Prioritized Random MAC Optimization via Graph-based Analysis

Motivated by the analogy between successive interference cancellation and iterative belief-propagation on erasure channels, irregular repetition slotted ALOHA (IRSA) strategies have received a lot of attention in the design of medium access control protocols. The IRSA schemes have been mostly analyzed for theoretical scenarios for homogenous sources, where they are shown to substantially improve the system performance compared to classical slotted ALOHA protocols. In this work, we consider generic systems where sources in different importance classes compete for a common channel. We propose a new prioritized IRSA algorithm and derive the probability to correctly resolve collisions for data from each source class. We then make use of our theoretical analysis to formulate a new optimization problem for selecting the transmission strategies of heterogenous sources. We optimize both the replication probability per class and the source rate per class, in such a way that the overall system utility is maximized. We then propose a heuristic-based algorithm for the selection of the transmission strategy, which is built on intrinsic characteristics of the iterative decoding methods adopted for recovering from collisions. Experimental results validate the accuracy of the theoretical study and show the gain of well-chosen prioritized transmission strategies for transmission of data from heterogenous classes over shared wireless channels

Toward a Robust Sparse Data Representation for Wireless Sensor Networks

Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into a sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding of the data. The sparse codes are generated at the activation of the hidden layer using a sparsity nomination constraint and a shrinking mechanism. Our analysis using real data samples shows that the proposed method outperforms conventional sparsity-inducing methods.Comment: 8 page

A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1

We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control