Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

Safaei, Bardia; Mohammadsalehi, Ali Asghar; Khoosani, Kimia Talaei; Zarbaf, Saba; Monazzah, Amir Mahdi Hosseini; Samie, Farzad; Bauer, Lars; Henkel, Jörg; Ejlali, Alireza

doi:10.1109/access.2020.3022793

Cited by 39 publications

(14 citation statements)

References 253 publications

(324 reference statements)

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“…An extended survey on a significant amount of mobility models and their impact on RPL protocol is presented in [25]. The authors provide a comprehensive taxonomy, a classification of the mobility models and they conduct a comparison based on their main specification.…”

Section: Related Workmentioning

confidence: 99%

Active Connectivity Fundamentals for TSCH Networks of Mobile Robots

Orfanidis¹,

Pop²,

Fafoutis³

2022

Preprint

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Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard which have been proven to be one of the most reliable options when it comes to industrial applications. TSCH has been designed to be utilized in static network topologies. Thus, if an application scenario requires a mobile network topology, TSCH does not perform reliably. In this paper we introduce active connectivity for mobile application scenarios, such as mobile robots. This is a feature that enables the option to regulate physical characteristics such as the speed of a node as it moves, in order to keep being connected to the TSCH network. We model the active connectivity approach through a basic example where two nodes are moving towards the same direction to infer the main principles of the introduced approach. We evaluate the active connectivity feature through simulations and quantify trade-off between connectivity and application-layer performance.

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Section: Related Workmentioning

confidence: 99%

Active Connectivity Fundamentals for TSCH Networks of Mobile Robots

Orfanidis¹,

Pop²,

Fafoutis³

2022

Preprint

View full text Add to dashboard Cite

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“…By using the historical data and learning from past events, it can improve the performance based on the dynamic changes [28]. The Q-learning/SARSA technique, which is recently been used in many emerging applications, such as robotics, and Unmanned Aerial Vehicles (UAV) [29,30], uses the RL technique to perform the runtime management/optimization of the system properties in single or multi-core processors. The general Q-learning/SARSA technique consists of the three main components [31,32], including: (1) a discrete set of states S = {s 1 , s 2 , ..., s l }, (2) a discrete set of actions A = {a 1 , a 2 , ..., a k }, and (3) reward function R. The states and actions determine the rows and columns of the Q-table of the learning-based algorithm, respectively (shown in Figure 2).…”

Section: Learning-based System Properties Optimizationmentioning

confidence: 99%

Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems

et al. 2022

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In Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks in emergency modes, various design-time scheduling policies have been recently presented. These techniques are mostly pessimistic, as the occurrence of worst-case scenario at run-time is a rare event. Nevertheless, they lead to an under-utilized system due to frequent drops of Low-Criticality (LC) tasks, and creation of unused slack times due to the quick execution of high-criticality tasks. Accordingly, this paper proposes a novel optimistic scheme, that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behaviour of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks penalty and preventing frequent drops of LC tasks in the future. Based on an extensive set of experiments, our observations have shown that the proposed approach exploits accumulated dynamic slack generated at run-time, by 9.84% more on average compared to existing works, and is able to reduce the deadline miss rate by up to 51.78%, and 33.27% on average, compared to state-of-the-art works.

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“…These protocols are not able to handle rapid topological changes in the network in a timely and accurate manner. Further, it has been shown that various mobility patterns impact the performance of the standard RPL protocol significantly [1]. This occurs due to the continuous relocation of mobile nodes and the delayed readjustments of RPL by the 'trickle' algorithm.…”

Section: Introductionmentioning

confidence: 99%

SDMob: SDN-Based Mobility Management for IoT Networks

Rabet

Selvaraju

Fotouhi

et al. 2022

JSAN

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Internet-of-Things (IoT) applications are envisaged to evolve to support mobility of devices while providing quality of service in the system. To keep the connectivity of the constrained nodes upon topological changes, it is of vital importance to enhance the standard protocol stack, including the Routing Protocol for Lossy Low-power Networks (RPL), with accurate and real-time control decisions. We argue that devising a centralized mobility management solution based on a lightweight Software Defined Networking (SDN) controller provides seamless handoff with reasonable communication overhead. A centralized controller can exploit its global view of the network, computation capacity, and flexibility, to predict and significantly improve the responsiveness of the network. This approach requires the controller to be fed with the required input and to get involved in the distributed operation of the standard RPL. We present SDMob, which is a lightweight SDN-based mobility management architecture that integrates an external controller within a constrained IoT network. SDMob lifts the burden of computation-intensive filtering algorithms away from the resource-constrained nodes to achieve seamless handoffs upon nodes’ mobility. The current work extends our previous work, by supporting multiple mobile nodes, networks with a high density of anchors, and varying hop-distance from the controller, as well as harsh and realistic mobility patterns. Through analytical modeling and simulations, we show that SDMob outperforms the baseline RPL and the state-of-the-art ARMOR in terms of packet delivery ratio and end-to-end delay, with an adjustable and tolerable overhead. With SDMob, the network provides close to 100% packet delivery ratio (PDR) for a limited number of mobile nodes, and maintains sub-meter accuracy in localization under random mobility patterns and varying network topologies.

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Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

Cited by 39 publications

References 253 publications

Active Connectivity Fundamentals for TSCH Networks of Mobile Robots

Active Connectivity Fundamentals for TSCH Networks of Mobile Robots

Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems

SDMob: SDN-Based Mobility Management for IoT Networks

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