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Thiemo Voigt

Researcher at Uppsala University

Publications -  360
Citations -  17312

Thiemo Voigt is an academic researcher from Uppsala University. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 52, co-authored 338 publications receiving 16036 citations. Previous affiliations of Thiemo Voigt include Research Institutes of Sweden & Swedish Institute of Computer Science.

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Proceedings ArticleDOI

Protothreads: simplifying event-driven programming of memory-constrained embedded systems

TL;DR: It is shown that protothreads significantly reduce the complexity of a number of widely used programs previously written with event-driven state machines, with a memory overhead of only two bytes per protothread.
Journal ArticleDOI

Routing Attacks and Countermeasures in the RPL-Based Internet of Things

TL;DR: This paper implements and demonstrates well-known routing attacks against 6LoWPAN networks running RPL as a routing protocol, and highlights novel security features in the IPv6 protocol and exemplifies the use of these features for intrusion detection in the IoT by implementing a lightweight heartbeat protocol.
Journal ArticleDOI

Lithe: Lightweight Secure CoAP for the Internet of Things

TL;DR: Lithe-an integration of DTLS and CoAP for the IoT is presented and a novel DTLS header compression scheme is proposed that aims to significantly reduce the energy consumption by leveraging the 6LoWPAN standard.
Proceedings Article

Making TCP/IP Viable for Wireless Sensor Networks

TL;DR: A number of mechanisms that are intended to enable the use of TCP/IP for wireless sensor networks are presented: spatial IP address assignment, shared context header compression, application overlay routing, and distributed TCP caching (DTC).
Proceedings ArticleDOI

Run-time dynamic linking for reprogramming wireless sensor networks

TL;DR: It is shown that run-time dynamic linking is an effective method for reprogramming even resource constrained wireless sensor nodes, and a combination of native code and virtual machine code provide good energy efficiency.