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The recent advances in microelectronics and communications have led to the development of large-scale IoT networks, where tremendous sensory data is generated and needs to be processed. To support real-time processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of edge nodes. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of edge nodes and improves the throughput between IoT and edge nodes.

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LoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the network may be broken early. Such an energy unfairness can be mitigated by deploying more gateways. However, with more gateways, more end devices may choose small spreading factors to reach closer gateways, increasing the collision probability. In this paper, we propose a networking solution for LoRa networks, namely EF-LoRa, that can achieve energy fairness among end devices by carefully allocating network resources, including frequency channels, spreading factors and transmission power. We develop a LoRa network model to study the energy consumption of the end devices, considering the unique features of LoRa networks such as the LoRaWAN MAC protocol and capacity limitation of a gateway. We formulate the energy fairness allocation as an optimization problem and propose a greedy allocation algorithm to achieve max-min fairness of energy efficiency. Extensive simulation results show that EF-LoRa can improve the energy fairness by 177.8%, compared to the stateof-the-art solutions.

Federated Learning (FL) has been employed for tremendous privacy-sensitive applications, where distributed devices collaboratively train a global model. In Industrial Internetof-Things (IIoT) systems, training latency is the key performance metric as the automated manufacture usually requires timely processing. The existing works increase the number of effective devices to accelerate the training. However, devices in IIoT systems are usually densely deployed, increasing the number of clients can potentially cause serious interference and prolonged training latency. In this paper, we propose RaFed, a resource allocation scheme for FL. We formulate the problem of reducing training latency as an optimization problem, which is proved to be NP-hard. We propose a heuristic to select appropriate devices to achieve a good trade-off between the interference and convergence time. We conduct experiments using an RGB-D dataset in an IIoT system. The results show that compared to the state-of-the-art works, RaFed significantly reduces the latency by 29.9%.

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Multichannel communication is an important means to improve the reliability of low-power Internet-of-Things (IoT) networks. Typically, data transmissions in IoT networks are often required to be delivered before a given deadline, making deadline-driven channel allocation an essential task. The existing works on time-division multiple access often fail to establish channel schedules to meet the deadline requirement, as they often assume that transmissions can be successful within one transmission slot. Besides, the allocation and link estimation incur considerable overhead for the IoT nodes. In this article, we propose an edge-based channel allocation (ECA) for unreliable IoT networks. In ECA, we explicitly consider the impact of allocation sequences and employ a recurrent-neural-networkbased channel estimation scheme. We utilize link quality and retransmission opportunities to maximize the packet delivery ratio before deadline. The allocation algorithms are executed on edge servers such that: 1) the channel allocation can be updated more frequently to deal with the wireless dynamics; 2) the allocation results can be obtained in real time; and 3) channel estimation can be more accurate. Extensive evaluation results show that ECA can significantly improve the reliability of deadline-driven IoT networks.