Data Centric Storage Technologies: Analysis and Enhancement

This paper surveys the most relevant works of Data Centric Storage (DCS) for Wireless Sensor Networks. DCS is a research area that covers data dissemination and storage inside an ad-hoc sensor network. In addition, we present a Quadratic Adaptive Replication (QAR) scheme for DCS, which is a more adaptive multi-replication DCS system and outperforms previous proposals in the literature by reducing the overall network traffic that has a direct impact on energy consumption. Finally, we discuss the open research challenges for DCS

Mining of popular paths with privacy protection and its applications.

Cheong Chi Hong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 81-86).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem statement --- p.1Chapter 1.2 --- Major contributions --- p.4Chapter 1.3 --- Thesis organization --- p.4Chapter 2 --- Smart Card System --- p.6Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Related Work --- p.11Chapter 2.2.1 --- Mining Customer Behaviors --- p.11Chapter 2.2.2 --- Privacy Preserving Data Mining --- p.12Chapter 2.2.3 --- Definitions of Privacy --- p.13Chapter 2.3 --- Model --- p.14Chapter 2.4 --- Algorithms --- p.17Chapter 2.4.1 --- Baseline Algorithm --- p.17Chapter 2.4.2 --- Privacy Equation --- p.19Chapter 2.4.3 --- Random Subsequence Selection Algorithm (RSSA) --- p.20Chapter 2.4.4 --- Popular Item Selection Algorithm (PISA) --- p.21Chapter 2.5 --- Analysis --- p.26Chapter 2.5.1 --- Accuracy --- p.26Chapter 2.5.2 --- Analysis of RSSA: Determine te values --- p.27Chapter 2.5.3 --- Analysis of RSSA: Accuracy --- p.30Chapter 2.5.4 --- Analysis of PISA --- p.33Chapter 2.5.5 --- Theoretical Proof of PISA --- p.41Chapter 2.5.6 --- Privacy Protection --- p.42Chapter 2.6 --- Simulations --- p.45Chapter 3 --- Transportation System --- p.48Chapter 3.1 --- Introduction --- p.49Chapter 3.2 --- Related Work --- p.51Chapter 3.3 --- Model --- p.56Chapter 3.4 --- Algorithms --- p.60Chapter 3.5 --- Simulations --- p.63Chapter 4 --- Enhanced Features in a Smart Card System --- p.67Chapter 4.1 --- Adding Time Information --- p.68Chapter 4.1.1 --- Motivation --- p.68Chapter 4.1.2 --- Time Intervals --- p.68Chapter 4.1.3 --- Original Graph --- p.69Chapter 4.1.4 --- New Graph --- p.70Chapter 4.1.5 --- Adding the New Graph into the Model --- p.72Chapter 4.1.6 --- Rewriting the Definition of a Path --- p.73Chapter 4.1.7 --- Drawback of Adding Time Information --- p.73Chapter 4.2 --- Generalization --- p.75Chapter 4.2.1 --- Motivation --- p.75Chapter 4.2.2 --- Generalization --- p.75Chapter 4.3 --- Specialization vs. Generalization --- p.75Chapter 5 --- Conclusion --- p.79Bibliography --- p.8

Efficient and Secure Network Services in Wireless Sensor Networks.

Wireless sensor networks (WSNs) have been deployed for environment monitoring and surveillance. A message delivery service is one of the most fundamental services for WSNs, thus making its efficiency and effectiveness important. A widely-adopted protocol for message delivery in WSNs is a geographic forward routing (GFR), in which messages are greedily forwarded to their destinations. In this thesis, we develop network services complementary to the existing GFR for efficient and secure message delivery in WSNs. We first develop a distributed location service protocol (DLSP) for message delivery to mobile nodes. Since GFR represents destinations of messages with destinations' geographic locations, the knowledge of location of mobile nodes is necessary to ensure correct message delivery. In DLSP, mobile nodes select some sensor nodes as their location servers, and publish the mobiles' location information to the location servers. Sensor nodes contact those location servers to retrieve the current location of mobile nodes when needed. DLSP provides systematic methods for mobile nodes to select location servers and publish their location to those servers, and for sensor nodes to query mobiles' location. We then design an algorithm called Traverse for hole boundary detection and geographic forward routing with hole avoidance (GFRHA) for efficient message routing. Traverse identifies boundaries of holes, i.e., areas without any functioning sensor node. GFRHA then utilizes the identified hole information to route messages around holes while being forwarded before they encounter holes. This way, the message path lengths, and subsequently the message delay and energy consumption, can be significantly reduced, depending on hole shapes and source and destination locations. We also develop attack-resilient collaborative message authentication (ARCMA) for message delivery. ARCMA is designed to tolerate node-capture attacks, in which attackers obtain valid keys by compromising physically-exposed sensor nodes, and use the keys to generate forged messages. To defend against such attacks, in ARCMA, messages are collaboratively authenticated by a set of sensor nodes rather than by one node. The security of ARCMA does not degrade unless attackers simultaneously compromise more than a certain number of sensor nodes.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64831/1/mgcho_1.pd

Access Control in Wireless Sensor Networks

Wireless sensor networks consist of a large amount of sensor nodes, small low-cost wireless computing devices equipped with different sensors. Sensor networks collect and process environmental data and can be used for habitat monitoring, precision agriculture, wildfire detection, structural health monitoring and many other applications. Securing sensor networks calls for novel solutions, especially because of their unattended deployment and strong resource limitations. Moreover, developing security solutions without knowing precisely against what threats the system should be protected is impossible. Thus, the first task in securing sensor networks is to define a realistic adversary model. We systematically investigate vulnerabilities in sensor networks, specifically focusing on physical attacks on sensor node hardware. These are all attacks that require direct physical access to the sensor nodes. Most severe attacks of this kind are also known as node capture, or node compromise. Based on the vulnerability analysis, we present a novel general adversary model for sensor networks. If the data collected within a sensor network is valuable or should be kept confidential then the data should be protected from unauthorized access. We determine security issues in the context of access control in sensor networks in presence of node capture attacks and develop protocols for broadcast authentication that constitute the core of our solutions for access control. We develop broadcast authentication protocols for the case where the adversary can capture up to some threshold t sensor nodes. The developed protocols offer absolute protection while not more than t nodes are captured, but their security breaks completely otherwise. Moreover, security in this case comes at a high cost, as the resource requirements for the protocols grow rapidly with t. One of the most popular ways to overcome impossibility or inefficiency of solutions in distributed systems is to make the protocol goals probabilistic. We therefore develop efficient probabilistic protocols for broadcast authentication. Security of these protocols degrades gracefully with the increasing number of captured nodes. We conclude that the perfect threshold security is less appropriate for sensor networks than the probabilistic approach. Gracefully degrading security offers better scalability and saves resources, and should be considered as a promising security paradigm for sensor networks

Accurate Data Approximation in Constrained Environments

Several data reduction techniques have been proposed recently as methods for providing fast and fairly accurate answers to complex queries over large quantities of data. Their use has been widespread, due to the multiple benefits that they may offer in several constrained environments and applications. Compressed data representations require less space to store, less bandwidth to communicate and can provide, due to their size, very fast response times to queries. Sensor networks represent a typical constrained environment, due to the limited processing, storage and battery capabilities of the sensor nodes. Large-scale sensor networks require tight data handling and data dissemination techniques. Transmitting a full-resolution data feed from each sensor back to the base-station is often prohibitive due to (i) limited bandwidth that may not be sufficient to sustain a continuous feed from all sensors and (ii) increased power consumption due to the wireless multi-hop communication. In order to minimize the volume of the transmitted data, we can apply two well data reduction techniques: aggregation and approximation. In this dissertation we propose novel data reduction techniques for the transmission of measurements collected in sensor network environments. We first study the problem of summarizing multi-valued data feeds generated at a single sensor node, a step necessary for the transmission of large amounts of historical information collected at the node. The transmission of these measurements may either be periodic (i.e., when a certain amount of measurements has been collected), or in response to a query from the base station. We then also consider the approximate evaluation of aggregate continuous queries. A continuous query is a query that runs continuously until explicitly terminated by the user. These queries can be used to obtain a live-estimate of some (aggregated) quantity, such as the total number of moving objects detected by the sensors

Data centric storage framework for an intelligent wireless sensor network

In the last decade research into Wireless Sensor Networks (WSN) has triggered extensive growth in flexible and previously difficult to achieve scientific activities carried out in the most demanding and often remote areas of the world. This success has provoked research into new WSN related challenges including finding techniques for data management, analysis, and how to gather information from large, diverse, distributed and heterogeneous data sets. The shift in focus to research into a scalable, accessible and sustainable intelligent sensor networks reflects the ongoing improvements made in the design, development, deployment and operation of WSNs. However, one of the key and prime pre-requisites of an intelligent network is to have the ability of in-network data storage and processing which is referred to as Data Centric Storage (DCS). This research project has successfully proposed, developed and implemented a comprehensive DCS framework for WSN. Range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven the research focus. The architecture of the deployed network, referred to as Disk Based Data Centric Storage (DBDCS), was inspired by the magnetic disk storage platter consisting of tracks and sectors. The core contributions made in this research can be summarized as: a) An optimally synchronized routing algorithm, referred to Sector Based Distance (SBD) routing for the DBDCS architecture; b) DCS Metric based Similarity Searching (DCSMSS) with the realization of three exemplar queries – Range query, K-nearest neighbor query (KNN) and Skyline query; and c) A Decentralized Distributed Erasure Coding (DDEC) algorithm that achieves a similar level of reliability with less redundancy. SBD achieves high power efficiency whilst reducing updates and query traffic, end-to-end delay, and collisions. In order to guarantee reliability and minimizing end-to-end latency, a simple Grid Coloring Algorithm (GCA) is used to derive the time division multiple access (TDMA) schedules. The GCA uses a slot reuse concept to minimize the TDMA frame length. A performance evaluation was conducted with simulation results showing that SBD achieves a throughput enhancement by a factor of two, extension of network life time by 30%, and reduced end-to-end latency. DCSMSS takes advantage of a vector distance index, called iDistance, transforming the issue of similarity searching into the problem of an interval search in one dimension. DCSMSS balances the load across the network and provides efficient similarity searching in terms of three types of queries – range query, k-query and skyline query. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. DDEC encoded the acquired information into n fragments and disseminated across n nodes inside a sector so that the original source packets can be recovered from any k surviving nodes. A lost fragment can also be regenerated from any d helper nodes. DDEC was evaluated against 3-Way Replication using different performance matrices. The results have highlighted that the use of erasure encoding in network storage can provide the desired level of data availability at a smaller memory overhead when compared to replication