QUOIN: Incentive Mechanisms for Crowd Sensing Networks

Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although which information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants, including data collectors, service providers, and service consumers, to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowd-sensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study, and experimental results demonstrate that it is efficient and effective in collecting valuable information for crowd-sensing applications

Cellular throughput optimization by game-based power adjustment and outband D2D communication

Abstract Device-to-device (D2D) communication is a promising option for the fifth generation (5G) mobile communication network to reduce energy consumption and increase throughput, which makes high throughput applications possible. Also, the improvement for energy and spectrum efficiency is critical in such applications. Without occupation of cellular spectrum resources, outband D2D communication is increasingly applied to high throughput applications to increase spectrum supply. However, energy efficiency is still a key issue that needs to be addressed. Moreover, the overall energy efficiency in cellular networks is severely limited by cell-edge devices. Therefore, in this paper, we apply multi-hop relay-aided outband D2D communication to cellular networks and propose a game-based power adjustment method to address throughput optimization problem. Firstly, we model an interaction relationship of power adjustment for each transmission path as a potential game, where a new utility function is designed for each player (i.e., the receiving end of a transmission path) to evaluate its action gain and to determine whether taking action or not. And then, it is proved that the utility function is an ordinal potential function (OPF) and the game of power adjustment is an ordinal potential game (OPG), which guarantees the convergence of game decision process. Moreover, we propose a new game decision algorithm, which has quicker convergence speed than the existing typical algorithm. In addition, we design a network-assisted distributed processing architecture for solving throughput optimization problem, including receiving mode selection, verification for relay selection, and transmission power adjustment, which can ease the burden of centralized processing. The experimental results show that our scheme is superior to the existing typical work in terms of throughput, delay, energy efficiency, continuous service ability, and convergence performance

An Efficient Radio Access Resource Management Scheme Based on Priority Strategy in Dense mmWave Cellular Networks

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are usually used to overcome severe signal path loss problem and improve signal coverage quality. The combination of directional transmission and network densification poses a challenge to radio access resource management. The existing work presented an effective solution for dense mmWave wireless local area networks (WLANs). However, this scheme cannot adapt to network expansion when it is applied directly to dense mmWave cellular networks. In addition, there is still room for improvement in terms of energy efficiency and throughput. Therefore, we firstly propose an efficient hierarchical beamforming training (BFT) mechanism to establish directional links, which allows all the small cell base stations (SBSs) to participate in the merging of training frames to adapt to network expansion. Then, we design a BFT information-aided radio access resource allocation algorithm to improve the downlink energy efficiency of the entire mmWave cellular network by reasonably selecting beam directions and optimizing transmission powers and beam widths. Simulation results show that the proposed hierarchical BFT mechanism has the smaller overhead of BFT than the existing BFT mechanism, and the proposed BFT information-aided radio access resource allocation algorithm outperforms the existing corresponding algorithm in terms of average energy efficiency and throughput per link

Correction to: Improving cellular downlink throughput by multi-hop relay-assisted outband D2D communications

The original publication [1] misses three algorithms. The missing ones can be found in this Erratum

Improving cellular downlink throughput by multi-hop relay-assisted outband D2D communications

Abstract One goal of the fifth-generation (5G) cellular network is to support much higher data capacity (e.g., 1000 times higher than today), where device-to-device (D2D) communication is one of the key enabling technologies. In this paper, we focus on the D2D relaying functionality to improve cellular downlink throughput. Based on the shortages of the latest relevant work, we propose a new scheme that leverages multi-hop relay-assisted outband D2D communications. First of all, by extending two-hop connection to three-hop connection, our scheme can cut down receiving bit error ratio (BER) of cell edge nodes far away from a cellular base station (BS), which improves cellular downlink throughput. Then, it balances network lifetime and throughput by the proposed ratio of income and expenditure (RIE) metric with respect to remaining energy and throughput. Moreover, it reduces the computational overhead of searching relay and also ensures that the optimal relay is selected by the adjustment of searching scope. Compared with the most relevant work, our scheme outperforms it in terms of throughput, delay, and network lifetime

Enhancing Cellular Coverage Quality by Virtual Access Point and Wireless Power Transfer

The ultradensification deploying for cellular networks is a direct and effective method for the improvement of network capacity. However, the benefit is achieved at the cost of network infrastructure investment and operating overheads, especially when there is big gap between peak-hour Internet traffic and average one. Therefore, we put forward the concept of virtual cellular coverage area, where wireless terminals with high-end configuration are motivated to enhance cellular coverage quality by both providing RF energy compensation and rewarding free traffic access to Internet. This problem is formulated as the Stackelberg game based on three-party circular decision, where a Macro BS (MBS) acts as the leader to offer a charging power to Energy Transferring Relays (ETRs), and the ETRs and their associating Virtual Access Points (VAPs) act as the followers to make their decisions, respectively. According to the feedback from the followers, the leader may readjust its strategy. The circular decision is repeated until the powers converge. Also, the better response algorithm for each game player is proposed to iteratively achieve the Stackelberg-Nash Equilibrium (SNE). Theoretical analysis proves the convergence of the proposed game scheme, and simulation results demonstrate its effectiveness

Performance Optimization in UAV-Assisted Wireless Powered mmWave Networks for Emergency Communications

In this paper, we explore how a rotary-wing unmanned aerial vehicle (UAV) acts as an aerial millimeter wave (mmWave) base station to provide recharging service and radio access service in a postdisaster area with unknown user distribution. The addressed optimization problem is to find out the optimal path starting and ending at the same recharging point to cover a wider area under limited battery capacity, and it can be transformed to an extended multiarmed bandit (MAB) problem. We propose the two improved path planning algorithms to solve this optimization problem, which can improve the ability to explore the unknown user distribution. Simulation results show that, in terms of the total number of served user equipment (UE), the number of visited grids, the amount of data, the average throughput, and the battery capacity utilization level, one of our algorithms is superior to its corresponding comparison algorithm, while our other algorithm is superior to its corresponding comparison algorithm in terms of the number of visited grids

Improving Energy Efficiency of Multimedia Content Dissemination by Adaptive Clustering and D2D Multicast

While achieving desired performance, there exist still many challenges in current cellular networks to support the multimedia content dissemination services. The conventional multimedia transmission schemes tend to serve all multicast group members with the data rate supported by the receiving user with the worst channel condition. The recent work discusses how to provide satisfactory quality of service (QoS) for all receiving users with different quality of experience (QoE) requirements, but the energy efficiency improvement of multimedia content dissemination is not its focus. In this paper, we address it based on adaptive clustering and device-to-device (D2D) multicast and propose an energy-efficient multimedia content dissemination scheme under a consistent QoE constraint. Our scheme extends the recent work with the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, which can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members. In the proposed scheme, we jointly consider the cellular multicast, intracluster D2D multicast, and intercluster D2D multicast for designing the energy-efficient multimedia content dissemination scheme. In particular, we formulate the energy-efficient multicast transmission problem as a Stackelberg game model, where the macro base station (MBS) is the leader and the candidate D2D cluster heads (DCHs) are the followers. Also, the MBS acts as the buyer who buys the power from the candidate DCHs for intracluster and intercluster D2D multicast communications, and the candidate DCHs act as the sellers who earn reward by helping the MBS with D2D multicast communications. Through analyzing the above game model, we derive the Stackelberg equilibrium as the optimal allocation for cellular multicast power, intracluster D2D multicast power, and intercluster D2D multicast power, which can maximize the MBS’s utility function. Finally, the proposed scheme is verified through the simulation experiments designed in this paper