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IEEE Transactions on Mobile Computing

IEEE Transactions on Mobile Computing

Archives Papers: 612
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Structure-Free Broadcast Scheduling for Duty-Cycled Multihop Wireless Sensor Networks
Quan ChenZhipeng CaiLianglun ChengHong GaoJianzhong Li
Keywords:SchedulesBroadcastingWireless sensor networksOptimal schedulingApproximation algorithmsScheduling algorithmsElectronic mailBroadcast schedulingminimum latencystructure-freeduty-cycledwireless sensor networks
Abstracts:Broadcasting is an essential operation in wireless networks for disseminating the message from the source node to all other nodes. Unfortunately, the problem of Minimum Latency Broadcast Scheduling (MLBS) in duty-cycled wireless sensor networks is not well studied. In existing works, the construction of broadcast tree and the scheduling of transmissions are conducted separately, where a tree-based structure is used as the input of the scheduling algorithm. Relying on a pre-determined tree may result in a much large latency even using the optimal scheduling method. Thus, the MLBS problem in duty-cycled WSNs without the above limitation is investigated in this paper. First, to avoid relying on a pre-determined structure, a two-step scheduling algorithm is proposed to construct the broadcast tree and compute a collision-free schedule simultaneously. To the best of our knowledge, this is the first work that can integrate these two kinds of operations together. Second, a novel transmission mode, i.e., concurrent broadcasting, is first introduced for wireless networks and several techniques are designed to further improve the broadcast latency. Third, the multiple messages broadcasting and all-to-all broadcasting algorithms, which can generate a series of broadcast schedules independently without a pre-determined tree, are also proposed by taking care of the collisions in both the current and the previous broadcast schedules. Finally, the theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in terms of latency.
Robust Channel Estimation in Multiuser Downlink 5G Systems Under Channel Uncertainties
Azadeh PourkabirianMohammad Hossein Anisi
Keywords:Channel estimationWireless communicationUncertaintyQuality of serviceGames5G mobile communicationInterferenceChannel estimationgame theoryminimax optimizationquality of service guarantees5G networks
Abstracts:In wireless communication, the performance of the network highly relies on the accuracy of channel state information (CSI). On the other hand, the channel statistics are usually unknown, and the measurement information is lost due to the fading phenomenon. Therefore, we propose a channel estimation approach for downlink communication under channel uncertainty. We apply the Tobit Kalman filter (TKF) method to estimate the hidden state vectors of wireless channels. To minimize the maximum estimation error, a robust minimax minimum estimation error (MSE) estimation approach is developed while the QoS requirements of wireless users is taken into account. We then formulate the minimax problem as a non-cooperative game to find an optimal filter and adjust the best behavior for the worst-case channel uncertainty. We also investigate a scenario in which the actual operating point is not exactly known under model uncertainty. Finally, we investigate the existence and characterization of a saddle point as the solution of the game. Theoretical analysis verifies that our work is robust against the uncertainty of the channel statistics and able to track the true values of the channel states. Additionally, simulation results demonstrate the superiority of the model in terms of MSE value over related techniques.
RL-Recruiter+: Mobility-Predictability-Aware Participant Selection Learning for From-Scratch Mobile Crowdsensing
Yunfan HuJiangtao WangBo WuSumi Helal
Keywords:SensorsTask analysisTrajectoryRobot sensing systemsReinforcement learningElectronic mailCrowdsensingMobile crowdsensingparticipant selectioncold start
Abstracts:Participant selection is a fundamental research issue in Mobile Crowdsensing (MCS). Previous approaches commonly assume that adequately long periods of candidate participants’ historical mobility trajectories are available to model their patterns before the selection process, which is not realistic for some new MCS applications or platforms. The sparsity or even absence of mobility traces will incur inaccurate location prediction, thus undermining the deployment of new MCS applications. To this end, this paper investigates a novel problem called “From-Scratch MCS” (FS-MCS for short), in which we study how to intelligently select participants to minimize such a “cold-start” effect. Specifically, we propose a novel framework based on reinforcement learning, named RL-Recruiter+. With the gradual accumulation of mobility trajectories over time, RL-Recruiter+ is able to make a good sequence of participant selection decisions for each sensing slot. Compared to its previous version, RL-Recruiter, Re-Recruiter+ jointly considers both the previous coverage and current mobility predictability when training the participant selection decision model. We evaluate our approach experimentally based on two real-world mobility datasets. The results demonstrate that RL-Recruiter+ outperforms the baseline approaches, including RL-Recruiter under various settings.
Partial Synchronization to Accelerate Federated Learning Over Relay-Assisted Edge Networks
Zhihao QuSong GuoHaozhao WangBaoliu YeYi WangAlbert Y. ZomayaBin Tang
Keywords:SynchronizationTrainingData modelsConvergenceMobile computingWireless sensor networksMachine learningFederated learningpartial synchronization parallelrelay-assisted edge network
Abstracts:Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a global model with highly distributed data located on mobile devices. Aiming to optimize the communication efficiency for gradient aggregation and model synchronization among large-scale devices, we propose a relay-assisted FL framework. By breaking the traditional transmission-order constraint and exploiting the broadcast characteristic of relay nodes, we design a novel synchronization scheme named Partial Synchronization Parallel (PSP), in which models and gradients are transmitted simultaneously and aggregated at relay nodes, resulting in traffic reduction. We prove that PSP has the same convergence rate as the sequential synchronization approaches via rigorous analysis. To further accelerate the training process, we integrate PSP with any unbiased and error-bounded compression technologies and prove that the convergence properties of the resulting scheme still hold. Extensive experiments are conducted in a distributed cluster environment with real-world datasets and the results demonstrate that our proposed approach reduces the training time up to 37 percent compared to state-of-the-art methods.
Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns
Feiyang SunPinghui WangJunzhou ZhaoNuo XuJuxiang ZengJing TaoKaikai SongChao DengJohn C.S. LuiXiaohong Guan
Keywords:Market researchWireless fidelityPredictive modelsLoad modelingAdaptation modelsWireless networksConvolutionUser mobilitygraph convolutionmobile computingtime series analysis
Abstracts:Understanding mobile data traffic and forecasting future traffic trend is beneficial to wireless carriers and service providers who need to perform resource allocation and energy saving management. However, predicting wireless traffic accurately at large-scale and fine-granularity is particularly challenging due to the following two factors: the spatial correlations between the network units (i.e., a cell tower or an access point) introduced by user arbitrary movements, and the time-evolving nature of user movements which frequently changes with time. In this paper, we use a time-evolving graph to formulate the time-evolving nature of user movements, and propose a model Graph-based Temporal Convolutional Network (GTCN) to predict the future traffic of each network unit in a wireless network. GTCN can bring significant benefits to two aspects. (1) GTCN can effectively learn intra- and inter-time spatial correlations between network units in a time-evolving graph through a node aggregation method. (2) GTCN can efficiently model the temporal dynamics of the mobile traffic trend from different network units through a temporal convolutional layer. Experimental results on two real-world datasets demonstrate the efficiency and efficacy of our method. Compared with state-of-the-art methods, the improvement of the prediction performance of our GTCN is 3.2 to 10.2 percent for different prediction horizons. GTCN also achieves 8.4× faster on prediction time.
Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning
Dian ShiLiang LiTomoaki OhtsukiMiao PanZhu HanH. Vincent Poor
Keywords:Device-to-device communicationResource managementGamesReinforcement learningTrainingPower controlInterferenceDeep reinforcement learningstackelberg gameD2D communicationsresource allocation
Abstracts:Device-to-Device (D2D) communication enabling direct data transmission between two mobile users has emerged as a vital component for 5G cellular networks to improve spectrum utilization and enhance system capacity. A critical issue for realizing these benefits in D2D-enabled networks is to properly allocate radio resources while coordinating the co-channel interference in a time-varying communication environment. In this paper, we propose a Stackelberg game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which allows D2D users to make smart power control and channel allocation decisions in a distributed manner. In particular, we define a crucial Stackelberg Q-value (ST-Q) to guide the learning direction, which can be calculated based on the equilibrium achieved in the Stackelberg game. With the guidance of the Stackelberg equilibrium, our approach converges faster with fewer iterations than the general MADRL method and thereby exhibits better performance in handling the network dynamics. After the initial training, each agent can infer timely D2D resource allocation strategies with distributed execution. Extensive simulations are conducted to validate the efficacy of our proposed scheme in developing timely resource allocation strategies. The results also show that our method outperforms the general MADRL based approach in terms of the average utility, channel capacity, and training time.
FD-JCAS Techniques for mmWave HetNets: Ginibre Point Process Modeling and Analysis
Christodoulos SkouroumounisConstantinos PsomasIoannis Krikidis
Keywords:InterferenceRadarSensorsCorrelationStochastic processesGeometryCellular networksFull-duplexmillimeter-wavecooperative detectionGinibre point processstochastic geometry
Abstracts:In this paper, we study the co-design of full-duplex (FD) radio with joint communication and radar sensing (JCAS) techniques in millimeter-wave (mmWave) heterogeneous networks (HetNets). Spectral co-existence of radar and communication systems causes mutual interference between the two systems, compromising both the data exchange and sensing capabilities. Focusing on the detection performance, we propose a cooperative detection technique, which exploits the sensing information from multiple base stations (BSs), aiming at enhancing the probability of successfully detecting an object. Three combining rules are considered, namely the <italic>OR</italic>, the <italic>Majority</italic> and the <italic>AND</italic> rule. In real-world network scenarios, the locations of the BSs are spatially correlated, exhibiting a repulsive behavior. Therefore, we model the spatial distribution of the BSs as a <inline-formula><tex-math notation="LaTeX">$beta$</tex-math><alternatives><mml:math><mml:mi>&#x03B2;</mml:mi></mml:math><inline-graphic xlink:href="skouroumounis-ieq1-3076856.gif"/></alternatives></inline-formula>-Ginibre point process (<inline-formula><tex-math notation="LaTeX">$beta$</tex-math><alternatives><mml:math><mml:mi>&#x03B2;</mml:mi></mml:math><inline-graphic xlink:href="skouroumounis-ieq2-3076856.gif"/></alternatives></inline-formula>-GPP), which can characterize the repulsion among the BSs. By using stochastic geometry tools, analytical expressions for the detection performance of <inline-formula><tex-math notation="LaTeX">$beta$</tex-math><alternatives><mml:math><mml:mi>&#x03B2;</mml:mi></mml:math><inline-graphic xlink:href="skouroumounis-ieq3-3076856.gif"/></alternatives></inline-formula>-GPP-based FD-JCAS systems are expressed for each of the considered combining rule. Furthermore, by considering temporal interference correlation, we evaluate the probability of successfully detecting an object over two different time slots. O- r results demonstrate that our proposed technique can significantly improve the detection performance when compared to the conventional non-cooperative technique.
Exploiting Channel Polarization for Reliable Wide-Area Backscatter Networks
Guochao SongWei WangHang YangDongchen ZhangPeng GaoTao Jiang
Keywords:BackscatterPolar codesIterative decodingThroughputEncodingDecodingReceiversWide-area backscatter networkshigh reliabilitychannel polarizationlow latency
Abstracts:A long-standing vision of backscatter networks is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatter networks toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further towards breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter networks. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. Furthermore, we devise a new metric to calculate log-likelihood ratio for accurate decoding, and present a stopping criterion of iterations to reduce decoding latency. We build a prototype PCB tag, and our experiments show that it achieves up to 11.5&#x00D7; throughput improvement over the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.
Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions
Arash BozorgchenaniSetareh MaghsudiDaniele TarchiEkram Hossain
Keywords:Task analysisEdge computingServersBase stationsHeuristic algorithmsDelaysVehicle dynamicsVehicular edge computing (VEC)computation offloadingheterogeneous networksoff-policy learningon-line learningbandit theory
Abstracts:With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.
Collective De-Anonymization of Social Networks With Optional Seeds
Jiapeng ZhangLuoyi FuHuan LongGui'e MengFeilong TangXinbing WangGuihai Chen
Keywords:Social networking (online)Complexity theorySpread spectrum communicationSolid modelingProbesMobile computingInternetSocial network de-anonymizationnetwork collectiveness
Abstracts:As Internet users interacting with their different friends in different social networks, the de-anonymization problem has been raising improving concern. Since the assailants may de-anonymize a social network by matching it with a correlated sanitized network and identifying anonymized user identities, multifarious arts study on the theoretical conditions or practical algorithms for correctly de-anonymizing a social network. Except for the structural information of these social networks, there has also been bounteous works taking advantage of some pre-identified seed nodes for reference in the anonymized network. In this paper, we systematically probe the theoretical conditions and algorithmic approaches for correctly matching two different-sized social networks by leveraging the multi-hop neighborhood relationships. A limited number of seeds are also taken into consideration as auxiliary information. To this end, we introduce the de-anonymization problem with the aid of the collectiveness and the collective adjacency disagreements, which are the collection of disagreements of different multi-hop adjacency matrices. We theoretically demonstrate that minimizing the collective adjacency disagreements can help match two social networks even in a very sparse circumstance, as it significantly enlarges the difference between the mismatched node pairs and the correctly matched pairs. Besides, the seeds is proved to bring positive influence in improving the de-anonymization accuracy. Algorithmically, we relax the domain of the matching function to continuum and adopt the conditional gradient descending method on the collective-form objective, to efficiently minimize the collective adjacency disagreements of two networks. We conduct tremendous experiments on different networks with or without seeds, the results of which return desirable de-anonymization accuracies and reveal the advantages of the collectiveness: the collectiveness manifests rich structural information, ther- by most nodes can be correctly matched with their correspondences even in some sparse networks, where merely utilizing the 1-hop adjacency relationships might fail to work.
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