Jennifer Yick

Bio: Jennifer Yick is an academic researcher from University of California, Davis. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 7, co-authored 9 publications receiving 5535 citations.

Analysis of a prediction-based mobility adaptive tracking algorithm

Abstract: Target tracking in wireless sensor networks requires efficient coordination among sensor nodes. Existing methods have focused on tree-based collaboration, selective activation, and group clustering. This paper presents a prediction-based adaptive algorithm for tracking mobile targets. We use adaptive Kalman filtering to predict the future location and velocity of the target. This location prediction is used to determine the active tracking region which corresponds to the set of sensors that needs to be "lighted". The velocity prediction is used to adaptively determine the size of the active tracking region, and to modulate the sampling rate as well. In this paper, we quantify the benefits of our approach in terms of energy consumed and accuracy of tracking for different mobility patterns. Our simulation results show that advance resource reservation coupled with adaptively changing the size of the active tracking region and the sampling rate reduces the overall energy consumed for tracking without affecting the accuracy in tracking.

Optimizing placement of beacons and data loggers in a sensor network - a case study

Abstract: Localization and clustering of sensor nodes are important services in a sensor network since the nodes are typically deployed in an ad-hoc manner into an infrastructure-less terrain. When beacons are used for localization, there are two critical design issues: 1) to maximize the lifetime of the beacons and 2) to maximize the coverage area. With clustering, the goal is to minimize the energy dissipation of the sensor network. In this paper, we consider the placement of beacons and data loggers (that act as cluster heads) in the Cosumnes River Preserve, which is a joint collaborative restoration project between the Cosumnes Research Consortium at University of California at Davis (UCD) and The Nature Conservancy. Currently, there are many types of sensors deployed in the preserve which are wired to data loggers. Our objective is to determine the minimum number and placement of beacons and data loggers for wireless sensors deployed in the preserve. We formulated an optimization problem which is solved by integer linear program (ILP).

Efficient aggregation of multiple classes of information in wireless sensor networks.

Abstract: Congestion in a Wireless Sensor Network (WSN) can lead to buffer overflow, resource waste and delay or loss of critical information from the sensors. In this paper, we propose the Priority-based Coverage-aware Congestion Control (PCC) algorithm which is distributed, priority-distinct, and fair. PCC provides higher priority to packets with event information in which the sink is more interested. PCC employs a queue scheduler that can selectively drop any packet in the queue. PCC gives fair chance to all sensors to send packets to the sink, irrespective of their specific locations, and therefore enhances the coverage fidelity of the WSN. Based on a detailed simulation analysis, we show that PCC can efficiently relieve congestion and significantly improve the system performance based on multiple metrics such as event throughput and coverage fidelity. We generalize PCC to address data collection in a WSN in which the sensor nodes have multiple sensing devices and can generate multiple types of information. We propose a Pricing System that can under congestion effectively collect different types of data generated by the sensor nodes according to values that are placed on different information by the sink. Simulation analysis show that our Pricing System can achieve higher event throughput for packets with higher priority and achieve fairness among different categories. Moreover, given a fixed system capacity, our proposed Pricing System can collect more information of the type valued by the sink.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

An Information Framework for Creating a Smart City Through Internet of Things

Abstract: Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services.

Flying Ad-Hoc Networks (FANETs)

Abstract: One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.