In recent years, the effort of promoting versatile, easy to manage routing schemes, as a replacement to OSPF has gathered momentum particularly in the context of large-scale enterprise networks, data center networks and software-defined wide area networks (SD-WANs). Such routing schemes rely on embedding the network into a geometric/topological space (e.g. a binary tree) to facilitate multi-path routing with reduced state maintenance and quick recovery in localized failure scenarios. In this work, we propose a systematic framework to embed the network topology into a hierarchical binary virtual-identityspace that is particularly amenable to multi-path routing. Our methodology firstly involves a relaxed form of the connected graph bi-partitioning problem that exploits a geometric embedding of the network in an n-dimensional Euclidean space (n being the number of hosts in the network) based on the Moore-Penrose pseudo inverse of the Laplacian for the graph associated with the network. The edges of the network are mapped to a weight distribution that helps construct a spanning tree from the core of the network towards the periphery, thereby providing a point of symmetry in the network to facilitate balanced bipartitions. This, in turn, yields a (nearly) full balanced binary tree embedding of the network and consequently a good virtual-id space. We also explore the binary identity assignment problem in another point of view by using bi-connected graph as the input graph to introduce a recursive bipartition algorithm. Through rigorous theoretical analysis and experimentation, we demonstrate that our methods perform well within reasonable bounds of computational complexity.

We present AMPLES, Approximate Matching of Persistent LExicon using Search-Engines, to address the Mobile-Application-Identification (MApId) problem in network traffic at a per-flow granularity. We transform MApId into an information-retrieval problem where lexical similarity of short-text-documents is used as a metric for classification tasks. Specifically, a network-flow, observed at an intercept-point, is treated as a semi-structured-text-document and modified into a flow-query. This query is then run against a corpus of documents pre-indexed in a search-engine. Each index-document represents an application, and consists of distinguishable identifiers from the metadata-file and URL-strings found in the application's executable-archive. The search-engine acts as a kernel function, generating a score distribution vis-'a-vis the index-documents, to determine a match. This extends the scope of MApId to fuzzy-classification mapping a flow to a family of apps when the score distribution is spread-out. Through experiments over an emulator-generated test-dataset (400 K applications and 13.5 million flows), we obtain over 80% flow coverage and about 85% application coverage with low false-positives (4%) and nearly no false-negatives. We also validate our methodology over a real network trace. Most importantly, our methodology is platform agnostic, and subsumes previous studies, most of which focus solely on the application coverage.

We present SAMPLES: Self Adaptive Mining of Persistent LExical Snippets; a systematic framework for classifying network traffic generated by mobile applications. SAMPLES constructs conjunctive rules, in an automated fashion, through a supervised methodology over a set of labeled flows (the training set).

Resolving geo-identities of addresses in emerging economies where users rely primarily on short messaging as the means of querying, poses several daunting challenges: lack of proper addressing schemes, non-availability of cartographic information and non-standardized nomenclature of geo-spatial entities such as streets and avenues, to name a few.

Despite the rapid growth in cellular data traffic, we know very little about the (operational) cellular data service network (CDSN) infrastructure. A key step in the process of developing any such understanding is to first understand the locations and distribution of the basestations in the CDSN infrastructure that serve as physical access points for end users for communicating with the underlying network. Such knowledge not only can provide critical insight into the the CDSN infrastructure, but can also guide the development of innovative (e.g. location-aware) services and applications. In this paper we propose a novel approach for mapping the CDSN basestation infrastructure via (explicit) user geo-intent. The intuition behind the proposed approach is to exploit specific geo-locations (i.e. geo-intent) contained in user queries to location-based services, and correlate them with basestation id's to geo-map the CDSN infrastructure. To investigate the validity of our approach, we employ data (RADIUS/RADA data sessions and application sessions) collected at the core IP network inside a CDSN. We develop heuristics for identifying user geo-intent and for geo-mapping the CDSN infrastructure - in particular, the basestations - and evaluate their efficacy using a subset of basestations with ground-truth GPS locations.

This paper deals with a natural disasters management system based on location aware distributed sensor networks. Optimal path discovery for routing of information packets is an essential means to achieve maximum life-time in wireless sensor networks. The authors propose a system based on hierarchical transmission of packets from sensor nodes to the base station by identifying a path from one head to a subsequent head along the route. The algorithm divides the entire sensor network into logical concentric zones based on energy of transmission whereby the packet is transmitted from a head-node to one of the head-nodes in the next zone with lesser depth. This is done on the basis of local angular deviation between two communicating heads, with the least deviant node being preferred. This leads to local route discovery and alternative path information maintenance, with the locations of only those heads in the subsequent zone to which the packet can be communicated within transmission-energy constraints, recorded and maintained. Over a period of time 'data highways' are formed and packets are routed automatically along previously discovered routes once they reach any head which is a part of a previously discovered route. The implementation heavily uses the location awareness of sensor nodes for optimal routing and hence is applicable only to those situations where such data can be made available at the time of installation. We further provide the concept of multiple memberships of sensor nodes to different heads within its area of reach thereby handling disaster conditions where a head fails without notification to its primary members