arXiv subjects

Ram Ramanathan

Publications and source records attributed to Ram Ramanathan.

8 recordsLinked to original sources

arXiv2026

SQEEZ: Energy-efficient Location Sharing for Mobile Ad Hoc Networks

Periodic network-wide dissemination of node location data is crucial for shared situational awareness and collaborative mapping in mobile ad hoc and mesh networks for public safety, disaster relief, and military. A key challenge is to provide maximally accurate location information with minimal energy expenditure on part of the nodes. We present SQEEZ: a mechanism for reducing the Position Location Information (PLI) load that combines two orthogonal techniques: (1) adaptive suppression of location updates; and (2) temporal and inline compression of update packets. We describe the SQEEZ suppression and compression algorithms, analyze the tradeoff between location error and energy consumption, and introduce a new metric called \textit{Error-Penalized-Energy (EPE)} that normalizes the energy metric using the error incurred. Our simulation results show that, in the range of parameters studied, SQEEZ improves the EPE-efficiency and scalability in a 30-node random waypoint scenario by up to 4.4x and 2.3x respectively; and increases the EPE-efficiency by 7.5x in a 9-node real-world network trace. Compression provides larger improvements than suppression at high mobilities and vice-versa at low mobilities.

cs.NI↗

arXiv2025

Mesh Augmentation of LoRaWAN-based IoT Networks

LoRaWAN is a leading standard and technology for low-power, long-range Internet-of-Things (IoT) communications. However, its single-hop architecture results in limited effective range and excessive power consumption for end devices, especially when deployed in large, remote and RF-challenged environments. Existing solutions are either incompatible with LoRaWAN, or limit relaying to a single hop. We present LIMA, a protocol for augmenting an existing or new LoRaWAN deployment with a mesh network of LIMA Routers. LIMA increases the effective coverage range well beyond the maximum LoRa range via multi-hopping, and significantly reduces the energy consumed by end-devices. LIMA requires no changes to the end-device, the servers or the LoRaWAN standard. LIMA builds routes using reverse path forwarding, tunnels LoRaWAN messages over LIMA, provides transparent extension of the existing Adaptive Data Rate (ADR), and suppresses duplicate forwarding if the device is directly reachable from the Gateway. Simulations using Network Simulator 3 (ns-3) show that LIMA increases the delivery rate, scalability, ED energy consumption by up to 5x, 8x and 12.6x respectively, and reduces latency by up to 2.3x. Table-top and outdoor testing with a prototype constructed using a commercial gateway as a starting point confirm that LIMA can be successfully deployed within an existing LoRaWAN system, and can provide range and energy gains transparently.

cs.NI↗

arXiv2018

Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification

We propose a novel subgraph image representation for classification of network fragments with the targets being their parent networks. The graph image representation is based on 2D image embeddings of adjacency matrices. We use this image representation in two modes. First, as the input to a machine learning algorithm. Second, as the input to a pure transfer learner. Our conclusions from several datasets are that (a) deep learning using our structured image features performs the best compared to benchmark graph kernel and classical features based methods; and, (b) pure transfer learning works effectively with minimum interference from the user and is robust against small data.

cs.CV↗

arXiv2017

An efficient alternative to Ollivier-Ricci curvature based on the Jaccard metric

We study Ollivier-Ricci curvature, a discrete version of Ricci curvature, which has gained popularity over the past several years and has found applications in diverse fields. However, the Ollivier-Ricci curvature requires an optimal mass transport problem to be solved, which can be computationally expensive for large networks. In view of this, we propose two alternative measures of curvature to Ollivier-Ricci which are motivated by the Jaccard coefficient and are demonstrably less computationally intensive, a cheaper Jaccard (JC) and a more expensive generalized Jaccard (gJC) curvature metric. We show theoretically that the gJC closely matches the Ollivier-Ricci curvature for Erdos-Renyi graphs in the asymptotic regime of large networks. Furthermore, we study the goodness of approximation between the proposed curvature metrics and Ollivier-Ricci curvature for several network models and real networks. Our results suggest that in comparison to an alternative curvature metric for graphs, the Forman-Ricci curvature, the gJC exhibits a reasonably good fit to the Ollivier-Ricci curvature for a wide range of networks, while the JC is shown to be a good proxy only for certain scenarios.

cs.SI↗

arXiv2016

Generative Models for Global Collaboration Relationships

When individuals interact with each other and meaningfully contribute toward a common goal, it results in a collaboration, as can be seen in many walks of life such as scientific research, motion picture production, or team sports. The artifacts resulting from a collaboration (e.g. papers, movies) are best captured using a hypergraph model, whereas the relation of who has collaborated with whom is best captured via an abstract simplicial complex (SC). In this paper, we propose a generative algorithm GeneSCs for SCs modeling fundamental collaboration relations, primarily based on preferential attachment. The proposed network growth process favors attachment that is preferential not to an individual's degree, i.e., how many people has he/she collaborated with, but to his/her facet degree, i.e., how many maximal groups or facets has he/she collaborated within. Unlike graphs, in SCs, both facet degrees (of nodes) and facet sizes are important to capture connectivity properties. Based on our observation that several real-world facet size distributions have significant deviation from power law-mainly due to the fact that larger facets tend to subsume smaller ones-we adopt a data-driven approach. We seed GeneSCs with a facet size distribution informed by collaboration network data and randomly grow the SC facet-by-facet to generate a final SC whose facet degree distribution matches real data. We prove that the facet degree distribution yielded by GeneSCs is power law distributed for large SCs and show that it is in agreement with real world co-authorship data. Finally, based on our intuition of collaboration formation in domains such as collaborative scientific experiments and movie production, we propose two variants of GeneSCs based on clamped and hybrid preferential attachment schemes, and show that they perform well in these domains.

physics.soc-ph↗

arXiv2012

Channel Assignment in Dense MC-MR Wireless Networks: Scaling Laws and Algorithms

We investigate optimal channel assignment algorithms that maximize per node throughput in dense multichannel multi-radio (MC-MR) wireless networks. Specifically, we consider an MC-MR network where all nodes are within the transmission range of each other. This situation is encountered in many real-life settings such as students in a lecture hall, delegates attending a conference, or soldiers in a battlefield. In this scenario, we show that intelligent assignment of the available channels results in a significantly higher per node throughput. We first propose a class of channel assignment algorithms, parameterized by T (the number of transceivers per node), that can achieve $Θ(1/N^{1/T})$ per node throughput using $Θ(TN^{1-1/T})$ channels. In view of practical constraints on $T$, we then propose another algorithm that can achieve $Θ(1/(\log_2 N)^2)$ per node throughput using only two transceivers per node. Finally, we identify a fundamental relationship between the achievable per node throughput, the total number of channels used, and the network size under any strategy. Using analysis and simulations, we show that our algorithms achieve close to optimal performance at different operating points on this curve. Our work has several interesting implications on the optimal network design for dense MC-MR wireless networks.

cs.NI↗

arXiv2012

Dynamic Shortest Path Algorithms for Hypergraphs

A hypergraph is a set V of vertices and a set of non-empty subsets of V, called hyperedges. Unlike graphs, hypergraphs can capture higher-order interactions in social and communication networks that go beyond a simple union of pairwise relationships. In this paper, we consider the shortest path problem in hypergraphs. We develop two algorithms for finding and maintaining the shortest hyperpaths in a dynamic network with both weight and topological changes. These two algorithms are the first to address the fully dynamic shortest path problem in a general hypergraph. They complement each other by partitioning the application space based on the nature of the change dynamics and the type of the hypergraph.

cs.DS↗

arXiv2010

Modeling and Analysis of Time-Varying Graphs

We live in a world increasingly dominated by networks -- communications, social, information, biological etc. A central attribute of many of these networks is that they are dynamic, that is, they exhibit structural changes over time. While the practice of dynamic networks has proliferated, we lag behind in the fundamental, mathematical understanding of network dynamism. Existing research on time-varying graphs ranges from preliminary algorithmic studies (e.g., Ferreira's work on evolving graphs) to analysis of specific properties such as flooding time in dynamic random graphs. A popular model for studying dynamic graphs is a sequence of graphs arranged by increasing snapshots of time. In this paper, we study the fundamental property of reachability in a time-varying graph over time and characterize the latency with respect to two metrics, namely store-or-advance latency and cut-through latency. Instead of expected value analysis, we concentrate on characterizing the exact probability distribution of routing latency along a randomly intermittent path in two popular dynamic random graph models. Using this analysis, we characterize the loss of accuracy (in a probabilistic setting) between multiple temporal graph models, ranging from one that preserves all the temporal ordering information for the purpose of computing temporal graph properties to one that collapses various snapshots into one graph (an operation called smashing), with multiple intermediate variants. We also show how some other traditional graph theoretic properties can be extended to the temporal domain. Finally, we propose algorithms for controlling the progress of a packet in single-copy adaptive routing schemes in various dynamic random graphs.

cs.NI↗