LoRaWAN is a Low Power Wide Area Network technology featuring long transmission ranges and a simple MAC layer, which can support sensor data collection, control applications and reliable services thanks to the flexibility offered by a large set of configurable system parameters. However, the impact of such parameters settings on the system’s performance is often difficult to predict, depending on several factors. To ease this task, in this paper, we provide a mathematical model to estimate the performance of a LoRaWAN gateway serving a set of devices that may or may not employ confirmed traffic. The model features a set of parameters that can be adjusted to investigate different gateway and end-device configurations, making it possible to carry out a systematic analysis of various trade-offs. The results given by the proposed model are validated through realistic ns-3 simulations that confirm the ability of the model to predict the system performance with high accuracy, and assess the impact of the assumptions made in the model for tractability.

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design anad hoctracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. We analyze the trade-off in setting the main parameters of the communication system and Adaptive Data Rate (ADR) scheme, showing how our tracking system can handle large swarms of drones at distances up to 4 km. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a swarm of UAVs, significantly outperforming baseline tracking approaches.

Many IoT scenarios, such as smart cities, wild life monitoring, or smart agriculture, involve thousands of battery-powered devices. The disposal and replacement of such batteries represent an important economical and environmental cost. To realize Green IoT solutions, it is therefore desirable to adopt battery-less energy-neutral devices that can harvest power from renewable sources, such as solar or wind energy and store it in much more sustainable capacitors. The limited and inconstant energy supply and the limited energy storage capacity of such devices, however, require special care in the design of communication and computational processes, which have a major impact on the energy consumption of the devices. In this work, we explore multiple elements that could affect the device energy and communication capabilities of LoRaWAN devices. We propose and compare different energy-aware packet transmission algorithms, and test them in a scenario where values for the harvested power are collected from real testbeds. We show that the number of successfully transmitted packets can be doubled by using an energy-aware design approach.

In Internet of Things (IoT) applications, hundreds of sensors collect and transmit data to a central gateway, allowing applications such as smart cities, smart home, and smart agriculture. Scalability and energy efficiency are two common requirements for this type of IoT systems that can be tackled by providing the sensor nodes with energy harvesting capabilities, so that they can operate even without batteries, exploiting the renewable energy sources, with a dramatic reduction in the manufacturing, management, and decommissioning costs. In this work, we outline our approach for network-wide and energy-aware performance optimization of energy harvesting IoT networks employing LoRaWAN, one of the most widespread technologies for the IoT. We describe a viable method to enable more efficient energy-aware operations on battery-less IoT devices, presenting promising preliminary results.

3GPP has recently introduced NB-IoT, a new mobile communication standard offering a robust and energy-efficient connectivity option to the rapidly expanding market of the Internet-of-Things (IoT) devices. To unleash its full potential, end devices are expected to work in a plug-and-play fashion, with zero or minimal configuration of parameters, still exhibiting excellent energy efficiency. We performed the most comprehensive set of empirical measurements with commercial IoT devices and different operators to date, quantifying the impact of several parameters to energy consumption. Our findings prove that parameters' settings do impact energy consumption, so proper configuration is necessary. We shed light on this aspect by first illustrating how the nominal standard operational modes map into real current consumption patterns of NB-IoT devices. Furthermore, we investigated which device-reported metadata metrics better reflected performance and implemented an algorithm to automatically identify device state in the current time-series logs. We worked with two major western European operators to provide a measurement-driven analysis of energy consumption and network performance of two popular NB-IoT boards under different parameter configurations. We observed that energy consumption is mostly affected by the paging interval in connected state, set by the base station. However, not all operators correctly implement such settings. Furthermore, under the default configuration, energy consumption in not strongly affected by packet size nor by signal quality, unless it is extremely bad. Our observations indicate that simple modifications to the default parameters' settings can yield great energy savings.

In this article, we evaluate the performance of a LoRaWAN network in industrial scenarios where different Industrial Internet of Things (IIoT) end nodes communicate to a central controller in order to provide monitoring and sensing information to optimize the efficiency of industrial processes and reduce costs. In particular, we consider confirmed and unconfirmed traffic, multigateway deployments, the usage of different classes of devices, and a nonstandard channel plan. Furthermore, we analyze the higher-layer impact of different models of LoRa PHY layer with industrial channel models. We show that, with proper configuration, LoRaWAN is able to serve IIoT sensing applications with a packet success rate over 90%, providing at the same time limited communication delays.

Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance. Although Feature Selection with Consensus (FeSC) implements a very robust architecture for feature selection and classification, results are still negatively affected by the limited size of the dataset. In the future, further investigations could determine to what extent size could cause a drop in the performance of FeSC in this and other gesture recognition applications.

LoRaWAN is an emerging low-power wide-area network (LPWAN) technology, which is gaining momentum thanks to its flexibility and ease of deployment. Conversely to other LPWAN solutions, LoRaWAN indeed permits the configuration of several network parameters that affect different network performance indexes, such as energy efficiency, fairness, and capacity, in principle making it possible to adapt the network behavior to the specific requirements of the application scenario. Unfortunately, the complex and sometimes elusive interactions among the different network components make it rather difficult to predict the actual effect of a certain parameters setting, so that flexibility can turn into a stumbling block if not deeply understood. In this article, we shed light on such complex interactions for a single-gateway (GW) system by analyzing the effect of some built-in features and configurations, including the GW's limitations in terms of duty cycle and the number of parallel reception paths, the number of allowed retransmissions for confirmed traffic, and the preconfigured data rate used in downlink transmissions. The simulation-based analysis reveals various tradeoffs and highlights some inefficiencies in the design of the LoRaWAN standard. Furthermore, we show how significant performance gains can be obtained by wisely setting the system parameters, possibly in combination with some novel network management policies (e.g., enabling selective prioritization of downlink transmissions at the GW).

LoRaWAN is gaining momentum in the arena of IoT connectivity technologies thanks to the low cost, ease of deployment, and support for adaptable transmission rates and bidirectional communications. The research community has then been working to develop suitable analytical models that can be instrumental in the study of this technology. In this work we propose a mathematical model that makes it possible to accurately estimate the packet success probability of a LoRaWannetwork in presence of bi-directional traffic, i.e., both uplink (UL) and downlink (DL) transmissions, and that accounts for the most critical features of the LoRa chipset and the LoRaWanstandard. The proposed model, furthermore, makes it possible to study the effect of different parameters configurations, thus offering a valid tool to investigate possible improvements to the system configuration. The proposed model is first validated by comparison with some accurate simulation results and, then, its potential is exemplified by analyzing the system settings that yield the best network performance.