Urs Hunkeler

MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) pose novel challenges compared with traditional networks. To answer such challenges a new communication paradigm, data-centric communication, is emerging. One form of data-centric communication is the publish/subscribe messaging system. Compared with other data-centric variants, publish/subscribe systems are common and wide-spread in distributed computing. Thus, extending publish/subscribe systems intoWSNs will simplify the integration of sensor applications with other distributed applications. This paper describes MQTT-S [1], an extension of the open publish/subscribe protocol message queuing telemetry transport (MQTT) [2] to WSNs. MQTT-S is designed in such a way that it can be run on low-end and battery-operated sensor/actuator devices and operate over bandwidth-constraint WSNs such as ZigBee-based networks. Various protocol design points are discussed and compared. MQTT-S has been implemented and is currently being tested on the IBM wireless sensor networking testbed [3]. Implementation aspects, open challenges and future work are also presented.

A power-efficient wireless sensor network for continuously monitoring seismic vibrations

We present a novel power-efficient wireless sensor network for continuously monitoring and analyzing seismic vibrations with sensor nodes and forwarding the retrieved information with low-cost relay nodes to backend applications. The applied vibration sensing algorithms are derived from the DIN 4150–3 standard. All nodes in the network are battery-powered and equipped with an IEEE 802.15.4 compatible radio transceiver. The nodes communicate with each other by executing a novel power-efficient protocol stack, which provides all network functions required by the vibration-sensing application and uses a publish/subscribe messaging protocol for communicating between the network nodes and the backend applications. Results obtained in certification and field tests show that the proposed vibration-sensing solution is standard-compliant, and that the wireless vibration sensor network (WVSN) exhibits excellent performance in terms of packet delivery rate, latency, and power efficiency.

A case for centrally controlled wireless sensor networks

In this article we present the Intelligent, Manageable, Power-Efficient and Reliable Internetworking Architecture (IMPERIA), a centrally managed architecture for large-scale wireless sensor networks (WSNs). We discuss the advantages of a centralized management over distributed approaches and derive our design by rigorously minimizing the amount of state information on individual sensor nodes and all sources of message collision during network operations. The result is a clustered multi-hop TDMA protocol that globally synchronizes the network and collects data at ultra-low power consumption. We present the end-to-end architecture and detail the algorithms we developed for (a) efficient network topology discovery and link quality estimation, (b) combined routing and clustering for pre-defined basestations, and (c) the scheduling of the medium access for multi-cluster and multi-channel data collection. IMPERIA has been implemented on TinyOS and IBMs Mote Runner and successfully deployed in applications for vibration sensing as well as datacenter energy management. This article summarizes the performance results from simulations, laboratory experiments, and deployment measurements that support our design decisions.

A quality-of-information-aware framework for data models in wireless sensor networks

Wireless sensor networks are used to monitor a given environment, such as indoor heating conditions or the micro-climate of glaciers. They offer a low-cost solution that provides a high data density. Usually the user of such a sensor network has a good idea of how, knowing the environment, the sensed values should behave. This idea can be expressed as a data model. Such models can be used to detect anomalies, compress data, or combine data from many inexpensive sensors to increase the quality of the measurements. This paper presents a framework to process arbitrary sensor-network data models. The framework can then be used to distribute the model processing into the wireless sensor network. Quality of information criteria are used to determine the performance of the models. A prototype of the framework is presented together with a comparison of two existing stochastic data model approaches for wireless sensor networks.

A Code Generator for Distributing Sensor Data Models

As wireless sensor networks mature, it becomes clear that the raw data collected by this technology can only be used in a meaningful way if it can be analyzed automatically. Describing the behavior of the data with a model, and then looking at the parameters of the model, or detecting differences between the model and the real data, is how experimental data is typically used in other fields. The work presented here aims at facilitating the use of sensor data models to describe the expected behavior of the sensor observations. The processing of such models can be pushed into the wireless sensor network to eliminate redundant information as early in the data collection chain as possible, thus minimizing both bandwidth requirements and energy consumption.