Abstract

Internet of Things (IoT) is evolving to multi-application scenarios in smart cities, which demand specific traffic patterns and requirements. Multi-applications share resources from a single multi-hop wireless networks, where smart devices collaborate to send collected data over a Low-Power and Lossy Networks (LLNs). Routing Protocol for LLNs (RPL) emerged as a routing protocol to be used in IoT scenarios where the devices have limited resources. Instances are RPL mechanisms that play a key role in order to support the IoT scenarios with multiple applications, but it is not standardized yet. Although there are related works proposing multiple instances in RPL on the same IoT network, those works still have limitations to support multiple applications. For instance, there is a lack of flexibility and dynamism in management of multiple instances and service differentiation for applications. In this context, the goal of this work is to develop a solution called DYNAmic multiple RPL instanceS for multiple ioT applicatIons (DYNASTI), which provides more dynamism and flexibility by managing multiple instances of RPL. As a result of this, the traffic performance of multiple applications is enhanced through the routing, taking into consideration the distinct requirements of the applications. In addition, DYNASTI enables the support of sporadic applications as well as the coexistence between regular and sporadic applications. DYNASTI achieved results that demonstrate a significant improvement in reducing the number of control messages, which resulted in increased packet received, decreased end-to-end delay, reduced energy consumption, and an improvement in service differentiation to multiple applications.

Highlights

  • The recent emerging advances on the Internet of Things (IoT) [1,2] can be found several smart mobile and fixed devices.When the resources of these smart devices are combined each other, they offer new monitoring and control capabilities which are able to perform intelligent activities [3,4].There are a lot of IoT wireless technologies and protocols (e.g., Long Range (LoRa)/LoRaWAN, NarrowBand-IoT (NB-IoT), LTE-M, SigFox, ZigBee, and among others [5,6,7]) that can be applied to provide communication for the smart devices

  • We assessed (i) the overhead of the DODAG Information Object (DIO) and Destination Advertisement Object (DAO) in order to measure the impact of the control message interruption, (ii) the end-to-end delay for successfully received messages in the sink, assessing the impact they can have on different numbers of nodes and applications, (iii) the energy consumption which measures the total energy spent by the sensor nodes, and (iv) lost packets

  • Static Instances approach does not employ mechanisms of instance scheduling nor management of messages; they employ multiple instances, every application must be associated with a single instance which runs during the entire network lifetime in this approach

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Summary

Introduction

The recent emerging advances on the Internet of Things (IoT) [1,2] can be found several smart mobile and fixed devices (e.g., sensors, cameras, 3D glasses, actuators, and several others). This article proposes DYNAmic multiple RPL instanceS for multiple ioT applicatIons (DYNASTI), a solution that extends the implementation of multiple instances in RPL, in order to provide more dynamicity and flexibility to routing, more optimizing QoS and traffic performance. This solution allows that multiple IoT applications with different requirements communicate in the same LLN. To the best our knowledge to date, there is no work in the literature addressing the shortcomings of existing multi-instance developments allowing them to be performed with multiple applications with different traffic requirements and patterns on the same LLNs through a management dynamic and flexible of instances.

RPL Background
Related Work
Concepts and Definitions
DYNASTI Architecture
Instance Scheduler
Dissemination and Storing Information of RPL Instance Scheduling
4: Update the instances statuses
Implementation Notes
Performance Evaluation and Results
Network Configuration Environment
Performance Metrics
Application Scenario
Selection of the Scheduling of Instances
Results
Varying the Number of Nodes
Varying the Number of Applications
Varying the Application Message Send Intervals
Results Summary
Conclusions and Future Work
Objective
Full Text
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