MULTI-SCALE SCHEDULING TECHNIQUES FOR SIGNAL PROCESSING SYSTEMS

A variety of hardware platforms for signal processing has emerged, from distributed systems such as Wireless Sensor Networks (WSNs) to parallel systems such as Multicore Programmable Digital Signal Processors (PDSPs), Multicore General Purpose Processors (GPPs), and Graphics Processing Units (GPUs) to heterogeneous combinations of parallel and distributed devices. When a signal processing application is implemented on one of those platforms, the performance critically depends on the scheduling techniques, which in general allocate computation and communication resources for competing processing tasks in the application to optimize performance metrics such as power consumption, throughput, latency, and accuracy. Signal processing systems implemented on such platforms typically involve multiple levels of processing and communication hierarchy, such as network-level, chip-level, and processor-level in a structural context, and application-level, subsystem-level, component-level, and operation- or instruction-level in a behavioral context. In this thesis, we target scheduling issues that carefully address and integrate scheduling considerations at different levels of these structural and behavioral hierarchies. The core contributions of the thesis include the following. Considering both the network-level and chip-level, we have proposed an adaptive scheduling algorithm for wireless sensor networks (WSNs) designed for event detection. Our algorithm exploits discrepancies among the detection accuracy of individual sensors, which are derived from a collaborative training process, to allow each sensor to operate in a more energy efficient manner while the network satisfies given constraints on overall detection accuracy. Considering the chip-level and processor-level, we incorporated both temperature and process variations to develop new scheduling methods for throughput maximization on multicore processors. In particular, we studied how to process a large number of threads with high speed and without violating a given maximum temperature constraint. We targeted our methods to multicore processors in which the cores may operate at different frequencies and different levels of leakage. We develop speed selection and thread assignment schedulers based on the notion of a core's steady state temperature. Considering the application-level, component-level and operation-level, we developed a new dataflow based design flow within the targeted dataflow interchange format (TDIF) design tool. Our new multiprocessor system-on-chip (MPSoC)-oriented design flow, called TDIF-PPG, is geared towards analysis and mapping of embedded DSP applications on MPSoCs. An important feature of TDIF-PPG is its capability to integrate graph level parallelism and actor level parallelism into the application mapping process. Here, graph level parallelism is exposed by the dataflow graph application representation in TDIF, and actor level parallelism is modeled by a novel model for multiprocessor dataflow graph implementation that we call the Parallel Processing Group (PPG) model. Building on the contribution above, we formulated a new type of parallel task scheduling problem called Parallel Actor Scheduling (PAS) for chip-level MPSoC mapping of DSP systems that are represented as synchronous dataflow (SDF) graphs. In contrast to traditional SDF-based scheduling techniques, which focus on exploiting graph level (inter-actor) parallelism, the PAS problem targets the integrated exploitation of both intra- and inter-actor parallelism for platforms in which individual actors can be parallelized across multiple processing units. We address a special case of the PAS problem in which all of the actors in the DSP application or subsystem being optimized can be parallelized. For this special case, we develop and experimentally evaluate a two-phase scheduling framework with three work flows --- particle swarm optimization with a mixed integer programming formulation, particle swarm optimization with a simulated annealing engine, and particle swarm optimization with a fast heuristic based on list scheduling. Then, we extend our scheduling framework to support general PAS problem which considers the actors cannot be parallelized

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A Systematic Approach to Constructing Families of Incremental Topology Control Algorithms Using Graph Transformation

In the communication systems domain, constructing and maintaining network topologies via topology control (TC) algorithms is an important cross-cutting research area. Network topologies are usually modeled using attributed graphs whose nodes and edges represent the network nodes and their interconnecting links. A key requirement of TC algorithms is to fulfill certain consistency and optimization properties to ensure a high quality of service. Still, few attempts have been made to constructively integrate these properties into the development process of TC algorithms. Furthermore, even though many TC algorithms share substantial parts (such as structural patterns or tie-breaking strategies), few works constructively leverage these commonalities and differences of TC algorithms systematically. In previous work, we addressed the constructive integration of consistency properties into the development process. We outlined a constructive, model-driven methodology for designing individual TC algorithms. Valid and high-quality topologies are characterized using declarative graph constraints; TC algorithms are specified using programmed graph transformation. We applied a well-known static analysis technique to refine a given TC algorithm in a way that the resulting algorithm preserves the specified graph constraints. In this paper, we extend our constructive methodology by generalizing it to support the specification of families of TC algorithms. To show the feasibility of our approach, we reneging six existing TC algorithms and develop e-kTC, a novel energy-efficient variant of the TC algorithm kTC. Finally, we evaluate a subset of the specified TC algorithms using a new tool integration of the graph transformation tool eMoflon and the Simonstrator network simulation framework.Comment: Corresponds to the accepted manuscrip

Towards Spatial Queries over Phenomena in Sensor Networks

Today, technology developments enable inexpensive production and deployment of tiny sensing and computing nodes. Networked through wireless radio, such senor nodes form a new platform, wireless sensor networks, which provide novel ability to monitor spatiotemporally continuous phenomena. By treating a wireless sensor network as a database system, users can pose SQL-based queries over phenomena without needing to program detailed sensor node operations. DBMS-internally, intelligent and energyefficient data collection and processing algorithms have to be implemented to support spatial query processing over sensor networks. This dissertation proposes spatial query support for two views of continuous phenomena: field-based and object-based. A field-based view of continuous phenomena depicts them as a value distribution over a geographical area. However, due to the discrete and comparatively sparse distribution of sensor nodes, estimation methods are necessary to generate a field-based query result, and it has to be computed collaboratively ‘in-the-network’ due to energy constraints. This dissertation proposes SWOP, an in-network algorithm using Gaussian Kernel estimation. The key contribution is the use of a small number of Hermite coefficients to approximate the Gaussian Kernel function for sub-clustered sensor nodes, and processes the estimation result efficiently. An object-based view of continuous phenomena is interested in aspects such as the boundary of an ‘interesting region’ (e.g. toxic plume). This dissertation presents NED, which provides object boundary detection in sensor networks. NED encodes partial event estimation results based on confidence levels into optimized, variable length messages exchanged locally among neighboring sensor nodes to save communication cost. Therefore, sensor nodes detect objects and boundaries based on moving averages to eliminate noise effects and enhance detection quality. Furthermore, the dissertation proposes the SNAKE-based approach, which uses deformable curves to track the spatiotemporal changes of such objects incrementally in sensor networks. In the proposed algorithm, only neighboring nodes exchange messages to maintain the curve structures. Based on in-network tracking of deformable curves, other types of spatial and spatiotemporal properties of objects, such as area, can be provided by the sensor network. The experimental results proved that our approaches are resource friendly within the constrained sensor networks, while providing high quality query results

Distance-based sensor node localization by using ultrasound, RSSI and ultra-wideband - A comparision between the techniques

Wireless sensor networks (WSNs) have become one of the most important topics in wireless communication during the last decade. In a wireless sensor system, sensors are spread over a region to build a sensor network and the sensors in a region co-operate to each other to sense, process, filter and routing. Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. WSNs have so many applications in different areas such as health-care, monitoring and control, rescuing and military; they all depend on nodes being able to accurately determine their locations. This master’s thesis is focused on distance-based sensor node localization techniques; Received signal strength indicator, ultrasound and ultra-wideband. Characteristics and factors which affect these distance estimation techniques are analyzed theoretically and through simulation the quality of these techniques are compared in different scenarios. MDS, a centralized algorithm is used for solving the coordinates. It is a set of data analysis techniques that display the structure of distance-like data as a geometrical picture. Centralized and distributed implementations of MDS are also discussed. All simulations and computations in this thesis are done in Matlab. Virtual WSN is simulated on Sensorviz. Sensorviz is a simulation and visualization tool written by Andreas Savvides.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format