The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.

LoRaWAN is one of the most popular protocols to build low-power wide area networks. Unfortunately, it adopts a star topology, which limits network coverage and may also cause an unnecessary decrease in energy efficiency as well as scalability. In fact, end-devices that are deployed far away from a gateway need to increase their transmission power or spreading factor (SF) to sustain reliable communications, which increases their energy expenditure as well as the size of the collision domain. The only alternative is the deployment of additional gateways or dedicated relay nodes, which results in higher costs and deployment efforts. In this work, we introduce LoRaHop, an extension of LoRaWAN that enriches end-devices with the ability to form a mesh network and to seamlessly relay packets to/from a gateway, thereby providing LoRaWAN networks with multihop support for both uplink and downlink messaging. LoRaHop leverages concurrent transmissions to enable a reliable and efficient data collection or dissemination over the mesh network, as well as to simplify network formation. Furthermore, LoRaHop embeds a mechanism that simplifies rendezvous across devices and that minimizes the impact of mesh operations on existing LoRaWAN transmissions. We implement LoRaHop on off-the-shelf LoRa end-devices (ensuring their interoperability with commercial LoRaWAN gateways and network servers), and evaluate its performance on an outdoor testbed. Our results show that LoRaHop can effectively extend the coverage of an LoRaWAN network while improving reliability by up to 98.33% and reducing energy consumption by up to 48.02%. Our findings further demonstrate that using LoRaHop to create a multihop LoRaWAN network that communicates using low SFs brings significant benefits in terms of energy efficiency and scalability compared to the use of a single-hop LoRaWAN network using high SFs.

This paper presents EMU, a framework that enables the emulation, snipping, and multiplexing of LoRa chirps on commercial IoT devices equipped with low-power sub-GHz transceivers, including those supporting LoRa itself. Chirp snipping consists in artificially removing a sequence of chips and in putting the radio in low-power mode, which allows to reduce energy consumption while still commu-nicating reliably. Chirp multiplexing exploits the gaps introduced by chirp snipping to transmit portions of another chirp on a sep-arate channel, which allows to concurrently transmit two LoRa packets and to increase the throughput. We build EMU as a modu-lar framework and implement support for off-the-shelf LoRa and non-LoRa transceivers. We then evaluate its performance by com-paring the reliability, efficiency, and receiver sensitivity achieved by EMU with that of traditional LoRa for different physical layer settings. We finally showcase EMU's ability to send packets over two channels simultaneously, thereby improving the uplink throughput of LoRaWan, and demonstrate that even non-LoRa transceivers employing EMU can communicate to a LoRaWan gateway, enabling new use cases and expanding the applicability of LoRa technology.

Recently, several datasets shedding light on connectivity aspects in real-world LoRa networks have been provided to the community. However, they typically only involve a limited number of nodes, deal with unidirectional communication only, or focus on very specific physical layer settings. More importantly, existing datasets typically lack fine-grained environmental information such as the temperature in the surroundings of each node, which is known to have a strong impact on communication performance. In this work, we provide the community with a comprehensive dataset that fills all these gaps. We have collected detailed connectivity information in an outdoor LoRa network composed of 21 nodes for more than four months. Our dataset does not only focus on network-level performance (e.g., the average number of correctly-exchanged packets), but sheds light on link-level information such as the received signal strength, signal-to-noise ratio, and the number of available neighbours over time. We further collect environmental information from an online weather site, as well as the on-board temperature of each node in the network, which varies considerably across the deployed locations. We collect all this information while perpetually changing physical layer settings such as the spreading factor and the RF channel. A preliminary analysis of our dataset, which is available in Zenodo1, reveals that temperature has a significant correlation with the link quality and connectivity in the outdoor LoRa network, confirming the findings of earlier studies.

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This paper fills this gap and provides the first extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by de-synchronization and beating on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based flooding protocols in the presence of radio interference on a large-scale, and derive important insights on how the used physical layer affects their dependability.

The increasing congestion of the RF spectrum is a key challenge for low-power wireless networks using concurrent transmissions. The presence of radio interference can indeed undermine their dependability, as they rely on a tight synchronization and incur a significant overhead to overcome packet loss. In this paper, we present Harmony, a new data collection protocol that exploits the benefits of concurrent transmissions and embeds techniques to ensure a reliable and timely packet delivery despite highly congested channels. Such techniques include, among others, a data freezing mechanism that allows to successfully deliver data in a partitioned network as well as the use of network coding to shorten the length of packets and increase the robustness to unreliable links. Harmony also introduces a distributed interference detection scheme that allows each node to activate various interference mitigation techniques only when strictly necessary, avoiding unnecessary energy expenditures while finding a good balance between reliability and timeliness. An experimental evaluation on real-world testbeds shows that Harmony outperforms state-of-the-art protocols in the presence of harsh Wi-Fi interference, with up to 50% higher delivery rates and significantly shorter end-to-end latencies, even when transmitting large packets.

This paper presents a real-time monocular SLAM algorithm which combines points and line segments. We extend traditional point-based SLAM system with line features which are usually abundant in man-made scenes. The system is more robust and accurate than traditional point-based and direct-based monocular SLAM algorithms. In order to improve the timeliness of multi-feature based SLAM, we propose a novel feature level parallel processing framework and a fast line matching algorithm. For improving the reconstruction accuracy of 3D line segments which is usually affected by unreliable line endpoints, a sample point-based 3D reconstruction algorithm for line segments is proposed. Our system is implemented based on a popular monocular SLAM known as ORB-SLAM and tested on the TUM RGB-D benchmark. The experiment results demonstrate that the proposed system performs better than current state-of-the-art visual SLAM with respect to accuracy.

Concurrent transmission, a novel communication paradigm, has been shown to effectively achieve reliable and energy-efficient flooding in low-power wireless networks. With multiple nodes exploiting a receive-and-forward scheme, this technique works effectively in flooding-based networks, i.e., in one-to-many scenarios. However, application-level scheduling has to be introduced for data collection in wireless sensor networks (WSNs). In this paper, we propose Packet-in-Packet (PiP), an energy-efficient paradigm requiring no application-level scheduling for timely data collection in low-power WSNs. PiP builds on concurrent transmissions, exploiting constructive interference and the capture effect to achieve high reliability and low latency. Moreover, PiP uses a packet concatenation capability to gather single-hop information in a best-effort manner. As a result, PiP significantly reduces collection time. We further compare PiP with a state-of-the-art protocol by extensive experiments in a 30-node FlockLab testbed. Experimental results show that PiP highly reduces collection time (i.e., rounds) and achieves good performance in terms of high reliability of approximately 98.7% and high energy efficiency in all experimental scenarios of the real-world testbed.

Concurrent transmission, a novel communication paradigm, has been shown to effectively achieve reliable and energy-efficient flooding in low-power wireless networks. With multiple nodes exploiting a receive-and-forward scheme, this technique works effectively in one-to-many scenarios. However, application-level scheduling has to be introduced for data collection applications. In this paper, we propose Packet-in-Packet (PiP), an energy-efficient paradigm requiring no application-level scheduling for timely data collection in low-power wireless sensor networks. PiP builds on concurrent transmissions, exploiting constructive interference and the capture effect to achieve high reliability, low latency, and high energy efficiency. Moreover, PiP uses a packet concatenation capability to gather single-hop information in a best-effort manner. Experimental results show that PiP achieves high reliability in all experimental scenarios of a real-world testbed.