Computation offloading has been extensively studied in recent years for the internet of vehicles (IoV), where roadside units (RSUs) are deployed to assist computation offloading. However, it is still challenging to decide when to offload regarding to multiple factors, such as load differences among RSUs, a vehicle’s moving speed, and a vehicle’s energy constraint. In this paper, an optimal offloading decision method is proposed based on optimal stopping theory (OST) to decide when to offload considering the aforementioned factors. Firstly, two offloading decision problems with and without energy constraint are constructed to find the optimal RSU which can minimize expected cost, where the expected cost is determined by the decision on offloading to the current RSU or continuing observing the next RSU. Then, OST is utilized to solve these two problems. Specifically, a sequence of thresholds are pre-calculated based on the OST. An offloading decision can be made by comparing the load of current RSU with the threshold. Moreover, some facts on a vehicle’s moving speed in the environment without energy constraint and the number of observations on RSUs in the environment with energy constraint are revealed. What’s more, the optimal moving speed which can minimize the expected cost is also provided. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed method. The effects of a vehicle’s moving speed and the number of observations on the performance of the proposed method are also verified. Comparing to the benchmarks, the proposed method can achieve superior performance in terms of cost and hit ratio, and has comparable performance with the best offloading method which has full RSUs’ load information. Moreover, the proposed method is robust to the estimation deviation of RSUs’ load distribution.

Network attached storage (NAS) devices are widely deployed for personal data storage. However, their distributed architecture and limited inspection interfaces pose significant challenges for comprehensive performance, security, and privacy analysis. In this paper, we first establish a threat model for NAS ecosystems. Then, we present a systematic framework NASchecker for discovering performance optimization mechanisms, security threats, and privacy leakage in NAS devices. By analyzing traffic generated during varied file operations on crafted files, NASchecker infers implemented optimizations and identifies security flaws within the traffic (e.g., susceptibility to passive sniffing and replay attacks). NASchecker also integrates NAS-specific protocol fuzzing and firmware reverse engineering to uncover deep-seated command injection, memory corruption, and improper access control vulnerabilities. NASchecker compares personally identifiable information (PII) leaked in traffic against declarations in privacy policies to detect privacy compliance issues. We evaluated NASchecker on twelve commercial NAS devices. Our results reveal that none of the tested devices employ file compression or deduplication. From a security standpoint, ten devices are vulnerable to passive sniffing and seven to replay attacks. Moreover, seven devices are affected by command injection, four by memory corruption, and eleven by improper access control. From a privacy perspective, four devices leaked PIIs that were not disclosed in their respective privacy policies. After reporting the findings to the manufacturers, we have been acknowledged by several manufacturers, resulting in the assignment of 20 CVEs and 6 NVDB entries (16 of them are rated as high severity). These findings validate NASchecker’s effectiveness and underscore the urgent need for improved design and testing practices of NAS.

Hyperspectral images (HSIs) with high spatial resolution are challenging to obtain directly due to sensor limitations. Deep learning is able to provide an end-to-end reconstruction solution from low to high spatial resolution. Nevertheless, existing deep learning-based methods have two main drawbacks. First, deep networks with self-attention mechanisms often require a trade-off between internal resolution, model performance and complexity, leading to the loss of fine-grained, high-resolution features. Second, there are visual discrepancies between the reconstructed hyperspectral image (HSI) and the ground truth because they focus on spatial-spectral domain learning. In this paper, a novel super-resolution algorithm for HSIs, named SRDNet, is proposed by using a dual-domain network with hybrid convolution and progressive upsampling to exploit both spatial-spectral and frequency information of the hyperspectral data. In this approach, we design a self-attentive pyramid structure (HSL) to capture interspectral self-similarity in the spatial domain, thereby increasing the receptive range of attention and improving the feature representation of the network. Additionally, we introduce a hyperspectral frequency loss (HFL) with dynamic weighting to optimize the model in the frequency domain and improve the perceptual quality of the HSI. Experimental results on three benchmark datasets show that SRDNet effectively improves the texture information of the HSI and outperforms state-of-the-art methods. The code is available at https://github.com/LTTdouble/SRDNet.

This paper first develops an analytical framework to investigate the performance of uplink (UL)/downlink (DL) decoupled access in cellular vehicle-to-everything (C-V2X) networks, in which a vehicle's UL/DL can be connected to different macro/small base stations (MBSs/SBSs), separately. Using the stochastic geometry analytical tool, the UL/DL decoupled access C-V2X is modeled as a Cox process, and we obtain the following theoretical results, i.e., 1) the probability of different UL/DL joint association cases i.e., both the UL and DL are associated with the different MBSs or SBSs, or they are associated with different types of BSs; 2) the distance distribution of a vehicle to its serving BSs in each case; 3) the spectral efficiency of UL/DL in each case; and 4) the UL/DL coverage probability of MBS/SBS. The analyses reveal the insights and performance gain of UL/DL decoupled access. Through extensive simulations, the accuracy of the proposed analytical framework is validated. Both the analytical and simulation results show that UL/DL decoupled access can improve spectral efficiency. The theoretical results can be directly used for estimating the statistical performance of a UL/DL decoupled access C-V2X network.

With the rapid development of Internet of Vehicles (IoV), an increasing number of vehicular users (VUEs) will connect to the Internet via 5G and beyond 5G(B5G) networks, which makes the spectrum resource becoming extremely scarce. However, the traditional spectrum allocation method cannot well adapt to the explosive growth of IoV traffic, and an efficient spectrum management for the IoV in B5G networks is an urgent challenge. In this paper, we propose a Stackelberg game and federated learning assisted spectrum sharing framework for the IoV. First, we develop a power control strategy for the region nodes considering its revenue and energy consumption, while VUEs can dynamically change their spectrum resource request strategy to maximize their revenue. We find the Stackelberg equilibrium using the alternating direction method of multiplier (ADMM) algorithm. To maximize the global revenue of region nodes, we leverage federated learning to realize the interaction between the region nodes and the central node. In specific, each region node utilizes deep learning to fit the relationship between the allocated power and its revenue. Then, they upload the network parameters to the central node. After collecting the network parameters from all region nodes, the central node can make the global decision about the spectrum allocation to the region nodes. Numerous simulation results verify the effectiveness of the proposed framework compared with the benchmark methods.

Novel empirical path-loss and root mean square delay spread (RDS) models for smart office scenarios are proposed. The effects of person density on the path loss and RDS are investigated based on the extensive measurements at 2.3–2.5 GHz. First, both of the measured path loss and RDS data are modeled as the dual log-distance functions. It is caused by the regular structure and furniture in the office environment. Second, in the proposed path-loss model, the path-loss exponents and the additional attenuation factor are modeled as quadratic functions of the person density. Meanwhile, the RDS is found to be uncorrelated with the person density. These phenomena reveal that the persons in the environments can be regarded as absorbers rather than scatters. Then, the accuracy of the proposed models is validated by the measured data and compared with two traditional models. Finally, the effect of the persons’ movements on the path loss and RDS is investigated, and the proposed models are extended to millimeter wave bands by a ray tracing technology. The proposed models and results can provide necessary information for link budget and algorithm design for the Internet of Things smart office scenarios.

Mobile edge computing (MEC) enables computing services at the network edge closer to mobile users (MUs) to reduce network transmission latency and energy consumption. Deploying edge computing servers in small base stations (SBSs), operators make profit by offering MUs with computing services, while MUs purchase services to solve their own computation tasks quickly and energy-efficiently. In this context, it is of particular importance to optimize computing resource allocation and computing service pricing in each SBS, subject to its limited computing and communication resources. To address this issue, we formulate an optimization problem of computing resource management and trading in small-cell networks and tackle this problem using a two-tier matching. Specifically, the first tier targets at the association algorithm between MUs and SBSs to achieve maximum social welfare, and the second tier focuses on the collaboration algorithm among SBSs to make efficient usage of limited computing resources. We further show that the two proposed algorithms contribute to stable matchings and achieve weak Pareto optimality. In particular, we verify that the first algorithm arrives at a competitive equilibrium. Simulation results demonstrate that our proposed algorithms can achieve a better network social welfare than baseline algorithms while retaining a close-optimal performance.

A sharded blockchain with the Proof-of-Stake (PoS) consensus protocol has advantages in increasing throughput and reducing energy consumption, enabling the resource-limited participants to manage transactions and in a decentralized way and obtain rewards at a lower cost, e.g., Internet-of-Things (IoT) users. However, the latest PoS (e.g., Casper) requires a steep security deposit, which is the key to provide more robust security guarantees than Proof of Work, but not practical for the owners of heterogeneous IoT devices. This article considers any individual and institute who owns the IoT devices as the potential participant and focuses on designing the proper security deposits in a practical scenario with hidden information and hidden action. To bridge blockchain and the IoT users, we study the problem of balancing the security incentive and the economic incentive under two cases: 1) stake oriented and 2) effort oriented. We propose two joint models under the contract theory framework to efficiently address the problems: 1) joint adverse selection and moral hazard and 2) joint adverse selection and tournament. Both optimal contracts can provide a maximized profit for blockchain. The optimal rewards and security deposits for different types of participants can be determined accordingly. Simulations indicate that the proposed models can overcome asymmetric information and offer feasible contracts. Moreover, it demonstrates that both joint models can provide an economic incentive for the participants without reducing security incentives for the sharded blockchain.

Wireless caching technologies have been proposed to relieve the transmission pressures, especially, the transmission redundancy on back-haul channels. In this paper, we consider a two-layer caching network, consisting of traditional macro-cell base station (MBS) aided back-haul channels and small-cell base stations (SBSs) aided local links. The network service provider (NSP), who is in charge of the two layers, leases its resources of the secondary layer, i.e., coverage of the SBSs, to content providers (CPs) for making extra profits and releasing pressures on the back-haul channels. At the same time, CPs will evaluate whether they are provided with proper incentives to pre-cache their files in the SBSs. Considering different quality of services (QoS) provided by the two layers as well as the economical impact of the traditional layer on the secondary layer, the NSP designs the optimal incentive mechanisms within the framework of contract theory for maximizing its own profits. First, we formulate the utility of the NSP and CPs. Then, the minimum transmission requirement, reserve price and limited resources are considered as constraints in designing the optimal contract. Also, some important properties of these constraints are analyzed to facilitate the optimal contract determination process. At last, an optimal contract determination scheme is proposed, based on which the optimal coverage set is determined first, and then the corresponding optimal prices are derived with the aid of equal cost line. Numerical results are provided to demonstrate the effectiveness of the proposed optimal contract in increasing the NSP's profits and incentivizing CPs to transmit on the secondary layer.

Computation offloading over fog computing has the potential to improve reliability and reduce latency in future networks. This paper considers a scenario where roadside units (RSUs) are installed for offloading tasks to the computation nodes including nearby fog nodes and a cloud center. To guarantee the reliable communication, we formulate the first subproblem of power allocation, and leverage the conditional value-at-risk approach to analyze the successful transmission probability in the worse-case channel condition. To complete computation tasks with low latency, we formulate the second subproblem of task allocation into a multi-period generalized assignment problem (MPGAP), which aims at minimizing the total delay by offloading tasks to the `right' fog nodes at `right' period. Then, we propose a modified branch-and-bound algorithm to derive the optimal solution and a heuristic greedy algorithm to obtain approximate performance. In addition, the master problem is proposed as a non-convex optimization problem, which considers both the reliability-guaranteed and delay-sensitive requirements. We design the Lagreedy algorithm by combining the subgradient algorithm and the heuristic algorithm. Comprehensive evaluations demonstrate that the Lagreedy is able to obtain the shortest delay with a high power consumption, while the branch-and-bound algorithm can achieve both shorter delay and lower power consumption with reliability guarantees.

The security of spatial modulation (SM) aided networks can always be improved by reducing the desired link's power at the cost of degrading its bit error ratio performance and assuming the power consumed to artificial noise (AN) projection (ANP). We formulate the joint optimization problem of maximizing the secrecy rate (Max-SR) over the transmit antenna selection and ANP in the context of secure SM-aided networks. In order to solve this problem, we provide a pair of solutions, namely joint and separate solutions. Specifically, an accurate approximation of the SR is used for reducing the computational complexity, and the optimal AN covariance matrix (ANCM) is found by convex optimization for any given active antenna group (AAG). Then, given a large set of AAGs, simulated annealing mechanism is invoked for optimizing the choice of AAG, where the corresponding ANCM is recomputed by this optimization method as well when the AAG changes. To further reduce the complexity of the above-mentioned joint optimization, a low-complexity two-stage separate optimization method is also proposed. Moreover, when the number of transmit antennas tends to infinity, the Max-SR problem becomes equivalent to that of maximizing the ratio of the desired user's signal-to-interference-plus-noise ratio to the eavesdropper's. Thus, our original problem reduces to a fractional programming problem and a significant computational complexity reduction can be achieved. Finally, our simulation results verify the efficiency of the proposed methods in terms of the SR performance attained.

One of the key metrics in measuring system quality of service (QoS) is the delay performance. Most existing papers have focused on the studies of decreasing transmission delay. However, as the wireless communication traffic increasing dramatically, queueing delay in the wireless networks becomes a non-negligible issue. Martingale theory, which fits any arrival and service process, providing a much tighter delay bound compared to the effective bandwidth theory, has been proposed to analyze the system queueing delay bound, especially in a bursty traffic scenario. In this paper, we propose to study the resource allocation problem based on the delay bounds derived from martingale theory. In specific, we first revisit some basic knowledge about stochastic network calculus, and present the delay bounds derived from martingale theory in certain typical bursty service models. Then, we setup a resource allocation problem in a computation offloading scenario, where multiple computation nodes with distinct computation capacities are considered. User's computation tasks are usually bursty, and are required to be executed within a limited time. We propose to minimize the system delay violation probability by properly allocating the computation tasks to different computation nodes. A closed-form solution is derived for the computation offloading problem, using a special kind of water-filling policy. Moreover, we discuss two potential models of martingale-based resource allocation, and provide the corresponding system architectures. Finally, numerical results are presented to demonstrate the performances of the proposed scheme. The proposed water-filling scheme achieves a smaller system delay violation probability compared to the benchmark.

Computation offloading over fog and cloud is critical to improve service quality and efficiency of future networks. Mobile vehicles have also been considered as potential fog nodes by sparing their computation capability to nearby users. In this paper, we propose a multi-layer computation offloading architecture, consisting of the user layer, mobile fog layer, fixed fog layer and cloud layer. Multiple wireless roadside units (RSUs) are deployed in the network to collect computation tasks from user layer, and offload the tasks to other layers. Each layer has distinct multi-dimensional characteristics, such as different transmission rates and computation capabilities. The computation tasks may consume different communication and computation resources when they are uploaded to different layers. However, the available resources of each layer are limited. Consider that each user will pay for the offloaded computation tasks according to their sizes, we aim to maximize the total profits of computation offloading from the infrastructure perspective. Specifically, the offloading problem is formulated as a generalized multidimensional multiple knapsack problem (MMKP), in which each layer is considered as a large knapsack and the computation tasks are treated as items. We propose a modified branch-and-bound algorithm to obtain the optimal solution, and a heuristic greedy method to obtain approximate performance with much lower computational overhead. A comprehensive simulation is conducted to compare the proposed two algorithms. Simulation results demonstrate that the proposed computation offloading architecture together with the task allocation algorithms can achieve the purpose of maximizing the total profits of offloaded tasks.

A large number of computation-hungry mobile applications have led to an ever-increasing computation demands. Mobile edge computing (MEC) has been considered as an emerging paradigm to alleviate the demand effectively by offloading the computationally intensive tasks from mobile devices (MD) to the adjacent MEC servers. It is expected that the quality of computation experience, e.g., the computing energy and the execution latency, can be greatly improved by the MEC. In this paper, we will investigate the computing task offloading problem via the MEC in the context of small-cell base-station (SBS) networks, where each SBS is equipped with an MEC server. Specifically, we first formulate the optimization problem to minimize the objective, i.e., the weighted sum of energy consumption and execution duration. The parameters to be optimized are the allocations of the MD's tasks to be offloaded to the MEC servers. Then we propose a novel belief propagation (BP) algorithm to optimize the task allocation in a distributed manner. Next, we develop the factor graph according to the network topology and decompose the object function into multiple local utilities to fit the factor graph. Finally, we transform local utilities into the estimations of marginal distributions and propose a distributed BP algorithm to solve the estimations. Simulations demonstrate that our BP algorithm can effectively approach the optimal solutions via exhaustive search.

With the burst of mobile data, it is necessary to make use of idle mobile equipment for caching space. Caching in the femto-cells is proposed for reducing transmission latency between the WiFi points and its mobile users (MU) and better user service. In this paper, we firstly take the copyright of the files as the allocation resource and the WiFi points with caching space want to rent these copyrights. We propose a multi-rounds double auction mechanism for this problem and take the popularity parameter of the files as the quality weight. This game can help multiple content providers (CP) lease copyrights of the files to multiple WiFi points effectively. Different from traditional double auctions, it will take the failed buyers and sellers into consideration and they are allowed to change their requests in the next auction process. This mechanism can largely improve efficiency of the game and is budget balanced. We also prove that the allocation process is monotone and with the set of the critical payment rule, we prove the truthfulness of the mechanism. Additionally, we prove that the mechanism can form a conditional equilibrium. Simulation results verify the effectiveness of the proposed mechanism and compare with the traditional one-round double auction.

A large-scale fully digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog PA (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer-Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.

Wireless caching is an efficient mechanism for reducing downloading delay and reducing the traffic pressure over backhual channels by caching some popular content, e.g., video clips, in small base stations (SBSs). In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs) and mobile users (MUs). The NSP leases its SBSs to VRs in order to earn profits, while the VRs store popular videos into the lent SBSs, thereby gaining profits from providing better services to the MUs. We conceive the system within the framework of contract theory by designing the optimal quality-price contract. We establish the profit function of NSP and VRs and solve the profit maximization problem through contract theory. Numerical results validate the effectiveness of our incentive mechanism for the system.

Evidences indicate that wireless video traffic has played an important role in cellular networks. Caching mechanisms which store popular contents into local small-cell base stations (SBSs) in cellular networks are proposed to further reduce transmission delay and release the traffic pressure over backhaul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs) and multiple mobile users (MUs). The NSP as a network facility monopoly releases its resources to the VRs in order to maximize its own profits. The distribution of VR's type is known to the NSP, while the actual type of a given VR is not known. We research on such an information asymmetric market within the framework of contract theory, formulated as an adverse selection problem. The MUs and SBSs are modeled as two independent Poisson point processes, and the directly downloading probability from the adjacent SBS is derived via stohastic geometry theory. Based on the probability, we formulate the utility functions of the NSP and the VRs. Then, the optimal contract problem is constructed. Also, we provide the feasibility of the contract, and the optimal contract is proposed when VR's popularity parameter γ takes different values. Numerical results are provided to show the optimal quality and the optimal price designed for each VR.

Recently, content-aware-enabled distributed caching relying on local small-cell base stations (SBSs), namely, smallcell caching, has been intensively studied for reducing transmission latency as well as alleviating the traffic load over backhaul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several content providers (CPs), and multiple mobile users (MUs). The NSP, as a network facility monopolist in charge of the SBSs, leases its resources to the CPs for gaining profits. At the same time, the CPs are intended to rent the SBSs for providing better downloading services to the MUs. We focus on solving the profit maximization problem for the NSP within the framework of contract theory. To be specific, we first formulate the utility functions of the NSP and the CPs by modeling the MUs and SBSs as two independent Poisson point processes. Then, we develop the optimal contract problem for an information asymmetric scenario, where the NSP only knows the distribution of CPs' popularity among the MUs. Also, we derive the necessary and sufficient conditions of feasible contracts. Lastly, the optimal contract solutions are proposed with different CPs' popularity parameter γ. Numerical results are provided to show the optimal quality and the optimal price designed for each CP. In addition, we find that the proposed contract-based mechanism is superior to the benchmarks from the perspective of maximizing the NSP's profit.

In this paper, spatial channel pairing (SCP) is introduced to coherent combining at the relay in relay networks. Closed-form solution to optimal coherent combining is derived. Given coherent combining, the approximate SCP solution is presented. Finally, an alternating iterative structure is developed. Simulation results and analysis show that, given the symbol error rate and data rate, the proposed alternating iterative structure achieves signal-to-noise ratio gains over existing schemes in maximum ratio combining (MRC) plus matched filter, MRC plus antenna selection, and distributed space-time block coding due to the use of SCP and iterative structure.

The FCC new regulation for cognitive use of the TV white space spectrum provides a new means for improving traditional cellular network performance. But it also introduces a number of technical challenges. This paper studies one of the challenges: given the significant differences in the propagation property and the transmit power limitations between the cellular band and the TV white space, how both bands can be jointly utilized such that the benefit from the TV white space is maximized for overall cellular network performance improvement. Both analytical and simulation results are provided.

Orthogonal frequency division multiple access (OFDMA) and Single Carrier Frequency Division Multiple Access (SC-FDMA) are the two major multiple access schemes for 4G wireless communications. In long term evolution (LTE) the downlink multiple access scheme is based on OFDMA and the uplink multiple access scheme is based on SC-FDMA. In this letter, we derive the fundamental performance difference between OFDMA and SC-FDMA, and present the general performance comparison between them. Analytical results show that OFDMA performance upper bounds SC-FDMA in the sense of capacity. The conclusion is further confirmed by numerical results.

In this paper, we review the signal-to-leakage-plus-noise ratio (SLNR) transmit precoding criterion and the active antenna selection (AAS) strategy in a multi-user MIMO system. We then address the limitations of the original SLNR and AAS schemes and provide a solution that generalizes the SLNR precoding model by incorporating the antenna-receiver structure into the optimization process to further improve the multi-user MIMO performance under various receiver structures.