A Novel Low-Complexity Compressed Data Aggregation Method for Energy-Constrained IoT Networks

Amarlingam, M and Durga Prasad, KVV and Rajalakshmi, P and Channappayya, SS and Sastry, CS (2020) A Novel Low-Complexity Compressed Data Aggregation Method for Energy-Constrained IoT Networks. In: IEEE Transactions on Green Communications and Networking, 4 (3). pp. 717-730.

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Abstract

Sensor nodes used in typical monitoring applications of the Internet of Things (IoT) are an on-board resource (energy, memory, computational capability) constrained devices. The existing data aggregation algorithms have proven that compressed sensing (CS) is promising for energy efficient data aggregation. However, these methods compromise on at least one of energy efficiency, on-node computational complexity and recovery fidelity. In this paper, we propose a novel CS-aided low-complexity compressed data aggregation (LCCDA) method that divides the network into constrained overlapped clusters thereby offering an optimal trade-off among energy consumption, on-node comSensor nodes used in typical monitoring applications of the Internet of Things (IoT) are an on-board resource (energy, memory, computational capability) constrained devices. The existing data aggregation algorithms have proven that compressed sensing (CS) is promising for energy efficient data aggregation. However, these methods compromise on at least one of energy efficiency, on-node computational complexity and recovery fidelity. In this paper, we propose a novel CS-aided low-complexity compressed data aggregation (LCCDA) method that divides the network into constrained overlapped clusters thereby offering an optimal trade-off among energy consumption, on-node computational complexity and recovery error. We show that the measurement matrix constructed from constrained overlapped clustering satisfies the restricted isometry property (RIP) that guarantees the recovery of the aggregated data. We make use of the graph Laplacian eigenbasis, that is based on the weight adjacency matrix, for finding the sparse representation of the measured data from randomly deployed networks, which enables the high fidelity recovery for aggregated data at the sink node. Through numerical experiments, we demonstrate that the proposed LCCDA method is capable of delivering the data to the sink with high recovery fidelity while achieving significant energy savings.

Item Type:	Journal Article
Publication:	IEEE Transactions on Green Communications and Networking
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Complex networks; Compressed sensing; Computational complexity; Computational efficiency; Computer system recovery; Economic and social effects; Energy efficiency; Energy utilization; Matrix algebra; Numerical methods; Recovery; Sensor nodes, Compressive sensing; Computational capability; Constrained devices; Internet of thing (IOT); Monitoring applications; Numerical experiments; Restricted isometry properties (RIP); Sparse representation, Internet of things
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	14 Feb 2023 08:45
Last Modified:	14 Feb 2023 08:45
URI:	https://eprints.iisc.ac.in/id/eprint/80236

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