Development of mobile agent framework in wireless sensor networks for multi-sensor collaborative processing

Recent advances in processor, memory and radio technology have enabled production of tiny, low-power, low-cost sensor nodes capable of sensing, communication and computation. Although a single node is resource constrained with limited power, limited computation and limited communication bandwidth, these nodes deployed in large number form a new type of network called the wireless sensor network (WSN). One of the challenges brought by WSNs is an efficient computing paradigm to support the distributed nature of the applications built on these networks considering the resource limitations of the sensor nodes. Collaborative processing between multiple sensor nodes is essential to generate fault-tolerant, reliable information from the densely-spatial sensing phenomenon. The typical model used in distributed computing is the client/server model. However, this computing model is not appropriate in the context of sensor networks. This thesis develops an energy-efficient, scalable and real-time computing model for collaborative processing in sensor networks called the mobile agent computing paradigm. In this paradigm, instead of each sensor node sending data or result to a central server which is typical in the client/server model, the information processing code is moved to the nodes using mobile agents. These agents carry the execution code and migrate from one node to another integrating result at each node. This thesis develops the mobile agent framework on top of an energy-efficient routing protocol called directed diffusion. The mobile agent framework described has been mapped to collaborative target classification application. This application has been tested in three field demos conducted at Twentynine palms, CA; BAE Austin, TX; and BBN Waltham, MA

Energy Efficient Designs for Collaborative Signal and Information Processing inWireless Sensor Networks

Collaborative signal and information processing (CSIP) plays an important role in the deployment of wireless sensor networks. Since each sensor has limited computing capability, constrained power usage, and limited sensing range, collaboration among sensor nodes is important in order to compensate for each other’s limitation as well as to improve the degree of fault tolerance. In order to support the execution of CSIP algorithms, distributed computing paradigm and clustering protocols, are needed, which are the major concentrations of this dissertation. In order to facilitate collaboration among sensor nodes, we present a mobile-agent computing paradigm, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. We further conduct extensive performance evaluation versus the traditional client/server-based computing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we propose a hybrid computing paradigm that adopts different computing models within different clusters of sensor nodes. Either the client/server or the mobile agent paradigm can be employed within clusters or between clusters according to the different cluster configurations. This new computing paradigm can take full advantages of both client/server and mobile agent computing paradigms. Simulations show that the hybrid computing paradigm performs better than either the client/server or the mobile agent computing. The mobile agent itinerary has a significant impact on the overall performance of the sensor network. We thus formulate both the static mobile agent planning and the dynamic mobile agent planning as optimization problems. Based on the models, we present three itinerary planning algorithms. We have showed, through simulation, that the predictive dynamic itinerary performs the best under a wide range of conditions, thus making it particularly suitable for CSIP in wireless sensor networks. In order to facilitate the deployment of hybrid computing paradigm, we proposed a decentralized reactive clustering (DRC) protocol to cluster the sensor network in an energy-efficient way. The clustering process is only invoked by events occur in the sensor network. Nodes that do not detect the events are put into the sleep state to save energy. In addition, power control technique is used to minimize the transmission power needed. The advantages of DRC protocol are demonstrated through simulations

Emma: An accurate, efficient, and multi-modality strategy for autonomous vehicle angle prediction

Autonomous driving and self-driving vehicles have become the most popular selection for customers for their convenience. Vehicle angle prediction is one of the most prevalent topics in the autonomous driving industry, that is, realizing real-time vehicle angle prediction. However, existing methods of vehicle angle prediction utilize only single-modal data to achieve model prediction, such as images captured by the camera, which limits the performance and efficiency of the prediction system. In this paper, we present Emma, a novel vehicle angle prediction strategy that achieves multi-modal prediction and is more efficient. Specifically, Emma exploits both images and inertial measurement unit (IMU) signals with a fusion network for multi-modal data fusion and vehicle angle prediction. Moreover, we design and implement a few-shot learning module in Emma for fast domain adaptation to varied scenarios (e.g., different vehicle models). Evaluation results demonstrate that Emma achieves overall 97.5% accuracy in predicting three vehicle angle parameters (yaw, pitch, and roll), which outperforms traditional single-modalities by approximately 16.7%–36.8%. Additionally, the few-shot learning module presents promising adaptive ability and shows overall 79.8% and 88.3% accuracy in 5-shot and 10-shot settings, respectively. Finally, empirical results show that Emma reduces energy consumption by 39.7% when running on the Arduino UNO board

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Advances and Applications of Dezert-Smarandache Theory (DSmT), Vol. 1

The Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning is a natural extension of the classical Dempster-Shafer Theory (DST) but includes fundamental differences with the DST. DSmT allows to formally combine any types of independent sources of information represented in term of belief functions, but is mainly focused on the fusion of uncertain, highly conflicting and imprecise quantitative or qualitative sources of evidence. DSmT is able to solve complex, static or dynamic fusion problems beyond the limits of the DST framework, especially when conflicts between sources become large and when the refinement of the frame of the problem under consideration becomes inaccessible because of vague, relative and imprecise nature of elements of it. DSmT is used in cybernetics, robotics, medicine, military, and other engineering applications where the fusion of sensors\u27 information is required

Advances and Applications of DSmT for Information Fusion

This book is devoted to an emerging branch of Information Fusion based on new approach for modelling the fusion problematic when the information provided by the sources is both uncertain and (highly) conflicting. This approach, known in literature as DSmT (standing for Dezert-Smarandache Theory), proposes new useful rules of combinations

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

Single Hydrophone Underwater Localization Approach in Sallow Waters

Applications of underwater signal processing are essential for environmental monitoring. Remote monitoring and passive sound source localization in an underwater environment can provide great insight into geological studies, environmental changes and marine lives monitoring. While various methods are available for Localization, they mostly employ arrays of hydrophones, requiring synchronization or prior knowledge of the source signals, which can prove costly, complicated, and hard to maintain. Remote monitoring applications require very high-range passive localization methods; and, given the frequency-selective nature of ambient noise and other channel parameters, current localization methods have short-distance range estimation or high localization error for long distances. The modal analysis makes it possible to study and localize sounds propagated over long distances using one passive hydrophone without a need for prior knowledge of the source or synchronization. This dissertation presents four new stand-alone multi/single hydrophone localization algorithms based on modal dispersion analysis to localize impulsive sound sources in a noisy shallow water environment. The first algorithm is named as selective multi-modal pair (SMP), enables utilizing modals with any wavenumbers as opposed to previously proposed methods based on only on the modes with sequential wavenumbers. The algorithm extracts the dispersion curves of the received signal to be compared against the dispersion curves computed using a custom channel. then chooses the most effective modes (that result in the lowest localization error) , estimated the range of a sound source. The resulting estimated range is the range that makes the best match between the selected modal dispersion curves and the estimated dispersion curves. Numerical results, using both simulated and actual recorded sounds of whale and underwater explosion show that the proposed algorithm can localize underwater sounds with high accuracy when the signal-to-noise ratio varies from 28dB to 45dB. The second Localization algorithm is named selective weighted genetic algorithm (SW-GA). This algorithm employs two weighting functions based on geolocation information of the source and a selective scaling function for the selection of the most noise resistive modal pairs. The proposed weighted localization scaling and selection functions are designed to ensure convergence towards the correct range estimation. We analyzed and compared this algorithm using the same signals and SNR scenarios as before and shows a better2D localization performance and noise resistivity compared with previously proposed methods. The third and fourth algorithms, named Weighted Multi-Modal (WMM)and Multi-Modal (MM) employs all available modal pairs instead of just a few or a sequential selection. They compute a modal contribution matrix based on all available modal pairs with common frequencies. Furthermore, the weighted version of the algorithm employs the contribution matrix for assigning weights based on contribution/noise resistivity to all modal pairs. Employing all available modes results in an algorithm capable of localizing signals with higher frequencies and the weighting function increase accuracy in low SNR environments. The noise performance analysis of both the WMM algorithm; and the non-weighted version yields considerable improvements in localization of sound sources in the presence of high level of ambient noise over other algorithms used in this work