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
Labels:

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 similarity measures for text data. Object o is an out
Post a Comment
Read more

TableSense: Spreadsheet Table Detection with Convolutional Neural Networks

 - By Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, Dongmei Zhang Microsoft Research, Beijing 100080, China. Beihang University, Beijing 100191, China Paper Link Abstract Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for tab
2 comments
Read more

DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY

-By  Raghavendra Chalapathy  University of Sydney,  Capital Markets Co-operative Research Centre (CMCRC)  Sanjay Chawla  Qatar Computing Research Institute (QCRI),  HBKU  Paper Link Anomaly detection also known as outlier detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions Hawkins defines an outlier as an observation that deviates so significantly from other observations as to arouse suspicion that it was generated by a different mechanism. Aim of this paper is two-fold, First is a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore the adoption of these methods
1 comment
Read more