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

Techniques for Wireless Channel Modeling in Harsh Environments

With the rapid growth in the networked environments for different industrial, scientific and defense applications, there is a vital need to assure the user or application a certain level of Quality of Service (QoS). Environments like the industrial environment are particularly harsh with interference from metal structures (as found in the manufacturing sector), interference generated during wireless propagation, and multipath fading of the radio frequency (RF) signal all invite novel mitigation techniques. The challenge of achieving the benefits like improved energy efficiency using wireless is closely coupled with maintaining network QoS requirements. Assessment and management of QoS needs to occur, allowing the network to adapt to changes in the RF, information, and operational environments. The capacity to adapt is paramount to maintaining the required operational performance (throughput, latency, reliability and security). This thesis address the need for accurate radio channel modeling techniques to improve the performance of the wireless communication systems. Multiple different channel modeling techniques are considered including statistical models, ray tracing techniques, finite time-difference technique, transmission line matrix method (TLM), and stochastic differential equation-based (SDE) dynamic channel models. Measurement of ambient RF is performed at several harsh industrial environments to demonstrate the existence of uncertainty in channel behavior. Comparison of various techniques is performed with metrics including accuracy, applicability, and computational efficiency. SDE- and TLM-based methods are validated using indoor and outdoor measurements. Fast, accurate techniques for modeling multipath fading in harsh environments is explored. Application of dynamic channel models is explored for improving QoS of wireless communication system. The TLM-based models provide accurate site-specific path loss calculations taking into consideration materials and propagation characteristics of propagating environment. The validation studies confirm the technique is comparable with existing channel models. The TLM-based channel models is extended to compute the site-specific multipath characteristics of the radio channel eliminating the need for experimental measurement. The TLM-based simulator is also integrated with packet-level network simulator to perform end to end-to-end site specific calculation of wireless network performance. The SDE-channel models provide accurate online estimations of the channel performance along with accurate one-step prediction of the signal strength. The validation studies confirm the accuracy of the technique. Application of the SDE-based models for adaptive antenna control is formulated using online recursive estimation

On the Reversibility of Newton-Raphson Root-Finding Method

Reversibility of a computational method is the ability to execute the method forward as well as backward. Reversible computational methods are generally useful in undoing incorrect computation in a speculative execution setting designed for efficient parallel processing. Here, reversibility is explored of a common component in scientific codes, namely, the Newton-Raphson root-finding method. A reverse method is proposed that is aimed at retracing the sequence of points that are visited by the forward method during forward iterations. When given the root, along with the number of iterations, of the forward method, this reverse method is aimed at backtracking along the reverse sequence of points to finally recover the original starting point of the forward method. The operation of this reverse method is illustrated on a few example functions, serving to highlight the method's strengths and shortcomings

Cybersecurity through Real-Time Distributed Control Systems

Critical infrastructure sites and facilities are becoming increasingly dependent on interconnected physical and cyber-based real-time distributed control systems (RTDCSs). A mounting cybersecurity threat results from the nature of these ubiquitous and sometimes unrestrained communications interconnections. Much work is under way in numerous organizations to characterize the cyber threat, determine means to minimize risk, and develop mitigation strategies to address potential consequences. While it seems natural that a simple application of cyber-protection methods derived from corporate business information technology (IT) domain would lead to an acceptable solution, the reality is that the characteristics of RTDCSs make many of those methods inadequate and unsatisfactory or even harmful. A solution lies in developing a defense-in-depth approach that ranges from protection at communications interconnect levels ultimately to the control system s functional characteristics that are designed to maintain control in the face of malicious intrusion. This paper summarizes the nature of RTDCSs from a cybersecurity perspec tive and discusses issues, vulnerabilities, candidate mitigation approaches, and metrics

The Development of Localized Algorithms in Wireless Sensor Networks

Advances in sensor technology and wireless communications have made networked microsensors possible, where each sensor individually senses the environment but collaboratively achieves complex information gathering and dissemination tasks. These networked sensors, however, possess several characteristics that have challenged many aspects of traditional computer network design, such as the scalability issue caused by the sheer amount of sensor nodes, the infrastructureless network, and the stringent resource onboard the sensors. These new features call for a re-design of overall structure of applications and services. It has been widely accepted that practical localized algorithms is probably the best solution to wireless sensor networks. In this article, we discuss recent research results on localized algorithms design in supporting services and applications in sensor networks

Distributed computing paradigms for collaborative signal and information processing in sensor networks

Abstract — In sensor networks, collaborative processing between multiple sensor nodes is essential in order to complement for each other’s sensing capability, tolerate faults, and provide reliable information. The client/server-based paradigm is typical for distributed processing. However, it is not the most efficient in the context of sensor networks. In this paper, we present a mobileagent-based paradigm to carry out collaborative processing, 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. This approach has great potential in providing energy-efficient and scalable collaborative processing with low latency. We design two metrics (execution time and energy consumption) and use simulation tools to quantitatively measure the performance of different computing models in collaborative processing. 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 develop a cluster-based hybrid computing paradigm to combine the advantages of both paradigms. We analyze two different scenarios in hybrid computing and simulation results show that there is always one scenario that performs better than either the client/server- or mobile-agent-based paradigm

The Mobile Agent Framework for Collaborative Processing in Sensor Networks ∗

This chapter discusses the distributed computing paradigms used to support the col-laborative processing in sensor networks. Sensor networks form a typical distributed envi-ronment and the most popular computing paradigm deployed has been the client/server-based, where all the clients send the raw data to a processing center for data dissemi-nation, as illustrated in Fig. 1 (a). In some applications where the size of raw data is very large, the clients can perform some local processing and send a compressed version of the raw data or simply the local processing results to the processing center, as illus-trated in Fig. 1 (b). This scheme is widely used in distributed detection [33]. Sometimes, the client/server-based processing can be carried out hierarchically with multiple levels of processing centers as illustrated in Fig. 1 (c) to solve scalability problems [1]. Although popular, the client/server-based distributed computing is not suitable for applications developed in sensor networks as the sensor network possesses some unique characteristics that the client/server-based approach cannot accommodate. Here, we sum-marize these features as follows

Early Detection of Breast Cancer using Thermal Texture Maps

This paper focuses on the discussion of using thermal infrared imaging (TIR) in early detection of breast cancer. We use the term thermal texture maps to represent the images captured from TIR imaging. Even though the heat emanating onto the surface from the cancerous tissue can be successfully modeled using the Pennes bio-heat equation, the complexity of the boundary conditions associated with the biological body makes it impractical to solve the inverse problem. This paper presents a new method for analyzing a thermal system based on an analogy to electrical circuit theory; referred to as thermal-electric analog. We demonstrate how the analog can be used to estimate the depth of the heat source, and furthermore, help understand the metabolic activities undergoing within the human body. The method has been used in early breast cancer detection and has achieved high sensitivity. Several breast cancer study cases are given to show the e#ectiveness of the method. On-going clinical study results are provided as well

RF and Microwave Systems Group

One of the popular methods for breast cancer detection is to make comparisons between contralateral images. When the images are relatively symmetrical, small asymmetries may indicate a suspicious region. In thermal infrared (IR) imaging, asymmetry analysis normally needs human intervention because of the difficulties in automatic segmentation. In order to provide a more objective diagnostic result, we describe an automatic approach to asymmetry analysis in thermograms. It includes automatic segmentation and supervised pattern classi-fication. Experiments have been conducted based on images provided by Elliott Mastology 1 Center (Inframetrics 600M camera) and Bioyear, Inc. (Microbolometer uncooled camera)