Showing papers on "Intelligent sensor published in 2010"

Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Abstract: In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree.

Energy Harvesting for Autonomous Wireless Sensor Networks

Abstract: Wireless sensor nodes (WSNs) are employed today in many different application areas, ranging from health and lifestyle to automotive, smart building, predictive maintenance (e.g., of machines and infrastructure), and active RFID tags. Currently these devices have limited lifetimes, however, since they require significant operating power. The typical power requirements of some current portable devices, including a body sensor network, are shown in Figure 1.

Design and implementation of smart home energy management systems based on zigbee

Abstract: Today, organizations use IEEE802.15.4 and ZigBee to effectively deliver solutions for a variety of areas including consumer electronic device control, energy management and efficiency home and commercial building automation as well as industrial plant management. The Smart home energy network has gained widespread attentions due to its flexible integration into everyday life. This next generation green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. The green home energy network gradually forms a complex system to process various tasks. Developing this trend, we suggest a new Smart Home Energy Management System (SHEMS) based on an IEEE802.15.4 and ZigBee (we call it as a "ZigBee sensor network"). The proposed smart home energy management system divides and assigns various home network tasks to appropriate components. It can integrate diversified physical sensing information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. We develop a new routing protocol DMPR (Disjoint Multi Path based Routing) to improve the performance of our ZigBee sensor networks. This paper introduces the proposed home energy control system's design that provides intelligent services for users. We demonstrate its implementation using a real environment.

Using Heterogeneous Wireless Sensor Networks in a Telemonitoring System for Healthcare

Abstract: Ambient intelligence has acquired great importance in recent years and requires the development of new innovative solutions. This paper presents a distributed telemonitoring system, aimed at improving healthcare and assistance to dependent people at their homes. The system implements a service-oriented architecture based platform, which allows heterogeneous wireless sensor networks to communicate in a distributed way independent of time and location restrictions. This approach provides the system with a higher ability to recover from errors and a better flexibility to change their behavior at execution time. Preliminary results are presented in this paper.

A Sub- $\mu$ W Embedded CMOS Temperature Sensor for RFID Food Monitoring Application

Abstract: An ultra-low power embedded CMOS temperature sensor based on serially connected subthreshold MOS operation is implemented in a 0.18 μm CMOS process for passive RFID food monitoring applications. Employing serially connected subthreshold MOS as sensing element enables reduced minimum supply voltage for further power reduction, which is of utmost importance in passive RFID applications. Both proportional-to-absolute-temperature (PTAT) and complimentary-to-absolute-temperature (CTAT) signals can be obtained through proper transistor sizing. With the sensor core working under 0.5 V and digital interfacing under 1 V, the sensor dissipates a measured total power of 119 nW at 333 samples/s and achieves an inaccuracy of + 1/-0.8°C from - 10°C to 30°C after calibration. The sensor is embedded inside the fabricated passive UHF RFID tag. Measurement of the sensor performance at the system level is also carried out, illustrating proper sensing operation for passive RFID applications.

An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks With Energy-Hungry Sensors

Abstract: Energy conservation techniques for wireless sensor networks generally assume that data acquisition and processing have energy consumption that is significantly lower than that of communication. Unfortunately, this assumption does not hold in a number of practical applications, where sensors may consume even more energy than the radio. In this context, effective energy management should include policies for an efficient utilization of the sensors, which become one of the main components that affect the network lifetime. In this paper, we propose an adaptive sampling algorithm that estimates online the optimal sampling frequencies for sensors. This approach, which requires the design of adaptive measurement systems, minimizes the energy consumption of the sensors and, incidentally, that of the radio while maintaining a very high accuracy of collected data. As a case study, we considered a sensor for snow-monitoring applications. Simulation experiments have shown that the suggested adaptive algorithm can reduce the number of acquired samples up to 79% with respect to a traditional fixed-rate approach. We have also found that it can perform similar to a fixed-rate scheme where the sampling frequency is known in advance.

Compressive Sensing on a CMOS Separable-Transform Image Sensor

Abstract: This paper demonstrates a computational image sensor capable of implementing compressive sensing operations. Instead of sensing raw pixel data, this image sensor projects the image onto a separable 2-D basis set and measures the corresponding expansion coefficients. The inner products are computed in the analog domain using a computational focal plane and an analog vector-matrix multiplier (VMM). This is more than mere postprocessing, as the processing circuity is integrated as part of the sensing circuity itself. We implement compressive imaging on the sensor by using pseudorandom vectors called noiselets for the measurement basis. This choice allows us to reconstruct the image from only a small percentage of the transform coefficients. This effectively compresses the image without any digital computation and reduces the throughput of the analog-to-digital converter (ADC). The reduction in throughput has the potential to reduce power consumption and increase the frame rate. The general architecture and a detailed circuit implementation of the image sensor are discussed. We also present experimental results that demonstrate the advantages of using the sensor for compressive imaging rather than more traditional coded imaging strategies.

A Wireless Soil Moisture Smart Sensor Web Using Physics-Based Optimal Control: Concept and Initial Demonstrations

Abstract: This paper introduces a new concept for a smart wireless sensor web technology for optimal measurements of surface-to-depth profiles of soil moisture using in-situ sensors. The objective of the technology, supported by the NASA Earth Science Technology Office Advanced Information Systems Technology program, is to enable a guided and adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of spaceborne soil moisture sensors. A potential application for this technology is the validation of products from the Soil Moisture Active/Passive (SMAP) mission. Spatially, the total variability in soil-moisture fields comes from variability in processes on various scales. Temporally, variability is caused by external forcings, landscape heterogeneity, and antecedent conditions. Installing a dense in-situ network to sample the field continuously in time for all ranges of variability is impractical. However, a sparser but smarter network with an optimized measurement schedule can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic physics-based hydrologic and sensor modeling system. The overall design of the smart sensor web-including the control architecture, physics-based hydrologic and sensor models, and actuation and communication hardware-is presented in this paper. We also present results illustrating sensor scheduling and estimation strategies as well as initial numerical and field demonstrations of the sensor web concept. It is shown that the coordinated operation of sensors through the control policy results in substantial savings in resource usage.

Analysis of Flip Ambiguities for Robust Sensor Network Localization

Abstract: Erroneous local geometric realizations in some parts of a network due to the sensitivity to certain distance-measurement errors with respect to some neighboring sensor locations is a major problem in wireless sensor-network localization, which may, in turn, affect the reliability of the localization of the whole or a major portion of the sensor network. This phenomenon is well described using the notion of ?flip ambiguity? in rigid graph theory. In this paper, we present a formal geometric analysis of flip-ambiguity problems in planar sensor networks via quantification of the likelihood of flip ambiguities in arbitrary sensor neighborhood geometries. Based on this analysis, we establish a robustness criterion to detect flip ambiguities in such neighborhood geometries. In addition to the analysis, the established robustness criterion is embedded in localization algorithms to enhance the reliability of the produced location estimates by eliminating neighborhoods with flip ambiguities from being included in the localization process.

Iso-Map: Energy-Efficient Contour Mapping in Wireless Sensor Networks

Abstract: Contour mapping is a crucial part of many wireless sensor network applications. Many efforts have been made to avoid collecting data from all the sensors in the network and producing maps at the sink, which is proven to be inefficient. The existing approaches (often aggregation based), however, suffer from heavy transmission traffic and incur large computational overheads on each sensor node. We propose Iso-Map, an energy-efficient protocol for contour mapping, which builds contour maps based solely on the reports collected from intelligently selected ?isoline nodes? in wireless sensor networks. Iso-Map achieves high-quality contour mapping while significantly reducing the generated traffic from O(n) to O(?n), where n is the total number of sensor nodes in the field. The pernode computation overhead is also restrained as a constant. We conduct comprehensive trace-driven simulations to verify this protocol, and demonstrate that Iso-Map outperforms the previous approaches in the sense that it produces contour maps of high fidelity with significantly reduced energy cost.

On Optimal Information Capture by Energy-Constrained Mobile Sensors

Abstract: A mobile sensor is used to cover a number of points of interest (PoIs), where dynamic events appear and disappear according to the given random processes. The sensor, which is of sensing range r, visits the PoIs in a cyclic schedule and gains information about any event that falls within its range. We consider the temporal dimension of the sensing as given by a utility function, which specifies how much information is gained about an event as a function of the cumulative sensing or observation time. The quality of monitoring (QoM), i.e., the fraction of information captured about all events, depends on the speed of the sensor and has been analyzed in an earlier paper for different utility functions. The prior work, however, does not consider the energy of motion, which is an important constraint for mobile sensor coverage. In this paper, we analyze the expected Information captured Per unit of Energy consumption (IPE) as a function of the event type (in terms of the utility function), the event dynamics, and the speed of the mobile sensor. Our analysis uses a realistic energy model of motion, and it allows the sensor speed to be optimized for information capture. The case of multiple sensors will also be discussed. Extensive simulation results verify and illustrate the analytical results.

Distributed Sensor Perception via Sparse Representation

Abstract: In this paper, sensor network scenarios are considered where the underlying signals of interest exhibit a degree of sparsity, which means that in an appropriate basis, they can be expressed in terms of a small number of nonzero coefficients. Following the emerging theory of compressive sensing (CS), an overall architecture is considered where the sensors acquire potentially noisy projections of the data, and the underlying sparsity is exploited to recover useful information about the signals of interest, which will be referred to as distributed sensor perception. First, we discuss the question of which projections of the data should be acquired, and how many of them. Then, we discuss how to take advantage of possible joint sparsity of the signals acquired by multiple sensors, and show how this can further improve the inference of the events from the sensor network. Two practical sensor applications are demonstrated, namely, distributed wearable action recognition using low-power motion sensors and distributed object recognition using high-power camera sensors. Experimental data support the utility of the CS framework in distributed sensor perception.

Multifunctional Intelligent Autonomous Parking Controllers for Carlike Mobile Robots

Abstract: An increasing number of carlike mobile robot (CLMR) studies have addressed the issues of autonomous parking and obstacle avoidance. An autonomous parking controller can provide convenience to a novice driver. However, if the controller is not designed adequately, it may endanger the car and the driver. Therefore, this paper presents a novel multifunctional intelligent autonomous parking controller that is capable of effectively parking the CLMR in an appropriate parking space, by integrating sensor data capable of obtaining the surrounding data of the robot. An ultrasonic sensor array system has been developed with group-sensor firing intervals. A binaural approach to the CLMR has been adopted for providing complete contactless sensory coverage of the entire workspace. The proposed heuristic controller can obtain the posture of a mobile robot in a parking space. In addition, the controller can ensure the ability of the CLMR to withstand collision to guarantee safe parking. Moreover, the CLMR can recognize the parking space and the obstacle position in dynamic environment. Therefore, the proposed controller installed in a car could ensure safe driving. Finally, practical experiments demonstrate that the proposed multifunctional intelligent autonomous parking controllers are feasible and effective.

The BehaviorScope framework for enabling ambient assisted living

Abstract: The in-house monitoring of elders using intelligent sensors is a very desirable service that has the potential of increasing autonomy and independence while minimizing the risks of living alone. Because of this promise, the efforts of building such systems have been spanning for decades, but there is still a lot of room for improvement. Driven by the recent technology advances in many of the required components, in this article, we present a scalable framework for detailed behavior interpretation. Our framework supports in-house monitoring of elders using an intelligent gateway and a set of cheap commercially available sensors, in addition to more advanced camera-based human localization sensors and a client for GPS-enabled mobile phones that provides monitoring when outdoors. In this article, we report our experiences and present our current progress in three main components: sensors, middleware, and behavior interpretation mechanisms spanning from simple programmable rule-based alerts to algorithms for extracting the temporal routines of individuals.

Development of intelligent robot hand using proximity, contact and slip sensing

Abstract: To achieve the skillful task like the human, many researchers have been working on robot hand. An interaction with vision and tactile information are indispensable for realization of skillful tasks. In the existing research, the method using a camera to get the vision information is often found. But, in the boundary area of a non-contact phase and a contact phase, there are problem that lack of sensor information because the influence of occlusion comes up to surface. We devise to introduce the proximity sensor in this area. And we call the robot hand which is equipped with proximity, tactile and slip sensor “intelligent robot hand”. In this research, we show the constitution example of the intelligent robot hand and propose the method to realize Pick&Place as concrete task.

Human activity recognition based on Evolving Fuzzy Systems.

Abstract: Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.

A Soldier Health Monitoring System for Military Applications

Abstract: With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.

A real-time control using wireless sensor network for intelligent energy management system in buildings

Abstract: Industrial evolution brings major new challenges due to increasing energy demands. This phenomenon encourages the improvement of control methodologies that reduce resource requirements. It has been lately observed that the building sector contributes considerably to final energy demand. For example, electricity used in France by this sector has reached 284TWh, accounting for 65% of all electricity consumed in 2007 (434TWh) [1], and this situation continues to increase. Moreover, the link between increased CO2 emissions and the use of energy is also considered, particularly in the building environment. 404 million tones of CO2 gas is emitted in France, and 22.6% originates from this sector [2].In the light of developments in microelectro-mechanical systems (MEMS), along with progress made in communication and embedded smart sensors, the building sector has a huge potential for mitigating demand. This paper deals with techniques and advanced load management strategies for BEMS. First, we present the architecture of this system that exploits several communication techniques. We then describe an application for a heating control which is based on the wireless sensor network (WSN). This application uses an innovative realtime control method that allows peak consumption to be reduced while maintaining thermal comfort. This method is tested and the experiment results demonstrate that the proposed method is able to control heating loads to adapt to any problems that may arise (by taking into account changing price, signals from energy provider and distribution system operator, etc).

Back-Tracking Based Sensor Deployment by a Robot Team

Abstract: We propose a novel localized carrier-based sensor placement algorithm, named Back-Tracking Deployment (BTD). Mobile robots (carriers) carry static sensors and drop them at visited empty vertices of a virtual square, triangular or hexagonal grid in a bounded 2D environment. A single robot will move forward along the virtual grid in open directions with respect to a pre-defined order of preference until a dead end is reached. Then it back tracks to the nearest sensor adjacent to an empty vertex on its backward path. The robot resumes regular forward moving and sensor dropping from there. To save movement steps, the back tracking is performed along a locally identified shortcut. We extend the algorithm to support multiple robots, which move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise it will take over the back-track path of another robot, by consulting with neighboring sensors. We prove that BTD terminates in finite time and produces full coverage when no sensor failures occur. We also describe an approach to handle sensor faults. Through extensive simulation we show that BTD far outperforms the only competing algorithm LRV [2] in robot moves and robot messages.

Wireless Intelligent Sensor and Actuator Network - A Scalable Platform for Time-synchronous Applications of Structural Health Monitoring

Abstract: Wireless sensor networks have attracted attention as a possible solution for applications of periodic and continuous structural health monitoring. Ensuring synchronous data acquisition across wireless nodes in large networks of sensors spatially distributed on a structure is of critical importance for many methods of structural health monitoring, especially those based on analysis of vibration. In this article we present a novel Wireless Intelligent Sensor and Actuator Network (WISAN) addressing the issue of scalability for applications of structural health monitoring. We also present a novel time synchronization algorithm that can keep the synchronization error between any number of globally distributed sensors nodes less than ±23 μs. We show proof of stability for the time synchronization algorithm. We validate WISAN in laboratory experiments, testing the actual time synchronization between randomly selected sensors in a complex network. Finally, we validate WISAN in a field experiment by reconstructing...

Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

Abstract: The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

Animal Situation Tracking Service Using RFID, GPS, and Sensors

Abstract: With the technology of sensor, RFID, and GPS, many researches are recently being carried out on monitoring animal behavior and interactions with the environment. Plus, the technology is applicable to develop new zoological systems for animal trace ability, identification, and anti-theft. Yet, there is a lack of studies of providing zoos with intelligent animal tracking and management services based on RFID, GPS, and sensors. Accordingly, in this paper, we propose an intelligent animal situation tracking service for zoological gardens, based on GPS, RFID, and sensors. Firstly, we present a service scenario of animal situation tracking and introduce the tracking system configuration. The proposed system can provide real-time animal situation information such as the current location, bodily temperature, and pictures. Plus, it can track the animals based on the information even when they escape their cages. Next, we design and implement a prototype of the proposed animal situation tracking system.

A Logical Approach to Home Healthcare with Intelligent Sensor-Network Support

Abstract: This paper describes an intelligent home healthcare system characterized by a wireless sensor network (WSN) and a reasoning component. The aim of the system is to allow constant and unobtrusive monitoring of a patient in order to enhance autonomy and increase quality of life. Data collected by the sensor network are used to support a reasoning component, which is based on answer set programming (ASP), in performing three main reasoning tasks: (i) continuous contextualization of the physical, mental and social state of a patient, (ii) prediction of possibly risky situations and (iii) identification of plausible causes for the worsening of a patient's health. Starting from different data sources (sensor data, test results, inference results) the reasoning component applies expressive logic rules aimed at correct interpretation of incomplete or inconsistent contextual information, and evaluates correlation rules expressed by clinicians. The expressive power of ASP allows efficient enough reasoning to support prevention, while declarativity simplifies rule-specification and allows automatic encoding of knowledge. Preliminary evaluations show that the combination of an ASP-based reasoning component and a WSN is a good solution for creating a home-based healthcare system.

A Smart Sensor Web for Ocean Observation: Fixed and Mobile Platforms, Integrated Acoustics, Satellites and Predictive Modeling

Abstract: In many areas of Earth science, including climate change research and operational oceanography, there is a need for near real-time integration of data from heterogeneous and spatially distributed sensors, in particular in situ and space-based sensors. The data integration, as provided by a smart sensor web, enables numerous improvements, namely, (1) adaptive sampling for more efficient use of expensive space-based and in situ sensing assets, (2) higher fidelity information gathering from data sources through integration of complementary data sets, and (3) improved sensor calibration. Our ocean-observing smart sensor web presented herein is composed of both mobile and fixed underwater in situ ocean sensing assets and Earth Observing System satellite sensors providing larger-scale sensing. An acoustic communications network forms a critical link in the web, facilitating adaptive sampling and calibration. We report on the development of various elements of this smart sensor web, including (a) a cable-connected mooring system with a profiler under real-time control with inductive battery charging; (b) a glider with integrated acoustic communications and broadband receiving capability; (c) an integrated acoustic navigation and communication network; (d) satellite sensor elements; and (e) a predictive model via the Regional Ocean Modeling System interacting with satellite sensor control.

Design and implementation of power-aware LED light enabler with location-aware adaptive middleware and context-aware user pattern

Abstract: Recent advances in ubiquitous technologies facilitate location-aware and power-aware systems that can provide predefined services. Recent research efforts are based on control mechanisms for standby power reduction. Conventional systems are only designed for power reduction of the consumer electronics. However, due to their architectural limitations, the recent systems are not flexible with respect to LED light control for power reduction. We need to consider efficient autonomous power control based on intelligent devices and the power-aware service prediction in networked environments. In this paper, we propose a power-aware LED light enabler with light sensors, motion sensors and network interfaces. The LED light enabler also communicates with context-aware middleware using an intelligent power gateway that adaptively determines the optimal power control by analyzing user living patterns using sensing data obtained by devices. Our power-aware LED light enabler with adaptive middleware dynamically reconfigures the power-aware services. The proposed adaptive middleware facilitates the learning mechanism which analyzes the illumination and the user activity, and controls the LED lights only when users exist around the devices. Our enabler reduces power consumption up to 58% in comparison to a basic lighting system at the real office testbed.

Low-Density Wireless Sensor Networks for Localization and Tracking in Critical Environments

Abstract: In this paper, the problem of localizing and tracking mobile nodes acting in a fixed wireless sensor network (WSN) is addressed. A strategy is proposed based on an empirical map of the received signal-strength distribution that is generated by the WSN and on a stochastic model of the mobile-node behavior. This approach results in being well suited for low-density setups and critical environments. The theoretical background and the architecture of the system are presented, together with simulations to validate the design phase. Also, the system is implemented into a real-time framework, and its performance is tested in an industrial indoor environment.

A New Intelligent Remote Control System for Home Automation and Reduce Energy Consumption

Abstract: This paper presents the design and implementation of an internet-based smart remote control system for home automation, dedicated to power management that adapts power consumption to available power resources according to user comfort and cost criteria. Sensors and home appliances are connected to the designed and implemented control panel and then they are monitored and controlled from every corner of the world through the Internet cloud. The system is scalable and allows additional appliances to be added to it with no major changes to its core. New communication format is proposed to enable communication between the control panel and the server as well. To verify the principle operation of the design, some home applications are experimentally tested. Experimental results show the efficiency and accuracy of proposed intelligent control system in terms of energy saving and being user friendly.

Development of high-sensitivity accelerometer board for structural health monitoring

Abstract: State-of-the-art wireless smart sensor technology enables a dense array of sensors to be distributed through a structure to provide an abundance of structural information. However, the relatively low resolution of the MEMS sensors that are generally adopted for wireless smart sensors limits the network's ability to measure lowlevel vibration often found in the ambient vibration response of building structures. To address this problem, development of a high-sensitivity acceleration board for the Imote2 platform using a low-noise accelerometer is presented. The performance of this new sensor board is validated through extensive laboratory testing. In addition, the use of the high-sensitivity accelerometer board as a reference sensor to improve the capability to capture structural behavior in the smart sensor network is discussed.

Detection of intelligent mobile target in a mobile sensor network

Abstract: We study the problem of a mobile target (the mouse) trying to evade detection by one or more mobile sensors (we call such a sensor a cat) in a closed network area. We view our problem as a game between two players: the mouse, and the collection of cats forming a single (meta-)player. The game ends when the mouse falls within the sensing range of one or more cats. A cat tries to determine its optimal strategy to minimize the worst case expected detection time of the mouse. The mouse tries to determine an optimal counter movement strategy to maximize the expected detection time. We divide the problem into two cases based on the relative sensing capabilities of the cats and the mouse. When the mouse has a sensing range smaller than or equal to the cats', we develop a dynamic programming solution for the mouse's optimal strategy, assuming high level information about the cats' movement model. We discuss how the cats' chosen movement model will affect its presence matrix in the network, and hence its payoff in the game. When the mouse has a larger sensing range than the cats, we show how the mouse can determine its optimal movement strategy based on local observations of the cats' movements. We further present a coordination protocol for the cats to collaboratively catch the mouse by: 1) forming opportunistically a cohort to limit the mouse's degree of freedom in escaping detection; and 2) minimizing the overlap in the spatial coverage of the cohort's members. Extensive experimental results verify and illustrate the analytical results, and evaluate the game's payoffs as a function of several important system parameters.

Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms

Abstract: Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks—the Cascade Correlation Neural Network (CCNNs)—to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes.