Configuring heterogeneous wireless sensor networks under quality-of-service constraints

Wireless sensor networks (WSNs) are useful for a diversity of applications, such as structural monitoring of buildings, farming, assistance in rescue operations, in-home entertainment systems or to monitor people's health. A WSN is a large collection of small sensor devices that provide a detailed view on all sides of the area or object one is interested in. A large variety of WSN hardware platforms is readily available these days. Many operating systems and protocols exist to support essential functionality such as communication, power management, data fusion, localisation, and much more. A typical sensor node has a number of settings that affect its behaviour and the function of the network itself, such as the transmission power of its radio and the number of measurements taken by its sensor per minute. As the number of nodes in a WSN may be very large, the collection of independent parameters in these networks – the configuration space – tends to be enormous. The user of the WSN would have certain expectations on the Quality of Service (QoS) of the network. A WSN is deployed for a specific purpose, and has a number of measurable properties that indicate how well the network's task is being performed. Examples of such quality metrics are the time needed for measured information to reach the user, the degree of coverage of the area, or the lifetime of the network. Each point in the configuration space of the network gives rise to a certain value in each of the quality metrics. The user may place constraints on the quality metrics, and wishes to optimise the configuration to meet their goals. Work on sensor networks often focuses on optimising only one metric at the time, ignoring the fact that improving one aspect of the system may deteriorate other important performance characteristics. The study of trade-offs between multiple quality metrics, and a method to optimally configure a WSN for several objectives simultaneously – until now a rather unexplored field – is the main contribution of this thesis. There are many steps involved in the realisation of a WSN that is fulfilling a task as desired. First of all, the task needs to be defined and specified, and appropriate hardware (sensor nodes) needs to be selected. After that, the network needs to be deployed and properly configured. This thesis deals with the configuration problem, starting with a possibly heterogeneous collection of nodes distributed in an area of interest, suitable models of the nodes and their interaction, and a set of task-level requirements in terms of quality metrics. We target the class of WSNs with a single data sink that use a routing tree for communication. We introduce two models of tasks running on a sensor network – target tracking and spatial mapping – which are used in the experiments in this thesis. The configuration process is split in a number of phases. After an initialisation phase to collect information about the network, the routing tree is formed in the second configuration phase. We explore the trade-off between two attributes of a tree: the average path length and the maximum node degree. These properties do not only affect the quality metrics, but also the complexity of the remaining optimisation trajectory. We introduce new algorithms to efficiently construct a shortest-path spanning tree in which all nodes have a degree not higher than a given target value. The next phase represents the core of the configuration method: it features a QoS optimiser that determines the Pareto-optimal configurations of the network given the routing tree. A configuration contains settings for the parameters of all nodes in the network, plus the metric values they give rise to. The Pareto-optimal configurations, also known as Pareto points, represent the best possible trade-offs between the quality metrics. Given the vastness of the configuration space, which is exponential in the size of the network, it is impossible to use a brute-force approach and try all possibilities. Still our method efficiently finds all Pareto points, by incrementally searching the configuration space, and discarding potential solutions immediately when they appear to be not Pareto optimal. An important condition for this to work is the ability to compute quality metrics for a group of nodes from the quality metrics of smaller groups of nodes. The precise requirements are derived and shown to hold for the example tasks. Experimental results show that the practical complexity of this algorithm is approximately linear in the number of nodes in the network, and thus scalable to very large networks. After computing the set of Pareto points, a configuration that satisfies the QoS constraints is selected, and the nodes are configured accordingly (the selection and loading phases). The configuration process can be executed in either a centralised or a distributed way. Centralised means that all computations are carried out on a central node, while the distributed algorithms do all the work on the sensor nodes themselves. Simulations show run times in the order of seconds for the centralised configuration of WSNs of hundreds of TelosB sensor nodes. The distributed algorithms take in the order of minutes for the same networks, but have a lower communication overhead. Hence, both approaches have their own pros and cons, and even a combination is possible in which the heavy work is performed by dedicated compute nodes spread across the network. Besides the trade-offs between quality metrics, there is a meta trade-off between the quality and the cost of the configuration process itself. A speed-up of the configuration process can be achieved in exchange for a reduction in the quality of the solutions. We provide complexity-control functionality to fine-tune this quality/cost trade-off. The methods described thus far configure a WSN given a fixed state (node locations, environmental conditions). WSNs, however, are notoriously dynamic during operation: nodes may move or run out of battery, channel conditions may fluctuate, or the demands from the user may change. The final part of this thesis describes methods to adapt the configuration to such dynamism at run time. Especially the case of a mobile sink is treated in detail. As frequently doing global reconfigurations would likely be too slow and too expensive, we use localised algorithms to maintain the routing tree and reconfigure the node parameters. Again, we are able to control the quality/cost trade-off, this time by adjusting the size of the locality in which the reconfiguration takes place. To conclude the thesis, a case study is presented, which highlights the use of the configuration method on a more complex example containing a lot of heterogeneity

System data communication structures for active-control transport aircraft, volume 2

The application of communication structures to advanced transport aircraft are addressed. First, a set of avionic functional requirements is established, and a baseline set of avionics equipment is defined that will meet the requirements. Three alternative configurations for this equipment are then identified that represent the evolution toward more dispersed systems. Candidate communication structures are proposed for each system configuration, and these are compared using trade off analyses; these analyses emphasize reliability but also address complexity. Multiplex buses are recognized as the likely near term choice with mesh networks being desirable for advanced, highly dispersed systems

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Topology Control and Pointing in Free Space Optical Networks

Free space optical (FSO) communication provides functionalities that are different from fiber optic networks and omnidirectional RF wireless communications in that FSO is optical wireless (no infrastructure installation cost involving fibers) and is highly directional (no frequency interference). Moreover, its high-speed data transmission capability is an attractive solution to the first or last mile problem to bridge to current fiber optic network and is a preferable alternative to the low data rate directional point-to-point RF communications for inter-building wireless local area networks. FSO networking depends critically on pointing, acquisition and tracking techniques for rapidly and precisely establishing and maintaining optical wireless links between network nodes (physical reconfiguration), and uses topology reconfiguration algorithms for optimizing network performance in terms of network cost and congestion (logical reconfiguration). The physical and logical reconfiguration process is called Topology Control and can allow FSO networks to offer quality of service by quickly responding to various traffic demands of network users and by efficiently managing network connectivity. The overall objective of this thesis research is to develop a methodology for self-organized pointing along with the associated autonomous and precise pointing technique as well as heuristic optimization methods for Topology Control in bi-connected FSO ring networks, in which each network node has two FSO transceivers. This research provides a unique, autonomous, and precise pointing method using GPS and local angular sensors, which is applicable to both mobile and static nodes in FSO networking and directional point-to-point RF communications with precise tracking. Through medium (264 meter) and short (40 meter) range pointing experiments using an outdoor testbed on the University of Maryland campus in College Park, sub-milliradian pointing accuracy is presented. In addition, this research develops fast and accurate heuristic methods for autonomous logical reconfiguration of bi-connected ring network topologies as well as a formal optimality gap measure tested on an extensive set of problems. The heuristics are polynomial time algorithms for a congestion minimization problem at the network layer and for a multiobjective stochastic optimization of network cost and congestion at both the physical and network layers

Enhancing Real-time Embedded Image Processing Robustness on Reconfigurable Devices for Critical Applications

Nowadays, image processing is increasingly used in several application fields, such as biomedical, aerospace, or automotive. Within these fields, image processing is used to serve both non-critical and critical tasks. As example, in automotive, cameras are becoming key sensors in increasing car safety, driving assistance and driving comfort. They have been employed for infotainment (non-critical), as well as for some driver assistance tasks (critical), such as Forward Collision Avoidance, Intelligent Speed Control, or Pedestrian Detection. The complexity of these algorithms brings a challenge in real-time image processing systems, requiring high computing capacity, usually not available in processors for embedded systems. Hardware acceleration is therefore crucial, and devices such as Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. These devices can assist embedded processors by significantly speeding-up computationally intensive software algorithms. Moreover, critical applications introduce strict requirements not only from the real-time constraints, but also from the device reliability and algorithm robustness points of view. Technology scaling is highlighting reliability problems related to aging phenomena, and to the increasing sensitivity of digital devices to external radiation events that can cause transient or even permanent faults. These faults can lead to wrong information processed or, in the worst case, to a dangerous system failure. In this context, the reconfigurable nature of FPGA devices can be exploited to increase the system reliability and robustness by leveraging Dynamic Partial Reconfiguration features. The research work presented in this thesis focuses on the development of techniques for implementing efficient and robust real-time embedded image processing hardware accelerators and systems for mission-critical applications. Three main challenges have been faced and will be discussed, along with proposed solutions, throughout the thesis: (i) achieving real-time performances, (ii) enhancing algorithm robustness, and (iii) increasing overall system's dependability. In order to ensure real-time performances, efficient FPGA-based hardware accelerators implementing selected image processing algorithms have been developed. Functionalities offered by the target technology, and algorithm's characteristics have been constantly taken into account while designing such accelerators, in order to efficiently tailor algorithm's operations to available hardware resources. On the other hand, the key idea for increasing image processing algorithms' robustness is to introduce self-adaptivity features at algorithm level, in order to maintain constant, or improve, the quality of results for a wide range of input conditions, that are not always fully predictable at design-time (e.g., noise level variations). This has been accomplished by measuring at run-time some characteristics of the input images, and then tuning the algorithm parameters based on such estimations. Dynamic reconfiguration features of modern reconfigurable FPGA have been extensively exploited in order to integrate run-time adaptivity into the designed hardware accelerators. Tools and methodologies have been also developed in order to increase the overall system dependability during reconfiguration processes, thus providing safe run-time adaptation mechanisms. In addition, taking into account the target technology and the environments in which the developed hardware accelerators and systems may be employed, dependability issues have been analyzed, leading to the development of a platform for quickly assessing the reliability and characterizing the behavior of hardware accelerators implemented on reconfigurable FPGAs when they are affected by such faults

ReSCon '09, Research Student Conference: Book of Abstracts

The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

A Decentralized Architecture for Active Sensor Networks

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms

Efficient Passive Clustering and Gateways selection MANETs

Passive clustering does not employ control packets to collect topological information in ad hoc networks. In our proposal, we avoid making frequent changes in cluster architecture due to repeated election and re-election of cluster heads and gateways. Our primary objective has been to make Passive Clustering more practical by employing optimal number of gateways and reduce the number of rebroadcast packets

Energy Efficient Downstream Communication in Wireless Sensor Networks

This dissertation studies the problem of energy efficient downstream communication in Wireless Sensor Networks (WSNs). First, we present the Opportunistic Source Routing (OSR), a scalable, reliable, and energy-efficient downward routing protocol for individual node actuation in data collection WSNs. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node’s upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter mechanism to effectively and efficiently encode a downward source-route in OSR, which enables a significant reduction of the length of source-route field in the packet header. OSR is scalable to very large-size WSN deployments, since each resource-constrained node in the network stores only the set of its direct children. The probabilistic nature of the Bloom filter passively explores opportunistic routing. Upon a delivery failure at any hop along the downward path, OSR actively performs opportunistic routing to bypass the obsolete/bad link. The evaluations in both simulations and real-world testbed experiments demonstrate that OSR significantly outperforms the existing approaches in scalability, reliability, and energy efficiency. Secondly, we propose a mobile code dissemination tool for heterogeneous WSN deployments operating on low power links. The evaluation in lab experiment and a real world WSN testbed shows how our tool reduces the laborious work to reprogram nodes for updating the application. Finally, we present an empirical study of the network dynamics of an out-door heterogeneous WSN deployment and devise a benchmark data suite. The network dynamics analysis includes link level characteristics, topological characteristics, and temporal characteristics. The unique features of the benchmark data suite include the full path information and our approach to fill the missing paths based on the principle of the routing protocol

Software-based and regionally-oriented traffic management in Networks-on-Chip

Since the introduction of chip-multiprocessor systems, the number of integrated cores has been steady growing and workload applications have been adapted to exploit the increasing parallelism. This changed the importance of efficient on-chip communication significantly and the infrastructure has to keep step with these new requirements. The work at hand makes significant contributions to the state-of-the-art of the latest generation of such solutions, called Networks-on-Chip, to improve the performance, reliability, and flexible management of these on-chip infrastructures