Towards Real-time Wireless Sensor Networks

Wireless sensor networks are poised to change the way computer systems interact with the physical world. We plan on entrusting sensor systems to collect medical data from patients, monitor the safety of our infrastructure, and control manufacturing processes in our factories. To date, the focus of the sensor network community has been on developing best-effort services. This approach is insufficient for many applications since it does not enable developers to determine if a system\u27s requirements in terms of communication latency, bandwidth utilization, reliability, or energy consumption are met. The focus of this thesis is to develop real-time network support for such critical applications. The first part of the thesis focuses on developing a power management solution for the radio subsystem which addresses both the problem of idle-listening and power control. In contrast to traditional power management solutions which focus solely on reducing energy consumption, the distinguishing feature of our approach is that it achieves both energy efficiency and real-time communication. A solution to the idle-listening problem is proposed in Energy Efficient Sleep Scheduling based on Application Semantics: ESSAT). The novelty of ESSAT lies in that it takes advantage of the common features of data collection applications to determine when to turn on and off a node\u27s radio without affecting real-time performance. A solution to the power control problem is proposed in Real-time Power Aware-Routing: RPAR). RPAR tunes the transmission power for each packet based on its deadline such that energy is saved without missing packet deadlines. The main theoretical contribution of this thesis is the development of novel transmission scheduling techniques optimized for data collection applications. This work bridges the gap between wireless sensor networks and real-time scheduling theory, which have traditionally been applied to processor scheduling. The proposed approach has significant advantages over existing design methodologies:: 1) it provides predictable performance allowing for the performance of a system to be estimated upon its deployment,: 2) it is possible to detect and handle overload conditions through simple rate control mechanisms, and: 3) it easily accommodates workload changes. I developed this framework under a realistic interference model by coordinating the activities at the MAC, link, and routing layers. The last component of this thesis focuses on the development of a real-time patient monitoring system for general hospital units. The system is designed to facilitate the detection of clinical deterioration, which is a key factor in saving lives and reducing healthcare costs. Since patients in general hospital wards are often ambulatory, a key challenge is to achieve high reliability even in the presence of mobility. To support patient mobility, I developed the Dynamic Relay Association Protocol -- a simple and effective mechanism for dynamically discovering the right relays for forwarding patient data -- and a Radio Mapping Tool -- a practical tool for ensuring network coverage in 802.15.4 networks. We show that it is feasible to use low-power and low-cost wireless sensor networks for clinical monitoring through an in-depth clinical study. The study was performed in a step-down cardiac care unit at Barnes-Jewish Hospital. This is the first long-term study of such a patient monitoring system

Scratchpad Memory Management For Multicore Real-Time Embedded Systems

Multicore systems will continue to spread in the domain of real-time embedded systems due to the increasing need for high-performance applications. This research discusses some of the challenges associated with employing multicore systems for safety-critical real-time applications. Mainly, this work is concerned with providing: 1) efficient inter-core timing isolation for independent tasks, and 2) predictable task communication for communicating tasks. Principally, we introduce a new task execution model, based on the 3-phase execution model, that exploits the Direct Memory Access (DMA) controllers available in modern embedded platforms along with ScratchPad Memories (SPMs) to enforce strong timing isolation between tasks. The DMA and the SPMs are explicitly managed to pre-load tasks from main memory into the local (private) scratchpad memories. Tasks are then executed from the local SPMs without accessing main memory. This model allows CPU execution to be overlapped with DMA loading/unloading operations from and to main memory. We show that by co-scheduling task execution on CPUs and using DMA to access memory and I/O, we can efficiently hide access latency to physical resources. In turn, this leads to significant improvements in system schedulability, compared to both the case of unregulated contention for access to physical resources and to previous cache and SPM management techniques for real-time systems. The presented SPM-centric scheduling algorithms and analyses cover single-core, partitioned, and global real-time systems. The proposed scheme is also extended to support large tasks that do not fit entirely into the local SPM. Moreover, the schedulability analysis considers the case of recovering from transient soft errors (bit flips caused by a single event upset) in several levels of memories, that cannot be automatically corrected in hardware by the ECC unit. The proposed SPM-centric scheduling is integrated at the OS level; thus it is transparent to applications. The proposed scheme is implemented and evaluated on an FPGA platform and a Commercial-Off-The-Shelf (COTS) platform. In regards to real-time task communication, two types of communication are considered. 1) Asynchronous inter-task communication, between either sequential tasks (single-threaded) or parallel tasks (multi-threaded). 2) Intra-task communication, where parallel threads of the same application exchange data. A new task scheduling model for parallel tasks (Bundled Scheduling) is proposed to facilitate intra-task communication and reduce synchronization overheads. We show that the proposed bundled scheduling model can be applied to several parallel programming models, such as fork-join and DAG-based applications, leading to improved system schedulability. Finally, intra-task communication is governed by a predictable inter-core communication platform. Specifically, we propose HopliteRT, a lean and predictable Network-on-Chip that connects the private SPMs

Mixed Criticality Systems - A Review : (13th Edition, February 2022)

This review covers research on the topic of mixed criticality systems that has been published since Vestal’s 2007 paper. It covers the period up to end of 2021. The review is organised into the following topics: introduction and motivation, models, single processor analysis (including job-based, hard and soft tasks, fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, related topics, realistic models, formal treatments, systems issues, industrial practice and research beyond mixed-criticality. A list of PhDs awarded for research relating to mixed-criticality systems is also included

Model evolution for the realization of complex systems

George Box said, “All models are wrong, but some are useful.” In the design of complex systems, types of complexity need to be managed. Giving the complexities that a decision maker may encounter, corresponding adjustments or improvements should be made to the design. In this dissertation, it is defined that all kinds of engineering design are comprised of four stages – formulation, approximation, exploration and evaluation – and the four stages form the model evolution loop or design evolution loop. By running the design evolution loop iteratively, a designer can handle the complexities and improve the design. Such improvements include but not limited to more robust to uncertainties, more efficient in design evolutions, easier interpretations of phenomena, etc. In the design of complex systems, as lack of data and information, heuristics are used to proceed the design, so that designers can explore the solution space and gain insight to improve the design. Those heuristics include but not limit to model structures, sub-problems identification and integration, approximation rules, and scale of details incorporated in the model. There is lacking mechanisms to evaluate the quality of the design associated with the heuristics. In this dissertation, it is hypothesized that by running the design evolution loop and exploring the solution space, designers can do the things as follows to improve the design. • Evaluating system performances associated with various heuristics (structure of the model, critical parameter setting, rules making, etc.). • Replacing the heuristics with insight obtained from exploration of the solution space to improve the design. • Managing the complexity of module structure, such as analyzing and simplifying the structure of a large number of goals. • Interpreting the behavior and the property of the model into the knowledge that supports the decision making. • Capturing and managing newly observed properties or a more detailed complexity that are not incorporated into the modeling at first – the emergent properties. • Automating the steps in the above. The intellectual merits in this dissertation are the expandable computational framework for designing complex systems and managing multiple types of uncertainty– the design evolution loop, and the methods fitting into it. By using satisficing strategy and incorporating machine learning to explore the solution space, heuristics in each of the four stages (formulation, approximation, exploration, and evaluation) can be updated or replaced by knowledge gained from experiments, calculations and analyses. In addition, knowledge on tradeoffs between different categories of design requirement – such as (but not limited to) approximation accuracy, computational complexity, design preference diversity, reformulation flexibility, and the degree of design automation – can be collected, stored and reused

Performanzanalyse für Multi-Core Multi-Mode Systeme mit gemeinsam genutzten Ressourcen - Verfahren und Anwendung auf AUTOSAR -

In order to implement multi-core systems for single-mode and multi-mode real-time applications, as can be found in modern automobiles, their development process requires appropriate methods and tools for timing and performance verification. In this context, this thesis proposes first novel approaches for the analysis of worst-case blocking-times and response-times for single-mode real-time applications that share resources in partitioned multi-core systems. For this purpose a compositional performance analysis methodology is adopted and extended to take into account the contention of tasks on the processor cores and on the shared resources under different combinations of processor scheduling policies and shared resource arbitration strategies. Highly relevant is the compatibility of the proposed analysis methods with the specifications of the automotive AUTOSAR standard, which defines the combination of (1) preemptive, non-preemptive and cooperative core local scheduling with (2) lock-based arbitration of core local shared resources and spinlock-based arbitration of inter-core shared resources. Further, this thesis proposes novel timing analysis solutions for multi-mode distributed real-time systems. For such systems, the settling time of a mode change, called mode change transition latency, is identified as an important system parameter that has been neglected before. This thesis contributes a novel analysis algorithm which gives a maximum bound on each mode change transition latency of multi-mode distributed applications. Knowing the settling time of each mode change, the impact of multiple mode changes and of the possible overload situations can be handled in the early development phases of real-time systems. Finally, an approach for safely handling shared resources across mode changes is presented and a corresponding timing analysis method is contributed. The new analysis solution combines modeling and analysis elements of the multi-core and multi-mode related analysis solutions and focuses on the specification of the AUTOSAR standard. This enables system designers to handle the timing behavior of more complex systems in which the problems of mode management, multi-core scheduling and shared resource arbitration coexist. The applicability and usefulness of the contributed analysis solutions are highlighted by experimental evaluations, which are enabled by the implementation of the proposed analysis methods in a performance analysis tool framework.Um Multicore-Systeme für die Umsetzung zeitkritischer Single- und Multi-Mode Anwendungen in sicherheitskritischen Umgebungen einsetzen zu können, werden in dem Entwicklungsprozess geeignete Analysemethoden und Tools zur Bestimmung des Zeitverhaltens und der Performanz benötigt. Als erster Beitrag dieser Dissertation werden neue Analyseverfahren eingeführt, um die Worst-Case-Antwortzeiten und -Blockierungszeiten für statische Echtzeitanwendungen in Single-Mode eingebetteten Multicore-Systemen mit gemeinsam genutzten Ressourcen zu bestimmen. Die entwickelten Verfahren nutzen einen existierenden kompositionellen Performanzanalyseansatz und erweitern diesen, um verschiedene Kombinationen von partitionierenden Multiprozessor-Schedulingverfahren und –Synchronisationsmechanismen behandeln zu können. Besonders praxisrelevant ist die Möglichkeit, die Kombination von (1) preemptives, nicht-preemptives sowie kooperatives Prozessor-Scheduling und (2) Spinlock-basierten Synchronisationsmechanismen zu analysieren, die heute in AUTOSAR-konformen Automotive-Softwarearchitekturen standardisiert sind. Als zweiter Beitrag wird in dieser Dissertation ein neuer Ansatz für die Analyse der zeitlichen Auswirkungen von mehreren Szenarienübergängen in vernetzten Multi-Mode eingebetteten Systemen eingeführt. Als erste konstruktive Maßnahme ermöglicht das in dieser Arbeit präsentierte Verfahren die Berechnung der Einschwingzeit jedes Szenarioübergangs und leistet dadurch eine wichtige Hilfestellung beim Systementwurf. Auf diese Weise können die Auswirkungen der Szenarienübergänge, einschließlich der zeitlich begrenzten Überlastsituationen, kontrolliert und in den Systementwurf frühzeitig einbezogen werden. Als letzter Beitrag dieser Dissertation wird ein Ansatz für die Handhabung der Zugriffskonflikte auf gemeinsam genutzten Ressourcen in Multi-Mode eingebetteten Multicore-Systemen präsentiert und eine entsprechende Analysemethode eingeführt. Die neue Analyse kombiniert Modellierungs- und Analyse-Elemente der vorher in dieser Arbeit eingeführten Analyseansätze, und ermöglicht die Untersuchung des ungünstigsten Zeitverhaltens viel komplexer eingebetteten Multicore-Systemen. Dabei werden erneut Spezifikationen der AUTOSAR-Standards berücksichtigt. Nicht zuletzt werden alle Analysemethoden in eine Toolumgebung implementiert und für verschiedene Experimente, die deren praktische Anwendbarkeit hervorheben, angewendet

one6G white paper, 6G technology overview:Second Edition, November 2022

6G is supposed to address the demands for consumption of mobile networking services in 2030 and beyond. These are characterized by a variety of diverse, often conflicting requirements, from technical ones such as extremely high data rates, unprecedented scale of communicating devices, high coverage, low communicating latency, flexibility of extension, etc., to non-technical ones such as enabling sustainable growth of the society as a whole, e.g., through energy efficiency of deployed networks. On the one hand, 6G is expected to fulfil all these individual requirements, extending thus the limits set by the previous generations of mobile networks (e.g., ten times lower latencies, or hundred times higher data rates than in 5G). On the other hand, 6G should also enable use cases characterized by combinations of these requirements never seen before, e.g., both extremely high data rates and extremely low communication latency). In this white paper, we give an overview of the key enabling technologies that constitute the pillars for the evolution towards 6G. They include: terahertz frequencies (Section 1), 6G radio access (Section 2), next generation MIMO (Section 3), integrated sensing and communication (Section 4), distributed and federated artificial intelligence (Section 5), intelligent user plane (Section 6) and flexible programmable infrastructures (Section 7). For each enabling technology, we first give the background on how and why the technology is relevant to 6G, backed up by a number of relevant use cases. After that, we describe the technology in detail, outline the key problems and difficulties, and give a comprehensive overview of the state of the art in that technology. 6G is, however, not limited to these seven technologies. They merely present our current understanding of the technological environment in which 6G is being born. Future versions of this white paper may include other relevant technologies too, as well as discuss how these technologies can be glued together in a coherent system

Scheduling in CDMA-based wireless packet networks.

Thesis (M.Sc. Eng.)-University of Natal, Durban, 2003.Modern networks carry a wide range of different data types, each with its own individual requirements. The scheduler plays an important role in enabling a network to meet all these requirements. In wired networks a large amount of research has been performed on various schedulers, most of which belong to the family of General Processor Sharing (GPS) schedulers. In this dissertation we briefly discuss the work that has been done on a range of wired schedulers, which all attempt to differentiate between heterogeneous traffic. In the world of wireless communications the scheduler plays a very important role, since it can take channel conditions into account to further improve the performance of the network. The main focus of this dissertation is to introduce schedulers, which attempt to meet the Quality of Service requirements of various data types in a wireless environment. Examples of schedulers that take channel conditions into account are the Modified Largest Weighted Delay First (M-LWDF), as well as a new scheduler introduced in this dissertation, known as the Wireless Fair Largest Weighted Delay First (WF-LWDF) algorithm. The two schemes are studied in detail and a comparison of their throughput, delay, power, and packet dropping performance is made through a range of simulations. The results are compared to the performance offour other schedulers. The fairness ofM-LWDF and WFLWDF is determined through simulations. The throughput results are used to establish Chernoff bounds of the fairness of these two algorithms. Finally, a summary is given of the published delay bounds of various schedulers, and the tightness of the resultant bounds is discussed

Performance Modeling and Optimization of Resource Allocation in Cloud Computing Systems

Cloud computing offers on-demand network access to the computing resources through virtualization. This paradigm shifts the computer resources to the cloud, which results in cost savings as the users leasing instead of owning these resources. Clouds will also provide power constrained mobile users accessibility to the computing resources. In this thesis, we develop performance models of these systems and optimization of their resource allocation. In the performance modeling, we assume that jobs arrive to the system according to a Poisson process and they may have quite general service time distributions. Each job may consist of multiple number of tasks with each task requiring a virtual machine (VM) for its execution. The size of a job is determined by the number of its tasks, which may be a constant or a variable. In the case of constant job size, we allow different classes of jobs, with each class being determined through their arrival and service rates and number of tasks in a job. In the variable case a job generates randomly new tasks during its service time. The latter requires dynamic assignment of VMs to a job, which will be needed in providing service to mobile users. We model the systems with both constant and variable size jobs using birth-death processes. In the case of constant job size, we determined joint probability distribution of the number of jobs from each class in the system, job blocking probabilities and distribution of the utilization of resources for systems with both homogeneous and heterogeneous types of VMs. We have also analyzed tradeoffs for turning idle servers off for power saving. In the case of variable job sizes, we have determined distribution of the number of jobs in the system and average service time of a job for systems with both infinite and finite amount of resources. We have presented numerical results and any approximations are verified by simulation. The performance results may be used in the dimensioning of cloud computing centers. Next, we have developed an optimization model that determines the job schedule, which minimizes the total power consumption of a cloud computing center. It is assumed that power consumption in a computing center is due to communications and server activities. We have assumed a distributed model, where a job may be assigned VMs on different servers, referred to as fragmented service. In this model, communications among the VMs of a job on different servers is proportional to the product of the number of VMs assigned to the job on each pair of servers which results in a quadratic network power consumption in number of job fragments. Then, we have applied integer quadratic programming and the column generation method to solve the optimization problem for large scale systems in conjunction with two different algorithms to reduce the complexity and the amount of time needed to obtain the solution. In the second phase of this work, we have formulated this optimization problem as a function of discrete-time. At each discrete-time, the job load of the system consists of new arriving jobs during the present slot and unfinished jobs from the previous slots. We have developed a technique to solve this optimization problem with full, partial and no migration of the old jobs in the system. Numerical results show that this optimization results in significant operating costs savings in the cloud computing systems

Software Defined Applications in Cellular and Optical Networks

abstract: Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201