Operations on the battlefield are becoming increasingly dependent on tactical networks as more robots, drones, and mobile devices are deployed. A tactical network is considered a disconnected, intermittent, limited environment, considering adversary attacks on network infrastructure. While tactical radios transmit voice messages over tactical networks, recipients may suffer message delays owing to limited bandwidth. Herein, we propose a new codec algorithm that converts voice to text using a voice recognition algorithm to deliver semantic information with minimal bandwidth consumption. We also provide an adaptive codec selection algorithm that selects the appropriate encoding algorithm according to the network capacity based on the trade-off between the compression rate and sound quality. The algorithm exploits the text-based voice codec algorithm in the minimal network environment; otherwise, it adaptively selects the best audio codec considering the network condition. Experiment results show that the proposed text-based codec can deliver messages under extremely scarce network conditions.

In military operations, Commander's Intent describes the desired end state and purpose of the operation, expressed in a concise and clear manner. Command by intent is a paradigm that empowers subordinate units to exercise measured initiative to meet mission goals and accept prudent risk within commander's intent. It improves agility of military operations by allowing exploitation of local opportunities without an explicit directive from the commander to do so. This paper discusses what the paradigm entails in terms of architectural decisions for data fusion systems tasked with real-time information collection to satisfy operational mission goals. In our system, information needs of decisions are expressed at a high level, and shared among relevant nodes. The selected nodes, then, jointly operate to meet mission information needs by forwarding and caching relevant data without explicit directives regarding the objects to fetch and sources to contact. A preliminary evaluation of the system is presented using a target tracking application, set in the context of a NATO-based mission scenario, called Anglova. Evaluation results show that delegating some decision authority to the data fusion system (in terms of objects to fetch and sources to contact) allows it to save more network resources, while also increasing mission success rate. The system is therefore particularly well-suited to operation in partially denied or contested environments, where resource bottlenecks caused by adversarial activity impair one's ability to collect real-time information for mission-critical decision making.

This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from traditional architectures lies in interfaces that allow applications to specify their underlying decision logic. This specification, in turn, allows the system to reason about most effective means to meet information needs of decisions, resulting in simultaneous optimization of decision accuracy, cost, and speed. The paper discusses the overall vision of decision-driven execution, outlining preliminary work and novel challenges.

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.

Recent work suggested that, in the age of data overload produced by sensors, social media, and IoT devices, a key new type of network transport protocols will be one that offers representative summaries of requested data, retrieved at a consumer-controlled degree of granularity. Given the over-abundance of data, consumers will seldom need all data on a topic, but rather will increasingly favor an appropriate sampling for summarization purposes. The paper explores such sampling as a novel service enabled by information-centric networking paradigms that name data objects, not hosts. By naming data objects, it becomes possible to selectively retrieve them, but the properties of the resulting sampling depend on the naming scheme. This paper describes an automated object naming service, called Espresso, that facilitates content sampling over information- centric networks. We show how Espresso, combined with a trivial retrieval policy, translates the sampling problem into a naming problem, and customizes the naming to different applications' sampling needs. Experimental results show that the computational overhead of automated naming is affordable. The service is first evaluated in simulation, demonstrating a higher sampled-data utility to the consumer, while balancing retrieved data importance and diversity. Social network applications are then introduced, where naming is produced by Espresso. Results demonstrate the advantages of Espresso, compared to baselines, in terms of retrieving meaningful media data summaries.

In this demo, we introduce the tweet-based newsfeed summary service, called iApollo, running on a named data network (NDN) stack. This novel application provides a customized newsfeed service to individual readers based on their interests. Data sampling is essential in iApollo because of the large volume of tweets. Espresso, the automatic naming agent, translates this sampling problem into the simple tree traversal by constructing a hierarchical namespace for the given set of tweets. Two types of tree traversals are introduced: a modified breadth-first-search (BFS) and depth-first-search (DFS) which result in generating headline and complementary news, respectively. This allows readers to quickly achieve the best semantic understanding of the news topic with the minimal number of data retrievals. The newsfeeds are transmitted over NDN because NDN caching can reduce the retrieval delay while releasing burden on the end server. Furthermore, Espresso is well-matched to NDN as it naturally resolves NDN's naming requirement. The application demonstrates not only the tweet-newsfeed service, but also the great synergy between Espresso and NDN. This work can be extended into a general software framework for content summarization.

The advent of social networks, mobile sensing, and the Internet of Things herald an age of data overload, where the amount of data generated and stored by various data services exceeds application consumption needs. In such an age, an increasingly important need of data clients will be one of data sub-sampling. This need calls for novel data dissemination protocols that allow clients to request from the network a representative sampling of data that matches a query. In this paper, we present the design of a new transport-layer dissemination protocol, called InfoMax, that allows applications to request such a data sampling. InfoMax exploits the recently proposed named-data-networking (NDN) stack that makes networks aware of hierarchical data names, as opposed to IP addresses. Assuming that named objects with longer prefixes are semantically more similar, InfoMax has the property of minimizing semantic redundancy among delivered data items, hence offering the best coverage of the requested topic with the fewest bytes. The paper discusses the design of InfoMax, its experimental evaluation, and example applications.