Current advances in technology, sensor collection, data storage, and data distribution have afforded more complex, distributed, and operational information fusion systems (IFSs). IFSs notionally consist of low-level (data collection, registration, and association in time and space) and high-level information fusion (user coordination, situational awareness, and mission control), which require a common ontology for effective communication and data processing. In this paper, we describe the ontology reference model developed as part of the uncertainty representation and reasoning evaluation framework (URREF). The URREF ontology is intended to provide guidance for defining the actual concepts and criteria that together comprise the comprehensive uncertainty evaluation framework being developed by the Evaluation of Technologies for Uncertainty Representation Working Group (ETURWG).

Towards unbiased evaluation of uncertainty reasoning: The URREF ontology

The original Joint Directors of Laboratories (JDL) model was developed in the early 90's, with revisits in 1998, and 2004. Today, with new technologies of big data, cloud computing, and machine analytics, there is an ever increasing need for integration of people and machines. The original JDL model focused on the data fusion (correlation, filtering, and association) issues, while today there is an increasing emphasis on an integrated approach to information exploitation over sensors, users, and missions using enterprise architectures, interoperability standards, and intelligence to the edge. Given these recent changes to computation and distributed access, we examine ways for extending the JDL model from 1998 to support exploitation functions and information management for situation awareness, massive data analytics for contextual awareness, and domain-specific needs for mission awareness.

http://fusion.isif.org/proceedings/fusion2013/html/pdf/Wednesday,%2010%20July%202013/10.30-12.10/4.Special%20Session%20%20Barbaros%20A/5-019_373%20Revisiting%20the%20JDL.pdf

Revisiting the JDL model for information exploitation

In this paper, unsupervised clustering of normal vessel traffic patterns is proposed and implemented, where patterns are represented as the momentary location, speed and course of tracked vessels. The learnt cluster models are used for anomaly detection in sea traffic. The Gaussian Mixture Model is used as cluster model and a greedy version of the Expectation-Maximization algorithm is used as clustering algorithm. The models have been trained and evaluated using real recorded sea traffic. A qualitative analysis reveals that the most distinguishing anomalies found in the traffic are vessels crossing sea lanes and vessels traveling close to and in the opposite direction of sea lanes. In order to detect complex anomalies involving multiple vessels and/or behavior that develop over time, a more sophisticated pattern model should be developed. Yet, the generality of the proposed model is stressed, as it is potentially applicable to other domains involving surveillance of moving objects.

/pdf/anomaly-detection-for-sea-surveillance-2yq8rc0c7g.pdf

Anomaly detection for sea surveillance

High-level information fusion (situation and threat assessment, process and user refinement) requires novel solutions for the operational transition of information fusion designs. Low-level (signal processing, object state estimation and characterization) is well-vetted in the community as compared to high-level information fusion (control and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), information management (ontologies, protocols) systems design (scenario-based, user-based, distributed-agent) and evaluation (measures of performance/effectiveness, and empirical case studies).

High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges

While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

BlendCAC: A Smart Contract Enabled Decentralized Capability-Based Access Control Mechanism for the IoT

: Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e., classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 Information Fusion (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) userfusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies for knowledge representation, presentation, and reasoning for situation assessment.

Issues and Challenges in Situation Assessment (Level 2 Fusion)

SAWA is a situation awareness assistant being developed by Versatile Information Systems, Inc. During the process of its development, several lessons were learned about advantages and limitations of certain approaches, techniques, and technologies as they are applied to situation awareness. This paper begins with an overview of SAWA and then focuses on some of the more significant lessons learned. These include the pros and cons of leveraging semantic Web technologies, the handling of time-varying attributes, and the processing of uncertainty.

/pdf/lessons-learned-from-developing-sawa-a-situation-awareness-4pv7yj1dc0.pdf

Lessons learned from developing SAWA: a situation awareness assistant

During the 2016 SPIE DSS conference, nine panelists were invited to highlight the trends and opportunities in
cyber-physical systems (CPS) and Internet of Things (IoT) with information fusion. The world will be ubiquitously
outfitted with many sensors to support our daily living thorough the Internet of Things (IoT), manage infrastructure
developments with cyber-physical systems (CPS), as well as provide communication through networked information
fusion technology over the internet (NIFTI). This paper summarizes the panel discussions on opportunities of
information fusion to the growing trends in CPS and IoT. The summary includes the concepts and areas where
information supports these CPS/IoT which includes situation awareness, transportation, and smart grids.

John S. Salerno

Papers

Issues and Challenges in Situation Assessment (Level 2 Fusion)

Lessons learned from developing SAWA: a situation awareness assistant

Panel summary of cyber-physical systems (CPS) and Internet of Things (IoT) opportunities with information fusion