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Thanks to rapid technological advances in the Internet of Things (IoT), a smart public safety (SPS) system has become feasible by integrating heterogeneous computing devices to collaboratively provide public protection services. While a service oriented architecture (SOA) has been adopted by IoT and cyberphysical systems (CPS), it is difficult for a monolithic architecture to provide scalable and extensible services for a distributed IoT based SPS system. Furthermore, traditional security solutions rely on a centralized authority, which can be a performance bottleneck or single point failure. Inspired by microservices architecture and blockchain technology, this paper proposes a BLockchain-ENabled Decentralized Microservices Architecture for Smart public safety (BlendMAS). Within a permissioned blockchain network, a microservices based security mechanism is introduced to secure data access control in a SPS system. The functionality of security services are decoupled into separate containerized microservices that are built using a smart contract, and deployed on edge and fog computing nodes. An extensive experimental study verified that the proposed BlendMAS is able to offer a decentralized, scalable and secured data sharing and access control to distributed IoT based SPS system.

Multiview learning has shown promising potential in many applications. However, most techniques are focused on either view consistency, or view diversity. In this paper, we introduce a novel multiview boosting algorithm, called Boost.SH, that computes weak classifiers independently of each view but uses a shared weight distribution to propagate information among the multiple views to ensure consistency. To encourage diversity, we introduce randomized Boost.SH and show its convergence to the greedy Boost.SH solution in the sense of minimizing regret using the framework of adversarial multiarmed bandits. We also introduce a variant of Boost.SH that combines decisions from multiple experts for recommending views for classification. We propose an expert strategy for multiview learning based on inverse variance, which explores both consistency and diversity. Experiments on biometric recognition, document categorization, multilingual text, and yeast genomic multiview data sets demonstrate the advantage of Boost.SH (85%) compared with other boosting algorithms like AdaBoost (82%) using concatenated views and substantially better than a multiview kernel learning algorithm (74%).

INTRODUCTIONInformation fusion utilizes a collection of data sources for uncertainty reduction, coverage extension, and situation awareness. Future information fusion solutions require systems design [1], coordination with users [2], metrics of performance [3], and methods of multilevel security [4]. A current trend that can enable all of these services is cloud computing. Cloud computing as defined by NIST is:Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. [5] Cloud computing provides capabilities (on-demand self service, broad network access, resource pooling, rapid elasticity, and measured service) over different types of clouds (private, community, public, and hybrid).An area of growing popularity of cloud-based applications is robotic systems [6], [7]. These applications of cloud services have implications for avionics systems in telerobotics [8], space communications [9], multisensor fusion [10], wide-area motion imagery [11], and information management [12]; not to mention numerous other emerging applications. Based on the services provided over a distributed network, cloud computing supports large scale data processing and analytics which is essential for future avionics systems designs.In this paper, we present a comprehensive view on a system level information fusion design using cloud computing technology. A systematic comparison among four different distributed computing paradigms is provided, which illustrates the advantages and constraints of cluster computing, peer-to-peer (P2P) computing, grid computing, and cloud computing. A holistic distributed cloud-enabled robotics framework for information fusion is proposed using robotic systems. The system-level design principles of service-based architectures are highlighted in this framework where we considered the implementation of both the cloud and robot networks with additional security features. In addition, on the cloud side, we include a virtual machine (VM) and a physical machine into our framework as dynamic computing clusters. A preliminary performance evaluation through a case study based on a video tracking application is demonstrated to highlight the advantages of cloud computing. Figure 1 compares the hardware and software stacks of four major distributed computing paradigms in order of development: cluster computing, peer-to-peer (P2P) computing, grid computing, and cloud computing. The solid arrows indicate the layers on which users run their application directly and dashed arrows indicate layers controlled by the middleware layer. It is important to note that cloud computing has emerged as a method for large data management [13] that is useful for distributed avionics systems.The lowest level of the stack is the networking infrastructure, which connects physical computers. Sca...

Situational awareness (SA) is defined as the perception of entities in the environment, comprehension of their meaning, and projection of their status in near future. From an Air Force perspective, SA refers to the capability to comprehend and project the current and future disposition of red and blue aircraft and surface threats within an airspace. In this article, we propose a model for SA and dynamic decision-making that incorporates artificial intelligence and dynamic data-driven application systems to adapt measurements and resources in accordance with changing situations. We discuss measurement of SA and the challenges associated with quantification of SA. We then elaborate a plethora of techniques and technologies that help improve SA ranging from different modes of intelligence gathering to artificial intelligence to automated vision systems. We then present different application domains of SA including battlefield, gray zone warfare, military- and air-base, homeland security and defense, and critical infrastructure. Finally, we conclude the article with insights into the future of SA.