Enrico Casini

Infrastructure, middleware, and applications for portable cellular devices in tactical edge networks

The tactical environment requires lightweight devices that are still sufficiently powerful to run a variety of applications to support dismounted soldiers. In the commercial market, competition and demand have driven the mobile phone platform to become a highly capable, programmable environment with devices that have excellent processing, storage, and display capabilities given their cost and size. The Android platform from Google has further increased access to these devices by providing an open source platform that can be programmed in Java and C++. This paper describes a set of middleware services designed to support tactical applications on Android devices, as well as improve communications capabilities from cellular devices. We also describe a candidate set of Android applications to support Tactical users.

Enhancing decision-making by leveraging human intervention in large-scale sensor networks

Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of data is to rely on parallelized approaches based on architectures such as MapReduce. However, fully-automated processing without human intervention is error prone. Supporting human involvement in processing pipelines of data in a variety of contexts such as warfare, cyber security, threat monitoring, and malware analysis leads to improved decision-making. Although this kind of human-machine collaboration seems straightforward, involving a human operator into an automated processing pipeline presents some challenges. For example, due to the asynchronous nature of the human intervention, care must be taken to ensure that once a user-made correction or assertion is introduced, all necessary adjustment and reprocessing is performed. In addition, to make the best use of limited resources and processing capabilities, reprocessing of data in light of such corrections must be minimized. This paper introduces an innovative approach for human-machine integration in decisionmaking for large-scale sensor networks that rely on the popular Hadoop MapReduce framework.

Extending Service-Oriented Architectures to the tactical edge

Service Oriented Architectures allow for seamless integration of heterogeneous systems and extensive service reuse; characteristics that led to a wide adoption of this paradigm in the enterprise and military environment. While SoAs are currently deployed in tactical environments mainly at higher-echelon levels, it is necessary to allow for the exchange of information all the way down to the edge nodes deployed on the ground and back. Because most SoAs implementations were designed to work on reliable infrastructure networks, porting SoAs to the tactical environment requires a complete redesign of the protocol stack to support unreliable, transiently disconnected networks. In this paper, we present our approach to integrate the U.S. Marine Corpss Marine Command and Control Systems and Applications SoA (MC2SA SoA) with DisServicePro, a middleware that supports proactive dissemination and information on demand in tactical edge networks.

DDAM: Dynamic network condition detection and communication adaptation in Tactical Edge Networks

Tactical Edge Networks provide one of the most challenging communication environments. In order to cope with node mobility, constrained resources, and link unreliability, communication solutions designed for Tactical Edge Networks typically present highly configurable interfaces to be adaptable for various networking conditions. However, the extreme dynamicity and heterogeneity of tactical scenarios call for network-aware, adaptive communication systems that continuously re-tune their configuration parameters to match the ever-changing network conditions. This paper presents the Dynamic Detect and Adapt Mechanism (DDAM) of the Agile Communication Middleware, a distributed solution to perform network monitoring and communication adaptation specifically designed for Tactical Edge Networks. The present work focuses on two components of the DDAM: NetSensor, which provides efficient monitoring of the network status, and NetSupervisor, which is responsible for characterizing the network technology used to connect a pair of nodes in the network. The presented results show that our solution can accurately identify the technology used to establish links between nodes.

PeerTalk: A Push-to-Talk and Instant Messaging Service for Tactical Networks

Mobile multi-service peer-to-peer architectures allow the establishment of multimedia sessions, like Push-to-Talk (PTT), Push-to-Talk over Cellular (PoC) and Instant Messaging (IM), between a group of mobile peers with limited or no use of centralized servers. PTT enables several users on different peers to speak with each other while using a single, half-duplex, communication channel, such that only one user speaks at a time while all other users listen. Enabling PTT on this type of architecture for tactical networks provides reduced transmission delay, increased robustness and higher fault tolerance compared to hybrid and pure client-server architectures. There are many challenges facing the implementation of PTT/IM on tactical networks, given the intrinsic nature of unreliable and transiently connected communications that is typical of these environments. This paper presents a new approach called PeerTalk, a cross-platform service operating on a mobile multi-service peer-to-peer architecture enabling a robust, distributed PTT and IM service. The proposed approach is designed to operate efficiently and resiliently over cellular and Wi-Fi tactical networks.

Peer-to-Peer Cooperative Networking for Cellular Mobile Devices

Cellular mobile devices, and in particular smartphones, have become ubiquitous. While bandwidth has steadily increased from 2G devices with Edge to 3G and now 3G LTE (4G), so has the demand for bandwidth intensive applications and streaming of multimedia content. Supporting high densities of such users in urban environments has become a challenge. In this paper, we describe an approach to peer-to-peer cooperative networking that exploits the WiFi interface in peer-to-peer mode in order to reduce the demand on the cellular network while at the same time increasing the reliability of data delivery. We describe multiple scenarios that benefit from such middleware and present some experimental results.

A content and context-aware solution for network state exchange in tactical networks

Efficient network communications are essential to enable network-centric warfare. However, tactical edge networks present an extremely challenging and heterogeneous networking environment due to heterogeneous architectures, dynamic topologies, unwanted adversarial behavior, interference, and other wireless channel conditions. In order to support end-user applications and guarantee system performance and interoperability in such constrained environments, it is critical to adapt the volume and type of traffic generated by applications to the continuously varying network conditions. To this end, we extended the Agile Computing Middleware (ACM) with capabilities specifically designed to provide network state detection and adaptation in constrained networks. In this paper, we present SENSEI (for Smart Estimation of Network StatE Information), a set of components of the ACM that provides effective strategies for the dissemination of network state information. SENSEI implements a content- and context-aware clustering-based algorithm for the distribution of network state information that can significantly reduce the overhead associated with network state information sharing.

Leveraging human oversight and intervention in large-scale parallel processing of open-source data

The popularity of cloud computing along with the increased availability of cheap storage have led to the necessity of elaboration and transformation of large volumes of open-source data, all in parallel. One way to handle such extensive volumes of information properly is to take advantage of distributed computing frameworks like Map-Reduce. Unfortunately, an entirely automated approach that excludes human intervention is often unpredictable and error prone. Highly accurate data processing and decision-making can be achieved by supporting an automatic process through human collaboration, in a variety of environments such as warfare, cyber security and threat monitoring. Although this mutual participation seems easily exploitable, human-machine collaboration in the field of data analysis presents several challenges. First, due to the asynchronous nature of human intervention, it is necessary to verify that once a correction is made, all the necessary reprocessing is done in chain. Second, it is often needed to minimize the amount of reprocessing in order to optimize the usage of resources due to limited availability. In order to improve on these strict requirements, this paper introduces improvements to an innovative approach for human-machine collaboration in the processing of large amounts of open-source data in parallel.