Self-organization and management of wireless sensor networks

Wireless sensor networks (WSNs) are a newly deployed networking technology consisting of multifunctional sensor nodes that are small in size and communicate over short distances. These sensor nodes are mainly in large numbers and are densely deployed either inside the phenomenon or very close to it. They can be used for various application areas (e.g. health, military, home). WSNs provide several advantages over traditional networks, such as large-scale deployment, highresolution sensed data, and application adaptive mechanisms. However, due to their unique characteristics (having dynamic topology, ad-hoc and unattended deployment, huge amount of data generation and traffic flow, limited bandwidth and energy), WSNs pose considerable challenges for network management and make application development nontrivial. Management of wireless sensor networks is extremely important in order to keep the whole network and application work properly and continuously. Despite the importance of sensor network management, there is no generalize solution available for managing and controlling these resource constrained WSNs. In network management of WSNs, energy-efficient network selforganization is one of the main challenging issues. Self-organization is the property which the sensor nodes must have to organize themselves to form the network. Selforganization of WSNs is challenging because of the tight constraints on the bandwidth and energy resources available in these networks. A self organized sensor network can be clustered or grouped into an easily manageable network. However, existing clustering schemes offer various limitations. For example, existing clustering schemes consume too much energy in cluster formation and re-formation. This thesis presents a novel cellular self-organizing hierarchical architecture for wireless sensor networks. The cellular architecture extends the network life time by efficiently utilizing nodes energy and support the scalability of the system. We have analyzed the performance of the architecture analytically and by simulations. The results obtained from simulation have shown that our cellular architecture is more energy efficient and achieves better energy consumption distribution. The cellular architecture is then mapped into a management framework to support the network management system for resource constraints WSNs. The management framework is self-managing and robust to changes in the network. It is application-co-operative and optimizes itself to support the unique requirements of each application. The management framework consists of three core functional areas i.e., configuration management, fault management, and mobility management. For configuration management, we have developed a re-configuration algorithm to support sensor networks to energy-efficiently re-form the network topology due to network dynamics i.e. node dying, node power on and off, new node joining the network and cells merging. In the area of fault management we have developed a new fault management mechanism to detect failing nodes and recover the connectivity in WSNs. For mobility management, we have developed a two phase sensor relocation solution: redundant mobile sensors are first identified and then relocated to the target location to deal with coverage holes. All the three functional areas have been evaluated and compared against existing solutions. Evaluation results show a significant improvement in terms of re-configuration, failure detection and recovery, and sensors relocation

Challenges in the Locomotion of Self-Reconfigurable Modular Robots

Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life

Concepts and Approaches for Mars Exploration

Abstracts describe missions, mission elements or experiments for consideration in the 2005-2020 time frame. Also the technologies and the support necessary to achieve the results are discussed.NASA Headquarters; Lunar and Planetary Institutehosted by Lunar and Planetary Institute ; sponsored by NASA Headquarters, Lunar and Planetary Institute ; convener Scott Hubbard

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Aeronautical Engineering: A continuing bibliography, supplement 116

This bibliography lists 550 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1979

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Land-use/land-cover change and vulnerability to landslide disasters in Kurseong (Darjeeling Himalayas), India

This three-article dissertation (TAD) examines the drivers and impacts of Land-use/land-cover Change (LULCC) on the social-ecological system (SES) in a Himalayan region, prone to landslide disasters. The study region is based in Kurseong, a district subdivision in eastern India, and is home to agrarian communities who work primarily in tea plantations and smallholdings. This dissertation is grounded in integrated theoretical frameworks of Land System Science (LSS), Disaster research and Political Ecology (PE), and employs a mix of remote sensing, archival and ethnographic research methods. Article one identifies LULCC subjected to landslides over the last three decades (1988 - 2019), and explores the proximate and underlying drivers behind local land-use practices and decisions. Article two computes the multidimensional ways in which local people are vulnerable, by adopting a multidimensional livelihood vulnerability index (MLVI) framework, and explores with a political ecology chain of explanation, why vulnerabilities continue to exist. Article three illustrates farmer adaptations to a postcolonial agricultural system, their vulnerabilities and resilience with limited entitlements and access to resources. The GIS and Remote Sensing analyses show an increase in forest cover from 1988 to 2019 (45 - 54%), and a decrease in total landslide area (225.54 - 162.56 ha) over the same period. However, landslide vulnerabilities intensified in heavily settled and deforested areas, inferring a more complex influence of broad land changes at local levels. The MLVI in selected areas further shows farming communities to be multidimensionally vulnerable in varying degrees to several socio-economic stressors. Finally, a decentralized and decolonized political ecology approach tracks the historical and social roots of local adaptations to infrastructural constraints, limited social and economic capitals, and environmental disasters. Such adaptations to the SES are both sustainable and maladaptive, and is defined by adopting the phrase 'clumsy solutions to wicked problems.' The frameworks employed in this research brings together multiple paradigms to help identify the underlying socio-economic and political drivers behind environmental changes, and complex ramifications of environmental impacts on society. Thus, this study simultaneously contributes to a local geography in the Himalayas, as well as transdisciplinary and integrated research concepts for Global Environmental Change research

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Seismic Waves

The importance of seismic wave research lies not only in our ability to understand and predict earthquakes and tsunamis, it also reveals information on the Earth's composition and features in much the same way as it led to the discovery of Mohorovicic's discontinuity. As our theoretical understanding of the physics behind seismic waves has grown, physical and numerical modeling have greatly advanced and now augment applied seismology for better prediction and engineering practices. This has led to some novel applications such as using artificially-induced shocks for exploration of the Earth's subsurface and seismic stimulation for increasing the productivity of oil wells. This book demonstrates the latest techniques and advances in seismic wave analysis from theoretical approach, data acquisition and interpretation, to analyses and numerical simulations, as well as research applications. A review process was conducted in cooperation with sincere support by Drs. Hiroshi Takenaka, Yoshio Murai, Jun Matsushima, and Genti Toyokuni

Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario