Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT

Industrial automation deployments constitute challenging environments where moving IoT machines may produce high-definition video and other heavy sensor data during surveying and inspection operations. Transporting massive contents to the edge network infrastructure and then eventually to the remote human operator requires reliable and high-rate radio links supported by intelligent data caching and delivery mechanisms. In this work, we address the challenges of contents dissemination in characteristic factory automation scenarios by proposing to engage moving industrial machines as device-to-device (D2D) caching helpers. With the goal to improve reliability of high-rate millimeter-wave (mmWave) data connections, we introduce the alternative contents dissemination modes and then construct a novel mobility-aware methodology that helps develop predictive mode selection strategies based on the anticipated radio link conditions. We also conduct a thorough system-level evaluation of representative data dissemination strategies to confirm the benefits of predictive solutions that employ D2D-enabled collaborative caching at the wireless edge to lower contents delivery latency and improve data acquisition reliability

Effects of 3D Deployments on Interference and SINR in 5G New Radio Systems

Lately, the extremely high frequency (EHF) band has become one of the factors enabling fifth-generation (5G) mobile cellular technologies. By offering large bandwidth, New Radio (NR) systems operating in the lower part of EHF band, called millimeter waves (mmWave), may satisfy the extreme requirements of future 5G networks in terms of both data transfer rate and latency at the air interface. The use of highly directional antennas in prospective mmWave-based NR communications systems raises an important question: are conventional two-dimensional (2D) cellular network modeling techniques suitable for 5G NR systems? To address this question, we introduced a novel, three-dimensional framework for evaluating the performance of emerging mmWave band wireless networks. The proposed framework explicitly takes into account the blockage effects of propagating mmWave radiation, the vertical and planar directivities at transceiver antennas, and the randomness of user equipment (UE), base station (BS), and blocker heights. The model allows for different levels of accuracy, encompassing a number of models with different levels of computational complexity as special cases. Although the main metric of interest in this thesis is the signal-to-interference-plus-noise ratio (SINR), the model can be extended to obtain the Shannon rate of the channel under investigation. The proposed model was numerically evaluated in different deployment cases and communication scenarios with a wide range of system parameters. We found that randomness of UE and BS heights and vertical directionality of the mmWave antennas are essential for accurate evaluation of system performance. We also showed that the results of traditional 2D models are too optimistic and greatly overestimate the actual SINR. In contrast, fixed-height models that ignore the impact of height on the probability of exposure to interference are too pessimistic. Furthermore, we evaluated the models that provide the best trade-off between computational complexity and accuracy in specific scenarios and provided recommendations regarding their use for practical assessment of mmWave-based NR systems

Maximum Throughput Scheduling for Multi-connectivity in Millimeter-Wave Networks

Multi-connectivity is emerging as promising solution to provide reliable communications and seamless connectivity at the millimeter-wave frequency range. Due to the obstacles that cause frequent interruptions at such high frequency range, connectivity to multiple cells can drastically increase the network performance in terms of throughput and reliability by coordination among the network elements. In this paper, we propose an algorithm for the link scheduling optimization that maximizes the network throughput for multi-connectivity in millimeter-wave cellular networks. The considered approach exploits a centralized architecture, fast link switching, proactive context preparation and data forwarding between millimeter-wave access points and the users. The proposed algorithm is able to numerically approach the global optimum and to quantify the potential gain of multi-connectivity in millimeter-wave cellular networks

Facilitating Internet of Things on the Edge

The evolution of electronics and wireless technologies has entered a new era, the Internet of Things (IoT). Presently, IoT technologies influence the global market, bringing benefits in many areas, including healthcare, manufacturing, transportation, and entertainment. Modern IoT devices serve as a thin client with data processing performed in a remote computing node, such as a cloud server or a mobile edge compute unit. These computing units own significant resources that allow prompt data processing. The user experience for such an approach relies drastically on the availability and quality of the internet connection. In this case, if the internet connection is unavailable, the resulting operations of IoT applications can be completely disrupted. It is worth noting that emerging IoT applications are even more throughput demanding and latency-sensitive which makes communication networks a practical bottleneck for the service provisioning. This thesis aims to eliminate the limitations of wireless access, via the improvement of connectivity and throughput between the devices on the edge, as well as their network identification, which is fundamentally important for IoT service management. The introduction begins with a discussion on the emerging IoT applications and their demands. Subsequent chapters introduce scenarios of interest, describe the proposed solutions and provide selected performance evaluation results. Specifically, we start with research on the use of degraded memory chips for network identification of IoT devices as an alternative to conventional methods, such as IMEI; these methods are not vulnerable to tampering and cloning. Further, we introduce our contributions for improving connectivity and throughput among IoT devices on the edge in a case where the mobile network infrastructure is limited or totally unavailable. Finally, we conclude the introduction with a summary of the results achieved