Towards mission-driven summarization for tactical networks

In October 2017, the U.S. Army established six army modernization priorities to build a more lethal force. Considering the increasing importance of information superiority, it is not surprising that the Army Network has been selected as one of the six modernization priorities. One of the main reasons behind this decision is that, in the future, battlespace will become more contested by an adversary's sophisticated attacks on network infrastructure. Indeed, modern jamming threats are significantly different than in the 1980s, and the military is looking for a solution to support communication in this contested environment. There are two directions to address the problem. The first approach would be to make the network systems more robust and reliable, such as by adding in routing and security solutions. Another approach is more efficient network resource management that includes minimizing the transmitted data volume to save network bandwidth consumption. There are two representative techniques in this case: compression and summarization. Compression represents the original data with fewer bytes and is widely used for multi-media data. Summarization, or sampling, selects only a subset of the data from the whole set. In the current thesis, we propose a new mission-driven summarization layer that understands the application needs and optimizes the network resources using the sampling technique. This work is similar to the existing summarization efforts but is distinguished by three key factors. First, its nature is mission driven. This layer offers declarative application program interfaces to the applications to analyze the application needs so that the layer can flexibly manages network resources on behalf of applications. This is different than the traditional approach, such as time-driven and event-driven approaches where applications need to micromanage network resources. The second difference is generality. Rather than devising the optimized algorithm for a particular application as in state-of-the-art researches, this work is designed to provide summarization as a service. Hence, we focus on an easy-to-port feature across different application contexts. This generality calls for simplicity by observing a fundamental trade-off between generality and complexity. That is, more sophisticated solutions tend to have more assumptions and are more difficult to apply to the wider application domain. The proposed solution is designed to be simple and widely applicable yet effective.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Towards mission-driven summarization for tactical networks

In October 2017, the U.S. Army established six army modernization priorities to build a more lethal force. Considering the increasing importance of information superiority, it is not surprising that the Army Network has been selected as one of the six modernization priorities. One of the main reasons behind this decision is that, in the future, battlespace will become more contested by an adversary's sophisticated attacks on network infrastructure. Indeed, modern jamming threats are significantly different than in the 1980s, and the military is looking for a solution to support communication in this contested environment. There are two directions to address the problem. The first approach would be to make the network systems more robust and reliable, such as by adding in routing and security solutions. Another approach is more efficient network resource management that includes minimizing the transmitted data volume to save network bandwidth consumption. There are two representative techniques in this case: compression and summarization. Compression represents the original data with fewer bytes and is widely used for multi-media data. Summarization, or sampling, selects only a subset of the data from the whole set. In the current thesis, we propose a new mission-driven summarization layer that understands the application needs and optimizes the network resources using the sampling technique. This work is similar to the existing summarization efforts but is distinguished by three key factors. First, its nature is mission driven. This layer offers declarative application program interfaces to the applications to analyze the application needs so that the layer can flexibly manages network resources on behalf of applications. This is different than the traditional approach, such as time-driven and event-driven approaches where applications need to micromanage network resources. The second difference is generality. Rather than devising the optimized algorithm for a particular application as in state-of-the-art researches, this work is designed to provide summarization as a service. Hence, we focus on an easy-to-port feature across different application contexts. This generality calls for simplicity by observing a fundamental trade-off between generality and complexity. That is, more sophisticated solutions tend to have more assumptions and are more difficult to apply to the wider application domain. The proposed solution is designed to be simple and widely applicable yet effective.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

SVC-TChain: Incentivizing good behavior in layered P2P video streaming

Video streaming applications based on Peer-to-Peer (P2P) systems are popular for their scalability, which is hard to achieve with traditional client-server approaches. In particular, layered video streaming has been much-studied due to its ability to differentiate users' streaming qualities in heterogeneous user environments. Previous work, however, has shown that user misbehavior (e.g., free-riding and protocol deviation) poses a serious threat to P2P systems that are not equipped with proper incentive mechanisms. We propose a method to disincentivize such misbehavior. Our SVC-TChain is a layered P2P video streaming method based on scalable video coding (SVC), which uses the recently proposed T-Chain incentive mechanism to discourage free-riding. After introducing T-Chain, we present the first analytical framework to study SVC piece selection with multiple video layers, using it to efficiently choose SVC-TChain's optimal piece selection parameters and thus discourage deviations from the piece selection policy. Extensive experimental results show that SVC-TChain outperforms layered extensions of BiTos and Give-to-Get, two popular P2P video streaming approaches, both in the absence of user misbehavior and when some users misbehave

Athena: Towards Decision-Centric Anticipatory Sensor Information Delivery

The paper introduces a new direction in quality-of-service-aware networked sensing that designs communication protocols and scheduling policies for data delivery that are optimized specifically for decision needs. The work complements present decision monitoring and support tools and falls in the larger framework of decision-driven resource management. A hallmark of the new protocols is that they are aware of the inference structure used to arrive at decisions (from logical predicates), as well as the data (and data quality) that need to be furnished to successfully evaluate the unknowns on which these decisions are based. Such protocols can therefore anticipate and deliver precisely the right data, at the right level of quality, from the right sources, at the right time, to enable valid and timely decisions at minimum cost to the underlying network. This paper presents the decision model used and the protocol design philosophy, reviews the key recent results and describes a novel system, called Athena, that is the first to embody the aforementioned data delivery paradigm. Evaluation results are presented that compare the performance of decision-centric anticipatory information delivery to several baselines, demonstrating its various advantages in terms of decision timeliness, validity and network resources used. The paper concludes with a discussion of remaining future challenges in this emerging area