Massive IoT including the large number of resource-constrained IoT devices has gained great attention. IoT devices generate enormous traffic, which causes network congestion. To manage network congestion, multi-channel-based algorithms are proposed. However, most of the existing multi-channel algorithms require strict synchronization, an extra overhead for negotiating channel assignment, which poses significant challenges to resource-constrained IoT devices. In this paper, a distributed channel selection algorithm utilizing the tug-of-war (TOW) dynamics is proposed for improving successful frame delivery of the whole network by letting IoT devices always select suitable channels for communication adaptively. The proposed TOW dynamics-based channel selection algorithm has a simple reinforcement learning procedure that only needs to receive the acknowledgment (ACK) frame for the learning procedure, while simply requiring minimal memory and computation capability. Thus, the proposed TOW dynamics-based algorithm can run on resource-constrained IoT devices. We prototype the proposed algorithm on an extremely resource-constrained single-board computer, which hereafter is called the cognitive-IoT prototype. Moreover, the cognitive-IoT prototype is densely deployed in a frequently-changing radio environment for evaluation experiments. The evaluation results show that the cognitive-IoT prototype accurately and adaptively makes decisions to select the suitable channel when the real environment regularly varies. Accordingly, the successful frame ratio of the network is improved.

In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.

In recent years, the demand for new applications using various Internet of Things (IoT) devices has led to an increase in the number of devices connected to wireless networks. However, owing to the limitation of available frequency resources for IoT devices, the degradation of the communication quality caused by channel congestion is a practical problem in developing IoT technology. Many IoT devices have hardware and software limitations that prevent centralized channel allocation, and congestion is even more severe in massive IoT networks without a central controller. Therefore, developing a distributed and sophisticated channel selection algorithm is necessary. In previous studies, the channel selection of each IoT device was modeled as a multi-armed bandit (MAB) problem, and a wireless channel selection method based on the MAB algorithm, which is a simple reinforcement learning, was proposed. In particular, it has been shown that the MAB algorithm of tug-of-war (TOW) dynamics can efficiently select channels with much lower computational complexity and power compared with other reinforcement learning-based channel-selection methods. This paper proposes a distributed channel selection method based on TOW dynamics in fully decentralized networks. We evaluate the effectiveness of the proposed method and other distributed channel-selection methods on the communication success rate in massive IoT networks by experiments and simulations. The results show that the proposed method improves the communication success rate more than other distributed channel selection methods even in a dense and dynamic network environment.

This paper overviews the concept of the Piggy-back Network to enable upcoming Beyond 5G society supported by autonomous mobilities, the key technologies, and the prototyping on a service robot platform. The data transfer principle in the Piggy-back Network is based on the store-carry-forwarding (SCF) between cross-industrial autonomous mobilities such as service robots, wheelchairs, vehicles, buses, etc., with autonomous moving capabilities, which are practicing indoor or outdoor in a city or town. It is suggested that three types of technologies; to exploit the proximity time at a maximum, to create or increase the proximity chances, and to manage the distributed proximity chances, should be pursued. It is shown that the Shannon’ s communication capacity theorem is still useful to estimate the channel capacity achieved by a passing wireless communication using an extremely high frequency band by introducing an antenna model suitably. This paper focuses on a technology to enlarge the data volume that can be transferred by the passing communications and proposes to control the mobilities with “Stop/Slow-down & Go” or “Catch-up & Rendezvous” policy. It is demonstrated by prototyping a service robot platform that the mobility control with the “Stop & Go” policy increases the transferable data size with a little time loss.

As one of the technologies to enable Beyond 5G society, a Piggy-back Network has been proposed as a communication system based on Store-Carry-Forwarding with high-speed millimeter-wave links. In this paper, we evaluate the end-to-end throughput of optimized Piggy-back Networks. We formulate optimization of the data transfer route in the Piggy-back Network as a pickup and delivery problem and apply a heuristic optimization algorithm to the formulated problem. The results show that the optimized Piggy-back Network enables high throughput even for long-distance communication.

The Piggyback Network has been proposed as one of the technologies to enable Beyond 5G society. The Piggyback Network provides a high-speed data transfer system with frequency radio, such as millimeter-wave (mmW) links and Store-Carry-Forward (SCF) based communication among multiple autonomous mobilities. Communicating with mmW enables high-speed and large-capacity data transfers. Autonomous mobilities include service robots, cars, and unmanned aerial vehicles (UAV). Simple calculations demonstrated the end-to-end throughput performance when a single mobility unit transfers a single piece of data. This paper considers expansion to handle multiple mobilities and data. To realize this data transfer system, we require data delivery assignment to autonomous mobility and route optimization. We propose local search algorithm for minimizing cost value based on the total time required for data to be delivered to the destination. This paper evaluates the data transfer performance of the Piggyback Network by UAVs in a large-scale field. If multiple data and delivery routes are appropriately assigned to multiple mobilities that can communicate at 10Gbps and move at 100km/h, the Piggyback Network can transfer 100GB of data within approximately 2 min, which corresponds to a throughput of approximately 3000Mbps. If the data size is 1000GB and mobilities can move at 10Gbps, data transfer is completed in approximately 4 min, which achieves approximately 30000Mbps, faster than 5G and long term evolution (LTE) networks.