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The Ambient Internet of Things (A-IoT) has emerged as a critical direction for achieving effective connectivity as the IoT system evolves to 6G. However, the introduction of A-IoT technologies, particularly involving backscatter modulation, poses numerous challenges for system design and network operations. This paper surveys current standardization efforts, highlights potential challenges, and explores future directions for A-IoT. It begins with a comprehensive overview of ongoing standardization initiatives by the 3rd Generation Partnership Project (3GPP) on A-IoT, providing a solid foundation for further technical research in both industry and academia. Building upon this groundwork, the paper conducts an analysis of critical enabling technologies for A-IoT. Moreover, a comprehensive A-IoT demonstration system is designed to showcase the practical viability and efficiency of A-IoT techniques, supported by field experiments. We finally address ongoing challenges associated with A-IoT technologies, providing valuable insights for future research endeavors.

Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues. However, finding the right parameters for an AQM scheme is very challenging due to the dynamics of the IP networks. In addition, this problem becomes even more complex in inter-domain scenarios where several organizations interconnect each other with the limitation of not sharing raw and private data. As a result, existing AQM schemes have not been widely employed despite their advantages. Therefore, we present a solution that tackles the challenges of tuning the AQM parameters for inter-domain congestion control scenarios where the network management goes beyond an organization's domain. We then introduce the Federated Intelligence for AQM (FIAQM) architecture, which enhances the existing AQM schemes by leveraging the Federated Learning approach. The proposed FIAQM framework is capable of dynamically adjusting the AQM parameters in a multi-domain setting, which is hard to achieve with the conventional AQM solutions working alone. To this end, FIAQM uses an artificial neural network, trained in a federated manner, to predict beyond-own-domain congestion and an intelligent AQM parameter tuner. The evaluation results show that FIAQM can effectively improve the performance of the inter-domain connections by reducing the congestion on their links while preserving the network data private within each participating domain.

Emerging vertical applications enabled by connected devices and smart infrastructures have created an ever-increasing demand for high data rates over 5th-Generation (5G) and beyond wireless networks. Deployment of dense small cells (SCs) and millimeter wave (mmWave) communication systems have become inevitable in future wireless networks. Consequently, it is more accurate to model such networks in the 3D space due to the spatially distributed nature of the SCs, locations of the devices, radio resources and propagation environment. Accurate estimation of location-specific path loss parameters is then essential for efficient utilization of radio resources and management of dynamic coverage in 3D SC networks. In the paper, a framework for location-specific path loss estimation is developed for efficient radio resource management, based on the principle of crowdsensing together with Linear Algebra (LA) and machine learning (ML) techniques considering 2.5 GHz and 28 GHz bands. The corresponding procedure for capturing dynamic coverage of a SC base station (BS) serving to an arbitrary cluster is proposed and examined based on its 3D propagation characteristics. Results show that the accuracy of 3D channel parameter estimation using gradient descent ML techniques is superior compared to LA technique and can achieve over 98% estimation accuracy. It is shown that using the proposed process, parameters can be extrapolated for the slightly extended 3D communication distances from the cluster boundary for the worst-case locations of devices based on already estimated propagation parameters with accuracy over 74% for certain distances. Although numerical results are presented for a single amorphous 3D cell of a wireless network, the framework given in the paper can be extended to any arbitrary 3D wireless cellular network.

In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a location-based $k$ -means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods.

Patent citations are significant components of patents, which play a vital role in the implementation of patent analysis. However, most of the existed models only focus on the text of patents and do not realize that citations can remedy missing information in the text. A method for citation modeling in patent analysis is proposed to generate patent citation trees in this paper. Correspondingly, a specific neural network is designed for extracting abstract features in patent citation trees. Then, on the basis of extracted features, a new citation-based vector space model (CVSM) combining citations with text of the patent database is constructed for the subsequent applications. An experiment is conducted based on real patents of USPTO. The experimental results show that the proposed CVSM has good performances in several applications, which demonstrate the effectiveness of the proposed CVSM.

We consider practical hardware implementation of polar decoders. To reduce latency due to the serial nature of successive cancellation, existing optimizations improve parallelism with two approaches, i.e., multi-bit decision or reduced path splitting. In this paper, we combine the two procedures into one with an error-pattern-based architecture. It simultaneously generates a set of candidate paths for multiple bits with pre-stored patterns. For rate-1 (R1) or single parity-check nodes, we prove that a small number of deterministic patterns are required to guarantee performance preservation. For general nodes, low-weight error patterns are indexed by syndrome in a look-up table and retrieved in $O(1)$ time. The proposed flip-syndrome-list decoder fully parallelizes all constituent code blocks without sacrificing performance, and thus is suitable for ultra-low-latency applications. Meanwhile, two code construction optimizations are presented to further reduce complexity and improve performance.

Dynamic spectrum access (DSA) emerging as an effective way of improving the utilization of the scarce spectrum has attracted great attention in the communication field. A key challenge in DSA is to perform an efficient spectrum sensing and sharing mechanism. In this paper, aiming at achieving a maximal spectrum utilization, we propose a privacy-aware crowdsourced spectrum sensing and multi-user sharing mechanism for DSA. Particularly, in the sensing stage, the advanced mobile crowd sensing is adapted to economically provide sufficient candidate sensing helpers for a sensing requestor. Considering the individual rationality and energy consumption, an incentive mechanism based on both monetary and social motivation is designed to motivate the finial participations of the sensing helpers. Moreover, with the increasing attention to individual privacy, a social network and location-based k-anonymity grouping algorithm are proposed to prevent each helper's privacy being attacked by the malicious requestor or mobile users. Then, for a sensing requestor, aiming at achieving a target detection performance with minimal payment, a truthful reverse auction-based winning group selection algorithm is designed. Furthermore, in the transmitting stage, a realistic scenario is considered where multiple transmitters may discover the same idle spectrum based on sensing helpers' detections and will transmit data simultaneously. Thus, we model this problem as a potential-game-based multi-user transmission mechanism where all the transmitters act as game players and will jointly adjust their transmission powers to maximize the global throughput. Accordingly, we also take advantage of an improved differential evolution algorithm for obtaining a better equilibrium solution in a decentralized way. Both the theoretical analysis and the simulation results prove the rationality and superiority of our proposed algorithms.

In this paper, to detect non-technical loss (NTL) of meter manipulating/malfunctioning and bypassing at the same time, we propose the intermediate monitor meter (IMM)-based power distribution network model with the concept of unit networks (UNs) dividing the network into smallest and independent networks to analyze the power flow in detail and effectively detect the NTL. We then propose an NTL detection algorithm to solve linear system of equations (LSE) constructed by energy balance analysis with IMMs and the collector, and describe the hardware architecture of IMMs. Simulation results show that the proposed NTL detection algorithm successively detects the honesty of energy consumers as well as the energy loss from bypassing. Furthermore, it is observed the proposed detection framework is time-efficient and detection accuracy is at least 95% when variability of meter reading value is 80%.

As an emerging paradigm enabling mobile devices to leverage additional computation resources from nearby MEC servers (MSs), mobile edge computing (MEC) has drawn great attention from academia to industry. Unlike the conventional cloud server, the MEC provides a medium-scale and portable computation ability at MSs without relying on the time-consuming and capacity-constrained backhaul. However, the MEC offloading process is still highly sensitive to the fluctuation of both radio and computing resources. In this paper, considering the independent variation of the wireless channel conditions and computing tasks, we propose a Mixed-timescale Joint Computational offloading and Wireless resource allocation (MJCW) algorithm for latency-critical applications, aiming at minimizing the total energy consumption. Through such a new approach, the original NP-hard problem is decoupled into a short-term stage problem seeking for the allocation of physical power and subcarrier and a long-term stage problem of task offloading and frequency scaling. The simulation results show that the proposed algorithm achieves excellent performance in energy saving in comparison with conventional schemes and realizes higher utilization of green energy by adjusting the energy price of MSs.

With the capacity of achieving spectrum efficient wireless communications, cognitive radio enabled advanced metering infrastructure (CR-AMI) networks are expected to enhance the efficiency and practicability of future smart grids. CR-AMI networks which have been recognized as a fundamental component of the smart grid ecosystem, are practically utilized as a static multi-hop wireless mesh network. This paper focuses on the development of a novel routing protocol for low power and lossy networks based routing protocol for enhancing the energy efficiency in CR-AMI networks. For meeting the requirements of green communications in smart grids, the proposed routing protocol adopts the energy efficiency over virtual distance as the core of routing mechanism such that the energy-efficient route can be achieved. Furthermore, the protocol provides protection for primary users whilst meeting the utility requirements of secondary users. System-level evaluation indicates that the proposed protocol performs better than existing routing protocols for CR-AMI networks.

Nowadays, Internet of Things (IoT) devices are rapidly proliferating to support a vast number of end-to-end (E2E) services and applications, which require reliable device authentication for E2E data security. However, most low-cost IoT end devices with limited computing resources have difficulties in executing the increasingly complicated cryptographic security protocols, resulting in increased vulnerability of the virtual authentication credentials to malicious cryptanalysis. An attacker possessing compromised credentials could be deemed legitimate by the conventional cryptography-based authentication. Although inherently robust to upper-layer unauthorized cryptanalysis, the device-to-device physical-layer (PHY) authentication is practically difficult to be applied to the E2E IoT scenario and to be integrated with the existing, well-established cryptography primitives without any conflict. This paper proposes an enhanced E2E IoT device authentication that achieves seamless integration of PHY security into traditional asymmetric cryptography-based authentication schemes. Exploiting the collaboration of several intermediate nodes (e.g., edge gateway, access point, and full-function device), multiple radio-frequency features of an IoT device can be estimated, quantized, and used in the proposed PHY identity-based cryptography for key protection. A closed-form expression of the generated PHY entropy is derived for measuring the security enhancement. The evaluation results of our cross-layer authentication demonstrate an elevated resistance to various computation-based impersonation attacks. Furthermore, the proposed method does not impose any extra implementation overhead on resource-constrained IoT devices.

A novel random code-domain non-orthogonal multiple access (NOMA) framework, rateless multiple access (RMA), is studied in this paper. In RMA, instead of granting each user specific radio resources in a fixed and centralized manner, the access point simply assigns to it a random access control function, according to which the user independently chooses a pseudo-random number of symbols each time and transmits their weighted sum until receiving an acknowledgment that indicates the successful recovery of its information. In this way, the transmission process of each individual user as well as that of all the users as a whole, resembles a special linear superposition rateless encoder and, thus, information can be retrieved with the low-complexity belief propagation (BP) algorithm at the access point. In this paper, the asymptotic throughput of RMA with BP decoding is analyzed. Then, in order to bridge the performance gap between BP decoding and the optimal MAP (maximum aposteriori) decoding, we apply the most recently developed coding technique of spatial coupling to RMA and propose an enhanced version, SC-RMA, in which all the user codewords are properly spatially-coupled across different subsets of REs. We prove that SC-RMA asymptotically approaches the channel sum-rate capacity with high adaptability and low-signaling overhead, which makes it a viable candidate for massive access.

Sparsely spread code division multiple access (SCDMA) is a non-orthogonal superposition coding scheme that allows concurrent communications between a base station and multiple users over a common channel. However, the detection performance of an SCDMA system is mainly determined by its signature matrix, which should be sparse to facilitate the belief propagation (BP) detection. On the other hand, to guarantee good maximum likelihood (ML) detection performance, the minimum Euclidean distance for the equivalent signal constellation after multi-user superposition should be maximized. In this paper, a code distance analysis is proposed for SCDMA systems with a finite number of users and spreading lengths. Based on this analysis, good signature matrices whose factor graphs have very few short cycles and possess large superposed signal constellation distances are designed. The proposed signature matrices have both good BP and ML detection performances. Moreover, their BP detection performances exactly converge to their ML detection performances with few iterations. It is worth pointing out that the proposed signature matrix design could be directly applied to the 5G non-orthogonal multiple access systems.

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.

The automotive industry has recently shifted from developing advanced vehicles to concentrating on safety and comfort, which stimulates the development of new intelligent vehicles with advanced driver assistance systems and autonomous driving [item 1) in the Appendix]. However, the current technology is still far from meeting the practical requirements of autonomous driving. The existing control approaches are mainly based on expensive sensors and cameras equipped in vehicles. Thanks to the rapid development of advanced communication and computation techniques, vehicles easily communicate and cooperate to each other [item 2) in the Appendix]. Meanwhile, the control of vehicles become more reliable and cost-efficient with the communication between vehicles [item 3) in the Appendix]. Therefore, communication, computing and control (3C) will play a critical role in the development of next generation intelligent vehicular systems [item 4) in the Appendix].

With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day. How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system. Collaborative filtering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations. In this paper, we first propose a framework of the CF recommender system based on various user data including user ratings and user behaviors. Key features of these two kinds of data are discussed. Moreover, several typical CF algorithms are classified as memory-based approaches and model-based approaches and compared. Two case studies are presented in an effort to validate the proposed framework.