distributed sensors
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Low-power wide-area network technologies such as long-range wide-area network (LoRaWAN) are promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data is a critical system function. This article considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN’s one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices’ clocks at all times. However, we show that this approach is susceptible to a frame delay attack consisting of malicious frame collision and delayed replay. Real experiments show that the attack can affect the end devices in large areas up to about 50,000, m 2 . In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, we propose a LoRaTS gateway design that integrates a commodity LoRaWAN gateway and a low-power software-defined radio receiver to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa’s chirp spread spectrum modulation, we develop signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa’s default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. We also investigate and implement a more crafty attack that uses advanced radio apparatuses to eliminate the frequency biases. To address this crafty attack, we propose a pseudorandom interval hopping scheme to enhance our frequency bias tracking approach. Extensive experiments show the effectiveness of our approach in deployments with real affecting factors such as temperature variations.

The Internet of Things (IoT) connects billions of sensors to share and collect data at any time and place. The Advanced Metering Infrastructure (AMI) is one of the most important IoT applications. IoT supports AMI to collect data from smart sensors, analyse and measure abnormalities in the energy consumption pattern of sensors. However, two-way communication in distributed sensors is sensitive and tends towards security and privacy issues. Before deploying distributed sensors, data confidentiality and privacy and message authentication for sensor devices and control messages are the major security requirements. Several authentications and encryption protocols have been developed to provide confidentiality and integrity. However, many sensors in distributed systems, resource constraint smart sensors, and adaptability of IoT communication protocols in sensors necessitate designing an efficient and lightweight security authentication scheme. This paper proposes a Payload Encryption-based Optimisation Scheme for lightweight authentication (PEOS) on distributed sensors. The PEOS integrates and optimises important features of Datagram Transport Layer Security (DTLS) in Constrained Application Protocol (CoAP) architecture instead of implementing the DTLS in a separate channel. The proposed work designs a payload encryption scheme and an Optimised Advanced Encryption Standard (OP-AES). The PEOS modifies the DTLS handshaking and retransmission processes in PEOS using payload encryption and NACK messages, respectively. It also removes the duplicate features of the protocol version and sequence number without impacting the performance of CoAP. Moreover, the PEOS attempts to improve the CoAP over distributed sensors in the aspect of optimised AES operations, such as parallel execution of S-boxes in SubBytes and delayed Mixcolumns. The efficiency of PEOS authentication is evaluated on Conitki OS using the Cooja simulator for lightweight security and authentication. The proposed scheme attains better throughput while minimising the message size overhead by 9% and 23% than the existing payload-based mutual authentication PbMA and basic DTLS/CoAP scheme in random network topologies with less than 50 nodes.

Magnetoelectric (ME) sensors with a form factor of a few millimeters offer a comparatively low magnetic noise density of a few pT/Hz in a narrow frequency band near the first bending mode. While a high resonance frequency (kHz range) and limited bandwidth present a challenge to biomagnetic measurements, they can potentially be exploited in indirect sensing of non-magnetic quantities, where artificial magnetic sources are applicable. In this paper, we present the novel concept of an active magnetic motion sensing system optimized for ME sensors. Based on the signal chain, we investigated and quantified key drivers of the signal-to-noise ratio (SNR), which is closely related to sensor noise and bandwidth. These considerations were demonstrated by corresponding measurements in a simplified one-dimensional motion setup. Accordingly, we introduced a customized filter structure that enables a flexible bandwidth selection as well as a frequency-based separation of multiple artificial sources. Both design goals target the prospective application of ME sensors in medical movement analysis, where a multitude of distributed sensors and sources might be applied.

This paper proposes a novel spiral-wound, optic-fiber sensor to monitor the corrosion of steel bars. At the same time, the winding parameters, such as winding angle and pitch, were first theoretically deduced. Then, to decrease light loss, a practically distributed sensor wound onto the protective mortar layer was developed by increasing the winding curvature radius. The spiral distributed sensors were experimentally verified for their feasibility. Experimental results showed that the spiral fiber strain depended on the thickness of the protective mortar layer. Furthermore, the spiral distributed strain well reflected the cracking process of concrete. In addition, the concrete cracking time depended on the thickness of the protective concrete layer. Accordingly, this method is feasible for evaluating the initial and final cracking behaviors of concrete structures and provides a sight for steel bar corrosion.

Epidural anesthesia is a pain management process that requires the insertion of a miniature needle through the epidural space located within lumbar vertebrae. The use of a guidance system for manual insertion can reduce failure rates and provide increased efficiency in the process. In this work, we present and experimentally assess a guidance system based on a network of fiber optic distributed sensors. The fibers are mounted externally to the needle, without blocking its inner channel, and through a strain-to-shape detection method reconstruct the silhouette of the epidural device in real time (1 s). We experimentally assessed the shape sensing methods over 25 experiments performed in a phantom, and we observed that the sensing system correctly identified bending patterns typical in epidural insertions, characterized by the different stiffness of the tissues. By studying metrics related to the curvatures and their temporal changes, we provide identifiers that can potentially serve for the (in)correct identification of the epidural space, and support the operator through the insertion process by recognizing the bending patterns.

Abstract This paper presents a multi-point strain monitoring method based on distributed sensors, which can monitor the multi-point stress of prestressed tendons in concrete. Firstly, the special sensor protection device is installed in the designated position of the component. Then, the sensor is placed in the device and attached to the monitored prestressing tendon. After concrete pouring, the device can form a closed space around the sensor to ensure that the sensor can freely expand and move in a small range when the prestressed tendon is tensioned. The proposed method is applied to the monitoring of the construction period and initial operation period of a large-span spatial structure, and the research results show that the method proposed in this paper can well realize the multi-point monitoring of the prestressed tendon strain in the concrete, and effectively solve the engineering problems; through the analysis of the long-term monitoring data, we can accurately grasp the influence of each construction condition on the structure stress, and clarify the actual stress state and long-term change trend of the structure. The obtained monitoring data play a positive role in guiding the construction of prestressed structure, and the construction safety can be objectively evaluated by analyzing the monitoring data.