The objective of this article is real-time detection of a change-point in the baseline distribution of the frequency signal generated by Phasor Measurement Units (PMUs) that could indicate potential for voltage collapse, false data injection, or other security threats. The alarm flags an anomaly event when the cumulative sum (CUSUM) of the log-likelihood departures of the Hurst exponent of the frequency from its baseline statistic exceeds a threshold set up as a compromise between the conflicting objectives of minimum detection delay and acceptable False Alarm Rate. As main theoretical contribution, an extra protection layer is developed that provides an estimate of the time to the alarm event, giving the Transmission System Operator (TSO) or an autonomous agent more time to deploy proactive measures to avoid large catastrophic system states. The proposed method is illustrated by a retrospective analysis of the 2012 Indian blackout that reveals that 10-12 minutes before the voltage collapse, a significant increase in the Hurst exponent of the frequency could have been detected. Conceptually, this shows that PMUs provide significant value towards ensuring autonomous cognition in the power grid by enabling the abnormal events to be fault-detected in an unsupervised fashion.

A dynamic routing policy, referred to as Heat-Diffusion (HD), is developed for multihop uniclass wireless networks subject to random traffic, time-varying topology and inter-channel interference. The policy uses only current condition of queue occupancies and channel states, with requiring no knowledge of traffic and topology. Besides throughput optimality, HD minimizes an average quadratic routing cost defined by endowing each channel with a time-varying cost factor. Further, HD minimizes average network delay in the class of routing policies that base decisions only on current condition of traffic congestion and channel states. Further, in this class of routing policies, HD provides a Pareto optimal tradeoff between average routing cost and average network delay, meaning that no policy can improve either one without detriment to the other. Finally, HD fluid limit follows graph combinatorial heat equation, which can open a new way to study wireless networks using heat calculus, a very active area of pure mathematics.

A new routing policy, named Heat-Diffusion (HD), is developed for multihop wireless networks subject to stochastic arrivals, time-varying topology, and inter-channel interference, using only current queue congestion and current channel states, without requiring the knowledge of topology and arrivals. Besides throughput optimality, HD minimizes a quadratic routing cost defined by endowing each channel with a cost-factor. It also minimizes average total queue congestion, and so average network delay, within the class of routing policies that base decision only on current queue lengths and current channel states. Further, within this class, HD provides a Pareto optimal tradeoff between average delay and average routing cost, meaning that no policy can improve either one without detriment to the other. Finally, HD fluid limit follows graph combinatorial heat equation that opens a new way to study wireless networks using heat calculus, a very active area of pure mathematics.

Minimum cost routing is considered on multiclass multihop wireless networks influenced by stochastic arrivals, inter-channel interference, and time-varying topology. Endowing each air link with a cost factor, possibly time-varying and different for different classes, we define the Dirichlet routing cost as the square of the link packet transmissions weighted by the link cost-factors. Our recently-proposed Heat-Diffusion (HD) routing protocol [3] is extended to minimize this cost, while ensuring queue stability for all stabilizable traffic demands, and without requiring any information about network topology or packet arrivals. This is the first time in literature that such a multiclass routing penalty can be minimized at network layer subject to queue stability. Further, when all links are of unit cost factor, our protocol here reduces to the one in our recent paper [4], leading to minimum average network delay among all routing protocols that act based only on current queue congestion and current channel states. Our approach is based on mapping a communication network into an electrical network by showing that the fluid limit of wireless network under our routing protocol follows Ohm's law on a nonlinear resistive network.

We propose a new scheduling and routing approach, the Heat Diffusion (HD) protocol, using combinatorial analogue of the heat equation in mathematical physics. The algorithm holds for systems subject to time-varying network conditions with general packet arrivals and random topology states, including ad-hoc networks with mobility. Compared to the well-known backpressure policy, the HD protocol is generalized in form and optimized in performance, which considers link penalties and node capacities in the routing. It mitigates the packet looping behavior of backpressure and attempts to communicate less over links of higher costs and with the nodes of lower capacities. While HD policy shows benefits over backpressure, it is developed using the same underlying control laws. Therefore, it can easily leverage all the theoretical works that have been done in improving the original backpressure. For the same reason, it provides a relatively easy path-way to modify existing applications of backpressure to the optimized versions using HD protocol.

Buffer management in queuing systems plays an important role in addressing the tradeoff between efficiency measured in terms of overall packet loss and fairness measured in terms of individual source packet loss. Complete partitioning (CP) of a buffer with the best fairness characteristic and complete sharing (CS) of a buffer with the best efficiency characteristic are at the opposite ends of the spectrum of buffer management techniques. Dynamic partitioning buffer management techniques aim at addressing the tradeoff between efficiency and fairness. Ease of implementation is the key issue when determining the practicality of a dynamic buffer management technique. In this paper, two novel dynamic buffer management techniques for queuing systems accommodating self-similar traffic patterns are introduced. The techniques take advantage of the adaptive learning power of perceptron neural networks when applied to arriving traffic patterns of queuing systems. Relying on the water-filling approach, our proposed techniques are capable of coping with the tradeoff between packet loss and fairness issues. Computer simulations reveal that both of the proposed techniques enjoy great efficiency and fairness characteristics as well as ease of implementation.

Reducing packet loss and increasing overall efficiency in multiple source queuing systems is one of the most important issues in the design of traffic control algorithms. On the other hand, the other important issue in such systems is to provide every individual source with the ability to take advantage of a fair portion of the shared available resources such as buffer space or server bandwidth. In this paper a novel technique for reducing packet loss in a class of queuing systems with self-similar traffic patterns is introduced. The technique takes advantage of the modeling power of neural networks to offer a dynamic buffer management scheme capable of efficiently addressing the trade off between packet loss and fairness issues.