A key aspect of many resource allocation problems is the need for the resource controller to compute a function, such as the max or arg max, of the competing users' metrics. Information must be exchanged between the competing users and the resource controller in order for this function to be computed. In many practical resource controllers, the competing users' metrics are communicated to the resource controller, which then computes the desired extremization function. However, in this paper, it is shown that information rate savings can be obtained by recognizing that the controller only needs to determine the result of this extremization function. If the extremization function is to be computed losslessly, the rate savings are shown in most cases to be at most 2 b independent of the number of competing users. Motivated by the small savings in the lossless case, simple achievable schemes for both the lossy and interactive variants of this problem are considered. It is shown that both of these approaches have the potential to realize large rate savings, especially in the case where the number of competing users is large. For the lossy variant, it is shown that the proposed simple achievable schemes are in fact close to the fundamental limit given by the rate distortion function.

Network traffic anomaly detection is now considered a surer approach to early detection of malware than signature-based approaches and is best accomplished with traffic data collected from multiple locations. Existing open-source tools are primarily signature-based, or do not facilitate integration of traffic data from multiple locations for real-time analysis, or are insufficiently modular for incorporation of newly proposed approaches to anomaly detection. In this paper, we describe DataMap, a new modular open-source tool for the collection and real-time analysis of sampled, anonymized, and filtered traffic data from multiple WiFi locations in a network and an example of its use in anomaly detection.

We consider the optimal transmission of distributed correlated discrete memoryless sources across a network with capacity constraints. We present several previously undiscussed structural properties of the set of feasible rates and transmission schemes. These properties are then applied to develop a characterization of an optimal solution and its connection to the corner points of the Slepian-Wolf rate region.

In this paper, we consider the problem of communicating over an Additive White Gaussian Noise (AWGN) channel with an unknown noise power without feedback. For the case where the noise power is drawn from a distribution with K distinct values, we model the channel as a K-user broadcast channel, with a user for each of the K possible noise powers. We propose two metrics for measuring the loss of using a broadcast code compared to an omniscient transmitter. Using these metrics, we solve for the optimal power allocation in a superposition code. For the case of K = 2, we prove several interesting properties of the optimal power allocation and the minimum loss.