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Fan, Kai-WeiOn Structure-less and Everlasting Data Collection in Wireless Sensor Networks
Doctor of Philosophy, The Ohio State University, 2008, Computer and Information Science

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of this dissertation is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure.

We propose the first structure-free data aggregation technique that achieves high efficiency. Based on this technique, we propose two semi-structured approaches to support scalability. We conduct large scale simulations and real experiments on a testbed to validate our design. The results show that our protocols can perform similar to an optimum structured approach which has global knowledge of the event and the network.

In addition to conserving energy through efficient data aggregation, renewable energy sources are required for sensor networks to support everlasting monitoring services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing data services without interruptions caused by battery runouts is non-trivial. Moreover, most environment monitoring applications require data collection from all nodes at a steady rate. The objective is to design a solution for fair and high throughput data extraction from all nodes in the network in presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate for each node in the network, such that no node will ever run out of energy. We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, while the distributed algorithm computes the optimal rate when the routes are pre-determined. We prove the optimality for both the centralized and the distributed algorithms, and use a testbed with 158 sensor nodes to validate the distributed algorithm.

Committee:

Prasun Sinha (Advisor); Anish Arora (Committee Member); David Lee (Committee Member)

Subjects:

Computer Science

Keywords:

Data Aggregation; Sensor Networks; Structure-less; Rechargeable Sensor Networks

CHUGH, SHRUTIAN ENERGY EFFICIENT COLLABORATIVE FRAMEWORK FOR EVENT NOTIFICATION AND DATA AGGREGATION IN WIRELESS SENSOR NETWORKS
MS, University of Cincinnati, 2004, Engineering : Computer Science
Wireless sensor networks consist of a large number of low power devices equipped with RF links for communication that have numerous military, civil and environmental monitoring applications. The energy constraints due to limited battery power present several design challenges. In this thesis we propose a cluster based framework and a localized and deterministic schedule based on TDMA/FDMA MAC protocol for the nodes in a neighborhood to communicate with each other. We also present a three phase collaboration algorithm that entails exchanging messages to determine the local maximum in a region in terms of received energy from the source. This helps in minimizing the redundancy in reporting events and saves energy. We also introduce a robust data aggregation approach for queries targeting selective regions of the network. Extensive simulations show that there is a significant reduction in the overall network traffic and in the energy expended by the nodes.

Committee:

Dr. DHARMA AGRAWAL (Advisor)

Subjects:

Computer Science

Keywords:

Wireless Sensor Networks; Collaborative Processing; Event Notification; Data Aggregation

JHAVER, RISHIDISCOVERY OF LINEAR TRAJECTORIES IN GEOGRAPHICALLY DISTRIBUTED DATASETS
MS, University of Cincinnati, 2003, Engineering : Computer Science
We work with temporal data stored in distributed databases that are spread over a region. We have considered a sensor network where a lot of sensor nodes are spread in a grid like manner. These sensor nodes are capable of storing data and thus act as a separate dataset. The entire network of these sensors act as a set of distributed datasets. An algorithm is introduced that mines global temporal patterns from these datasets and results in the discovery of linear trajectories of moving objects under supervision. Each of these datasets has its local temporal dataset along with spatial data and the geographical coordinates of a given object or target. The main objective here is to perform in-network aggregation between the data contained in the various datasets to discover global spatio-temporal patterns; the main constraint is that there should be minimal communication among the participating nodes. We present the algorithm and analyze it in terms of the communication costs. The cost of our algorithm is much smaller than that of the alternative in which the data must be transferred to a single site and then mined. In addition to this, we vary the requirements of our algorithm slightly and present a variant of it that enhances its performance in terms of the overall complexity of computations. We go on to show that the while the efficiency of the algorithm increases in terms of the number of messages exchanged between nodes, the amount of information available to all the nodes in the system decrease. The advantages and drawbacks of this variant of our algorithm is also presented.

Committee:

Dr. Raj Bhatnagar (Advisor)

Subjects:

Computer Science

Keywords:

distributed data sets; data aggregation; in-network aggregation; temporal databases; sensor data sets

Banerjee, TorshaEnergy Efficient Data Representation and Aggregation with Event Region Detection in Wireless Sensor Networks
PhD, University of Cincinnati, 2008, Engineering : Computer Science

Unlike conventional networks, wireless sensor networks (WSNs) are limited in power, have much smaller memory buffers, and possess relatively slower processing speeds. These characteristics necessitate minimum transfer and storage of information in order to prolong the network lifetime. In this dissertation, we exploit the spatio-temporal nature of sensor data to approximate the current values of the sensors based on readings obtained from neighboring sensors and itself.

We propose a Tree based polynomial REGression algorithm, (TREG) that addresses the problem of data compression in wireless sensor networks. Instead of aggregated data, a polynomial function (P) is computed by the regression function, TREG. The coefficients of P are then passed to achieve the following goals: (i) The sink can get attribute values in the regions devoid of sensor nodes, and (ii) Readings over any portion of the region can be obtained at one time by querying the root of the tree. As the size of the data packet from each tree node to its parent remains constant, the proposed scheme scales very well with growing network density or increased coverage area.

Since physical attributes exhibit a gradual change over time, we propose an iterative scheme, UPDATE_COEFF, which obviates the need to perform the regression function repeatedly and uses approximations based on previous readings. Extensive simulations are performed on real world data to demonstrate the effectiveness of our proposed aggregation algorithm, TREG. Results reveal that for a network density of 0.0025 nodes/m2, a complete binary tree of depth 4 could provide the absolute error to be less than 6%. A data compression ratio of about 0.02 is achieved using our proposed algorithm, which is almost independent of the tree depth. In addition, our proposed updating scheme makes the aggregation process faster while maintaining the desired error bounds.

We also propose a Polynomial-based scheme that addresses the problem of Event Region Detection (PERD) for WSNs. When a single event occurs, a child of the tree sends a Flagged Polynomial (FP) to its parent, if the readings approximated by it falls outside the data range defining the existing phenomenon. After the aggregation process is over, the root having the two polynomials, P and FP can be queried for FP (approximating the new event region) instead of flooding the whole network. For multiple such events, instead of computing a polynomial corresponding to each new event, areas with same data range are combined by the corresponding tree nodes and the aggregated coefficients are passed on. Results reveal that a new event can be detected by PERD while error in detection remains constant and is less than a threshold of 10%. As the node density increases, accuracy and delay for event detection are found to remain almost constant, making PERD highly scalable.

Whenever an event occurs in a WSN, data is generated by closeby sensors and relaying the data to the base station (BS) make sensors closer to the BS run out of energy at a much faster rate than sensors in other parts of the network. This gives rise to an unequal distribution of residual energy in the network and makes those sensors with lower remaining energy level die at much faster rate than others. We propose a scheme for enhancing network Lifetime using mobile cluster heads (CH) in a WSN. To maintain remaining energy more evenly, some energy-rich nodes are designated as CHs which move in a controlled manner towards sensors rich in energy and data. This eliminates multihop transmission required by the static sensors and thus increases the overall lifetime of the WSN. We combine the idea of clustering and mobile CH to first form clusters of static sensor nodes.

A collaborative strategy among the CHs further increases the lifetime of the network. Time taken for transmitting data to the BS is reduced further by making the CHs follow a connectivity strategy that always maintain a connected path to the BS.

Spatial correlation of sensor data can be further exploited for dynamic channel selection in Cellular Communication. In such a scenario within a licensed band, wireless sensors can be deployed (each sensor tuned to a frequency of the channel at a particular time) to sense the interference power of the frequency band. In an ideal channel, interference temperature (IT) which is directly proportional to the interference power, can be assumed to vary spatially with the frequency of the sub channel. We propose a scheme for fitting the sub channel frequencies and corresponding ITs to a regression model for calculating the IT of a random sub channel for further analysis of the channel interference at the base station. Our scheme, based on the readings reported by Sensors helps in Dynamic Channel Selection (S-DCS) in extended C-band for assignment to unlicensed secondary users. S-DCS proves to be economic from energy consumption point of view and it also achieves accuracy with error bound within 6.8%. Again, users are assigned empty sub channels without actually probing them, incurring minimum delay in the process. The overall channel throughput is maximized along with fairness to individual users.

Committee:

Dr. Dharma Agrawal (Advisor)

Subjects:

Remote Sensing

Keywords:

wireless sensor; data aggregation; polynomial regression; energy efficiency; mobile; faulty cognitive radio

SHARMA, ANURAGEXPLOITING SPATIAL CORRELATION USING TREE BASED POLYNOMIAL REGRESSION IN A THREE DIMENSIONAL WIRELESS SENSOR NETWORK
MS, University of Cincinnati, 2007, Engineering : Computer Engineering
A Wireless Sensor Network (WSN) consists of a large number of sensor nodes dispersed over a chosen area for monitoring purposes. Information about an event can be captured by the surrounding sensor nodes. Observations from the sensor nodes which are in close proximity are highly correlated. This is called Spatial Correlation. In this thesis, we propose a scheme to exploit the spatial correlation of data in a three dimensional sensor network by using polynomial regression technique. The scheme involves creation of a binary tree in the network, such that the network has two types of nodes viz., Tree nodes and Sensing nodes. The sensing nodes sense the physical attribute and report their position coordinates (x, y, z) and the sensed value to the nearest tree node. The tree nodes, on the other hand, fit a polynomial function on the received values and transmit the coefficients of regression to the parent tree node. The process starts with the leaf tree nodes and stops at the root. At the end of the process, the root has the polynomial function (Attribute value as a function of space coordinates) for the entire sensor network. When the sink queries the root, instead of flooding the entire network, the root can use the polynomial function to compute the attribute value at any location within the boundary. This saves a lot of energy in the sensor network. Simulations have been performed for different tree heights and different sensor nodes density. Results presented in graphical form indicate that a tree with a depth of four provides accurate values, with minimum error. Concluding remarks and plans for future work have also been presented.

Committee:

Dr. Dharma Agrawal (Advisor)

Subjects:

Computer Science

Keywords:

three dimensional wireless sensor network; data aggregation; polynomial regression

Chakraborty, SuryadipData Aggregation in Healthcare Applications and BIGDATA set in a FOG based Cloud System
PhD, University of Cincinnati, 2016, Engineering and Applied Science: Computer Science and Engineering
The Wireless Body Area Sensor Network (WBASN) is a wireless network of wearable computing devices including few medical body sensors which capture and transmit different physiological data wirelessly to a monitoring base station. When a physiological sensor continuously senses and generates huge amount of data, the network might become congested due to heavy traffic and it might lead to starvation and ineffectiveness of the WBASN system. This had led to the beginning of our first problem in this research which is the use of aggregation of data so as to reduce the traffic, enhancing the network life time, and saving the network energy. This research also focuses on dealing with huge amount of healthcare data which is widely known today as `BIGDATA’. Our research investigates the use of BIGDATA and ways to analyze them using a cloud based architecture that we have proposed as FOG Networks which improves the use of cloud architecture. During the work of data aggregation, we propose to use of the statistical regression polynomial of the order 4, and 8. Due to computation, we performed the 6th order coefficient computation and analyzed our results with real-time patient data with compression ratio and correlation coefficients. We also focus on studying the energy saving scenarios using our method and investigate how the node failure scheme would be handled. While focusing on building a polynomial based data aggregation approach in the WBASN system which involves summing and aggregating of wireless body sensors data of the patient's, we noticed the problem of dealing with thousand and millions of patients data when we run a WBASN system for continuous monitoring purpose. We could not also deal with such big amount of data in the small storage of the physiological sensors with small computation abilities of them. So, there is an immediate necessity of an architecture and tools to deal with these thousands of data commonly known today as the BIGDATA. To analyze the BIGDATA, we propose to implement a robust cloud-based structure that uses Hadoop based map reduce system and get some meaningful interpretation of the patient's monitoring data for the medical practitioners, doctors and medical representatives in a very time-efficient manner. As getting thousands of BIGDATA with patient’s secured health information is a proprietary and licensed issue, we examined our cloud based BIGDATA architecture using the Twitter and Google N-gram data which are freely available in public domain. In our next proposed task, we plan to implement a robust and scalable architecture of the existing cloud system which itself takes care of the short comings of the public cloud architecture such as Amazon S3, Microsoft Azure etc. Therefore, we propose to use of a newly introduced system known as the FOG networks that significantly helps the clients (medical workers monitoring the patient’s vital parameters) to easily assess, interpret and analyze the patient’s data of injuries, health parameter performance, and improvement in the health condition, associated vital parameters and emergency data arise very efficiently and more effectively.

Committee:

Dharma Agrawal, D.Sc. (Committee Chair); Amit Bhattacharya, Ph.D. (Committee Member); Rui Dai, Ph.D. (Committee Member); Chia Han, Ph.D. (Committee Member); Carla Purdy, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Wireless body area sensor networks;Data aggregation;Cloud computing;Fog computing

Li, HailongAnalytical Model for Energy Management in Wireless Sensor Networks
PhD, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science and Engineering
Wireless sensor networks (WSNs) are one type of ad hoc networks with data-collecting function. Because of the low-power, low-cost features, WSN attracts much attention from both academia and industry. However, since WSN is driven by batteries and the multi-hop transmission pattern introduces energy hole problem, energy management of WSN became one of fundamental issues. In this dissertation, we study the energy management strategies for WSNs. Firstly, we propose a packets propagation scheme for both deterministic and random deployment of WSNs so to prolong their lifetime. The essence of packets propagation scheme is to control transmission power so as to balance the energy consumption for the entire WSN. Secondly, a characteristic correlation based data aggregation approach is presented. Redundant information during data collection can be effectively mitigated so as to reduce the packets transmission in the WSN. Lifetime of WSN is increased with limited overhead. Thirdly, we also provide a two-tier lifetime optimization strategy for wireless visual sensor network (VSN). By deploying redundant cheaper relay nodes into existing VSN, the lifetime of VSN is maximized with minimal cost. Fourthly, our two-tier visual sensor network deployment is further extended considering multiple base stations and image compression technique. Last but not the least, description of UC AirNet WSN project is presented. At the end, we also consider future research topics on energy management schemes for WSN.

Committee:

Dharma Agrawal, D.Sc. (Committee Chair); Kenneth Berman, Ph.D. (Committee Member); Yizong Cheng, Ph.D. (Committee Member); Chia Han, Ph.D. (Committee Member); Wen Ben Jone, Ph.D. (Committee Member)

Subjects:

Computer Engineering

Keywords:

Wireless Sensor Networks;Wireless Visual Sensor Network;Energy Management;Data Aggregation;Gaussian Random Distribution;Lifetime Optimization;