Tackling Mobility in Low Latency Deterministic Multihop IEEE 802.15.4e Sensor Network

Al-Nidawi, Yaarob; Yahya, Harith; Kemp, Andrew H.

doi:10.1109/jsen.2015.2500502

Cited by 23 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the simulations, the random waypoint mobility model is used because it fairly reflects the actual mobility behaviour in WSNs and IoT applications [13] [14].…”

Section: B Gtm-rpl Game Formulationmentioning

confidence: 99%

See 1 more Smart Citation

A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications

2018

Self Cite

View full text Add to dashboard Cite

“…In the simulations, the random waypoint mobility model is used because it fairly reflects the actual mobility behaviour in WSNs and IoT applications [13] [14].…”

Section: B Gtm-rpl Game Formulationmentioning

confidence: 99%

“…Where g(λ k , r) is the pseudo-gradient of σ(λ k , r) and ▽ k φ k (λ k ) is given by: 13, its Jacobian matrix can be defined by G(λ k , r) as:…”

Section: Theorem 32mentioning

confidence: 99%

A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications

2018

Self Cite

View full text Add to dashboard Cite

“…There is a performance evaluation with respect to variations in the superframes in which the authors of [13] provide an insight about the relationship of superframe size, base timeslot size and data payload. A mobility-aware (MA-LLDN) scheme has been implemented for LLDN in [14], in which the authors claim that their approach minimizes both latency and energy consumption when compared to the standard LLDN enabled network.…”

Section: Related Workmentioning

confidence: 99%

Worst-case bound analysis for the time-critical MAC behaviors of IEEE 802.15.4e

Kurunathan

Severino

Koubâa

et al. 2017

2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS)

View full text Add to dashboard Cite

With an advancement towards the paradigm ofInternet of Things (IoT), in which every device will be interconnected and communicating with each other, the field of wirelesssensor networks has helped to resolve an ever-growing demandin meeting deadlines and reducing power consumption. Amongseveral standards that provide support for IoT, the recentlypublished IEEE 802.15.4e protocol is specifically designed to meetthe QoS requirements of industrial applications. IEEE 802.15.4eprovides five Medium-Access Control (MAC) behaviors, includingthree that target time-critical applications: Deterministic andSynchronous Multichannel Extension (DSME); Time SlottedChannel Hopping (TSCH) and Low Latency Deterministic Network (LLDN). However, the standard and the literature do notprovide any worst-case bound analysis of these behaviors, thusit is not possible to effectively predict their timing performancein an application and accurately devise a network in accordanceto such constraints. This paper fills this gap by contributingnetwork models for the three time-critical MAC behaviors usingNetwork Calculus. These models allow deriving the worst-caseperformance of the MAC behaviors in terms of delay andbuffering requirements. We then complement these results bycarrying out a thorough performance analysis of these MACbehaviors by observing the impact of different parameters. Abstract-With an advancement towards the paradigm of Internet of Things (IoT), in which every device will be interconnected and communicating with each other, the field of wireless sensor networks has helped to resolve an ever-growing demand in meeting deadlines and reducing power consumption. Among several standards that provide support for IoT, the recently published IEEE 802.15.4e protocol is specifically designed to meet the QoS requirements of industrial applications. IEEE 802.15.4e provides five Medium-Access Control (MAC) behaviors, including three that target time-critical applications: Deterministic and Synchronous Multichannel Extension (DSME); Time Slotted Channel Hopping (TSCH) and Low Latency Deterministic Network (LLDN). However, the standard and the literature do not provide any worst-case bound analysis of these behaviors, thus it is not possible to effectively predict their timing performance in an application and accurately devise a network in accordance to such constraints. This paper fills this gap by contributing network models for the three time-critical MAC behaviors using Network Calculus. These models allow deriving the worst-case performance of the MAC behaviors in terms of delay and buffering requirements. We then complement these results by carrying out a thorough performance analysis of these MAC behaviors by observing the impact of different parameters. Worst-Case BoundAnalysis for the Time-

show abstract

“…Every DODAG is formed according to the defined Objective Function (OF) which determines the routing metrics that will be used for selecting the preferred parent. Many applications require some of the nodes to be mobile which creates an extra challenge to routing especially when nodes move at high speeds or in an unpredictable pattern [3][4] [5]. RPL was originally designed for static networks but there are some efforts that proved it can be used for some mobile WSNs with a few alterations and enhancements [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic RPL for multi-hop routing in IoT applications

Kharrufa

Al-Kashoash

Al-Nidawi

et al. 2017

2017 13th Annual Conference on Wireless on-Demand Network Systems and Services (WONS)

Self Cite

View full text Add to dashboard Cite

Abstract-The Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard routing protocol for the Internet of Things (IoT). This paper investigates the use of RPL in dynamic networks and presents an enhanced RPL for different applications with dynamic mobility and diverse network requirements. This implementation of RPL is designed with a new dynamic Objective-Function (D-OF) to improve the Packet Delivery Ratio (PDR), end-to-end delay and energy consumption while maintaining low packet overhead and loop-avoidance. We propose a controlled reverse-trickle timer based on received signal strength identification (RSSI) readings to maintain high responsiveness with minimum overhead and consult the objective function when a movement or an inconsistency is detected to help nodes make an informed decision. Simulations are done using Cooja with random waypoint mobility scenario for healthcare applications considering multi-hop routing. The results show that the proposed dynamic RPL (D-RPL) adapts to the nodes mobility and has a higher PDR, slightly lower end-to-end delay and reasonable energy consumption compared to related existing protocols.

show abstract

Tackling Mobility in Low Latency Deterministic Multihop IEEE 802.15.4e Sensor Network

Cited by 23 publications

References 41 publications

A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications

A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications

Worst-case bound analysis for the time-critical MAC behaviors of IEEE 802.15.4e

Dynamic RPL for multi-hop routing in IoT applications

Contact Info

Product

Resources

About