Using visual lifelogs to automatically characterise everyday activities

Visual lifelogging is the term used to describe recording our everyday lives using wearable cameras, for applications which are personal to us and do not involve sharing our recorded data. Current applications of visual lifelogging are built around remembrance or searching for specific events from the past. The purpose of the work reported here is to extend this to allow us to characterise and measure the occurrence of everyday activities of the wearer and in so doing to gain insights into the wearer's everyday behaviour. The methods we use are to capture everyday activities using a wearable camera called SenseCam, and to use an algorithm we have developed which indexes lifelog images by the occurrence of basic semantic concepts. We then use data reduction techniques to automatically generate a profile of the wearer's everyday behaviour and activities. Our algorithm has been evaluated on a large set of concepts investigated from 13 users in a user experiment, and for a group of 16 popular everyday activities we achieve an average F-score of 0.90. Our conclusions are that the the technique we have presented for unobtrusively and ambiently characterising everyday behaviour and activities across individuals is of sufficient accuracy to be usable in a range of applications

A Self-organizing Hybrid Sensor System With Distributed Data Fusion For Intruder Tracking And Surveillance

A wireless sensor network is a network of distributed nodes each equipped with its own sensors, computational resources and transceivers. These sensors are designed to be able to sense specific phenomenon over a large geographic area and communicate this information to the user. Most sensor networks are designed to be stand-alone systems that can operate without user intervention for long periods of time. While the use of wireless sensor networks have been demonstrated in various military and commercial applications, their full potential has not been realized primarily due to the lack of efficient methods to self organize and cover the entire area of interest. Techniques currently available focus solely on homogeneous wireless sensor networks either in terms of static networks or mobile networks and suffers from device specific inadequacies such as lack of coverage, power and fault tolerance. Failing nodes result in coverage loss and breakage in communication connectivity and hence there is a pressing need for a fault tolerant system to allow replacing of the failed nodes. In this dissertation, a unique hybrid sensor network is demonstrated that includes a host of mobile sensor platforms. It is shown that the coverage area of the static sensor network can be improved by self-organizing the mobile sensor platforms to allow interaction with the static sensor nodes and thereby increase the coverage area. The performance of the hybrid sensor network is analyzed for a set of N mobile sensors to determine and optimize parameters such as the position of the mobile nodes for maximum coverage of the sensing area without loss of signal between the mobile sensors, static nodes and the central control station. A novel approach to tracking dynamic targets is also presented. Unlike other tracking methods that are based on computationally complex methods, the strategy adopted in this work is based on a computationally simple but effective technique of received signal strength indicator measurements. The algorithms developed in this dissertation are based on a number of reasonable assumptions that are easily verified in a densely distributed sensor network and require simple computations that efficiently tracks the target in the sensor field. False alarm rate, probability of detection and latency are computed and compared with other published techniques. The performance analysis of the tracking system is done on an experimental testbed and also through simulation and the improvement in accuracy over other methods is demonstrated

Resource-aware Video Multicasting via Access Gateways in Wireless Mesh Networks

This paper studies video multicasting in large-scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy and low-capacity multihop wireless paths. A set of heuristic-based algorithms is described that together aim to maximize reliable network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the dynamic group management algorithm. These algorithms use different approaches to arrange nodes involved in video multicasting into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are presented, showing that our multicasting algorithms can achieve up to 40 percent more throughput than other related published approaches

Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior

Large-scale data collection by means of wireless sensor network and internet-of-things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this study, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm---built upon belief-propagation principles---that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing.Comment: accepted to IEEE Transactions on Communication

Reconfigurable middleware architectures for large scale sensor networks

Wireless sensor networks, in an effort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task reconfiguration and high-level object recomposition. Through the layered approach of SENSIX, the software developer creates a domain-specific sensing architecture by defining a customized task specification and utilizing object inheritance. In addition, SENSIX performs better at large scales (on the order of 1000 nodes or more) than other sensor network middleware which do not include such unified facilities for vertical integration

A hierarchical group model for programming sensor networks

A hierarchical group model that decouples computation from hardware can characterize and aid in the construction of sensor network software with minimal overhead. Future sensor network applications will move beyond static, homogeneous deployments to include dynamic, heterogeneous elements. These sensor networks will also gain new users, including casual users who will expect intuitive interfaces to interact with sensor networks. To address these challenges, a new computational model and a system implementing the model are presented. This model ensures that computations can be readily reassigned as sensor nodes are introduced or removed. The model includes methods for communication to accommodate these dynamic elements. This dissertation presents a detailed description and design of a computational model that resolves these challenges using a hierarchical group mechanism. In this model, computation is tasked to logical groups and split into collective and local components that communicate hierarchically. Local computation is primarily used for data production and publishes data to the collective computation. Similarly, collective computation is primarily used for data aggregation and pushes results back to the local computation. Finally, the model includes data-processing functions interposed between local and collective functions that are responsible for data conversion. This dissertation also presents implementations and applications of the model. Implementations include Kensho, a C-based implementation of the hierarchical group model, that can be used for a variety of user applications. Another implementation, Tables, presents a spreadsheet-inspired view of the sensor network that takes advantage of hierarchical groups for both computation and communication. Users are able to specify both local and collective functions that execute on the sensor network via the spreadsheet interface. Applications of the model are also explored. One application, FUSN, provides a set of methods for constructing filesystem-based interfaces for sensor networks. This demonstrates the general applicability of the model as applied to sensor network programming and management interfaces. Finally, the model is applied to a novel privacy algorithm to demonstrate that the model isn\u27t strictly limited to programming interfaces

Evaluation and Analysis of Node Localization Power Cost in Ad-Hoc Wireless Sensor Networks with Mobility

One of the key concerns with location-aware Ad-hoc Wireless Sensor Networks (AWSNs) is how sensor nodes determine their position. The inherent power limitations of an AWSN along with the requirement for long network lifetimes makes achieving fast and power-efficient localization vital. This research examines the cost (in terms of power) of network irregularities on communications and localization in an AWSN. The number of data bits transmitted and received are significantly affected by varying levels of mobility, node degree, and network shape. The concurrent localization approach, used by the APS-Euclidean algorithm, has significantly more accurate position estimates with a higher percentage of nodes localized, while requiring 50% less data communications overhead, than the Map-Growing algorithm. Analytical power models capable of estimating the power required to localize are derived. The average amount of data communications required by either of these algorithms in a highly mobile network with a relatively high degree consumes less than 2.0% of the power capacity of an average 560mA-hr battery. This is less than expected and contrary to the common perception that localization algorithms consume a significant amount of a node\u27s power

A Crosslayer Routing Protocol (XLRP) for Wireless Sensor Networks

The advent of wireless sensor networks with emphasis on the information being routed, rather than routing information has redefined networking from that of conventional wireless networked systems. Demanding that need for contnt based routing techniques and development of low cost network modules, built to operate in large numbers in a networked fashion with limited resources and capabilities. The unique characteristics of wireless sensor networks have the applicability and effectiveness of conventional algorithms defined for wireless ad-hoc networks, leading to the design and development of protocols specific to wireless sensor network. Many network layer protocols have been proposed for wireless sensor networks, identifying and addressing factors influencing network layer design, this thesis defines a cross layer routing protocol (XLRP) for sensor networks. The submitted work is suggestive of a network layer design with knowledge of application layer information and efficient utilization of physical layer capabilities onboard the sensor modules. Network layer decisions are made based on the quantity of information (size of the data) that needs to be routed and accordingly transmitter power leels are switched as an energy efficient routing strategy. The proposed routing protocol switches radio states based on the received signal strength (RSSI) acquiring only relevant information and piggybacks information in data packets for reduced controlled information exchange. The proposed algorithm has been implemented in Network Simulator (NS2) and the effectiveness of the protocol has been proved in comparison with diffusion paradigm

Design and Implementation of Large-Scale Wireless Sensor Networks for Environmental Monitoring Applications

Environmental monitoring represents a major application domain for wireless sensor networks (WSN). However, despite significant advances in recent years, there are still many challenging issues to be addressed to exploit the full potential of the emerging WSN technology. In this dissertation, we introduce the design and implementation of low-power wireless sensor networks for long-term, autonomous, and near-real-time environmental monitoring applications. We have developed an out-of-box solution consisting of a suite of software, protocols and algorithms to provide reliable data collection with extremely low power consumption. Two wireless sensor networks based on the proposed solution have been deployed in remote field stations to monitor soil moisture along with other environmental parameters. As parts of the ever-growing environmental monitoring cyberinfrastructure, these networks have been integrated into the Texas Environmental Observatory system for long-term operation. Environmental measurement and network performance results are presented to demonstrate the capability, reliability and energy-efficiency of the network

Anchor-Free Localization in Mixed Wireless Sensor Network Systems

Recent technological advances have fostered the emergence of Wireless Sensor Networks (WSNs), which consist of tiny, wireless, battery-powered nodes that are expected to revolutionize the ways in which we understand and construct complex physical systems. A fundamental property needed to use and maintain these WSNs is ``localization\u27\u27, which allows the establishment of spatial relationships among nodes over time. This dissertation presents a series of Geographic Distributed Localization (GDL) algorithms for mixed WSNs, in which both static and mobile nodes can coexist. The GDL algorithms provide a series of useful methods for localization in mixed WSNs. First, GDL provides an approximation called ``hop-coordinates\u27\u27, which improves the accuracy of both hop-counting and connectivity-based measurement techniques. Second, GDL utilizes a distributed algorithm to compute the locations of all nodes in static networks with the help of the hop-coordinates approximation. Third, GDL integrates a sensor component into this localization paradigm for possible mobility and as a result allows for a more complex deployment of WSNs as well as lower costs. In addition, the development of GDL incorporated the possibility of manipulated communications, such as wormhole attacks. Simulations show that such a localization system can provide fundamental support for security by detecting and localizing wormhole attacks. Although several localization techniques have been proposed in the past few years, none currently satisfies our requirements to provide an accurate, efficient and reliable localization for mixed WSNs. The contributions of this dissertation are: (1) our measurement technique achieves better accuracy both in measurement and localization than other methods; (2) our method significantly improves the efficiency of localization in updating location in mixed WSNs by incorporating sensors into the method; (3) our method can detect and locate the communication that has been manipulated by a wormhole in a network without relying on a central server