arXiv subjects

Anish Arora

Publications and source records attributed to Anish Arora.

29 records · Page 2Linked to original sources

arXiv2021

Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks

We present a bulk data collection service, Harvest, for energy constrained wireless sensor nodes. To increase spatial reuse and thereby decrease latency, Harvest performs concurrent, pipelined exfiltration from multiple nodes to a base station. To this end, it uses a distance-k coloring of the nodes, notably with a constant number of colors, which yields a TDMA schedule whereby nodes can communicate concurrently with low packet losses due to collision. This coloring is based on a randomized CSMA approach which does not exploit location knowledge. Given a bounded degree of the network, each node waits only O$(1)$ time to obtain a unique color among its distance-k neighbors, in contrast to the traditional deterministic distributed distance-k vertex coloring wherein each node waits O$(Δ^{2})$ time to obtain a color. Harvest offers the option of limiting memory use to only a small constant number of bytes or of improving latency with increased memory use; it can be used with or without additional mechanisms for reliability of message forwarding. We experimentally evaluate the performance of Harvest using 51 motes in the Kansei testbed. We also provide theoretical as well as TOSSIM-based comparison of Harvest with Straw, an extant data collection service implemented for TinyOS platforms that use one-node at a time exfiltration. For networks with more than 3-hops, Harvest reduces the latency by at least 33% as compared to that of Straw.

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arXiv2020

Middle-mile Network Optimization in Rural Wireless Meshes

The status quo of limited broadband connectivity in rural areas motivates the need for fielding alternatives such as long-distance wireless mesh networks. A key aspect of fielding wireless meshes cost-effectively is planning how to connect the last-mile networks to the core network service providers (i.e., the network between the edge access terminals and the landline / optical fiber terminals) with minimal infrastructure cost and throughput constraints. This so-called middle-mile network optimization, which includes topology construction, tower height assignment, antenna and orientation selection, as well as transmit power assignment, is known to be a computationally hard problem. In this paper, we provide the first polynomial time approximation solution for a generalized version of the middle-mile network optimization problem, wherein point-to-point (i.e., WiFi p2p) links are deployed to bridge last-mile networks. Our solution has a cost performance ratio of $O(\ln{|A|}+\frac{|B|}{|A|}+\frac{|A|+|B|}γ)$, where A and B respectively denote the number of terminals and non-terminals and $γ$ is the ratio of $\frac{link\ capacity}{terminal\ demand}$. Furthermore, our solution extends to hybrid networks, i.e., point-to-multipoint (i.e., WiFi p2mp) or omnidirectional (i.e., TV White Space) can serve as hyperlinks in addition to point-to-point links, to further reduce the cost of wireless links. We provide a complementary heuristic for our middle-mile network optimization solution that adds hyperlinks if and only if they reduce the cost.

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arXiv2019

One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification

Edge sensing with micro-power pulse-Doppler radars is an emergent domain in monitoring and surveillance with several smart city applications. Existing solutions for the clutter versus multi-source radar classification task are limited in terms of either accuracy or efficiency, and in some cases, struggle with a trade-off between false alarms and recall of sources. We find that this problem can be resolved by learning the classifier across multiple time-scales. We propose a multi-scale, cascaded recurrent neural network architecture, MSC-RNN, comprised of an efficient multi-instance learning (MIL) Recurrent Neural Network (RNN) for clutter discrimination at a lower tier, and a more complex RNN classifier for source classification at the upper tier. By controlling the invocation of the upper RNN with the help of the lower tier conditionally, MSC-RNN achieves an overall accuracy of 0.972. Our approach holistically improves the accuracy and per-class recalls over ML models suitable for radar inferencing. Notably, we outperform cross-domain handcrafted feature engineering with time-domain deep feature learning, while also being up to $\sim$3$\times$ more efficient than a competitive solution.

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arXiv2019

Achieving Throughput via Fine-Grained Path Planning in Small World DTNs

We explore the benefits of using fine-grained statistics in small world DTNs to achieve high throughput without the aid of external infrastructure. We first design an empirical node-pair inter-contacts model that predicts meetings within a time frame of suitable length, typically of the order of days, with a probability above some threshold, and can be readily computed with low overhead. This temporal knowledge enables effective time-dependent path planning that can be respond to even per-packet deadline variabilities. We describe one such routing framework, REAPER (for Reliable, Efficient and Predictive Routing), that is fully distributed and self-stabilizing. Its key objective is to provide probabilistic bounds on path length (cost) and delay in a temporally fine-grained way, while exploiting the small world structure to entail only polylogarithmic storage and control overhead. A simulation-based evaluation confirms that REAPER achieves high throughput and energy efficiency across the spectrum of ultra-light to heavy network traffic, and substantially outperforms state-of-the-art single copy protocols as well as sociability-based protocols that rely on essentially coarse-grained metrics.

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arXiv2018

SONYC: A System for the Monitoring, Analysis and Mitigation of Urban Noise Pollution

We present the Sounds of New York City (SONYC) project, a smart cities initiative focused on developing a cyber-physical system for the monitoring, analysis and mitigation of urban noise pollution. Noise pollution is one of the topmost quality of life issues for urban residents in the U.S. with proven effects on health, education, the economy, and the environment. Yet, most cities lack the resources to continuously monitor noise and understand the contribution of individual sources, the tools to analyze patterns of noise pollution at city-scale, and the means to empower city agencies to take effective, data-driven action for noise mitigation. The SONYC project advances novel technological and socio-technical solutions that help address these needs. SONYC includes a distributed network of both sensors and people for large-scale noise monitoring. The sensors use low-cost, low-power technology, and cutting-edge machine listening techniques, to produce calibrated acoustic measurements and recognize individual sound sources in real time. Citizen science methods are used to help urban residents connect to city agencies and each other, understand their noise footprint, and facilitate reporting and self-regulation. Crucially, SONYC utilizes big data solutions to analyze, retrieve and visualize information from sensors and citizens, creating a comprehensive acoustic model of the city that can be used to identify significant patterns of noise pollution. These data can be used to drive the strategic application of noise code enforcement by city agencies to optimize the reduction of noise pollution. The entire system, integrating cyber, physical and social infrastructure, forms a closed loop of continuous sensing, analysis and actuation on the environment. SONYC provides a blueprint for the mitigation of noise pollution that can potentially be applied to other cities in the US and abroad.

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arXiv2017

EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks

This paper describes EZ-AG, a structure-free protocol for duplicate insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a token that performs a self-repelling random walk in the network and aggregates information from nodes when they are visited for the first time. A self-repelling random walk of a token on a graph is one in which at each step, the token moves to a neighbor that has been visited least often. While self-repelling random walks visit all nodes in the network much faster than plain random walks, they tend to slow down when most of the nodes are already visited. In this paper, we show that a single step push phase at each node can significantly speed up the aggregation and eliminate this slow down. By doing so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of overhead, EZ-AG outperforms existing structure-free data aggregation by a factor of at least log(N) and achieves the lower bound for aggregation message overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3 simulations in networks ranging from 100 to 4000 nodes under different mobility models and node speeds. We also describe a hierarchical extension for EZ-AG that can produce multi-resolution aggregates at each node using only O(NlogN) messages, which is a poly-logarithmic factor improvement over existing techniques.

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arXiv2017

Coverage characteristics of self-repelling random walks in mobile ad-hoc networks

A self-repelling random walk of a token on a graph is one in which at each step, the token moves to a neighbor that has been visited least often (with ties broken randomly). The properties of self-repelling random walks have been analyzed for two dimensional lattices and these walks have been shown to exhibit a remarkable uniformity with which they visit nodes in a graph. In this paper, we extend this analysis to self-repelling random walks on mobile networks in which the underlying graph itself is temporally evolving. Using network simulations in ns-3, we characterize the number of times each node is visited from the start until all nodes have been visited at least once. We evaluate under different mobility models and on networks ranging from 100 to 1000 nodes. Our results show that until about 85% coverage, duplicate visits are very rare highlighting the efficiency with which a majority of nodes in the network can be visited. Even at 100% coverage, the exploration overhead (the ratio of number of steps to number of unique visited nodes) remains low and under 2. Our analysis shows that self-repelling random walks are effective, structure-free tools for data aggregation in mobile ad-hoc networks.

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arXiv2015

Census: Fast, scalable and robust data aggregation in MANETs

This paper describes Census, a protocol for data aggregation and statistical counting in MANETs. Census operates by circulating a set of tokens in the network using biased random walks such that each node is visited by at least one token. The protocol is structure-free so as to avoid high messaging overhead for maintaining structure in the presence of node mobility. It biases the random walks of tokens so as to achieve fast cover time; the bias involves short albeit multi-hop gradients that guide the tokens towards hitherto unvisited nodes. Census thus achieves a cover time of O(N/k) and message overhead of O(Nlog(N)/k) where N is the number of nodes and k the number of tokens in the network. Notably, it enjoys scalability and robustness, which we demonstrate via simulations in networks ranging from 100 to 4000 nodes under different network densities and mobility models.

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arXiv2015

On the repair time scaling wall for MANETs

The inability of practical MANET deployments to scale beyond about 100 nodes has traditionally been blamed on insufficient network capacity for supporting routing related control traffic. However, this paper points out that network capacity is significantly under-utilized by standard MANET routing algorithms at observed scaling limits. Therefore, as opposed to identifying the scaling limit for MANET routing from a capacity stand-point, it is instead characterized as a function of the interaction between dynamics of path failure (caused due to mobility) and path repair. This leads to the discovery of the repair time scaling wall, which is used to explain observed scaling limits in MANETs. The factors behind the repair time scaling wall are identified and techniques to extend the scaling limits are described.

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arXiv2014

Extract Secrets from Wireless Channel: A New Shape-based Approach

Existing secret key extraction techniques use quantization to map wireless channel amplitudes to secret bits. This pa- per shows that such techniques are highly prone to environ- ment and local noise effects: They have very high mismatch rates between the two nodes that measure the channel be- tween them. This paper advocates using the shape of the channel instead of the size (or amplitude) of the channel. It shows that this new paradigm shift is significantly ro- bust against environmental and local noises. We refer to this shape-based technique as Puzzle. Implementation in a software-defined radio (SDR) platform demonstrates that Puzzle has a 63% reduction in bit mismatch rate than the state-of-art frequency domain approach (CSI-2bit). Exper- iments also show that unlike the state-of-the-art received signal strength (RSS)-based methods like ASBG, Puzzle is robust against an attack in which an eavesdropper can pre- dict the secret bits using planned movements.

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arXiv2014

A Little Prediction Goes a Long Way: Routing in Semi-Deterministic Delay Tolerant Networks

Realizing delay-capacity in intermittently connected mobile networks remains a largely open question, with state-of-the-art routing schemes typically focusing either on delay or on capacity. We show the feasibility of routing with both high goodput and desired delay constraints, with REAPER (for Reliable, Efficient, and Predictive Routing), a fully distributed convergecast routing framework that jointly optimizes both path length and path delay. A key idea for efficient instantiation of REAPER is to exploit predictability of mobility patterns, in terms of a semi-deterministic model which appropriately captures several vehicular and human inter-contact patterns. Packets are thus routed using paths that are jointly optimal at their time of arrival, in contrast to extant DTN protocols which use time-average metrics for routing. REAPER is also self-stabilizing to changes in the mobility pattern. A simulation-based evaluation confirms that, across the spectrum of ultra-light to heavy traffics, REAPER achieves up to 135% and 200% higher throughput and up to 250% and 1666% higher energy efficiency than state-of-the-art single-copy protocols MEED-DVR and PROPHET, which optimize a single metric only, specifically, expected delay and path probability respectively.

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