Energy-efficient broadcast in mobile networks subject to channel randomness

© 2002-2012 IEEE. Wireless communication in a network of mobile devices is a challenging and resource-demanding task, due to the highly dynamic network topology and the wireless channel randomness. This paper investigates information broadcast schemes in 2-D mobile ad hoc networks where nodes are initially randomly distributed and then move following a random direction mobility model. Based on an in-depth analysis of the popular susceptible-infectious-recovered epidemic broadcast scheme, this paper proposes a novel energy and bandwidth-efficient broadcast scheme, named the energy-efficient broadcast scheme, which is able to adapt to fast-changing network topology and channel randomness. Analytical results are provided to characterize the performance of the proposed scheme, including the fraction of nodes that can receive the information and the delay of the information dissemination process. The accuracy of analytical results is verified using simulations driven by both the random direction mobility model and a real-world trace

Multirealization of Linear Systems

For multiple-model adaptive control systems, “multi-controller” architecture can be efficiently implemented (multirealized) by means of a “state-shared” parameter-dependent feedback system. Necessary and sufficient conditions for the multirealization of a family of linear multivariable systems based on matrix fractional descriptions are presented. The problem of the minimal generic multirealization of a set of linear systems is introduced and solved. © 2005, The Institute of Electrical and Electronics Engineers, Inc. All rights reserved

Multirealization of Linear Systems

For multiple-model adaptive control systems, “multi-controller” architecture can be efficiently implemented (multirealized) by means of a “state-shared” parameter-dependent feedback system. Necessary and sufficient conditions for the multirealization of a family of linear multivariable systems based on matrix fractional descriptions are presented. The problem of the minimal generic multirealization of a set of linear systems is introduced and solved. © 2005, The Institute of Electrical and Electronics Engineers, Inc. All rights reserved

Local average consensus in distributed measurement of spatial-temporal varying parameters: 1D case

© 2014 Elsevier. Ltd All rights reserved. We study a new variant of consensus problems, termed 'local average consensus', in networks of agents. We consider the task of using sensor networks to perform distributed measurement of a parameter which has both spatial (in this paper 1D) and temporal variations. Our idea is to maintain potentially useful local information regarding spatial variation, as contrasted with reaching a single, global consensus, as well as to mitigate the effect of measurement errors. We employ two schemes for computation of local average consensus: exponential weighting and uniform finite window. In both schemes, we design local average consensus algorithms to address first the case where the measured parameter has spatial variation but is constant in time, and then the case where the measured parameter has both spatial and temporal variations. Our designed algorithms are distributed, in that information is exchanged only among neighbors. Moreover, we analyze both spatial and temporal frequency responses and noise propagation associated with the algorithms. The tradeoffs of using local consensus, as compared to standard global consensus, include higher memory requirement and degraded noise performance. Arbitrary updating weights and random spacing between sensors are also analyzed in the proposed algorithms

Estimating distances via connectivity in wireless sensor networks

Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum-likelihood estimator to estimate distances between a pair of neighboring nodes in a static wireless sensor network using their local connectivity information, namely the numbers of their common and non-common one-hop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log-normal (shadowing) model as special cases. Under the log-normal model, we investigate the impact of the log-normal model uncertainty; we numerically evaluate the bias and standard deviation associated with our method, which show that for long distances our method outperforms the method based on received signal strength; and we provide a Cramér-Rao lower bound analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on implementing the proposed method on the basis of measurement data from a realistic environment and applying it in connectivity-based sensor localization, the advantages of the proposed method are confirmed. Copyright © 2012 John Wiley & Sons, Ltd

Multi-realization of nonlinear systems

The system multi-realization problem is to find a state-variable realization for a set of systems, sharing as many parameters as possible. A multi-realization can be used to efficiently implement a multi-controller architecture for Multiple Model Adaptive Control (MMAC). We extend the linear multi-realization problem to nonlinear systems. The problem of minimal multi-realization of a set of MIMO systems is introduced and solved for feedback linearizable systems. ©2009 IEEE

Controlling the shape and scale of triangular formations using landmarks and bearing-only sensing

© 2016 TCCT. This work considers the scenario where three agents that can sense only bearings use two landmarks to control their formation shape. We define a method of relating the known distance separating the landmarks back to the edge lengths of the triangular formation. The result is used to define a formation control law that incorporates inter-agent distance constraints. We prove a strong exponential convergence result and show how one can extend the controller such that global stability from any initial position is possible

Network coding based wireless broadcast with performance guarantee

© 2014 IEEE. Wireless broadcast has been increasingly used to deliver information of common interest to a large number of users. There are two major challenges in wireless broadcast: the unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large. In this paper, by resorting to stochastic geometry analysis, we develop a network coding based broadcast scheme that allows a base station (BS) to broadcast a given number of packets to a large number of users, without user acknowledgment, while being able to provide a performance guarantee on the probability of successful delivery. Further, the BS only has limited statistical information about the environment including the spatial distribution of users (instead of their exact locations and number) and the wireless propagation model. Performance analysis is conducted. On that basis, an upper and a lower bound on the number of packet transmissions required to meet the performance guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper are useful for designing efficient and reliable wireless broadcast strategies

When does the algebraic Riccati equation have a negative semi-definite solution?

Find a reasonable necessary and sufficient frequency domain condition, i.e, a condition in terms of the rational matrix ∂W, or possibly in terms of the two-variable rational matrix W, for the existence of a real symmetric negative semi-definite solution of the algebraic Riccati equation

Backward Nonlinear Smoothing Diffusions

We present a backward diffusion flow (i.e., a backward-in-time stochastic differential equation) whose marginal distribution at any (earlier) time is equal to the smoothing distribution when the terminal state (at a later time) is distributed according to the filtering distribution. This is a novel interpretation of the smoothing solution in terms of a nonlinear diffusion (stochastic) flow. This solution contrasts with, and complements, the (backward) deterministic flow of probability distributions (viz. a type of Kushner smoothing equation) studied in a number of prior works. A number of corollaries of our main result are given, including a derivation of the time-reversal of a stochastic differential equation, and an immediate derivation of the classical Rauch--Tung--Striebel smoothing equations in the linear setting