Improving Energy Efficiency of MQTT-SN in Lossy Environments Using Seed-Based Network Coding

Schütz, Bertram; Bauer, Jan; Aschenbruck, Nils

doi:10.1109/lcn.2017.87

Cited by 14 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The design and implementation of the coding and its components are based on the lightweight, microservices software architecture [52],focused on distributed coding, deployed in the cloud, and capable of integration at the application layer to create composite services. The functionality of these microservices enables meeting expectations for deployment elasticity, heterogeneity, and distribution [53]. Each component of these coding services centers around using the RESTful paradigm for native applications, typically through HTTP APIs.…”

Section: A Efficient Fulcrum Coding In 5g Cloud Microservicesmentioning

confidence: 99%

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Julio,

Garcia,

Díaz

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: A Efficient Fulcrum Coding In 5g Cloud Microservicesmentioning

confidence: 99%

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Julio,

Garcia,

Díaz

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…MQTT-SN forwarders are responsible for transporting messages to GW. The gateways used are of two types [11], [12]:…”

Section: Mqtt-snmentioning

confidence: 99%

Efficiency of integration between sensor networks and clouds

Tsvetanov

Pandurski

2022

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Numerous wireless sensor networks (WSN) applications include monitoring and controlling various conditions in the environment, industry, healthcare, medicine, military affairs, agriculture, etc. The life of sensor nodes largely depends on the power supply type, communication ability, energy storage capacity and energy management mechanisms. The collection and transmission of sensor data streams from sensor nodes lead to the depletion of their energy. At the same time, the storage and processing of this data require significant hardware resources. Integration between clouds and sensor networks is an ideal solution to the limited computing power of sensor networks, data storage and processing. One of the main challenges facing systems engineers is to choose the appropriate protocol for integrating sensor data into the cloud structure, taking into account specific system requirements. This paper presents an experimental study on the effectiveness of integration between sensor networks and the cloud, implemented through three protocols HTTP, MQTT and MQTT-SN. A model for studying the integration of sensor network - Cloud with the communication models for integration - request-response and publish- subscribe, implemented with HTTP, MQTT and MQTT-SN. The influence of the number of transmitted data packets from physical sensors to the cloud on the transmitted data delay to the cloud, the CPU and memory load was studied. After evaluating the results of sensor network and cloud integration experiments, the MQTT protocol is the most efficient in terms of data rate and power consumption.

show abstract

“…The transport protocol uses UDP (User Datagram Protocol) and has the advantage of supporting multicast, but CoAP has a disadvantage in that it is difficult to apply to time critical systems, due to limited QoS policy and asynchronous connection. MQTT-SN (MQTT for Sensor Networks) is a protocol for sensor nodes operating in a process similar to MQTT [ 20 , 21 ]. The benefit is that it can be applied to devices with multicast support and low computing performance.…”

Section: Related Workmentioning

confidence: 99%

DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

Park

Kim

2018

Sensors

View full text Add to dashboard Cite

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

show abstract

Improving Energy Efficiency of MQTT-SN in Lossy Environments Using Seed-Based Network Coding

Cited by 14 publications

References 14 publications

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Efficiency of integration between sensor networks and clouds

DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

Contact Info

Product

Resources

About