@conference {
	title = {Multi-scale Analysis of Long Range Dependent Traffic for Anomaly Detection in Wireless Sensor Networks},
	author = {Zheng, Shanshan., Baras, John S.},
	year = {2011},
	month = {12/2011},
	abstract = {Anomaly detection is important for the correct functioning of wireless sensor networks. Recent studies have shown that node mobility along with spatial correlation of the monitored phenomenon in sensor networks can lead to observation data that have long range dependency, which could significantly increase the difficulty of anomaly detection. In this paper, we develop an anomaly detection scheme based on multiscale analysis of the long range dependent traffic to address this challenge. In this proposed detection scheme, discrete wavelet transform is used to approximately de-correlate the traffic data and capture data characteristics in different time scales. The remaining dependencies are then captured by a multi-level hidden Markov model in the wavelet domain. To estimate the model parameters, we propose an online discounting Expectation Maximization (EM) algorithm, which also tracks variations of the estimated models over time. Network anomalies are detected as abrupt changes in the tracked model variation scores. We evaluate our detection scheme numerically using typical long range dependent time series.},
	address = {Orlando USA},
	booktitle = {50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011)},
	pages = {pp. 4060-4065}
}
@conference {
	title = {Power Allocation Policy for Distributed Estimation in Wireless Networks},
	author = {Matei, Ion., Baras, John S.},
	year = {2011},
	month = {12/2011},
	abstract = {We consider a sensor network monitoring a stochastic process. The sensors exchange information over wireless communication channels affected by noise. The goal of the sensors is to compute accurate estimates of the state of the stochastic process under limited energy available for communications. We introduce a distributed algorithm which computes a power allocation scheme aimed at ensuring accurate state estimates and at saving communication energy. The distributed algorithm arise from solving an approximate convex optimization problem with constraints. We present how the power allocation scheme can be used for performing distributed estimation in the case where measurements are shared between sensors.},
	address = {Orlando, FL},
	booktitle = {50th IEEE Conference on Decision and Control Conference (CDC-ECC 2011)},
	pages = {pp. 7275-7280}
}
@conference {
	title = {Learning Behaviors for Coordination in Networked Systems and a Generalized Consensus Protocol},
	author = {Hovareshti, Pedram., Baras, John S.},
	year = {2011},
	month = {12/2011},
	abstract = {We consider the problem of coordination for efficient joint decision making in networks of autonomous agents. When making a decision, an agent is influenced by its knowledge about others{\textquoteright} behaviors. Agents{\textquoteright} understanding of others{\textquoteright} behaviors is shaped through observing their actions over a long time. We have modeled the decision making on whether to cooperate in a group effort as a result of a series of two-person games between agents, where the payoff of each agent is computed as the sum of its payoffs from each of these games. The agents initially have different behaviors. In order to maximize their pay-off, they need to learn the others{\textquoteright} behavior and coordinate with them. We consider a behavior learning algorithm for a class of behavior functions and study its effects on the emergence of coordination in the network. The conditions under which the learning algorithm converges are studied. We show that for a class of linear functions the learning algorithm results in an extension of non-homogeneous consensus protocol to the more general case of block-stochastic matrices.},
	address = {Orlando, Florida},
	booktitle = {50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011)},
	pages = {pp. 2447-2452}
}
@conference {
	title = {Distributed Medium Access and Opportunistic Scheduling for Ad-Hoc Networks: An Analysis of the Constant Access Time Problem},
	author = {Chen, Hua., Hovareshti, Pedram., Baras, John S.},
	year = {2011},
	month = {12/2011},
	abstract = {In this paper, we study the problem of medium access and distributed opportunistic scheduling to exploit channel fluctuations for wireless ad-hoc networks. This work focuses on the Constant Access Time (CAT) problem, where the total time duration of channel probing and data transmission is slotted into fixed block length. In particular, we consider the dependence between channel rates at different time instances during the channel probing phase and its impact on the overall system throughput. We first analyze the system performance of the CAT problem under independent channel rate assumption and compare our result to the existing work on the Constant Data Time (CDT) problem. We then propose a protocol to reduce the channel probing costs based on this analysis. We show analytical results of the proposed protocol for systems with a sufficiently large number of users. We compare the system performance of the proposed protocol with that of the original protocol under independent channel rate assumption. We mathematically prove that the proposed protocol improves the system performance.},
	address = {Houston Texas},
	booktitle = {IEEE Globecom 2011},
	pages = {pp. 1-6}
}
