Integrated data and energy transfer (IDET) is considered as a key enabler of 6G, as it can provide both wireless energy transfer (WET) and wireless data transfer (WDT) services towards low power devices. Thanks to the extra degree of freedom provided by fluid antenna (FA), incorporating FA into IDET systems presents a promising approach to enhance energy efficiency performance. This paper investigates a FA assisted IDET system, where the transmitter is equipped with multiple FAs and transmits wireless signals to the data receiver (DR) and the energy receiver (ER), both of which are equipped with a single traditional antenna. The switching delay and energy consumption induced by port selection are taken into account in IDET system for the first time. We aim to obtain the optimal beamforming vector and the port selection strategy at the transmitter, in order to maximize the short-term and long-term WET efficiency, respectively. The instant sub-optimal solution is obtained by alternatively optimizing the beamforming vector and port selection in each transmission frame, while a novel constrained soft actor critic (C-SAC) algorithm is proposed to find the feasible policy of port selection from the long-term perspective. Simulation results demonstrate that our scheme is able to achieve greater gain in terms of both the short-term and long-term WET efficiency compared to other benchmarks, while not degrading WDT performance.

This article considers a two-hop wireless-powered relay network consisting of multiple sources, multiple destinations, and one relay. The relay can receive energy from the sources and forward data to the destinations. We focus on the source selection problem during the energy transfer process and the resource allocation problem during the data transmission process. First, the relay can choose among all sources based on the transferred energy from the sources. A credit mechanism is introduced for the relay to achieve optimal selection. Second, a Stackelberg differential game-based model is adopted for the resource allocation problem in the data transmission process, using the differential equation to describe the dynamic variation of energy, and the Stackelberg game to describe the relationships between the sources and the relay. In the proposed approach, both sources and relays consider energy consumption and energy revenue. To find the optimal solutions, an adaptive dynamic programming-based algorithm is utilized. The Lyapunov-based stability analysis shows that the system has uniform ultimate boundedness and convergence. Finally, the trained neural networks can achieve optimal resource allocation strategies. Through extensive simulation experiments, the effectiveness of the proposed algorithm is verified.

A combination of Industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realize Industry 4.0 and beyond. However, scheduling more IIoT devices engaged in FL contributes to accelerated learning speed, but resulting in increased learning cost in terms of energy consumption and model accuracy reduction. In this article, we investigate the tradeoff between learning speed and cost in a three-layer FL-enabled IIoT system. Particularly, a weighted learning utility function is designed by capturing such a tradeoff. We aim to maximize the weighted learning utility in an FL training round by jointly optimizing the edge association as well as the allocations of resource block, computation capacity, and transmit power of the IIoT device. The resulting problem is a nonconvex and mixed-integer optimization problem, and consequently, it is difficult to solve. We thereby decompose the original problem into three subproblems, and then propose an overall alternating optimization algorithm to solve the subproblems iteratively until convergence. Via experimental results, it is demonstrated that the proposed scheme significantly improves the system-wide learning utility as compared to other baseline schemes. It is also shown that the proposed scheme can achieve the optimized tradeoff between learning speed and learning cost.

Integrated data and energy transfer (IDET) is capable of providing both stable wireless energy transfer (WET) and wireless data transfer (WDT) services towards massive low-power devices. In order to enhance both the resource efficiency for WDT and the energy harvesting performance for WET, a novel reflective index modulation (RIM) scheme is proposed in an intelligent reflecting surface (IRS)-assisted IDET system, where various group number of IRS elements are activated for WDT and WET, respectively, while the index of activated IRS elements can also deliver additional data in the reflective domain. The bit error ratio (BER), the achievable data rate of the data receiver (DR) and the average energy harvesting power at the energy receiver (ER) are then derived into the closed form. The phase shifters of the IRS are then optimized, in order to maximize average energy harvesting power at the ER by guaranteeing the BER and the data rate constraints of the DR. Simulation results validate our theoretical analysis, which also demonstrate that RIM scheme is capable of improving the joint WDT and WET performance in the IRS-IDET system.

Thanks to the characteristic that the signals can be reflected to a pre-defined oritention, intelligent reflecting surface (IRS) is becoming a promising technology to improve the efficiency of wireless communications. In this paper, a reflective group number based index modulation (RGNIM) scheme is proposed by exploiting the activating states of the reflecting elements of the IRS, where different group number of activated IRS elements can carry additional information in the reflective domain. The bit error ratio (BER) performance is then theoretically analysed under the imperfect channel estimation by adopting the maximum likelihood (ML) detection approach. Simulation results validate and evaluate the BER performance of the RGNIM assisted system, which also demonstrate that our RGNIM scheme outperforms other IRS aided index modulation schemes.

This article studies the integration of laser-beamed wireless power transfer (WPT) into high-altitude platform (HAP)-aided multiaccess edge computing (MEC) systems for the HAP-connected aerial user equipments (AUEs). By discretizing the 3-D coverage space of the HAP, we present a multitier tile grid-based spatial structure to provide the aerial locations in the form of tile grids to AUEs for laser charging. We identify a new privacy vulnerability caused by the openness during the WPT signaling transfer in the presence of a terrestrial adversary, which is able to launch the attacks by distributing the false tile grids to the AUEs. To address this vulnerability and enhance the location privacy of AUEs, we then propose a Colonel Blotto (CB) game framework to formulate the competitive tile grid allocation problem for the HAP and the adversary. The attack-defense interaction between the adversary and the HAP as a defender in their tile grid allocations to the AUEs is formulated as a CB game, which models the competition of two players for limited resources over multiple battlefields. Moreover, we derive the mixed-strategy Nash equilibria of the game for both symmetric and asymmetric tile grids between two players. Simulation results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

The Space-Air-Ground Integrated Networks (SAGINs) converges the rapidly developing ground, aerial and satellite communication networks to provide users with efficient and personalized services through the multi-layer network architecture. The complex structure, inconsistent communication protocol, and incompatible equipment significantly affect the information transmission efficiency in SAGINs. The application of Software Defined Network (SDN) technology promotes global deployment of the SAGIN resources. Aiming at the information transmission bottleneck between controller and switch nodes in the software-defined SAGINs, a dynamic handover transmission control scheme based on the queuing game model is proposed. Considering long transmission delay links in the SAGINs, the traditional queuing game model is improved to reduce the number of information interactions. The users’ service value are defined as the random distribution to describe the various requirements in the network. In addition, the relationship between the social welfare and the arrival rate of the transmission control system is discussed under two modes of observable and unobservable controller queues. Through theoretical analysis and calculation, the unique handover arrival rate is obtained to make the social welfare equal in the two modes. When the arrival rate is less than the handover threshold, the controller queue is set to unobservable mode, and otherwise to observable mode. Then, the system can operate in an optimal way to obtain the maximum benefit. At last, numerical and system simulation verify the effectiveness of the scheme.

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

In this paper, the energy efficient virtual machine (VM) placement and virtual resource allocation problem for the mobile edge computing (MEC) system is explored. Particularly, we develop an optimization framework of energy consumption minimization for computing and offloading by jointly optimizing the VM placement matrix and the number of physical machines (PMs). To resolve this problem, we transform the optimization problem into a non-uniform weighted hypergraph model. In this model, the weight of hyperedge is defined as the negative of the accumulated energy consumption for computing at VM instances hosted by one PM. Based on the hypergraph model, a hypergraph matching algorithm by utilizing the local search policy is proposed for finding the maximum-weight subset of vertex-disjoint hyperedges, aiming to obtain an optimal VM placement, i.e., (M*)-perfect matching. Furthermore, the virtualized resources are further allocated to user equipments (UEs) in the form of multiple VM instances via the optimal VM placement to meet the requirement of workloads. Simulation results are presented to demonstrate the effectiveness of the proposed hypergraph matching algorithm over the alternative benchmark algorithm.

Database-driven Cognitive Radio Network (CRN) has been proposed to replace the requirement of spectrum sensing of terminal devices so that the operation of users is simplified. However, location privacy issues introduce a big challenge for securing database-driven CRN due to spectrum availability information. The existing works consider either PU or SU's location privacy while not the both. In this study, we identify a unified attack framework in which a curious user could infer a target's location based on the spectrum availability/utilization information. Further, we propose a location privacy protection mechanism, which allows both SU and PU to protect their location privacy by adopting a series of countermeasures. The location privacy and spectrum utility are the trade-off. In the countermeasures of location privacy preserving spectrum query process, both SU and database aim to maximize the location privacy with constraints of spectrum utility. Thus, they can obtain higher location privacy level with sacrifice of spectrum utility as long as the spectrum utility meets the requirements. We evaluate the unified attack and defence approaches based on simulation and demonstrate the effectiveness of the proposed location privacy preserving approaches.

In this study, the authors explore a joint cross‐layer optimised routing and dynamic power allocation for intermittently connected deep space backbone layer in deep space information networks via predictable contacts. First, the authors build up the payoff function and state dynamics based on differential game theory via two defined cost function paradigms. Then they establish a general differential game model for dynamic power allocation by the associated payoff function and state dynamics, and further propose the theoretical results of dynamic power allocation in a cooperative or non‐cooperative manner. To describe the routing metric, they introduce the concept of hybrid link homeostasis which illustrates the connection between the predictable contact and the transmitted power along the corresponding backbone link. In addition, they propose a polynomial time algorithm of cross‐layer optimised routing, which realises joint routing selection, transmitted power allocation and predictable contact schedule simultaneously. The numerical results demonstrate the effectiveness and feasibility of the authors proposed joint cross‐layer optimised routing and dynamic power allocation.

Tagging systems are known to be particularly vulnerable to tag spam. Due to the self-organization and self-maintenance nature of Peer-to-Peer (P2P) overlay networks, users in the P2P tagging systems are more vulnerable to tag spam than the centralized ones. This paper proposes SpamResist, a novel social reliability-based mechanism. For each tag search, SpamResist client groups the search respondents into two categories, namely unfamiliar peers and interacted peers according to the fact whether the client has interacted with such respondents. For the two different categories of peers, the client computes their reliability degrees, and then utilizes these reliability degrees as weights to rank search results. To obtain higher quality search results, we propose a socially-enhanced mechanism, considering social friends can share their previous experience and help improve both the performance and convergence of SpamResist. Finally, the experimental results illustrate that SpamResist can effectively defend against tag spam and work better than the existing search models in P2P tagging systems.

Deceptive behaviors of peers in peer-to-peer (P2P) content sharing systems have become a serious problem due to the features of P2P overlay networks such as anonymity, self-organization, etc. This paper presents Sorcery, a novel active challenge-response mechanism based on the notion that one side of interaction with dominant information can detect whether the other side is telling a lie. To make each client obtain the dominant information, our approach introduces social network to the P2P content sharing system; thus, the client can establish friend-relationships with peers who are either acquaintances in reality or those reliable online friends. Using the confidential voting histories of friends as own dominant information, the client can challenge the content providers with the overlapping votes of both his friends and the content provider, thus detecting whether the content provider is a deceiver. Moreover, Sorcery provides the punishment mechanism which can reduce the impact brought by deceptive behaviors, and our work also discusses some key practical issues. The experimental results illustrate that Sorcery can effectively address the problem of deceptive behaviors, and work better than the existing reputation models.