Thesis (M.S.)--University of North Texas, August, 2008. / Title from title page display. Includes bibliographical references.

Thesis (M.S.)--Boise State University, 2009. / Includes abstract. Includes bibliographical references (leaves 67-69).

Thesis (M.S.)--Boise State University, 2009. / Title from t.p. of PDF file (viewed Apr. 23, 2010). Includes abstract. Includes bibliographical references (leaves [67-69]).

Thesis (M.Sc.). / Written for the School of Computer Science. Title from title page of PDF (viewed 2009/08/07). Includes bibliographical references.

Thesis (M.S.)--Rutgers University, 2008. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 83-85).

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: Charles G. Boncelet, Dept. of Computer & Information Sciences. Includes bibliographical references.

No description available.

Due to the combination of constrained power, low duty cycle, and high mobility, neighbor discovery is one of the most challenging problems in wireless sensor networks. Existing discovery designs can be divided into two types: pairwise-based and group-based. The former schemes suffer from high discovery delay, while the latter ones accelerate the discovery process but increase transmission package size or incur too much energy overhead, far from practical. Guided by the Talk More Listen Less (TMLL) principle (published in 2016), in which beacons are not necessarily placed in the wakeup slots, we propose two different versions of a group-based protocol we called Talk Half Listen Half (THLH). For the first time, a group-based protocol uses the Channel Occupancy Rate (COR), one of the fundamental novel components of the TMLL model, for performance improvements, in the same way, Duty Cycle (DC) was used in previous group-based protocols. Both versions of the protocol use low transmission overhead in comparison with previous group-based discoveries. After analyzing pros and cons of each approach, we arrived at the conclusion that both behave the best for networks where the average number of new neighbors per slot (β) is low, a metric that sets the bases for performance comparisons of any current/future work with variable COR usage. We also derived a formula that links this new metric with the worst case avg. COR usage of our proposed protocols. Finally, simulation results show that our protocol can improve the average discovery latency and worst case latency close to 50% given low β values.

abstract: Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied. Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated. Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared. Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area. Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

In the multiratecasting problem in wireless sensor networks, the source sensor is usually required to report to multiple destinations at dif- ferent rates for each of them. We present a MST-based rate-aware cost-efficient multiratecast routing protocol (MSTRC). The proposed MSTRC examines only one set partition of destinations at each for- warding step. A message split occurs when the locally-built minimum spanning tree (MST) over the current node and the set of destina- tions has multiple edges originated at the current node. Destinations spanned by each of these edges are grouped together, and for each of these subsets the best neighbor is selected as the next hop. We also suggested a novel face recovery mechanism to deal with void ar- eas, when no neighbor provides positive progress toward destinations. It constructs a MST of current node and destinations without the progress via neighbors, and for each set partition of destinations cor- responding to an edge e in MST, the face routing keeps going until a node that is closer to one of these destinations is found, allowing for greedy continuation, while the process repeats for the remaining desti- nations similarly. Our experimental results demonstrate that MSTRC is highly rate-efficient in all scenarios, and unlike existing solutions, it is adaptive to destination rate deviations.