Long Range (LoRa) is a low-power wireless communication technology for long-range connectivity, extensively used in the Internet of Things. Several works in the literature have analytically characterized the performance of LoRa networks, with particular focus on scalability and reliability. However, most of the related models are limited, as they cannot account for factors that occur in practice, or make strong assumptions on how devices are deployed in the network. This article proposes an analytical model that describes the delivery ratio in a LoRa network with device-level granularity. Specifically, it considers the impact of several key factors that affect real deployments, including multiple gateways and channel variation. Therefore, the proposed model can effectively evaluate the delivery ratio in realistic network topologies, without any restrictions on device deployment or configuration. It also accurately characterizes the delivery ratio of each device in a network, as demonstrated by extensive simulations in a wide variety of conditions, including diverse networks in terms of node deployment and link-level parameter settings. The proposed model provides a level of detail that is not available in the state of the art, and it matches the simulation results within an error of a few percentage points.
LoRa is one of the most popular technologies for low-power wide area networks. It offers long-range communication with a low energy consumption, which makes it ideal for many applications in the Internet of things. The performance of LoRa networks depends on the communication parameters used by individual nodes. Several works have proposed different solutions, typically running on a central network server, to select these parameters. However, existing approaches have not addressed the need to (re-)assign parameters when channel conditions suddenly vary due to additional traffic, changes in the weather or the presence of obstacles. Moreover, allocation strategies that require a central entity to decide communication parameters do not scale due to the large number of configuration packets that must be sent to the nodes. To address these issues, this work proposes NoReL, a distributed game-theoretic approach that allows nodes to autonomously update their parameters and maximize their packet delivery ratio. NoReL is based on a stochastic variant of no-regret learning, which is proven to reach an -coarse correlated equilibrium in LoRa networks. Extensive simulations show that NoReL achieves a higher delivery ratio than the state of the art in both static and dynamic environments, with an improvement up to 12%. Index Terms-LoRa, no-regret learning, game theory, Internet of Things, low power wide area networks I. INTRODUCTIONLow-Power Wide Area Networks (LPWANs) offer longrange wireless communication with low energy consumption at low data rates [1]. This makes them ideal in the Internet of Things (IoT), especially for scenarios where sensors infrequently send small packets. Long Range (LoRa) is one of the most popular LPWAN technologies. It achieves communication ranges of 3-5 km in urban areas and 10-15 km in rural environments [2]. LoRa offers a scalable network architecture [3] where nodes simply send packets to all gateways in range, which in turn forward them to a central network server that filters out duplicates. For these reasons, LoRa has been used in several application scenarios, smart
Cross-Technology Communication (CTC) allows direct message exchange between devices with different (i.e., incompatible) wireless communication standards. CTC is particularly suitable to allow for coordination between heterogeneous devices sharing the same spectrum, as in the Internet of Things. Existing research on CTC has focused on enabling communications for diverse technologies with the goal of achieving a high throughput. However, it did not address how to establish a link suitable for CTC, which is necessary for successful data exchange. This article specifically addresses such a problem by introducing CTC-CEM (CTC Channel Establishment with Multiple nodes), a scheme to establish a CTC channel involving the use of multiple nodes in a network. CTC-CEM employs duty-cycling and leverages network density to reduce energy consumption, while keeping a low discovery latency. In particular, CTC-CEM defines different discovery protocols to reliably detect co-located networks. Moreover, it addresses the selection of multiple CTC nodes as a set cover problem, and includes an optimization technique based on dynamic programming to balance the energy consumption in the whole network. Extensive simulations show that CTC-CEM effectively distributes the energy consumption in the network, increasing fairness by 97% after optimization. Furthermore, the latency in establishing a channel with CTC-CEM is two orders of magnitude lower than that for device discovery in duty-cycled networks.
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