Skip to main content

CoAPing up with IOT



Internet of Things (IOT)  has been called the Third Wave in information industry following the computer and the Internet. There are hundreds of protocols supported by IoT. Of the many protocols, wireless protocols play an important role in IoT development.  IOT is represented as a global network which intelligently connects all the objects no matter devices, systems or human, it is with self-configuring capabilities based on standard and interoperable protocols and formats.


IoT needs to integrate various sensors, computer and communication equipment, which are using different communication protocols. Wireless Protocols are mainly used in three layers, which are PHY/MAC layer, Network/Communication layer and Application layer.

Constrained Application Protocol (CoAP) is one of the latest application layer protocol developed by IETF for smart devices to connect to Internet. As many devices exist as components in vehicles and buildings with constrained resources, it leads a lot of variation in power computing, communication bandwidth etc. Thus lightweight protocol CoAP is intended to be used and considered as a replacement of HTTP for being an IoT application layer protocol.


Application layer usually employ HTTP to provide web service, but HTTP has high computation complexity, low data rate and high energy consumption. Therefore, IETF has developed several lightweight protocols, e.g., CoAP, Embedded Binary HTTP (EBHTTP), Lean Transport Protocol (LTP). The Constrained Application Protocol (CoAP) is a specialized web transfer protocol for use with constrained nodes and constrained (e.g., low-power, lossy) networks.

CoAP Features


  • Constrained web protocol fulfilling M2M requirements
  • Security binding to Datagram Transport Layer Security (DTLS)
  • Asynchronous message exchanges
  • Low header overhead and parsing complexity.
  • URI and Content-type support.
  • Simple proxy and caching capabilities
  • UDP binding with optional reliability supporting unicast and multicast requests.
  • A stateless HTTP mapping allowing proxies to be built providing access to CoAP resources via HTTP in a uniform way or for HTTP simple interfaces to be realized alternatively over CoAP.


CoAP Vs HTTP

Unlike HTTP based protocols, CoAP operates over UDP instead of using complex congestion control as in TCP. CoAP is based on REST architecture, which is a general design for accessing
Internet resources. In order to overcome disadvantage in constrained resource, CoAP need to optimize the length of datagram and provide reliable communication. On one side, CoAP provides URI, REST method such as GET, POST, PUT, and DELETE. On the other side, based on lightweight UDP protocol, CoAP allows IP multicast, which satisfies group communication for IoT. To compensate for the unreliability of UDP protocol, CoAP defines a re-transmission mechanism and provides resource discovery mechanism with resource description. 




With the completion of the CoAP specification, it is expected that there will be million of devices deployed in various application domains in the future. These applications range from smart energy, smart grid, building control, intelligent lighting control, industrial control systems, asset tracking, to environment monitoring. CoAP would become the standard protocol to enable interaction between devices and to support IoT applications

Popular posts from this blog

ABOD and its PyOD python module

Angle based detection By  Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek  Ludwig-Maximilians-Universität München  Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By  Yue Zhao   Zain Nasrullah   Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada  Zheng Li jk  Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that  uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing.  Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based sim...

Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems

  Ranwa Al Mallah , Godwin Badu-Marfo , Bilal Farooq image Courtesy: Comparitech Abstract Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructure-based configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The threat is due to the intrinsic characteristics of the wireless medium and is caused by the characteristics of the vehicular networks such as high node-mobility and rapidly changing topology. Most cyber defense techniques depend on highly reliable and connected networks. This paper explores falsified informat...

MLOps Drivenby Data Quality using ease.ml techniques

 Cedric Renggli, Luka Rimanic, Nezihe Merve Gurel, Bojan Karlas, Wentao Wu, Ce Zhang ETH Zurich Microsoft Research Paper Link ease.ml reference paper link Image courtesy 99designes Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong dependency between the quality of a machine learning model and the quality of the data used to train or perform evaluations. In this work, we demonstrate how different aspects of data quality propagate through various stages of machine learning development. By performing joint analysis of the impact of well-known data quality dimensions and the downstream machine learning process, we show that different components of a typical MLOps pipeline can be efficiently designed, providing both a technical and theoretical perspective. Courtesy: google The term “MLOps” is used when this DevOps process is specifically applied to ML. Diffe...