Next-generation wireless networks are progressing beyond conventional connectivity to incorporate emerging sensing and computing capabilities. This convergence gives rise to integrated systems that enable not only uninterrupted communication, but also environmental awareness, intelligent decision-making, and novel applications that take advantage of these combined features. At the same time, this integration brings substantial security challenges. As computing, sensing, and communication become more tightly intertwined, the overall complexity of the system increases, creating new vulnerabilities and expanding the attack surface. The widespread deployment of data-heavy artificial intelligence applications further amplifies concerns regarding data security and privacy. This paper presents a comprehensive survey of security and privacy threats, along with potential countermeasures, in integrated wireless systems. We first review physical-layer security techniques for communication networks, and then investigate the security and privacy implications of semantic and pragmatic communications and their associated cross-layer design methodologies. For sensing functionalities, we pinpoint security and privacy risks at the levels of signal sources, propagation channels, and sensing targets, and summarize state-of-the-art defense strategies for each. The growing computational requirements of these applications drive the need for distributed computing over the network, which introduces additional risks such as data leakage, weak authentication, and multiple points of failure. We subsequently discuss secure coded computing approaches that can help overcome several of these challenges. Finally, we introduce unified security frameworks tailored to integrated communication–sensing–computing architectures, offering an end-to-end perspective on protecting future wireless systems.

This work investigates the time-dependent statistical characteristics (SCs) of age of information (AoI) in V2I periodic-updating. The D/G/1/1 (non-)preemptive discrete-time (slotted) queue model is employed. The entire AoI process is split into a set of slot-specific AoI processes w.r.t. updating period (UP). A Markov multi-dimensional age process w.r.t. UP is used to track the evolution of slot-specific AoI. Accordingly, the time-dependent AoI distribution and expected AoI (EAoI) are derived, forming a framework to study the time-dependent AoI SCs under periodic updating. Based on the AoI SCs, we optimize the decision-making (DM) process at roadside unit to minimize age upon decisions, improving information freshness when conducting DMs. A quasiperiodic DM mechanism is regarded, where one DM slot is probabilistically determined in each UP. The optimal DM probabilities are obtained in closed-form. In the D/Geo/1/1 case, we find that the preemption-induced EAoI gains are identical among slots; for extremely reliable/error-prone channels, conducting DM at the second/middle slot in UP is optimal. Numerical results verify the effectiveness of theoretical analyses and DM process optimization.

The proliferation of IoT devices has led to an exponential increase in data generation, creating new opportunities for data marketplaces. However, due to the security and privacy issues arising from the sensitive nature of IoT data, as well as the need for efficient management of vast amounts of IoT data, a robust solution is necessary. Therefore, this paper proposes a secure data sharing framework with fine-grained access control and privacy protection for the internet of things (IoT) data marketplace. For fine-grained access control of the data in the proposed protocol, we develop the hidden attributes and encryption outsourced key-policy attribute-based encryption (HAEO-KP-ABE) that outsources high-complex operations to peripheral devices with high capability to reduce the computation burden of IoT device. It achieves data privacy by hiding attributes in the ciphertext and by preventing entities that do not hold the data consumer’s secret key material (including SA/CS) from running the match test on stored ciphertexts before decryption. It also has an efficient match test algorithm which can verify that the hidden attributes of the ciphertext match the access policy of the data consumer’s private key without revealing those attributes. We demonstrate the proposed protocol satisfies the security features required for the data sharing process in an IoT data marketplace environment. Furthermore, we evaluate the execution time of the proposed protocol according to the number of attributes and show the practicality and efficiency of the proposed protocol compared to the related works.

This work investigates the time-dependent statistical characteristics of age of information (AoI) in the real-time Internet of Things systems with wireless periodic status updating. The periodic updating system is modeled as a discrete-time D/G/1/1 non-preemptive queue. The entire AoI process w.r.t. slot is split into a set of slot-specific AoI processes w.r.t. updating period. A Markov high-dimensional age process is introduced to track the evolution of the slot-specific AoI. Accordingly, the time-dependent AoI distribution, expected AoI (EAoI), and the time-average AoI are derived, forming a framework to study the time-dependent AoI statistical characteristics under periodic updating. Numerical results verify the effectiveness of theoretical analyses and usefulness of packet retransmission. It is found that the EAoI varies non-monotonously and greatly w.r.t. slot in an updating period; the EAoI variations are distinct for the update transmission times with different distributions; and the EAoI gains of channel enhancement differ greatly among slots.

Secure data transmission and real-time state updates are critical yet challenging requirements for Cyber-Physical Systems (CPS) to maintain stability under adversarial conditions. While existing works have separately explored covert communications for security and Age of Information (AoI) optimization for timeliness, their interdependencies remain unaddressed, leading to suboptimal trade-offs between detection resistance, control performance, and resource efficiency. To bridge this gap, this paper proposes a novel co-design framework that jointly optimizes covert communication and AoI-aware control strategies. First, we rigorously derive a linear relationship between average AoI and control cost, termed the Cost of Update Delay (CoUD), which quantifies how outdated information exacerbates state fluctuations and increases stabilization efforts. Building on Kosta et al.’s Geo/Geo/1 queuing model, a closed-form expression for average AoI is further established as a function of sampling rate and packet delivery probability, explicitly linking communication parameters to control efficacy. Subsequently, this paper formulate a constrained optimization problem to minimize CoUD while guaranteeing covertness against eavesdroppers, leveraging Dinkelbach’s transformation and Lagrangian duality to decouple nonlinear constraints, derive optimal sampling rates, and transmission powers. Simulation results demonstrate that the proposed co-design framework achieves significant reductions in CoUD and superior freshness compared to baseline methods, while robustly maintaining covertness requirements. Notably, the proposed integration of sampling rate adaptation into the detection error rate model markedly enhances both AoI performance and resource efficiency, outperforming state-of-the-art disjoint designs. Therefore, this work provides a unified methodology to harmonize security, timeliness, and stability in resource-constrained CPS.

As the demand on time-sensitive contents increases, data freshness recently becomes an important performance metric in the cache-enabled networks. Therefore, in this paper, we design the joint cache replacement and content update algorithm in the cloud-edge networks considering the data freshness at both the cloud server and the edge server. We define a fresh content acquisition with cache hit (FACH) ratio as a performance metric, which shows the portion of users obtaining the requesting content from the edge server while satisfying the freshness constraint. To maximize the FACH ratio, we propose the reinforcement learning (RL)-based algorithm, named theintelligent Cache replacement and content Update(i-CU)algorithm. In the proposed algorithm, we newly suggest the score-based action decision to reduce the action space while guaranteeing the constraints of the problem. In the simulation results, we develop and evaluate thei-CUalgorithm for various datasets, which verifies that thei-CUalgorithm can achieve the higher FACH ratio compared to the existing baselines under the various network parameters.

Since the permissioned blockchain technology has been proposed to ensure data integrity in distributed systems, the low throughput and high latency have been recognized as major issues. In some applications, the data, available later than allowed time, can be useless, so the effective throughput is newly considered, defined as the average number of transactions per second, committed within the required latencies. For maximizing the effective throughput, we propose a novel intelligent transaction generation control (i-TGC) method to determine the transaction generation for each client. To improve performance in the dynamic environment of blockchain services based on real-time information, we employ the reinforcement learning (RL) for the i-TGC algorithm. Our experiment results show the i-TGC outperforms the probabilistic transaction generation control (p-TGC), which generates transactions randomly with the optimal probability that maximizes the effective throughput. We also verify the performance of the i-TGC for various environments with different block sizes, block generation timeout, traffic patterns, and the number of clients. The i-TGC can be a way to accelerate the usage of the permissioned blockchain for latency-sensitive services.

When the wireless networks become large, it is hard to connect all base stations (BSs) to the core networks via the wired backhaul due to the increased infrastructure cost. Therefore, the integrated access and backhaul (IAB) networks, where the BS connects to the core networks via the wireless backhaul, has been emerged. In this paper, we consider a millimeter wave (mmWave) beamforming design and backhaul rate assignment in IAB networks. On top of this system model, we derive a closed form expression of the expected sum data rate. Moreover. we formulate the expected sum data rate maximization problem concerning the beamforming design and backhaul rate assignment. Since the formulated problem is a complex form and coupled with the optimization variables, we define a slack variable and divide the original problem into two subproblems. Then, we propose an iterative algorithm to obtain the optimal solution. Moreover, we provide a low-complex algorithm for large-scale networks by using a genetic algorithm (GA)-based approach. Finally, from numerical simulations, we show that the proposed solutions achieve a higher achievable sum data rate than the baseline schemes. We also explore the impacts of the bandwidth partitioning ratio, the biased factor ratio, the density of the user and the number of macro base station (MBS)’s transmit antennas on the achievable sum data rate.

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), in which one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC) and then DC transmits the processed data back to a group of ground users to fulfill their diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Sensor Association Information (AomaI) metric by incorporating the multi-source and up-downlink aspects. Under this framework, we formulate the optimization problem aiming to minimize the average AomaI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose parallel optimization and primal-dual methods to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, then develop the data processing and results distribution scheduling strategies for the DC, and lastly propose the task-associated genetic algorithm (TAGA) and the improved Nawas-Enscore-Ham (INEH) algorithm to design the UAV’s visiting order. Simulation results demonstrate that uplink and downlink AoI influence each other, and the consideration of up-downlink AoI can effectively enhance the freshness of AomaI.

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), where one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC), intending to fulfill the users’ diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Source Information (AomsI) metric. Under this framework, we formulate the optimization problem aiming to minimize the average AomsI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose the parallel optimization method to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, and then propose the task-associated genetic algorithm (TAGA) to design the UAV’s visiting order. Simulation results demonstrate that the UAV tends to prioritize the collection of all SN data required by the same user during data collection.

Increasing demands for Quality of Experience (QoE) lead to massive connectivity and intensive computation in future vehicular networks. This article proposes a device-to-device (D2D)-based mobile edge computing (MEC) network architecture to provide effective communication connections and sufficient computing abilities for vehicular networks. However, the available communication and computing resources are limited in the D2D-based vehicular MEC networks, and an imbalanced resource allocation always leads to suboptimal optimization of overall performances. To address this challenge, we formulate a Lyapunov optimization method-based resource allocation framework to balance communication and computing by compromising energy efficiency (EE) and time delay. However, the long-term resource allocation framework is ineffective when it ignores the dynamic characteristics of vehicular networks, i.e., channel state changes due to the movement of vehicles and a dynamic queue backlog with data fluctuations. Considering the time-varying channel state and dynamic queue backlog, the proposed framework aims to balance resource allocations while primarily maintaining network stability. Finally, we propose a Lyapunov optimization-based long-term dynamic resource allocation algorithm to develop real-time allocation strategies. Simulation results illustrate that the proposed algorithm balances communication and computing resources by tuning the control parameter V. Furthermore, the results confirm that the proposed algorithm outperforms baseline algorithms in real-time transmission and offloading ability.

Positioning and sensing have long been an important area of research. Recently, this field has attracted more attention due to the rapid deployment of emerging applications and next-generation communication networks. On the one hand, emerging applications like extended reality (XR) and autonomous vehicle systems need to precisely “see” the physical world, thus greatly increasing the demands on positioning and sensing technologies. Moreover, these applications also require data rate communication links, and thus technologies like cellular networks and WiFi are excellent for supporting these applications. On the other hand, with the evolution of wireless networks, positioning, and sensing have also been considered important functions of future wireless networks that can further enhance communication performance. Although existing wireless communication has achieved significant success in the past several decades, achieving satisfying positioning and sensing performance for these emerging applications remains a challenge due to the complexity of the wireless environment and the stringent performance requirements.

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles, and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both academic and standard perspectives. The paper provides a comprehensive overview of indoor positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies, and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises, and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of existing efforts and new trends in the field of indoor positioning from both academic and standardization perspectives would be a useful reference to researchers in the field.

This is Part II of the double-part Special Issue (SI) on Positioning and Sensing Over Wireless Networks. The two-part SI aims to bring cutting-edge and novel contributions on positioning and sensing over wireless networks for future and emerging applications. The accepted 51 papers are arranged into eight groups: 1) fundamental performance analysis and optimization; 2) positioning and sensing with cellular networks; 3) positioning and sensing with WiFi networks; 4) positioning and sensing with emerging communication technologies; 5) positioning and sensing applications; 6) cooperative positioning and sensing; 7) reconfigurable intelligent surfaces (RIS)-assisted positioning and sensing; and 8) privacy and security. The contributions made by the papers in Part II are summarized as follows, which correspond to the last four paper groups.

This paper considers the joint optimization of resource allocation and power control for rate-splitting multiple access (RSMA) based low earth orbits (LEO) satellite-terrestrial networks, where resource sharing between terrestrial and LEO satellite communications is optimized and the LEO satellite serves multiple ground stations (GSs) simultaneously through RSMA technique. Particularly, to make full use of RSMA technique, the LEO satellite needs to appropriately schedule transmitting power to common and private streams. Therefore, the key issue is to jointly optimize the resource allocation and power control to fully utilize the benefits of resource sharing and RSMA. However, the combination of continuous power control and discrete resource allocation becomes the bottleneck for providing an effective solution with limited system information. To deal with this problem, we propose a deep reinforcement learning (DRL)-based framework which jointly employs deep Q-network (DQN) algorithm for discrete resource allocation and proximal policy optimization (PPO) algorithm for continuous power control to maximize a joint objective. Simulation experiments evaluate the performance of the proposed scheme compared with several baseline schemes and the results show the advantages of the proposed scheme.

As advanced V2X applications emerge in the connected and autonomous vehicle (CAV), the data communications between in-vehicle end-devices and outside nodes increase, which make the end-to-end (E2E) security to in-vehicle end-devices as the urgent issue to be handled. However, the E2E security with fine-grained access control still remains as a challenging issue for resource-constrained end-devices since the existing security solutions require complicated key management and high resource consumption. Therefore, this paper proposes a practical and secure vehicular communication protocol for the E2E security based on a new attribute-based encryption (ABE) scheme. In the ABE scheme, the outsourced computation is provided for encryption, and the computation cost for decryption constantly remains small, regardless of the number of attributes. The policy privacy can be ensured by the proposed ABE to support privacy-sensitive V2X applications, and the existing identity-based signature for outsourced signing is newly reconstructed. The protocol achieves the confidentiality, message authentication, identity anonymity, unlinkability, traceability, and reconfigurable outsourced computation, and this paper also shows the practical feasibility of the protocol via the performance evaluation.

The existing multiple-input single-output (MISO) design to transmit data reliably to multiple mobile users (MUs) has become insufficient as MUs require fresh data, not just data. Therefore, in this paper, we consider multi-user MISO networks, where a base station (BS) equipped with multiple antennas serves MUs that want to maintain data freshness. To analyze the data freshness in this network, we first define the age-of-information (AoI) violation ratio, which is the ratio of duration that the AoI is larger than the AoI violation threshold to a sensing period. We then obtain the upper bound on the AoI violation ratio using the smooth maximum/minimum unit. We consider an optimization problem that minimizes the AoI violation ratio of multiple MUs. We propose a generalized power iteration (GPI) precoding algorithm to find a principal precoding vector that satisfies a first-order optimality condition of the optimization problem. Furthermore, for the scenario where the BS has the imperfect channel state information (CSI) of MUs, we provide the upper bound on the AoI violation time using the lower bound on the ergodic spectral efficiency, and also design the GPI precoding algorithm. Simulation results show proposed methods outperform baseline methods and demonstrate the effect of network parameters on the AoI violation probability.

As the demand on artificial intelligence (AI)-based applications increases, the freshness of sensed data becomes crucial in the wireless sensor networks. Since those applications require a large amount of computation for processing the sensed data, it is essential to offload the computation load to the edge computing (EC) server. In this paper, we propose the sensing and computing decision (SCD) algorithms for data freshness in the EC-enabled wireless sensor networks. We define the η-coverage probability to show the probability of maintaining fresh data for more than η ratio of the network, where the spatial-temporal correlation of information is considered. We then propose the probability-based SCD for the single pre-charged sensor case with providing the optimal point after deriving the η-coverage probability. We also propose the reinforcement learning (RL)-based SCD by training the SCD policy of sensors for both the single pre-charged and multiple energy harvesting (EH) sensor cases, to make a real-time decision based on its observation. Our simulation results verify the performance of the proposed algorithms under various environment settings, and show that the RL-based SCD algorithm achieves higher performance compared to baseline algorithms for both the single pre-charged sensor and multiple EH sensor cases.

Channel state information (CSI) is an essential aspect of the frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) system since there is no reciprocity between the bidirectional channels. However, the CSI transmission often requires significant channel resources because there may be hundreds of antennas transmitting and receiving data simultaneously. In this paper, we design an dissymmetric convolution neural network for CSI feedback (DiscoCSINet). Specifically, we utilize the dissymmetric convolution blocks (Disco-Blocks) to address the CSI compression and decompression issue, where convolution's feature extraction capability can be enhanced. To improve the storage efficiency of the receiver, we also employ a lightweight approach of the DiscoCSINet. Furthermore, we explore the fusion strategies of multi-rate and multi-scenario, respectively, and strengthen the generalization capability of the DiscoCSINet in practical settings. Experiment results indicate that the proposed DiscoCSINet can notably enhance the NMSE and cosine similarity$\rho$, especially in outdoor scenarios. Additionally, the proposed lightweight approach and multi-model fusion strategies can greatly decrease the parameter amounts by over 80% and 89%, respectively, but the performance are only slightly decayed.

In this paper, we propose a joint sensing and computing decision algorithm for data freshness in edge computing (EC)-enabled wireless sensor networks. By introducing the data freshness at the presented networks, we define the$\eta$-coverage probability to show the probability of maintaining fresh data for more than$\eta$ratio of the network, where the spatial-temporal correlation of information is considered. To maximize the$\eta$-coverage probability in the networks with limited energy, we propose the reinforcement learning (RL)-based decision algorithm by training the policy of sensors. Our simulation results verify the performance of the proposed algorithm for different number of sensors and the computing energy. From the results, we show the proposed algorithm achieves higher$\eta$-coverage probability compared to the baseline algorithms.

In this paper, we analyze the non-linear age of information (AoI) performance in a point-to-point short packet communication system, where a transmitter generates packets based on status updates and transmits the packets to a receiver. Specifically, we investigate three packet management strategies, namely, the non-preemption with no buffer strategy, the non-preemption with one buffer strategy, and the preemption strategy. To characterize the level of the receiver's dissatisfaction on outdated data, we adopt a generalized$\alpha-\beta$AoI penalty function into the analysis and derive closed-form expressions for the average AoI penalty achieved by the three packet management strategies. Simulation results are used to corroborate our analysis and explicitly evaluate the impact of various system parameters, such as the coding rate and status update generation rate, on the AoI performance. Additionally, we find that the value of$\alpha$reflects the system transmission reliability.

With the development of the Internet of Things, the sensor usage has been elevated to a new level, and it becomes more crucial to maintain reliable sensor networks. In this article, we provide how to efficiently and reliably manage the sensor monitoring system for ensuring data freshness. A sensor transmits its sensing information regularly to the data center (DC), and the sensed information generally has the correlation over the space and the time. Hence, the accuracy of the estimated information from the sensed information decreases as the target location for information estimation is further away from the sensor or the Age of Information (AoI), the elapsed time from the information generation, increases. Therefore, by considering the effect of the AoI and the distance from the sensor on the estimation error, we newly define an error-tolerable sensing (ETS) coverage as the area that the estimated information at the DC is with smaller error than the target value. To analyze the impact of the AoI on the ETS coverage, we derive the$\eta $-coverage probability, which is the probability that the ETS coverage is greater than$\eta $ratio of the maximum sensor coverage, and the average ETS coverage. We also provide the optimal transmission power of the sensor, which minimizes the average energy consumption while guaranteeing certain level of the$\eta $-coverage probability. Numerical results validate the theoretical analysis and show the tendency of the optimal transmission power according to the maximum number of retransmissions. This article can pave the way to efficient design of the AoI-sensitive sensor networks.

The boom in Internet of Things has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination and aroused widespread attention from both academia and industry. In this paper, we develop a theoretical framework to evaluate the statistics of AoI, including its average and violation probability, in wireless networks under different types of sources and updating patterns. The analyses account for the randomness that arises from both the spatial deployment and temporal queueing dynamics, and its accuracy is verified through simulations. Based on the analytical results, we design a locally adaptive power control policy to optimize the sum of average AoI of all nodes, which allows each node to assign transmit power according to its local observation. The proposed scheme has low implementation complexity. Numerical results show that the proposed power control policy can significantly improve information freshness. The scheme is well adapted to variants of network environment and heterogeneous source-destination distance. Further, we evaluate the effect of the retransmission mechanism and updating patterns on the AoI performance.

In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)’ service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs’ service cache size, and an EC servers’ computing capability on the SSP.

In this paper, we consider beamforming vector allocation and backhaul link capacity allocation in millimeter wave (mmWave) integrated access and backhaul (IAB) networks. We consider out-of-band backhaul and each base station (BS) has a Line-of-Sight (LoS) probability. We then derive a sum ergodic capacity using stochastic geometry. We also formulate the sum ergodic capacity maximization problem subject to the beamforming vector and backhaul link capacity allocations of the macro base station (MBS). Then, we show the optimization problem is a convex problem, and hence, we obtain a global optimal solution for our optimization problem using the Karush-Kuhn-Tucker (KKT) condition. Finally, from numerical simulation, we show that the proposed solutions achieve higher sum ergodic capacity than baseline schemes. We also explore the impacts of a backhaul-access bandwidth partitioning ratio and a density of small base stations (SBSs).

In recent years, blockchain technology has been frequently exploited to address new security requirements for unmanned aerial vehicle (UAV)-assisted data collection (U-DC). However, the new latency to commit data to the blockchain ledger has emerged as a new issue. In this paper, therefore, we analyze the timely update probability (TUP) of the blockchain ledger, which is the probability that collected data from a UAV is updated in the blockchain within a given target latency. For analysis, we first define the TUP of the blockchain ledger in U-DC networks, using both the UAV communication and blockchain latencies. We then derive a closed-form expression of the TUP, and then validate our analysis. We conclude by exploring the impacts of some UAV communication and blockchain parameters on the TUP for their optimal deployment.

The boom in Internet of Things (IoT) has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination node and aroused widespread attention from both academia and industry. In this paper, we develop a locally adaptive power control policy for wireless ad hoc networks, which adjusts each node’s transmit power according to its local observation so as to optimize the sum of average AoI of all destination nodes. The proposed scheme has a low implementation complexity. Numerical results show that the proposed scheme is well adapted to variants of network environment and can significantly improve information freshness.

The Age of Information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems, where timeliness is of importance. In this article, the problem of characterizing and controlling the AoI is studied in the context of blockchain-enabled monitoring networks (BeMNs). In BeMN, status updates from sources are transmitted and recorded in a blockchain. To investigate the statistical characteristics of the AoI in BeMN, the transmission latency and the consensus latency are first rigorously modeled. Then, the average AoI, the AoI violation probability, and the peak AoI violation probability are derived in a closed form so as to quantify the performance of BeMN. Furthermore, a simplified form is derived for the AoI violation probability, and it is shown that this quantity can capture the upper or lower bounds of the actual AoI violation probability. Simulation results show that each BeMN parameters (i.e., target successful transmission probability, block size, and timeout) can have conflicting effects on the AoI-related performance. Subsequently, design insights are provided to maintain the freshness of the status data in BeMN. Then, experimental results with a real Hyperledger Fabric platform further validate the accuracy of our modeling and analysis.

In-vehicle communications are not designed for message exchange between the vehicles and outside systems originally. Thus, the security design of message protection is insufficient. Moreover, the internal devices do not have enough resources to process the additional security operations. Nonetheless, due to the characteristic of the in-vehicle network in which messages are broadcast, secure message transmission to specific receivers must be ensured. With consideration of the facts aforementioned, this work addresses resource problems by offloading secure operations to high-performance devices, and uses attribute-based access control to ensure the confidentiality of messages from attackers and unauthorized users. In addition, we reconfigure existing access control based cryptography to address new vulnerabilities arising from the use of edge computing and attribute-based access control. Thus, this paper proposes an edge computing-based security protocol with fine-grained attribute-based encryption using a hash function, symmetric-based cryptography, and reconfigured cryptographic scheme. In addition, this work formally proves the reconfigured cryptographic scheme and security protocol, and evaluates the feasibility of the proposed security protocol in various aspects using the CANoe software.

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, inappropriate task offloading to roadside units (RSUs) can lead to large energy consumption, which will result in negative economic, environmental, and performance impacts. Therefore, in this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks. We first establish a novel cooperative offloading model to multiple RSUs for given batch of moving vehicles, different from existing works that consider single vehicle only or static users. Then, we consider the total energy minimization by optimizing the task splitting ratio, computation resource, and communication resource, which is a challenging non-convex problem, and provide optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we extend our proposed scheme to the one for a more realistic scenario (i.e., online scenario), where batches of vehicles sequentially approach the RSUs. Finally, through numerical results, the impact of network parameters on the total energy consumption is analyzed, and we verify that our proposed solution consumes lower energy than baseline schemes.

In both academia and industry, multi-user multiple-input single-output (MU-MISO) techniques have shown enormous gains in spectral efficiency by exploiting spatial degrees of freedom. So far, an underlying assumption in most of the existing MU-MISO design has been that all the users use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering delay-constrained users whose blocklength tends to be finite. In this paper, we consider a heterogeneous setting in MU-MISO systems where delay-constrained users and delay-tolerant users coexist, called a DCTU-MISO network. To maximize the sum spectral efficiency in this system, we present the spectral efficiency for delay-tolerant users and provide a lower bound of the spectral efficiency for delay-constrained users. We consider an optimization problem that maximizes the sum spectral efficiency of delay-tolerant users while satisfying the latency constraint of delay-constrained users, and propose a generalized power iteration (GPI) precoding algorithm that finds a principal precoding vector. Furthermore, we extend a DCTU-MISO network to the multiple time slots scenario and propose a recursive GPI precoding algorithm. In simulation results, we validate proposed methods outperform baseline schemes and present the effect of network parameters on the average sum spectral efficiency.

Age of information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems where timeliness is of importance. In this paper, we explore the data freshness in the Hyperledger Fabric Blockchain-enabled monitoring network (HeMN) by leveraging the AoI metric. In HeMN, status updates from sources are transmitted through an uplink and recorded in a Hyperledger Fabric (HLF) network. To provide a stochastic guarantee of data freshness, we derive a closed-form of AoI violation probability by considering the transmission latency and the consensus latency. Then, we validate our analytic results through the implemented HLF platform. We also investigate the effect of the target successful transmission probability (STP) on the AoI violation probability.

In both academia and industry, the multiple-input multiple-output (MIMO) techniques have shown enormous gains in spectral efficiency by exploiting the spatial degrees of freedom. So far, an underlying assumption in most of existing multi-user multiple-input multiple-output (MU-MIMO) design has been that all the users in the system use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering the presence of delay-constrained users whose the blocklength tends to be finite. In this paper, we consider a heterogeneous setting in a MU-MEMO system where delay tolerant users and delay constrained users coexist. To maximize the sum spectral efficiency in this system, we first present the spectral efficiencies for delay-tolerant user and delay-constrained user as the Rayleigh quotient. We then derive a first-order optimality condition of the optimization problem that maximizes the sum spectral efficiency of total users and satisfies the latency requirement of delay-constrained users, and propose the generalized power iteration preceding algorithm. In the simulation results, we prove that the proposed method outperforms baseline schemes.

The mobile edge computing (MEC) has been introduced for providing computing capabilities at the edge of networks to improve the latency performance of wireless networks. In this paper, we provide the novel framework for MEC-enabled heterogeneous networks (HetNets), composed of the multi-tier networks with access points (APs) (i.e., MEC servers), which have different transmission power and different computing capabilities. In this framework, we also consider multiple-type mobile users with different sizes of computation tasks, and they offload the tasks to a MEC server, and receive the computation resulting data from the server. We derive the successful edge computing probability (SECP), defined as the probability that a user offloads and finishes its computation task at the MEC server within the target latency. We provide a closed-form expression of the approximated SECP for general case, and closed-form expressions of the exact SECP for special cases. This paper then provides the design insights for the optimal configuration of MEC-enabled HetNets by analyzing the effects of network parameters and bias factors, used in MEC server association, on the SECP. Specifically, it shows how the optimal bias factors in terms of SECP can be changed according to the numbers of user types and tiers of MEC servers, and how they are different to the conventional ones that did not consider the computing capabilities and task sizes.

In this paper, we consider a large-scale edge computing (EC)-enabled network. We consider multiple latency-sensitive services. We adopt a random service caching scheme and a computation resource allocation scheme at base stations (BSs). We first derive the successful service probability (SSP). Using tools from stochastic geometry and queuing theory, we formulate the SSP maximization problem with respect to (w.r.t.) the service caching distribution and computation resource allocation, which is a challenging non-convex problem due to the complicated form of the SSP. Using parallel successive convex approximation (SCA), we develop an efficient iterative algorithm to obtain a stationary point of the non-convex problem. Finally, by numerical simulations, we show that the proposed solution achieves a higher SSP than the baseline schemes. We also show the impacts of the cache size and service rate of EC servers.

Different from a terrestrial network, novel un-manned aerial vehicle (UAV) networks are necessary to efficiently serve aerial users (AUs) as well as ground users (GUs). In this paper, we model a base station (BS) antenna power gain, which is determined by the BS antenna tilt angle and the horizontal distance between BS and user (GU or AU). In order to efficiently serve both GUs and AUs, we propose two BS service schemes, inclusive-service base station (IS-BS) and exclusive-service base station (ES-BS) schemes. For each BS service scheme, we derive a network outage probability by taking into account the characteristics of the BS antenna power gain and channel components for line-of-sight (LoS) and non-line-of-sight (NLoS) environments. Finally, we show that the effect of the total BS density, the interfering BS density, and the user density on the optimal BS tilt angle and our proposed scheme.

In this paper, we propose a novel coordinated multipoint (CoMP) downlink transmission strategy for the ultra-dense network (UDN) environment. In this proposed CoMP transmission scheme, we exploit the average received link power (ARLP) of the base stations (BSs) with respect to the ARLP of the strongest BS to a typical user, to dynamically adjust the number of cooperating BSs serving that user in the network. This allows us to effectively manage the inter-cell interference in the UDN regime. Our numerical results and simulations show that the proposed scheme can out-perform the existing fixed number of BS based CoMP transmission scheme in the high BS density scenario.

Due to the fact that the higher density of mobile devices is expected, the fifth generation (5G) mobile networks introduce small cell networks (SCNs) to prevent exhausting radio resources. SCNs improve radio spectrum utilization by deploying more base stations (BSs) in the networks. Even though authenticated key exchange (AKE) is still essential to ensure entity authentication and confidentiality in mobile communications. Besides, user anonymity is required to guarantee the footprints of mobile communications being concealed. However, AKE with user anonymity may increase the latency of communications dramatically in total due to several times more frequency in SCNs. The increase of latency will be more serious when an AKE protocol supports user anonymity, where traceability and revocability to users are necessary. Thus, this paper presents a secure region-based handover scheme (ReHand) with user anonymity and fast revocation for SCNs. ReHand greatly reduces the communication latency when user equipments (UEs) roam between small cells within the region of a macro BS, i.e., eNB, and the computation costs due to the employment of symmetry-based cryptographic operations. Compared to the three related prior arts, ReHand dramatically reduces the latency from 82.92% to 99.99% by region-based secure handover. Nevertheless, this paper demonstrates the security of ReHand by theoretically formal proofs.

In this paper, we establish a foundation for the multi-layer aerial networks (MANs), which are modeled as K layer aerial networks (ANs), where each layer has unmanned aerial vehicles (UAVs) with different densities, floating altitudes, and transmission power. To make the framework applicable for various scenarios in MAN, we consider the transmitter- and the receiver-oriented node association rules as well as the air-to-ground and air-to-air channel models, which form line of sight links with a location-dependent probability. We then newly analyze the association probability, the main link distance distribution, successful transmission probability (STP), and area spectral efficiency (ASE) of MAN. The upper bounds of the optimal densities that maximize STP and ASE are also provided. Finally, in the numerical results, we show the optimal UAV densities of each AN that maximize the ASE and the STP decrease with the altitude of the network. We also show that when the total UAV density is fixed for two layer AN, the use of single layer in higher(lower) altitude only for all UAVs can achieve better performance for low(high) total density case. Otherwise, distributing UAVs in two layers, i.e., MAN, achieves better performance.

Wireless jamming is a critical challenge for sprawling and ubiquitous devices, which are connected using radio-frequency (RF) waves. Traditionally direct-sequence spread spectrum (DSSS) and its derivatives have been recognized as an effective jamming resilient technique. However, the effectiveness of DSSS relies on the use of either pre-shared secret code or a large bank of public codes, the generation, distribution, and management of which would be particularly difficult in future large-scale decentralized wireless networks. To tackle this problem, we present a framework which exploits the shared randomness inherent in wireless channels to generate and refresh secret seeds at each communicating node. We highlight why channel randomness cannot be used as it is and develop processing algorithms which ensure that RF based sources are suitable for entropy pooling and random seed generation.

In this paper, we propose a secret group-key generation scheme in physical layer, where an arbitrary number of multi-antenna LNs (LN) exist in mesh topology with a multi-antenna passive eavesdropper. In the first phase of the scheme, pilot signals are transmitted from selected antennas of all nodes and each node estimates channels linked to it. In the second phase, each node sequentially broadcasts a weighted combination of the estimated channel information using selected coefficients. The other LNs can obtain the channel information used for group-key generation while the eavesdropper cannot. Each node then can generate a group key by quantizing and encoding the estimated channels into keys. We apply well-known quantization schemes, such as scalar and vector quantizations, and compare their performance. To further enhance the key-generation performance, we also provide how to determine the antennas at each node used for group-key generation and the coefficients used in the broadcast phase. The simulation results verify the performance of the proposed secret group-key generation scheme using various key-related metrics. We also verify the practical robustness of our scheme by implementing a testbed using universal software radio peripheral. After generating secret common key among three nodes, we also test it using the National Institute of Standards and Technology test suit. The generated key passes the test and it is random enough for communication secrecy.

Unmanned aerial vehicle (UAV) wireless communication has recently been recognized to be inevitable and prevalent in the fifth-generation (5G) wireless networks. In this paper, we propose a secure transmission scheme for a wiretap channel, where a source communicates with a legitimate UAV in the presence of an eavesdropper. We consider the full-duplex active eavesdropper, which performs both eavesdropping and malicious jamming simultaneously. The source transmits artificial noise (AN) signals, in addition to information signals, to confuse this eavesdropper. By considering the ground-to-UAV channel model, we analyze the hybrid outage probability, which takes both the transmission outage probability and the secrecy outage probability into consideration. We further provide the asymptotic hybrid outage probability in a more compact form, where both the transmit power at the source and the jamming power at the eavesdropper become large with a fixed ratio. Through the analysis and the numerical results, we determine the optimal power allocation factor between information signals and AN signals as well as the operating height of UAV that minimize the hybrid outage probability. We also provide the most harmful antenna configuration of the eavesdropper to the UAV communications, and this paper can be a useful framework for the design of confidential UAV communication system.

Unlike terrestrial communications, unmanned aerial vehicle (UAV) communications have some advantages such as line-of-sight (LoS) environment and flexible mobility. However, the interference will be still inevitable. In this paper, we analyze the effect of the interference on the UAV communications by considering the LoS probability and different channel fadings for LoS and non-line-of-sight (NLoS) links, which are affected by the elevation angle of the communication link. We then derive a closed-form outage probability in the presence of an interfering node for all the possible scenarios and environments of main and interference links. After discussing the impacts of transmitting and interfering node parameters on the outage probability, we show the existence of the optimal height of the UAV that minimizes the outage probability. We also show the NLoS environment can be better than the LoS environment if the average received power of the interference is more dominant than that of the transmitting signal in UAV communications.

This paper considers the problem of secure communication over a 2-user Gaussian interference channel (GIC) with shared key of finite rate between the transmitter-receiver pair with strong secrecy constraint at the receiver. The main contributions of the paper lies in obtaining a novel achievable scheme which uses a combination of one-time pad, stochastic encoding and superposition based coding scheme, and outer bound on the secrecy capacity region of the 2-user GIC. The main novelty of the derivation of the outer bound lies in the selection of the side-information to be provided to the receiver and using the secrecy constraints at the receiver. The results highlight the role of secret key in the encoding of messages to enhance the system performance in interference limited scenarios.

Radio frequency identification (RFID) has been considered one of the imperative requirements for implementation of Internet-of-Things applications. It helps to solve the identification issues of the things in a cost-effective manner, but RFID systems often suffer from various security and privacy issues. To solve those issues for RFID systems, many schemes have been recently proposed by using the cryptographic primitive, called physically uncloneable functions (PUFs), which can ensure a tamper-evident feature. However, to the best of our knowledge, none of them has succeeded to address the problem of privacy preservation with the resistance of DoS attacks in a practical way. For instance, existing schemes need to rely on exhaustive search operations to identify a tag, and also suffer from several security and privacy related issues. Furthermore, a tag needs to store some security credentials (e.g., secret shared keys), which may cause several issues such as loss of forward and backward secrecy and large storage costs. Therefore, in this paper, we first propose a lightweight privacy-preserving authentication protocol for the RFID system by considering the ideal PUF environment. Subsequently, we introduce an enhanced protocol which can support the noisy PUF environment. It is argued that both of our protocols can overcome the limitations of existing schemes, and further ensure more security properties. By analyzing the performance, we have shown that the proposed solutions are secure, efficient, practical, and effective for the resource-constraint RFID tag.

Device-to-device (D2D) communication is mainly launched by the transmission requirements between devices for specific applications such as proximity services in long-term evolution advanced networks, and each application will form a group of registered devices for the network-covered and network-absent D2D communications. During the applications of D2D communication, each device needs to identify the other devices of the same group in proximity by their group identity. This leads to the exposure of group information, by which the usage of applications can be analyzed by eavesdroppers. Hence, this paper introduces network-covered and network-absent authenticated key exchange protocols for D2D communications to guarantee accountable group anonymity, end-to-end security to network operators, as well as traceability and revocability for accounting and management requirements. We formally prove the security of those protocols, and also develop an analytic model to evaluate the quality of authentication protocols by authentication success rate in D2D communications. Besides, we implement the proposed protocols on android mobile devices to evaluate the computation costs of the protocols. We also evaluate the authentication success rate by the proposed analytic model and prove the correctness of the analytic model via simulation. Those evaluations show that the proposed protocols are feasible to the performance requirements of D2D communications.

In this paper, we study the stability region of the two-user broadcast channel (BC) with bursty data arrivals and security constraints. It is assumed that one of the receivers has a secrecy constraint, i.e., its packets need to be kept secret from the other receiver, which is defined based on signal to interference noise ratio. The receiver with secrecy constraint has full-duplex capability to send a jamming signal for improving its service rate. The stability region of the two-user BC with secrecy constraint is characterized for the general decoding case. Then, assuming two different decoding schemes, the respective stability regions are derived. The full-duplex operation of receiver results in self-interference, and the effect of imperfect self-interference cancelation on the stability region is also investigated. The stability region of the BC with a secrecy constraint, where the receivers do not have full duplex capability can be obtained as a special case of the results derived in this paper. In addition, the paper considers the problem of maximizing the saturated throughput of the queue for which there is no secrecy constraint under minimum service guarantees for the other queue. The results provide new insights on the effect of the secrecy constraint on the stability region of the BC. It is found that the stability region with secrecy constraint is sensitive to the degree of self-interference cancelation.

We propose a secure transmission scheme for a wiretap channel, where a source transmits to a legitimate destination in the presence of a full- duplex eavesdropper. We assume that the full- duplex eavesdropper performs both eavesdropping and malicious jamming simultaneously. In order to combat the full-duplex active eavesdropper, the source transmits artificial noise (AN) signals, apart from information signals, to confuse the eavesdropper. We adopt the hybrid outage probability as the performance metric, which takes both the transmission outage probability and the secrecy outage probability into consideration. We derive a compact expression for the hybrid outage probability. Utilizing the derived expression, we determine the optimal power allocation factor between information signals and AN signals that minimizes the hybrid outage probability. We also examine the asymptotic hybrid outage probability in the high signal-to-noise ratio regime. Furthermore, we compare the secrecy performance achieved by a full-duplex active eavesdropper with that achieved by a passive eavesdropper, showing that active eavesdropping can be more harmful to the legitimate system.

We develop a framework for downlink heteroge- neous cellular networks with line-of-sight (LoS) and non-line- of- sight (NLoS) transmissions. Using stochastic geometry, we derive a tight approximation of the achievable downlink rate that enables us to compare the performance between densifying small cells and expanding base station (BS) antenna arrays. Interestingly, we find that adding small cells into a sparse network improves the achievable rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spacial densification will lose its benefits and further impair the network capacity. To this end, we present the optimal small cell density that maximizes the rate as practical deployment guidance. In contrast, expanding macro BS antenna array can always increase the capacity until reaching an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from deployment of small cells. Therefore, small cells are preferred for low rate requirements due to the rapid rate gain, and the massive MIMO is preferred for higher rate requirements due to better achievable rate.

In smart grid, the local data aggregator of an area collects the total electrical consumption of the area by aggregating the measurements of smart meters (SMs). A great number of schemes based on the cryptography have been proposed to guarantee the privacy of the individual measurements of the SMs. However, the cryptographic systems require the complicated computations and key-management infrastructural support as well as introduce information leaking risks. In this paper, we propose a novel physical channel-based scheme for privacy preservation in data aggregation, which can work without a cryptographic system. The SMs preserve the privacy of the measurements by adding the jamming signals, which are constructed by scaled channels. The jamming signals are designed to be cancelled with each other at the aggregator. Concurrently, our scheme can resist different types of attacks such as eavesdropping, compromising, and differential attacks even in the case that they are colluding. The simulation results show that the mean squared error (MSE) of the total measurements is significantly lower than that of the traditional scheme while the privacy of the individual measurements is high.

In this paper, the role of unidirectional limited rate transmitter cooperation is studied for the two-user symmetric Z interference channel (Z-IC) with secrecy constraints at the receivers, in achieving two conflicting goals simultaneously: mitigating interference and ensuring secrecy. First, the problem is studied under the linear deterministic model. A novel scheme for partitioning the encoded messages and outputs based on the relative strengths of the signal and interference is proposed. The partitioning reveals the side information that needs to be provided to the receiver and facilitates the development of tight outer bounds on the secrecy capacity region. The achievable schemes for the deterministic model use a fusion of cooperative precoding and transmission of a jamming signal. The optimality of the proposed scheme is established for the deterministic model for all possible parameter settings. The insights obtained from the deterministic model are used to derive inner and outer bounds on the secrecy capacity region of the two-user Gaussian symmetric Z-IC. The achievable scheme for the Gaussian model uses stochastic encoding in addition to cooperative precoding and transmission of a jamming signal. For the Gaussian case, the secure sum generalized degrees of freedom (GDOF) is characterized and shown to be optimal for the weak/moderate interference regime. It is also shown that the secure sum capacity lies within 2 bits/s/Hz of the outer bound for the weak/moderate interference regime for all values of the capacity of the cooperative link. Interestingly, in the deterministic model, it is found that there is no penalty on the capacity region of the Z-IC due to the secrecy constraints at the receivers in the weak/moderate interference regimes. Similarly, it is found that there is no loss in the secure sum GDOF for the Gaussian case due to the secrecy constraint at the receiver, in the weak/moderate interference regimes. The results highlight the importance of cooperation in facilitating secure communication over the Z-IC.

Wireless content caching in small cell networks (SCNs) has recently been considered as an efficient way to reduce the data traffic and the energy consumption of the backhaul in emerging heterogeneous cellular networks. In this paper, we consider a cluster-centric SCN with combined design of cooperative caching and transmission policy. Small base stations (SBSs) are grouped into disjoint clusters, in which in-cluster cache space is utilized as an entity. We propose a combined caching scheme, where part of the cache space in each cluster is reserved for caching the most popular content in every SBS, while the remaining is used for cooperatively caching different partitions of the less popular content in different SBSs, as a means to increase local content diversity. Depending on the availability and placement of the requested content, coordinated multi-point technique with either joint transmission or parallel transmission is used to deliver content to the served user. Using Poisson point process for the SBS location distribution and a hexagonal grid model for the clusters, we provide analytical results on the successful content delivery probability of both transmission schemes for a user located at the cluster center. Our analysis shows an inherent tradeoff between transmission diversity and content diversity in our cooperation design. We also study the optimal cache space assignment for two objective functions: maximization of the cache service performance and the energy efficiency. Simulation results show that the proposed scheme achieves performance gain by leveraging cache-level and signal-level cooperation and adapting to the network environment and user quality-of-service requirements.

We develop a framework for downlink heterogeneous cellular networks with line-of-sight (LoS) and non-LoS transmissions. Using stochastic geometry, we derive tight approximation of average downlink rate that enables us to compare the performance between densifying small cells and expanding base station (BS) antenna arrays. Interestingly, we find that adding small cells into the network improves the downlink rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spatial densification will lose its benefits and further impair the network capacity. To this end, we provide the optimal small cell density that maximizes the rate via numerical results for practical deployment guidance. In contrast, expanding macro BS antenna array can always benefit the capacity until an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from the deployment of small cells. Furthermore, we find that allocating part of antennas to distributed small cell BSs works better than centralizing all antennas at the macro BS, and the optimal allocation proportion is also given numerically for practical configuration reference. In summary, this paper provides a further understanding on how to leverage small cells and massive MIMO in future heterogeneous cellular networks deployment.

For a user cooperation system with multiple antennas, we consider trust degree-based cooperation techniques to explore the influence of the trustworthiness between users on the communication systems. For the system with two communication pairs, when one communication pair achieves its quality of service (QoS) requirement, they can help the transmission of the other communication pair according to the trust degree, which quantifies the trustworthiness between users in the cooperation. For given trust degree, we investigate the user cooperation strategies, which include the power allocation and precoder design for various antenna configurations. For single-input-single-output and multiple-input-single-output (MISO) cases, we provide the optimal power allocation and beamformer design that maximize the expected achievable rates while guaranteeing the QoS requirement. For a single-input-multiple-output (SIMO) case, we resort to semidefinite relaxation technique and block coordinate update method to solve the corresponding problem, and guarantee the rank-one solutions at each step. For a multiple-input-multiple-output (MIMO) case, as MIMO is the generalization of MISO and SIMO, the similarities among their problem structures inspire us to combine the methods from MISO and SIMO together to efficiently tackle MIMO case. Simulation results show that the trust degree information has a great effect on the performance of the user cooperation in terms of the expected achievable rate, and the proposed user cooperation strategies achieve high achievable rates for give trust degree.

This article proposes a secure and energy efficient user authentication protocol, which can preserve the user anonymity for roaming service in the mobile network. Compared to other state of the art solutions, the proposed scheme has several considerable advantages. Firstly, no encryption/ decryption, modular and exponential operations have been introduced in our design. Instead, it uses the low cost function such as HMAC and exclusive-OR operations to accomplish the goals of authentication and key agreement. This makes the protocol more suitable for battery-powered mobile devices. Secondly, the proposed scheme can resolve several existing security issues like forgery attack, known session key attack, etc., with the limited computation and communication overheads which are indeed essential for offering a secure and expeditious roaming services in mobile communication environment.

The trustworthiness of nodes in a network is practically a complicated concept and strongly depends on the social relationship among the nodes. For a certain model of trustworthiness, the scheme can be optimized to achieve the highest average secrecy rate based on pre-defined information about possibly malicious nodes. In such a scheme, the secrecy rate is considered as a function of the maximum achievable rate (MAR) at the destination, the MAR at the malicious nodes and the corresponding probabilities. However, in some cases, the trustworthiness model is not available, for example, an engineer does not know if a certain node is maliciously eavesdropping, such a function cannot be derived. Consequently, we need to consider the performance of a scheme in a wide range of trustworthiness levels. To evaluate the performance for such a case, in this paper, we propose a novel concept for comparing these leaking rates which is called leaking rate region. We derive the closed form of leaking rate region for the case of two untrusted relays with multiple antennas at the source and the destination.

In this paper, we propose an adaptive cluster-centric small cell network with cooperative caching and transmission design, in which small base stations (SBSs) are grouped into disjoint clusters and in-cluster cache space is utilized as an entity. We design a combined caching scheme where part of the available cache space is reserved for caching the most popular content in every SBS, while the remaining is used to increase the content diversity. Depending on the availability of the requested content, either joint transmission (JT) or parallel transmission (PT) is used to deliver the content to the served user. We provide analytical results on the successful content delivery probability of both schemes for a user located at the cluster center. The optimal cache utilization strategy to maximize the cache service probability is determined as a function of network parameters, revealing an interesting tradeoff between transmission diversity and content diversity. Simulation results show that our proposed cooperative design finely combines the benefits of cache-level and signal-level cooperation schemes.

Honeypots have been considered as one of the methods to ensure security for networks in the Internet of Things (IoT) realm. In this paper, we study the problem of defending against attacks in honeypot-enabled networks by looking at a game-theoretic model of deception involving an attacker and a defender. The attacker may try to deceive the defender by employing different types of attacks ranging from a suspicious to a seemingly normal activity, while the defender in turn can make use of honeypots as a tool of deception to trap attackers. The problem is modeled as a Bayesian game of incomplete information, where equilibria are identified for both the one-shot game and the repeated game versions. Our results showed that there is a threshold for the frequency of active attackers, above which both players will take deceptive actions and below which the defender can mix up his/her strategy while keeping the attackers success rate low.

In this work, the stability region of the broadcast channel (BC) is explored, where the receiver, for which the packets need to be kept secret from the other receiver has full-duplex (FD) capability. The effect of self-interference on the stability region has also been investigated. In particular, the stability region is characterized under two types of decoding schemes: (a) both the receivers perform treating interference as noise and (b) receiver with full-duplex capability performs successive decoding and other receiver treats interference as noise. As a by product of the analysis, the stability region of the BC with secrecy constraint where the receivers do not have FD capability can be obtained as a special case of the results derived in this paper. It is found that if the self-interference cancelation at the full-duplex receiver is not efficient, then the gain obtained in secure throughput due to transmission of the jamming signal by the FD receiver can diminish. In particular, it is found that the stability region with secrecy constraint is sensitive to the coefficient of self-interference cancelation under certain cases.

In modern days, breakthroughs in information and communications technologies lead to more and more devices of every imaginable type being connected to the Internet. This also strengthens the need for protection against cyber-attacks, as virtually any devices with a wireless connection could be vulnerable to malicious hacking attempts. Meanwhile, honeypot-based deception mechanism has been considered as one of the methods to ensure security for modern networks in the Internet of Things (IoT). In this paper, we address the problem of defending against attacks in honeypot-enabled networks by looking at a game-theoretic model of deception involving an attacker and a defender. The attacker may try to deceive the defender by employing different types of attacks ranging from a suspicious to a seemingly normal activity, while the defender in turn can make use of honeypots as a tool of deception to trap attackers. The problem is modeled as a Bayesian game of incomplete information, where equilibria are identified for both the one-shot game and the repeated game versions. Our results show that there is a threshold for the frequency of active attackers, above which both players will take deceptive actions and below which the defender can mix up his/her strategy while keeping the attacker's success rate low.

Recently, based on the homomorphic signatures, the authentication schemes, such as homomorphic subspace signature (HSS) and key predistribution-based tag encoding (KEPTE), have been proposed to resist against pollution attacks in network coding. In this paper, we show that there exists an efficient multi-generation pollution attack on HSS and KEPTE. In particular, we show that using packets and their signatures of different generations, the adversary can create invalid packets and their corresponding signatures that pass the verification of HSS and KEPTE at intermediate the nodes as well as at the destination nodes. After giving a more generic attack, we analyze the cause of the proposed attack. We then propose the improved key distribution schemes for HSS and KEPTE, respectively. Next, we show that the proposed key distribution schemes can combat against the proposed multi-generation pollution attacks. Finally, we analyze the computation and communication costs of the proposed key distribution schemes for HSS and KEPTE, and by implementing experiments, we demonstrate that the proposed schemes add acceptable burden on the system.

For a confidential communication, we develop the transmission strategies by taking into account trust degree among users, which shows how much a user can be trusted in terms of an eavesdropping attack. For a single potential eavesdropper (PE) case, we derive an optimal power allocation for confidential message and jamming signal to maximize an expected secrecy rate with respect to the trust degree. In order to further increase the expected secrecy rate, by exploiting relaying transmission via PE, we derive the optimal transmission strategy and corresponding power allocation based on the threshold of trust degree. Furthermore, to investigate the effect of the accuracy of trust degree, we analyze the expected secrecy rate loss caused by an estimation error of the trust degree and provide the robust power allocation based on imperfect trust degree. Finally, we extend the results to the case of confidential communication in the presence of multiple PEs. Our analytical and numerical results show that the proposed strategies achieve substantially higher secrecy rates than that without exploiting the trust degree of users.

In this paper, we propose a physical-layer secret key generation scheme for multiantenna legitimate nodes with the help of multiple untrusted relays, equipped with multiple antennas. The untrusted relays conform to the relaying transmission protocol of legitimate nodes, but they also eavesdrop the confidential information of an legitimate transmitter. The key generation scheme is designed with zero forcing (ZF) and minimum mean square error (MMSE) channel estimators for non-, partially, and fully colluding modes of untrusted relays. Furthermore, we propose a scheme adaptive to channel coherence time. Specifically, to achieve a higher secret key rate (SKR) within the channel coherence time, the number of relay and legitimate nodes' antennas are optimally determined and the most suitable antennas are selected for the key generation. Our results show that the proposed scheme achieves a higher SKR than a prior work, and non- and partially colluding modes provide a higher SKR than the fully colluding mode through the proposed scheme. We also verify that exploiting more antennas of untrusted relays does not always enhance the SKR by showing the existence of the optimal number of antennas of the relays participating in the scheme.

The concept of mobile user equipment (UE) relay (UER) has been introduced to support device-to-device (D2D) communications for enhancing communication reliability. However, as the UER needs to use its own power for other UE's data transmission, relaying information in D2D communication may be undesirable for the UER. To overcome this issue, motivated by the recent advances in energy harvesting (EH) techniques, we propose a D2D communication provided EH heterogeneous cellular network (D2D-EHHN), where UERs harvest energy from an access point (AP) and use the harvested energy for D2D communication. We develop a framework for the design and analysis of D2D-EHHN by introducing the EH region (EHR) and modeling the status of harvested energy using Markov chain. The UER distribution is derived, and a transmission mode selection scheme including the efficient UER selection method is proposed. The network outage probability is derived in close form to measure the performance of D2D-EHHN. Based on our analysis results, we explore the effects of network parameters on the outage probability and the optimal offloading bias in terms of the outage probability. Particularly, we show that having a high EH efficiency enhances the performance of D2D-EHHN, but can also degrade, especially for dense network.

This work develops a tractable model to analyze the performance of downlink heterogeneous cellular networks (HCNs) with both power-grid-connected base stations (PG-BSs) and energy harvesting small cell access points (EH-SAPs). Each EH-SAP forms a personal cell that is active (and is available to serve others) only when its own priority user requests service and its battery contains sufficient energy to transmit. By modeling the battery dynamics of an EH-SAP as a discrete-time Markov chain and by considering a practical power consumption model, the rate coverage, network throughput, and energy efficiency are derived as functions of the PG-BS and EH-SAP densities, transmission powers, cell association biases, and energy harvesting capabilities. Cell association biases control the traffic load among different tiers and transmission powers affect EH-SAPs' probability of being active. Exact expressions of the performance metrics are first derived for the general multitier scenario and are then used to obtain approximate closed-form expressions for two-tier networks using the mean load approximation. The analytic results of the two-tier network provide valuable insights on the achievable performance and the choice of system parameters, and are further validated with numerical results.

For communication secrecy, we develop the cooperative transmission strategies by taking into account the degree of trust between users as well as physical wireless channels. For a given trust degree information, we derive an optimal power allocation for confidential message and jamming signal to maximize an expected secrecy rate with respect to the trust degree information. In order to further increase the expected secrecy rate, by adopting cooperative transmission via user relaying, we derive the optimal transmission strategy and corresponding power allocation based on the trust degree information. Our analytical and numerical results show that the proposed strategies achieve substantially higher secrecy rates than that without exploiting the trust degree information of users.

Secret group key generation based on physical layer in wireless communications has a lot of practical applications. A few studies focused on the theoretical bounds of the secret key rate based on the sets of received signals rather than a particular secret key generation scheme. We propose a secret group key generation scheme for an arbitrary number of legitimate nodes, n, in mesh topology in the presence of a passive eavesdropper. In the scheme, after general pilot signal transmissions at all nodes, each node broadcasts a weighted combination of its received signals with optimized coefficients, so that legitimate nodes can obtain the information of channels used for group key generation while the eavesdropper cannot. We also apply different quantization schemes for quantizing and encoding the estimated channels into keys. To provide detailed transmissions and processing steps of group key generation, we also describe the proposed scheme for 4-node mesh topology case. The simulation results show that the proposed scheme achieves a higher secret group key rate and a lower key disagreement rate than a benchmark scheme.

We investigate the degrees of freedom (DoF) and secrecy DoF for a general framework of multiway relay networks named wireless MIMO switching, where a number of users exchange information via a common relay. Each round of data exchange consists of one uplink transmission from users to relay and one downlink transmission from relay back to users. The data exchange model is unicast, i.e., every user transmits one message and intends to receive one message from one other user. We categorize unicast patterns using the notion of orbit borrowed from abstract algebra. Roughly speaking, an orbit is a minimum subset of users such that data exchange is closed within this subset. We analyze the achievable DoF of wireless MIMO switching with various numbers of orbits. Particularly, the DoF capacity for unicast with one and two orbits are established. Furthermore, we study communication secrecy with an untrusted relay in wireless MIMO switching. We present an achievable secrecy sum rate and the corresponding achievable secrecy DoF by assuming a non-regenerative relay. Then, we focus on unicast patterns with one and two orbits, and show that this achievable lower bound is actually the secrecy DoF capacity based on a novel genie-aided technique. Our results build a bridge between the DoF and the secrecy DoF in multiway relaying. The methodology of the proof can be generally applied to analyze the secrecy DoF in other relay networks.

In this paper, we develop an analytical framework to evaluate the network throughput and traffic offloading efficiency in heterogeneous cellular networks (HCNs) with multiple energy-harvesting personal cells. The network consists of a tier of power-grid connected base-stations (BSs) and multiple tiers of energy-harvesting (personal) small cell access points (EH-SAPs). Each EH-SAP is personally owned by a priority user and becomes active only when its priority user requests communication and when its residual battery energy is sufficient to support the transmission. The energy arrival at each EH-SAP is modeled as a Bernoulli random process and the battery energy dynamics are modeled as a discrete time Markov chain. The network throughput is derived and its dependence on the energy arrival probability, cell association bias, and transmission power are examined based on the analysis. We show that, when the transmission power of EH-SAPs is sufficiently small, network throughput can be increased by increasing the density of EH-SAPs and by offloading more traffic to them. Our results offer insights on the design of spectral-efficient HCNs with EH-SAPs.

Cognitive radio networks using relays have been introduced to enhance secondary network performance by having secondary users (SUs) transmit in two hops. This paper develops a framework for a random cognitive relay network (RCRN) where the primary, secondary, and relay nodes are randomly distributed according to Poisson point processes (PPPs) to investigate the effect of the random nature of the relays on secondary outage performance. We first derive the optimal relay location in terms of the outage probability of the secondary network. In addition, we provide the outage probability of the RCRN based on the optimal relay selection strategy that compares the instantaneous channel state information (CSI) of all relays. We then propose an efficient relay selection strategy that reduces the selection complexity and the feedback burden by limiting candidate relays to those located close to the optimal relay location. This paper sheds new light on the design of an efficient RCRN with respect to elements such as the effects of network parameters and the candidate relay set size on the RCRN outage probability.

Full-duplex (FD) radio has been introduced for bidirectional communications on the same temporal and spectral resources so as to maximize spectral efficiency. In this paper, motivated by the recent advances in FD radios, we provide a foundation for HDHNs, composed of multi-tier networks with a mixture of APs, operating either in bidirectional FD mode or downlink HD mode. Specifically, we characterize the network interference from FD-mode cells, and derive the HDHN throughput by accounting for AP spatial density, self-IC capability, and transmission power of APs and users. By quantifying the HDHN throughput, we present the effect of network parameters and the self-interference cancellation (IC) capability on the HDHN throughput, and show the superiority of FD mode for larger AP densities (i.e., larger network interference and shorter communication distance) or higher self-IC capability. Furthermore, our results show operating all APs in FD or HD achieves higher throughput compared to the mixture of two mode APs in each tier network, and introducing hybrid-duplex for different tier networks improves the heterogenous network throughput.

In this paper, we investigate the artificial noise-aided jamming design for a transmitter equipped with large antenna array in Rician fading channels. We figure out that when the number of transmit antennas tends to infinity, whether the secrecy outage happens in a Rician channel depends on the geometric locations of eavesdroppers. In this light, we first define and analytically describe the secrecy outage region (SOR), indicating all possible locations of an eavesdropper that can cause secrecy outage. After that, the secrecy outage probability (SOP) is derived, and a jamming-beneficial range, i.e., the distance range of eavesdroppers which enables uniform jamming to reduce the SOP, is determined. Then, the optimal power allocation between messages and artificial noise is investigated for different scenarios. Furthermore, to use the jamming power more efficiently and further reduce the SOP, we propose directional jamming that generates jamming signals at selected beams (mapped to physical angles) only, and power allocation algorithms are proposed for the cases with and without the information of the suspicious area, i.e., possible locations of eavesdroppers. We further extend the discussions to multiuser and multi-cell scenarios. At last, numerical results validate our conclusions and show the effectiveness of our proposed jamming power allocation schemes.

Full-duplex (FD) radio has been introduced for bidirectional communications on the same temporal and spectral resources so as to maximize spectral efficiency. In this paper, motivated by the recent advances in FD radios, we provide a foundation for downlink hybrid-duplex heterogeneous networks (HDHNs), composed of multi-tier networks with a mixture of access points (APs) operating either in FD mode or half-duplex (HD) mode. Specifically, we characterize the network interference from FD-mode cells, and derive the HDHN throughput by accounting for AP spatial density, self-interference cancellation (IC) capability, and transmission power of APs and users. By quantifying the HDHN throughput, we present the effect of network parameters and the self-IC capability on the HDHN throughput, which shows the superiority of FD mode for larger AP densities (i.e., larger network interference and shorter communication distance) or higher self-IC capability.

Wireless mobile social networks have been introduced to consider characteristics of mobile user's behaviors for network configuration. The device-to-device (D2D) communication is to increase the network spectral efficiency by allowing users to communicate directly with the corresponding destinations. This paper develops a framework for the design of reliable D2D communication provided wireless mobile social network (D2D-MSN), and introduces a D2D- MSN throughput as a new measurement of the spectral efficiency achieved by users communicating successfully. Our framework accounts for spatial user distributions and the link distance-based mode selection scheme that allows the D2D mode only for the users with shorter link distance than a threshold. We also account for the communication distance distribution defined in a social context to decrease the communication probability density according to the link distance with a power exponent. This research offers insights on the optimal configuration of the D2D-MSN, and also provide a new perspective on the effect of the link distance distribution on the D2D communication performance.