VTechWorks Repository :: Browsing by Author "Chen, Ing-Ray"

Browsing by Author "Chen, Ing-Ray"

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Abnormal Pattern Recognition in Spatial Data
Kou, Yufeng (Virginia Tech, 2006-11-29)
In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic congestion points, pinpoint military targets in satellite images, determine possible locations of oil reservoirs, and detect water pollution incidents. Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on "global comparison" and identifies deviations from the remainder of the entire data set. In contrast, spatial outlier detection concentrates on discovering neighborhood instabilities that break the spatial continuity. In recent years, a number of techniques have been proposed for spatial outlier detection. However, they have the following limitations. First, most of them focus primarily on single-attribute outlier detection. Second, they may not accurately locate outliers when multiple outliers exist in a cluster and correlate with each other. Third, the existing algorithms tend to abstract spatial objects as isolated points and do not consider their geometrical and topological properties, which may lead to inexact results. This dissertation reports a study of the problem of abnormal spatial pattern recognition, and proposes a suite of novel algorithms. Contributions include: (1) formal definitions of various spatial outliers, including single-attribute outliers, multi-attribute outliers, and region outliers; (2) a set of algorithms for the accurate detection of single-attribute spatial outliers; (3) a systematic approach to identifying and tracking region outliers in continuous meteorological data sequences; (4) a novel Mahalanobis-distance-based algorithm to detect outliers with multiple attributes; (5) a set of graph-based algorithms to identify point outliers and region outliers; and (6) extensive analysis of experiments on several spatial data sets (e.g., West Nile virus data and NOAA meteorological data) to evaluate the effectiveness and efficiency of the proposed algorithms.
Adaptation in Reputation Management Systems for Ad hoc Networks
Refaei, Mohamed Tamer (Virginia Tech, 2007-02-02)
An ad hoc network adopts a decentralized unstructured networking model that depends on node cooperation for key network functionalities such as routing and medium access. The significance of node cooperation in ad hoc networks makes network survival particularly sensitive to insider node behavior. The presence of selfish or malicious nodes in an ad hoc network could greatly degrade the network performance and might even result in a total communication breakdown. Consequently, it is important for both security and performance reasons to discourage, expose, and react to such damaging misbehavior. Reputation management systems have been proposed to mitigate against such misbehavior in ad hoc networks. The functions of a reputation management system are to evaluate nodes' quality of behavior based on their cooperation (evaluation), distinguish between well-behaved and misbehaving nodes (detection), and appropriately react to misbehaving nodes (reaction). A significant number of reputation management systems have been proposed for ad hoc networks to date. However, there has been no attempt to consolidate all current research into a formal framework for reputation management systems. The lack of a formal framework is a potential weakness of the research field. For example, a formal comparison of proposed reputation management systems has remained difficult, mainly due to the lack of a formal framework upon which the comparison could be based. There is also a lack of formal metrics that could be used for quantitative evaluation and comparison of reputation management systems. Another major shortcoming in this research field is the assumption that the functions of reputation management (evaluation, detection, and reaction) are carried out homogeneously across time and space at different nodes. The dynamic nature of ad hoc networks causes node behavior to vary spatially and temporally due to changes in the local and network-wide conditions. Reputation management functions do not adapt to such changes, which may impact the system accuracy and promptness. We herein recognize an adaptive reputation management system as one where nodes carry out the reputation management functions heterogeneously across time and space according to the instantaneous perception of each of its surrounding network conditions. In this work, we address the above concerns. We develop a formal framework for reputation management systems upon which design, evaluation, and comparison of reputation management systems can be based. We define and discuss the different components of the framework and the interactions among them. We also define formal metrics for evaluation of reputation management systems. The metrics assess both, the effectiveness (security issues) of a reputation management system in detecting misbehavior and limiting its negative impact on the network, and its efficiency (performance issues) in terms of false positives and overhead exerted by the reputation management system on the network. We also develop ARMS, an autonomous reputation management system, based on the formal framework. The theoretical foundation of ARMS is based on the theory of Sequential Probability Ratio Test introduced by Wald. In ARMS, nodes independently and without cooperation manage their reputation management system functions. We then use ARMS to investigate adaptation in reputation management systems. We discuss some of the characteristics of an adaptive reputation management system such as sensitivity, adaptability, accuracy, and promptness. We consider how the choice of evaluation metric, typically employed by the evaluation function for assessment of node behavior, may impact the sensitivity and accuracy of node behavior evaluation. We evaluate the sensitivity and accuracy of node behavior evaluation using a number of metrics from the network and medium access layer. We then introduce a time-slotted approach to enhance the sensitivity of the evaluation function and show how the duration of an evaluation slot can adapt according to the network activity to enhance the system accuracy and promptness. We also show how the detection function can adapt to the network conditions by using the node's own behavior as a benchmark to set its detection parameters. To the best of our knowledge, this is the first work to explore the adaptation of the reputation management functions in ad hoc networks.
Adaptive Asymmetric Slot Allocation for Heterogeneous Traffic in WCDMA/TDD Systems
Park, JinSoo (Virginia Tech, 2004-07-28)
Even if 3rd and 4th generation wireless systems aim to achieve multimedia services at high speed, it is rather difficult to have full-fledged multimedia services due to insufficient capacity of the systems. There are many technical challenges placed on us in order to realize the real multimedia services. One of those challenges is how efficiently to allocate resources to traffic as the wireless systems evolve. The review of the literature shows that strategic manipulation of traffic can lead to an efficient use of resources in both wire-line and wireless networks. This aspect brings our attention to the role of link layer protocols, which is to orchestrate the transmission of packets in an efficient way using given resources. Therefore, the Media Access Control (MAC) layer plays a very important role in this context. In this research, we investigate technical challenges involving resource control and management in the design of MAC protocols based on the characteristics of traffic, and provide some strategies to solve those challenges. The first and foremost matter in wireless MAC protocol research is to choose the type of multiple access schemes. Each scheme has advantages and disadvantages. We choose Wireless Code Division Multiple Access/Time Division Duplexing (WCDMA/TDD) systems since they are known to be efficient for bursty traffic. Most existing MAC protocols developed for WCDMA/TDD systems are interested in the performance of a unidirectional link, in particular in the uplink, assuming that the number of slots for each link is fixed a priori. That ignores the dynamic aspect of TDD systems. We believe that adaptive dynamic slot allocation can bring further benefits in terms of efficient resource management. Meanwhile, this adaptive slot allocation issue has been dealt with from a completely different angle. Related research works are focused on the adaptive slot allocation to minimize inter-cell interference under multi-cell environments. We believe that these two issues need to be handled together in order to enhance the performance of MAC protocols, and thus embark upon a study on the adaptive dynamic slot allocation for the MAC protocol. This research starts from the examination of key factors that affect the adaptive allocation strategy. Through the review of the literature, we conclude that traffic characterization can be an essential component for this research to achieve efficient resource control and management. So we identify appropriate traffic characteristics and metrics. The volume and burstiness of traffic are chosen as the characteristics for our adaptive dynamic slot allocation. Based on this examination, we propose four major adaptive dynamic slot allocation strategies: (i) a strategy based on the estimation of burstiness of traffic, (ii) a strategy based on the estimation of volume and burstiness of traffic, (iii) a strategy based on the parameter estimation of a distribution of traffic, and (iv) a strategy based on the exploitation of physical layer information. The first method estimates the burstiness in both links and assigns the number of slots for each link according to a ratio of these two estimates. The second method estimates the burstiness and volume of traffic in both links and assigns the number of slots for each link according to a ratio of weighted volumes in each link, where the weights are driven by the estimated burstiness in each link. For the estimation of burstiness, we propose a new burstiness measure that is based on a ratio between peak and median volume of traffic. This burstiness measure requires the determination of an observation window, with which the median and the peak are measured. We propose a dynamic method for the selection of the observation window, making use of statistical characteristics of traffic: Autocorrelation Function (ACF) and Partial ACF (PACF). For the third method, we develop several estimators to estimate the parameters of a traffic distribution and suggest two new slot allocation methods based on the estimated parameters. The last method exploits physical layer information as another way of allocating slot to enhance the performance of the system. The performance of our proposed strategies is evaluated in various scenarios. Major simulations are categorized as: simulation on data traffic, simulation on combined voice and data traffic, simulation on real trace data. The performance of each strategy is evaluated in terms of throughput and packet drop ratio. In addition, we consider the frequency of slot changes to assess the performance in terms of control overhead. We expect that this research work will add to the state of the knowledge in the field of link-layer protocol research for WCDMA/TDD systems.
Algorithmic Distribution of Applied Learning on Big Data
Shukla, Manu (Virginia Tech, 2020-10-16)
Machine Learning and Graph techniques are complex and challenging to distribute. Generally, they are distributed by modeling the problem in a similar way as single node sequential techniques except applied on smaller chunks of data and compute and the results combined. These techniques focus on stitching the results from smaller chunks as the best possible way to have the outcome as close to the sequential results on entire data as possible. This approach is not feasible in numerous kernel, matrix, optimization, graph, and other techniques where the algorithm needs access to all the data during execution. In this work, we propose key-value pair based distribution techniques that are widely applicable to statistical machine learning techniques along with matrix, graph, and time series based algorithms. The crucial difference with previously proposed techniques is that all operations are modeled on key-value pair based fine or coarse-grained steps. This allows flexibility in distribution with no compounding error in each step. The distribution is applicable not only in robust disk-based frameworks but also in in-memory based systems without significant changes. Key-value pair based techniques also provide the ability to generate the same result as sequential techniques with no edge or overlap effects in structures such as graphs or matrices to resolve. This thesis focuses on key-value pair based distribution of applied machine learning techniques on a variety of problems. For the first method key-value pair distribution is used for storytelling at scale. Storytelling connects entities (people, organizations) using their observed relationships to establish meaningful storylines. When performed sequentially these computations become a bottleneck because the massive number of entities make space and time complexity untenable. We present DISCRN, or DIstributed Spatio-temporal ConceptseaRch based StorytelliNg, a distributed framework for performing spatio-temporal storytelling. The framework extracts entities from microblogs and event data, and links these entities using a novel ConceptSearch to derive storylines in a distributed fashion utilizing key-value pair paradigm. Performing these operations at scale allows deeper and broader analysis of storylines. The novel parallelization techniques speed up the generation and filtering of storylines on massive datasets. Experiments with microblog posts such as Twitter data and GDELT(Global Database of Events, Language and Tone) events show the efficiency of the techniques in DISCRN. The second work determines brand perception directly from people's comments in social media. Current techniques for determining brand perception, such as surveys of handpicked users by mail, in person, phone or online, are time consuming and increasingly inadequate. The proposed DERIV system distills storylines from open data representing direct consumer voice into a brand perception. The framework summarizes the perception of a brand in comparison to peer brands with in-memory key-value pair based distributed algorithms utilizing supervised machine learning techniques. Experiments performed with open data and models built with storylines of known peer brands show the technique as highly scalable and accurate in capturing brand perception from vast amounts of social data compared to sentiment analysis. The third work performs event categorization and prospect identification in social media. The problem is challenging due to endless amount of information generated daily. In our work, we present DISTL, an event processing and prospect identifying platform. It accepts as input a set of storylines (a sequence of entities and their relationships) and processes them as follows: (1) uses different algorithms (LDA, SVM, information gain, rule sets) to identify themes from storylines; (2) identifies top locations and times in storylines and combines with themes to generate events that are meaningful in a specific scenario for categorizing storylines; and (3) extracts top prospects as people and organizations from data elements contained in storylines. The output comprises sets of events in different categories and storylines under them along with top prospects identified. DISTL utilizes in-memory key-value pair based distributed processing that scales to high data volumes and categorizes generated storylines in near real-time. The fourth work builds flight paths of drones in a distributed manner to survey a large area taking images to determine growth of vegetation over power lines allowing for adjustment to terrain and number of drones and their capabilities. Drones are increasingly being used to perform risky and labor intensive aerial tasks cheaply and safely. To ensure operating costs are low and flights autonomous, their flight plans must be pre-built. In existing techniques drone flight paths are not automatically pre-calculated based on drone capabilities and terrain information. We present details of an automated flight plan builder DIMPL that pre-builds flight plans for drones tasked with surveying a large area to take photographs of electric poles to identify ones with hazardous vegetation overgrowth. DIMPL employs a distributed in-memory key-value pair based paradigm to process subregions in parallel and build flight paths in a highly efficient manner. The fifth work highlights scaling graph operations, particularly pruning and joins. Linking topics to specific experts in technical documents and finding connections between experts are crucial for detecting the evolution of emerging topics and the relationships between their influencers in state-of-the-art research. Current techniques that make such connections are limited to similarity measures. Methods based on weights such as TF-IDF and frequency to identify important topics and self joins between topics and experts are generally utilized to identify connections between experts. However, such approaches are inadequate for identifying emerging keywords and experts since the most useful terms in technical documents tend to be infrequent and concentrated in just a few documents. This makes connecting experts through joins on large dense graphs challenging. We present DIGDUG, a framework that identifies emerging topics by applying graph operations to technical terms. The framework identifies connections between authors of patents and journal papers by performing joins on connected topics and topics associated with the authors at scale. The problem of scaling the graph operations for topics and experts is solved through dense graph pruning and graph joins categorized under their own scalable separable dense graph class based on key-value pair distribution. Comparing our graph join and pruning technique against multiple graph and join methods in MapReduce revealed a significant improvement in performance using our approach.
Attack and Defense with Hardware-Aided Security
Zhang, Ning (Virginia Tech, 2016-08-26)
Riding on recent advances in computing and networking, our society is now experiencing the evolution into the age of information. While the development of these technologies brings great value to our daily life, the lucrative reward from cyber-crimes has also attracted criminals. As computing continues to play an increasing role in the society, security has become a pressing issue. Failures in computing systems could result in loss of infrastructure or human life, as demonstrated in both academic research and production environment. With the continuing widespread of malicious software and new vulnerabilities revealing every day, protecting the heterogeneous computing systems across the Internet has become a daunting task. Our approach to this challenge consists of two directions. The first direction aims to gain a better understanding of the inner working of both attacks and defenses in the cyber environment. Meanwhile, our other direction is designing secure systems in adversarial environment.
Automated Seed Point Selection in Confocal Image Stacks of Neuron Cells
Bilodeau, Gregory Peter (Virginia Tech, 2013-07-25)
This paper provides a fully automated method of finding high-quality seed points in 3D space from a stack of images of neuron cells. These seed points may then be used as initial starting points for automated local tracing algorithms, removing a time consuming required user interaction in current methodologies. Methods to collapse the search space and provide rudimentary topology estimates are also presented.
Biologically-inspired Network Memory System for Smarter Networking
Mokhtar, Bassem Mahmoud Mohamed Ali (Virginia Tech, 2014-02-24)
Current and emerging large-scale networks, for example the current Internet and the future Internet of Things, target supporting billions of networked entities to provide a wide variety of services and resources. Such complexity results in network-data from different sources with special characteristics, such as widely diverse users and services, multiple media (e.g., text, audio, video, etc.), high-dimensionality (i.e., large sets of attributes) and various dynamic concerns (e.g., time-sensitive data). With huge amounts of network data with such characteristics, there are significant challenges to a) recognize emergent and anomalous behavior in network traffic and b) make intelligent decisions for efficient and effective network operations. Fortunately, numerous analyses of Internet traffic have demonstrated that network traffic data exhibit multi-dimensional patterns that can be learned in order to enable discovery of data semantics. We claim that extracting and managing network semantics from traffic patterns and building conceptual models to be accessed on-demand would help in mitigating the aforementioned challenges. The current Internet, contemporary networking architectures and current tools for managing large network-data largely lack capabilities to 1) represent, manage and utilize the wealth of multi-dimensional traffic data patterns; 2) extract network semantics to support Internet intelligence through efficiently building conceptual models of Internet entities at different levels of granularity; and 3) predict future events (e.g., attacks) and behaviors (e.g., QoS of unfamiliar services) based on learned semantics. We depict the limited utilization of traffic semantics in networking operations as the “Internet Semantics Gap (ISG)”. We hypothesize that endowing the Internet and next generation networks with a “memory” system that provides data and semantics management would help resolve the ISG and enable “Internet Intelligence”. We seek to enable networked entities, at runtime and on-demand, to systematically: 1) learn and retrieve network semantics at different levels of granularity related to various Internet elements (e.g., services, protocols, resources, etc.); and 2) utilize extracted semantics to improve network operations and services in various aspects ranging from performance, to quality of service, to security and resilience. In this dissertation, we propose a distributed network memory management system, termed NetMem, for Internet intelligence. NetMem design is inspired by the functionalities of human memory to efficiently store Internet data and extract and utilize traffic data semantics in matching and prediction processes, and building dynamic network-concept ontology (DNCO) at different levels of granularity. The DNCO provides dynamic behavior models for various Internet elements. Analogous to human memory functionalities, NetMem has a memory system structure comprising short-term memory (StM) and long-term memory (LtM). StM maintains highly dynamic network data or data semantics with lower levels of abstraction for short time, while LtM keeps for long time slower varying semantics with higher levels of abstraction. Maintained data in NetMem can be accessed and learned at runtime and on-demand. From a system’s perspective, NetMem can be viewed as an overlay network of distributed “memory” agents, called NMemAgents, located at multiple levels targeting different levels of data abstraction and scalable operation. Our main contributions are as follows: • Biologically-inspired customizable application-agnostic distributed network memory management system with efficient processes for extracting and classifying high-level features and reasoning about rich semantics in order to resolve the ISG and target Internet intelligence. • Systematic methodology using monolithic and hybrid intelligence techniques for efficiently managing data semantics and building runtime-accessible dynamic ontology of correlated concept classes related to various Internet elements and at different levels of abstraction and granularity that would facilitate: ▪ Predicting future events and learning about new services; ▪ Recognizing and detecting of normal/abnormal and dynamic/emergent behavior of various Internet elements; ▪ Satisfying QoS requirements with better utilization of resources. We have evaluated the NetMem’s efficiency and effectiveness employing different semantics reasoning algorithms. We have evaluated NetMem operations over real Internet traffic data with and without using data dimensionality reduction techniques. We have demonstrated the scalability and efficiency of NetMem as a distributed multi-agent system using an analytical model. The effectiveness of NetMem has been evaluated through simulation using real offline data sets and also via the implementation of a small practical test-bed. Our results show the success of NetMem in learning and using data semantics for anomaly detection and enhancement of QoS satisfaction of running services.
BioSENSE: Biologically-inspired Secure Elastic Networked Sensor Environment
Hassan Eltarras, Rami M. (Virginia Tech, 2011-01-25)
The essence of smart pervasive Cyber-Physical Environments (CPEs) is to enhance the dependability, security and efficiency of their encompassing systems and infrastructures and their services. In CPEs, interactive information resources are integrated and coordinated with physical resources to better serve human users. To bridge the interaction gap between users and the physical environment, a CPE is instrumented with a large number of small devices, called sensors, that are capable of sensing, computing and communicating. Sensors with heterogeneous capabilities should autonomously organize on-demand and interact to furnish real-time, high fidelity information serving a wide variety of user applications with dynamic and evolving requirements. CPEs with their associated networked sensors promise aware services for smart systems and infrastructures with the potential to improve the quality of numerous application domains, in particular mission-critical infrastructure domains. Examples include healthcare, environment protection, transportation, energy, homeland security, and national defense. To build smart CPEs, Networked Sensor Environments (NSEs) are needed to manage demand-driven sharing of large-scale federated heterogeneous resources among multiple applications and users. We informally define NSE as a tailorable, application agnostic, distributed platform with the purpose of managing a massive number of federated resources with heterogeneous computing, communication, and monitoring capabilities. We perceive the need to develop scalable, trustworthy, cost-effective NSEs. A NSE should be endowed with dynamic and adaptable computing and communication services capable of efficiently running diverse applications with evolving QoS requirements on top of federated distributed resources. NSEs should also enable the development of applications independent of the underlying system and device concerns. To our knowledge, a NSE with the aforementioned capabilities does not currently exist. The large scale of NSEs, the heterogeneous node capabilities, the highly dynamic topology, and the likelihood of being deployed in inhospitable environments pose formidable challenges for the construction of resilient shared NSE platforms. Additionally, nodes in NSE are often resource challenged and therefore trustworthy node cooperation is required to provide useful services. Furthermore, the failure of NSE nodes due to malicious or non-malicious conditions represents a major threat to the trustworthiness of NSEs. Applications should be able to survive failure of nodes and change their runtime structure while preserving their operational integrity. It is also worth noting that the decoupling of application programming concerns from system and device concerns has not received the appropriate attention in most existing wireless sensor network platforms. In this dissertation, we present a Biologically-inspired Secure Elastic Networked Sensor Environment (BioSENSE) that synergistically integrates: (1) a novel bio-inspired construction of adaptable system building components, (2) associative routing framework with extensible adaptable criteria-based addressing of resources, and (3) management of multi-dimensional software diversity and trust-based variant hot shuffling. The outcome is that an application using BioSENSE is able to allocate, at runtime, a dynamic taskforce, running over a federated resource pool that would satisfy its evolving mission requirements. BioSENSE perceives both applications and the NSE itself to be elastic, and allows them to grow or shrink based upon needs and conditions. BioSENSE adopts Cell-Oriented-Architecture (COA), a novel architecture that supports the development, deployment, execution, maintenance, and evolution of NSE software. COA employs mission-oriented application design and inline code distribution to enable adaptability, dynamic re-tasking, and re-programmability. The cell, the basic building block in COA, is the abstraction of a mission-oriented autonomously active resource. Generic cells are spontaneously created by the middleware, then participate in emerging tasks through a process called specialization. Once specialized, cells exhibit application specific behavior. Specialized cells have mission objectives that are being continuously sought, and sensors that are used to monitor performance parameters, mission objectives, and other phenomena of interest. Due to the inherent anonymous nature of sensor nodes, associative routing enables dynamic semantically-rich descriptive identification of NSE resources. As such, associative routing presents a clear departure from most current network addressing schemes. Associative routing combines resource discovery and path discovery into a single coherent role, leading to significant reduction in traffic load and communication latency without any loss of generality. We also propose Adaptive Multi-Criteria Routing (AMCR) protocol as a realization of associative routing for NSEs. AMCR exploits application-specific message semantics, represented as generic criteria, and adapts its operation according to observed traffic patterns. BioSENSE intrinsically exploits software diversity, runtime implementation shuffling, and fault recovery to achieve security and resilience required for mission-critical NSEs. BioSENSE makes NSE software a resilient moving target that : 1) confuses the attacker by non-determinism through shuffling of software component implementations; 2) improves the availability of NSE by providing means to gracefully recover from implementation flaws at runtime; and 3) enhances the software system by survival of the fittest through trust-based component selection in an online software component marketplace. In summary, BioSENSE touts the following advantages: (1) on-demand, online distribution and adaptive allocation of services and physical resources shared among multiple long-lived applications with dynamic missions and quality of service requirements, (2) structural, functional, and performance adaptation to dynamic network scales, contexts and topologies, (3) moving target defense of system software, and (4) autonomic failure recovery.
BRIoT: Behavior Rune Specification-Based Misbehavior Detection for IoT-Embedded Cyber-Physical Systems
Sharma, Vishal; You, Ilsun; Vim, Kangbin; Chen, Ing-Ray; Cho, Jin-Hee (IEEE, 2019)
The identification of vulnerabilities in a mission-critical system is one of the challenges faced by a cyber-physical system (CPS). The incorporation of embedded Internet of Things (IoT) devices makes it tedious to identify vulnerability and difficult to control the service-interruptions and manage the operations losses. Rule-based mechanisms have been considered as a solution in the past. However, rule-based solutions operate on the goodwill of the generated rules and perform assumption-based detection. Such a solution often is far from the actual realization of the IoT runtime performance and can be fooled by zero-day attacks. Thus, this paper takes this issue as motivation and proposes better lightweight behavior rule specification-based misbehavior detection for the IoT-embedded cyber-physical systems (BRIoT). The key concept of our approach is to model a system with which misbehavior of an IoT device manifested as a result of attacks exploiting the vulnerability exposed may be detected through automatic model checking and formal verification, regardless of whether the attack is known or unknown. Automatic model checking and formal verification are achieved through a 2-layer Fuzzy-based hierarchical context-aware aspect-oriented Petri net (HCAPN) model, while effective misbehavior detection to avoid false alarms is achieved through a Barycentric-coordinated-based center of mass calculation method. The proposed approach is verified by an unmanned aerial vehicle (UAV) embedded in a UAV system. The feasibility of the proposed model is demonstrated with high reliability, low operational cost, low false-positives, low false-negatives, and high true positives in comparison with existing rule-based solutions.
A Class of Call Admission Control Algorithms for Resource Management and Reward Optimization for Servicing Multiple QoS Classes in Wireless Networks and Its Applications
Yilmaz, Okan (Virginia Tech, 2008-11-17)
We develop and analyze a class of CAC algorithms for resource management in wireless networks with the goal not only to satisfy QoS constraints, but also to maximize a value or reward objective function specified by the system. We demonstrate through analytical modeling and simulation validation that the CAC algorithms developed in this research for resource management can greatly improve the system reward obtainable with QoS guarantees, when compared with existing CAC algorithms designed for QoS satisfaction only. We design hybrid partitioning-threshold, spillover and elastic CAC algorithms based on the design techniques of partitioning, setting thresholds and probabilistic call acceptance to use channel resources for servicing distinct QoS classes. For each CAC algorithm developed, we identify optimal resource management policies in terms of partitioning or threshold settings to use channel resources. By comparing these CAC algorithms head-to-head under identical conditions, we determine the best algorithm to be used at runtime to maximize system reward with QoS guarantees for servicing multiple service classes in wireless networks. We study solution correctness, solution optimality and solution efficiency of the class of CAC algorithms developed. We ensure solution optimality by comparing optimal solutions achieved with those obtained by ideal CAC algorithms via exhaustive search. We study solution efficiency properties by performing complexity analyses and ensure solution correctness by simulation validation based on real human mobility data. Further, we analyze the tradeoff between solution optimality vs. solution efficiency and suggest the best CAC algorithm used to best tradeoff solution optimality for solution efficiency, or vice versa, to satisfy the system's solution requirements. Moreover, we develop design principles that remain applicable despite rapidly evolving wireless network technologies since they can be generalized to deal with management of 'resources' (e.g., wireless channel bandwidth), 'cells' (e.g., cellular networks), "connections" (e.g., service calls with QoS constraints), and "reward optimization" (e.g., revenue optimization in optimal pricing determination) for future wireless service networks. To apply the CAC algorithms developed, we propose an application framework consisting of three stages: workload characterization, call admission control, and application deployment. We demonstrate the applicability with the optimal pricing determination application and the intelligent switch routing application.
Collaborative Computing Cloud: Architecture and Management Platform
Khalifa, Ahmed Abdelmonem Abuelfotooh Ali (Virginia Tech, 2015-03-11)
We are witnessing exponential growth in the number of powerful, multiply-connected, energy-rich stationary and mobile nodes, which will make available a massive pool of computing and communication resources. We claim that cloud computing can provide resilient on-demand computing, and more effective and efficient utilization of potentially infinite array of resources. Current cloud computing systems are primarily built using stationary resources. Recently, principles of cloud computing have been extended to the mobile computing domain aiming to form local clouds using mobile devices sharing their computing resources to run cloud-based services. However, current cloud computing systems by and large fail to provide true on-demand computing due to their lack of the following capabilities: 1) providing resilience and autonomous adaptation to the real-time variation of the underlying dynamic and scattered resources as they join or leave the formed cloud; 2) decoupling cloud management from resource management, and hiding the heterogeneous resource capabilities of participant nodes; and 3) ensuring reputable resource providers and preserving the privacy and security constraints of these providers while allowing multiple users to share their resources. Consequently, systems and consumers are hindered from effectively and efficiently utilizing the virtually infinite pool of computing resources. We propose a platform for mobile cloud computing that integrates: 1) a dynamic real-time resource scheduling, tracking, and forecasting mechanism; 2) an autonomous resource management system; and 3) a cloud management capability for cloud services that hides the heterogeneity, dynamicity, and geographical diversity concerns from the cloud operation. We hypothesize that this would enable 'Collaborative Computing Cloud (C3)' for on-demand computing, which is a dynamically formed cloud of stationary and/or mobile resources to provide ubiquitous computing on-demand. The C3 would support a new resource-infinite computing paradigm to expand problem solving beyond the confines of walled-in resources and services by utilizing the massive pool of computing resources, in both stationary and mobile nodes. In this dissertation, we present a C3 management platform, named PlanetCloud, for enabling both a new resource-infinite computing paradigm using cloud computing over stationary and mobile nodes, and a true ubiquitous on-demand cloud computing. This has the potential to liberate cloud users from being concerned about resource constraints and provides access to cloud anytime and anywhere. PlanetCloud synergistically manages 1) resources to include resource harvesting, forecasting and selection, and 2) cloud services concerned with resilient cloud services to include resource provider collaboration, application execution isolation from resource layer concerns, seamless load migration, fault-tolerance, the task deployment, migration, revocation, etc. Specifically, our main contributions in the context of PlanetCloud are as follows. 1. PlanetCloud Resource Management • Global Resource Positioning System (GRPS): • Global mobile and stationary resource discovery and monitoring. A novel distributed spatiotemporal resource calendaring mechanism with real-time synchronization is proposed to mitigate the effect of failures occurring due to unstable connectivity and availability in the dynamic mobile environment, as well as the poor utilization of resources. This mechanism provides a dynamic real-time scheduling and tracking of idle mobile and stationary resources. This would enhance resource discovery and status tracking to provide access to the right-sized cloud resources anytime and anywhere. • Collaborative Autonomic Resource Management System (CARMS): Efficient use of idle mobile resources. Our platform allows sharing of resources, among stationary and mobile devices, which enables cloud computing systems to offer much higher utilization, resulting in higher efficiency. CARMS provides system-managed cloud services such as configuration, adaptation and resilience through collaborative autonomic management of dynamic cloud resources and membership. This helps in eliminating the limited self and situation awareness and collaboration of the idle mobile resources. 2. PlanetCloud Cloud Management Architecture for resilient cloud operation on dynamic mobile resources to provide stable cloud in a continuously changing operational environment. This is achieved by using trustworthy fine-grained virtualization and task management layer, which isolates the running application from the underlying physical resource enabling seamless execution over heterogeneous stationary and mobile resources. This prevents the service disruption due to variable resource availability. The virtualization and task management layer comprises a set of distributed powerful nodes that collaborate autonomously with resource providers to manage the virtualized application partitions.
A component-based approach to proving the correctness of the Schorr-Waite algorithm
Singh, Amrinder (Virginia Tech, 2007-08-09)
This thesis presents a component-based approach to proving the correctness of programs involving pointers. Unlike previous work, our component-based approach supports modular reasoning, which is essential to the scalability of systems. Specifically, we specify the behavior of a graph-marking algorithm known as the Schorr-Waite algorithm, implement it using a component that captures the behavior and performance benefits of pointers, and prove that the implementation is correct with respect to the specification. We use the Resolve language in our example, which is an integrated programming and specification language that supports modular reasoning. The behavior of the algorithm is fully specified using custom definitions, pre- and post-conditions, and a complex loop invariant. Additional operations for the Resolve pointer component are introduced that preserve the accessibility of a system. These operations are used in the implementation of the algorithm. They simplify the proof of correctness and make the code shorter.
Cooperative Autonomous Resilient Defense Platform for Cyber-Physical Systems
Azab, Mohamed Mahmoud Mahmoud (Virginia Tech, 2013-02-28)
Cyber-Physical Systems (CPS) entail the tight integration of and coordination between computational and physical resources. These systems are increasingly becoming vital to modernizing the national critical infrastructure systems ranging from healthcare, to transportation and energy, to homeland security and national defense. Advances in CPS technology are needed to help improve their current capabilities as well as their adaptability, autonomicity, efficiency, reliability, safety and usability. Due to the proliferation of increasingly sophisticated cyber threats with exponentially destructive effects, CPS defense systems must systematically evolve their detection, understanding, attribution, and mitigation capabilities. Unfortunately most of the current CPS defense systems fall short to adequately provision defense services while maintaining operational continuity and stability of the targeted CPS applications in presence of advanced persistent attacks. Most of these defense systems use un-coordinated combinations of disparate tools to provision defense services for the cyber and physical components. Such isolation and lack of awareness of and cooperation between defense tools may lead to massive resource waste due to unnecessary redundancy, and potential conflicts that can be utilized by a resourceful attacker to penetrate the system. Recent research argued against the suitability of the current security solutions to CPS environments. We assert the need for new defense platforms that effectively and efficiently manage dynamic defense missions and toolsets in real-time with the following goals: 1) Achieve asymmetric advantage to CPS defenders, prohibitively increasing the cost for attackers; 2) Ensure resilient operations in presence of persistent and evolving attacks and failures; and 3) Facilitate defense alliances, effectively and efficiently diffusing defense intelligence and operations transcending organizational boundaries. Our proposed solution comprehensively addresses the aforementioned goals offering an evolutionary CPS defense system. The presented CPS defense platform, termed CyPhyCARD (Cooperative Autonomous Resilient Defenses for Cyber-Physical systems) presents a unified defense platform to monitor, manage, and control the heterogeneous composition of CPS components. CyPhyCARD relies on three interrelated pillars to construct its defense platform. CyPhyCARD comprehensively integrates these pillars, therefore building a large scale, intrinsically resilient, self- and situation-aware, cooperative, and autonomous defense cloud-like platform that provisions adequate, prompt, and pervasive defense services for large-scale, heterogeneously-composed CPS. The CyPhyCARD pillars are: 1) Autonomous management platform (CyberX) for CyPhyCARD's foundation. CyberX enables application elasticity and autonomic adaptation to changes by runtime diversity employment, enhances the application resilience against attacks and failures by multimodal recovery mechanism, and enables unified application execution on heterogeneously composed platforms by a smart employment of a fine-grained environment-virtualization technology. 2) Diversity management system (ChameleonSoft) built on CyberX. ChameleonSoft encrypts software execution behavior by smart employment of runtime diversity across multiple dimensions to include time, space, and platform heterogeneity inducing a trace-resistant moving-target defense that works on securing CyPhyCARD platform against software attacks. 3) Evolutionary Sensory system (EvoSense) built on CyberX. EvoSense realizes pervasive, intrinsically-resilient, situation-aware sense and response system to seamlessly effect biological-immune-system like defense. EvoSense acts as a middle layer between the defense service provider(s) and the Target of Defense (ToD) creating a uniform defense interface that hides ToD's scale and heterogeneity concerns from defense-provisioning management. CyPhyCARD is evaluated both qualitatively and quantitatively. The efficacy of the presented approach is assessed qualitatively, through a complex synthetic CPS attack scenario. In addition to the presented scenario, we devised multiple prototype packages for the presented pillars to assess their applicability in real execution environment and applications. Further, the efficacy and the efficiency of the presented approach is comprehensively assessed quantitatively by a set of custom-made simulation packages simulating each CyPhyCARD pillar for performance and security evaluation. The evaluation illustrated the success of CyPhyCARD and its constructing pillars to efficiently and effectively achieve its design objective with reasonable overhead.
COSTA: Composite Trust-Based Asset-Task Assignment in Mobile Ad Hoc Networks
Cho, Jin-Hee; Al-Hamadi, Hamid; Chen, Ing-Ray (IEEE, 2019)
In mobile ad hoc networks (MANETs), asset-task assignment problems have been explored with vastly different approaches. Considering the unique characteristics of MANET environments, such as no centralized trusted entity, a lack of resources, and high-security vulnerabilities, resource allocation is not a trivial problem particularly for situations where a mobile team aims to successfully complete a common mission. The existing approaches have studied asset-task assignment problems by best matching a node's functionality and requirements of a given task. In this paper, we propose a task assignment protocol using the concept of multidimensional trust, namely, CompoSite Trust-based Assignment (COSTA), aiming to maximize the completion ratio of a common mission consisting of multiple tasks by balancing trust and risk in executing them. Based on the core concept of trust defined as the willingness to take the risk in performing a given task, COSTA selects qualified nodes for a given task while meeting an acceptable risk level for executing multiple tasks contributing to successful mission completion. Given a mission consisting of dynamic multiple tasks, we model each task with importance, urgency, and difficulty characteristics and use them for selecting qualified members. In addition, we model a node's risk behavior (i.e., risk-seeking, risk-neutral, and risk-averse) and investigate its impact on mission performance where a payoff is given for member selection and task execution. We formulate an optimization problem for the task assignment using integer linear programming (ILP). Our simulation results validated with ILP solutions demonstrate the existence of an optimal acceptable risk level that best balances trust and risk so as to maximize the mission completion ratio. We conduct a comprehensive comparative analysis and show that COSTA achieves a higher mission completion ratio while incurring a lower communication overhead compared with non-trust-based counterparts.
Design and Analysis of Adaptive Fault Tolerant QoS Control Algorithms for Query Processing in Wireless Sensor Networks
Speer, Ngoc Anh Phan (Virginia Tech, 2008-04-17)
Data sensing and retrieval in WSNs have a great applicability in military, environmental, medical, home and commercial applications. In query-based WSNs, a user would issue a query with QoS requirements in terms of reliability and timeliness, and expect a correct response to be returned within the deadline. Satisfying these QoS requirements requires that fault tolerance mechanisms through redundancy be used, which may cause the energy of the system to deplete quickly. This dissertation presents the design and validation of adaptive fault tolerant QoS control algorithms with the objective to achieve the desired quality of service (QoS) requirements and maximize the system lifetime in query-based WSNs. We analyze the effect of redundancy on the mean time to failure (MTTF) of query-based cluster-structured WSNs and show that an optimal redundancy level exists such that the MTTF of the system is maximized. We develop a hop-by-hop data delivery (HHDD) mechanism and an Adaptive Fault Tolerant Quality of Service Control (AFTQC) algorithm in which we utilize "source" and "path" redundancy with the goal to satisfy application QoS requirements while maximizing the lifetime of WSNs. To deal with network dynamics, we investigate proactive and reactive methods to dynamically collect channel and delay conditions to determine the optimal redundancy level at runtime. AFTQC can adapt to network dynamics that cause changes to the node density, residual energy, sensor failure probability, and radio range due to energy consumption, node failures, and change of node connectivity. Further, AFTQC can deal with software faults, concurrent query processing with distinct QoS requirements, and data aggregation. We compare our design with a baseline design without redundancy based on acknowledgement for data transmission and geographical routing for relaying packets to demonstrate the feasibility. We validate analytical results with extensive simulation studies. When given QoS requirements of queries in terms of reliability and timeliness, our AFTQC design allows optimal "source" and "path" redundancies to be identified and applied dynamically in response to network dynamics such that not only query QoS requirements are satisfied, as long as adequate resources are available, but also the lifetime of the system is prolonged.
Design and Analysis of Algorithms for Efficient Location and Service Management in Mobile Wireless Systems
Gu, Baoshan (Virginia Tech, 2005-09-30)
Mobile wireless environments present new challenges to the design and validation of system supports for facilitating development of mobile applications. This dissertation concerns two major system-support mechanisms in mobile wireless networks, namely, location management and service management. We address this research issue by considering three topics: location management, service management, and integrated location and service management. A location management scheme must effectively and efficiently handle both user location-update and location-search operations. We first quantitatively analyze a class of location management algorithms and identify conditions under which one algorithm may perform better than others. From insight gained from the quantitative analysis, we design and analyze a hybrid replication with forwarding algorithm that outperforms individual algorithms and show that such a hybrid algorithm can be uniformly applied to mobile users with distinct call and mobility characteristics to simplify the system design without sacrificing performance. For service management, we explore the notion of location-aware personal proxies that cooperate with the underlying location management system with the goal to minimize the network communication cost caused by service management operations. We show that for cellular wireless networks that provide packet services, when given a set of model parameters characterizing the network and workload conditions, there exists an optimal proxy service area size such that the overall network communication cost for service operations is minimized. These proxy-based mobile service management schemes are shown to outperform non-proxy-based schemes over a wide range of identified conditions. We investigate a class of integrated location and service management schemes by which service proxies are tightly integrated with location databases to further reduce the overall network signaling and communication cost. We show analytically and by simulation that when given a user's mobility and service characteristics, there exists an optimal integrated location and service management scheme that would minimize the overall network communication cost for servicing location and service operations. We demonstrate that the best integrated location and service scheme identified always performs better than the best decoupled scheme that considers location and service managements separately.
Design and Analysis of Intrusion Detection Protocols in Cyber Physical Systems
Mitchel, Robert Raymondl III (Virginia Tech, 2013-04-23)
In this dissertation research we aim to design and validate intrusion detection system (IDS) protocols for a cyber physical system (CPS) comprising sensors, actuators, control units, and physical objects for controlling and protecting physical infrastructures.
The design part includes host IDS, system IDS and IDS response designs. The validation part includes a novel model-based analysis methodology with simulation validation. Our objective is to maximize the CPS reliability or lifetime in the presence of malicious nodes performing attacks which can cause security failures. Our host IDS design results in a lightweight, accurate, autonomous and adaptive protocol that runs on every node in the CPS to detect misbehavior of neighbor nodes based on state-based behavior specifications. Our system IDS design results in a robust and resilient protocol that can cope with malicious, erroneous, partly trusted, uncertain and incomplete information in a CPS. Our IDS response design results in a highly adaptive and dynamic control protocol that can adjust detection strength in response to environment changes in attacker strength and behavior. The end result is an energy-aware and adaptive IDS that can maximize the CPS lifetime in the presence of malicious attacks, as well as malicious, erroneous, partly trusted, uncertain and incomplete information.
We develop a probability model based on stochastic Petri nets to describe the behavior of a CPS incorporating our proposed intrusion detection and response designs, subject to attacks by malicious nodes exhibiting a range of attacker behaviors, including reckless, random, insidious and opportunistic attacker models. We identify optimal intrusion detection settings under which the CPS reliability or lifetime is maximized for each attacker model. Adaptive control for maximizing IDS performance is achieved by dynamically adjusting detection and response strength in response to attacker strength and behavior detected at runtime. We conduct extensive analysis of our designs with four case studies, namely, a mobile group CPS, a medical CPS, a smart grid CPS and an unmanned aircraft CPS. The results show that our adaptive intrusion and response designs operating at optimizing conditions significantly outperform existing anomaly-based IDS techniques for CPSs.
Design and Analysis of QoS-Aware Key Management and Intrusion Detection Protocols for Secure Mobile Group Communications in Wireless Networks
Cho, Jin-Hee (Virginia Tech, 2008-11-12)
Many mobile applications in wireless networks such as military battlefield, emergency response, and mobile commerce are based on the notion of secure group communications. Unlike traditional security protocols which concern security properties only, in this dissertation research we design and analyze a class of QoS-aware protocols for secure group communications in wireless networks with the goal to satisfy not only security requirements in terms of secrecy, confidentiality, authentication, availability and data integrity, but also performance requirements in terms of latency, network traffic, response time, scalability and reconfigurability. We consider two elements in the dissertation research: design and analysis. The dissertation research has three major contributions. First, we develop three "threshold-based" periodic batch rekeying protocols to reduce the network communication cost caused by rekeying operations to deal with outsider attacks. Instead of individual rekeying, i.e., performing a rekeying operation right after each group membership change event, these protocols perform batch rekeying periodically. We demonstrate that an optimal rekey interval exists that would satisfy an imposed security requirement while minimizing the network communication cost. Second, we propose and analyze QoS-aware intrusion detection protocols for secure group communications in mobile ad hoc networks to deal with insider attacks. We consider a class of intrusion detection protocols including host-based and voting-based protocols for detecting and evicting compromised nodes and examine their effect on the mean time to security failure metric versus the response time metric. Our analysis reveals that there exists an optimal intrusion detection interval under which the system lifetime metric can be best traded off for the response time performance metric, or vice versa. Furthermore, the intrusion detection interval can be dynamically adjusted based on the attacker behaviors to maximize the system lifetime while satisfying a system-imposed response time or network traffic requirement. Third, we propose and analyze a scalable and efficient region-based group key management protocol for managing mobile groups in mobile ad hoc networks. We take a region-based approach by which group members are broken into region-based subgroups, and leaders in subgroups securely communicate with each other to agree on a group key in response to membership change and member mobility events. We identify the optimal regional area size that minimizes the network communication cost while satisfying the application security requirements, allowing mobile groups to react to network partition/merge events for dynamic reconfigurability and survivability. We further investigate the effect of integrating QoS-aware intrusion detection with region-based group key management and identify combined optimal settings in terms of the optimal regional size and the optimal intrusion detection interval under which the security and performance properties of the system can be best optimized. We evaluate the merits of our proposed QoS-aware security protocols for mobile group communications through model-based mathematical analyses with extensive simulation validation. We perform thorough comparative analyses against baseline secure group communication protocols which do not consider security versus performance tradeoffs, including those based on individual rekeying, no intrusion detection, and/or no-region designs. The results obtained show that our proposed QoS-aware security protocols outperform these baseline algorithms. â
The Design and Implementation of the Tako Language and Compiler
Vasudeo, Jyotindra (Virginia Tech, 2006-05-05)
Aliasing complicates both formal and informal reasoning and is a particular problem in object-oriented languages, where variables denote references to objects rather than object values. Researchers have proposed various approaches to the aliasing problem in object-oriented languages, but all use reference semantics to reason about programs. This thesis describes the design and implementation of Tako—a Java-like language that facilitates value semantics by incorporating alias-avoidance. The thesis describes a non-trivial application developed in the Tako language and discusses some of the object-oriented programming paradigm shifts involved in translating that application from Java to Tako. It introduces a proof rule for procedure calls that uses value semantics and accounts for both repeated arguments and subtyping.
Design, Implementation and Analysis of Wireless Ad Hoc Messenger
Cho, Jin-Hee (Virginia Tech, 2004-07-26)
Popularity of mobile devices along with the presence of ad hoc networks requiring no infrastructure has contributed to recent advances in the field of mobile computing in ad hoc networks. Mobile ad hoc networks have been mostly utilized in military environments. The recent advances in ad hoc network technology now introduce a new class of applications. In this thesis, we design, implement and analyze a multi-hop ad hoc messenger application using Pocket PCs and Microsoft .Net Compact Framework. Pocket PCs communicate wirelessly with each other using the IEEE 802.11b technology without the use of an infrastructure. The main protocol implemented in this application is based on Dynamic Source Routing (DSR), which consists of two important mechanisms, Route Discovery and Route Maintenance. We adopt DSR since DSR operates solely based on source routing and "on-demand" process, so each packet does not have to transmit any periodic advertisement packets or routing information. These characteristics are desirable for the ad hoc messenger application for which a conversation is source-initiated on-demand. To test our application easily, we have developed a testing strategy by which a mobility configuration file is pre-generated describing the mobility pattern of each node generated based on the random waypoint mobility model. A mobility configuration file thus defines topology changes at runtime and is used by all nodes to know whether they can communicate with others in a single-hop or multi-hops during an experimental run. We use five standard metrics to test the performance of the wireless ad hoc messenger application implemented based on DSR, namely, (1) average latency to find a new route, (2) average latency to deliver a data packet, (3) delivery ratio of data packets, (4) normalized control overhead, and (5) throughput. These metrics test the correctness and efficiency of the wireless ad hoc messenger application using the DSR protocol in an 802.11 ad hoc network that imposes limitations on bandwidth and resources of each mobile device. We test the effectiveness of certain design alternatives for implementing the ad hoc messenger application with these five metrics under various topology change conditions by manipulating the speed and pause-time parameters in the random waypoint model. The design alternatives evaluated include (1) Sliding Window Size (SWS) for end-to-end reliable communication control; (2) the use of per-hop acknowledgement packets (called receipt packets) deigned for rapid detection of route errors by intermediate nodes; and (3) the use of cache for path look-up during route discovery and maintenance. Our analysis results indicate that as the node speed increases, the system performance deteriorates because a higher node speed causes the network topology to change more frequently under the random waypoint mobility model, causing routes to be broken. On the other hand, as the pause time increases, the system performance improves due to a more stable network topology. For the design alternatives evaluated in our wireless ad hoc messenger, we discover that as SWS increases, the system performance also increases until it reaches an optimal SWS value that maximizes the performance due to a balance of a higher level of data parallelism introduced and a higher level of medium contention in 802.11 because of more packets being transmitted simultaneously as SWS increases. Beyond the optimal SWS, the system performance deteriorates as SWS increases because the heavy medium contention effect outweighs the benefit due to data parallelism. We also discover that the use of receipt packets is helpful in a rapidly changing network but is not beneficial in a stable network. There is a break-even point in the frequency of topology changes beyond which the use of receipt packets helps quickly detect route errors in a dynamic network and would improve the system performance. Lastly, the use of cache is rather harmful in a frequently changing network because stale information stored in the cache of a source node may adversely cause more route errors and generate a higher delay for the route discovery process. There exists a break-even point beyond which the use of cache is not beneficial. Our wireless ad hoc messenger application can be used in a real chatting setting allowing Pocket PC users to chat instantly in 802.11 environments. The design and development of the dynamic topology simulation tool to model movements of nodes and the automatic testing and data collection tool to facilitate input data selection and output data analysis using XML are also a major contribution. The experimental results obtained indicate that there exists an optimal operational setting in the use of SWS, receipt packets and cache, suggesting that the wireless ad hoc messenger should be implemented in an adaptive manner to fine-tune these design parameters based on the current network condition and performance data monitored to maximize the system performance.

Browsing by Author "Chen, Ing-Ray"

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