SPARCS: Stream-processing architecture applied in real-time cyber-physical security

In this paper, we showcase a complete, end-To-end, fault tolerant, bandwidth and latency optimized architecture for real time utilization of data from multiple sources that allows the collection, transport, storage, processing, and display of both raw data and analytics. This architecture can be applied for a wide variety of applications ranging from automation/control to monitoring and security. We propose a practical, hierarchical design that allows easy addition and reconfiguration of software and hardware components, while utilizing local processing of data at sensor or field site ('fog computing') level to reduce latency and upstream bandwidth requirements. The system supports multiple fail-safe mechanisms to guarantee the delivery of sensor data. We describe the application of this architecture to cyber-physical security (CPS) by supporting security monitoring of an electric distribution grid, through the collection and analysis of distribution-grid level phasor measurement unit (PMU) data, as well as Supervisory Control And Data Acquisition (SCADA) communication in the control area network

Named data networking for efficient IoT-based disaster management in a smart campus

Disasters are uncertain occasions that can impose a drastic impact on human life and building infrastructures. Information and Communication Technology (ICT) plays a vital role in coping with such situations by enabling and integrating multiple technological resources to develop Disaster Management Systems (DMSs). In this context, a majority of the existing DMSs use networking architectures based upon the Internet Protocol (IP) focusing on location-dependent communications. However, IP-based communications face the limitations of inefficient bandwidth utilization, high processing, data security, and excessive memory intake. To address these issues, Named Data Networking (NDN) has emerged as a promising communication paradigm, which is based on the Information-Centric Networking (ICN) architecture. An NDN is among the self-organizing communication networks that reduces the complexity of networking systems in addition to provide content security. Given this, many NDN-based DMSs have been proposed. The problem with the existing NDN-based DMS is that they use a PULL-based mechanism that ultimately results in higher delay and more energy consumption. In order to cater for time-critical scenarios, emergence-driven network engineering communication and computation models are required. In this paper, a novel DMS is proposed, i.e., Named Data Networking Disaster Management (NDN-DM), where a producer forwards a fire alert message to neighbouring consumers. This makes the nodes converge according to the disaster situation in a more efficient and secure way. Furthermore, we consider a fire scenario in a university campus and mobile nodes in the campus collaborate with each other to manage the fire situation. The proposed framework has been mathematically modeled and formally proved using timed automata-based transition systems and a real-time model checker, respectively. Additionally, the evaluation of the proposed NDM-DM has been performed using NS2. The results prove that the proposed scheme has reduced the end-to-end delay up from 2% to 10% and minimized up to 20% energy consumption, as energy improved from 3% to 20% compared with a state-of-the-art NDN-based DMS