Abstract

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.

Highlights

  • Introduction iationsWith the advent of the Internet-of-Things (IoT) and its revolutionary role in numerous application domains such as healthcare, industrial automation, and environmental monitoring, there is an increasing demand for seamless support of mobile nodes (MNs)

  • To address the low reliability of the Routing Protocol for Lossy Low-power Networks (RPL) protocol in mobile IoT applications, we have proposed SDMob

  • An edge device collaborates with the distributed nodes to provide seamless handoff for the mobile nodes

Read more

Summary

Related Works

We provide an overview of some of the main related works, which are focused on mobility support in RPL, localization algorithms within the routing protocol, and edge or fog computing architecture in IoT networks enabled with SDN devices. Upon receiving the DIO packet, each node selects its preferred parent (based on some objective function) and schedules relaying a DIO packet with its non-decreasing rank to further advertise the network. Upward traffic can be routed after DIO packet transmission, but for downward routing, the root node (or parent in storing mode) gets notified about the high-rank nodes only after transmission of Destination Advertisement Object (DAO) packets. In non-storing mode, it is only the root that maintains the downward routes. This mode scales better, since the memory footprint at intermediate nodes does not increase with the size of the network. SDMob uses the non-storing mode of RPL so that the controller can manipulate the source-routed downward packets. SDMob allows the baseline version of RPL to converge with arbitrary metrics and injects higher priority routes from the controller

Mobility-Aware RPL Routing
Location Estimation Models for Enhancing Routing Protocols
SDN-Enabled IoT Network Architectures
SDMob Architecture
Basic SDMob Architecture
Collision Avoidance between Control Plane and Data Plane
Downward Routing Process
Integration to the Objective Functions
Enhanced SDMob
Handling Anchor Density
New Route Projection Packet Format
Filter Design
Analytical Model and Evaluation
Analytical Model
Analytical Evaluation
Relation between the Analytical Evaluations and Simulations
Performance Evaluation
An Overview of the ARMOR
Scaling to Multiple Mobile Nodes
Scaling to Networks with High Density or Hop-Distance
Mobility Patterns
Velocity
Path Loss Variance
Conclusions
Objective
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call