LoRa is a low-power long-range IoT standard that uses the chirp spread spectrum technique, and we have strived to further extend its coverage by utilizing the direct device-to-device (D2D) links to construct a multi-hop relay network. In LoRa, the spreading factor (SF) is an important parameter, which not only provides great flexibility between the data rate and sensitivity but also presents a new dimension for multiple accesses. Our approach to improving the capacity of a multihop LoRa network is to attempt to off-load the data traffic into several subnets by utilizing this multiple-access dimension. Each subnet rooted at a sink node is allocated a specific SF on the basis of network clustering. This enables packet transmission in parallel with multiple SFs to become feasible. To allow such parallel transmissions, our considerations are: 1) ensuring the connectivity of all subnets; 2) off-loading the traffic according to the number of nodes, data rates, and network topologies of each subnet; and 3) shortening the airtime of each subnet by reducing the hop count. Toward these objectives, we present a tree-based SF clustering algorithm (TSCA) to conduct SF allocation in a multihop LoRa network. The TSCA focuses on balancing the airtime between the subnets while ensuring connectivity. Furthermore, we use simulations to show that our approach can significantly increase the network performance compared with other approaches. We additionally deploy a real-chip experiment to evaluate the feasibility of parallel transmission in practice. INDEX TERMS Low-power wide area network (LPWAN), LoRa, spreading factor (SF) allocation algorithm, tree-based spreading factor clustering algorithm (TSCA), multi-hop network.
We study interaction between gradient-based sub carrier allocation algorithms and TCP traffic sources in a downlink single-hop OFDMA wireless system. Specifically, we are interested in evaluating the maximum-rate (Max SNR), Proportional Fair (PF), and queue-based Max-Weight (QBMW) subcarrier schedulers, in both non-iterative and iterative versions, with long-lived TCP fluid traffic. Using system throughput and the Jain's fairness index as bench marks, our simulation results show that even when the users are homogeneous, QBMW leads to suboptimal throughput and extreme unfairness in the presence of TCP traffic. On the other hand, when the users are heterogeneous, Max-SNRis also unfair and gives suboptimal system throughput. How ever, in both cases, the iterative PF gives the best throughput as well as the best fairness among all considered schedulers, due to its slow variation in the sub carrier allocation, which is preferred by TCP.
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