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

High Accuracy Distributed Target Detection and Classification in Sensor Networks Based on Mobile Agent Framework

High-accuracy distributed information exploitation plays an important role in sensor networks. This dissertation describes a mobile-agent-based framework for target detection and classification in sensor networks. Specifically, we tackle the challenging problems of multiple- target detection, high-fidelity target classification, and unknown-target identification. In this dissertation, we present a progressive multiple-target detection approach to estimate the number of targets sequentially and implement it using a mobile-agent framework. To further improve the performance, we present a cluster-based distributed approach where the estimated results from different clusters are fused. Experimental results show that the distributed scheme with the Bayesian fusion method have better performance in the sense that they have the highest detection probability and the most stable performance. In addition, the progressive intra-cluster estimation can reduce data transmission by 83.22% and conserve energy by 81.64% compared to the centralized scheme. For collaborative target classification, we develop a general purpose multi-modality, multi-sensor fusion hierarchy for information integration in sensor networks. The hierarchy is com- posed of four levels of enabling algorithms: local signal processing, temporal fusion, multi-modality fusion, and multi-sensor fusion using a mobile-agent-based framework. The fusion hierarchy ensures fault tolerance and thus generates robust results. In the meanwhile, it also takes into account energy efficiency. Experimental results based on two field demos show constant improvement of classification accuracy over different levels of the hierarchy. Unknown target identification in sensor networks corresponds to the capability of detecting targets without any a priori information, and of modifying the knowledge base dynamically. In this dissertation, we present a collaborative method to solve this problem among multiple sensors. When applied to the military vehicles data set collected in a field demo, about 80% unknown target samples can be recognized correctly, while the known target classification ac- curacy stays above 95%

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

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

Distributed Environment Control Using Wireless Sensor/Actuator Networks for Lighting Applications

We propose a decentralized algorithm to calculate the control signals for lights in wireless sensor/actuator networks. This algorithm uses an appropriate step size in the iterative process used for quickly computing the control signals. We demonstrate the accuracy and efficiency of this approach compared with the penalty method by using Mote-based mesh sensor networks. The estimation error of the new approach is one-eighth as large as that of the penalty method with one-fifth of its computation time. In addition, we describe our sensor/actuator node for distributed lighting control based on the decentralized algorithm and demonstrate its practical efficacy

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Virtual visual sensors and their application in structural health monitoring

Wireless sensor networks are being increasingly accepted as an effective tool for structural health monitoring. The ability to deploy a wireless array of sensors efficiently and effectively is a key factor in structural health monitoring. Sensor installation and management can be difficult in practice for a variety of reasons: a hostile environment, high labour costs and bandwidth limitations. We present and evaluate a proof-of-concept application of virtual visual sensors to the well-known engineering problem of the cantilever beam, as a convenient physical sensor substitute for certain problems and environments. We demonstrate the effectiveness of virtual visual sensors as a means to achieve non-destructive evaluation. Major benefits of virtual visual sensors are its non-invasive nature, ease of installation and cost-effectiveness. The novelty of virtual visual sensors lies in the combination of marker extraction with visual tracking realised by modern computer vision algorithms. We demonstrate that by deploying a collection of virtual visual sensors on an oscillating structure, its modal shapes and frequencies can be readily extracted from a sequence of video images. Subsequently, we perform damage detection and localisation by means of a wavelet-based analysis. The contributions of this article are as follows: (1) use of a sub-pixel accuracy marker extraction algorithm to construct virtual sensors in the spatial domain, (2) embedding dynamic marker linking within a tracking-by-correspondence paradigm that offers benefits in computational efficiency and registration accuracy over traditional tracking-by-searching systems and (3) validation of virtual visual sensors in the context of a structural health monitoring application

Resource management for target tracking in wireless sensor networks

Master'sMASTER OF ENGINEERIN