We apply a fluid-reservoir model to study the Age-of-Information (AoI) of update packets through energy-harvesting transmitters. The model is closer to how energy is stored and depleted in reality, and can reveal the system behavior for different settings of packet arrival rates, service rates, and energy charging and depletion rates. We present detailed results for both finite and infinite transmitter buffers and an infinite energy reservoir, and some indicative results for a finite reservoir. The results are derived for the mean AoI in the case of an infinite transmitter buffer and an infinite reservoir, and for the mean peak AoI for the remaining cases. The results show that, similar to a system without energy constraints, the transmitter buffer should be kept to a minimum in order to avoid queueing delays and maintain freshness of updates. Furthermore, a high update packet rate is only helpful in energy-rich regimes, whereas in energy-poor regimes more frequent updates deplete the energy reservoir and result in higher AoI values.
5G New Radio (NR) represents a shift in mobile telephony whereby the network architecture runs containerized software on commodity hardware. In preparation, numerous 4G software stacks have been developed to test the containerization of core network functions and the interfaces with radio access network (RAN) protocols. In this work one such stack, developed by the OpenAirInterface Software Alliance (OSA), is used to create a low-cost, simplified mobile network. Commercial off-the-shelf (COTS) user equipment (UE) is then connected to the network to demonstrate how a major buffer overflow vulnerability present in certain Global Navigation Satellite System (GNSS) chipsets can be leveraged to enable a spoofed network attack. Finally, the theoretical attack method is extended to 5G NR networks.
The last few years have seen an explosive growth in the Internet of Things (IoT) as billions of new low-powered devices are connected to the network in every sector, from household items to sensors, healthcare devices, and industrial controls. Devices often rely on vertical architectures, with each type of device requiring a separate platform for control, data sharing, and storage. New horizontal architectures are needed to allow sharing of resources between devices to optimize hardware efficiency. In addition, new networking paradigms such as Software Defined Networking (SDN) are required to efficiently manage increased delay-sensitive network demand. This broad range of new technologies and skills make entry difficult for students and new researchers, and innovative practical testbeds for IoT systems and SDN will be required for training and research. We propose an IoT testbed with multiple networking layers and heterogeneous devices to simultaneously support networking research, anomaly detection, and security principles applied specifically to IoT for education and research, providing a complex yet practical hands-on environment made entirely of open-source tools and Commercial Off-The-Shelf (COTS) materials that can be replicated for use by others seeking to build such a system.
In this paper, we propose a software defined network (SDN) architecture suitable for optical intra-rack data center networks (DCNs). Thus in an optical DCN, we distinguish the intra-rack communication from the inter-rack one and we focus on the intra-rack network This is implemented by the passive optical interconnection among the rack servers using a set of dedicated data wavelengths. In this way, low power consumption is required. A SDN controller is introduced to play an administrative role in the optical intra-rack network, by determining the servers' transmission coordination. Core of the proposed SDN configuration is the optical communication link that is assumed to connect each rack server with the SDN controller, aiming to ensure the perfect synchronization among the control and the data planes operations. Based on this assurance, we propose a synchronous transmission SDN-based pre-transmission coordination medium access control (SPC-MAC) protocol that the SDN controller follows in order to instruct the servers to transmit without packet losses due to collisions in the intra-rack network In this way, high throughput and low delay are achieved for diverse intra-rack network configurations. Simulation results prove that the proposed software defined optical intra-rack DCN in conjunction with the proposed SPC-MAC protocol reach high bandwidth exploitation, while they provide high scalability, access fairness and reliability.
Users are often interested in a specific type of data (user-preferred data) from large volumes of data streaming into the system in real-time. An efficient system that only stores user-preferred data from these data can reduce storage space by storing only the user-preferred data and discarding the remaining data. It also reduces the search latency which allows the users to search for relevant information in a timely manner. The motivation behind this research is to devise a technique that filters streaming data and stores only the filtered data for post-processing, thereby saving storage space and reduce search latency. A proof-of-concept prototype for this technique has been built on Apache Spark by leveraging the parallel processing framework for streaming data and its machine learning library. The performance of the prototype subjected to synthetically generated streaming data is analyzed. The analysis of experimental results demonstrates the efficacy of this technique and provides insights into system behavior and performance.
