With the explosive growth in communication de mand for the fifth generation (5G) mobile communication system, research into access based on new radio in the unlicensed band (NR-U) has become key to improve network performance. Existing coexistence methods typically simulate NR-U as WiFi nodes and dynamically adjust their listen-before-talk (LBT) related parameters for coexistence. However, these approaches struggle to reliably manage channel occupancy, resulting in wasted channel time resources and limited coexistence perfor mance. In light of this, this paper proposes a channel occupancy notification-based fair coexistence (CON-FC) method for NR-U and WiFi, addressing the interference and fairness issues between the two types of networks. The method optimizes the channel utilization efficiency of the NR-U system through a reliable channel occupancy notification mechanism based on the Clear-to-Send (CTS) frame and Physical Layer Convergence Procedure (PLCP) header of valid WiFi communication frame, and the design of policies and algorithms for NR-U accesses, which reduces ineffective fallbacks andWiFi device conflicts. The results of the simulation test show that, compared to the traditional LBT method, CON-FC improves the fairness index by an average of 7.43% and increases overall throughput by 11.53%-71.82% in various network configurations. This method not only meets the fairness requirements of the 3rd Generation Partnership Project (3GPP) but also significantly enhances overall system perfor mance, providing a reliable solution for the efficient coexistence of NR-U and WiFi in the unlicensed spectrum.

The deployment of 5G NR-U (New Radio in Unlicensed Spectrum) technology has become a key strategy for expanding network capacity. However, the traditional listen before talk (LBT) coexistence mechanism suffers from unstable channel occupancy in heterogeneous scenarios involving NR-U and WiFi. The existing performance analysis models mostly assume that WiFi networks are in a saturated state, which deviates from the unsaturated service characteristics commonly found in real networks. For this reason, this paper proposes a fair coexistence mechanism, NS-CON-FC (Non-Saturated Channel Occupancy Notification-Fair Coexistence), based on channel occupancy notification, aiming to improve the reliability of NR-U channel occupancy and achieve fair coexistence with WiFi networks. This mechanism effectively suppresses potential conflicts of WiFi devices by proactively sending composite occupancy notification frames (combining 802.11a CTS frame with 802.11n PLCP header) from NR-U to pre-declare the transmission time slots. In this paper, a system model comprising unsaturated WiFi nodes is constructed, and an optimization problem is established to maximize the total system throughput while adhering strictly to 3GPP fairness constraints. The analytical solution of the optimal time allocation strategy is obtained through mathematical derivation, which provides a low-complexity parameter configuration guide for practical deployment. Simulation results show that, compared with the traditional LBT scheme, NS-CON-FC improves the total throughput by 30.7% while maintaining the fairness constraint, thereby verifying the effectiveness of the proposed mechanism.

Abstract this study was to discuss the application value of the embedded intelligent fetal heart rate monitoring system (EI system) based on empirical mode decomposition (EMD) algorithm in the monitoring of pregnant women with pregnancy bacterial infection complications, aiming to provide a reference basis for timely nursing of pregnancy bacterial infection complications. In this study, the blind source separation method was introduced to denoise the screened intrinsic modal function (IMF) components based on the EI system and the EMD algorithm. The signal to noise ratio (SNR) based on the EMD optimization algorithm was compared with that of other algorithms. Two hundred and sixty‐eight pregnant women in the obstetric clinic were selected as the research objects and divided into an observation group (abnormal fetal heart rate) and a control group (normal fetal heart rate). The pregnant women in both groups accepted the conventional nursing methods, and the EI system was adopted to monitor the fetal heart rate. The probability of abnormal fetal heart monitoring of pregnant women in observation group and control group was analysed. At the same time, the probability of bacterial infection complications was analysed. It was found that the EI system based on EMD algorithm showed obviously higher SNR in contrast to other algorithms. The waveforms of fetal heart monitoring in the observation group and the control group were significantly different (χ2 = 11.215, p < 0.05); microscopic examination of pregnant women secretion in the bacterial infection group revealed the presence of a large number of pathogenic bacteria; it was found that the infection rate of vaginosis was significantly different between the control group and the observation group (p < 0.01); there was a significant difference between the observation group and the control group in the complication of premature rupture of fetal membrane caused by bacteria (χ2 = 11.2, p < 0.01). In conclusion, the EI system based on the EMD algorithm could be undertaken as a tool for the preliminary diagnosis of pregnancy bacterial infection complications in pregnant women, which provided relevant basis for later nursing.

In most of the existing mobile applications for data collection and data analytics, either the privacy issue is frequently neglected or the privacy options are not configurable by the participants. This paper proposes configurable privacy level by potential crowdsourcing participants who are able to choose their desirable privacy level and get paid based on the quality of data they provide to the originator in Device to Device network (D2D). Combining with the encryption technique, not only the user’s privacy is protected, but also the encryption complexity is reduced. We first formulate the participant recruitment process into an optimization problem from the participants’ perspective by considering the competition and collaboration among existing and candidate participants in order to achieve the best utility. Then we design a distributed approximate scheme that relies on participants’ local knowledge to complete the overall recruitment task. We implement the approximate approach in Dell Streak tablets and carry out a campus-scale experiment for 21 days, plus run simulations for more extensive and detailed evaluation under various task settings. The results demonstrate the efficiency of the proposed approaches and disclose valuable insights for practical considerations in D2D based crowdsourcing.

In mobile opportunistic networks, a common assumption is that nodes are able to complete as much data exchange as needed during a communication opportunity, which is, however, not the case in practice due to limited wireless link bandwidth. Beyond that, the storage capacity at a node is also limited, further impacting the communication efficiency. In this paper, we explore delay sensitive and network resource constrained traffic allocation for mobile device-to-device crowdsourcing. With the requirement of restricted node storage and link bandwidth, we first formulate a non-linear traffic allocation optimization problem that would be at least as hard as NP-hard. In order to practically solve it, based on the submodular property, we propose an approximation algorithm and a distributed heuristic in the same design principle. We implement the latter on Dell Streak tablets and deploy an experiment with 21 nodes for a period of three weeks. Moreover, we extract the implementation codes from the prototype and run simulations using the Haggle trace to study its performance trend. The experiment and simulation outcomes verify that the proposed mechanisms achieve the close-to-optimal performance with affordable computation complexity, which can be easily implanted in practical network environment.

With the remarkable proliferation of intelligent wireless devices and active mobile users, we have witnessed a rapid increasing trend of mobile crowdsourcing applications. While crowdsourcing does not depend on any specific underlying network, the device-to-device (D2D)-based crowdsourcing is highly desired when the originator of a crowdsourcing task cannot directly reach out to the participants or the conventional approaches for data transportation are costly. The marriage of crowdsourcing and D2D creates new, interesting research problems, mainly due to the unique non-deterministic setting in D2D. In this work, we focus on the problem of how to efficiently distribute a crowdsourcing task and recruit participants based on D2D communications. We formally formulate the minimum-cost crowdsourcing (MCC) problem in D2D networks, which explores a multi-dimensional design space to seek an optimal solution that minimizes the total crowdsourcing cost while satisfying the coverage probability over the field of interest. We introduce an approximation algorithm based on a reduced solution space and formally prove that its cost is bounded by a desired approximation ratio in comparison with the optimal solution. We further propose a lightweight online heuristic that inherits the same design philosophy but implements it in a distributed manner. We prototype the proposed online scheme in Android and carry out experiments in a campus environment, involving 21 Dell Streak tablets carried by students for a period of 15 days. We also extract the algorithm codes from our prototype and perform extensive simulations based on Haggle trace. The results demonstrate the efficiency of the proposed heuristics and reveal empirical insights into the design tradeoffs and practical considerations in D2D-based crowdsourcing.

Device-to-Device (D2D) networks impose a significant challenge on delay-sensitive crowdsourcing due to the highly nondeterministic and intermittent network connectivity. Under this setting, the paper investigates a participant recruitment problem in which an initial set of recruited nodes, which we call seeds, need to make an optimal decision on what other nodes to recruit to perform the crowdsourcing task. These seeds face the dilemma that recruiting more nodes increases their own payment but on the other hand also increases the risk of being excluded from the crowdsourcing task. As a first attack to this problem, we propose a dynamic programming algorithm. However, it is a centralized solution and hence the practicality is compromised. Therefore, we introduce two distributed alternatives. One is based on the divide-and-conquer paradigm by first partitioning a network into a set of opportunistic Voronoi cells and then running an optimization algorithm in each cell. The other is a task-splitting scheme which recursively delegates the recruitment task to newly joined nodes. We implemented our proposed solutions on an Android-based prototype and built a testbed using 25 Dell Streak tablets. Our experiments which lasted for 24 days demonstrate that the distributed schemes approximate the theoretical optimum with affordable complexity. Moreover, we conducted simulations with a much larger scale and more diverse settings. The simulation results corroborate the experimental data and confirm that our proposed distributed solutions closely approach the performance of the centralized solution while satisfying the optimization goal under different network configurations.

This work addresses the problem of how to enable efficient data query in a Mobile Ad-hoc SOcial Network (MASON), formed by mobile users who share similar interests and connect with one another by exploiting Bluetooth and/or WiFi connections. The data query in MASONs faces several unique challenges including opportunistic link connectivity, autonomous computing and storage, and unknown or inaccurate data providers. Our goal is to determine an optimal transmission strategy that supports the desired query rate within a delay budget and at the same time minimizes the total communication cost. To this end, we propose a centralized optimization model that offers useful theoretic insights and develop a distributed data query protocol for practical applications. To demonstrate the feasibility and efficiency of the proposed scheme and to gain useful empirical insights, we carry out a testbed experiment by using 25 off-the-shelf Dell Streak tablets for a period of 15 days. Moreover, extensive simulations are carried out to learn the performance trend under various network settings, which are not practical to build and evaluate in laboratories.

In the Cloud manufacturing environment, there are a huge number of cloud services, which are dynamic and changeful. The management of cloud services is very difficult. The paper presents a hypergraph clustering-based method to manage cloud manufacturing services. The clustering-based Cloud Manufacturing Service Management Model is presented, which contains three layers: manufacturing resources, cloud manufacturing service instances and cloud manufacturing service classes. Based on hypergraph, we establish an efficient organizational structure of the cloud service resources, the relationship between cloud services mapped to a weighted hypergraph and accomplish the cloud service clustering based on hypergraph partitioning. Based on the proposed method, we build a cloud service management prototype on Shandong Province cloud manufacturing platform for the effective organization and management of cloud services. It significantly benefits key service management tasks, such as service discovery and composition. These tasks can be first applied to a small number of abstract services and then mapped to the larger scale actual services. Services in one clustering can be interchangeable, which can improve system stability and reliability.

In this work we study the problem of delay-constrained data transmission in mobile opportunistic networks. In contrast to the single-copy single-path and multi-copy multi-path routing schemes that have been discussed in the literature, we aim to determine an optimal single-copy multi-path transmission strategy that satisfies delay requirement and at the same time minimizes communication cost. We first propose a centralized optimal formulation, and then develop a distributed routing algorithm under practical network settings. We implement the proposed algorithm on Dell Streak tablets and carry out an experiment with 25 nodes for a period of two weeks. Moreover, we extract the algorithm codes from our prototype and run simulations based on the Haggle trace to study its performance trends under various network settings.