Helper Selection Research Articles

Uncertainty Minimization for Personalized Federated Semi-Supervised Learning

Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable convergence in FL applications. Model personalization methods have been studied to overcome this problem. However, existing approaches are mainly under the prerequisite of fully labeled data, which is unrealistic in practice due to the requirement of expertise. The primary issue caused by partial-labeled condition is that, clients with deficient labeled data can suffer from unfair performance gain because they lack adequate insights of local distribution to customize the global model. To tackle this problem, 1) we propose a novel personalized semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents), thus to enhance their perception of local data; 2) based on this paradigm, we design an uncertainty-based data-relation metric to ensure that selected helpers can provide trustworthy pseudo labels instead of misleading the local training; 3) to mitigate the network overload introduced by helper searching, we further develop a helper selection protocol to achieve efficient communication with acceptable performance sacrifice. Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partially labeled data, especially in highly heterogeneous setting.

RAIM: A Reverse Auction-Based Incentive Mechanism for Mobile Data Offloading Through Opportunistic Mobile Networks

Offloading cellular traffic through Opportunistic Mobile Networks (OMNs) has been an effective method to ease the traffic burden of cellular networks. However, providing data offloading services consumes a lot of resources. Since nodes in OMNs are rational and selfish, they will not be willing to provide data offloading services if they are not properly rewarded. Therefore, it is important to exploit incentive mechanisms to motivate nodes to provide data offloading services. This paper proposes a Reverse Auction-based Incentive Mechanism, named RAIM. In RAIM, reverse auction is used as the incentive mechanism, and the incentive-driven data offloading process is modeled as Non-Linear Integer Programming (NLIP) from the business point of view, aiming to minimize the cost of the Content Service Provider (CSP). Then, a heuristic method named Decay-based Helper Selection Method (DBHSM) is proposed to resolve the problem. Moreover, a payment rule based on the standard Vickrey-Clarke-Groves scheme is proposed to ensure the individual rationality and truthfulness properties of DBHSM. Finally, real mobility trace-driven simulation results show that DBHSM outperforms other baseline methods in terms of the CSP's cost and the offloading rate under different scenarios.

A novel D2D\u2013MEC method for enhanced computation capability in cellular networks

Device-to-device (D2D) communications and mobile edge computing (MEC) used to resolve traffic overload problems is a trend in the cellular network. By jointly considering the computation capability and the maximum delay, resource-constrained terminals offload parts of their computation-intensive tasks to one nearby device via a D2D connection or an edge server deployed at a base station via a cellular connection. In this paper, a novel method of cellular D2D–MEC system is proposed, which enables task offloading and resource allocation meanwhile improving the execution efficiency of each device with a low latency. We consider the partial offloading strategy and divide the task into local and remote computing, both of which can be executed in parallel through different computational modes. Instead of allocating system resources from a macroscopic view, we innovatively study both the task offloading strategy and the computing efficiency of each device from a microscopic perspective. By taking both task offloading policy and computation resource allocation into consideration, the optimization problem is formulated as that of maximized computing efficiency. As the formulated problem is a mixed-integer non-linear problem, we thus propose a two-phase heuristic algorithm by jointly considering helper selection and computation resources allocation. In the first phase, we obtain the suboptimal helper selection policy. In the second phase, the MEC computation resources allocation strategy is achieved. The proposed low complexity dichotomy algorithm (LCDA) is used to match the subtask-helper pair. The simulation results demonstrate the superiority of the proposed D2D-enhanced MEC system over some traditional D2D–MEC algorithms.

Analysis of Factors Affecting the Selection of Midwives and Shaman Labor Workers in Haria State Saparua District

Maternal mortality ratio (MMR) is an important indicator of a country’s public health. MMR is currently thehighest number in Southeast Asia. The coverage of childbirth in 2018 Haria State is 66.67%, still far fromthe Target Of Central Maluku Regency in 2018 which is 76.27%. There are 14 active child shamans who aretrained shamans and there are still many wild shamans who do maternity assistance, while only 3 midwives.The purpose in this study is to analyze the factors that influence the selection of midwives and shamanlaborers in Haria State saparua sub-district. This research is quantitatively descriptive using cross sectionaldesign. The population in this study was mothers who performed childbirth, either stillborn or born alive inthe working area of Porto Haria public health center saparua sub-district in 2018. The sampling used totalsampling, namely all mothers who did childbirth helped by midwives as many as 62 respondents and shamansas many as 31 respondents in Haria State in 2018. Data analysis uses univariate and bivariate analysis. Theresults showed no influence between the number (parity) of children on the selection of maternity helpersin Haria State saparua sub-district (p value=0.769), There is a work influence (p value = 0.012), knowledge(p value = 0.000) and attitude (p value = 0.000) on the selection of labor helpers in Haria State saparua subdistrict.It is important to improve the mother’s knowledge of healthy childbirth and instill a positive attitudetowards health workers so that the mother will choose a healthcare person as a helper in childbirth.

Fog-Assisted Privacy Preservation Scheme for Location-Based Services Based on Trust Relationship

With advancements in smart mobile devices and their capabilities, location-based services have gained utmost importance, as its individual and social benefits are enormous. Users of location-based services have a concern to the security issues posed by its usage as the location service providers track the users' interests, behavior, and identity information. Most of the location-based services are launched from mobile phones that have stringent resources; hence incorporating encryption schemes becomes tedious, and further, dual identity attacks uncover the encrypted message. A fog-assisted privacy protection scheme for location-based service (FPriLBS) employs a semi-trusted third party as a fog server to eliminate redundant queries submitted to the location service provider in addition to the trusted helper selection scheme which hides the real identity of the user from the fog server. The experimental results show that the proposed FPriLBS outperforms the existing schemes in terms of processing time and processing cost.

The effects of perceived helpers’ cost on the helper selection

The effects of perceived helpers’ cost on the helper selection

Social-Aware Spectrum Sharing and Caching Helper Selection Strategy Optimized Multicast Video Streaming in Dense D2D 5G Networks

The expected explosion of video traffic in 5G ultradense networks will pose many challenges to the Internet service providers, e.g., degraded capacity and unfair quality of service (QoS). The most efficient solution is resource allocation that does not require any network architecture changes. This article proposes a social-aware spectrum sharing and caching helper selection (SSC) strategy that exploits the resources of the mobile users (MUs), i.e., downlink spectrum resources for sharing and caching storage resources for multicasting, to offload the videos in dense device-to-device (D2D) 5G networks. Particularly, we consider physical and social attributes of the MUs to formulate an SSC optimization problem. The SSC problem is solved to optimally control: which MUs, namely sharing mobile users (SUs), that share the downlink spectrum resources and which MUs, namely caching mobile helpers, that cache the requested videos for D2D multicast communications by reusing the shared downlink spectrum resources. The objective is to maximize the system capacity while satisfying the constraints on capacity fluctuation among the receiving mobile users (RUs) and target signal-to-interference-plus-noise ratio at the SUs, to guarantee the RUs and the SUs high QoS fairness. Simulation results are shown to demonstrate the benefits of the SSC strategy.

Downlink Resource Sharing and Caching Helper Selection Control Maximized Multicast Video Delivery Capacity in Dense D2D 5G Networks

In 5G ultra-dense networks, a large number of mobile users (MUs) request a huge amount of high data rate video traffic causing a peak congestion situation at the macro base station (MBS) and small-cell base stations. This situation certainly reduces the total video capacity delivered to the MUs. In this paper, we exploit the available spectrum and storage resources of the MUs as well as the wireless broadcast nature of device-to-device (D2D) communications to propose a joint downlink resource sharing and caching helper selection (DRS-CHS) control to maximize the multicast video delivery capacity in dense D2D 5G networks. We assume that the MUs are divided into different clusters in which they can communicate with each other by D2D communications. There are two types of MUs in each cluster including the requesting users (RUs) that request the video and the caching helpers (CHs) that have cached the video. In addition, there are some sharing users (SUs) that can share their downlink resources with the CHs and the RUs for D2D multicast communications. A DRS-CHS optimization problem is then formulated and solved for an optimal control process of how to select a CH in each cluster and how to assign an SU to share its downlink resource with the selected CH such that the total video delivery capacity multicasted from the CHs to the RUs in all clusters is maximized. Simulation results demonstrate the benefits of the proposed DRS-CHS control solution compared to other conventional benchmarks.

A detailed review of D2D cache in helper selection

With the rapid development of communication networks, the information interaction between heterogeneous networks such as the Internet of Things (IoT) and vehicle ad-hoc networks (VANETs) is becoming more and more common. In cellular networks, the proximity devices may share files directly without going through the eNBs, which is named Device-to-Device (D2D) communications. It has been considered as a potential technological component for the next generation of communication. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the back-haul links and long latency. Cyber-social networks seamlessly integrate people’s daily lives and social activities, which has a prominent contribution to the social relationship of mobile users in D2D communication. In this paper, we make an exhaustive review on the state-of-the-art research efforts on D2D caching. We first give an overview of D2D helper selection, including the network frame, computing method, and social-aware attribute. Next, a comprehensive survey of issues on D2D helper selection is presented. Finally, open research challenges and future directions are presented as well.

Proximity as a Service for the Use Case of Access Enhancement Via Cellular Network-Assisted Mobile Device-to-Device

Device-to-Device (D2D) communication is a way to treat the User Equipments (UEs) not as terminals, but as a part of the network (helpers) for service provisioning. We propose a generic framework, namely Proximity as a Service (PaaS), formulate the helper selection problem, and design and prove a heuristic helper selection policy, ContAct based Proximity (CAP), which increases the service connectivity and continuity. Design Of Experiment (DOE) is a statistical methodology that rigorously designs and conducts an experiment, and maximizes the information obtained from that experiment. We apply DOE to explore the relationship (analytic expression) between four inputs (factors) and four metrics (responses). Since different factors have different regression levels, a unified four level full factorial experiment and cubic multiple regression analysis have been carried out. Multiple regression equations are provided to estimate the different contributions and the interactions between factors. Results show that transmission range and user density are dominant and monotonically increasing, but transmission range should be restricted because of interference and energy-efficiency. After obtaining the explicit close form expressions between factors and responses, optimal values of key factors are derived. A methodology (the e-constraint method) to solve the multiple-objective optimization problem has been provided and a Pareto-Optimal set of factors has been found through iteration. The fluctuation of the iterations is small and a specific solution can be chosen based on the particular scenarios (city center or countryside with different user density). The methodology of optimization informs the design rules of the operator, helping to find the optimal networking solution.

Optimizing Content Placement and Delivery in Wireless Distributed Cache Systems Through Belief Propagation

Wireless distributed cache system (WDCS) is a promising technique to offload traffic and improve energy efficiency in content-centric networks. In this paper, the content sharing problem is considered by minimizing the average energy consumption for content caching and delivering in device-to-device (D2D) enabled cellular networks. The problem is formulated as a joint optimization problem of multiple interactive variables, which is NP-hard. In order to solve this problem, we first decompose the original problem into three subproblems, which are content helper (CH) selection, content caching, and content delivering. Specifically, in CH selection, we propose a PageRank based distributed algorithm to estimate the centrality of mobile users (MUs) by jointly analyzing their social relationships and channel conditions. Based on the selected CHs, the content caching and content delivering are formulated into two weighted matching problems. Two belief propagation (BP) based distributed algorithms are then proposed by considering content popularity and channel states to solve the aforementioned problems. Furthermore, based on the correlation of the three subproblems, a heuristic-based alternating iterative optimization method (HAIOM) is proposed to improve the average energy consumption performance of the content delivering of the WDCS. Simulation results show that by jointly optimizing the CH selection, content caching, and content delivering, the proposed scheme is capable of reducing the average energy consumption and the average content delivery delay, as well as of increasing the caching hit ratio.

Smart Contracts for Spectrum Sensing as a Service

Mobile network operators (MNO) can opportunistically use the licensed bands of the primary users (PU) provided that they monitor the spectrum and stop their transmission upon detection of the PU. As deploying spectrum sensors may be prohibitively expensive, the MNO can buy spectrum sensing service from sensing helpers in its proximity. However, such a trade requires a framework with three key functions: helper selection, faulty or malicious helper identification, and payment to honest helpers. Here, we introduce Spass which provides these functions and facilitates a fair exchange between the entities without a trusted third party via smart contracts (SC) running on a blockchain network. While payments via SCs seem conceptually simple, realizing it is difficult due to the cost of using SC functions which might be prohibitive as write/computation operations on the SCs might have a cost, e.g., in Ethereum. Considering our design goals and SC-related overhead, we derive the optimal Spass parameters maximizing the MNO's profit. Moreover, we propose a K-means clustering approach to identify independent malicious helpers, and using both lossless and lossy compression on the helpers' sensing report to decrease the cost of write operations. Via simulations, we show under which conditions Spass-powered service leads to a profitable business for an MNO.

PEMILIHAN TENAGA PENOLONG PERSALINAN DI DESA PANANCANGAN KECAMATAN CIBADAK KABUPATEN LEBAK PROVINSI BANTEN TAHUN 2017

Background : Maternal deaths in Lebak district in 2016 were 38 cases from 21,292 live births. One cause is the help of birth by paraji (not nakes). Coverage coverage of Nakes at Puskesmas Cibadak in 2016 is only reached (71,6%) from target should (90%). While coverage in Panancangan village in 2016 (46.2%). Every year the delivery by health personnel in Panancangan village is always low and has never reached the target set. Objectives : Knowledge “Factors Associated With Selection of Labor Helpers in Panancangan Village, Cibadak Sub-district, Lebak District, Banten Province, 2017”. Methodology: using Cross Sectional design. The population of this study were all maternal mothers from October to December 2017, a total sample of 58 people. The research instrument used questionnaires. Results of analysis: Most (55.17%) of respondents chose labor on non-Nakes, (53.45%) respondents had good knowledge, 56.90% of housewives were close to Faskes, 58.62% health insurance, (53.45%) mother get support from husband / family, (55,17%) mother get support of health worker. Conclusions and suggestions: There is a relationship between the selection of auxiliary maternity helpers (p-palue 0,000), health insurance ownership (p value = 0,000; OR = 18,000), husband / family support (ppalue 0,000), and nakes support value = 0,000; OR = 42,857). While there is no significant relationship is the distance to health facilities (p value = 0.149). Suggestion: Health worker in Panancangan Village further increase counseling, increase pregnant mother class, socialization of P4K, partnership of midwife and paraji shaman to all deliveries safe and assisted by health personnel.

D2D Computation Offloading Optimization for Precedence-Constrained Tasks in Information-Centric IoT

This paper proposes a computation offloading scheme for precedence-constrained tasks in a base station-assisted device-to-device (D2D) scenario for the information-centric Internet of Things (IC-IoT). When specified precedence among subtasks cannot be described as simple sequential or parallel relations in a task, the selection of task execution helper for subtasks offloading becomes complex due to the constraints of latency and resources. We define this type of precedence and aim to minimize the time and financial cost of computation task offloading for the user by optimizing subtask-helper pairs. This problem is modeled as a dynamic generalized multi-resource-constrained assignment problem. The optimal offloading policy is offered by searching minimum weight matchings in a bipartite graph. Computer simulations indicate the effectiveness of the proposed approach compared with the random helper selection and priority-based offloading scheme.

Reputation Systems Impact on Help Seeking in MOOC Discussion Forums

Many interactional archetypes from outside of learning contexts are being adapted and widely used for online learning environments without consideration for some of the side effects relevant to learner outcomes. Of particular concern is the effectiveness of help exchange in these learning environments. To address this need, this article explores how the reputation system features of up/downvoting, badges, and displayed expertise impact student helper selection in a peer help exchange system within a MOOC discussion forum. We draw from Expectancy Value Theory for Help Sources as a theoretical framework for positioning the work. Results from our field experiment show that up/downvoting has a negative impact on help seeking which is mitigated by the positive effect of Help Giver badges. The mechanism behind these results are then explored in a survey experiment investigating reputation systems impact on students expectancies, values, and costs for a help source.

On the performance of cooperative vehicular networks under antenna correlation at RSU

Vehicular communications is gradually becoming mature after decades of exciting developments and thriving advances. Resultantly, these advances have opened new possibilities for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to meet the requirements of safety applications and future self-driving technologies. While performance limits of single link vehicular communications have been well analyzed in the literature, only incremental growth has been shown in the domain of multi-antenna communications. Another major concern is that the existing works mostly assume independent fading at the antennas mounted on road side unit (RSU), thus neglecting the impact of channel correlation. Our work addresses this issue by evaluating packet error probability for two renowned antenna correlation models i.e., constant correlation (CC) and exponential correlation (EC), under Nakagami-m fading. We also consider cooperation between intermediate vehicles to ensure reliable communication from the source vehicle to the RSU. More specifically, we derive closed-form expressions of packet error probability for three cooperative techniques, namely, single helper selection (SHS), multi-hop cooperative selection (MCS) and multiple helper selection (MHS). We quantify the performance variations for different numbers of intermediate helper vehicles, and for varying values of fading parameter and correlation coefficients. Finally, we validate our mathematical derivations by performing extensive simulations in MATLAB.

Locally Repairable Regenerating Codes: Node Unavailability and the Insufficiency of Stationary Local Repair

Recent works by Ahmad et al. and by Hollmann studied the concept of “locally repairable regenerating codes (LRRCs)” that successfully combines the functional repair and partial information exchange of regenerating codes (RCs) with the much-desired local repairability feature of locally repairable codes (LRCs). One important issue that needs to be addressed by any local repair schemes (including both LRCs and LRRCs) is that sometimes designated helper nodes may be temporarily unavailable, the result of various reasons that include multiple failures, degraded reads, or power-saving strategies to name a few. Under the setting of LRRCs with temporary node unavailability, this paper studies the impact of different helper selection methods. It proves that with node unavailability, all existing methods of helper selection, including those used in RCs and LRCs, can be insufficient in terms of achieving the optimal repair-bandwidth. For some scenarios, it is necessary to combine LRRCs with a new class of helper selection methods, termed dynamic helper selection , to achieve optimal repair-bandwidth. This paper also compares the performance of different classes of helper selection methods and answers the following fundamental question: is one method of helper selection intrinsically better than the other? for various scenarios.

Miss and Forward: Exploiting Diversity With Intrasession Network Coding

Faced with explosive mobile data demand, ultrasense network is emerging as a solution for the 5G networks, which introduces new opportunities for space diversity. In this paper, we propose Miss-and-Forward (MF), a new paradigm for intrasession network coding, in which a special relay called “helper” is assigned to exploit the rich diversity. In accordance, a network coding based scheme is designed, which has the ability to restore the “missed” information and in the meanwhile retains the benefit of state-of-the-art batched sparse coding. Mathematically, we show that the source of throughput gain is the higher ranked end-to-end transfer matrix. Besides, we provide a systematical design to address some practical issues such as helper selection and rank distribution estimation. Both numerical and simulation results show that our method significantly outperforms fountain codes and existing network coding schemes.

When Can Intelligent Helper Node Selection Improve the Performance of Distributed Storage Networks?

The concept of distributed storage networks (DSNs) mostly follows two main modeling assumptions: Any $k$ out of $n$ surviving nodes should be able to reconstruct the protected file; and if one node fails, the replacement node can access $d$ helper nodes to repair its content either functionally or exactly. Two major existing approaches for DSNs are the so-called regenerating codes (RCs) and locally repairable codes (LRCs), which have different design philosophies and focus on distinct applications. Instead of being limited by the framework of either RCs or LRCs, this work answers a fundamental question for general DSNs: For an arbitrarily given $(n,k,d)$ value, whether there exists an intelligent helper node selection design that can strictly improve the storage-bandwidth tradeoff when compared with naive blind helper selection. Surprisingly, the answer is negative for a large set of $(n,k,d)$ values. Namely, for those $(n,k,d)$ values even the best helper selection design offers no gain over a blind solution. We call those $(n,k,d)$ values indifferent-to-helper-selection (ITHS). The main contribution of this work is a necessary and sufficient condition that characterizes whether an $(n,k,d)$ value is ITHS. As a fundamental study, this work assumes functional repair with unlimited computing power for encoding/decoding and focuses on the fundamental performance limits of intelligent helper selection. A new helper selection scheme, termed family helper selection , is proposed and used in the achievability analysis. For some scenarios, the proposed scheme is indeed optimal (as good as any helper selection one can design).

Mobility as a Service (MaaS): A D2D-Based Information Centric Network Architecture for Edge-Controlled Content Distribution

It has been envisaged that in future 5G networks user devices will become an integral part by participating in the transmission of mobile content traffic typically through device-to-device (D2D) technologies. In this context, we promote the concept of mobility as a service, where content-aware mobile network edge is equipped with necessary knowledge on device mobility in order to distribute popular mobile content items to interested clients via a small number of helper devices. Toward this end, we present a device-level information centric networking architecture that is able to perform intelligent content distribution operations according to necessary context information on mobile user mobility and content characteristics. Based on such a platform, we further introduce device-level online content caching and offline helper selection algorithms in order to optimize the overall system efficiency. In particular, this paper sheds distinct light on the importance of user mobility data analytics based on which helper selection can lead to overall system optimality. Based on representative user mobility models, we conducted realistic simulation experiments and modeling which have proven the efficiency in terms of both network traffic offloading gains and user-oriented performance improvements. In addition, we show how the framework can be flexibly configured to meet specific delay tolerance constraints according to specific context policies.