Predictable Reliability In Inter-Vehicle Communications

Predictably reliable communication in wireless networked sensing and control systems (WSC) is a basic enabler for performance guarantee. Yet current research efforts are either focus on maximizing throughput or based on inaccurate interference modelling methods, which yield unsatisfactory results in terms of communication reliability. In this dissertation, we discuss techniques that enable reliable communication in both traditional wireless sensor networks and highly mobile inter-vehicle communication networks. We focus our discussion on traditional wireless sensor networks in Chapter 2 where we discuss mechanisms that enable predictable and reliable communications with no centralized infrastructures. With the promising results in Chapter 2, we extend our methods to inter-vehicle communication networks in Chapter 3. We focus on the broadcast communication paradigm and the unique challenges in applying the PRK interference model into broadcast problems in highly mobile inter-vehicle communication networks. While Chapter 2 and Chapter 3 focus on average reliability, we switch our problem to a more challenging aspect: guaranteeing short-term per-packet reception probability in Chapter 4. Specifically, we describe the PRKS protocol in Chapter 2 which considers unicast transmission paradigm in traditional static wireless sensor networks. PRKS uses the PRK interference model as a basis for interference relation identification that captures characteristics of wireless communications. For communication reliability control, we design a controller that runs at each link receiver and is able to control the average link reliability to be no lower than an application requirement as well as minimizing reliability variation. We further evaluate PRKS with extensive ns-3 simulations. The CPS protocol described in Chapter 3 considers an one-hop broadcast problem in multi-hop inter-vehicle communication networks. We analyze the challenges of applying the PRK model in this particular setting and propose an approximated PRK model, i.e., gPRK model, that addresses the challenges. We further design principles that CPS uses to instantiate the gPRK model in inter-vehicle communications. We implement the CPS scheduling framework in an integrated platform with SUMO and ns-3 to evaluate our design. In Chapter 4, we conservatively estimate the background interference plus noise while nodes are receiving packets. In the meantime, receivers decide minimum power levels their sender should use and feedback their decisions to their senders. Senders fuse feedbacks and choose a power level that guarantees expected packet reception probability at each receivers’ side. We notice in our evaluation that guaranteeing short-term reliability causes extra concurrency loss

Power control for predictable communication reliability in wireless cyber-physical systems

Wireless networks are being applied in various cyber-physical systems and posed to support mission-critical cyber-physical systems applications. When those applications require reliable and low-latency wireless communication, ensuring predictable per-packet communication reliability is a basis. Due to co-channel interference and wireless channel dynamics (e.g. multi-path fading), however, wireless communication is inherently dynamic and subject to complex uncertainties. Power control and MAC-layer scheduling are two enablers. In this dissertation, cross-layer optimization of joint power control and scheduling for ensuring predictable reliability has been studied. With an emphasis on distributed approaches, we propose a general framework and additionally a distributed algorithm in static networks to address small channel variations and satisfy the requirements on receiver-side signal-to-interference-plus-noise-ratio (SINR). Moreover, toward addressing reliability in the settings of large-scale channel dynamics, we conduct an analysis of the strategy of joint scheduling and power control and demonstrate the challenges. First, a general framework for distributed power control is considered. Given a set of links subject to co-channel interference and channel dynamics, the goal is to adjust each link\u27s transmission power on-the-fly so that all the links\u27 instantaneous packet delivery ratio requirements can be satised. By adopting the SINR high-delity model, this problem can be formulated as a Linear Programming problem. Furthermore, Perron-Frobenius theory indicates the characteristic of infeasibility, which means that not all links can nd a transmission power to meet all the SINR requirements. This nding provides a theoretical foundation for the Physical-Ratio-K (PRK) model. We build our framework based on the PRK model and NAMA scheduling. In the proposed framework, we dene the optimal K as a measurement for feasibility. Transmission power and scheduling will be adjusted by K and achieve near-optimal performance in terms of reliability and concurrency. Second, we propose a distributed power control and scheduling algorithm for mission-critical Internet-of-Things (IoT) communications. Existing solutions are mostly based on heuristic algorithms or asymptotic analysis of network performance, and there lack eld-deployable algorithms for ensuring predictable communication reliability. When IoT systems are mostly static or low mobility, we model the wireless channel with small channel variations. For this setting, our approach adopts the framework mentioned above and employs feedback control for online K adaptation and transmission power update. At each time instant, each sender will run NAMA scheduling to determine if it can obtain channel access or not. When each sender gets the channel access and sends a packet, its receiver will measure the current SINR and calculate the scheduling K and transmission power for the next time slot according to current K, transmission power and SINR. This adaptive distributed approach has demonstrated a signicant improvement compared to state-of-the-art technique. The proposed algorithm is expected to serve as a foundation for distributed scheduling and power control as the penetration of IoT applications expands to levels at which both the network capacity and communication reliability become critical. Finally, we address the challenges of power control and scheduling in the presence of large-scale channel dynamics. Distributed approaches generally require time to converge, and this becomes a major issue in large-scale dynamics where channel may change faster than the convergence time of algorithms. We dene the cumulative interference factor as a measurement of impact of a single link\u27s interference. We examine the characteristic of the interference matrix and propose that scheduling with close-by links silent will be still an ecient way of constructing a set of links whose required reliability is feasible with proper transmission power control even in the situation of large-scale channel dynamics. Given that scheduling alone is unable to ensure predictable communication reliability while ensuring high throughput and addressing fast-varying channel dynamics, we demonstrate how power control can help improve both reliability at each time instant and throughput in the long-term. Collectively, these ndings provide insight into the cross-layer design of joint scheduling and power control for ensuring predictable per-packet reliability in the presence of wireless network dynamics and uncertainties

Multi-Cell, Multi-Channel Scheduling with Probabilistic Per-Packet Real-Time Guarantee

For mission-critical sensing and control applications such as those to be enabled by 5G Ultra-Reliable, Low-Latency Communications (URLLC), it is critical to ensure the communication quality of individual packets. Prior studies have considered Probabilistic Per-packet Real-time Communications (PPRC) guarantees for single-cell, single-channel networks with implicit deadline constraints, but they have not considered real-world complexities such as inter-cell interference and multiple communication channels. Towards ensuring PPRC in multi-cell, multi-channel wireless networks, we propose a real-time scheduling algorithm based on \emph{local-deadline-partition (LDP)}. The LDP algorithm is suitable for distributed implementation, and it ensures probabilistic per-packet real-time guarantee for multi-cell, multi-channel networks with general deadline constraints. We also address the associated challenge of the schedulability test of PPRC traffic. In particular, we propose the concept of \emph{feasible set} and identify a closed-form sufficient condition for the schedulability of PPRC traffic. We propose a distributed algorithm for the schedulability test, and the algorithm includes a procedure for finding the minimum sum work density of feasible sets which is of interest by itself. We also identify a necessary condition for the schedulability of PPRC traffic, and use numerical studies to understand a lower bound on the approximation ratio of the LDP algorithm. We experimentally study the properties of the LDP algorithm and observe that the PPRC traffic supportable by the LDP algorithm is significantly higher than that of a state-of-the-art algorithm

Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward

Over the last decade, society and industries are undergoing rapid digitization that is expected to lead to the evolution of the cyber-physical continuum. End-to-end deterministic communications infrastructure is the essential glue that will bridge the digital and physical worlds of the continuum. We describe the state of the art and open challenges with respect to contemporary deterministic communications and compute technologies: 3GPP 5G, IEEE Time-Sensitive Networking, IETF DetNet, OPC UA as well as edge computing. While these technologies represent significant technological advancements towards networking Cyber-Physical Systems (CPS), we argue in this paper that they rather represent a first generation of systems which are still limited in different dimensions. In contrast, realizing future deterministic communication systems requires, firstly, seamless convergence between these technologies and, secondly, scalability to support heterogeneous (time-varying requirements) arising from diverse CPS applications. In addition, future deterministic communication networks will have to provide such characteristics end-to-end, which for CPS refers to the entire communication and computation loop, from sensors to actuators. In this paper, we discuss the state of the art regarding the main challenges towards these goals: predictability, end-to-end technology integration, end-to-end security, and scalable vertical application interfacing. We then present our vision regarding viable approaches and technological enablers to overcome these four central challenges. Key approaches to leverage in that regard are 6G system evolutions, wireless friendly integration of 6G into TSN and DetNet, novel end-to-end security approaches, efficient edge-cloud integrations, data-driven approaches for stochastic characterization and prediction, as well as leveraging digital twins towards system awareness.Comment: 22 pages, 8 figure

The Alive-in-Range Medium Access Control Protocol to Optimize Queue Performance in Underwater Wireless Sensor Networks, Journal of Telecommunications and Information Technology, 2017, nr 4

Time synchronization between sensor nodes to reduce the end-to-end delay for critical and real time data monitoring can be achieved by cautiously monitoring the mobility of the mobile sink node in underwater wireless sensor networks. The Alive-in-Range Medium Access Control (ARMAC) protocol monitors the delay of sensitive, critical and real-time data. The idea evolves as it involves reduction in duty cycle, precise time scheduling of active/sleep cycles of the sensors, monitoring the mobility of the sink node with the selection of appropriate queues and schedulers. The model for the path loss due to attenuation of electromagnetic wave propagation in the sea water is explained. The three-path reflection model evaluating reflection loss from the air-water and watersand interfaces as a function of distance between sensors and water depth is introduced. The algorithms for effective path determination and optimum throughput path determination are elaborated. The results verify that implementation of the Alive-in-Range MAC protocol has reduced the total number of packets dropped, the average queue length, the longest time in queue, the peak queue length and the average time in queue significantly, making it relevant for critical and real-time data monitoring

Predictable Reliability In Inter-Vehicle Communications

Predictably reliable communication in wireless networked sensing and control systems (WSC) is a basic enabler for performance guarantee. Yet current research efforts are either focus on maximizing throughput or based on inaccurate interference modelling methods, which yield unsatisfactory results in terms of communication reliability. In this dissertation, we discuss techniques that enable reliable communication in both traditional wireless sensor networks and highly mobile inter-vehicle communication networks. We focus our discussion on traditional wireless sensor networks in Chapter 2 where we discuss mechanisms that enable predictable and reliable communications with no centralized infrastructures. With the promising results in Chapter 2, we extend our methods to inter-vehicle communication networks in Chapter 3. We focus on the broadcast communication paradigm and the unique challenges in applying the PRK interference model into broadcast problems in highly mobile inter-vehicle communication networks. While Chapter 2 and Chapter 3 focus on average reliability, we switch our problem to a more challenging aspect: guaranteeing short-term per-packet reception probability in Chapter 4. Specifically, we describe the PRKS protocol in Chapter 2 which considers unicast transmission paradigm in traditional static wireless sensor networks. PRKS uses the PRK interference model as a basis for interference relation identification that captures characteristics of wireless communications. For communication reliability control, we design a controller that runs at each link receiver and is able to control the average link reliability to be no lower than an application requirement as well as minimizing reliability variation. We further evaluate PRKS with extensive ns-3 simulations. The CPS protocol described in Chapter 3 considers an one-hop broadcast problem in multi-hop inter-vehicle communication networks. We analyze the challenges of applying the PRK model in this particular setting and propose an approximated PRK model, i.e., gPRK model, that addresses the challenges. We further design principles that CPS uses to instantiate the gPRK model in inter-vehicle communications. We implement the CPS scheduling framework in an integrated platform with SUMO and ns-3 to evaluate our design. In Chapter 4, we conservatively estimate the background interference plus noise while nodes are receiving packets. In the meantime, receivers decide minimum power levels their sender should use and feedback their decisions to their senders. Senders fuse feedbacks and choose a power level that guarantees expected packet reception probability at each receivers’ side. We notice in our evaluation that guaranteeing short-term reliability causes extra concurrency loss

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa