Online Algorithm for Opportunistic Handling of Received Packets in Vehicular Networks

Ala Al-Fuqaha, Ammar Gharaibeh, Ihab Mohammed, Sayed Jahed Hussini, Abdallah Khreishah, Issa Khalil

Research output: Contribution to journalArticle

Abstract

In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast; sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492% more cumulative severity compared to the three other baseline algorithms.

Original languageEnglish
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
Publication statusAccepted/In press - 14 Mar 2018

Fingerprint

Scheduling
Vehicular ad hoc networks
Processing
Communication

Keywords

  • online algorithm
  • packet scheduling.
  • VANET

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

Online Algorithm for Opportunistic Handling of Received Packets in Vehicular Networks. / Al-Fuqaha, Ala; Gharaibeh, Ammar; Mohammed, Ihab; Hussini, Sayed Jahed; Khreishah, Abdallah; Khalil, Issa.

In: IEEE Transactions on Intelligent Transportation Systems, 14.03.2018.

Research output: Contribution to journalArticle

Al-Fuqaha, Ala ; Gharaibeh, Ammar ; Mohammed, Ihab ; Hussini, Sayed Jahed ; Khreishah, Abdallah ; Khalil, Issa. / Online Algorithm for Opportunistic Handling of Received Packets in Vehicular Networks. In: IEEE Transactions on Intelligent Transportation Systems. 2018.
@article{51d368c5095f468ba1be922bb59d2ea4,
title = "Online Algorithm for Opportunistic Handling of Received Packets in Vehicular Networks",
abstract = "In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast; sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492{\%} more cumulative severity compared to the three other baseline algorithms.",
keywords = "online algorithm, packet scheduling., VANET",
author = "Ala Al-Fuqaha and Ammar Gharaibeh and Ihab Mohammed and Hussini, {Sayed Jahed} and Abdallah Khreishah and Issa Khalil",
year = "2018",
month = "3",
day = "14",
doi = "10.1109/TITS.2018.2809917",
language = "English",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Online Algorithm for Opportunistic Handling of Received Packets in Vehicular Networks

AU - Al-Fuqaha, Ala

AU - Gharaibeh, Ammar

AU - Mohammed, Ihab

AU - Hussini, Sayed Jahed

AU - Khreishah, Abdallah

AU - Khalil, Issa

PY - 2018/3/14

Y1 - 2018/3/14

N2 - In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast; sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492% more cumulative severity compared to the three other baseline algorithms.

AB - In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast; sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492% more cumulative severity compared to the three other baseline algorithms.

KW - online algorithm

KW - packet scheduling.

KW - VANET

UR - http://www.scopus.com/inward/record.url?scp=85043792570&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85043792570&partnerID=8YFLogxK

U2 - 10.1109/TITS.2018.2809917

DO - 10.1109/TITS.2018.2809917

M3 - Article

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

ER -