Federated Unlearning (FU) has emerged as a promising paradigm for effectively removing the influence of specific data of clients from the global model in federated learning. It can further enhance personal data privacy for individual clients and eliminate the impact of malicious attacks like poisoning. Due to these benefits, many FU methods have been analyzed and proposed. Yet, they largely overlook external threats against FU systems, such as gradient inversion attacks that reconstruct client data from shared gradients, posing serious privacy risks to other participating clients. Motivated by this, we propose DIARY, a Differential prIvacy IntegrAted fedeRated recoverY framework to address these dual threats. DIARY presents a privacy budget allocation method, whose insight lies in adaptively assigning appropriate privacy budgets to various training and recovery phases to fully utilize the global privacy budget of each client, balancing the trade-off between privacy and utility. Furthermore, DIARY introduces a novel Federated noise-Immune aNomaly Detection (FIND) module. The deep integration of FIND with two-level selective storage and model rollback mechanisms contributes to model recovery, while significantly reducing the associated overhead. Finally, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods are conducted to validate the effectiveness of DIARY.

LoRaWAN is a compelling low-cost solution for large-scale indoor Internet of Things (IoT) data backhaul, owing to its strong penetration capability and low power consumption. However, its default pure ALOHA access mechanism leads to severe channel contention, substantial packet loss, and reduced throughput under dense, concurrent transmissions. To overcome this, we propose a lightweight out-of-band (OOB) synchronization scheme that integrates a time division multiple access (TDMA) mechanism into commercial LoRaWAN Class A networks. Unlike approaches requiring gateway scheduling, frequent downlink signaling, or custom hardware, our method introduces a single low-cost node providing millisecond-level alignment via a dedicated OOB synchronization channel. End devices seamlessly access this channel by briefly retuning their existing LoRa transceivers. Consequently, the scheme imposes zero downlink overhead during the steady-state reporting phase, requires no hardware modifications to gateways or end devices, and remains fully backward-compatible. This design enables collision-free scheduled channel access within the configured nominal resource capacity, thereby improving throughput and reducing contention. Real-world experiments using an indoor positioning prototype demonstrate that the proposed TDMA-LoRaWAN architecture improves system throughput by over 30% and reduces the packet loss rate from 25.8% to 5.02% in a 20-node indoor deployment. Furthermore, large-scale simulations corroborate these empirical findings, support the scalability analysis under larger network sizes, and indicate improved energy efficiency per successful packet in dense network settings. These combined results demonstrate the effectiveness of the proposed approach for dense indoor IoT data collection and indicate its practical potential under high uplink reporting demands.

In future space-air-ground integrated networks, time synchronization technology serves as the cornerstone for realizing network communication. In this network, the self-synchronization within the network achieved via communication links has advantages such as autonomy and higher synchronization accuracy compared to the timekeeping of the Global Navigation Satellite System (GNSS), and it is applied in various complex environments. Therefore, in the time synchronization technology of integrated networks, more attention should be paid to the issue that space-air/ground links are affected by dynamic interference. Such interference can lead to a decline in time synchronization accuracy and fluctuations in network performance. To address these challenges, this paper proposes a Neighbor Aware Reinforcement Learning (NARL) time synchronization framework. A lightweight neighbor node interaction mechanism is designed to control synchronization overhead and effectively prevent broadcast information explosion among nodes. Specifically, for partial space-air/ground link interference, we develop the NARL master-slave synchronization (NARL-MSS) algorithm, which constructs an online scoring model to enable rapid synchronization response while enhancing long-term performance through RL. Under full space-air/ground link interference, an NARL distribution synchronization (NARL-DS) algorithm is employed to identify optimal synchronization link sets, jointly optimizing synchronization accuracy and network overhead. Simulations demonstrate that NARL-MSS can achieve an 80% improvement in synchronization errors, while NARL-DS exhibits excellent synchronization convergence and significantly reduces synchronization costs.

The pinching-antenna systems (PASS), which activate small dielectric particles along a dielectric waveguide, has recently emerged as a promising paradigm for flexible antenna deployment in next-generation wireless communication networks. While most existing studies assume rectangular indoor layouts with full coverage waveguide, practical deployments may involve geometric constraints, partial coverage, and non-negligible waveguide attenuation. This paper presents the first analytical investigation of PASS in a circular indoor environment, encompassing both full coverage and partial coverage waveguide configurations with/without propagation loss. A unified geometric– propagation framework is developed that jointly captures pinching-antenna placement, Internet of Things (IoT) device location distribution, and waveguide attenuation. Closed-form expressions for the outage probability and average achievable rate are derived for four scenarios, with accuracy validated via extensive Monte-Carlo simulations. The analysis reveals that, under the partial coverage waveguide scenario with propagation loss, the system performance demonstrates a non-monotonic trend with respect to the waveguide length, and the optimal length decreases as the attenuation coefficient increases. Numerical results further quantify the interplay between deployment strategy, waveguide propagation loss, and coverage geometry, offering practical guidelines for performance-oriented PASS design.

Traditional unimodal sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users’ diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing, decoding, and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse and higher-accuracy sensing services.

From the perspective of user experience, when optimizing resource provisioning in networks, we have to maximize social utility, which is an abstraction of what users can obtain from the service provided by a network. In quantum networks, unlike their counterparts, circuit-switched classical networks, (i) the utility obtained by a demand is not always concave for the number of Entanglement Connections (ECs) we provision to it; and (ii) each demand requires a different amount of quantum resources over each link along the path to establish an EC. As a result, the Social Utility Maximization (SUM) problem is more challenging than in classic circuit-switched networks. In this paper, we propose an approach also called SUM to maximize social utility in quantum networks by provisioning an appropriate number of ECs (and corresponding resources) to demands. We first formulate the SUM problem and analyze it based on Lagrangian relaxation and duality techniques. Accordingly, we derive the optimal EC provisioning scheme for a given Lagrangian multiplier, depending on whether the utility function of each demand is convex, concave, or sigmoid-like. After that, a primal-dual iteration algorithm is proposed to determine the optimal EC provisioning scheme to maximize social utility. We conduct extensive simulations to demonstrate that SUM outperforms the state-of-the-art approach to maximizing quantum network throughput,i.e., EFiRAP, by up to 58.4%.

Space simultaneous information and power transfer (SSIPT) extends simultaneous wireless information and power transfer (SWIPT) from terrestrial to space-based scenarios for efficient energy utilization beyond the limitations of photovoltaic systems. A large-aperture antenna is required to provide sufficient gain to compensate for the severe path loss caused by the extremely long transmission distance in the SSIPT system. However, most existing antennas employ phased-array (PA) architectures that depend on costly hardware components, making it difficult to achieve the aforementioned requirements under constrained budgets. In this paper, we propose a reconfigurable holographic surface (RHS)-assisted SSIPT system. Specifically, RHSs, which can be implemented entirely with low-cost, commercially available components, offer a promising alternative to conventional PAs for realizing cost-efficient SSIPT. The serial-feed architecture of RHSs introduces radiation power coupling among adjacent elements, which significantly affects the transmission characteristics and invalidates conventional PA-based SWIPT analytical models. To address this issue, we develop a new analytical SSIPT framework that accurately captures the serial coupling effect and design amplitude-controlled beamforming schemes that depart fundamentally from phase-shift-based approaches, which improve rate-energy (R-E) performance. In simulations, we demonstrate that under the same hardware cost, the RHS achieves an expanded R-E region compared with the PA. Therefore, the compact and low-cost characteristics of RHSs make them more suitable for SSIPT scenarios with limited payload and cost.

Aiming at the real-time computing problems in large-scale internet of things devices (IoTDs) scenarios, a framework for terahertz (THz) -based mobile edge computing (MEC) network with multi-unmanned aerial vehicles (UAV) collaboration is proposed. In this framework, a utility model based on latency and connection scheduling is first presented. Its significance lies in enabling high-priority tasks to obtain more computing resources, thereby reducing computing latency. Then, we formulate an optimization problem that jointly optimizes connection scheduling, computing resource allocation, and UAV flight trajectories under the objective of maximizing the average utility of IoTDs. To solve this Mixed Integer Nonlinear Programming Problem (MINLP), we use Deep Reinforcement Learning (DRL) based on learning rate decay and Prioritized Experience Replay (PER) to optimize the UAVs trajectories, and design a low-time complexity heuristic algorithm to solve the connection scheduling and resolve the computing resource allocation by an iterative algorithm. Subsequently, to evaluate the performance of our proposed algorithm, we compare it with Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), and Particle Swarm Optimization (PSO). Simulation results show that our proposed algorithm significantly improves the average utility of IoTDs and reduces the latency of high-priority tasks. Besides, our proposed algorithm has better convergence than the above algorithms.

To efficiently implement Terahertz (THz) communications in the 6G era, ultra-massive multiple-input multiple-output (UM-MIMO) technique is considered essential. However, effective wideband THz UM-MIMO transmissions necessitate low-cost yet accurate channel estimation (CE) methods. In this article, we investigate the wideband THz UM-MIMO CE problem under hybrid near- and far-field propagation, molecular absorption, and multi-path reflection. The CE problem is reformulated into a compressed sensing (CS)-aided counterpart (CSCE), exploiting the inherent sparsity of THz UM-MIMO channels to reduce pilot overhead. Our key contributions are: 1) after analyzing the inefficiency of conventional Bayesian learning (BL)-based CSCE frameworks in solving this CE task, we propose a deep unfolding (DU)-aided BL (DUBL) CE algorithm, in which the unfolded expectation-maximization (EM) iteration is implemented through a carefully tailored deep neural network (DNN) architecture; 2) we design a staged offline training procedure equipped with a dedicated loss function to ensure efficient DUBL training; and 3) we conduct a detailed complexity analysis that explicitly quantifies the computational cost of each unrolled layer, thereby characterizing the online inference overhead of the proposed DUBL method. Simulation results demonstrate that the DUBL solution offers substantial THz UM-MIMO CE gains over representative baselines, while complexity comparison highlights its enhanced real-time inference.

Deep learning (DL) has emerged as a powerful tool for addressing the intricate challenges inherent in communication and sensing systems, significantly enhancing the intelligence of future sixth-generation (6G) networks. While substantial research has demonstrated the potential of DL-based techniques, challenges remain in ensuring robustness, generalization, and interpretability under highly dynamic and unpredictable environments. To address these limitations, this paper introduces a family of mathematically grounded and modularized models, termed bedrock models, designed for seamless integration into communication and sensing systems. Unlike traditional black-box neural architectures, each bedrock module inherits its structure from classical physical-layer operators, enabling a key rollback capability: when the environment becomes adverse or uncertain, the trainable model can deterministically revert to a closed-form classical solution by simply resetting its parameters. This ensures that the system does not perform worse than the well-understood baseline, while retaining the flexibility of AI enhancements under favorable conditions. In communication systems, bedrock models achieve notable performance gains and exhibit strong transferability, supporting direct parameter reuse across tasks and modulation schemes. In sensing applications, the integration of bedrock models significantly improves performance, reducing delay and Doppler estimation errors by an order of magnitude. Additionally, a transmitter-side pre-equalization strategy is proposed, enabling real-time waveform adaptation using sensing information, while preserving the structure of a pretrained communication model. This approach allows the system to mitigate doubly dispersive channels and maintain near-optimal performance. Extensive simulations validate the effectiveness, robustness, and deployment readiness of the proposed bedrock models across diverse scenarios in both communication and sensing domains.

Seamlessly unifying communication and sensing, sixth-generation (6G) networks are poised to transform into intelligent platforms with high spectral–energy efficiency and real-time environmental awareness. In the low-altitude economy, unmanned aerial vehicles (UAVs) enable air–ground integrated sensing and communication (ISAC) for applications such as logistics and inspection, yet most studies focus on single-UAV or homogeneous-agent designs. In contrast, this paper proposes a multi-UAV cooperative ISAC system that enables heterogeneous-agent collaboration between multiple UAVs and a ground base station (BS) for joint target sensing, tracking, and communication. The system is formulated as a posterior Cramér–Rao bound (PCRB) minimization problem under communication performance constraints, utilizing joint trajectory–beamforming optimization. To tackle the NP-hard nature of this problem, we design a curriculum-based heterogeneous-agent proximal policy optimization (C-HAPPO) algorithm, where curriculum learning guides progressive policy refinement and Kronecker/QR decomposition mitigates action dimensionality. Simulation results show that the proposed approach achieves more than a 30% improvement in sensing performance, faster convergence, and higher tracking accuracy than existing baselines, demonstrating its scalability and effectiveness for complex multi-UAV ISAC scenarios.

With the rapid advancement of Internet of Things and artificial intelligence, device automation systems have become increasingly integrated with physical environments, introducing new security challenges for Trigger-Action Programming. An improper configuration of TAP rules may lead to severe cascading threats. However, existing methods typically rely on predefined safety properties and fail to capture the underlying semantic dependencies and interactions among rules. To address these limitations, we propose TAPGuard, a semantics-enhanced framework for TAP rule linkage modeling and cascading threat detection. Specifically, we identify two types of cascading threats: explicit threats, which arise from direct device interactions, and implicit threats, which are induced by shared environmental variables and may propagate across semantically related but structurally disconnected rules. TAPGuard leverages large language models to extract structured semantic elements from natural language rule descriptions and incorporates a semantic alignment module to assess the functional similarity between rules. Building on this, we propose a dual-relation context encoder incorporating node-level and semantic-level attention to model heterogeneous dependencies and enable multi-hop relational reasoning in the heterogeneous TAP rule graph. We evaluate TAPGuard on a real-world smart home dataset and demonstrate its effectiveness in detecting cascading threats. Experimental results show that TAPGuard significantly outperforms state-of-the-art graph-based baselines.

The unmanned/uncrewed aerial vehicle (UAV) assisted mobile edge computing (MEC) technology has become a viable and flexible solution for providing computation offloading and energy charging services for ground users, especially in scenarios with terrible direct links. Therefore, latency has become one of the crucial design issues subject to the energy limitations of the UAV and users. Motivated by this, we study a latency-aware air ground hybrid MEC system with an assistant UAV and a ground base station (GBS) to serve and charge multiple users under both the time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols. The task completion latency minimization problems are formulated by jointly optimizing the time slot scheduling, CPU frequency allocation, UAV's three dimensional (3D) trajectory design, transmit power allocation, as well as the number of required time slots. To address the formulated mixed integer non-convex optimization problems, we introduce an efficient alternating optimization algorithm with a double-loop structure. In the outer loop, we constantly adjust the number of time slots by employing the bisection search method and determine the search range via feasibility check. In the inner loop, we first transform the original subproblem into an equivalent problem that maximizes the minimum computation completion ratio of the users. Then we further deconmpose this transformed problem into four subproblems, which can be solved by a proposed iterative algorithm. Extensive experiments are con ducted to illustrate the efficacy and superiority of the proposed algorithm over the other benchmark schemes in minimizing the task completion latency, particularly in scenarios where the computing resource is limited or the density of users is high.

Despite the widespread adoption of vision sensors in edge applications, such as surveillance, video transmission consumes substantial spectrum resources. Semantic communication (SC) offers a solution by extracting and compressing information at the semantic level, but traditional SC without sensing capabilities faces inefficiencies due to the repeated transmission of static frames in edge videos. To address this challenge, we propose an SC with computer vision sensing (SCCVS) framework for edge video transmission. The framework first introduces a compression ratio (CR) adaptive SC (CRSC) model, capable of adjusting CR based on whether the frames are static or dynamic, effectively conserving spectrum resources. Simultaneously, we present a knowledge distillation (KD)-based approach to ensure the efficient learning of the CRSC model. Additionally, we implement a computer vision (CV)-based sensing model (CVSM) scheme, which intelligently perceives the scene changes by detecting the movement of the sensing targets. Therefore, CVSM can assess the significance of each frame through in-context analysis and provide CR prompts to the CRSC model based on real-time sensing results. Moreover, both CRSC and CVSM are designed as lightweight models, ensuring compatibility with resource-constrained sensors commonly used in practical edge applications. Experimental results show that SCCVS improves transmission accuracy by approximately 70% and reduces transmission latency by about 89% compared with baselines. We also deploy this framework on an NVIDIA Jetson Orin NX Super, achieving an inference speed of 14 ms per frame with TensorRT acceleration and demonstrating its real-time capability and effectiveness in efficient semantic video transmission.

Immersive volumetric video streaming in extended reality (XR) demands ultra-low motion-to-photon (MTP) latency, which conventional edge-centric architectures struggle to meet due to per-frame computationally intensive rendering tightly coupled with user motion. To address this challenge, we propose an Open Radio Access Network (O-RAN)-integrated playback framework that jointly orchestrates radio, compute, and content resources in near real time (Near-RT) control loop. The system formulates the rendered-pixel ratio as a continuous control variable and jointly optimizes it over the Open Cloud (O-Cloud) compute, gNB transmit power, and bandwidth under a Weber-Fechner quality of experience (QoE) model, explicitly balancing resolution, computation, and latency. A Soft Actor-Critic (SAC) agent with structured action decomposition and QoE-aware reward shaping resolves the resulting high-dimensional control problem. Experiments on a 5G O-RAN testbed and system simulations show that SAC reduces median MTP latency by above $11\%$ and improves both mean QoE and fairness, demonstrating the feasibility of RIC-driven joint radio-compute-content control for scalable, latency-aware immersive streaming.

Evaluating network-wide fairness is challenging because it is not a static property but one highly sensitive to Service Level Agreement (SLA) parameters. This paper introduces a complete analytical framework to transform fairness evaluation from a single-point measurement into a proactive engineering discipline centered on a predictable sensitivity landscape. Our framework is built upon a QoE-Imbalance metric whose form is not an ad-hoc choice, but is uniquely determined by a set of fundamental axioms of fairness, ensuring its theoretical soundness. To navigate the fairness landscape across the full spectrum of service demands, we first derive a closed-form covariance rule. This rule provides an interpretable, local compass, expressing the fairness gradient as the covariance between a path’s information-theoretic importance and its parameter sensitivity. We then construct phase diagrams to map the global landscape, revealing critical topological features such as robust “stable belts” and high-risk “dangerous wedges”. Finally, an analysis of the landscape’s curvature yields actionable, topology-aware design rules, including an optimal “Threshold-First” tuning strategy. Ultimately, our framework provides the tools to map, interpret, and navigate the landscape of system sensitivity, enabling the design of more robust and resilient networks.

With the rising demand for high-rate and timely communications, fog radio access networks (F-RANs) offer a promising solution. This work investigates age of information (AoI) performance in F-RANs, consisting of multiple content users (CUs), enhanced remote radio heads (eRRHs), and content providers (CPs). Time-critical CUs need rapid content updates from CPs but cannot communicate directly with them; instead, eRRHs act as intermediaries. CUs decide whether to request content from a CP and which eRRH to send the request to, while eRRHs decide whether to command CPs to update content or use cached content. We study two broad classes of policies: (i) oblivious policies, where decision-making is independent of historical information, and (ii) non-oblivious policies, where decisions are influenced by historical information. We first derive closed-form expressions for the average AoI of eRRHs under both policy types. Due to the complexity of calculating closed-form expressions for CUs, we then derive general upper bounds for their average AoI. Next, we identify optimal policies for both types. Under both optimal policies, each CU requests content from each CP at an equal rate. When demand is low or resources are limited, all requests are consolidated to a single eRRH; when demand is high and resources are ample, requests are evenly distributed among eRRHs. eRRHs command content from each CP at an equal rate under an optimal oblivious policy, while prioritize the CP with the highest age under an optimal non-oblivious policy. Our numerical results validate these theoretical findings. We further extend our analytical framework to two generalized scenarios, and simulations confirm the validity of our conclusions.

Conventional entanglement routing approaches decide the Entanglement Paths (EPs) to establish Entanglement Connections (ECs) before trying to create Entanglement Links (ELs). By doing so, very few EL failures will result in a low network throughput. In this paper, we study how to choose the EPs to establish ECs after knowing which ELs are successfully created. This is called the Deferred EP Selection (DEPS) problem. DEPS is a generalized integer multi-commodity flow problem and we cannot solve it quickly with conventional optimization methods. To address this issue, we propose a Deep Reinforcement Learning based EP Selection (DRLEPS) approach. The salient features of DRLEPS include (i) by controlling the number of candidate EPs, DRLEPS can achieve a trade-off between time complexity and the EC establishment rate; and (ii) using candidate EPs as input, DRLEPS is robust to request variation; and (iii) by training neural networks with different topologies, a model derived by DRLEPS can be applied to various networks (even with a different number of nodes) without fine-tune. Through extensive simulations, we show that even in a network with 200 nodes, DRLEPS can solve the DEPS problem in 0.39 seconds with a Nvidia GeForce 3090 GPU. It outperforms the approach always establishing ECs through the EP with the largest success probability by up to 23.4% in EC establishment rate. It also outperforms the Integer Linear Programming (ILP) based scheme, which can achieve the maximum EC establishment rate, by up to 184.2x in network throughput.

Quantum Key Distribution (QKD) is considered to be the ultimate solution to communication security. However, current QKD devices, especially quantum photon sources, are expensive, and they can generate secret keys only at a low rate. In this paper, we first consider homogeneous trusted-relay-based QKD networks where every request has the same amount of secret key requirement and every photon source has the same key distribution rate, and design an approach named RPSP to not only minimize the number of photon sources needed in a network to ensure at least one feasible relay path exists for any potential QKD requests but also save the time to complete a batch of QKD requests by jointly optimizing the routing of relay paths and the provisioning of photon sources to distribute secret keys. Then, we extend RPSP to RPSP-HN which can be applied to heterogeneous networks where requests have different secret key requirements and photon sources distribute keys at different rates. Furthermore, we also extend RPSP to RPSP-HY, which considers that some of the nodes in a network is untrusted. Compared with existing works, RPSP and its extensions focus on more practical scenarios where only some of the nodes are equipped with photon sources and they leverage optical switching to enable dynamic photon source provisioning such that we can utilize QKD devices more efficiently. Extensive simulations show that compared with baseline schemes, RPSP, RPSP-HN, and RPSP-HY can save up to 33%, 37%, and 25% of the time to complete a batch of QKD requests in homogeneous, heterogeneous, and hybrid QKD networks, respectively.

Entanglement routing plays a vital role in supporting various applications in quantum networks. Existing works on entanglement routing either ignored the Entangled Photon Source (EPS) placement issue or simply assumed a pool of EPSes at a centralized location that can provision entanglement over arbitrary quantum links. In this paper, we propose LIGHTER and fidelity-aware LIGHTER (named F-LIGHTER) to solve the joint EPS placement and entanglement routing problem based on the assumption that EPSes are co-located with quantum nodes and each EPS can send one entangled photon at a time to one of its adjacent nodes only. The salient features of LIGHTER and F-LIGHTER include (i) LIGHTER and F-LIGHTER use a demand-agnostic EPS placement scheme to maximize network throughput and fairness for all feasible Entanglement Connection EC) establishment demands, and (ii) most requested ECs can be established over Entanglement Paths (EPs) determined offline, and only a small percentage of them will be established over online calculated EPs, resulting in fast and efficient entanglement routing. Extensive simulations show that compared with schemes without proper EPS placement or entanglement routing, LIGHTER can improve the network throughput by up to 175.6% and 37.0%, respectively. When the fidelity is considered, the network throughput improvement achieved by F-LIGHTER will be up to 135.0% and 21.5%, respectively.

Integrated sensing, communications, and computation (ISCC) is becoming increasingly critical, particularly for enabling advanced intelligent applications. This paper proposes an ISCC framework for edge-intelligent networks, where edge intelligent devices (EIDs) cooperatively sense multiple mobile targets and simultaneously offload radar sensing data to a base station (BS) equipped with an edge server for processing. To address the time-varying nature of the network, we develop an online resource management strategy that maximizes the long-term average weighted sum rate (AWSR), subject to queue stability, average power constraints, and quality-of-service (QoS) requirements. Using the Lyapunov drift-plus-penalty framework, the original stochastic optimization problem is decomposed into a sequence of deterministic subproblems across time slots. At each time slot, sensing scheduling, transmit beamforming for both sensing and communications, receive beamforming for radar echoes, and computing resource allocation at the BS are jointly optimized through an efficient alternating optimization algorithm based on the current system state. Simulation results validate the effectiveness of the proposed online strategy, showing superior performance over baseline methods and revealing the influence of key parameters. In particular, a trade-off is observed between the AWSR and queue backlogs, which can be flexibly tuned via control parameters.

This paper investigates a full-space covert communication (CC) scheme aided by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which can resist the position randomness of the eavesdropper (PRE). In contrast to prevailing STAR-RIS assisted CC schemes assuming an eavesdropper with fixed position, the proposed CC scheme considers an eavesdropper which randomly locate at both sides of the STAR-RIS, leading to 360° eavesdropping risks. To restrict the eavesdropper’s ability to detect the CCs, the covert user is designed with two antennas working in full-duplex mode. One of the antennas is employed to receive the desired covert messages, while the other produces jamming signals at various power levels to impede the eavesdropper’s detection. Except the covert user, the base station (BS) also needs to serve a public user, which has certain quality of service (QoS) requirement. In order to construct a robust covert constraint, we analyze and derive a closed-form formula for the eavesdropper’s minimum detection error probability (DEP) in the worst-case situation. Subsequently, an optimization problem is established to maximize the covert rate of the system, through joint optimizing the bandwidth allocation, the active beamforming of the BS, and the passive beamforming of STAR-RIS, while adhering to the covert constraint and QoS requirement for the public user. An iterative algorithm is provided to tackle this non-convex optimization problem, utilizing the semi-definite relaxation (SDR) method and the augmented Lagrange technique. The simulation results demonstrate that the proposed STAR-RIS assisted CC scheme exhibits superior performance in resisting the full-space eavesdropping compared to other benchmark schemes, thereby validating the efficacy of the proposed scheme.

Due to the innovative application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain with lower hardware complexity. Together with the aid of a circular antenna arrangement in H-MIMO, integrated data and energy multicast (IDEM) can fully exploit the near-field channel to realize wider range of energy focusing and higher achievable rate. In this paper, we focus on beamforming design and investigate the IDEM systems that maximize the minimum rate of data users (DUs) while meeting the energy harvesting requirements for energy users (EUs). Specifically, we first derive the closed-form near-field resolution function in 3D space and show the asymptotic spatial orthogonality of near-field channel for circular antenna arrays. Then, we design an asymptotically optimal fully-digital beamformer based on the spatial orthogonality. After that, we apply the alternating optimization to develop H-MIMO beamforming scheme, where the digital beamformer is given in closed form while the analog beamformers of three different control modes are obtained numerically, respectively. Scaling schemes are also investigated to further improve the IDEM performance. Numerical results verify the correctness of the resolution function and asymptotic orthogonality and demonstrate that the proposed beamforming schemes outperform benchmark schemes, with very low complexity.

Sensing-assisted communication schemes have recently garnered significant research attention. In this work, we design a dual-function reconfigurable intelligent surface (RIS), integrating both active and passive elements, referred to as the reconfigurable intelligent sensing surface (RISS), to enhance communication. By leveraging sensing results from the active elements, we propose communication enhancement and robust interference suppression schemes for both near-field and far-field models, implemented through the passive elements. These schemes remove the need for base station (BS) feedback for RISS control, simplifying the communication process by replacing traditional channel state information (CSI) feedback with real-time sensing from the active elements. The proposed schemes are theoretically analyzed and then validated using software-defined radio (SDR). Experimental results demonstrate the effectiveness of the sensing algorithms in real-world scenarios, such as direction of arrival (DOA) estimation and radio frequency (RF) identification recognition. Moreover, the RISS-assisted communication system shows strong performance in communication enhancement and interference suppression, particularly in near-field models.

Movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) have emerged as two promising technologies for enhancing wireless communication performance, owing to their capability to dynamically reshape and manipulate the propagation environment. Motivated by this potential, this paper investigates the joint utilization of the additional degrees of freedom introduced by MAs (through antenna repositioning) and RIS (via optimized reflection) to effectively mitigate computation distortion in over-the-air computation (AirComp) systems. Specifically, we formulate an optimization problem aimed at minimizing the mean square error (MSE) between the target function values and their estimates, through jointly optimizing the receive beamformer at the access point, RIS reflection phase shifts, and transmit coefficients as well as antenna positions of AirComp users. To address the non-convex nature of the formulated problem, we develop a computationally efficient algorithm capitalizing alternating optimization technique, the penalty-dual decomposition method, and the particle swarm optimization enhanced by a dynamic neighborhood pruning mechanism. Next, we further extend the optimization framework to a more practical case with discrete MA positions. Extensive simulation results demonstrate that the joint optimization of RIS beamforming and MA positioning substantially reduces the computation MSE, compared to the separate MA-enhanced AirComp and RIS-aided AirComp schemes. Moreover, the proposed algorithm achieves comparable performance to the penalty function-based method, while incurring significantly lower computational complexity.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems based on the concept of exploiting constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Moreover, an “indirect power scaling” method is proposed for the precoding design based on DAC quantization to enhance the ISAC performance. From the simulation results, we can observe that the proposed precoding schemes can achieve satisfactory ISAC performance with low complexity. In the considered ISAC systems, increasing the quantization bits for DAC and PS quantizations can improve the ISAC performance, and the gain for DAC quantization is more pronounced.

Joint waveform and beamforming design is demonstrated as an effective approach to improve the wireless energy transfer (WET) performance. Meanwhile, the emerging technique of reconfigurable holographic surface (RHS) without the half-wavelength limitation is able to achieve a higher spatial gain. Hence, the RHS-assisted WET system is designed to enhance WET performance by optimizing the waveform and beamforming. However, the ideal linear power amplifier is always conceived, while the nonlinearity of the power amplifier is ignored. In this paper, a more practical nonlinear power amplifier (NPA)-based RHS-assisted WET system is proposed, where the energy harvesting (EH) model by considering the channel aging effect is derived to adapt to the time-varying channel. In order to improve the WET performance, a predistortion technique is adopted to tackle the nonlinear input-output characteristic of NPAs, while the waveform and beamforming are jointly optimized. Simulation results demonstrate that the RHS outperforms the phased array benchmark having the same size on the WET performance, when the NPAs work in the unsaturated region. Furthermore, the impact of the NPAs on the WET performance by considering channel aging effect is demonstrated, while the waveform design is invalid when the NPAs work near the saturation region.

The bistatic Integrated Sensing and Communication (ISAC) is poised to become a key application for next generation communication networks (e.g., B5G/6G), providing simultaneous sensing and communication services with minimal changes to existing network infrastructure and hardware. However, a significant challenge in bistatic cooperative sensing is clock asynchronism, arising from the use of different clocks at far separated transmitters and receivers. This asynchrony leads to Timing Offsets (TOs) and Carrier Frequency Offsets (CFOs), potentially causing sensing ambiguity. Traditional synchronization methods typically rely on static reference links or GNSS-based timing sources, both of which are often unreliable or unavailable in UAVbased bistatic ISAC scenarios. To overcome these limitations, we propose a Time-Varying Offset Estimation (TVOE) framework tailored for clock-asynchronous bistatic ISAC systems, which leverages the geometrically predictable characteristics of the Line-of-Sight (LoS) path to enable robust, infrastructure-free synchronization. The framework treats the LoS delay and the Doppler shift as dynamic observations and models their evolution as a hidden stochastic process. A state-space formulation is developed to jointly estimate TO and CFO via an Extended Kalman Filter (EKF), enabling real-time tracking of clock offsets across successive frames. Furthermore, the estimated offsets are subsequently applied to correct the timing misalignment of all Non-Line-of-Sight (NLoS) components, thereby enhancing the high-resolution target sensing performance. Extensive simulation results demonstrate that the proposed TVOE method improves the estimation accuracy by 60%.

In response to the “security blind zone” challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into the CC systems and proposes the corresponding CC transmission scheme. Specifically, we first develop the signal processing model of the FD-RIS, which considers effective control of harmonic signals by leveraging the time-delay techniques. The joint distance-angle beamforming capability is then validated through its normalized beampattern. Based on this model, we then construct an FD-RIS-assisted CC system under a multi-warden scenario and derive an approximate closed-form expression for the covert constraints by considering the worst-case eavesdropping conditions and utilizing the logarithmic moment-generating function. An optimization problem is formulated which aims at maximizing the covert user’s achievable rate under covert constrains by jointly designing the time delays and modulation frequencies. To tackle this non-convex problem, an iterative algorithm with assured convergence is proposed to effectively solve the time-delay and modulation frequency variables. To evaluate the performance of the proposed scheme, we consider three communication scenarios with varying spatial correlations between the covert user and wardens. Simulation results demonstrate that FD-RIS can significantly improve covert performance, particularly in angular-overlap scenarios where traditional RIS experiences severe degradation. These findings further highlight the effectiveness of FD-RIS in enhancing CC robustness under challenging spatial environments.

Utilizing communication signals to extract motion parameters has emerged as a key direction in Vehicle-to-Everything (V2X) networks. Accurately modeling the relationship between communication signals and sensing performance is critical for the advancement of such systems. Unlike prior work that relies primarily on qualitative analysis, this paper derives the Cramér-Rao Bound (CRB) for radar parameter estimation in the context of Orthogonal Frequency Division Multiplexing (OFDM) waveforms and Uniform Planar Array (UPA) configurations. Recognizing that vehicles may act as extended targets, we propose two New Radio (NR)-V2X-compatible beamforming schemes tailored to different phases of the communication process. During the initial beam establishment phase, we develop a beamforming approach based on the union of predictive error ellipses, which enhances scatterer localization through temporally assisted beam training. In the beam adjustment phase, we introduce an adaptive narrowest-beam strategy that leverages the positions of scatterers and the communication receiver (CR), enabling effective tracking with reduced complexity. The beam design problem is addressed using the minimum enclosing ellipse algorithm and tailored antenna control methods. Simulation results validate the proposed approach, showing up to a 32.4% improvement in achievable rate with a 32×32 transmit antenna array and a 5.2% gain with an 8×8 array, compared to conventional beam sweeping under identical SNR conditions.

Low Earth-orbit (LEO) satellite communications have attracted increasing attention as an effective complement to terrestrial networks for global coverage. However, inherent challenges, such as severe Doppler shifts, significantly degrade the estimation accuracy of instantaneous channel state information (CSI) and accordingly the beamforming performance. To address these issues, this paper proposes an end-to-end sensing-aided deep learning network (SANet) for beamforming in LEO satellite communications. The SANet enhances the sum-rate performance by extracting moving information from radar echo signals. Specifically, the sensing-aided hypernetwork employs an integrated sensing and communications (ISAC) framework to extract the relative velocity between the LEO satellite and ground users from radar echoes. This velocity information is then used to adjust the weights of the beamforming recurrent neural network (RNN), effectively mitigating the Doppler effects. Numerical results demonstrate the proposed SANet significantly outperforms the state-of-the-art beamforming approaches, achieving an approximate 25% improvement in sum-rate over the conventional approaches under identical parameter settings.

Modern network systems have grown considerably in scale and complexity. Deep learning technology has thus become indispensable for unlocking the full potential. Therefore, the demand for high-precision fine-grained data has grown significantly. In this paper, we introduce the critical yet unexplored problem of super-resolution for network traffic data, aiming to reconstruct fine-grained data (i.e., data sampled at high frequencies) from coarse-grained data (i.e., data sampled at low frequencies). Inspired by image super-resolution techniques, we first transform network traffic data into images to expose their inherent periodic patterns. Moreover, we successfully migrate vision backbone network to the temporal super-resolution task. Based on this foundational network, we design a novel cross-attention super-resolution generative adversarial network (SRGAN) model that integrates our cross-attention mechanism to jointly capture local-global temporal correlations. Experimental results on real-world network traffic datasets demonstrate that our model effectively performs super-resolution on traffic network data, outperforming several state-of-the-art models.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems with the assistance of constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes based on them. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Simulation results have been implemented to validate the superiority of the proposed algorithms. When the number of elements in the quantization sets is fixed, the ISAC performance of the precoding scheme based on DAC quantization is superior to that of the precoding scheme based on PS quantization, thanks to the more dispersed level distribution of DAC quantization.

The flourishing of Large Language Models (LLMs) calls for increasingly ultra-scale training. In this paper, we share our experience in designing, deploying, and operating our novel Astral datacenter infrastructure, along with operational lessons and evolutionary insights gained from its production use. Astral has three important innovations: (i) a same-rail interconnection network architecture on tier-2, which enables the scaling of LLM training. To physically deploy this high-density infrastructure, we introduce a distributed high-voltage direct current power system and a new air-liquid integrated cooling system. (ii) a full-stack monitoring system featuring cross-host and hierarchical logging correlation, which diagnoses failures at scale and precisely localizes root causes. (iii) an operator-granular forecasting component Seer that efficiently generates operator execution timelines with acceptable accuracy, aiding in fault diagnosis, model tuning, and network architecture upgrading. Astral infrastructure has been gradually deployed over 18 months, supporting LLM training and inference for multiple customers.

Simultaneous wireless information and power transfer (SWIPT) is a promising technology for the upcoming sixth-generation (6G) communication networks, enabling internet of things (IoT) devices and sensors to extend their operational lifetimes. In this paper, we propose a SWIPT scheme by projecting the interference signals from both intra-wireless information transfer (WIT) and inter-wireless energy transfer (WET) into the null space, simplifying the system into a point-to-point WIT and WET problem. Upon further analysis, we confirm that dedicated energy beamforming is unnecessary. In addition, we develop a low-complexity algorithm to solve the problem efficiently, further reducing computational overhead. Numerical results validate our analysis, showing that the computational complexity is reduced by 97.5% and 99.96% for the cases of KI= KE= 2, M = 4 and KI= KE= 16, M = 64, respectively.

Thanks to the transmit antenna array design, the integrated data and energy transfer (IDET) performance can be readily improved by exploiting spatial degrees of freedom (DoFs). Recently, the 6-dimensional movable antennas (6DMA), which includes three spatial translation dimensions and three spatial rotation dimensions, has been proposed to further improve the spatial DoFs in higher dimensional spaces. In this paper, a 6DMA-assisted IDET system is investigated, where the 6DMA is utilized to spatially align the UPA surface with the IDET receivers for pursuing the maximal spatial gain. By adopting the two time-scale optimization approach, the long-term 6DMA spatial orientation is optimized with the statistical channel state information (CSI) and the short-term optimal IDET beamforming is achieved with the instantaneous CSI. Simulation results demonstrate that our proposed scheme can improve the IDET performance significantly compared to the traditional fixed-position antenna (FPA) and 2-dimensional movable antenna (2DMA) benchmarks.

In the mobile network, digital twin (DT)-aided artificial intelligence (AI)-empowered link control is vital to enhance the performance of wireless communication. This paper proposes a deep reinforcement learning (DRL)-convex optimization enhanced time-frequency domain power allocation scheme to reduce the long-term average bit error rate (BER) in multi-user orthogonal frequency division multiplexing (OFDM) systems. To alleviate performance loss caused by trial-and-error during the training period of DRL algorithms, we design a novel practical DT-aided “prediction-then-decision” autonomous wireless link control framework considering the periodic interaction mechanism between the DT and its physical counterpart. A Transformer-based channel generator Mucomformer is implemented in the DT layer to generate large amounts of multi-user virtual channel state information (CSI) in future transmission frames. In addition, the DRL agent is trained over the DT channel in advance and executed in the real-world OFDM system to generate the optimal transmission strategy by considering the interaction mechanism between the DT and the physical counterpart. The simulation results demonstrate that the proposed Mucomformer has lower average prediction error of 2.51 dB compared to the Transformer baseline. The DRL and convex-based power allocation scheme further outperforms the classic strategy. Moreover, the practical DT-aided autonomous link control framework effectively mitigates the performance impairment, achieves an average BER performance gain 45.65% higher than that without DT and achieves faster convergence during the whole training period.

The Internet of Bio-Nano Things (IoBNT) integrates biosensors, nanorobots, and molecular communication, significantly extending the functionality of traditional IoT systems on a nano-scale. It holds promise for targeted drug delivery and real-time health monitoring applications. However, IoBNT faces critical challenges, including high delay, low network reliability, and congestion, primarily due to biological environments’ complex and dynamic nature. DNA emerges as an ideal information carrier for IoBNT due to its high information density, longevity, biocompatibility, and robustness against environmental interference. These properties make DNA uniquely suited for reliable and efficient communication within IoBNT, with additional functionalities in bio-sensing and DNA computing. This paper proposes AoI-OptiIoBNT, an innovative routing and packet forwarding strategy designed to optimize DNA-based information flow in IoBNT. AoI-OptiIoBNT combines an Age of Information (AoI)-driven approach with a Markov Decision Process (MDP)-based routing algorithm to mitigate delay and congestion. It incorporates a multi-retransmission strategy to enhance network reliability and introduces a Yin-Yang Coding (YYC) mechanism to reduce error rates and improve decoding accuracy. Simulation results demonstrate that AoI-OptiIoBNT substantially improves the efficiency, reliability, and overall performance of IoBNT networks. It offers a robust framework for addressing congestion, packet loss, and delay, making it a promising solution for advancing IoBNT applications.

Wireless energy transfer (WET) technology enables Internet of Things (IoT) networks to have longer lifetime without the need of frequent battery replacements. This article proposes a payload-adaptive hybrid MAC (PAH-MAC) protocol by considering both contention and time-slot allocation among sensors for large-scale data collection scenarios within WET-enhanced IoT networks. The PAH-MAC protocol employs different access strategies based on the payload size of data packets. Furthermore, leveraging synchronization mechanisms, the coordinator periodically dispatches energy packets to replenish the battery of all sensors. The stationary performance of PAH-MAC protocol is analyzed by invoking Markov chains. Considering the traffic variations caused by changes in the number of sensors in the network, a slot adaptive adjustment algorithm is proposed to maximize the throughput and energy performance. Therefore, the coordinator adaptively adjusts the duration of the contention period and the energy transmission period within the next superframe according to access conditions observed in current superframe. Another transmission power adjustment algorithm is proposed for sensors to improve their energy efficiency. According to our simulation, our proposed protocol consumes less energy in low-traffic scenarios than the classic carrier-sense multiple access/CA and another baseline protocol. Furthermore, in high-traffic scenarios, our proposed protocol achieves higher throughput, lower latency, and lower energy consumption than its counterpart.

This article investigates a joint optimization framework for intelligent reflecting surface (IRS)-enhanced integrated sensing, communication, and powering systems. In this framework, the base station transmits signals for simultaneous radar sensing, as well as multi-user information and power transmissions. We aim at maximizing the minimum harvested power among all users, while satisfying beampattern gain requirements for multi-target sensing and signal-to-interference-plus-noise constraints of users. To tackle this strictly non-convex problem, we employ the block coordinate descent technique to iteratively optimize the transmit beamformer of the base station, the phase shift matrix of the IRS, and the power splitting ratios of users. The semi-definite relaxation method is utilized to obtain the optimal transmit beamformer of the base station, and the tightness of the rank-one relaxation is demonstrated. Furthermore, we develop a penalty function-based algorithm and use successive convex approximation techniques to determine the optimal phase shift matrix of the IRS. Additionally, closed-form expressions are derived for the optimal power splitting ratios. Moreover, by exploiting the Bernstein-type inequality, we further designed the robust beamforming and power splitting scheme for considered systems under stochastic channel estimation errors. Numerical results demonstrate that the proposed IRS-enhanced method outperforms several benchmark methods in terms of the minimum harvested power among all users.

The thriving domain of the Internet of Bio-Nano Things (IoBNT) promises revolutionary advances in biomedicine, enabling biosensing, health monitoring, and therapeutic capabilities at the cellular level. A pivotal challenge, however, lies in devising reliable, efficient communication mechanisms within this bio-nano realm. This article introduces an emerging DNA-based molecular communication (MC) system utilizing a novel track-hopper mechanism that significantly enhances precision and control in molecular cargo transport. By leveraging DNA strands for information encoding and cargo transport, our track-hopper-based MCs (THMCs) IoBNT system achieves a symbiosis of high reliability, low delay, and precise directional control, surpassing traditional diffusion and motor-based methods. Through extensive theoretical analysis and simulation of network topology’s link and node response functions, we demonstrate the system’s superior performance in network delay and reliability metrics, underpinning its potential to redefine communication within IoBNT for applications ranging from health monitoring to disease detection. Our findings illuminate a path forward in bio-nano information exchange, offering a robust framework for the next generation of IoBNT systems.

Reconfigurable Intelligent Surfaces (RISs) and Non-Orthogonal Multiple Access (NOMA) can enhance Visible Light Communication (VLC) systems by mitigating signal blockage and improving spectrum utilization. While boosting communication efficiency is crucial, maintaining high illumination quality is equally important. This paper investigates a novel approach to simultaneously improving the sum rate (SR) and illumination uniformity (IU) in a RIS-assisted NOMA-based VLC system. Communication and illumination optimization problems are formulated as a non-convex mixed-integer non-linear programming problem, considering RIS placement, LED-user association, and power allocation. To the best of our knowledge, this is the first work to jointly optimize SR and IU with explicit consideration of RIS placement. We propose a joint optimization approach that leverages a differential evolution algorithm to optimize RIS placement. During each iteration, the obtained solutions are further refined using a block coordinate descent algorithm, which iteratively solves the decomposed sub-problems of LED-user association and power allocation. Simulation results show that the approach outperforms existing methods in both SR and IU. Moreover, RIS placement optimization is shown to be crucial, as neglecting it significantly degrades performance. Finally, the impacts of noise power and total LED power are analyzed, offering practical insights for parameter selection in VLC systems.

Deep learning-based applications have great potential for providing intelligent and personalized services in the Internet of Things (IoT). However, the resource limitation in IoT devices may significantly hinder deep learning applications in IoTs, especially when infrastructures are absent for critical environments. Unmanned Aerial Vehicle (UAV) based split learning can alleviate this problem, by offloading the major part of the deep learning training tasks from IoT devices to the UAV. Whereas, current studies often overlook the latent privacy challenges caused by the UAV and extra data transmissions. To address this issue while ensuring efficient split learning, we propose a novel privacy-preserving split learning architecture. Based on this architecture, we present an improved pipeline scheme to synchronize the training and communicating period between the UAV and the IoT device. Then, in the context of privacy preservation, we formulate an optimization model to minimize the system energy consumption by jointly optimizing model split points, UAV service slot allocation, and flight trajectories. Based on Block Coordinate Descent (BCD) and Successive Convex Approximation (SCA), we put forward HOTSS algorithm to find the optimized solution of this model. Simulation results show the fluctuating characteristic of energy consumption changed with the increase of the privacy preservation requirement, and show our approach can reduce overall system energy consumption by an average of 6.7% compared to the benchmark scheme.

In the wake of the swift evolution of technologies, such as the Internet of Things (IoT), the global data landscape is undergoing an exponential surge, propelling DNA storage into the spotlight as a prospective medium for contemporary cloud storage applications. This article introduces a semantic artificial intelligence-enhanced DNA storage (SemAI-DNA) paradigm, distinguishing itself from prevalent deep learning (DL)-based methodologies through two key modifications: 1) embedding a semantic extraction module at the encoding terminus, facilitating the meticulous encoding and storage of nuanced semantic information and 2) conceiving a forethoughtful multireads filtering model at the decoding terminus, leveraging the inherent multicopy propensity of DNA molecules to bolster the system fault tolerance, coupled with a strategically optimized decoder’s architectural framework. Numerical results demonstrate the SemAI-DNA’s efficacy, attaining 2.61 dB peak signal-to-noise ratio (PSNR) gain and 0.13 improvement in structural similarity index (SSIM) over conventional DL-based approaches.

With the advancement of nanotechnology, the Bio-Internet of Things(B-IoT) and nanonetworks have become important research focuses. Compared to electromagnetic communication, molecular communication is not constrained by device size and has outstanding low-power consumption and biocompatibility, making it a promising approach for realizing nanonetworks. However, when multiple nanomachines are present, using the same type of information molecule leads to severe inter-user interference. Since the available types of information molecules are limited, simply increasing their number is not feasible and also raises the detection complexity at the receiver. In this paper, we propose a non-negative signal-based code-division multiple access (NCMA) scheme that enables communication among multiple nanomachines using only one type of information molecule. By assigning unique orthogonal codewords to different users and employing a non-uniform sampling and quantization scheme, the proposed NCMA effectively distinguishes information from different users and enhances interference resistance. Simulation results demonstrate that this scheme can support up to 16 nanomachines communicating with each other using only a single type of information molecule, while significantly reducing inter-symbol interference, making it a viable and effective communication solution for B-IoT.

Satellite Internet of things (S-IoT) aims to provide globally covered network services. In this paper, we conceive an uplink grant-free random access scheme for S-IoT network, where ground devices transmit data packets to the low Earth orbit (LEO) satellite, reducing signaling cost and making efficient use of spectrum resources by employing the non-orthogonal multiple access scheme. The impact of high operational speed of the LEO satellite is also taken into account. We further propose an iterative Gaussian approximated message passing-aided sparse Bayesian learning (GAMP-SBL) algorithm to address the joint channel estimation (CE), active user identification (UID) and data detection (DD) problem, where the three steps interacts with each other during the iterative process. Simulation results have demonstrated that our proposed joint receiver design outperforms the existing AMP-based schemes in terms of bit error rate (BER), convergence speed, as well as false alarm rate (FAR).

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks.

In the evolving landscape of high-speed communication, the shift from traditional pilot-based methods to a Sensing-Oriented Approach (SOA) is anticipated to gain momentum. This paper delves into the development of an innovative Integrated Sensing and Communication (ISAC) framework, specifically tailored for beamforming and trajectory prediction processes. Central to this research is the exploration of an Unmanned Aerial Vehicle (UAV)-enabled communication system, which seamlessly integrates ISAC technology. This integration underscores the synergistic interplay between sensing and communication capabilities. The proposed system initially deploys omnidirectional beams for the sensing-focused phase, subsequently transitioning to directional beams for precise object tracking. This process incorporates an Extended Kalman Filtering (EKF) methodology for the accurate estimation and prediction of object states. A novel frame structure is introduced, employing historical sensing data to optimize beamforming in real-time for subsequent time slots, a strategy we refer to as ‘temporal-assisted’ beamforming. To refine the temporal-assisted beamforming technique, we employ Successive Convex Approximation (SCA) in tandem with Iterative Rank Minimization (IRM), yielding high-quality suboptimal solutions. Comparative analysis with conventional pilot-based systems reveals that our approach yields a substantial improvement of 156% in multi-object scenarios and 136% in single-object scenarios.

In communication and computation cooperative networks (3CNs), timely computation is crucial but not always guaranteed. There is a strong demand for a computational task to be completed within a given deadline. The time taken involves processing time, transmission time, and the impact of the deadline. However, a measure of such timeliness in 3CNs is lacking. To address this gap, we propose the novel concept of Age of Computing (AoC) to quantify computation freshness in 3CNs. Built on task timestamps, AoC serves as a practical metric for dynamic and complex real-world 3CNs. We evaluate AoC under two types of deadlines: (i) soft deadline, tasks can be fed back to the source if delayed beyond the deadline, but with additional latency; (ii) hard deadline, tasks delayed beyond the deadline are discarded. We investigate AoC in two distinct networks. In point-to-point, time-continuous networks, tasks are processed sequentially using a first-come, first-served discipline. We derive a general expression for the time-average AoC under both deadlines. Utilizing this expression, we obtain a closed-form solution for M/M/1-M/M/1 systems under soft deadlines and propose an accurate approximation for hard deadlines. These results are further extended to G/G/1-G/G/1 systems. Additionally, we introduce the concept of computation throughput, derive its general expression and an approximation, and explore the trade-off between freshness and throughput. In the multi-source, time-discrete networks, tasks are scheduled for offloading to a computational node. For this scenario, we develop AoC-based Max-Weight policies for real-time scheduling under both deadlines, leveraging a Lyapunov function to minimize its drift.

Conventional collaborative machine learning (CML) faces significant challenges in resource-constrained environments, such as emergency scenarios with limited power, bandwidth, and computing resources, leading to increased communication delays and energy consumption. To address these issues, this paper introducesAir-CoKD, a novel CML framework designed to reduce resource consumption and training latency while preserving model performance.Air-CoKDleverages knowledge distillation (KD) to minimize data transmission by avoiding the direct sharing of model parameters. It also integrates over-the-air computation (AirComp) to aggregate local logits, optimizing bandwidth utilization. To address the dimensional differences in local logits caused by the unbalanced device data class,Air-CoKDemploys orthogonal frequency division multiplexing (OFDM) to transmitting local logits for different target classes. To handle aggregation errors introduced by AirComp, we conduct a detailed analysis of error bounds. Specifically, we convert the Kullback-Leibler (KL) divergence, used in KD loss function, into a quadratic upper bound for precise error quantification and effective optimization. Based on these insights, we propose a strategy to manage bandwidth constraints, transmission power limits, and device energy budgets withinAir-CoKD. Extensive simulations demonstrate thatAir-CoKDsurpasses state-of-the-art methods, effectively balancing training efficiency and model performance. The framework proves to be a robust solution for CML in resource-constrained networks.

Autonomous aerial vehicles (AAV) can alleviate the computational burden on edge devices through assisted computing. However, with the increase in the number of Internet of Things Devices (IoTDs), it is essential to establish a task queue on the AAV to schedule computing tasks from IoTDs. In addition, the load fairness of AAVs should be optimized to fully utilize the computing resources. Therefore, a multi-AAV-assisted mobile edge computing (MEC) network framework based on the queuing model is proposed, which aims at optimizing the average delay of all user devices and the load fairness of AAVs. Firstly, we prove that the arrangement of tasks with different computing delays on the AAV queue can affect the user’s average delay, so a short-job-first (SJF) queuing model is proposed to minimize the average delay of users. On this basis, a joint optimization problem related to the AAV’s three-dimensional trajectory and user connection scheduling is formulated. A SJF based low-complexity connection scheduling algorithm is proposed and combined in a deep reinforcement learning (DRL) to solve this NP-hard problem. To evaluate the performance of the proposed algorithm, we compare it with deep deterministic policy gradient (DDPG), particle swarm optimization (PSO), random moving (RM), and local computing (LC). Simulation results show that our algorithm effectively reduces user average delay and enhances AAV load fairness. Finally, SJF is compared with the traditional first-come-first-served (FCFS) queuing model on different algorithms. The results indicate that the average delay of SJF is significantly lower than that of FCFS.

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low information density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: 1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure; 2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features; 3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system.

In this paper, we investigate the constructive interference (CI) based symbol-level precoding (SLP) design for integrated sensing and communication (ISAC) systems, where a multi-antenna base station (BS) serves multiple single-antenna communication users while simultaneously detecting targets of interest. Specifically, the minimum communication CI scaling factor among the users is maximized under radar performance constraint and power constraint. In order to solve the proposed optimization problem, two groups of approximate feasible domains are adopted to transform the optimization problem into convex. In order to improve the efficiency of the proposed precoding scheme, we adopt a modified Hooke-Jeeves pattern search algorithm for the convex subproblems. We further propose a weighted optimization scheme which considers the tradeoff between radar performance and communication performance as the objective function. By analyzing the Lagrangian function and Karush-Kuhn-Tucker (KKT) condition of the weighted optimization problem, we formulate the corresponding dual problem, which is a simple quadratic programming (QP) problem and can be easily solved. In addition, we further extend the proposed CI precoding scheme from symbol level to block level, in order to be more consistent with the currently used communication systems and achieve better ISAC performance. Extensive simulation results are provided to demonstrate the advantages and the effectiveness of the proposed symbol-scaling based CI-SLP design and CI-based block-level precoding (CI-BLP) design in ISAC systems.

A robust full-space physical layer security (PLS) transmission scheme is proposed in this paper considering the full-space wiretapping challenge of wireless networks supported by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Different from the existing schemes, the proposed PLS scheme takes account of the uncertainty on the eavesdropper’s position within the 360◦ service area offered by the STAR-RIS. Specifically, the large system analytical method is utilized to derive the asymptotic expression of the average security rate achieved by the security user, considering that the base station (BS) only has the statistical information of the eavesdropper’s channel state information (CSI) and the uncertainty of its location. To evaluate the effectiveness of the proposed PLS scheme, we first formulate an optimization problem aimed at maximizing the weighted sum rate of the security user and the public user. This optimization is conducted under the power allocation constraint, and some practical limitations for STAR-RIS implementation, through jointly designing the active and passive beamforming variables. A novel iterative algorithm based on the minimum mean-square error (MMSE) and cross-entropy optimization (CEO) methods is proposed to effectively address the established non-convex optimization problem with discrete variables. Simulation results indicate that the proposed robust PLS scheme can effectively mitigate the information leakage across the entire coverage area of the STAR-RIS-assisted system, leading to superior performance gain when compared to benchmark schemes encompassing traditional RIS-aided scheme.

This paper introduces a novel multi-user mobile edge computing (MEC) scheme facilitated by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and a unmanned aerial vehicle (UAV). Unlike existing MEC approaches, the proposed scheme enables bi-directional offloading, allowing users to concurrently offload tasks to the MEC servers located at ground base station (BS) and UAV with the support of the STAR-RIS. To evaluate the effectiveness of the proposed MEC scheme, we first formulate an optimization problem aiming at maximizing the energy efficiency of the system while ensuring the quality of service (QoS) constraints by jointly optimizing the resource allocation, user scheduling, passive beamforming of the STAR-RIS, and the UAV trajectory. A block coordinate descent (BCD) iterative algorithm designed with the Dinkelbach’s algorithm and the successive convex approximation (SCA) technique is proposed to effectively handle the formulated non-convex optimization problem characterized by significant coupling among variables. Simulation results indicate that the proposed STAR-RIS enhanced UAV-enabled MEC scheme possesses significant advantages in enhancing the system energy efficiency over other baseline schemes including the conventional RIS-aided scheme.

Intelligent reflecting surface (IRS) can significantly enhance the performance of data and energy integrated networks (DEIN) by adjusting its amplitude and/or phase. However, there is a lack of comprehensive performance analysis model for realistic DEIN where multiple cells exist rather than only one cell as assumed by most existing work. In this paper, we consider an IRS-assisted multi-cell DEIN. Specifically, in the downlink wireless energy transfer (WET) stage, the hybrid access point (HAP) in each cell broadcasts radio frequency (RF) energy signals to edge user equipments (UEs). Subsequently, during the uplink wireless information transfer (WIT) stage, the edge UEs employ the harvested energy to send their information to the HAP. We first represent the statistical characteristics of the signal-to-interference-plus-noise ratio (SINR) at the edge UE. Then, we derive the closed-form expressions for outage probability, ergodic rate and average symbol error probability of the edge UE in the typical cell. To gain more insights, we obtain the minimum required number of reflection elements and a sub-optimal solution for time allocation coefficients. Finally, extensive numerical results are provided to validate the correctness of the theoretical results.

In future applications of Internet of Everything (IoE), we need to timely and reliably collect multi-modal sensing data in order to monitor dynamic environment. To this end, we study a timely wireless powered crowdsensing system by enabling cooperative sensing among multiple sensors to increase reliability, by exploiting radio frequency (RF) based wireless power transfer (WPT) to address energy shortage of miniature sensors, and by minimising the average age of information (AoI) to guarantee the timeliness of the sensing data. We jointly optimise the inter-group sensors scheduling and the intra-group sensors scheduling for minimising the weighted AoI among multiple group of sensors. Since the optimisation problem is NP-hard, we propose a joint scheduling algorithm to obtain the optimal scheduling policy. Simulation results demonstrate the superiority of our scheme over the existing state of the art.

Compared to traditional passive reconfigurable intelligent surfaces (RISs), active RISs can actively amplify incident signals, enhancing signal strength, reducing path-loss, and extending coverage. In this paper, we investigate an active RIS-assisted full-duplex integrated sensing and communication (ISAC) system. By jointly designing the power allocation factor for communication user equipment (UE) and the reflecting coefficients of the active RIS, the long-term average sensing performance is maximized while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for all UEs. In this paper, we explore a deep reinforcement learning (DRL) algorithm to address the complex non-convex optimization problem, aiming to maximize long-term gains in system sensing performance. Simulation results indicate that the soft actor-critic (SAC) algorithm achieves superior performance compared to two benchmark algorithms, and that active RIS demonstrates enhanced capabilities over traditional RIS.

Performing video analytics tasks based on deep neural networks (DNNs) on resource-constrained mobile devices is extremely challenging because of the huge volume of video data and the computationally intensive nature of DNN. One promising solution is to offload tasks to the edge servers for execution. However, due to explosive growth in the number of end devices, more and more mobile devices are connected to the edge servers. This makes it difficult for the edge server to meet the specific service level objective (SLO) of on-edge video analytics when facing concurrent computing requests, especially in the real-time scene. To address this issue, this article presents EHCI, an on-edge high-throughput collaborative inference framework for real-time video analytics. On the mobile device, EHCI crops the key regions from the current video frame based on the local detection cache and offloads these regions to the edge server, which can significantly reduce bandwidth consumption and computation costs. Besides, considering concurrent DNN inference requests from multiple mobile devices, EHCI uses a key region patching method to achieve high-throughput DNN inference on the edge server, along with a scheduling algorithm to meet the SLO for each mobile device. It has been validated with testing that the EHCI outperforms the state-of-the-art technology by 159% in achieved throughput, reduces the average end-to-end delay by 36%, and the application accuracy sacrifice is within a reasonable range.

The exponential increase of data has motivated advances of data storage technologies. As a promising storage media, deoxyribonucleic acid (DNA) storage provides a much higher data density and superior durability, compared with state-of-the-art media. In this article, we provide a tutorial on DNA storage and its role in molecular communications (MCs). First, we introduce the fundamentals of DNA-based MCs and storage (MCS), discussing the basic process of performing DNA storage in MCS. Furthermore, we provide tutorials on how conventional coding schemes that are used in wireless communications can be applied to DNA-based MCS, along with numerical results. Finally, promising research directions on DNA-based data storage in MCs are introduced and discussed in this article.

This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted orthogonal time-frequency space (OTFS)-integrated sensing and communication (ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations to support multiple users. By employing ISAC, the UAV extracts position and velocity information from the user’s echo signals, and non-orthogonal power allocation is conducted to achieve a superior achievable rate. A 3D motion prediction topology is used to guide the NOMA transmission for multiple users, and a robust power allocation solution is proposed under perfect and imperfect channel estimation for max-min fairness (MMF) and maximum sum-rate (SR) problems. Simulation results demonstrate the superiority of the proposed NOMA-assisted OTFS-ISAC system over other systems in terms of achievable rate under both perfect and imperfect channel conditions with the aid of 3D motion prediction topology.

The future of the Internet of Things (IoT) holds great promise, particularly in the realm of healthcare, where the concept of Bio-IoT (B-IoT) has gained significant attention. B-IoT involves the coordination of monitoring and treatment within the human body using bio-implants that require communication. However, how to efficiently communicate among bio-implants is seldom studied. Molecular communication (MC), which uses molecules as information carriers, is a novel communication method of nano-devices for its excellent bio-compatibility and low energy consumption. In every part of the body, there is a micro-circulation network (MCN) responsible for substance exchange which can be utilized as a channel to deliver information efficiently by bio-implants. However, since the structure of MCN is complicated and the characteristics of blood flow vary, there is not yet a mature channel modeling on MCN, making it impossible to design and evaluate the performance of B-IoT. In this article, we address the need for efficient communication channels in B-IoT by exploring the potential of MCNs in MC. We have fully analyzed the characteristics of MCN and blood flow and derived the mathematical model of channel impulse response. We also built a simple end-to-end communication model based on MCN and analyzed its error probability and mutual information from a communication perspective. The numerical results have shown that MCN is an effective communication channel of MC for B-IoT in the scale of${\mu }m$and mm.

To support emerging environmentally-aware intelligent applications, a massive amount of data needs to be collected by sensor devices and transmitted to edge/cloud servers for further computation and analysis. However, due to the high deployment and operational cost, only depending on terrestrial infrastructures cannot satisfy the communication and computation requirements of sensor devices in the unexpected and emergency situations. To tackle this issue, this paper presents a digital twin-enabled space-air-ground integrated sensing, communication and computation network framework, where unmanned aerial vehicles (UAVs) serve as aerial edge access point to provide wireless access and edge computing services for ground sensor devices, and satellites provide access to cloud data center. In order to tackle the complex network environments and coupled multi-dimensional resources, the digital twin technique is utilized to realize real-time network monitoring and resource management, and the mapping deviation is also considered. To realize real-time data sensing and analysis, we formulate a maximum execution latency minimization problem while satisfying the energy consumption constraints and network resource restrictions. Based on the block coordinate descent method and successive convex approximation technique, we develop an efficient algorithm to obtain the optimal sensing time, transmit power, bandwidth allocation, UAV deployment position, data assignment strategy, and computation capability allocation scheme. Simulation results demonstrate that the proposed method outperforms several benchmark methods in terms of maximum execution latency among all sensor devices.

In order to transmit data and transfer energy to the low-power Internet of Things (IoT) devices, integrated data and energy networking (IDEN) system may be harnessed. In this context, we propose a bitwise end-to-end design for polar coded IDEN systems, where the conventional encoding/decoding, modulation/demodulation, and energy harvesting modules are replaced by the neural networks (NNs). In this way, the entire system can be treated as an AutoEncoder (AE) and trained in an end-to-end manner. Hence achieving global optimization. Additionally, we improve the common NN-based belief propagation (BP) decoder by adding an extra hypernetwork, which generates the corresponding NN weights for the main network under different number of iterations, thus the adaptability of the receiver architecture can be further enhanced. Our numerical results demonstrate that our BP-based end-to-end design is superior to conventional BP-based counterparts in terms of both the BER and power transfer, but it is inferior to the successive cancellation list (SCL)-based conventional IDEN system, which may be due to the inherent performance gap between the BP and SCL decoders.

Modulation scheme and power control simultaneously impact the performance of integrated data and energy transfer (IDET). Therefore, some efforts have been invested in deep reinforcement learning (DRL) algorithms to realize adaptive modulation (AM) and adaptive power control (APC), in order to achieve long-term performance improvement. However, the optimal DRL algorithm design for the long-term performance optimization having long-term constraints is still a challenge, while the optimal patterns of IDET-oriented joint AM and APC are not fully understood. This paper aims to maximize the long-term performance of energy harvesting (EH), while satisfying the long-term constraints of spectrum efficiency, bit-error-rate and transmit power, by jointly optimizing the modulation selection and transmit power allocation. Then, a novel DRL algorithm, named constrained parameterized action deep deterministic policy gradient (C-PADDPG), is proposed to find the feasible policy of joint AM and APC for the transformed constraint satisfaction problem. Meanwhile, the optimal policy is searched for via bisection method. Simulation results demonstrate that our solution can achieve significant gain on the long-term EH performance, compared to the traditional genetic algorithm-based solution and other DRL benchmark. Moreover, the communication-efficient and EH-efficient patterns of joint AM and APC generated by the C-PADDPG algorithm are explicitly illustrated and analyzed.

This paper presents a reconfigurable intelligent sensing surface (RISS) that combines passive and active elements to achieve simultaneous reflection and direction of arrival (DOA) estimation tasks. By utilizing DOA information from the RISS instead of conventional channel estimation, the pilot overhead is reduced and the RISS becomes independent of the hybrid access point (HAP), enabling efficient operation. Specifically, the RISS autonomously estimates the DOA of uplink signals from single-antenna users and reflects them using the HAP’s slowly varying DOA information. During downlink transmission, it updates the HAP’s DOA information and designs the reflection phase of energy signals based on the latest user DOA information. The paper includes a comprehensive performance analysis, covering system design, protocol details, receiving performance, and RISS deployment suggestions. We derive a closed-form expression to analyze system performance under DOA errors, and calculate the statistical distribution of user received energy using the moment-matching technique. We provide a recommended transmit power to meet a specified outage probability and energy threshold. Numerical results demonstrate that the proposed system outperforms the conventional counterpart by 2.3 dB and 4.7 dB for Rician factors$\kappa _{h}=\kappa _{G}=1$and$\kappa _{h}=\kappa _{G}=10$, respectively.

Millimeter wave (mmWave)-based unmanned aerial vehicle (UAV) communication is susceptible to blockages, even from humans. Previous studies that primarily focused only on static blockage may not accurately characterize the system performance. This paper investigates the coverage performance of mmWave UAV networks by jointly considering multiple types of blockages under finite homogeneous Poisson point process and Binomial point process, which are commonly employed in finite area scenarios with random and fixed number of UAVs, respectively. Particularly, we derive the average line-of-sight probability and coverage probability under static, dynamic, and self blockages. Simulations verify our theoretical results, demonstrating that: the above system performance predominantly depends on self-blockage if UAVs are at high altitudes. Conversely, at relatively low altitudes, all three types of blockages impact them, with static blockage being the dominant factor. To avoid self-blockage, UAV height should satisfy$h\!\gt \!h_{R}\!+\!\frac {r_{i}}{\tan \varphi _{b}}$, where$h_{R}$is the height of the user equipment (UE),$r_{i}$is the two-dimensional distance of the UAV-UE link,$\varphi _{b}$is the elevation angle between UE and UAV. The required height is proportional to$r_{i}$and increases as distance d between the user and UE decreases, as$\varphi _{b}$is proportional to d. The findings help on designing the network parameters. To our best knowledge, this is the first work to analyze the coverage of mmWave UAV networks under multiple types of blockages.

To satisfy the heterogeneous service requirements in future internet of things (IoT), this paper investigates the novel framework for intelligent reflecting surface (IRS)-aided wireless powered over-the-air computation (AirComp) and communication networks, where the IoT devices first harvest energy from the downlink signal sent by the base station, and then conduct the information transmissions and AirComp in the uplink. In particular, the IRS is used to improve the efficiency of wireless energy transfer, and alleviate the harmful interference between the communication and AirComp signals. To balance the performance of such an integrated system, we present two joint beamforming and reflection optimization problems via minimizing the computation distortion and maximizing the sum rate, respectively. To solve the non-convex problems, we develop the alternating optimization framework with proved convergence, in which the penalty function-based method and variable substitution technique are exploited to acquire the optimal solutions of beamformers and reflection parameters. Finally, simulation results show that the proposed method realizes significantly higher computation accuracy and communication rate, in comparison with several existing benchmark methods.

A novel Physical Layer Security (PLS) framework is conceived for enhancing the security of wireless communication systems by exploiting multi-domain polarization in Multiple-Input Multiple-Output (MIMO) systems. We design a sophisticated key generation scheme based on multi-domain polarization and the corresponding receivers. An in-depth analysis of the system’s secrecy rate is provided, demonstrating the confidentiality of our approach in the presence of eavesdroppers having strong computational capabilities. More explicitly, our simulation results and theoretical analysis corroborate the advantages of the proposed scheme in terms of its bit error rate (BER), block error rate (BLER), and maximum achievable secrecy rate. Our findings indicate that the innovative PLS framework effectively enhances the security and reliability of wireless communication systems. For example, in a$4\times 4$MIMO setup, the proposed PLS strategy exhibits an improvement of 2dB compared to conventional MIMO, systems at a BLER of$2\cdot 10^{-5}$while the eavesdropper’s BLER reaches 1.

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider 1) data privacy and security; 2) SC model adaptation for heterogeneous devices; 3) explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an adaptive client training strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL.

Thanks to the application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain by enabling the ability to generate any current distribution on the surface. With the aid of electromagnetic (EM) manipulation capability of H-MIMO, integrated data and energy transfer (IDET) system can fully exploit the EM channel to realize energy focusing and eliminate inter-user interference, which yields the concept of holographic IDET (H-IDET). In this paper, we investigate the beamforming designs for H-IDET systems, where the sum-rate of data users (DUs) are maximized by guaranteeing the energy harvesting requirements of energy users (EUs). In order to solve the non-convex functional programming, a block coordinate descent (BCD) based scheme is proposed, wherein the Fourier transform and the equivalence between the signal-to-interference-plus-noise ratio (SINR) and the mean-square error (MSE) are also conceived, followed by the successive convex approximation (SCA) and an initialization scheme to enhance robustness. Numerical results illustrate that our proposed H-IDET scheme outperforms benchmark schemes, especially the one adopting traditional discrete antennas. Besides, the near-field focusing using EM channel model achieves better performance compared to that using the traditional channel model, especially for WET where the EUs are usually close to the transmitter.

B5G/6G expands a new application scenario of joint communication and positioning which can simultaneously provide high-quality communication and positioning services. Millimeter wave (mmWave) and massive Multiple Input Multiple Output (MIMO) can help systems to achieve high-quality communication and generate high-directional beams to assist positioning. In this paper, the proposed Structured Perturbed Orthogonal Matching Pursuit (SPOMP) could alleviate the pilot pollution in massive MIMO systems and break the resolution of angular estimation. Based on the above super-resolution estimation, we develop a dynamic two-stage multi-cell user grouping scheme to reduce interference and improve resource utilization. Combined with the grouping results and our derived performance metric, a joint optimization problem for power and bandwidth allocation is proposed to maximize the comprehensive performance of joint communication and positioning while guaranteeing performance bounds. An effective cyclic iteration algorithm based on Alternating Direction Method of Multipliers (ADMM) is present to solve the proposed optimization problem. Numerical results show that the proposed joint user grouping and resource allocation scheme achieves a larger rate-accuracy region and a better multi-cell service performance balance compared to conventional schemes.

When wideband signals propagate in a rich-scatterer environment, we obtain abundant resolvable multiple transmission paths to form a number of virtual antennas. Therefore, substantial spatial gain can be attained by carefully waveforming in all these resolvable transmission paths without additional antennas. This resultant spatial gain is then exploited for improving the performance of integrated-data-and-energy-transfer (IDET) from a single transmitter to multiple receivers. We aim to maximise the downlink fair-throughput and sum-throughput, while satisfying the energy harvesting requirements by jointly optimising the waveformers at the transmitter and the power splitters at the receivers. A low-complexity fractional-programming (FP) based alternating algorithm is proposed to solve these non-convex optimisation problems. The non-convex wireless energy transfer (WET) constraints are transformed to be convex with a modified quadratic transform (MQT) method. As a result, the stationary points for both the fair-throughput and the sum-throughput maximisation problems are obtained. The numerical results demonstrate the advantage of our proposed algorithm over a minimum-mean-square-error (MMSE) scheme, a zero-forcing (ZF) scheme and a time-reversal (TR) scheme. Simulation results show that the wireless data transfer (WDT) performance of our scheme outperforms the single-input-single-output orthogonal-frequency-division-multiple-access (SISO-OFDMA) when the output direct current (DC) power requirement is high. When we have a practical individual subcarrier power constraint, the WDT performance of our scheme outperforms multiple-input-single-output orthogonal-frequency-division-multiplex-access (MISO-OFDMA).

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden’s minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of covert communications between a multi-antenna transmitter and a single-antenna receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions where one antenna is responsible for receiving the transmitted signals and the other one transmits the jamming signals with a varying power to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized in a covert constraint to guarantee the system performance. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on semi-definite relaxation (SDR) method and Dinkelbach’s algorithm is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS, which validates the effectiveness of the proposed algorithm as well as the superiority of STAR-RIS in guaranteeing the covertness of wireless communications.

Reconfigurable intelligent surface (RIS) is a promising technology to improve the spectral and energy efficiency of Internet of Things (IoT) systems. In this paper, we investigate an RIS-assisted simultaneous wireless information and power transmission (SWIPT) system by utilizing stochastic geometry. Moreover, we consider not only the case of random phase shift, but also the case where the phase shift of the RIS are aligned to thek-th IoT device. We first derive the closed-form expressions of the uplink outage probability and the average uplink data size for thek-th IoT device under the Rayleigh channel. Then, we extend the performance analysis to the Rician fading channel and multi-antenna scenarios. Finally, extensive numerical results have been carried out to verify the effectiveness of our derived results.

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.

Age of Information (AoI) has been studied recently for satisfying the timeliness requirements of the emergent applications, i.e. smart cities and smart wearables in future Internet of Everything (IoE). In order to integrate multiple information flows i wireless powered sensor network (WPSN), we propose age of sensing (AoS) for measuring freshness of the fused information, which is defined as the time elapsed from the generation moment of the sensing information used for generating the fresh reliable fused information. Specifically, a wireless power transfer (WPT) aided wireless sensor network (WSN) is studied. Multiple batteryless sensors cooperatively sense common objects and then timely upload the sensing information to a fusion center (FC). The FC fuses the correctly decoded information from different sensors and finally evaluates the reliability of the fused information. The number of time slots for WPT and the deployment of the sensors are then jointly optimised, in order to minimise the average AoS. A Fibonacci aroused integer programming approach is then proposed to solve the nonlinear integer problem having NP-hard complexity. Simulation results validate the accuracy of the theoretical analysis for the AoS, while also demonstrating that our algorithm achieves the superb performance by referring to the optimal exhaustive method.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of communications between transmitter and receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized as a covert constraint. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR- RIS, an iterative algorithm based on globally convergent version of method of moving asymptotes (GCMMA) is proposed to effectively solve the non-convex optimization problem. Simu-lation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS.

Empowering sensing capabilities is becoming increasingly important in future wireless networks. Meanwhile, intelligent reflecting surface (IRS) and wireless power transfer (WPT) have also received widespread attention as two key technologies to improve network spectrum efficiency and solve device energy shortage issues, respectively. To this end, we investigate an IRS-assisted and WPT-enabled integrated sensing and communication (ISAC) system. Specifically, a base station (BS), with the assistance of the IRS, has the dual functions of radar sensing as well as receiving the data information transmitted by Internet of Things (IoT) devices. IoT devices can charging itself by harvesting the power of radar signals transmitted from the BS. The sensing performance is studied by deriving an exact closed-form expression and an upper bound of the average radar estimation information rate. In addition, we derive an exact expression for the average data information rate to evaluate the communication performance of the system. The simulation results reveal that increasing the number of reflecting elements of the IRS can simultaneously enhance the radar sensing and communication performance.

Unmanned aerial vehicle (UAV) assisted wireless power transfer (WPT) enables the network to provide flexible and controllable energy supplement towards low-powered devices, which is considered as a promising technology in the future Internet of Things (IoT) networks. In this paper, the integrated communication and control (ICAC) for UAV assisted WPT is studied, aiming for mitigating the performance degradation caused by the WPT beam misalignment between the UAV and the low-power device. The optimal blocklength of the control signal is then obtained, in order to maximize the lower bound of the average energy harvesting power of the device by satisfying the control reliability constraint. Simulation results evaluate the performance of the ICAC system, which demonstrates that the WPT performance is a decrease-and-increase-then-decrease function with respect to the blocklength of the control signal. The performance of the ICAC system can be improved by adjusting the blocklength of the control signal.

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 paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter-wave (mmWave) system. Specifically, analytical derivations are performed to obtain the closed-form expression of the warden’s minimum detection error probability (DEP) considering the practical assumption. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

Unmanned aerial vehicle (UAV) swarm has been considered as a promising paradigm for conducting complex tasks in remote or hostile environments. In this paper, the integrated communication and control (ICAC) assisted formation management of UAV swarms is studied, where a head UAV is responsible for generating and transmitting wireless signals for controlling the flight states of the following UAVs remotely. The control performance, i.e., the control MSE, of the ICAC system is analysed theoretically. Then, the optimal resource allocation of sub-carriers and the control data rate is obtained by minimizing the fair control MSE among following UAVs. Simulation results evaluate the performance of the ICAC assisted UAV swarm system, which demonstrate that the control MSE is convex respect to the control data rate.

Simultaneous wireless information and power transfer (SWIPT) has been considered as a promising technique to address energy shortage of communication devices deployed in Internet of Everything (IoE). In this paper, we consider a multi-relay aided cooperative communication network with SWIPT, where relay stations (RSs) receive RF signal from a source node (SN) for energy harvesting and information reception by power splitters. A single activated RS then decodes and forwards information to the destination using the harvested energy. Selective-decode-and-forward (S-DF) protocol is adopted, where the activated RS forwards only when information is correctly decoded. By considering time-varying channels, we maximise the end-to-end (E2E) throughput by jointly designing the transmit beamformers for both SN and RSs, optimising transmit power and power splitters for RSs, as well as the RS selection. The original formulated non-convex optimisation problem with coupled variables is solved by invoking an iterative algorithm. Numerical results demonstrate that our design with SDF protocol outperforms that with the decode-and-forward (DF) protocol. Moreover, the impact of the imperfect channel state information (CSI) on the E2E throughput is also evaluated.

Abstract Mobile user terminals in 5G networks can generate massive computational workloads, which require sufficient computation and caching resources, and the processors of user terminals cannot tackle these workloads. Emerging mobile edge computing (MEC) has become the key to solving the computation problem by offloading computation‐intensive workloads to the MEC server. To make full use of the limited resources on the MEC side, service caching can pre‐store specific executable programs, databases, or libraries for executing offloaded workloads from user terminals. In this study, a service caching‐assisted MEC model is designed. According to the proposed MEC model, a decentralized model‐free deep reinforcement learning algorithm‐based server caching optimization policy (DDSCOP) is proposed to minimize the long‐term weighted average cost. Considering the time‐varying request workloads from user terminals, the stochastic channel state, and the arrival of renewable resources of the MEC server, DDSCOP can find the near‐optimal service caching decision‐making policy by training the neural networks, that is, whether the service caching can be hosted by the MEC server, and which MEC server hosts the service caching. The numerical experimental results verify the convergence and effectiveness of DDSCOP through extensive parameters configuration, and DDSCOP outperforms the three baseline algorithms.

Radio-frequency (RF) signals are capable of simultaneously transferring data and energy from a hybrid access point (HAP) toward battery-powered and batteryless wireless devices (WDs). Battery-powered and batteryless WDs with the capability of RF energy harvesting need a distributed access control protocol with collision avoidance to achieve higher energy efficiency. We study the performance of a data and energy integrated network (DEIN) that adopts an enhanced carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Each device in this network can switch to RF energy harvesting mode or data reception mode according to HAP’s instruction, and freezes its backoff counter when energy storage is insufficient. By invoking a 3-D Markov chain, we model the operating behaviors of batteryless WDs and an HAP in a DEIN. Apart from backoff operations of devices, the 3-D Markov chain also depicts their dynamic energy changes, including RF energy harvesting and energy consumption. WDs consume energy harvested from the HAP’s downlink transmissions for powering their data upload and random backoff. With the aid of the 3-D Markov chain, the upload throughput of devices can be obtained in semi-closed-form. Moreover, a decoupling method is proposed to approximate throughput performance with low complexity. The accuracy of our theoretical model is validated by simulation results. By characterizing the impact of various parameters on throughput performance, a design guideline for a DEIN with a distributed batteryless access protocol is provided.

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless-powered communication networks (FD-WPCNs). This article investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of the formulated nonconvex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart.

Machine learning is an essential technology providing ubiquitous intelligence in Internet of Things (IoT). However, the model training in machine learning demands tremendous computing resource, bringing heavy burden to the IoT devices. Meanwhile, in the Proof-of-Work (PoW)-based blockchains, miners have to devote large amount of computing resource to compete for generating valid blocks, which is frequently disputed for tremendous computing resource waste. To address this dilemma, we propose an Evolved-PoW (E-PoW) consensus that can integrate the matrix computations in machine learning into the process of blockchain mining. The integrated architecture, the elaborated schemes of transferring matrix computations from machine learning to blockchain mining, and the reward adjustment scheme to affect the activity of the miners are, respectively, designed for E-PoW in detail. E-PoW can keep the advantages of PoW in blockchain and simultaneously salvage the computing power of the miners for the model training in machine learning. We conduct experiments to verify the availability and effect of E-PoW. The experimental results show that E-PoW can salvage by up to 80% computing power from pure blockchain mining for parallel model training in machine learning.

This article studies a dynamic Mobile Edge Computing (MEC) system assisted by Unmanned Aerial Vehicles (UAVs) and Intelligent Reflective Surfaces (IRSs). We propose a scaleable resource scheduling algorithm to minimize the energy consumption of all UEs and UAVs in the MEC system with a variable number of UAVs. We propose a Multi-tAsk Resource Scheduling (MARS) framework based on Deep Reinforcement Learning (DRL) to solve the problem. First, we present a novel Advantage Actor-Critic (A2C) structure with the state-value critic and entropy-enhanced actor to reduce variance and enhance the policy search of DRL. Then, we present a multi-head agent with three different heads in which a classification head is applied to make offloading decisions and a regression head is presented to allocate computational resources, and a critic head is introduced to estimate the state value of the selected action. Next, we introduce a multi-task controller to adjust the agent to adapt to the varying number of UAVs by loading or unloading a part of weights in the agent. Finally, a Light Wolf Search (LWS) is introduced as the action refinement to enhance the exploration in the dynamic action space. The numerical results demonstrate the feasibility and efficiency of the MARS framework.

Future 6G is deemed to provide Simultaneous Wireless cOmputing and Power Transfer (SWOPT) services for addressing the shortage of local computation and that of energy at terminal devices in the era of the Internet of Everything (IoE). In this paper, we study a fog radio access network (F-RAN) consisting of enhanced remote radio heads (eRRHs) with multiple antennas and edge computing capabilities. The F-RAN simultaneously supports task offloading of computing users (CUs) and energy harvesting of energy users (EUs). Thanks to the global coordination of a central server, full cooperation among eRRHs in both computing and communication domains can substantially improve the SWOPT performance. Specifically, the energy harvesting performance of EUs is maximised by jointly optimising the bandwidth and the beam resources in the communication domain as well as the computing frequency and the offloading strategy in the computing domain. The original non-convex problem with mixed integer and continuous variables is then solved by sequential convex optimisations in the framework of a greedy algorithm with low complexity. Thorough simulation results demonstrate the advantage of our proposed resource scheduling scheme over the existing state-of-the-art counterparts.

As the number of Internet of Things Devices (IoTDs) increases, the building Structural Health Monitoring (SHM) system is subject to the enormous amount of data collected from sensors. To tackle this challenge, we investigate an Unmanned Aerial Vehicle (UAV)-enabled Wireless Powered Communication Network (WPCN) in a building SHM scenario where a UAV is dispatched to provide wireless charging and data relaying services for IoTDs on the building. For preventing the channel blockage caused by the building, we place the UAV and Access Points (APs) in specific trajectory and locations, respectively. To improve the system’s throughput, we maximize the minimum data volume among devices in a given period by formulating an optimization problem in which we jointly optimize the link schedule, the power and time allocation and the hovering positions of the UAV. However, the formulated problem is a mixed-integer nonlinear programming and is hard to solve. Therefore, we adopt a bottleneck-aware idea to reduce the dimensionality of the optimization variables in order to obtain a simplified problem that can be solved in a low-complexity way. Also, the Block Coordinate Descent (BCD) method is applied to reduce the complexity of the problem. Meanwhile, we further propose a method to deal with the heterogeneous problem for improving the generalizability of our algorithm. To estimate the performance of our proposed algorithm, we compare it with the Monte Carlo (MC) method, Game Theory (GT) and Particle Swarm Optimization (PSO). The simulation results indicate that our algorithm can obtain better performance.

Cell-free networking enables full cooperation among distributed access points (APs). This paper focuses on reducing the long-term energy consumption of a cell-free network in the downlink integrated data and energy transfer (IDET) for achieving energy sustainability. The resultant design includes both the AP classification on a large time-scale and the beamforming of the APs on a small time-scale in order to simultaneously satisfy the IDET requirements of data users and energy users. For dealing with binary integer actions (AP classification) and continuous actions (beamforming) together, we innovatively propose a stable double parameterized deep-Q-network (DP-DQN), which can be enhanced by a digital twin (DT) running in the intelligent core processor (ICP) so as to achieve faster and more stable convergence. Therefore, the cell-free network may avoid suffering from performance fluctuation during the training process. The simulation results demonstrate that our DP-DQN exceeds in convergence compared to other benchmarks while guaranteeing an optimal solution.

In an intelligent reflecting surface (IRS) aided wireless power transfer (WPT) system, a practical architecture of an energy receiver (ER) is proposed, which includes multiple receive antennas, an analog energy combiner, a power splitter and multiple energy harvesters. In order to maximise the output direct-current (DC) power, the transmit beamformer of the transmitter, the passive beamformer of the IRS, the energy combiner, and the power splitter of the ER are jointly optimised. The optimisation problem is equivalently divided into two sub-problems, which independently maximises the input RF power and the output DC power of the energy harvesters, respectively. A successive linear approximation (SLA) based algorithm with a low complexity is proposed to maximise the input RF power to the energy harvesters, which converges to a Karush-Kuhn-Tucker (KKT) point. We also propose an improved greedy randomized adaptive search procedure (I-GRASP) based algorithm having better performance to maximise the input RF power. Furthermore, the optimal power splitter for maximising the output DC power of the energy harvesters is derived in closed-form. The numerical results are provided to verify the performance advantage of the IRS-aided WPT and to demonstrate that conceiving the optimised energy combiner achieves better WPT performance than the deterministic counterpart.

Integrated data and energy transfer (IDET) has been considered as a key technology to tackle the energy shortage in the densely connected wireless networks. In this paper, a novel time index modulation (TIM) assisted IDET system is studied, where data information is delivered not only by the conventional modulation scheme, but also by the time index of the activated symbol durations for either wireless data transfer (WDT) or wireless energy transfer (WET). With the aid of theoretical analysis, the average energy harvesting performance as well as the achievable data rate between the transmitter and the receiver are both derived in the semi-closed form. Further, a population-based optimizer is conceived to obtain the power allocation of WDT and WET signals, in order to maximize the energy harvesting performance by satisfying the constraints of the achievable data rate. Simulation results validate our theoretical analysis, while they also demonstrate that the TIM assisted system can substantially increase the IDET performance.

In large-scale mobile edge computing (MEC) systems, the task latency, and energy consumption are important for massive resource-consuming and delay-sensitive Internet of Things Devices (IoTDs). Against this background, we propose a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task latency and energy consumption for all IoTDs, which can be formulated as a mixed-integer nonlinear programming. The DIRS framework includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. Specifically, we first introduce a novel multiagent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Second, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Levy flight search. Finally, an imitation acceleration scheme is presented to pretrain all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. The simulation results in three typical scenarios demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

Molecular communication and the Internet of Nanothings (IoNT) are emerging research hotspots recently, which show great potential in biomedical applications inside the human body. However, how to transmit information from inside body IoNTs to outside devices is seldomly studied. It is well known that the nervous system is responsible for perceiving the external environment and controlling the feedback signals. It exactly works like an interface between the external and internal environment. Inspired by this, this article proposes a novel concept that one can use the modified nervous system to communicate between IoNT devices andin vitroequipments. In our proposed system, nanomachines transmit signals via stimulating the nerve fiber by the electrode. Then, the signals transmit along nerve fibers and muscle fibers. Finally, they cause changes in surface electromyography (sEMG) signals, which can be decoded by the body surface receiver. This article presents the framework of this entire through-body communication system. Each part of the framework is also mathematically modeled. The error probability and mutual information of the system are derived from the communication theory perspective, which are evaluated and analyzed through numerical results. This study can pave the way for the connection of IoNTin vivoto external networks.

DNAs exist in nature and could provide solutions to computing, storage, and communications as existing ones approach their physical limits. Plenty of research work has been conducted on DNA-based computing, data storage and molecular communications (MCs), but largely in isolation. There is a lack of a unified place where the triples are put together to be discussed. This article aims to filling in this gap by providing an overview of each triplet. Starting from an overall description of DNA features and their reading and writing in practical terms, this article goes on to describe each of the three from three aspects: 1) requirements and differences from current electronic-dominating technologies; 2) their working principles; and 3) practical considerations. Recent advancement in each area is summarized and discussed. Furthermore, this article intends to call for researches that go beyond the boundary of each and encourages interconnection and joint research among the three. It proposes a molecular information and communication technology (ICT) system architecture with all its three components underpinned by DNAs. This article also identifies and discusses some new future directions, such as joint coding for storage and communications, directional DNA-based MCs, interfaces between molecular DNA systems and electronic systems. It is hoped that this article can spark more joint research across computing, storage, and communications in this exciting field of DNA-based molecular ICT systems.

In this article, we investigate multiple unmanned aerial vehicles (UAVs)-enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time-constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated, considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer nonconvex problem, we decompose the problem into two subproblems: 1) UAV trajectory and 2) hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed the MACO-based algorithm, is given by leveraging the MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm.

Energy self-sustainability is critically important for future Internet of Things (IoT) networks to support an ever-growing massive number of wireless devices with low maintenance cost and high spectrum/energy efficiency. Power-splitting (PS)-based simultaneous wireless information and power transfer (PS-SWIPT) is a promising solution to realize it. However, the performance of PS-SWIPT is severely influenced by the channel attenuation caused by the detrimental radio propagation environment. Intelligent reflecting surface (IRS) is an emerging technology that can reconfigure the incident signal with considerable array gain so as to improve the PS-SWIPT performance. Thus, in this article, we investigate the weighted sumrate (WSR) maximization problem of the IRS-assisted multi-input–multioutput (MIMO) PS-SWIPT IoT network with multiple low-power IoT PS-based devices (PSDs). The formulated problem is nonconvex and arduous to tackle due to the presence of the intricately coupled variables and the mutually exclusive constraints. To the best of our knowledge, the problem is not addressed yet and cannot be solved by employing the existing methods directly. To cope with the problem, we develop a joint optimization framework that decomposes the original problem into several subproblems that can be solved alternately. Simulation results confirm the effectiveness of IRS to improve the WSR of the PS-SWIPT energy self-sustainable IoT networks and demonstrate that the proposed algorithm outperforms benchmark methods considerably.

In order to satisfy timeliness requirements arising from environmental sensing applications, Age of Information (AoI) was proposed to characterize the freshness of the received updates. In this article, we consider a wireless powered relay aided communication network (WPRCN), in which a relay wirelessly powered by a hybrid-access point (H-AP) receives environmental monitoring information update from a sensor and forwards it to the H-AP. The long-term average AoI is studied since the decision correctness depends on the update timely uploaded by the relay. The wireless powered relay adopts either a decode-and-forward (DF) or an amplify-and-forward (AF) protocols, respectively, subject to the energy causality. We also consider the decoding cost of DF protocol owing to the relay’s limited energy storage. Since the expression of the average AoI is nonelementary, we propose a Taylor approximation-based algorithm to obtain its integral. We optimize the transmit power/the equivalent average power consumption of the relay for the sake of minimizing the average AoI in the whole WPRCN with different forwarding protocols. Our simulation results demonstrate the accuracy of the theoretical analysis, while the average AoI of the WPRCN is optimized by our power allocation scheme at the relay. The proposed algorithm is verified to achieve a great approximation effect.

By activating blocked users and altering successive interference cancellation (SIC) sequences, reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems. To evaluate the benefits between RISs and NOMA, a downlink RIS-aided multi-cell-NOMA network is investigated via stochastic geometry. We first introduce the unique path loss model for RIS reflecting channels. Then, we evaluate the angle distributions based on a Poisson cluster process (PCP) model, which theoretically demonstrates that the angles of incidence and reflection are uniformly distributed. Additionally, we derive closed-form analytical and asymptotic expressions for coverage probabilities of the paired NOMA users. Lastly, we derive the analytical expressions of the ergodic rate for both of the paired NOMA users and calculate the asymptotic expressions for the typical user. The analytical results indicate that 1) the achievable rates reach an upper limit when the length of RIS increases; 2) exploiting RISs can enhance the path loss intercept to improve the performance without influencing the bandwidth. The simulation results show that 1) RIS-aided networks have superior performance than the networks without RISs; and 2) the SIC order in NOMA systems can be altered since RISs are able to change the channel quality of NOMA users.

With the increasing demand for digitalization and participation in Industry 4.0, new challenges have emerged concerning the market of digital services to compensate for the lack of processing, computation, and other resources within Industrial Internet of Things (IIoTs). At the same time, the complexity of interplay among stakeholders has grown in size, granularity, and variation of trust. In this paper, we consider an IIoT resource market with heterogeneous buyers such as manufacturer owners. The buyers interact with the resource supplier dynamically with specific resource demands. This work introduces a broker between the supplier and the buyers, equipped with Distributed Ledger Technologies (DLT) providing a service for market security and trustworthiness. We first model the DLT-assisted IIoT market analytically to determine an offline solution and understand the selfish interactions among different entities (buyers, supplier, broker). Considering the non-cooperative heterogeneous buyers in the dynamic market, we then follow an independent learners framework to determine an online solution. In particular, the decision-making procedures of buyers are modeled as a Partially Observable Markov Decision Process which is solved using independent Q-learning. We evaluate both the offline and online solutions with analytical simulations, and the results show that the proposed approaches successfully maximize players’ satisfaction. The results further demonstrate that independent Q-learners achieve equilibrium in a dynamic market even without the availability of complete information and communication, and reach a better solution compared to that of centralized Q-learning.

A protocol of batteryless wireless devices massive access network is proposed in this paper. Energy harvesting is a major work for wireless devices(WDs) which has no stable energy source to transmit data, and the access point(AP) performs energy transmission when transmits data through wireless signals to meet the energy requirement for WDs. As a result, limited by energy, the traffic generated by WDs is sporadic. With this feature, the batteryless massive access(BLMA) protocol is designed to achieve massive access of WDs. First, on the basis of SCMA(Sparse Code Multiple Access), sparse codes are used to realize simultaneous transmission for WDs using different codes, and finite codes are allocated to massive WDs which size is greater than the codebook in an orderly manner. Then, different slots are alternately allocated to different WDs to avoid collisions caused by simultaneous transmission. Finally, the limit of slot occupancy is set according to the energy status of the WD to optimize the slots allocation. Simulation shows that the BLMA protocol could increase the device access amount and system throughput by at least six times compared to SCMA.

A typical unmanned aerial vehicle (UAV) aided communication system with multiple user equipments (UEs) is investigated in this paper. The UE's information and energy maximization problem is proposed by optimizing the UAV's trajectory, transmit power and hovering time. The problem is decomposed into four sub-problems: dividing UEs, solving hovering positions of the UAV, designing UAV's trajectory and optimizing problem. Then, a K-clustering algorithm combined with Fermat Problem is considered to solve some sub-problems. In addition, UAV's trajectory is obtained by using dynamic programming (DP) to solve the TSP problem. The numerical results verify that our proposed algorithms effectively reduce the number of hovering positions of the UAV. Besides, the proposed optimization problems also improve the performance of information and energy transmission compared with the energy optimization only and information optimization only schema.

In order to meet timeliness requirements arising from sensing applications, age of information (AoI) was proposed to characterise the freshness of the received updates. In this paper, we study the average AoI for a two-hop network, in which a relay wirelessly powered by a hybrid-access point (H-AP) receives environmental monitoring information update from a sensor and forwards it to the H-AP. We optimise the transmit power of the relay for the sake of minimising the average AoI in the whole wireless powered relay aided communication nework (WPRCN). The wireless powered relay adopts a decode-and-forward strategy by considering the energy causality. Our simulation results demonstrate the impact of the relay's transmit power on the average AoI, while the information freshness of the WPRCN is optimised by our power allocation scheme at the relay.

The Internet of Things (IoT) connected by software-defined networking (SDN) promises to bring great benefits to cyber-physical systems. However, the increased attack surface offered by the growing number of connected vulnerable devices and separation of SDN control and data planes could overturn the huge benefits of such a system. This article addresses the vulnerability of the trust relationship between the control and data planes. To meet this aim, we propose an edge computing-based Blockchain as a Service (BaaS), enabled by an external BaaS provider. The proposed solution provides verification of inserted flows through an efficient, edge-distributed, blockchain solution. We study two scenarios for the blockchain reward purpose: 1) information symmetry, in which the SDN operator has direct knowledge of the real effort spent by the BaaS provider and 2) information asymmetry, in which the BaaS provider controls the exposure of information regarding spent effort. The latter yields the so-called “moral hazard,” where the BaaS may claim higher than actual effort. We develop a novel mathematical model of the edge BaaS solution and propose an innovative algorithm of a fair reward scheme based on game theory that takes into account moral hazard. We evaluate the viability of our solution through analytical simulations. The results demonstrate the ability of the proposed algorithm to maximize the joint profits of the BaaS and SDN operator, i.e., maximizing the social welfare.

Considering not only reducing the energy consumption based on the fixed energy source but also capturing new energy by RF energy harvesting (EH) for Internet-of-Things (IoT) devices, an integrated energy-efficient strategy for IoT devices is proposed in this article. On the one hand, a node sampling scheduling algorithm based on matrix completion is designed. All the sampling data are handled as matrix elements. The basic idea is to reduce the sampling data and then reconstruct the complete data set with the technology of matrix completion by using the spatial-temporal correlation of all the sampling data. Thus, the IoT nodes can keep dormant more instead of working on data sampling. The energy consumption can be significantly reduced with very little data loss. On the other hand, an adaptive RF energy management strategy is introduced. Based on the self-designed data and energy integrated network (DEIN) system, both data and energy are transferred between the DEIN gateway (DEING) and the DEIN Node (DEINN). With the adaptive energy management strategy, they both automatically switch between the wireless information transfer (WIT) mode and the wireless energy transfer (WET) mode. Combining energy saving with EH, the energy efficiency can be greatly enhanced. The proposed integrated solution aims to decrease the energy consumption of IoT devices and provide them with constant new energy by RF EH. Thus, their battery lives can be prolonged. Both the effectiveness and the efficiency of the proposed integrated strategy have been validated with the simulation.

Relying on the propagation of modulated radio-frequency (RF) signals, we can achieve simultaneous wireless information and power transfer (SWIPT) to support low-power communication devices. In this paper, we proposed a unary coding based SWIPT encoder by considering a practical M-QAM. Markov chains are exploited for characterising coherent binary information source and for modelling the generation process of modulated symbols. Therefore, both mutual information and the average energy harvesting performance at the SWIPT receiver are analysed in semi-closed-form. With the aid of the genetic algorithm, the sub-optimal codeword distribution of the coded information source is obtained by maximising the average energy harvesting performance, while satisfying the requirement of the mutual information. Simulation results demonstrate the advantage of the SWIPT encoder. Moreover, a higher-level unary code and a lower-order M-QAM results in higher WPT performance, when the maximum transmit power of the modulated symbol is fixed.

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. UAVs can greatly support data collecting and processing for Internet of Things devices (IoTDs) in mobile edge computing (MEC) systems due to their advantages of high environmental flexibility. This paper focuses on the scenario where multiple heterogeneous rotary-wing UAVs complete data collection and processing missions cooperatively. This paper introduces an energy minimization problem for UAV-assisted MEC system which attempts to optimize route planning and task allocation of UAVs. The energy consumption of a UAV includes hovering energy and flight energy depending on its configuration. By jointly choosing optimal UAVs for tasks and routes, we aim to obtain a sub-optimal solution of allocating IoTD tasks to UAVs and UAV flying route design while minimizing energy consumption. The Ant Colony System (ACS) algorithm is employed to obtain a high-quality near-optimal solution to solve this optimization problem. Finally, the simulation results show the effectiveness and efficiency of our proposed solution.

In millimeter wave (mmWave) based communication system, hybrid beamforming is regarded as a pivotal technique for the sake of reducing the hardware complexity. In this paper, we studied the optimal hybrid beamforming and combiner design for a multi-user mmWave MIMO system in order to simultaneously multicast information to the information users (IUs) and transfer wireless power to the energy users (EUs). By considering practical non-linear RF energy harvester, our transceiver design aims for maximising the downlink multicast throughput, while satisfying the charging requirements of the EUs. This sub-optimal solution is obtained by a low-complexity algorithm. This algorithm firstly designs separately the beamformer and combiners by minimizing mean square error principle. Then given the resultant combiners, the algorithm update the hybrid multicast beamforming at the transmitter until it converges. Our transceiver design also achieves good detection performance for the IUs. The numerical results demonstrate the advantage of our joint transceiver design over the separated counterpart in terms of both the multicast throughput and the energy harvested.

In wireless powered communication networks (W-PCNs), full duplex (FD) is adopted owing to high efficiency. Furthermore, multi-antenna technique is also widely utilised, since it is capable substantially of improving communication throughput and reliability. In this paper, we study the optimal transmit beamforming and receive combining design for a multiantenna and FD aided hybrid access point (H-AP) in a WPCN, which aims for maximising the uplink sum-throughput of bat-teryless user equipments (UEs). Furthermore, self-interference cancellation (SIC) at the H-AP is also considered in the joint transmit beamforming and receive combining design. Since the optimisation problem is non-convex, a block descent method is exploited for finding the near-optimal solution. Our simulation results demonstrate that the sum-throughput obtained by our design outperforms the other counterparts. As the number of antennas increases, the performance of our design with self-interference approaches that without self-interference (or with perfect self-interference cancellation). Furthermore, our design is robust for accommodating increasing number of batteryless UEs.

Due to its long-distance propagation, radio-frequency (RF) signals have been relied upon for remotely charging low-power communication devices, which significantly extends the lifetime of battery-powered devices. Delivering both wireless information provision (WIP) and wireless energy provision (WEP) services in the RF band requires a holistic design of wireless information and energy provision (WIEP) for achieving energy self-sustainability in the next generation of wireless networks, such as 6G. However, most of the existing works studied the integrated WIEP performance by exploiting Gaussian distributed signals with the infinite alphabet, which cannot be realised in a practical communication system. Therefore, we investigate the WIEP performance by considering practical modulation schemes having finite alphabet in a well-known Nakagami-m wireless fading channel. Furthermore, we jointly optimise the transmit power allocation and the transmission switching threshold of a WIEP transmitter and the power splitting ratio of a WIEP receiver in order to maximise the attainable spectrum efficiency for WIP, while satisfying both the WEP requirement and the WIP reliability constraint. Numerical results are provided for characterising the rate-energy-reliability trade-off of different modulation schemes in various wireless fading conditions.

Mobile edge computing (MEC) has been proposed in recent years to process resource-intensive and delay-sensitive applications at the edge of mobile networks, which can break the hardware limitations and resource constraints at user equipment (UE). In order to fully use the MEC server resource, how to maximize the number of offloaded tasks is meaningful especially for crowded place or disaster area. In this paper, an optimal partial offloading scheme POSMU (Partial Offloading Strategy Maximizing the User task number) is proposed to obtain the optimal offloading ratio, local computing frequency, transmission power and MEC server computing frequency for each UE. The problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is NP-hard and challenging to solve. As such, we convert the problem into multiple nonlinear programming problems (NLPs) and propose an efficient algorithm to solve them by applying the block coordinate descent (BCD) as well as convex optimization techniques. Besides, we can seamlessly apply POSMU to UAV (Unmanned Aerial Vehicle) enabled MEC system by analyzing the 3D communication model. The optimality of POSMU is illustrated in numerical results, and POSMU can approximately maximize the number of offloaded tasks compared to other schemes.

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive ${h}$ -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.

In this article, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs), and unmanned aerial vehicles (UAVs), all with the mobile edge cloud installed to enable user equipments (UEs) or Internet of Things (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real time, while considering the dynamic environment. To this end, we propose a hybrid deep-learning-based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LS-FCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Second, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed-integer nonlinear programming (MINLP) problems and generate the sample data sets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Third, a DNN with the scheduling layer is introduced to provide the user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from the traditional DNN predictor, we only input one UE's information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN.

With the emerging concept of Wi-Fi radio as sensors, we are witnessing more device-free sensing applications. But we observe that most of the existing works of these applications are meant for simple indoor layout and are not adequate for complex cases, e.g., collapsed structures. In this article, we explore the feasibility of Wi-Fi Halow signals for the collapsed scenario as it can boost rescue efforts. To achieve this, we aim at two prime objectives of this article. First, we model debris constituent of common collapsed scenario materials, such as concrete, brick, glass, and lumber by conducting a field survey of an earthquake-affected area. After that, we consider signal propagation models for better coverage in this debris model by employing two methods. The first method is an integrated TOPSIS and Shannon entropy-based on a bijective soft set, which provides us an approximation tool to select the best Wi-Fi Halow signal coverage in debris. The second method composes two modified wireless signal propagation models, which are transmitter-receiver (TR) and Wi-Fi radar, respectively. We perform extensive simulations and figure out that low power transmission using Wi-Fi radar can yield better coverage, which is also verified by the Shannon entropy method.

This article studies an unmanned aerial vehicle (UAV)-enabled edge-cloud system, where UAV acts as a mobile edge computing (MEC) server interplaying with remote central cloud to provide computation services to ground terminals (GTs). The UAV-enabled edge-cloud system implements a virtualized network function, namely, mobile clone (MC), for each GT to help execute their offloaded tasks. Through such network function virtualization (NFV) implemented on top of the UAV-enabled edge-cloud system, GTs can have extended computation capability and prolonged battery lifetime. We aim to jointly optimize the allocation of resource and the UAV trajectory in the 3-D spaces to minimize the overall energy consumption of the UAV. The proposed solution, therefore, can extend the endurance of the UAV and support reliable MC functions for GTs. This article solves the complicated optimization problem through a block coordinate descent algorithm in an iterative way. In each iteration, the allocation of resource is modeled as a multiple constrained optimization problem given predefined UAV trajectory, which can be reformulated into a more tractable convex form and solved by successive convex optimization and Lagrange duality. Second, given the allocated resource, the optimization of the trajectory of rotary-wing/fixed-wing UAV can be formulated into a series of convex quadratically constrained quadratically program (QCQP) problems and solved by the standard convex optimization techniques. After the block coordinate descent algorithm converges to a prescribed accuracy, a high-quality suboptimal solution can be found. According to the simulation, the numerical results verify the effectiveness of our proposed solution in contrast to the baseline solutions.

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS.

With the increase of the number of electric vehicles (EVs), it is of vital importance to develop the efficient and effective charging scheduling schemes for all the EVs. In this article, we aim to maximize the social welfare of all the EVs, charging stations (CSs) and power plant (PP), by taking into account the changing demand of each EV, the changing price, the capacity and the congestion balance between different CSs. To this end, two efficient scheduling algorithms, i.e., Centralized Charging Strategy (CCS) and Distributed Charging Strategy (DCS) are proposed. CCS has a slightly better performance than the DCS, as it takes all the information and make the decision in the central control unit. On the other hand, DCS dose not require the private information from EVs and can make decentralized decision. Extensive simulation are conducted to verify the effectiveness of the proposed algorithms, in terms of the performance, congestion balance, and computing complexity.

The increasing data requirements of Internet applications have driven a dramatic surge in developing new programming paradigms and complex scheduling algorithms to handle data-intensive workloads. Due to the expanding volume and the variety of such flows, their raw data are often processed on Intermediate Processing Nodes (IPNs) before being sent to servers. However, the intermediate processing constraint is rarely considered in existing flow computing models. This paper aims to minimize the tardiness of data-intensive applications in the presence of intermediate processing constraint. Motivating cases show that the tardiness is affected by both IPN locations and flow dispatching strategies. Based on the observation that dispatching flows to IPNs is essentially building a matching between flows and IPNs, a novel solution is proposed based on matching theory. In the deployment phase, a tardiness-aware deferred acceptance algorithm is developed to optimize IPN locations. In the operation phase, the Power-of-D paradigm and matching theory are combined together to dispatch flows efficiently. Evaluation results show that our solution effectively minimizes the total tardiness of data-intensive applications in heterogeneous systems.

Radio frequency (RF) signals have been relied upon for both wireless information delivery and wireless charging to the massively deployed low-power Internet of Things (IoT) devices. Extensive efforts have been invested in physical layer and medium-access-control layer design for coordinating simultaneous wireless information and power transfer (SWIPT) in RF bands. Different from the existing works, we study the coding controlled SWIPT from the information theoretical perspective with practical transceiver. Due to its practical decoding implementation and its flexibility on the codeword structure, unary code is chosen for joint information and energy encoding. Wireless power transfer (WPT) performance in terms of energy harvested per binary sign and of battery overflow/underflow probability is maximised by optimising the codeword distribution of coded information source, while satisfying required wireless information transfer (WIT) performance in terms of mutual information. Furthermore, a Genetic Algorithm (GA) aided coding design is proposed to reduce the computational complexity. Numerical results characterise the SWIPT performance and validate the optimality of our proposed GA aided unary coding design.

As the number of communication devices rapidly grows, limited radio resources hardly accommodate the every-increasing tele-traffic. As a remedy, spatial modulation (SM) is capable of modulating additional information onto the index of transmit or receive antennas, which results in substantial improvement of spectrum efficiency. Moreover, radio frequency (RF) signal based simultaneous wireless information and power transfer (SWIPT) has attracted tremendous research interest, in order to relieve the energy-thirst of massively deployed low-power communication devices. In this paper, a receive spatial modulation (RSM) aided SWIPT system with finite alphabets is studied, in which three different transmission schemes are proposed, namely the general scheme, the superimposed scheme and the distinct scheme. Furthermore, the performance of these transmission schemes in the RSM aided SWIPT system is theoretically analysed. The energy harvested by the receiver is then maximised by jointly optimising the transmit power of the information signal and the covariance matrix of the energy signal as well as the power splitting ratio, while satisfying the quality of service of the wireless information transfer. At last, simulation results validate our theoretical analysis, while they also demonstrate that the distinct scheme has the best SWIPT performance among these three transmission schemes.

Radio frequency (RF) signal based simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technique for satisfying both the communication and charging requests of the massively deployed IoT devices. Different from the physical layer and the medium-access-control layer design for coordinating the SWIPT in the RF band, we study its coding-level control from the information theoretical perspective. Due to its practical implementation of the decoder and its flexibility on the codeword structure, the unary code is chosen as a potential joint information and energy encoder. By conceiving the classic Z-channel, the mutual information and the energy harvesting performance of the unary coding aided SWIPT transceiver is analysed. Furthermore, the optimal codeword distribution is obtained for maximising the mutual information, while satisfying the minimum energy harvesting requirement. Our theoretical analysis and the optimal coding design are demonstrated by the numerical results.

This paper considers a multi-drone enabled data collection system for smart cities, where there are two kinds of drones, i.e., Low Altitude Platforms (LAPs) and a High Altitude Platform (HAP). In the proposed system, the LAPs perform data collection tasks for smart cities and the solar-powered HAP provides energy to the LAPs using wireless laser beams. We aim to minimize the total laser charging energy of the HAP, by jointly optimizing the LAPs' trajectory and the laser charging duration for each LAP, subject to the energy capacity constraints of the LAPs. This problem is formulated as a mixed-integer and non-convex Drones Traveling Problem (DTP), which is a combinatorial optimization problem and NP-hard. We propose an efficient and novel search algorithm named Drones Traveling Algorithm (DTA) to obtain a near-optimal solution. Simulation results show that DTA can deal with the large-scale DTP (i.e., more than 400 data collection points) efficiently. Moreover, the DTA only uses 5 iterations to obtain the near-optimal solution whereas the normal Genetic Algorithm needs nearly 10000 iterations and still fails to obtain an acceptable solution.

In this paper, we investigate a mobile edge computing (MEC) architecture with the assistance of an unmanned aerial vehicle (UAV). The UAV acts as a computing server to help the user equipment (UEs) compute their tasks as well as a relay to further offload the UEs' tasks to the access point (AP) for computing. The total energy consumption of the UAV and UEs is minimized by jointly optimizing the task allocation, the bandwidth allocation and the UAV's trajectory, subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints, and the UAV's trajectory constraints. The formulated optimization problem is nonconvex, and we propose an alternating algorithm to optimize the parameters iteratively. The effectiveness of the algorithm is verified by the simulation results, where great performance gain is achieved in comparison with some practical baselines, especially in handling the computation- intensive and latency-critical tasks.

In this paper, we study a general Near-Far Computing Enhanced C-RAN (NFC-RAN), in which users can offload the tasks to the near edge cloud (NEC) or the far edge cloud (FEC). We aim to propose a profit-aware task allocation model by maximizing the profit of the edge cloud operators. We first prove that this problem can be transformed to a Multiple-Choice Multi-Dimensional 0-1 Knapsack Problem (MMKP), which is NP-hard. Then, we solve it by using a low complexity heuristic algorithm. The simulation results show that the proposed algorithm achieves a good tradeoff between the performance and the complexity compared with the benchmark algorithm.

In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i.e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground user equipments (UEs). Compared to the traditional fixed location MEC, UAV enabled MEC (i.e., UAVE) is particular useful in case of temporary events, emergency situations and on-demand services, due to its high flexibility, low cost and easy deployment features. However, operation of UAVE faces several challenges, two of which are how to achieve both 1) the association between multiple UEs and UAVs and 2) the resource allocation from UAVs to UEs, while minimizing the energy consumption for all the UEs. To address this, we formulate the above problem into a mixed integer nonlinear programming (MINLP), which is difficult to be solved in general, especially in the large-scale scenario. We then propose a Reinforcement Learning (RL)-based user Association and resource Allocation (RLAA) algorithm to tackle this problem efficiently and effectively. Numerical results show that the proposed RLAA can achieve the optimal performance with comparison to the exhaustive search in small scale, and have considerable performance gain over other typical algorithms in large-scale cases.

Industrial Internet of Things (IIoT) is a revolution which is changing the visage of industry in a profound manner. However, it brings many opportunities as well as many puzzles and challenges. Facing with billions of programmable IIoT devices, the traditional IIoT architecture based on cloud computing is no longer suitable, therefore, Mobile Edge Computing (MEC) has been seen as the promising technology to support IIoT business in 5G era. However, the existing mainstream MEC framework exposes numerous problems when supporting IIoT, such as complex development, low development reuse rate, poor software maintainability and mobility, poor flexibility, etc. Therefore, in order to solve the problems mentioned above, in this paper, we propose IIoT-MEC, a novel MEC framework specially for IIoT. We use Docker container to slice computing and storage resources of MEC server into numerous resource blocks (RBs). Based on the concept of virtualization, some RBs for “Device Function Virtualization (DFV)” are used to map physical devices into virtual devices and present in a set of normalized APIs, which shield the hardware development of diverse IIoT devices, so as to simplify the IIoT development into software development only. Some RBs are used to support the operation of IIoT services, in the form of distributed computing. On these basis, a flexible object-oriented IIoT development architecture is constructed. IIoT-MEC can overcome the drawbacks of the existing MEC framework in supporting IIoT. And the implementation procedure of IIoT-MEC is demonstrated with an application example. We also discuss how the IIoT-MEC would be used and what we need to do in future research.

Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the MapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.

Radio frequency (RF) signals can be relied upon for conventional wireless information transfer (WIT) and for challenging wireless power transfer (WPT), which triggers the significant research interest in the topic of simultaneous wireless information and power transfer (SWIPT). By further exploiting the advanced non-orthogonal-multiple-access (NOMA) technique, we are capable of improving the spectrum efficiency of the resource-limited SWIPT system. In our SWIPT system, a hybrid access point (H-AP) superimposes the modulated symbols destined to multiple WIT users by exploiting the power-domain NOMA, while WPT users are capable of harvesting the energy carried by the superposition symbols. In order to maximize the amount of energy transferred to the WPT users, we propose a joint design of the energy interleaver and the constellation rotation-based modulator in the symbol-block level by constructively superimposing the symbols destined to the WIT users in the power domain. Furthermore, a transmit power allocation scheme is proposed to guarantee the symbol-error-ratio (SER) of all the WIT users. By considering the sensitivity of practical energy harvesters, simulation results demonstrate that our scheme is capable of substantially increasing the WPT performance without any remarkable degradation of the WIT performance.

Recently, vehicular ad hoc networks (VANETs) have been attracting significant attention for their potential for guaranteeing road safety and improving traffic comfort. Due to high mobility and frequent link disconnections, it becomes quite challenging to establish a reliable route for delivering packets in VANETs. To deal with these challenges, an artificial spider geographic routing in urban VAENTs (ASGR) is proposed in this paper. First, from the point of bionic view, we construct the spider web based on the network topology to initially select the feasible paths to the destination using artificial spiders. Next, the connection-quality model and transmission-latency model are established to generate the routing selection metric to choose the best route from all the feasible paths. At last, a selective forwarding scheme is presented to effectively forward the packets in the selected route, by taking into account the nodal movement and signal propagation characteristics. Finally, we implement our protocol on NS2 with different complexity maps and simulation parameters. Numerical results demonstrate that, compared with the existing schemes, when the packets generate speed, the number of vehicles and number of connections are varying, our proposed ASGR still performs best in terms of packet delivery ratio and average transmission delay with an up to 15% and 94% improvement, respectively.

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

This paper considers an unmanned aerial vehicle based mobile edge computing (UAV based MEC) system, where we assume there is one UAV, acts as an edge cloud, providing data processing services to the Internet of things devices (IoTDs). We consider the UAV hovers at difference places for different time to receive and process data for IoTDs. We aim to minimize the energy consumption of the UAV, including its hovering energy and computation energy, by optimizing the hovering time, scheduling and resource allocation of the tasks received from IoTDs, subject to the quality of service (QoS) requirement of all the IoTDs and the computing resource available at UAV. This is formulated as a mixed-integer non-convex optimization problem, which is difficult to solve in general. We propose an efficient iterative algorithm to get a high-quality suboptimal solution. Simulation results show that our proposed method has a very good performance compared with the other benchmarks.

With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there are several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.

This paper presents a biologically inspired design and its implementation of macroscale mobile molecular communication systems. Our design of macroscale mobile molecular communication systems consists of a statically placed target and mobile nodes that autonomously move in the environment. The target keeps releasing molecules to create a concentration gradient in the environment, and mobile nodes sense the concentration gradient in the environment and move toward the target according to an algorithm inspired by bacterial chemotaxis. The proposed design of macroscale molecular communication systems is implemented using electrical sprays, mobile robots and alcohol molecules, and evaluated experientially. The proposed design and implementation provides a new tool to study mobile molecular communication systems.

We study a distributed coordination function (DCF) aided WLAN system, where the user stations (STAs) are capable of harvesting energy either from the AP's downlink data transmission or from the AP's dedicated downlink energy transfer. The energy harvested by the STAs is then exploited for powering their own uplink data transmission. Based on the classic carrier-sense-multiple-access with collision avoidance (CSMA/CA) protocol, a pair of enhanced versions are proposed for the sake of incorporating the active energy request of the STAs and the dedicated energy transfer of the AP. The first one is the energy-backoff-interval aided CSMA/CA (EBI-CSMA/CA) protocol. The other is the energy-packet aided CSMA/CA protocol (EP-CSMA/CA). According to our simulation results, both of the protocols are capable of substantially increasing the uplink throughput, when compared to their counterpart without the dedicated energy transfer. Furthermore, the EP-CSMA/CA protocol is more energy-efficient than the EBI-CSMA/CA protocol, since it does not require frequent control signalling exchange for the energy requests and responses.

Indoor positioning systems have gained a lot of attention in the last few years. With the introduction of the Internet of Things (IoT) paradigm, the knowledge of user's location has become crucial information to deliver the efficient and tailored Location Based Services (LBS), especially in indoor environments. In this paper we propose an AP (Access Point)-centred indoor positioning system that overcomes common limitations presented in conventional positioning systems, such as an excessive involvement of Mobile Devices (MDs). Our work merges ideas originally proposed in [1] and [2] to build an efficient, accurate and smart Probabilistic-FingerPrint (P-FP) algorithm that avoids the MD involvement and considers the signal strength measurements as a random variable in the positioning process. Numerical results, obtained in a real-world deployment, show better performance on positioning accuracy, energy consumption and latency with respect to the MD-based architecture.

This paper studies the power-efficient joint radio and computational resource allocation for two near-far mobile devices in a wireless powered mobile edge computing system. To overcome the double-near-far effect for the farther device, cooperative communications in the form of relaying via the nearer device is considered for offloading. The access point (AP)'s total transmit power minimization problem is formulated under the constraints of the computation tasks, which is equivalent to a min-max problem and can be optimally solved by a two-phase method. Numerical results not only show the significant performance improvement of the proposed scheme, but also demonstrate its effectiveness in handling computation-intensive latency-critical (CILC) tasks and resisting the double-near-far effect.

Vehicular Ad Hoc Networks (VANETs) have been attracting more and more interest. Designing one efficient routing protocol is one of the most important issues for urban VANETs. However, fast node movement, dynamic topology changes and complicated channel environments make it quite challenging. In this paper, a Delay-aware and Backbone-based Geographic Routing (DBGR) protocol for urban VANETs is proposed. This protocol comprehensively exploits the real-time traffic information in case of link connection and the historical traffic information when the link is disconnected to make a route selection for packet forwarding. Based on the current traffic condition, using the road weight evaluation scheme (RWE), each road segment can be assigned with an appropriate weight associated with the corresponding transmission delay, by which the weight matrix of the network topology can be built. Using the matrix, the optimized route with the minimum delay can be selected. Simulation results show that the proposed protocol outperforms existing protocols in terms of packet delivery ratio and end-to-end delay.

This paper studies the sustainable resource allocation for energy harvesting (EH) powered cloud radio access network (C-RAN), where EH powered remote radio units (RRUs) cooperatively transmit wireless energy and information to the energy-constrained mobile devices. To investigate network resource allocation problem, firstly, a general system utility optimization framework is proposed for the design of coordinated beamforming and power splitting algorithm based on Lyapunov optimization theory. The randomness of channel conditions and energy arrivals are considered in the framework. Secondly, an online algorithm is designed to maximize the system utility subject to energy sustainable constraints at the RRUs, signal-to-interference-plus-noise (SINR) and energy harvesting requirements of mobile devices. Specifically, there is no requirements of prior distribution knowledge about channel condition or energy arrival in the proposed online algorithm. Finally, performance analysis demonstrate that the proposed algorithm can achieve close-to-optimal system utility. In addition, extensive simulation results are provided to validate the theoretical analysis and to evaluate the performance of the proposed algorithm.

This paper investigates energy-efficient resource allocation for cooperative wireless powered cellular networks (WPCNs), where the cellular and device-to-device (D2D) users first harvest energy from the HAP and then the D2D user consumes a portion of power to help the cell-edge cellular user relay the data in exchange for some time from cellular user for D2D communications. Under the proposed cooperation scheme, we formulate an energy efficiency (EE) maximization problem. The energy beamforming vector, time assignment and power allocation are jointly optimized under the transmission rate requirements and available energy constraints of both D2D and cellular users. Based on the fractional programming theory and semi-definite relaxation (SDR) method, we transform the originally non-convex EE maximization problem into a standard convex problem. This allows us to design efficient resource allocation algorithm for achieving optimal solution. Extensive simulation results are provided to show the convergence rate of the proposed iterative algorithm and to demonstrate the EE improved by the proposed system than that of two baseline systems.

Realising both wireless information transfer (WIT) and wireless power transfer (WPT) in the same RF spectral band has imposed great challenges on the system design but the spectral efficiency can be substantially improved, which triggers the significant research interest in the simultaneous wireless information and power transfer (SWIPT). In order to fully exploit the limited spectral resources, a non-orthogonal-multiple-access (NOMA) technique aided SWIPT system is considered for simultaneously realising both of WIT and WPT, which consists of a single access point (AP), a single WPT user and a pair of WIT users. The AP delivers the requested information to the pair of WIT users by adopting the NOMA technique. In order to maximise the amount of energy received by the WPT user, a novel constellation rotation based modulation scheme is proposed for constructively combining the symbols requested by the pair of the WIT users by optimising the constellation rotation angle. By considering the sensitivity of the energy harvester, the simulation results demonstrate that our modulation scheme is capable of efficiently increasing the energy harvested by the WPT user without incurring additional symbol detection errors.

This paper introduces a Data and Energy Integrated Networks (DEINs), which consists of a Wi-Fi based Wireless Local Area Network (WLAN) and ZigBee based Wireless Sensor Network (WSN). The RF energy harvester aided ZigBee sensor nodes are designed as RF energy receiver. A designed smart gateway integrates both Wi-Fi and ZigBee interfaces serves as RF energy transmitter. The single Wi-Fi interface on the smart gateway is responsible for data communication as well as broadcasting RF energy. The RF energy is generated from broadcasting a sequential self-created unmeaning UDP packets with enhanced transmission power. Hence, transmitting RF energy is transmitting designed UDP packets, which will make the occupation of the downlink bandwidth of Wi-Fi functional data communication. As a result, an energy efficient prioritised bandwidth resource allocation algorithm is proposed for allocating optimum bandwidth resource to downlink data communication and RF energy transmission. This algorithm gives the consideration for lowest energy consumption of RF energy transmitter and relatively high downlink data speed during RF energy charging progress.

Recently, the industrial Internet of Things (IIoT) has drawn high attention in academia and industry in the context of industry 4.0. In the IIoT, smart IoT devices are adopted to improve production efficiency. But, these devices will generate huge amounts of production data, which need to be processed effectively. To support IIoT services efficiently, cloud computing is usually considered as one of the possible solutions. However, the IIoT services still suffer from the high-latency and unreliable links problem between cloud and IIoT terminals. To combat these issues, fog computing is a promising solution which extends computing and storage to the network edge. In this paper, we are motivated to integrate the fog computing to the cloud-based IIoT to build a cloud-fog integrated IIoT (CF-IIoT) network. To achieve the ultra-low service response latency, we introduce the distributed computing to the CF-IIoT network and propose leveraging the real-coded genetic algorithm for constrained optimization problem(RCGA-CO) algorithm to optimize the load balancing problem of the distributed cloud-fog network. Most importantly, considering the unreliable situation in the CF-IIoT (e.g., fog nodes damage, wireless links outage), we propose a task reallocation and retransmission mechanism to reduce the average service latency of the CF-IIoT network architecture. The performance evaluation results validate that the RCGA-CO-based CF-IIoT and our proposed mechanism can provide ultra-low latency service in IIoT scenario.

The indoor positioning technology plays a significant role in the scenarios of the Internet of Things which require indoor location context. In this paper, the WiFi signals under modern enterprise WiFi infrastructure, signal patterns between coexisting access points (APs), and signals’ correlation with indoor pathway map are investigated to address the problem of inconsistent WiFi signal observations. The sibling signal patterns (SSPs) are defined for the first time and processed to generate Beacon APs which have higher confidence in positioning. The spatial signal patterns are used to bring the estimated location into a limited area through signal coverage constraint (SCC). A positioning scheme using SSP and SCC is proposed and shows improved positioning accuracy. The proposed scheme is fully designed, implemented, and evaluated in a real-world environment, revealing its effectiveness and efficiency.

The cities in the world are in the process of quick transition toward more smart, automatic, responsive, and flexible societies. The Internet-of-Things (IoT) are expected to improve the intelligence of the cities, promote the interaction between the human and the environment, enhance the reliability, resilience, operational efficiency, and energy efficiency, as well as reduce costs and resource consumption. The development, adoption, and application of IoT technology into smart cities is of huge interest. Local authorities have partnered with startups, technology companies, research institutions, and universities to test and deploy IoT across all dimensions of urban life such as smart grid (SG), smart buildings, water management, connected healthcare and patient monitoring, environment/climate monitoring, connected cars, and smart transportation.

A typical data and energy integrated communication network (DEIN) conceives a conventional base station, which is capable of simultaneously transmitting the data and energy to user equipments (UEs) during the downlink (DL) transmissions by invoking the time-division-multiple-access (TDMA) protocol in the medium access control (MAC) layer. Several UEs operating in this DEIN are capable of harvesting the energy from the DL transmissions by adopting the power splitting (PS) technique and they are also capable of exploiting the harvested energy for powering their uplink (UL) data transmissions by invoking the TDMA protocol in the MAC layer. Both of the UL sum-throughput and the UL fair-throughput of the DEIN is maximized by deciding the duration of each time-slot during the DL/UL transmissions and by determining the optimal PS factor for each UE. Both of these optimization problems are finally solved by the classic method of Lagrange multipliers in close-form. An interesting observation shows that supporting lowthroughput data services during the DL transmissions does not degrade the wireless energy transfer and hence does not reduce the throughput of the UL transmissions.

Cloud radio access network (C-RAN) has emerged as a potential candidate of the next generation access network technology to address the increasing mobile traffic, while mobile cloud computing (MCC) offers a prospective solution to the resource-limited mobile user in executing computation intensive tasks. Taking full advantages of above two cloud-based techniques, C-RAN with MCC are presented in this paper to enhance both performance and energy efficiencies. In particular, this paper studies the joint energy minimization and resource allocation in C-RAN with MCC under the time constraints of the given tasks. We first review the energy and time model of the computation and communication. Then, we formulate the joint energy minimization into a non-convex optimization with the constraints of task executing time, transmitting power, computation capacity and fronthaul data rates. This non-convex optimization is then reformulated into an equivalent convex problem based on weighted minimum mean square error (WMMSE). The iterative algorithm is finally given to deal with the joint resource allocation in C-RAN with mobile cloud. Simulation results confirm that the proposed energy minimization and resource allocation solution can improve the system performance and save energy.

RF signals can be relied upon for transferring energy to those power-thirsty wireless devices. Thanks to the broadcast nature of wireless channels, dedicated RF signals for delivering information to specific devices can also be received by others for energy harvesting. Coordinating the conventional wireless information transfer and the wireless energy transfer (WET) requires a systematic design in all OSI layers, which yields data and energy integrated communication networks. Based on the classic carrier-sense-multiple-access with collision avoidance protocol, we originally propose a distributed multiple access protocol in the medium access control (MAC) layer for the indoor wireless-local-area-network (WLAN) powered by RF signal-based WET. The operation of our proposed protocol in the WET powered indoor WLAN is modeled by a multi-dimensional Markov chain, which is exploited for deriving the closed-form throughput of the WLAN studied. The simulation results demonstrate the accuracy of our theoretical analysis, which pave the way for the future optimization design in the MAC layer of WET powered indoor WLAN.

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.

This paper studies a mobile edge computing (MEC) system in which two mobile devices are energized by the wireless power transfer (WPT) from an access point (AP) and they can offload part or all of their computation-intensive latency-critical tasks to the AP connected with an MEC server or an edge cloud. This harvest-then-offload protocol operates in an optimized time-division manner. To overcome the doubly near-far effect for the farther mobile device, cooperative communications in the form of relaying via the nearer mobile device is considered for offloading. Our aim is to minimize the AP's total transmit energy subject to the constraints of the computational tasks. We illustrate that the optimization is equivalent to a min-max problem, which can be optimally solved by a two-phase method. The first phase obtains the optimal offloading decisions by solving a sum-energy-saving maximization problem for given an energy transmit power. In the second phase, the optimal minimum energy transmit power is obtained by a bisection search method. Numerical results demonstrate that the optimized MEC system utilizing cooperation has significant performance improvement over systems without cooperation.

Smart home environment is typically comprised of two parts: a home gateway and a number of home appliances. One of the challenges faced by ZigBee is its resource-efficient firmware update. A commonly used method is to compress the firmware before sending it to the ZigBee end nodes. The receiver side, i.e., the ZigBee end nodes, have limited resources in terms of storage and communication bandwidth. Hence the key issue here is how to make the decompression process use as little resources as possible. Based on the existing work this paper goes one step further to propose a nimble decompression algorithm called HHD, whose technical essence is to turn a Huffman tree that represents the firmware to an array which is much smaller. Its nimbleness is reflected in two aspects: storage usage and decompression time. Performance evaluations have shown that HHD has outperformed the conventional algorithms such as Huffman coding by fifty percent on average storage usage and by twenty folds on average decompression time usage.

In this paper, a congestion balanced green charging networks is proposed for the electric vehicles (EVs) in smart grid. Firstly, a problem about the congestion probability balance among the charging stations (CSs) is analyzed and formulated, and then a two-layer optimization model is established based on the profit functions of power plant (PP), CSs and EVs. In the first layer, the optimal generation capacities as well as the charging capacities of CSs are determined, while in the second layer, the sum of each CS's profit and that of the EVs which want to charge at the CS is formulated as a profit maximization problem. The two-layer optimization model solves the congestion probability balance problem in the iterative manner, and finally the congestion balanced smart charging algorithm (CBSCA) is obtained. By comparing with other benchmarks, the results show that CBSCA is converged in an acceptable time, and the congestion probabilities among the CSs are balanced.

Activating direct communications among mobile users (MUs) for sharing a content of common interest (CoCI) becomes an essential paradigm for realising the efficient content distribution in densely populated scenarios. Relying on the cooperative multicast among the MUs, a centralised fair resource allocation algorithm is proposed in this paper for improving the attainable content distribution delay. Apart from the physical wireless transmission, social aspects of MUs are also taken into account, where a MU only multicasts the CoCI to its social contacts. The content distribution process is further modelled by a discrete-time pure-birth-based Markov Chain (DT-PBMC). Relying on the DT-PBMC, we minimise the state-retention probability during each transmission frame in order to reduce the content distribution delay to its smallest possible. The classic branch-and-bound algorithm is invoked for obtaining the optimal resource allocation scheme. The simulation results demonstrate that the proposed algorithm outperforms its existing counterparts, while our novel resource allocation scheme may simultaneously achieve better fairness and reduced content distribution delay.

In this paper, the cooperative caching strategies are developed for a typical cache-enabled small cell network under heterogeneous file and network settings, where the neighboring base stations are enabled to collaborate to share the cached content. To make full usage of the content diversity in the caches, maximum distance separable (MDS) codes are used for content restructuring. The content placement and the cooperation policy among the neighboring base stations are jointly optimized to minimize the long-term average user attrition (UA) cost for fetching content from external storage subject to the cache capacity constraints. In addition to the unicast based cooperative caching scheme, a compound caching strategy, namely multicast-aware cooperative caching assuming fixed and dynamic cooperative policies, respectively, is developed to combine the merits of multicast-aware content delivery and cooperative content sharing. Mathematical analysis and simulation results are presented to illustrate the advantages of MDS coded cooperative caching strategies in terms of reducing the backhaul requirements.

Prolonging battery lifetime, enhancing computation capability and improving spectral efficiency have been the key design challenges in Internet of Things (IoT) era. This paper provides a novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer (WET), Mobile-Edge Computing (MEC) and Full-Duplex (FD). Specifically, the Hybrid Access-Point (HAP) (integrated with a MEC server) operates in FD mode to simultaneously broadcast energy and receive computation tasks to/from the mobile devices in the same band. Each mobile relies on the harvested energy to accomplish computation tasks by locally executing or (partial) offloading to the HAP. We concentrate on max-min energy efficiency optimization problem (MMEP) with the joint the optimization of the transmission power at the HAP, computation energy consumption and offloaded bits at each mobile device, time slots for energy transfer and computation offloading. We study the cases with perfect and imperfect self-interference cancellation at the HAP. To solve the non-convex MMEP, we apply the fractional programming theory and Block Coordinate Descent (BCD) method to design the algorithms with low complexity. Numerical results demonstrate that the proposed solutions outperform the baseline scheme in terms of the worst-case mobile EE. Moreover, the proposed algorithms can converge to the optimal solution through a few iterations.

Prolonging battery lifetime and enhancing computation capability have been the key challenges for designing the mobile devices in the Internet of Things (IoT) era. The investigation of Mobile-Edge Computing (MEC) with Wireless Energy Transfer (WET) is a promising solution to overcome such challenges. In this paper, we study the fundamental tradeoff between Energy Efficiency (EE) and delay in the multi-user wireless powered MEC systems. In order to tackle the randomness of channel conditions and task arrivals, we formulate a stochastic optimization problem to achieve the EE-delay tradeoff, which optimizes the network energy efficiency subject to the network stability, Central Processing Unit (CPU)-cycle frequency, peak transmission power, and energy causality constraints. Furthermore, we propose a joint computation allocation and resource management algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory, whose convexity is further proved. Specifically, the proposed algorithm with low complexity requires no prior distribution knowledge of channel conditions and task arrivals. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V ),O(V )] and provides a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impacts of various parameters to the system performance.

Through the self interference cancellation, the emerging full duplex (FD) technology possesses the potential to double the link capacity of wireless cellular networks. However, under the simultaneous uplink and downlink transmission in the same band, the co-channel interference (CCI) remains in FD cellular networks is becoming a prominent obstacle for FD performance improvement. In this paper, a novel co-channel interference cancellation (CCIC) scheme is proposed to improve the spectrum efficiency by cancelling CCI in FD cellular networks. Based on the CCIC scheme, the upper bound of the instantaneous end-to-end equivalent link capacity in a FD cellular network is derived. Then we provide a new closed-form expression for the general successful transmission probability that captures the joint effect of CCI and residual self-interference (RSI). The accurate match between simulation and theoretical results can verify the derivation. Besides, the obtained results indicate that the successful transmission probability under CCIC scheme increases with the decline of the channel fading which significantly outperforms the conventional FD schemes.

With the fast development of the cloud computing and virtualization techniques, computation resources can be allocated more dynamically and scalably on demand. This paper aims to study two types of computing, i.e., service computing and communication computing. We have proposed to have both computing resource in mobile operator's mobile cloud and investigated how to jointly allocate them with the objective of reducing mobile operator's power consumption and meanwhile, improving mobile users' experience. In this paper, we have introduced the computing power minimization problem, which is NP-hard. By applying several transformations and estimations, the problem can be solved by the branch and bound solution. Also, admission control is considered in this paper. Simulation results have shown that the proposed joint resource allocation solution has a very good performance and outperforms the traditional fixed data rate guarantee algorithm.

Cloud radio access network (C-RAN) and mobile edge computing (MEC) have emerged as promising candidates for the next generation access network techniques. Unfortunately, although MEC tries to utilize the highly distributed computing resources in close proximity to user equipments equipments (UE), C-RAN suggests to centralize the baseband processing units (BBU) deployed in radio access networks. To better understand and address such a conflict, this paper closely investigates the MEC task offloading control in C-RAN environments. In particular, we focus on perspective of matching problem. Our model smartly captures the unique features in both MEC and C-RAN with respect to communication and computation efficiency constraints. We divide the cross-layer optimization into the following three stages: (1) matching between remote radio heads (RRH) and UEs, (2) matching between BBUs and UEs, and (3) matching between mobile clones (MC) and UEs. By applying the Gale-Shapley Matching Theory in the duplex matching framework, we propose a multi-stage heuristic to minimize the refusal rate for user's task offloading requests. Trace-based simulation confirms that our solution can successfully achieve near-optimal performance in such a hybrid deployment.

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm.

The indoor positioning technology plays an important role in the application scenarios requiring indoor location. In this paper, the WiFi signals under modern enterprise WiFi infrastructure and signal patterns between coexisting access points (APs) are investigated. Sibling signal patterns are defined and processed to generate Beacon APs that have higher confidence for positioning. Then a positioning approach using Beacon APs is proposed and shows improved positioning accuracy. The proposed schemes are fully designed, implemented and evaluated in a real-world environment, revealing its effectiveness and efficiency.

In order to address the energy shortage in communication networks, RF signals are exploited for transferring energy to miniature devices, which yields wireless powered communication networks (WPCNs). A full-duplex aided hybrid-base-station (H-BS) is conceived in a WPCN for simultaneously transferring energy during downlink transmissions and receiving data during uplink transmissions. UEs may deplete all the energy received from the H-BS for supporting their own uplink transmissions. In this full-duplex WPCN, A joint time allocation and UE scheduling algorithm is proposed for the sake of maximising the sum-uplink-throughput of multiple UEs by further considering UEs' actual data uploading requirements. The numerical results demonstrate that the suboptimal solution is capable of achieving almost the same performance with its optimal counterparts, while our scheme outperforms other existing peers in terms of the sum-uplink-throughput.

Switching is an indispensable functionality in traditional computer networks. Inspired by computer networks design, this paper investigates the switching functionality for molecular communication networks. In particular,we design channel switches for molecular communication among biological cells through calcium signaling. First, we extend mathematical models of calcium signaling by incorporating gating models of gap junction channels. Second, we show how channel switches may be designed based on the mathematical models, with numerical results demonstrating the switching functionality. Further, we discuss design issues for practical application of channel switches. This paper shows through mathematical modeling and numerical experiments that channel switches are feasible and indicates that complex molecular communication networks may be designed using channel switches.

Caching at the wireless edge is a promising approach to dealing with massive content delivery in heterogeneous wireless networks, which have high demands on backhaul. In this paper, a typical cache-enabled small cell network under heterogeneous file and network settings is considered using maximum distance separable (MDS) codes for content restructuring. Unlike those in the literature considering online settings with the assumption of perfect user request information, we estimate the joint user requests using the file popularity information and aim to minimize the long-term average backhaul load for fetching content from external storage subject to the overall cache capacity constraint by optimizing the content placement in all the cells jointly. Both multicast-aware caching and cooperative caching schemes with optimal content placement are proposed. In order to combine the advantages of multicast content delivery and cooperative content sharing, a compound caching technique, which is referred to as multicast-aware cooperative caching, is then developed. For this technique, a greedy approach and a multicast-aware in-cluster cooperative approach are proposed for the small-scale networks and large-scale networks, respectively. Mathematical analysis and simulation results are presented to illustrate the advantages of MDS codes, multicast, and cooperation in terms of reducing the backhaul requirements for cache-enabled small cell networks.

Nowadays the indoor location context becomes an important element in a number of real applications. Use of WiFi signals to fulfil the location detection of WiFi-enabled devices is a promising approach. In this paper an AP (Access Point)- centred indoor positioning system is proposed to address some common concerns in the conventional MH (Mobile Handheld)-based positioning system, such as excessive involvement of MH, in particular for scenarios of positioning multiple MHs simultaneously. Meanwhile the popularly-used fingerprint technique is combined into the AP-centred architecture to achieve higher positioning accuracy. The proposed system is fully designed, implemented and tested in a real-world deployment. In the environment covered by the APs running the proposed system, the location of the WiFi-enabled MHs appearing in this environment can be computed by a positioning server without disturbing MHs. The accuracy of positioning result obtained from the AP- centred positioning system is evaluated in comparison with a traditional MH-based system in the real experiments. The proposed AP-centred system shows not only the feasibility of AP-centred positioning but also better performance on positioning accuracy and energy consumption of MH.

With the conversion capability from radio frequency into electricity, Radio Frequency based Energy Harvesting (RF-EH) has appeared as a promising means to overcome the battery exhaustion problem of mobile devices. However, despite the great efforts in RF-EH communication techniques made recently, a big drawback that limits the application of RF-EH is the large propagation loss of radio signal energy. This paper attempts to address this problem through deploying multiple energy sources in the network based on cognitive techniques that can perceive and analyze user behaviors. Under a Mobile Social Network (MSN) scenario, we conduct energy source deployment in combination with the throwbox deployment problem, which aims at improving data forwarding efficiency. Our focus is on both data forwarding (DF) and energy harvesting (EH) efficiency. We propose three deployment schemes, i.e., D-deployment, E-deployment and T-deployment. With a continuous-time Markov chain based mobility model, we formulate these deployment schemes as three optimization problems, respectively. Simulation results indicate that the proposed schemes outperform an existing deployment scheme in terms of both DF efficiency and EH efficiency.

Nervous cells and blood vessels form crucial circulating networks in human body. They are interdependent on biological level and communicate with each other via abundant interaction phenomena. These interactions are complex while worthy concerned, which may guide to implement controllable relaying communication across nervous and blood vascular heterogeneous channels. In this paper, we highlight the heterogeneous theme in area of molecular communication. We set up a basic framework based on the heterogeneous network interactions, in which two properties are proposed to make those interactions easier understood on communication level. Moreover, we establish a one-way single-threaded channel model as a case study based on the network framework, and give the mutual information expressions. We aim to explore the possibility of effective communication. The results show that settings of the adjustable system have a significant impact on the relaying performance, as well as the mutual information.

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operator's cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms nonoptimal solutions.

This paper studies the problem of optimal power allocation towards maximizing the throughput of point-to-point wireless communication systems with energy harvesting. A novel energy-transferring approach is proposed to analyze the throughput maximization problem with causality constraints, in which we study the transfer energy rather than the water level widely used in the existing literature. The proposed approach simplifies the power allocation as a linear function with respect to only two transfer energy variables, i.e., the energy transferred from the previous epoch and the energy transferred to the next epoch. Moreover, we prove that all the positive transfer energy variables can be determined by solving a set of linear equations with a special coefficient matrix derived from the KKT conditions for the dual problem. Based on the energy-transferring approach, we propose an iterative algorithm to obtain the optimal solution with a much lower complexity compared to those existing algorithms based on the directional water-filling structure results. Numerical studies verify the analytical results as well as the effectiveness of the proposed algorithm.

The increasing data requirement of Internet applications has driven a dramatic surge in developing new programming paradigms and complex scheduling algorithms to handle data-intensive workloads. Due to the expanding volume and the variety of such flows, their raw data are often processed on intermediate processing nodes before being sent to servers. The intermediate processing constraints are however not yet considered in existing task and flow computing models. In this paper, we aim to minimize the total tardiness of all flows in the presence of intermediate processing constraints. We build a model to consider Tardiness-aware Flow Scheduling with Processing constraints (TFS-P), which is unfortunately NP-Hard. Hence, we propose a heuristic Routing and Scheduling duplex MATching (RSMAT) framework based on the classic Gale-Shapley Matching Theory. We find that the problem can be well-addressed by classic Deferred Acceptance (DA) algorithm, in which the match is stable but inefficient for the model. We therefore propose the Tardiness-aware Deferred Acceptance algorithm with Dynamical Quota (TDA-DQ). This algorithm is enhanced by overcoming the inefficient stability and smartly considering the dynamical quota in the system. The evaluation compares TDA-DQ to the lower bound obtained by a modified subgradient optimization algorithm. The result indicates that TDA-DQ can achieve near-optimal performance for data-intensive applications.

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm.

In this paper, we consider a smart power model, where some subscribers share several energy providers and there are some malicious users in this power grid. The energy providers are managed by a power market scheduling center (PMSC), which broadcasts electricity price to subscribers and energy providers. The energy providers and subscribers update their capacities and energy consumption requirements, respectively, according to the electricity prices received. In order to identify the malicious users and the unstable energy providers, the mechanism of identification and processing (MIP) for the malicious users and unstable energy providers is proposed. By integrating the MIP, we proposed a heuristic algorithm called the dynamic pricing algorithm with malicious users and unstable energy providers (DPAMU) to get the optimal electricity price as well as the optimal power requirement and the load capacity. Finally, the simulation results show that the proposed DPAMU has good convergence performance and can shave and clip the peak load effectively.

Aiming at the current problems that most physical hosts in the cloud data center are so overloaded that it makes the whole cloud data center'load imbalanced and that existing load balancing approaches have relatively high complexity, this paper has focused on the selection problem of physical hosts for deploying requested tasks and proposed a novel heuristic approach called Load Balancing based on Bayes and Clustering (LB-BC). Most previous works, generally, utilize a series of algorithms through optimizing the candidate target hosts within an algorithm cycle and then picking out the optimal target hosts to achieve the immediate load balancing effect. However, the immediate effect doesn't guarantee high execution efficiency for the next task although it has abilities in achieving high resource utilization. Based on this argument, LB-BC introduces the concept of achieving the overall load balancing in a long-term process in contrast to the immediate load balancing approaches in the current literature. LB-BC makes a limited constraint about all physical hosts aiming to achieve a task deployment approach with global search capability in terms of the performance function of computing resource. The Bayes theorem is combined with the clustering process to obtain the optimal clustering set of physical hosts finally. Simulation results show that compared with the existing works, the proposed approach has reduced the failure number of task deployment events obviously, improved the throughput, and optimized the external services performance of cloud data centers.

Mobile social network (MSN) is a special kind of delay tolerant network that consists of mobile users with social characteristics. The existing social-aware data delivery algorithms usually ignore the energy cost of devices as well as the time-varying characteristic of user clustering in the vicinity of hotspots, which result in the degradation of the energy efficiency and the delay performance of data delivery in the MSN. This paper proposes an Energy Saving data Delivery (ESD) scheme, which can reduce energy consumption and data delivery delay. Moreover, the optimal number of data copies, the optimal set of destination hotspots and the route paths with the minimum energy cost are derived towards the highest energy efficiency for data delivery in a MSN. Simulation results indicate that the proposed ESD scheme outperforms the existing hotspotbased MSN schemes in terms of both energy cost and delay of data delivery. We also investigate the impact of the community similarity of users on the performance of data delivery.

In Mobile Social Networks (MSNs), data caching techniques are widely applied to enhance the performance of data delivery by using storage devices called throwboxes. A throwbox is usually placed at a particular place and acts as a stationary relay. When putting throwboxes into a network, the deployment and storage allocation of throwboxes are two fundamental problems. Although throwbox deployment has been studied, optimal storage allocation is often ignored in these approaches. In this paper, we investigate the two problems jointly. Contact strength between a user and a particular place is evaluated with the aid of the contact history of users. Moreover, a joint optimization model is established to calculate the optimal throwbox deployment and storage allocation. Simulation results show that the proposed scheme performs well in decreasing data loss incurred by storage saturation and improving the efficiency of data delivery.

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

Since wireless network virtualization (WNV) enables physical resources abstraction and sharing, the overall resources inefficiency can be reduced dramatically. This paper investigates a pricing-based energy efficient (EE) optimization problem for orthogonal frequency-division multiple Access (OFDMA) WNV. A typical WNV environment consists of an infrastructure provider (InP), virtual network operators (VNOs) and end users. The objective of this paper is to maximize VNOs' EE in bits per joule unit. This is achieved by allocating each VNO certain amount of power. The problem is formulated as a commercial market competition based on a pricing function. A non-cooperative game is applied and a power allocation algorithm is developed to search the Nash equilibrium which is the solution of this game. The Nash equilibrium indicates the best strategy that each VNO can employ. The performances of the proposed algorithm are obtained in a frequency selective fading environment. Evaluation results reveal the VNO adaptation of power sharing strategies and also shows the inefficiency of the Nash equilibrium.

In the context of Mobile Social Networks (MSNs), a type of wireless storage device called throwbox has emerged as a promising way to improve the efficiency of data delivery. Recent studies focus on the deployment of throwboxes to maximize data delivery opportunities. However, as a storage device , the storage usage of throwboxes has seldom been addressed by existing work. In this paper, the storage allocation of throwboxes is studied as two specific problems: (1) if throwboxes are fixed at particular places, how to allocate storage to the throwboxes; and (2) if throwboxes are deployable, how to conduct storage allocation in combination with throwbox deployment. Two optimization models are proposed to calculate the optimal storage allocation with a knowledge of the contact history of users. Real trace based simulations demonstrate that the proposed scheme is able to not only decrease data loss on throwboxes but also improve the efficiency of data delivery.

In this paper, we propose a joint relay and transmit/ receive (Tx/Rx) antenna mode selection scheme (RAMS) in the general full-duplex (FD) relay networks consisting of one source, one destination, and N FD amplify-and-forward (AF) relays. Each FD relay is equipped with two antennas, one for receiving and the other for transmitting. In the proposed scheme, each antenna of the FD relay is able to transmit/receive the signal. Each relay adaptively selects its Tx antenna and Rx antenna based on the instantaneous channel conditions, and the optimal single relay with the optimal Tx/Rx antenna configuration is selected to maximize the end-to-end signal to interference and noise ratio (SINR) of the FD relay system. The performance of the proposed scheme is analyzed. The closed-form expressions of the outage probability, average symbol error rate, and the ergodic capacity are derived. The analytical results are verified by the simulations. To reduce the error floor and capacity ceiling caused by the self-loop interference in FD relay, we propose a RAMS scheme with adaptive power allocation (RAMS-PA). We provide an upper bound and a lower bound of the end-to-end SINR for RAMS-PA scheme, and prove that the error floor can be removed in the RAMS-PA scheme. Results show that the proposed scheme achieves an extra spatial diversity in the medium SNR region due to the FD antenna selection at the relay nodes and considerably improve the system performance compared to the conventional FD relay selection scheme with fixed relay Tx and Rx antennas.

Following the wired network virtualization, virtualization of wireless networks becomes the next step aiming to provide network or infrastructure providers with the ability to manage and control their networks in a more dynamic fashion. The benefit of the wireless mobile network virtualization is a more agile business model where virtual mobile network operators (MNOs) can request and thus pay physical MNOs in a more pay-as-you-use manner. This paper presents some resource allocation algorithms for joint network virtualization and resource allocation of wireless networks. The overall algorithm involves the following two major processes: firstly, to virtualize a physical wireless network into multiple slices, each representing a virtual network, and secondly, to carry out physical resource allocation within each virtual network (or slice). In particular, the paper adopts orthogonal frequency division multiplexing (OFDM) as its physical layer to achieve more efficient resource utilization. Therefore, the resource allocation is conducted in terms of sub-carriers. Although the motivation and algorithm design are based on IEEE 802.16 or WiMAX networks, the principle and algorithmic essence are also applicable to other OFDM access-based wireless networks. The aim was to achieve the following design goals: virtual network isolation and resource efficiency. The latter is measured in terms of network throughput and packet delivery ratio. The simulation results show that the aforementioned goals have been achieved.

Neuro-spike communication is an important branch of molecular communications and has attracted much attention recently. Seminal works on the analyses of signal processing and channel models for the synaptic communication have recently been carried out. However, these works do not consider interference. In this paper, we propose an interference model for synaptic channels with particular focus on InterSymbol Interference (ISI) and Single-Input Single-Output (SISO) channel. We have investigated the overlapping between the two consecutively signals which are sent from a presynaptic terminal to a postsynaptic terminal and their interferences. Furthermore, important parameters of synaptic communication channel that are related to the ISI are also analyzed. The relationship between channel rate region and ISI is also studied.

In this paper, we propose a joint relay and antenna selection scheme in general full-duplex (FD) relay networks with one source, one destination and N FD amplify-and-forward (AF) relays. Each FD relay is equipped with two antennas, one for receiving and one for transmitting. We consider a joint antenna and relay selection scheme to optimize the end-to-end error performance. In the proposed scheme, each relay adaptively selects the transmit antenna and receive antenna based on the instantaneous channel conditions, and the optimal single relay with the optimal Tx/Rx antenna configuration is selected to optimize the end-to-end performance of the system transmission. This is in contrast to the conventional pure FD relay selection, where the Tx and Rx FD antenna of each relay are fixed. The proposed scheme achieves an extra space diversity due to the antenna selection at the relay nodes, and considerably improves the system performance compared to the conventional FD relay selection. Furthermore, closed-form expressions for the outage probability and average symbol error rate (SER) are derived. The analytical results are verified by the computer simulations. Results show that the proposed scheme outperforms the conventional full-duplex relay selection scheme with fixed relay Tx and Rx antennas.

Network virtualization provides a promising way to run multiple virtual networks (VNs) simultaneously on a shared infrastructure. It is critical to efficiently map VNs onto substrate resources, which is known as the VN embedding problem. Most existing studies restrict this problem in a single substrate domain, whereas the VN embedding process across multiple domains (i.e., inter-domain embedding) is more practical, because a single domain rarely controls an entire end-to-end path. Since infrastructure providers (InPs) are usually reluctant to expose their substrate information, the inter-domain embedding is more sophisticated than the intra-domain case. In this paper, we develop an efficient solution to facilitate the inter-domain embedding problem. We start with extending the current business roles by employing a broker-like role, virtual network provider (VNP), to make centralized embedding decisions. Accordingly, a reasonable information sharing scheme is proposed to provide VNP with partial substrate information meanwhile keeping InPs' confidential information. Then we formulate the embedding problem as an integer programming problem. By relaxing integer constraints, we devise an inter-domain embedding algorithm to handle online VN requests in polynomial time. Simulation results show that our solution outperforms other counterparts and achieves 80%-90% of the benchmarks in an ideal scenario where VNP has complete knowledge of all substrate information.

In Mobile Social Networks (MSNs), users with specific relationships are usually treated as a community for data sharing. However, the demand of data sharing among distributed strangers also exists. Those users that have the same interest but do not necessarily know or usually encounter each other can form a gossip community and share information. This paper proposes a data dissemination approach, i.e., the Gathering Point-aided Spreading (GPS) algorithm, which explores the encounter pattern of users and the aid of gathering points to facilitate data sharing in the gossip community. Based on the past encounter pattern, the GPS algorithm predicts the encounter probability among users and assigns the best users to carry the data for a wide spreading. Moreover, by storing a copy of data at the gathering points, GPS enables a further sharing of the data even the carriers leave the gathering points. With different utility functions, GPS can be modified into three versions (GPS-DR, GPS-DE and GPS-TR). Simulation experiments show that GPS outperforms SocialCast in both delivery ratio and delay in data sharing. In addition, among the three versions, GPS-DR and GPS-DE perform the best in terms of delivery ratio and delay respectively, while GPS-TR makes a tradeoff between them.

The full search scheme employed in H.264/AVC significantly improves the coding performance, but it also introduces a very high computational complexity which limits the applications in resource‐constrained mobile devices. In this study, the authors firstly present a discretisation total variation and orientation gradient‐based hierarchical intra‐prediction mode decision method for mobile video applications. By shrinking the candidate mode set in the rate–distortion optimisation (RDO) process, the proposed algorithm reduces the computational complexity and power consumption of the encoder. Furthermore, they extend the hierarchical algorithm to a complexity scalable version in which the coding complexity is measured on five levels by reserving various numbers of modes for RDO. Experimental results demonstrate that the proposed mode decision algorithm reduces the coding complexity significantly with negligible performance degradation and the proposed complexity scalable algorithm is effective and efficient for mobile video application.

In this study, the authors introduce adaptive modulation and coding in a two‐way amplify‐and‐forward (AF) relay network to improve the system performance. They consider a cooperative system where two user nodes exchange information with the assistance of multiple two‐way AF relays. In the proposed scheme, the user nodes adaptively choose the appropriate modulation and coding scheme to ensure that the frame error rate (FER) satisfies the system requirement; all relays are utilised to forward the received signals to the user terminals. Furthermore, they provide a better approximation of the cumulative distribution function of the destination signal‐to‐noise ratio; and thus, derive more accurate expressions including average spectral efficiency and average FER in closed‐form, over Rayleigh fading channels. The theoretical analysis is verified by numerical results.

Road networks have significant impact on mobility and network characteristics of wireless ad hoc networks. Discovering their characteristics and effects on mobility and network performance in urban environments is a fundamental research task. In this paper, we firstly study the graph attributes of road networks by sampling real road networks in main cities of Europe and USA. We propose a new graph metric, called characteristic central length, in order to estimate the average shortest-path length of a large-scale spatial network. We find that real road networks from Europe and USA have different patterns with regard to some graph attributes and a simple grid model is inadequate to describe them. Considering the diverse patterns of urban road networks caused by obstacles and shortcuts, we propose a random road network model, called the GRE model. The model is validated through fitting it to real road network samples using a genetic algorithm and simulation of delay-tolerant networks. The simulation results have shown that by extending the grid model with new probabilistic parameters, the GRE model has better capability on approximating real road networks. The simulation results have also shown that delay-tolerant networks operating on road networks may have better performance than scenarios without road networks.

Novel virtualized HPC centers provide isolated and configurable Virtual Clusters (VC) on shared distributed infrastructures as execution environments for parallel and distributed applications. These VCs are usually customized and deployed per job in runtime. Allocating physical resources for VC is known as Virtual Cluster Mapping (VCM) problem, which is a critical issue that affects both performance of the VC and resource utilization of the system. Most previous works treat all Virtual Machines (VMs) in a VC request equally. However, because sub-jobs in a parallel job usually perform different roles, the corresponding VMs in a VC that execute these sub-jobs respectively should have different levels of importance. Based on this argument, this paper introduces the concept of partial VC mapping in contrast to the full mapping methodology in the current literatures. To fulfill partial mapping, the important backbone communication structure of parallel job called Communication Skeleton (CS) is derived based on the network topology among virtual nodes. To generate the CS of a job, mechanisms for evaluating the importance of nodes are proposed. Eventually, a Topology-aware Partial Virtual Cluster Mapping algorithm (TOP-VCM) is proposed which is based on sub-graph isomorphism detection. TOP-VCM can fully satisfy the nodes/links requirements in CS to ensure the execution performance with only slight degradation of other trivial nodes/links to significantly reduce the mapping difficulty. Simulation results have shown that TOP-VCM has significantly improved the total revenue, the utilization of physical resources and the performance of mapping algorithm while satisfying the VC requirements.

Anonymity is an important topic in ad hoc networks. Previous unicast anonymous routing protocols in ad hoc networks usually assume that shared secret exists between the sender and the receiver and hide either the sender and the receiver or the intermediate nodes from the ad hoc network. Researches on anonymous multicast routing are nearly extinct. This paper proposes a multicast anonymous routing protocol, called topology hidden multicast routing (THMR), which not only makes the sender and the receivers anonymous, but also hides the intermediate route nodes from the adversary simultaneously. In addition, each node do not need to share secret with others before communications. THMR can prevent passive analysis attack as well as most active attacks that are based on route information. The denial-of-service attack to specific session can also be restrained. The simulative results show that THMR is able to discover anonymous routes effectively in an ad hoc network.

Internet of Things (IoT) is regarded as the next big thing for the Internet development. While much work has been focused on the sensors, communication and network aspects of IoT systems, this paper investigates into service provisioning in IoT environment. In particular the paper focuses on message scheduling in an IoT environment where things or sensors are clustered into groups with each group has a message broker that delivers the messages originated from the group to the ultimate receiver of the sensed data. The message scheduler operates on the brokers to decide which message to be transmitted first. While most message scheduling algorithms consider only application-layer features of messages (such as expiry time) the message scheduling algorithm proposed in this paper improve the overall IoT systems efficiency. Furthermore, via routing, energy efficiency has also become a salient feature of the proposed scheduling algorithm. The simulation results have shown the effectiveness and the efficiency of the proposed message scheduling algorithm both in terms of service response time and energy consumption.

Opportunistic networking is perhaps the only available option for the deployment of distributed mobile social networks, in which no form of infrastructure is present. There have been numerous approaches attempting to devise new socially-aware metrics, in order to increase the effectiveness and efficiency of the opportunistic routing algorithms. However, the routing procedure itself is usually not considered. This paper proposes a novel routing method which takes into account the phases of the opportunistic routing procedure. These are detected by checking the availability of information concerning the whereabouts of the destination. Thus, by identifying in which phase a message is in, the algorithm is able to choose the appropriate metric that should be utilized. Using similar metrics as existing routing schemes, this routing protocol is able to provide much higher delivery rates, minimizing the delay. The protocol has been implemented using a base system combining ad-hoc and opportunistic functionality, deployed in the OMNET++ simulation environment. The results presented, clearly demonstrate the benefits of using multi-phase opportunistic routing, when the mobility of the underlying network is driven by social relationships.

Molecular communication is a novel paradigm that uses molecules as an information carrier to enable nanomachines to communicate with each other. The two major components of a diffusion-based molecular communication are diffusion in the medium and the ligand-reception. In this paper, using the principles of diffusion theories and natural ligand-receptor binding mechanisms in biology, we first develop and present model for the molecular single-access channel. Then, we derive the capacity expressions of the molecular single-access channel. Furthermore, we extend it to multiple-access channel in which multiple transmitters communicate with a single receiver. The objective of this paper is to study the capacity of multiple-access channel which is effected by the parameters of diffusion channel and ligand-receptor binding mechanisms. The numerical results show that the overall channel capacity is restricted by the physical parameters of diffusion channel and ligand-receptors and some different characteristics are presented in multiple-access channel scenario comparing with single-access channel scenario.

This paper considers the network situation where the primary users (PUs) in a cognitive radio network have leased out the idled spectrum to the secondary users (SUs) via pricing-based dynamic spectrum allocations (DSAs). We take into account that a SU can serve as a cooperative relay for a PU, and, then, stimulate the PU to split more spectrum while maintaining the minimum transmission rate of the PU. Without using pricing-based mechanisms again, a resource-exchange based bargaining game is proposed to develop the incentive mechanism. Considering multiple SUs should compete with each other for the newly-obtained spectrum from a PU, the novelty of the game scheme is in taking explicitly account of that each PU and SU have their own minimum rate demands. Simulation results show the game guarantees the minimum rate requirement for the PU, and, at the same time, ensures each SU can get a fair rate-reward from the PU according to the level of contribution that it can make to compensate the PU's rate-loss.

Future Internet and more specifically Information-Centric Networks (ICNs), based on the Publish-Subscribe paradigm, is an area that has attracted a big interest lately. Especially topology management issues and network path calculations, whether inter or intra domain, are a major issue. In this paper, a simulation environment developed in OMNET++ is presented, that can be used to provide insight on how Future Internet architectures will cope with congestion and high demand. This environment follows the publish-subscribe approach introduced by PSIRP, but is mainly focused on the topology manager design and intends to enable the development of new improved network path calculation techniques.

In this paper, a multi-user cooperative game is proposed to stimulate selfish user nodes to participate in cooperative relaying in wireless ad hoc networks. Without using the traditional reputation-mechanisms or pricing-mechanisms, we resort to the resource-exchange mechanism by assuming a source node could reward the relaying nodes by, in return, forwarding data that are originated from these relaying nodes. Then the cooperation stimulating problem can be formulated as a multi-player cooperative bargaining game. We prove that there exists a unique Nash bargaining solution (NBS) of the game and propose a fast Particle Swarm Optimizer (PSO) algorithm to solve the NBS. Simulation results show that the NBS-based incentive strategy achieves social optimality, i.e., all cooperative nodes could achieve significant rate-gains in comparison with direct transmission. Moreover, the relaying nodes could also get fairness rewards by the source node according to the level of contribution that they have made to improve the performance of the source node.

Mobility is an important issue in the research of mobile delay-tolerant networks (DTNs). A simple grid model has been frequently used to simulate urban road networks in geographical restricted mobility models. However, by analyzing graph attributes of some urban road networks in main cities of Europe and USA, we discovered the discrepancy between real road network samples and the grid model. Based on the finding, we proposed a random graph-based road network model, called the Grid Model with Random Edges (GRE). The GRE model extends the basic grid model with new probabilistic parameters and thus has better capabilities to approximate real-world road networks. The model was validated through optimizing model parameter values using a genetic algorithm and comparing graph attributes of road networks generated by the model. It was demonstrated that the GRE model has better capability on approximating real road networks than the grid model, thus providing a better foundation for mobility modeling in mobile DTNs.

With the growth of mobile handsets and services provided over wireless networks, the need of dynamically managed environments is obvious. Network providers have already moved from E1/T1 lines to more scalable technologies, including EPONs (Ethernet Passive Optical Networks) and WiMax. These new technologies support service differentiation and Quality of Service (QoS). This work presents an overlay, cross-technology, signaling protocol which allows information exchange, enabling convergence in terms of common resource management, between different types of networks. This behavior is desirable mainly in the fixed-mobile convergence (FMC) area. The benefits of such a common, distributed, resource management scheme are presented in this work. A set of simulations performed using OMNet++, show the advantages of its use in converged EPON-WiMax networks.

In this paper, a cooperative game is proposed to perform a fair and efficient resource allocation for the time division multiple access (TDMA) based cooperative communication networks. In the considered system, two selfish user nodes can act as a source as well as a potential relay for each other. A transmission node with energy limitation is willing to seek cooperative relaying only if the data-rate achieved through cooperation is not lower than that achieved without cooperation by consuming the same amount of energy. The cooperative strategy of a node can be defined as the number of data-symbols and power that it is willing to contribute for relaying purpose. We formulate this two-node fair and efficient resource sharing problem as a bargaining game. Since the Nash bargaining solution (NBS) to the game is computationally complex to obtain, a low-complexity algorithm to search the suboptimal NBS is proposed. Simulation results show that the NBS results are fair in that both nodes could experience better performance than if they work independently. And the NBS results are efficient in that the performance loss of the game to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.

The massive increase of mobile handsets in conjunction with the new and more demanding services deployed over wireless networks lead to huge bandwidth requirements for both the wireless and the backhaul networks. Ethernet Passive Optical Network deployment has already been started to replace E-l/T-1 lines. In addition wireless broadband access is taking place mostly with the use of WiMax. Integration and convergence of the above technologies had been proven to have many benefits. Due to the nature and the complexity though of the scenarios in this area, a large scale simulator, supporting multiple services and QoS is needed in order to validate new network and resource management algorithms. In this paper a simulator developed using OMNet++ framework is presented. The designed modules and their functionality are listed. A discussion on how these could be used to form different convergence architectures and the major outcome/results of such a simulation are explained.

Wireless sensor networks (WSNs) are being deployed for an ever-widening range of applications, particularly due to their low power consumption and reliability. In most applications, the sensor data must be sent over the Internet or core network, which results in the WSN backhaul problem. Passive optical networks (PONs), a next-generation access network technology, are well suited form a backhaul which interfaces WSNs to the core network. In this paper a new structure is proposed which converges WSNs and PONs. It is modelled and analyzed through queuing theory by employing two M/M/1 queues in tandem. Numerical results show how the WSN and PON dimensions affect the average queue length, thus serving as a guideline for future resource allocation and scheduling of such a converged network.

A pricing-based non-cooperative game is proposed to stimulate cooperation and perform spectrum allocation in multi-relay cooperative transmission networks. The authors construct a buyers' market competition model to consider that multiple relays are willing to share their spectrum resources with a single user. Both the benefits of the relays and the user are concerned in the game. First, according to the current user's demand, the relays as sellers compete with each other to determine the price of relaying that can maximise their profits. Then to maximise its utility, the user purchases the optimal amount of spectrum resources from each relay. The existence of the Nash equilibrium (NE), that is, the solution of the game, is proved. Even though the NE can be obtained in a centralised manner, a distributed algorithm to search for the NE is developed, which is more applicable in practical systems. Also, the convergence conditions of the algorithm are also analysed. Furthermore, the authors have also proved that the NE is not efficient when considering the total relays' profits. Thus, a general method to find the global optimal solution that maximises the total relays' profits is given. Simulation results show, by using the game, that a reasonable spectrum allocation can be performed between the relays and the user.

Low-medium-altitude platforms (LMAPs) are being actively researched and developed as a key solution to improve the performance and services of emergency communications. In order to provide higher capacity, throughput and quality of service guarantee to territorial users in emergency scenarios, a LMAP-based WiMAX system, AirWiMAX, is presented in this paper. Firstly, a hierarchical AirWiMAX topology is presented. Secondly, a joint radio resource allocation is carried out simultaneously at the time, frequency and power domain for the AirWiMAX downlink. This problem is modelled as a cooperative game in which a fairness criterion is enforced. Simulation results show that compared to the other two typical resource allocation algorithms, that is, the max-rate algorithm and the max–min fairness algorithm, the proposed algorithm achieves a good trade-off between the overall system throughput and the fairness.

Abstract In this paper, we consider the problem of stimulating cooperation and resource allocation in cooperative transmission networks. We formulate this problem as a sellers' market competition where a relay is willing to share its resource with multiple users. We use a Stackelberg game to jointly consider the benefits of the relay and the users. Firstly, the relay determines the price of relaying according to the user demand. Secondly, the users purchase the optimal amount of resources to maximize their utilities. Although the Nash equilibrium, i.e., the solution of the game, can be obtained in a centralized manner, we develop a distributed algorithm to search the Nash equilibrium, which is more applicable in practical systems. Also, the convergence conditions of the algorithm are analyzed. Simulation results show, by using the distributed algorithm, the relay and the users could determine what price should ask for and how much bandwidth should buy, respectively. Copyright © 2010 John Wiley & Sons, Ltd.

Social recommendation, that an individual recommends an item to another, has gained popularity and success in web applications such as online sharing and shopping services. It is largely different from a traditional recommendation where an automatic system recommends an item to a user. In a social recommendation, the interpersonal influence plays a critical role but is usually ignored in traditional recommendation systems, which recommend items based on user-item utility. In this paper, we propose an approach to model the utility of a social recommendation through combining three factors, i.e. receiver interests, item qualities and interpersonal influences. In our approach, values of all factors can be learned from user behaviors. Experiments are conducted to compare our approach with three conventional methods in social recommendation prediction. Empirical results show the effectiveness of our approach, where an increase by 26% in prediction accuracy can be observed.

IEEE 802.16j is an extension of IEEE 802.16 to support relay mode operation. This paper applies IEEE 802.16j to vehicular networks to provide Internet access for high-way vehicles. It designs a utility function, which considers different types of services and effect of velocity on vehicular networks. It proposes a dynamic bandwidth allocation (DBA) algorithm specifically for base stations (BSs) in vehicular networks to support QoS requirements of different types of services. The objective of the proposed DBA is to allocate bandwidth from the BS to its serving relay stations (RSs) with QoS consideration. The simulation results have shown the effectiveness and efficiency of the proposed DBA algorithm.

The current status of a campus research testbed that is being constructed to allow for the exploration of digital service delivery and smart networked environments using different networking technologies is presented.

IEEE 802.16 has been regarded as a promising broadband wireless access technology for its large coverage, quality of service (QoS) support for different types of application, and easy deployment. IEEE 802.16j is an extension of IEEE 802.16 to support relay mode operation. This paper applies IEEE 802.16j to vehicular network to provide Internet access for high-way vehicles. In particular, it proposes a dynamic bandwidth allocation (DBA) algorithm specifically for relay stations (RS) to support QoS requirements of different types of service. The objective of the proposed DBA is to allocate bandwidth from relay station to its serving subscriber stations (SSs) with QoS consideration. The simulation results have shown the effectiveness and efficiency of the proposed DBA algorithm.

In this paper, we propose a Subproblem-dependent Heuristic (SH) for MOEA/D to deal with the Deployment and Power Assignment Problem (DPAP) in Wireless Sensor Networks (WSNs). The goal of the DPAP is to assign locations and transmit power levels to sensor nodes for maximizing the network coverage and lifetime objectives. In our method, the DPAP is decomposed into a number of scalar subproblems. The subproblems are optimized in parallel, by using neighborhood information and problem-specific knowledge. The proposed SH probabilistically alternates between two DPAP-specific strategies based on the subproblems objective preferences. Simulation results have shown that MOEA/D performs better than NSGA-II in several WSN instances.

The K-connected deployment and power assignment problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing both the network coverage and lifetime under K-connectivity constraints, in a single run. It is shown that the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem specific knowledge for increasing its efficiency. Since the K-connected DPAP requires constraint handling, several techniques are investigated and compared, including a DPAP-specific repair heuristic (RH) that transforms an infeasible network design into a feasible one and maintains the MOEA/D's efficiency simultaneously. This is achieved by alternating between two repair strategies, which favor one objective each. Simulation results have shown that the MOEA/D-RH performs better than the popular constrained NSGA-II in several network instances.

Wireless sensor networks deployment and power assignment problems (DPAPs) for maximizing the network coverage and lifetime respectively, have received increasing attention recently. Classical approaches optimize these two objectives individually, or by combining them together in a single objective, or by constraining one and optimizing the other. In this paper, the two problems are formulated as a multi-objective DPAP and tackled simultaneously. Problem-specific encoding representation and genetic operators are designed for the DPAP and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is specialized. The multi-objective DPAP is decomposed into many scalar subproblems which are solved simultaneously by using neighborhood information and network knowledge. Simulation results have shown the effectiveness of the proposed evolutionary components by providing a high quality set of alternative solutions without any prior knowledge on the objectives preference, and the superiority of our problem-specific MOEA/D approach against a state of the art MOEA.

In heterogeneous wireless networks, when a mobile host/handset (MH) with multiple wireless interfaces changes its location or requires a certain network service, the MH will require a switch between different wireless networks (namely vertical handoff). A congestion-aware proactive vertical handoff algorithm is proposed, which uses a data pre-deployment technology to realise soft handoff between cellular interface and ad hoc interface. Here, the vertical handoff algorithm is implemented in an experimental heterogeneous network structure called converged ad hoc and cellular network, which is an ad hoc overlay system considering the balancing of the traffic between adjacent cellular cells. By evaluations, it is shown that the proposed algorithm can realise low handoff delay and low packet losses, and help to ease congestion issue existing in the heterogeneous networks.

IEEE 802.16 and Ethernet Passive Optical Network (EPON) are two promising broadband access technologies for high-capacity wireless access networks and wired access networks, respectively. They each can be deployed to facilitate connection between the end users and the Internet but each of them suffers from some drawbacks if operating separately. To combine the bandwidth advantage of optical networks with the mobility feature of wireless communications, we propose a convergence of EPON and 802.16 networks in this paper. First, this paper starts with presenting the converged network architecture and especially the concept of virtual ONU-BS (VOB). Then, it identifies some unique research issues in this converged network. Second, the paper investigates a dynamic bandwidth allocation (DBA) scheme and its closely associated research issues. This DBA scheme takes into consideration the specific features of the converged network to enable a smooth data transmission across optical and wireless networks, and an end-to-end differentiated service to user traffics of diverse QoS (Quality of Service) requirements. This QoS-aware DBA scheme supports bandwidth fairness at the VOB level and class-of-service fairness at the 802.16 subscriber station level. The simulation results show that the proposed DBA scheme operates effectively and efficiently in terms of network throughput, average/maximum delay, resource utilization, service differentiation, etc.

Abstract The practical success of pervasive services running in mobile wireless networks relies largely on its flexibility in providing adaptive and cost‐effective services. Service discovery is an essential mechanism to achieve this goal. As an enhancement to our previous work for service discovery, that is, model‐based service discovery (MBSD), this paper proposes a location‐based service advertisement (SA) algorithm named as MBSD‐sa. MBSD‐sa advocates the importance of service location to the service availability and integrates the service location information together with the service semantic information into service information for advertisement. MBSD‐sa utilizes prediction to estimate the service location so as to reduce the number of SA messages (SAMs). Two complementary types of SA mechanisms (Types 1 and 2) are employed by MBSD‐sa to strike the balance between the SAM overhead and the accuracy of service information. The performance of MBSD‐sa is analyzed both numerically and using simulations. Copyright © 2008 John Wiley & Sons, Ltd.

IEEE 802.16 and Passive Optical Network (PON) are two promising broadband access technologies for high-capacity wireless and wired access networks, respectively. In order to better understand the co-existence of both network technologies and to determine whether closer cooperation in the bandwidth provisioning process is advantageous, an access network that utilizes a Gigabit PON (GPON) to backhaul 802.16 network traffic is evaluated. Typical to many network deployments, the equipment is from different manufacturers and has different management and control interfaces. This paper proposes the use of a control bridge that overlooks the operations of both the GPON and 802.16 networks in order to: (1) provide dynamic QoS mapping so as to reduce traffic delivery cost; and (2) to improve overall channel utilization through coordinated dynamic bandwidth allocation. The performance of the converged network under the control of the proposed control bridge is evaluated in terms of cost of data delivery, channel utilization, and service differentiation.

The creation of services is a complex activity that involves several tasks. Furthermore this complexity is augmented by the fact that supporting service creation environments are technology-specific. Consequently a technology-independent approach and framework are required to generate service creation environments and drive service creation. In this paper we present such an approach and a generic framework for supporting service creation. The approach realizes service creation via the phases of: (i) domain specific language definition, (ii) model definition and validation, (iii) model-to-model transformation and (iv) model-to-code generation. Each phase maps to a corresponding phase in service creation starting from service analysis to service implementation. The applicability of the approach and its accompanying framework is demonstrated via an example scenario that illustrates the automatic generation of a service creation environment for an online survey system.

Wireless sensor networks design requires high quality location assignment and energy efficient power assignment for maximizing the network coverage and lifetime. Classical deployment and power assignment approaches optimize these two objectives individually or by combining them together in a single objective or by constraining one and optimizing the other. In this article a multi-objective deployment and power assignment problem (DPAP) is formulated and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is specialized. Following the MOEA/D's framework the above multiobjective optimization problem (MOP) is decomposed into many scalar single objective problems. The sub-problems are solved simultaneously by using neighborhood information. Additionally, unique problem-specific, parameter-rising, genetic operators and local search heuristics were designed specifically for the DPAP. In addition, a new encoding scheme is designed to represent a WSN based on the DPAP's design variables. Simulation results show that MOEA/D provides a high quality set of alternative solutions without any prior knowledge on the objectives preference.

A dynamic bandwidth reservation (DBR) scheme for hybrid IEEE 802.16 wireless networks is investigated, in which 802.16 networks serve as the backhaul for client networks, such as WiFi hotspots and cellular networks. The DBR scheme implemented in the subscription stations (SSs) (co-locating with access pointers) consists of two components: connection admission controller (CAC), and bandwidth controller (BC). The CAC processes the received connection set-up requests from the client networks connected to the SSs. The BC manages the request and release of bandwidth from the base station (BS). It dynamically changes the reserved bandwidth between a small number of values. Hysteresis is incorporated in bandwidth release to reduce bandwidth request signalling load and connection blocking probability. An analytical model is proposed to evaluate the performances of reserved bandwidth, connection blocking probability and signalling load. The impacts of hysteresis mechanism and probability of reservation request blocking are taken into account. Simulation verifies the analytical model.

Offloading (also known cyber-foraging) is an approach to leverage the severity of resource constrained nature of mobile devices (such as PDAs) by migrating part of the computation of applications to some nearby resource-rich surrogates (e.g., desktop PCs). It is an essential mechanism for the execution of pervasive applications. However, the mobile nature of mobile devices and the unstable connectivity of wireless links all render a less predictability of the performance of a pervasive application running under the control of offloading systems. This paper proposes an analytical model to express the performance of fault- tolerant offloading systems in mobile wireless environments. We model the failure recovery time and total execution time of pervasive applications that run under the control of the fault- tolerant offloading systems. The model is analyzed numerically and primarily evaluated based on a real application.

Denial of service (DoS) attacks, and jamming in particular, present a significant threat to wireless networks because they are easy to mount and difficult to detect and prevent. We present and analyze a special type of DoS attack, called random packet destruction (RPD) that works by transmitting short periods of noise signals. The RPD DoS attack can effectively shut down a wireless network. Since the attacker does not need to pretend to be a legal user participating in the network, current anti-attack measures such as encryption, authentication and authorization cannot prevent these types of attacks. RPD DoS attacks are pervasive in nature and can potentially be launched against any wireless networks that are detectable. An attacker can launch RPD attacks against wireless networks used for mission critical systems to inflict serious damages on lives or properties. The paper presents for the first time, both theoretical analysis and performance simulations of WLANs when operating under RPD DoS attacks for a range of types of network traffic.

Integrated cellular networks (ICNs) are normally constructed by adding ad hoc overlay on cellular networks to solve the latter's flexibility and capacity expansion problems. In such networks, routing plays a critical role in finding a route to divert congested traffic from a congested cell to another less crowded cell. Much work has been conducted on routing protocols in ICNs, whereas no dedicated work has been found for an important aspect of routing, namely source selection. The process of a source selection can be an algorithm that is designed for selecting a proper pseudo-source to release its occupying channel to a blocked mobile user. Consequently, this pseudo-source diverts its ongoing call to another cell by using a free channel in a neighbour cell via a relaying route. Based on an introduction of a representative ICN infrastructure, three source selection algorithms are proposed. Both numerical analysis and evaluation results are presented, which show the efficiency of the algorithms and their different abilities in adapting to different network situations, such as traffic density and cell capacity.

This paper investigates the random channel access mechanism specified in the IEEE 802.16 standard for the uplink traffic in a point-to-multipoint (PMP) network architecture. An analytical model is proposed to study the impacts of the channel access parameters, bandwidth configuration and piggyback policy on the performance. The impacts of physical burst profile and non-saturated network traffic are also taken into account in the model. Simulations validate the proposed analytical model. It is observed that the bandwidth utilization can be improved if the bandwidth for random channel access can be properly configured according to the channel access parameters, piggyback policy and network traffic.

With the advantage of mobile devices and wireless communication technologies, service offloading, as an important branch of pervasive computing, attracts much research. In service offloading, by offloading part of computation in the mobile device to a powerful surrogate, the total service execution time can be reduced and mobile devices' battery lifetime can be greatly prolonged. In this paper, we focus on mobile ad hoc environments. Given a service's remote execution components, our goal is to find a surrogate and the path to the surrogate (i.e., the surrogate-path pair) so as to achieve a minimum surrogate execution time. Since the topology in mobile ad hoc environments can change frequently due to node mobility, we study the problem of combined routing and surrogate selection. The task is formulated as a combinatorial optimization problem and a combined routing and surrogate selection algorithm (CRoSS) is proposed. Based on cross-layer design principles, CRoSS enables the process of surrogate discovery and selection to be coalesced into the process of route discovery and maintenance of the underlying mobile ad hoc networks. It is analytically proofed that by utilizing the surrogate-path pair found by CRoSS the surrogate execution time is minimized. Simulation evaluations also show the constant efficiency of CRoSS.

Offloading is an approach to leverage the severity of resource constrained nature of mobile devices (such as PDAs, mobile phones) by migrating part of the computation of applications to some nearby resource-rich surrogates (e.g., desktop PCs, mobility support stations). It is an essential mechanism for the execution of pervasive services. However, the mobile nature of mobile devices and the unstable connectivity of wireless links all render a less predictability of the performance of a pervasive service running under the control of offloading systems. This paper proposes an analytical model to express the performance of offloading systems in mobile wireless environments. We investigate the surrogate unreachability when mobile devices move following random waypoint (RWP) mobility scheme. We model the failure recovery time and total execution time of pervasive applications that run under the control of offloading systems. Detailed evaluation and analysis results are reported and the results of this paper can be used as design guidance for pervasive service offloading systems.

Recently, game theory becomes a useful and powerful tool to research mobile ad hoc networks (MANETs). Wireless LANs (WLANs) can work under both infrastructure and ad hoc modes, and are the most widely used MANETs. In this paper, we propose a novel concept of incompletely cooperative game theory and use it to improve the performance of WLANs. In this game, firstly, each node estimates the current state of the game (i.e., the number of competing nodes) by detecting the channel. Secondly, each node changes its equilibrium strategy by tuning its local contention parameters based on the estimated game state. Finally, the game is repeated finitely to get the optimal performance. Our simulation results show that the incompletely cooperative game can increase system throughput, and decrease delay, jitter and packet-loss-rate.

In this paper, we present a novel 2-way handshake authentication protocol to locally authorize intra-domain roaming users for efficient authentication in mobile IP networks, which is based on authentication, authorization and accounting (AAA) architecture. We develop a detailed procedure to establish local security associations (SAs) for re-authentication using commitment schemes. By considering the traffic and mobility patterns of a mobile user (MU), as well as the message transmission time between the MU and its home AAA server, we provide a performance study for comparing the authentication latency of existing authentication protocol with our approach. The result shows that our protocol outperforms the existing authentication protocol.

Control phase plays a critical role in the performance of time-division multiple access (TDMA)-based networks. Within cluster-based wireless sensor networks, a nimble and adaptive control phase algorithm called NACPA to control the control phase of TDMA-based medium access control (MAC) in cluster-based sensor networks is proposed. This algorithm takes advantage of the wireless sensor hardware feature and presents a more accurate although simpler means to calculate the number of contention nodes in one round. On the basis of the analysis of the features of contention probability against the number of contention nodes, this algorithm can significantly reduce its computation complexity, rendering it practically feasible for resource-constrained sensor networks. Detailed analytical evaluation against two typical MAC algorithms (polling and carrier sense multiple access) is presented both in terms of packet transmission delay and average channel utilisation, the results of which, while also matching the simulation observation, have shown its effectiveness and efficiency.

Cost-effective topology control is critical in wireless sensor networks. While much research has been carried out in this aspect using various methods, no attention has been made on utilizing modern heuristics for this purpose. This paper proposes a memetic algorithm-based solution for energy-aware topology control for wireless sensor networks. This algorithm (called ToCMA), using a combination of problem-specific light-weighted local search and genetic algorithm, is able to solve the minimum energy network connectivity (MENC) this NP-hard problem in an approximated manner that performs better than the classical minimum spanning tree (MST) solution. The outcomes of ToCMA can also be utilized for various network optimization and fault-tolerant purposes.

A distributed algorithm is introduced which detects and recovers holes in the coverage provided by wireless sensor networks. It does not require coordinates, requiring only minimal connectivity information (for example, whether any two nodes are within either the sensing radius or twice the sensing radius.) The radio communications area is assumed to be larger than the sensed area. Two active nodes are called neighbors, and are said to be connected by a link, if their distance lies between these two values. Redundant nodes are likely to exist inside an active node's sensing range. If all connected neighbors of some active node A can form a ring via links between them, there is no large hole inside the ring. Otherwise A is a boundary node of a large hole. All boundary nodes and most holes can be detected with very low probability of error, and simulation results suggest that redundant nodes are selected efficiently for activation when recovering the hole.

This paper proposes a dynamic and adaptive algorithm (NACPA) to control the control phase of TDMA-based MAC that can be used in cluster-based wireless sensor networks. This algorithm takes advantage of the wireless sensor hardware feature and presents a more accurate while simpler means to calculate the number of contention nodes in one round. Based on the analysis of the features of contention probability against the number of contention nodes, this algorithm can significantly reduce its computation complexity rendering it practically feasible for resource-constrained sensor networks.

This paper proposes an adaptive routing protocol called ARFA for an integrated cellular and ad hoc heterogeneous network with flexible access (iCAR-FA). Based on the presentation of the iCAR-FA physical characteristics, the paper details the design issues and operation of ARFA. Detailed numerical analysis on route request rejection rate, which is along with some general evaluations, has indicated the effectiveness and efficiency of the ARFA protocol.

The practical success of the future generation mobile systems such as 4G relies largely on its flexibility in providing adaptive and cost-effective services. Service discovery is an essential mechanism to achieve this goal. Based on the investigation of the existing service discovery protocols, this paper proposes a new service discovery methodology for future generation mobile systems: model-based service discovery or MBSD. MBSD takes advantage of the OMG (Object Management Group) MDA (Model-Driven Architecture) technique. The system architecture of MBSD and its operation are presented and implemented. The proposed methodology is validated via a mobile service scenario.

Offloading is a kind of mechanism utilized in pervasive systems to leverage the severity of resource constraints of mobile devices by migrating part of the classes of a pervasive service/application to some resource-rich nearby surrogates. A pervasive service application needs to be partitioned prior to offloading. Such partitioning algorithms play a critical role in a high-performance offloading system. This paper proposes an adaptive (k+1) partitioning algorithm that partitions a given application into 1 unoffloadable partition and k offloadable partitions. Furthermore, these partitions satisfy the multiple constraints imposed by either application users or mobile device resources. Underpinning the partitioning algorithm is a dynamic multi-cost graph that models the costs of an application in terms of its component classes (including CPU cost, memory cost and communication cost), and a Heavy-Edge and Light-Vertex Matching (HELVM) algorithm to coarsen the multi-cost graph. An offloading toolkit implementing the above algorithms has been developed, upon which the evaluations are carried out. The outcomes of the evaluation have indicated a higher level of performance of our algorithm in terms of its efficiency and cost-effectiveness

With the advances in mobile terminals and wireless communications, the demand for mobile devices to run heavier applications (e.g., those running on their desktop PC) is on increase. Offloading is a novel approach to leverage the severity of resource constrained nature of mobile devices by migrating part of the computation of pervasive services to some nearby resource-rich surrogates (e.g., base stations, desktop PCs, servers). This paper proposes a runtime offloading system for pervasive services that considers multiple types of system resources and carries out service partitioning and partition offloading in a more adaptive and efficient manner. The system's service partitioning algorithm and partition offloading mechanism are presented in detail. The evaluation outcomes have indicated a higher level of efficiency of this service offloading system

This paper presents a proof-of-the-concept of a novel means to develop in an easy way, to execute in a more pervasive way and to maintain in a more sustainable way context-aware services. The essence of this approach is the integration of the policy-based management (PBM) technique and the MDA (model-driven architecture) technique. The presence of policies grants context-aware services the high flexibility and adaptability as their nature, whereas the introduction of MDA for context-aware service information model fundamentally solves the information model puzzle of current PBM. MDA's middleware-neutral feature also benefits the smooth evolution of context-aware services as a kind of software. The preliminary case study has proved the positive feasibility of this approach.

In order to bring together the higher speed of WLAN and the wider coverage of GPRS, solution from service's perspective is necessary. And this kind of integrated service should be context-aware in order to automatically adapt itself to the changing environment. This paper proposes to explore the applicability of using network-centric context-aware service to integrate WLAN and GPRS network environments. Starting from typical scenario description and requirement analysis, a policy-based context model is presented, which takes into account the real implementation of context-aware service in the underlying networks. A context-aware service scenario called modern professor is explored to exemplify this methodology based on the policy-based context-aware service system architecture.