Michael Mao Wang

Like all other types of wireless communications, massive Internet of Things (mIoT) communication is all about degrees of freedom. It holds the key to almost all mIoT challenging issues. However, it is also the most limited resource in mIoT due to its unique constraints. This paper derives a new class of waveforms, i.e., a canonical waveform, and explores the dimensions hidden in its underlying mathematical structure. The intrinsic characteristics and the degrees of freedom of the canonical waveform are studied and fully explored to reap the full potential of the waveform. Most significantly, a new dimension is uncovered and exploited as additional degrees of freedom to better address the mIoT challenges in energy efficiency, resource efficiency, and robustness under the mIoT constraint. The corresponding benefits are evaluated analytically and numerically in AWGN, frequency-flat, and frequency-selective channels. We demonstrate that no existing mIoT waveforms can fully address the mIoT challenges, whereas the new degrees of freedom from the canonical waveform offer a comprehensive solution.

An Internet of Things (IoT) network is a network of devices. In a massive IoT network, devices are often battery-powered, and the battery life determines the network’s lifespan. The goal of this article is thus to examine the inherent relationship between device intrinsic characteristics and network performance (lifespan and capacity). The findings lead to a simple but fundamental view of IoT network longevity and, more significantly, an IoT network longevity principle. This new perception of network longevity alludes to distinct characteristics of IoT devices from those used in existing technologies and systems. These distinctions manifested by the network performance are examined through in-depth theoretical analysis. An exemplary augmentation of characteristics on a practical device demonstrates and reinforces the importance of device characteristics on network performance and the power of the longevit principle.

Exploration for new dimensions to convey information is a non-trivial task for resource-limited communications. Interleaved Chirp Spreading LoRa (ICS-LoRa) constitutes a new multi-dimensional space from the interleaved versions of LoRa waveforms to convey extra bits. Nevertheless, it destroys the phase continuity of LoRa waveforms and degrades the spectral localization, offsetting the benefit from the increased symbol payload size. This letter fully elaborates the potential of the interleaving dimension and proposes a continuous phase ICS-LoRa scheme, which is proven to be more efficient in data rate improvement than ICS-LoRa. Furthermore, a low-complexity design from the proposed scheme is provided for implementation simplicity.

The massive Internet of Things (IoT) paradigm requires the communication device or transceiver to possess the following essential characteristics: 1) resource efficiency, 2) energy efficiency, 3) implementation efficiency, and 4) coverage. This paper discusses LoRa Modulation characteristics from these aspects. First, we review the fundamental characteristics of LoRa Modulation, derive the upper bounds, evaluate its baseline resource-and energy-efficiency performance against the bounds, and analyze its limitations and room for improvement. We then identify a crucial element of LoRa Modulation and uncover its high relevance to LoRa Modulation characteristics. Finally, based on this finding, we propose an augmented LoRa Modulation solution under the original LoRa Modulation framework, including the transceiver structure, that minimizes the performance gaps from the bounds and extends the coverage while maintaining LoRa Modulation implementation efficiency.

In recent years, LoRa has become one of the most popular solutions for Internet of Things (IoT) services owing to its low-cost and low-energy characteristics. Although with growing interest in the literature, little attention has been paid for LoRa on the preamble detection. In this letter, we give a detailed analysis on the preamble detection under the low-complexity transceiver implementation when with initial timing and frequency offsets. The effects of the offsets on preamble detection performance are analyzed and some unrevealed issues are addressed, leading to a robust design of the preamble.

The Internet of Things (IoT) is all about services . The proximity-based IoT service is a type of service that becomes available once an IoT object comes near other objects. This type of service typically involves proximity machine-type communication (MTC) that allows direct communication between objects in a spatiotemporal context. The IoT world has witnessed increasing cases of such applications in recent years and yet has not received sufficient attention in the IoT community. This tutorial addresses the unique requirements of this type of communication technology that vastly differs from the classic infrastructure-based wide-area cellular MTC technology in a descriptive and tutorial way to guide readers to an easy understanding of its unique yet complex requirements and characteristics. The tutorial utilizes an important emerging application in maritime IoT, particularly Maritime Autonomous Surface Shipping (MASS), as an example and offers an insightful and rigorous examination of the leading technology in today’s maritime communications arena. It highlights the issues to avoid in the development and standardization of proximity MTC in light of the recent spectrum assignment by ITU. A comprehensive service-centric solution is presented to address the requirements and issues. Different from other IoT literature in the field, this tutorial focuses on the unique issues in the proximity and service-centricity aspects of the proximity IoT paradigm. It provides a comprehensive description of the most relevant techniques to help bring researchers and application developers up to speed on how the different protocols of different layers fit together to deliver desired functionalities to meet the service-centric requirements through a practical design. This tutorial focuses on not only proliferation but especially the differentiation of proximity MTC from cellular MTC and service-centric networking from host-centric networking, as well as the significance of enabling virtual sensing in maritime autonomous surface shipping. Article invites comparisons, discussions, critiques, and, ultimately, new solutions in the field of proximity MTC and maritime IoT.

Sensor sharing is an important ability for vehicle platoon to reduce vehicle costs and improve driving safety. However, sensing data with a large amount of information is difficult to be shared by using traditional communication methods. Applying millimeter-wave communication can improve sensor sharing capabilities of vehicles, but millimeter wave has large propagation loss and is easy to be blocked by obstacles. Therefore, how to apply millimeter-wave communication to improve sensor sharing capabilities in vehicle platoon is still an open issue. To this end, we present a stochastic game approach and propose two distributed algorithms to achieve high-reliability and low-energy sensor sharing. First, we model the multihop millimeter-wave sensor sharing of the vehicle platoon as an optimization problem of reducing energy consumption while ensuring communication reliability, and then based on the characteristics of vehicle communication, we transform the optimization problem into a multiagent stochastic game. Second, by utilizing the characteristic that the immediate cost has nothing to do with the transfer function of the game, we prove that this stochastic game has equilibrium by proving that each subgame has a Nash equilibrium. Third, we propose an equilibrium-guided multiagent distributed (EGMD) algorithm to obtain the optimal action of each vehicle in the platoon, and then based on the analysis of the limitation of the approach, we present a multiagent distributed cooperation (MDC) algorithm to further reduce the system energy consumption. Finally, through simulation experiments, we evaluate a variety of approaches and demonstrate that our proposed methods can significantly reduce energy consumption while ensuring communication reliability.

In the era of ubiquitous computing, offloading certain computing tasks to the mobile ad hoc cloud (MAHC) could help mobile devices reduce execution time. However, if multiple resource demanders (RDs) offload tasks to the MAHC without a proper scheduling policy, it may cause unbalanced load distribution among resource providers (RPs), which will affect the overall quality of service (QoS). To this end, in this paper, we propose a multi-agent independent learning approach aiming to optimize the QoS of MAHC. Firstly, for the distributed MAHC, we formulate the QoS optimization model as a non-cooperative game, where each RD competes for maximizing its own utility. Secondly, based on the potential game theory, we prove the existence of Nash equilibrium. A multi-agent independent learning algorithm is then proposed to obtain the equilibrium points, and the convergence of this algorithm is analyzed. Simulation results confirm that the proposed approach helps balance the load distribution and enhances the QoS of MAHC.

The introduction of artificial intelligence (AI) into edge computing could significantly improve its quality of service. Connecting them into a system can provide services for a wider range. However, due to the mobility of the crowd and mobile devices, the load imbalance issue of these interconnected intelligent edge servers (IESs) will cause severe impacts on their service performance. To this end, we investigate load balancing for the distributed IESs from the game theoretic perspective and propose a state-based distributed learning algorithm. Firstly, by modelling the IES cost as the deviation between its execution time and the system average execution time, we formulate load balancing as a state-based game where each IES competes to maximize its own utility. Secondly, according to the definition of the recurrent state Nash equilibrium, we prove that this game has such an equilibrium by establishing a potential function at each reachable state. Finally, we propose a state-based distributed learning algorithm to obtain the pure Nash equilibrium strategy of each IES. Then, an ordinary differential equation is derived to prove the convergence of the algorithm. In comparison with existing works, our approach could largely improve load balancing for the distributed IESs and thus enhance their service performance.

The application of nonorthogonal multiple access (NOMA) technology to New Radio (NR) Vehicles-to-Everything (V2X) can further reduce communication delay and improve the system capacity of the vehicular network. However, due to the high mobility of the vehicles, it is difficult for the base station to obtain the channel information between vehicles in real time for resource allocation and vehicle transmission power control. To this end, we propose a two-stage scheme of centralized resource allocation and distributed power control to meet the NR V2X Mode 1 requirement while NOMA technology is employed in each vehicle group. First, at the beginning of a transmission period, the base station allocates resources to vehicle groups. For this centralized manner, we propose a graph-based matching approach to allocate resources to improve system capacity. Then, each vehicle group controls and adjusts transmission power for NOMA communication in the period. For this distributed manner, we propose a noncooperative game to control the power of the vehicle groups and then analyze the performance of the noncooperative game. Afterward, we further put forward a cooperative game approach to control the power of the vehicle group to increase system capacity. Compared with the noncooperative game, the proposed scheme can increase communication capacity by up to 5% and reduce transmission power consumption by 36%.