In this paper, we investigate the spectrum sensing performance of a distributed satellite clusters (DSC) under perturbation, aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak signals. Specifically, we propose a cooperative beamforming (BF) algorithm though random antenna array theory to fit the location characteristic of DSC and derive the average far-field beam pattern under perturbation. Then, a constrained optimization problem with maximizing the signal to interference plus noise ratio (SINR) is modeled to obtain the BF weight vectors, and an approximate expression of SINR is presented in the presence of the mismatch of signal steering vector. Finally, we derive the closed-form expression of the detection probability for the considered DSC over Shadowed-Rician fading channels. Simulation results are provided to validate our theoretical analysis and to characterize the impact of various parameters on the system performance.
The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper. The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process. Furthermore, a spectrum access scheme based on the prior knowledge enabled RL (PKRL) is designed, which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment. Compared with the existing methods, the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy. Simulation results show that, the convergence speed of the learning is improved by about 66% with the invariant average throughput.
In this paper, we investigate a geosynchronous earth orbit (GEO) and low earth orbit (LEO) coexisting satellite communication system. To decrease the interference imposed on the GEO user caused by LEO satellites, we propose a joint beam-management and power-allocation (JBMPA) scheme to maximize signal-to-interference plus noise ratio (SINR) at the GEO user, whilst maintaining the ongoing wireless links spanning from LEO satellites to their corresponding users. Specifically, we first analyze the overlapping coverage among GEO and LEO satellites, to obtain the LEO-satellite set in which their beams impose interference on the GEO user. Then, considering the traffic of LEO satellites in the obtained set, we design a beam-management method to turn off and switch interference beams of LEO satellites. Finally, we further propose a deep Q-network (DQN) aided power allocation algorithm to allocate the transmit power for the ongoing LEO satellites in the obtained set, whose beams are unable to be managed. Numerical results show that comparing with the traditional fixed beam with power allocation (FBPA) scheme, the proposed JBMPA can achieve a higher SINR and a lower outage probability, whilst guaranteeing the ongoing wireless transmissions of LEO satellites.
This paper investigates the anti-jamming trajectory design to safeguard the effective data collection, where a unmanned aerial vehicle (UAV) is dispatched to collect data over multiple sensor nodes(SNs) in jamming environment. Under the limited power and transmission range of SNs, we aim to minimize the UAV's flight energy consumption in a finite task period, by jointly optimizing SNs collection sequence and UAV flight trajectory. Firstly, we propose a general optimization framework which consists of path planning and trajectory optimization for the formulated non-convex problem. In the path planning phase, a dynamic programming (DP) algorithm is used to provide the initial path of the UAV, which is the shortest path to visit each SN. In the trajectory optimization phase, we introduce the concept of Communication Flight Corridor (CFC) to meet the non-convex constraints and apply a piecewise Bézier curve, based on Bernoulli polynomial, to represent the flight trajectory of the UAV, which can transform the optimization variables from infinite time variables to polynomial coefficients of finite order. Finally, we simulate the flight trajectory of UAV in hovering mode and continuous flight mode under different parameters, and the simulation results demonstrate the effectiveness of the proposed method.
Aiming at the physical layer security (PLS) secure transmission existing in the information backhaul link of the satellite-UAV integrated (SUI) network, a two-layer Stackelberg game model (TSGM) that can resist full-duplex (FD) eavesdropping and jamming attacks is proposed. The confrontation relationship between the UAV network and the attacker is established as the first layer Stackelberg game. The source UAV adjusts its own transmission power strategy according to the attacker's jamming strategy to resist malicious jamming attacks. The internal competition and cooperation relationship in UAV network is modeled as the second layer Stackelberg game, and the optimal cooperative UAV transmits jamming signal to the attacker to resist malicious eavesdropping attacks. Aiming at the "selfishness" of UAV nodes, a price incentive mechanism is established to encourage UAV to actively participate in cooperation, so as to maximize the advantages of cooperative communication. For the proposed TSGM, we construct the utility function and analyze the closed equilibrium solution of the game model, and design a three-stage optimal response iterative (TORI) algorithm to solve the game equilibrium. The simulation results show that the proposed TSGM can effectively increase the utility of the source UAV and improve the enthusiasm of cooperation compared with other power control models.
The space-air-ground integrated network (SAGIN) has gained widespread attention from academia and industry in recent years. It is widely applied in many practical fields such as global observation and mapping, intelligent transportation systems, and military missions.As an information carrier of air platforms, the deployment strategy of unmanned aerial vehicles (UAVs) is essential for communication systems' performance.In this paper, we discuss a UAV broadcast coverage strategy that can maximize energy efficiency (EE) under terrestrial users' requirements. Due to the non-convexity of this issue, conventional approaches often solve with heuristics algorithms or alternate optimization.To this end, we propose an iterative algorithm by optimizing trajectory and power allocation jointly.Firstly, we discrete the UAV trajectory into several stop points and propose a user grouping strategy based on the traveling salesman problem (TSP) to acquire the number of stop points and the optimization range.Then, we use the Dinkelbach method to dispose of the fractional form and transform the original problem into an iteratively solvable convex optimization problem by variable substitution and Taylor approximation. Numerical results validate our proposed solution and outperform the benchmark schemes in EE and mission completion time.
In application to time convolutive mixing model or frequency domain blind separation model for wireless receiving applications, frequency domain independent component analysis (FDICA) has been a very popular method but with adverse random permutation ambiguity influence. In order to solve this inherent problem in FDICA assisted multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) based the Internet of Thing (IoT) systems, this paper proposes an new detection mechanism, named independent vector analysis (IVA), for realizing blind adaptive signal recovery in MIMO IoT green communication to reduce inter-carrier interference (ICI) and multiple access interference (MAI). IVA jointly implements separation work for different frequency bin data while the FDICA deals with it separately. In IVA, the dependencies of frequency bins can be exploited in mitigating the random permutation problem. In addition, multivariate prior distributions are employed to preserve the inter-frequency dependencies for individual sources, which can result in separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed method.
In LEO (Low Earth Orbit) satellite communication system, the orbit height of the satellite is low, the transmission delay is short, the path loss is small, and the frequency multiplexing is more effective. However, it is an unavoidable technical problem of the significant Doppler effect caused by the interference between satellite networks and the high-speed movement of the satellite relative to the ground. In order to improve the target detection efficiency and system security of LEO satellite communication system, blind separation technology is an effective method to process the collision signals received by satellites. Because of the signal has good sparsity in Delay-Doppler domain, in order to improve the blind separation performance of LEO satellite communication system, orthogonal Time-Frequency space (OTFS) modulation is used to convert the sampled signal to Delay-Doppler domain. DBSCAN clustering algorithm is used to classify the sparse signal, so as to separate the original mixed signal. Finally, the simulation results show that the method has a good separation effect, and can significantly improve the detection efficiency of system targets and the security of LEO satellite communication system network.
Rate splitting multiple access (RSMA) has shown great potentials for the next generation communication systems. In this work, we consider a two-user system in hybrid satellite terrestrial network (HSTN) where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality. The non-convex weighted sum rate (WSR) problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error (WMMSE) algorithm. We propose to apply deep unfolding to solve the optimization problem, which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations. We also incorporate momentum accelerated projection gradient descent (PGD) algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping. The momentum and step size in deep unfolding network are selected as trainable parameters for training. As shown in the simulation results, deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.
In LEO (Low Earth Orbit) satellite communication systems, the satellite network is made up of a large number of satellites, the dynamically changing network environment affects the results of distributed computing. In order to improve the fault tolerance rate, a novel public blockchain consensus mechanism that applies a distributed computing architecture in a public network is proposed. Redundant calculation of blockchain ensures the credibility of the results; and the transactions with calculation results of a task are stored distributed in sequence in Directed Acyclic Graphs (DAG). The transactions issued by nodes are connected to form a net. The net can quickly provide node reputation evaluation that does not rely on third parties. Simulations show that our proposed blockchain has the following advantages: 1. The task processing speed of the blockchain can be close to that of the fastest node in the entire blockchain; 2. When the tasks' arrival time intervals and demanded working nodes(WNs) meet certain conditions, the network can tolerate more than 50% of malicious devices; 3. No matter the number of nodes in the blockchain is increased or reduced, the network can keep robustness by adjusting the task's arrival time interval and demanded WNs.
In the satellite-to-ground high-speed data transmission link, there are signal self-interference problems of symbols in the co-channel, as well as between orthogonal and polarized channels. A multichannel adaptive filter is designed by constructing a multichannel Wiener-Hopf equation, and the influence of five channel nonideal factors is suppressed to improve the BER performance. Experiments show that this method is effective to suppress the signal self-interference, and the BER floor is optimized from 1E-3 to 1E-7.
In this paper, we address the problem of multiple frequency-hopping (FH) signal parameters estimation in the presence of random missing observations. A space-time matrix with random missing observations is acquired by a uniform linear array (ULA). We exploit the inherent incomplete data processing capability of atomic norm soft thresholding (AST) to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals. The hopping time is obtained by the sudden changes of the spatial information, which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components. Then, the frequency of multiple signal components can be estimated precisely by AST within each segment. After obtaining the above two parameters of the hopping time and the frequency of signals, the direction of arrival (DOA) can be directly calculated by them, and the network sorting can be realized. Results of simulation show that the proposed method is superior to the existing technology. Even when a large portion of data observations is missing, as the number of array elements increases, the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.
Link flooding attack (LFA) is a fresh distributed denial of service attack (DDoS). Attackers can cut off the critical links, making the services in the target area unavailable. LFA manipulates legal low-speed flow to flood critical links, so traditional technologies are difficult to resist such attack. Meanwhile, LFA is also one of the most important threats to Internet of things (IoT) devices. The introduction of software defined network (SDN) effectively solves the security problem of the IoT. Aiming at the LFA in the software defined Internet of things (SDN-IoT), this paper proposes a new LFA mitigation scheme ReLFA. Renyi entropy is to locate the congested link in the data plane in our scheme, and determines the target links according to the alarm threshold. When LFA is detected on the target links, the control plane uses the method based on deep reinforcement learning (DRL) to carry out traffic engineering. Simulation results show that ReLFA can effectively alleviate the impact of LFA in SDN IoT. In addition, the rerouting time of ReLFA is superior to other latest schemes.
Biometric key is generated from the user's unique biometric features, and can effectively solve the security problems in cryptography. However, the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged easily. According to the characteristics of Electroencephalographic(EEG) signals such as the randomness, non-linear and non-stationary etc., it can significantly avoid these flaws. This paper proposes a novel method to generate keys based on EEG signals with end-edge-cloud collaboration computing. Using sensors to measure motor imagery EEG data, the key is generated via pre-processing, feature extraction and classification. Experiments show the total time consumption of the key generation process is about 2.45s. Our scheme is practical and feasible, which provides a research route to generate biometric keys using EEG data.
The one-time pad (OTP) is an application-layer encryption technique to achieve the information-theoretic security, and the physical-layer secret key generation (SKG) technique is a promising candidate to provide the random keys for OTP. In this paper, we propose a joint SKG and OTP encryption scheme with the aid of a reconfigurable intelligent surface (RIS) to boost secret key rate. To maximize the efficiency of secure communication, we divide the process of secure transmission into two stages: SKG and then encrypted packet transmission. Meanwhile, we design an optimal algorithm for allocating time slots for SKG to maximize SKG efficiency without security risk. Furthermore, we design a key updating protocol based on our SKG scheme for OTP encryption. Simulation results verify that our scheme can generate keys securely and efficiently, and significantly improve the secure communication performance in an intelligent IoT system.
Cloud computing provides powerful processing capabilities for large-scale intelligent Internet of things (IoT) terminals. However, the massive real-time data processing requirements challenge the existing cloud computing model. The edge server is closer to the data source. The end-edge-cloud collaboration offloads the cloud computing tasks to the edge environment, which solves the shortcomings of the cloud in resource storage, computing performance, and energy consumption. IoT terminals and sensors have caused security and privacy challenges due to resource constraints and exponential growth. As the key technology of IoT, Radio-Frequency Identification (RFID) authentication protocol tremendously strengthens privacy protection and improves IoT security. However, it inevitably increases system overhead while improving security, which is a major blow to low-cost RFID tags. The existing RFID authentication protocols are difficult to balance overhead and security. This paper designs an ultra-lightweight encryption function and proposes an RFID authentication scheme based on this function for the end-edge-cloud collaborative environment. The BAN logic proof and protocol verification tools AVISPA formally verify the protocol's security. We use VIVADO to implement the encryption function and tag's overhead on the FPGA platform. Performance evaluation indicates that the proposed protocol balances low computing costs and high-security requirements.
In industrial Internet of Things systems, state estimation plays an important role in multi-sensor cooperative sensing. However, the state information received by remote control center experiences random delay, which inevitably affects the state estimation performance. Moreover, the computation and storage burden of remote control center is very huge, due to the large amount of state information from all sensors. To address this issue, we propose a layered network architecture and design the mobile edge computing (MEC) enabled cooperative sensing scheme. In particular, we first characterize the impact of random delay on the error of state estimation. Based on this, the cooperative sensing and resource allocation are optimized to minimize the state estimation error. The formulated constrained minimization problem is a mixed integer programming problem, which is effectively solved with problem decomposition based on the information content of delivered data packets. The improved marine predators algorithm (MPA) is designed to choose the best edge estimator for each sensor to pretreat the sensory information. Finally, the simulation results show the advantage and effectiveness of proposed scheme in terms of estimation accuracy.
Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server. To conserve energy as well as maintain quality of service, low time complexity algorithm is proposed to complete task offloading and server allocation. In this paper, a multi-user with multiple tasks and single server scenario is considered for small network, taking full account of factors including data size, bandwidth, channel state information. Furthermore, we consider a multi-server scenario for bigger network, where the influence of task priority is taken into consideration. To jointly minimize delay and energy cost, we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation. We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems. To further reduce time cost and achieve near-optimal performance, we use convolutional neural networks to process mass data based on fully connected networks. Numerical results show that the proposed algorithm performs better than other offloading schemes, which can generate near-optimal offloading decision timely.
Peer-to-peer computation offloading has been a promising approach that enables resource-limited Internet of Things (IoT) devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers, the spare computation resources offered by peer devices are random and intermittent, which affects the offloading performance. The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions. In this work, we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals, dynamic inter-user interference, and opportunistic availability of peer devices. Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion, which takes into consideration its energy consumption, task delay, and task loss due to buffer overflow. The dynamic decision process among multiple requestors is formulated as a stochastic game. By constructing the post-decision states, a decentralized online offloading algorithm is proposed, where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations. Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms, especially in the scenarios with large task arrival probability or small helper availability probability.
In this paper, we propose a novel fuzzy matching data sharing scheme named FADS for cloud-edge communications. FADS allows users to specify their access policies, and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously. Specifically, we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments. Then, we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies. If the matching succeeds, the data can be decrypted. Otherwise, nothing will be revealed. In addition, FADS allows users to dynamically specify the policy for each time, which is an urgent demand in practice. A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack (IND-CCA) in random oracle model against probabilistic polynomial-time (PPT) adversary, and the desirable security properties of privacy and authenticity are achieved. Extensive experiments provide evidence that FADS is with acceptable efficiency.
In this work, we employ the cache-enabled UAV to provide context information delivery to end devices that make timely and intelligent decisions.Different from the traditional network traffic, context information varies with time and brings in the age-constrained requirement. The cached content items should be refreshed timely based on the age status to guarantee the freshness of user-received contents, which however consumes additional transmission resources. The traditional cache methods separate the caching and the transmitting, which are not suitable for the dynamic context information.We jointly design the cache replacing and content delivery based on both the user requests and the content dynamics to maximize the offloaded traffic from the ground network. The problem is formulated based on the Markov Decision Process (MDP). A sufficient condition of cache replacing is found in closed form, whereby a dynamic cache replacing and content delivery scheme is proposed based on the Deep Q-Network (DQN).Extensive simulations have been conducted. Compared with the conventional popularity-based and the modified Least Frequently Used (i.e., LFU-dynamic) schemes, the UAV can offload around 30% traffic from the ground network by utilizing the proposed scheme in the urban scenario, according to the simulation results.
Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation. In a platoon, a vehicle could play as a requester that employs another vehicles as performers for workload processing. An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making, which avoids the information collection from the performers and preserves their privacy. We model the interactions among the requester (leader) and multiple performers (followers) as a Stackelberg game. The requester incentivizes the performers to accept the workloads. We derive the Stackelberg equilibrium under complete information. Furthermore, deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers' information private. Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.
With the rapid developments of Internet of Things (IoT) and proliferation of embedded devices, large volume of personal data are collected, which however, might carry massive private information about attributes that users do not want to share. Many privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local devices. Unfortunately, they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined tasks. In this paper, we propose a novel task-free privacy-preserving data collection method via adversarial representation learning, called TF-ARL, to protect private attributes specified by users while maintaining data utility for unknown downstream tasks. To this end, we first propose a privacy adversarial learning mechanism (PAL) to protect private attributes by optimizing the feature extractor to maximize the adversary's prediction uncertainty on private attributes, and then design a conditional decoding mechanism (ConDec) to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized features. With the joint learning of PAL and ConDec, we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except privacy. Extensive experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.
Multi-mode power internet of things (PIoT) combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park. Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping. However, edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service (QoS) guarantee, inadaptability of resource management, and multi-mode access conflict. We propose an Adaptive learning based delAy-sensitive and seCure Edge-End Collaboration algorithm ($\text{ACE}^2$) to optimize multi-mode channel selection and split device power into artificial noise (AN) transmission and data transmission for secure data delivery. $\text{ACE}^2$ can achieve multi-attribute QoS guarantee, adaptive resource management and security enhancement, and access conflict elimination with the combined power of deep actor-critic (DAC), “win or learn fast (WoLF)” mechanism, and edge-end collaboration. Simulations demonstrate its superior performance in queuing delay, energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.
