CALCIUM SIGNALING: OVERVIEW AND RESEARCH DIRECTIONS OF A MOLECULAR COMMUNICATION PARADIGM

In the ongoing effort to build micro- and nanoscale machines, one of the key approaches is the bio-hybrid approach, which focuses on the use of biological constructs and engineered cells. As a natural extension of this concept to nanoscale communication, molecular communication is an umbrella term encompassing various communication systems that are built based on biological intra-and intercellular communication methods, most of which use molecules and molecular concentration as the information carrier. Compared to other proposed molecular communication systems such as diffusion-based communication and microtubular networks, calcium signaling is expected to provide a faster and more controllable system that is suitable for information dissemination and group behavior in nanoscale sensor networks. In this article, we give a general overview of calcium signaling, a novel communication paradigm that uses intercellular calcium waves in biology as a baseline, explain its capabilities, limitations, and some possible deployment scenarios. We also describe various open issues of this novel communication system and elaborate on some research directions for calcium signaling

Multimedia traffic classification with mixture of Markov components

We study multimedia traffic classification into popular applications to assist the quality of service (QoS) support of networking technologies, including but not limited to, WiFi. For this purpose, we propose to model the multimedia traffic flow as a stochastic discrete-time Markov chain in order to take into account the strong sequentiality (i.e. the dependencies across the data instances) in the traffic flow observations. This addresses the shortcoming of the prior techniques that are based on feature extraction which is prone to losing the information of sequentiality. Also, for investigating the best application of our Markov approach to traffic classification, we introduce and test three data driven classification schemes which are all derived from the proposed model and tightly related to each other. Our first classifier has a global perspective of the traffic data via the likelihood function as a mixture of Markov components (MMC). Our second and third classifiers have local perspective based on k-nearest Markov components (kNMC) with the negative loglikelihood as a distance as well as k-nearest Markov parameters (kNMP) with the Euclidean distance. We additionally introduce to the use of researchers a rich multimedia traffic dataset consisting of four application categories, e.g., video on demand, with seven applications, e.g., YouTube. In the presented comprehensive experiments with the introduced dataset, our local Markovian approach kNMC outperforms MMC and kNMP and provides excellent classification performance, 89% accuracy at the category level and 85% accuracy at the application level and particularly over 95% accuracy for live video streaming. Thus, in test time, the nearest Markov components with the largest likelihoods yield the most discrimination power. We also observe that kNMC significantly outperforms the state-of-the-art methods (such as SVM, random forest and autoencoder) on both the introduced dataset and benchmark dataset both at the category and application level