CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence optimization.However,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not high.To solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine.First,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position prediction.Second,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,respectively.Meanwhile,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

Participants Recruitment for Coverage Maximization by Mobility Predicting in Mobile Crowd Sensing

Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.

An Incentive Mechanism for Mobile Crowd Sensing in Vehicular Ad Hoc Networks

In the mobile crowdsensing of vehicular ad hoc networks (VANETs), in order to improve the amount of data collection, an effective method to attract a large number of vehicles is needed. Therefore, the incentive mechanism plays a dominant role in the mobile crowdsensing of vehicular ad hoc networks. In addition, the behavior of providing malicious data by vehicles as data collectors will have a huge negative impact on the whole collection process. Therefore, participants need to be encouraged to provide data honestly to obtain more available data. In order to increase data collection and improve the availability of collected data, this paper proposes an incentive mechanism for mobile crowdsensing in vehicular ad hoc networks named V-IMCS. Specifically, the Stackelberg game model, Lloyd’s clustering algorithm and reputation management mechanism are used to balance the competitive relationship between participants and process the data according to the priority order, so as to improve the amount of data collection and encourage participants to honestly provide data to obtain more available data. In addition, the effectiveness of the proposed mechanism is verified by a series of simulations. The simulation results show that the amount of available data is significantly higher than the existing incentive mechanism while improving the amount of data collection.

Hybrid Two-Phase Task Allocation for Mobile Crowd Sensing

As a result of the popularity of mobile devices,Mobile Crowd Sensing (MCS) has attracted a lot of attention. Task allocation is a significant problem in MCS. Most previous studies mainly focused on stationary spatial tasks while neglecting the changes of tasks and workers. In this paper,the proposed hybrid two-phase task allocation algorithm considers heterogeneous tasks and diverse workers.For heterogeneous tasks,there are different start times and deadlines. In each round,the tasks are divided into urgent and non-urgent tasks. The diverse workers are classified into opportunistic and participatory workers.The former complete tasks on their way,so they only receive a fixed payment as employment compensation,while the latter commute a certain distance that a distance fee is paid to complete the tasks in each round as needed apart from basic employment compensation. The task allocation stage is divided into multiple rounds consisting of the opportunistic worker phase and the participatory worker phase. At the start of each round,the hiring of opportunistic workers is considered because they cost less to complete each task. The Poisson distribution is used to predict the location that the workers are going to visit,and greedily choose the ones with high utility. For participatory workers,the urgent tasks are clustered by employing hierarchical clustering after selecting the tasks from the uncompleted task set.After completing the above steps,the tasks are assigned to participatory workers by extending the Kuhn-Munkres (KM) algorithm.The rest of the uncompleted tasks are non-urgent tasks which are added to the task set for the next round.Experiments are conducted based on a real dataset,Brightkite,and three typical baseline methods are selected for comparison. Experimental results show that the proposed algorithm has better performance in terms of total cost as well as efficiency under the constraint that all tasks are completed.

Toward Energy-Efficient and Trustworthy eHealth Monitoring System

The rapid technological convergence between Internet of Things （loT）, Wireless Body Area Networks （WBANs） and cloud computing has made e-healthcare emerge as a promising application domain, which has significant potential to improve the quality of medical care. In particular, patient-centric health monitoring plays a vital role in e-healthcare service, involving a set of important operations ranging from medical data collection and aggregation, data transmission and segregation, to data analytics. This survey paper firstly presents an architectural framework to describe the entire monitoring life cycle and highlight the essential service components. More detailed discussions are then devoted to {／em data collection} at patient side, which we argue that it serves as fundamental basis in achieving robust, efficient, and secure health monitoring. Subsequently, a profound discussion of the security threats targeting eHealth monitoring systems is presented, and the major limitations of the existing solutions are analyzed and extensively discussed. Finally, a set of design challenges is identified in order to achieve high quality and secure patient-centric monitoring schemes, along with some potential solutions.

Mobile crowd sensing task optimal allocation： a mobility pattern matching perspective

With the proliferation of sensor-equipped portable mobile devices, Mobile CrowdSensing （MCS） using smart devices provides unprecedented opportunities for collecting enormous surrounding data. In MCS applications, a crucial issue is how to recruit appropriate participants from a pool of available users to accomplish released tasks, satisfying both resource efficiency and sensing quality. In order to meet these two optimization goals simultaneously, in this paper, we present a novel MCS task allocation framework by aligning existing task sequence with users＇ moving regularity as much as possible. Based on the process of mobility repetitive pattern discovery, the original task allocation problem is converted into a pattern matching issue, and the involved optimization goals are transformed into pattern matching length and support degree indicators. To determine a trade-off between these two competitive metrics, we propose greedy- based optimal assignment scheme search approaches, namely MLP, MDP, IU1 and IU2 algorithm, with respect to matching length-preferred, support degree-preferred and integrated utility, respectively. Comprehensive experiments on real- world open data set and synthetic data set clearly validate the effectiveness of our proposed framework on MCS task optimal allocation.

Quality-Aware User Recruitment Based on Federated Learning in Mobile Crowd Sensing

With the rapid development of mobile devices,the use of Mobile Crowd Sensing(MCS)mode has become popular to complete more intelligent and complex sensing tasks.However,large-scale data collection may reduce the quality of sensed data.Thus,quality control is a key problem in MCS.With the emergence of the federated learning framework,the number of complex intelligent calculations that can be completed on mobile devices has increased.In this study,we formulate a quality-aware user recruitment problem as an optimization problem.We predict the quality of sensed data from different users by analyzing the correlation between data and context information through federated learning.Furthermore,the lightweight neural network model located on mobile terminals is used.Based on the prediction of sensed quality,we develop a user recruitment algorithm that runs on the cloud platform through terminal-cloud collaboration.The performance of the proposed method is evaluated through simulations.Results show that compared with existing algorithms,i.e.,Random Adaptive Greedy algorithm for User Recruitment(RAGUR)and Context-Aware Tasks Allocation(CATA),the proposed method improves the quality of sensed data by 23.5%and 38.8%,respectively.

A Survey on Task and Participant Matching in Mobile Crowd Sensing

Mobile crowd sensing is an innovative paradigm which leverages the crowd, i.e., a large group of people with their mobile devices, to sense various information in the physical world. With the help of sensed information, many tasks can be fulfilled in an efficient manner, such as environment monitoring, traffic prediction, and indoor localization. Task and participant matching is an important issue in mobile crowd sensing, because it determines the quality and efficiency of a mobile crowd sensing task. Hence, numerous matching strategies have been proposed in recent research work. This survey aims to provide an up-to-date view on this topic. We propose a research framework for the matching problem in this paper, including participant model, task model, and solution design. The participant model is made up of three kinds of participant characters, i.e., attributes, requirements, and supplements. The task models are separated according to application backgrounds and objective functions. Offline and online solutions in recent literatures are both discussed. Some open issues are introduced, including matching strategy for heterogeneous tasks, context-aware matching, online strategy, and leveraging historical data to finish new tasks.

VDCM: A Data Collection Mechanism for Crowd Sensing in Vehicular Ad Hoc Networks

With the rapid development of mobile devices,aggregation security and efficiency topics are more important than past in crowd sensing.When collecting large-scale vehicle-provided data,the data transmitted via autonomous networks are publicly accessible to all attackers,which increases the risk of vehicle exposure.So we need to ensure data aggregation security.In addition,low aggregation efficiency will lead to insufficient sensing data,making the data unable to provide data mining services.Aiming at the problem of aggregation security and efficiency in large-scale data collection,this article proposes a data collection mechanism(VDCM)for crowd sensing in vehicular ad hoc networks(VANETs).The mechanism includes two mechanism assumptions and selects appropriate methods to reduce consumption.It selects sub mechanism 1 when there exist very few vehicles or the coalition cannot be formed,otherwise selects sub mechanism 2.Single aggregation is used to collect data in sub mechanism 1.In sub mechanism 2,cooperative vehicles are selected by using coalition formation strategy and auction cooperation agreement,and multi aggregation is used to collect data.Two sub mechanisms use Paillier homomorphic encryption technology to ensure the security of data aggregation.In addition,mechanism supplements the data update and scoring steps to increase the amount of available data.The performance analysis shows that the mechanism proposed in this paper can safely aggregate data and reduce consumption.The simulation results indicate that the proposed mechanism reduces time consumption and increases the amount of available data compared with existing mechanisms.

Crowd sensing data delivery based on tangle DAG network

In view of the fact that current data delivery methods are not enough to meet the security requirements of today’s distributed crowd sensing,and the data delivery methods are not flexible enough,this paper proposes a crowd sensing data interaction method based on tangle directed acyclic graph(DAG)network.In this method,users and platforms are regarded as nodes of the network in the process of performing crowd sensing tasks.First,the heaviest chain is generated through the main chain strategy to ensure the stability of the network.Then,the hidden Markov model(HMM)prediction model is used to improve the correlation of the perceived data to improve the performance.Then,the confidential transaction and commitment algorithm is used to ensure the reliability of the transaction,overcome the security risks faced by the trusted third party,and simplify the group intelligence aware transaction mode.Finally,through simulation experiments,the security and feasibility of the group intelligence aware data delivery method based on tangle DAG network are verified.

Detecting Anomalous Bus-Driving Behaviors from Trajectories

In urban transit systems,discovering anomalous bus-driving behaviors in time is an important technique for monitoring the safety risk of public transportation and improving the satisfaction of passengers.This paper proposes a two-phase approach named Cygnus to detect anomalous driving behaviors from bus trajectories,which utilizes collected sensor data of smart phones as well as subjective assessments from bus passengers by crowd sensing.By optimizing support vector machines,Cygnus discovers the anomalous bus trajectory candidates in the first phase,and distinguishes real anomalies from the candidates,as well as identifies the types of driving anomalies in the second phase.To improve the anomaly detection performance and robustness,Cygnus introduces virtual labels of trajectories and proposes a correntropy-based policy to improve the robustness to noise,combines the unsupervised anomaly detection and supervised classification,and further refines the classification procedure,thus forming an integrated and practical solution.Extensive experiments are conducted on real-world bus trajectories.The experimental results demonstrate that Cygnus detects anomalous bus-driving behaviors in an effective,robust,and timely manner.