DEER: Simultaneous Multi-Modal Decentralized Energy Efficient Covert Routing

A fundamental challenge in covert routing is that meeting both covertness and throughput requirements often leads to increased transmit power, which can significantly elevate the overall energy consumption of the network. Therefore, it is important to achieve higher throughput and better energy efficiency while maintaining the required covertness. To this end, we propose a novel simultaneous multi-modal Decentralized Energy- Efficient covert Routing approach - DEER for a multi-hop heterogeneous network (HetNet). Unlike the prevailing single-modal approaches, DEER leverages the diversity of the available wireless communication technologies for simultaneous multi-modal routing. DEER aims to minimize the end-to-end total transmit power of the whole route in a decentralized fashion while maintaining the constraints on required throughput and covertness. DEER stems into two main steps: node-level optimization followed by network-level optimization using the proposed custom-tailored Dijkstra's based link state routing protocol to meet the constraints while minimizing the end-to-end total transmit power. We demonstrate by numerical analysis that DEER improves the energy efficiency by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$23.5 x$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.9 x$</tex> times in comparison to the baseline single-modal and naive simultaneous multimodal approaches respectively.

Authors

Institutions

Publication Details

Published
2025-03-24
DOI
https://doi.org/10.1109/wcnc61545.2025.10978544
Primary Topic
Internet Traffic Analysis and Secure E-voting
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DEER: Simultaneous Multi-Modal Decentralized Energy Efficient Covert Routing

Terrence J. Moore, Han-Bae Kong, Khandaker Foysal Haque, Fikadu T. Dagefu et al.
Internet Traffic Analysis and Secure E-voting
article

DEER: Simultaneous Multi-Modal Decentralized Energy Efficient Covert Routing

Terrence J. Moore, Han-Bae Kong, Khandaker Foysal Haque, Fikadu T. Dagefu, Francesco Restuccia
article en

Abstract

A fundamental challenge in covert routing is that meeting both covertness and throughput requirements often leads to increased transmit power, which can significantly elevate the overall energy consumption of the network. Therefore, it is important to achieve higher throughput and better energy efficiency while maintaining the required covertness. To this end, we propose a novel simultaneous multi-modal Decentralized Energy- Efficient covert Routing approach - DEER for a multi-hop heterogeneous network (HetNet). Unlike the prevailing single-modal approaches, DEER leverages the diversity of the available wireless communication technologies for simultaneous multi-modal routing. DEER aims to minimize the end-to-end total transmit power of the whole route in a decentralized fashion while maintaining the constraints on required throughput and covertness. DEER stems into two main steps: node-level optimization followed by network-level optimization using the proposed custom-tailored Dijkstra's based link state routing protocol to meet the constraints while minimizing the end-to-end total transmit power. We demonstrate by numerical analysis that DEER improves the energy efficiency by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$23.5 x$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.9 x$</tex> times in comparison to the baseline single-modal and naive simultaneous multimodal approaches respectively.

Northeastern University (US), DEVCOM Army Research Laboratory (US), United States Army Combat Capabilities Development Command (US)
National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research
Affordable and clean energy
Openalex Percentile: Top 96%
Internet Traffic Analysis and Secure E-voting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.