Glenn Ergeerts

Survey of the DASH7 Alliance Protocol for 433 MHz Wireless Sensor Communication

433 MHz is getting more attention for Machine-to-Machine communication. This paper presents the DASH7 Alliance Protocol, an active RFID alliance standard for 433 MHz wireless sensor communication based on the ISO/IEC 18000-7. First, the major differences of 433 MHz communication compared to more frequently used frequencies, such as 2.4 GHz and 868/920 MHz are explained. Subsequently, the general concepts of DASH7 Alliance Protocol are described, such as the BLAST networking topology and the different OSI layer implementations, in a top-down method. Basic DASH7 features such as the advertising protocol, ad-hoc synchronization and query based addressing are used to explain the different layers. Finally, the paper introduces a software stack implementation named OSS-7, which is an open source implementation of the DASH7 alliance protocol used for testing, rapid prototyping, and demonstrations.

DASH7 alliance protocol 1.0: Low-power, mid-range sensor and actuator communication

This paper presents the DASH7 Alliance Protocol 1.0. It is an industry alliance standard for wireless sensor and actuator communication using the unlicensed sub-1 GHz bands. The paper explains its historic relation to active RFID standards ISO 18000-7 for 433 MHz communication, the basic concepts and communication paradigms of the protocol. Since the protocol is a full OSI stack specification, the paper discusses the implementation of every OSI layer.

DASH7 Alliance Protocol in Monitoring Applications

In this paper we introduce important aspects of the recently published DASH7 Alliance Protocol v1.0 specification for wireless sensor and actuator networks. The main contribution of this paper is the discussion of the different communication schemes and the accompanying trade-offs which can be used when designing a DASH7 network. Finally, we describe two practical use cases as examples of how DASH7 can be used to efficiently solve specific problems as well as the hardware developed that uses energy harvesting.

Adaptive probabilistic model using angle of arrival estimation for IoT indoor localization

The industrial demands for accurate localization systems have been rapidly increasing after the introduction of the Internet of Things (IoT) concept. Self localization and tracking transmitting sources are considered essential parts of IoT applications. In this paper we studied the possibility of applying angle of arrival (AoA) estimations to localize an IoT transceiver device in an indoor environment. Furthermore, we propose an adaptive probabilistic model which works on top of the AoA estimation technique to improve the localization accuracy. The experimental results show the potential of using AoA-based localization for indoor environments. The results furthermore show that the proposed adaptive probabilistic model outperforms the traditional static probabilistic model in terms of the localization accuracy and the stability of the position estimate.

Large Scale Distributed Localization Based on RSS and Mass-Spring Model

Large scale localization has gained interest in the last few years together with wireless sensor networks. Applications such as a swarm of drones or a sensor network to control an environment already exist but do not cover scales up to thousands of nodes. In this paper, a method for large scale distributed localization is developed based on existing technologies including received signal strength and mass-spring model (MSM). First, we created a theoretical model based on specific indoor and outdoor tests. Based on this model, a scaled up simulation was done using an MSM-based algorithm. Using this simulation, an average error of up to 7.66 m could be achieved in a field of 100 m by 100 m.

Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware

The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks.

Multi-frequency sub-1 GHz radio tomographic imaging in a complex indoor environment

Unlike most currently available localization systems, tagless localization technologies do not require a target to wear a passive or active hardware device. Radio Tomographic Imaging (RTI) is one such technique, which operates based on the use of a tomographic radio frequency (RF) sensor network. The majority of RTI-systems communicate using a single frequency band: 2.4 GHz. The use of sub-1 GHz frequencies within RTI could potentially provide important benefits regarding energy efficiency, accuracy in complex indoor environments and size of the environments in which a system can be installed. We deployed a combined 433 MHz and 868 MHz RF sensor network in a complex indoor environment and performed localization when a human individual was present in the environment. Two different RTI-algorithms were investigated: a Bayesian-based method we developed earlier and an adaptation of an existing 2.4 GHz technique based on fade level. Both methods turned out to be capable of accurately locating individuals with a median error lower than 1 meter. This proves the feasibility of using a combination of sub-1 GHz frequencies in RTI for indoor localization in complex environments.

Combining multiple sub-1 GHz frequencies in Radio Tomographic Imaging

Conventional localization systems require the target to carry a tag, which can be highly impractical for some individuals, such as the elderly, or in some situations, such as in emergencies. This requirement can be alleviated by an emerging set of tagless or device-free localization systems, of which Radio Tomographic Imaging (RTI) is a common example. Most variations of this technique assume the use of radio frequency (RF) signals in the 2.4 GHz band, or combinations of lower frequencies with that band; however, using only lower frequencies might decrease the power consumption and the influence of the environment, and increase the range of the system. We tested the combination of 433 MHz and 868 MHz in a single RTI system. We studied two approaches to combine the RTI images generated by one frequency with the other: an approach based on literature and a newly developed approach based on probability theory. This paper compares the result of both approaches. We found that the result based on literature has a root-mean-square error (RMSE) of 1.09 m, while our approach has an RMSE of 0.54 m. While also improving the state-of-the-art fusion of two frequencies, we proved the feasibility of combining only frequencies under 1 GHz in an RTI system.