Fast Real-World Implementation of a Direction of Arrival Method for Constrained Embedded IoT Devices

Direction of arrival (DOA) methods are found in many applications, and in the case of the Internet of Things (IoT), it is used for indoor localization. However, the implementation of DOA in IoT devices poses a real challenge, since they are computationally expensive complex numerical methods that could easily lead to resource starvation, unacceptable execution time, and rapid depletion of batteries of small constrained embedded systems typically found in IoT networks. This paper contributes to alleviating that problem, it presents a fast low-power optimized version of a DOA method called Unitary TLS ESPRIT. The optimization exploits the radio communication system design to avoid two time-consuming executions of eigendecomposition, and instead, it applies two simple Power Method algorithms. The result is a lightweight version of ESPRIT that can attain sub-millisecond execution time. To prove the solution’s viability, we carried out experiments on energy consumption, memory footprint, accuracy, and execution time for three floating-point formats in a commercial constrained embedded IoT device series without any operating system and software layers. Experiments show the solution satisfies the hardware requirements and the floating-point precision fully operated by the Floating-Point Unit is found to be the best option.acceptedVersionPeer reviewe

Direction of Arrival Method for L-Shaped Array with RF Switch : An Embedded Implementation Perspective

This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction- finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly deplete the batteries of small embedded systems typically found in IoT networks. To address this challenge, the paper presents a novel Unitary R-D Root MUSIC for L-shaped arrays that is tailor-made for such devices utilizing a switching protocol defined by Bluetooth. The solution exploits the radio communication system design to speed up execution, and its root-finding method circumvents complex arithmetic despite being used for complex polynomials. The paper carries out experiments on energy consumption, memory footprint, accuracy, and execution time in a commercial constrained embedded IoT device series without operating systems and software layers to prove the viability of the implemented solution. The results demonstrate that the solution achieves good accuracy and attains an execution time of a few milliseconds, making it a viable solution for DOA implementation in IoT devices.Peer reviewe

A Wireless Sensor Network for Hospital Security: From User Requirements to Pilot Deployment

Increasing amount of Wireless Sensor Network (WSN) applications require low network delays. However, current research on WSNs has mainly concentrated on optimizing energy-efficiency omitting low network delays. This paper presents a novel WSN design targeted at applications requiring low data transfer delays and high reliability. We present the whole design flow from user requirements to an actual pilot deployment in a real hospital unit. The WSN includes multihop low-delay data transfer and energy-efficient mobile nodes reaching lifetime of years with small batteries. The nodes communicate using a low-cost low-power 2.4&#8201;GHz radio. The network is used in a security application with which personnel can send alarms in threatening situations. Also, a multitude of sensor measurements and actuator control is possible with the WSN. A full-scale pilot deployment is extensively experimented for performance results. Currently, the pilot network is in use at the hospital.</p

Low-Power Wireless Sensor NetworksProtocols, Services and Applications /

XIII, 83p. 32 illus.online resource

Wireless Positioning in IoT: A Look at Current and Future Trends

Connectivity solutions for the Internet of Things (IoT) aim to support the needs imposed by several applications or use cases across multiple sectors, such as logistics, agriculture, asset management, or smart lighting. Each of these applications has its own challenges to solve, such as dealing with large or massive networks, low and ultra-low latency requirements, long battery life requirements (i.e., more than ten years operation on battery), continuously monitoring of the location of certain nodes, security, and authentication. Hence, a part of picking a connectivity solution for a certain application depends on how well its features solve the specific needs of the end application. One key feature that we see as a need for future IoT networks is the ability to provide location-based information for large-scale IoT applications. The goal of this paper is to highlight the importance of positioning features for IoT applications and to provide means of comparing and evaluating different connectivity protocols in terms of their positioning capabilities. Our compact and unified analysis ends with several case studies, both simulation-based and measurement-based, which show that high positioning accuracy on low-cost low-power devices is feasible if one designs the system properly.publishedVersionPeer reviewe

Implementation of Embedded Multiple Signal Classification Algorithm for Mesh IoT Networks

Angle-of-Arrival (AoA) methods are an Internet of Things (IoT) application, which could be used, for example, in indoor localization. Anchor nodes have an array of antennas and could send the data via Ethernet cable to the cloud that calculates AoA. However, having cable connections means high installation costs, and constantly transferring big chunks of data over some IoT networks, such as mesh, is energy inefficient. The solution of this paper consists in executing AoA locally in anchor nodes. Thus, the paper presents an implementation of a Multiple Signal Classification (MUSIC) algorithm tailor- made for embedded system devices. It calculates a complex eigendecomposition via an equivalent real formulation. It has a detailed memory analysis of the implemented solution that shows its memory requirements satisfy commercial embedded systems for IoT, such as Nordic semiconductor System-on-Chip (SoC) of nRF52 Series and all their SoCs with direction-finding capability. Experiments show that reducing the floating-point precision to shrink its memory footprint does not impact the accuracy. It also shows that minimizing the execution time of its time-consuming peak-finding operation has a few effects on accuracy.acceptedVersionPeer reviewe