters If you are interested, please get in touch with me

11 Shameless Plug Paper on VoI in October IEEE Communications Magazine

12 Let s Get Started! Lance Kaplan Panel Members US Army Research Laboratory Michael Kolodny US Army Research Laboratory Mark Linderman US Air Force Research Laboratory Derya Cansever US Army CERDEC Sastry Kompella US Naval Research Laboratory Make note of your questions or them to me at nsuri@ihmc.us

13 Backup Slides

14 Value of Information Our approach Discretize information into Information Objects Determine value of information (for each IO) Use value estimation to prioritize and filter information Enable receiver and context-dependent delivery Maximize available network capacity Reduce operator data overload Question how to determine right / actionable information?

15 Defining Value of Information Many definitions in the literature for Value of Information Game Theory Price someone would pay for information before making a decision Information Value Theory Value obtained by a reduction in uncertainty / increase in profit Our definitions: Information Object (IO): A discrete piece of information (e.g., a picture, a sensor report, a document, a video clip, a track) Value of Information (VoI): A numerical value between 0 and 1 assigned to an IO VoI of 0 implies it is of no value VoI of 1 implies it is of maximal value VoI of an IO is relative to a recipient (e.g., a soldier) Different from Quality of Information (QoI), which is intrinsic to the IO 15

16 How is VoI Useful? Assumptions: Number of Information Objects (IOs) exceed network s ability to transport and deliver all of the information Delivering all of the IOs available overloads the soldier Determining the VoI for each IO allows us to sort the IOs in decreasing order by value Once sorted, algorithm can Send the most important IOs first, maximizing available network capacity If capacity is reduced, lower rated IOs are dynamically skipped Filter out IOs whose computed VoI is below a desirable threshold Reduce overload on soldier IO s delivered to applications such as ATAK 16

17 Value of Information (2) Question How to determine value of information? Abstract (working) definition information is of value if it causes the receiver to improve his/her situation awareness and/or course of action What is a (reasonably) computable approach to solving this question? Would need to take into account many factors Current mission, current activity, current location Mission Information Exchange Requirements (IER) Predicted location / activity in time (brings in temporal dimension) Type of information, source, pedigree, recency Quality of Information Vetting / validation of the information (sometimes, receiver dependent) Range of influence Information known a priori Receiver s preferences Receiver s social network and trust relationships I n f o r m a t i o n V a l u a t o r

18 Realization Proactive Dissemination Service (DSPro) Model mission (including routes, objectives, roles) Match available and incoming information against mission model Prioritize information dissemination based on relevance estimation Pre-stage information for edge users to increase availability Learn user preferences over time Realize a flexible push/pull architecture Operations Center Tactical Network Tactical Network Operations Center Tactical Network Information Valuator User 1 IO Information Valuator Analyst 1 User 1 User 3 Node Context 1 User 2 IO Node Context n IO Tactical Network User n Sensor Network IO Information Valuator IO Analyst 2 User n Sensor Network IO (a) From OC to Edge Network (a) From Edge Network to OC (c) At the Edge Network

Realization (2) Modeled Metadata Georeference / Spatial Extents Timestamp Expiration Time Source / Pedigree

representation includes: Identity Location Mission Name Task / Role Projected Path(s) Relevant Distance By

position t : expected time of use e : expiration time i : importance l : learned preference w : corresponding

19 Realization (2) Modeled Metadata Georeference / Spatial Extents Timestamp Expiration Time Source / Pedigree Type Classification Relevant Missions Priority Source Reliability Information Content User Context Initial representation includes: Identity Location Mission Name Task / Role Projected Path(s) Relevant Distance By Type of Object Selection Threshold Weights for Ranking VoI (d w d )+(t w t )+(e d : distance from the route / position t : expected time of use e : expiration time i : importance l : learned preference w : corresponding weight factor w e )+(i w Preference Importance i Proximity ) ( l wl Validity Time to Use ) 19

20 Enhancements Range of Influence History of Previously Delivered Information and Change Delta Account for Value Added by new Information Object Object Size

21 Range of Influence Estimate the Range of Influence of Tracks based on type (MIL2525 standard symbol) Configurable policy specifies range of influence for different symbol patterns Policies can depend on attributes such as Friendly, unknown, enemy Type of entity airborne, ground, sea

History and Change Delta Matchmaking History used in conjunction with distance to compute relevance of subsequent updates: A track that moves 100 m, but is 1 km away, is assigned a higher relevance

22 History and Change Delta Matchmaking History used in conjunction with distance to compute relevance of subsequent updates: A track that moves 100 m, but is 1 km away, is assigned a higher relevance than a track that moves 100 m, but is 10 km away. Object Metadata Node Context Area of Relevance Mil STD 2552 Type Client-Assigned Importance... Node Path Node Location Node Mission Node Role... Matchmaker Matchmaking History

23 Object Size Multiple Smaller Objects Vs Fewer Big Objects? Prioritization by Relevance/Object size ratio Filtering of undeliverable data Multiple Tracks ~ 15 KB Object Metadata Area of Relevance Mil STD 2552 Type Client-Assigned Importance... Node Context Node Path Node Location Node Mission Node Role... Network Topology Link capacity Link delay... Matchmaker Matchmaking History Single Image ~ 150 KB Scheduler

24 Initial Results (1) Scenario: Information Dissemination to the Tactical Edge SATCOM SATCOM SATCOM SATCOM MANET MANET MANET Tactical Operations Center Scenario includes dissemination of tracks and sensor reports to dismounted soldiers Soldiers can choose between three levels of selectivity Low (most inclusive), Medium, high (most selective)

Bandwidth Utilization (KB/sec) (Lower is

Improvement 1000 900 800 700 600 500 400 300

Low - DSPro Medium - Naïve Medium - DSPro

Improvement 26.85% to 45.39% (SATCOM) 31.

25 Bandwidth Utilization (KB/sec) (Lower is Better) Initial Results (2) DSPro Performance Improvement SATCOM MANET Baseline Low - Naïve Low - DSPro Medium - Naïve Medium - DSPro High - Naïve High - DSPro Performance Improvement 26.85% to 45.39% (SATCOM) 31.16% to 64.86% (MANET) Increasing Information Selectivity ---> 25

requirements as opposed to gathering everything Integrate C2 requirements of multiple missions Shaping of data: downsample

26 Way Forward Mission based Collection Control the number and type of ISR assets deployed / tasked to support mission requirements Control Drive/steer ISR collection using existing, deployed assets, control what is gathered to support mission requirements as opposed to gathering everything Integrate C2 requirements of multiple missions Shaping of data: downsample imagery, audio, video, summarize documents Based on mission requirements and network state Prioritize based on type; e.g. type of traffic VoIP

Broader Context Agile Computing Middleware Realizes Many Protocols for Discovery, Transport, Dissemination, Information Management Building the OODA Loop for the Network Source code for Mockets,

27 Broader Context Agile Computing Middleware Realizes Many Protocols for Discovery, Transport, Dissemination, Information Management Building the OODA Loop for the Network Source code for Mockets, DisService, NetProxy has been released under open source GPLv3 license Application Application Agile Computing Middleware Mockets Group Manager (Discovery) AgServe Dissemination Service DSPro ACM Network Proxy UDP Serial Representing Commander s Intent Wired / Wireless Network

28 Interconnected Puzzle Value of Information Mission and Information Requirements Confidence Computation and Actuation Information Ranking and Filtering Wearable Computing and Quantified Self Data Shaping Mediating Between Multiple Information Systems Social Network Analysis / Mining Data Analyzers / Aggregators Moving Computation to Data Software Agents for Offloading Tasks Anomaly Detection Resource Coordination and Allocation Sensor Tasking Quality of Service and Prioritization Tactical Clouds Opportunistic Resource Exploitation Network Awareness Intelligent Dissemination Resource Discovery Tactical Communications Network

Synthesizing Adaptive Protocols by Selective Enumeration (SYNAPSE)

Synthesizing Adaptive Protocols by Selective Enumeration (SYNAPSE) Problem Definition Solution Approach Benefits to End User Talk Overview Metrics Summary of Results to Date Lessons Learned & Future Work