Efficient Allocation Mechanism Research Articles

Optimizing task allocation with temporal‐spatial privacy protection in mobile crowdsensing

AbstractMobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection.

A Survey on Throughput Maximization in IoT Networks

This paper presents a comprehensive survey on Non-Orthogonal Multiple access for multiple access in wireless networks. As networks are now migrating towards the wireless domain as opposed to the wired domain, the need for more efficient bandwidth allocation mechanism is necessary. With the advent of big data applications, increasing number of users, increased bandwidth and data requirement yet limited bandwidth availability have become serious challenges. Hence future generation wireless networks would need improved multiple access techniques than their present day counterparts. Non-Orthogonal Multiple access is a technique in which multiple users data is separated in the power domain. The problems addressed by NOMA are low overall bandwidth for multiple users. This paper presents a comprehensive review and taxonomy of the NOMA based multiple access framework. Index Terms:- Non-Orthogonal Multiple Access, Wireless Networks, Quality of Service (QoS), Bit Error Rate, Throughput.

Disentangling the asymmetric effect of financialization on the green output gap

Insufficient green output can lead to environmental degradation while overinvestment may result in economic imbalances, social inequalities, and opportunity costs. Thus, sustaining an optimal level of green growth could restrict both green neglect and green grabbing. A higher level of financialization is believed to be an efficient allocation mechanism for green development. This research provides empirical insights into how financialization helps narrow the green growth gap. Using various estimation techniques such as the Hodrick-Prescott (HP) filter, Hamilton's filter, and the super-efficiency slack-based model, we examine the heterogeneous effects of financialization on the green growth gap by utilizing unbalanced panel data from 124 countries spanning the period 1980–2020. The results, obtained through bootstrapped quantile regression, indicate that financial access, depth, and efficiency contribute to improving insufficient green growth levels and curbing excessive investment in green initiatives. However, we find little or no evidence that financial stability or liberalization restricts overinvestment in low-carbon economic development or improves environmental stewardship when green neglect is observed. Additional analysis suggests that business cycle fluctuations, especially during recessionary periods, adversely affect the ability of financialization to optimally allocate green resources, but this effect is mitigated if financial development is countercyclical. Policymakers should prioritize promoting financial depth and efficiency, as well as provide access to finance, in order to support sustainable green growth and to avoid overinvestment in low-carbon economic development.

Optimal Resource Allocation for NOMA Wireless Networks

The non-orthogonal multiple access (NOMA) method is a novel multiple access technique that aims to increase spectral efficiency (SE) and accommodate enormous user accesses. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC), are performed at the receiving end to demodulate the necessary user signals. Although its basic signal waveform, like LTE baseline, could be based on orthogonal frequency division multiple access (OFDMA) or discrete Fourier transform (DFT)-spread OFDM, NOMA superimposes numerous users in the power domain. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the complexity of its receiver. Different power allocation techniques will have a direct impact on the system’s throughput. As a result, in order to boost the system capacity, an efficient power allocation mechanism must be investigated. This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution. The major goal is to maximize the users’ maximum weighted sum rate. The suggested algorithm’s most notable feature is that it converges to the global optimal solution. When compared to existing methods, simulation results reveal that the suggested technique has a better power allocation capability.

Locally efficient and strategy-proof allocation mechanisms in exchange economies

In this paper, we investigate whether efficiency and strategy-proofness of allocation mechanisms defined on a “local” preference set imply dictatorship. Although there is an extensive literature on the characterization of efficient and strategy-proof allocation mechanisms defined on the whole preference set, little attention has been given to a local characterization even in two-agent economies. This paper presents three results. First, we point out that locally efficient, strategy-proof, and nondictatorial allocation mechanisms exists even in two-agent economies, when boundary allocations can be efficient. Second, excluding such exceptional cases, we show that in economies where the number of goods equals or exceeds the number of agents, any efficient and strategy-proof allocation mechanism defined on any local preference domain is alternately dictatorial, that is, it always allocates the total amount of goods to some single agent, even if the receivers vary. Third, we clarify that the local characterization is generally an open question even with allocation conditions such as the minimum consumption guarantee, and show that efficiency and strategy-proofness are incompatible with allocation conditions when all agents have the same local preference set.

A Novel Dynamic Bandwidth Allocation Scheme for XGPON based Mobile Fronthaul for Small Cell CRAN

Time division multiplexing passive optical networks (TDM-PON) have emerged as a promising technology for mobile fronthaul for small cell CRAN. The performance of such TDM-based PON mobile fronthaul for small cell CRAN greatly depends on efficient dynamic bandwidth allocation (DBA) mechanisms. In this paper, a novel DBA mechanism based on the concept of dynamic service interval for ITU-based TDM-PON (XG-PON) for mobile fronthaul has been proposed. The proposed scheme is implemented using network simulator −3 (NS-3) simulation tool for evaluation of network performance parameters. The performance evaluation of the proposed DBA against the existing schemes such as optimized round robin (ORR) and Group-GIANT DBA shows a significant improvement in upstream mean delay, packet loss ratio and grant-request ratio. In addition to this, the proposed scheme shows that larger number of ONU(RRH) can be supported by the network for front haul uplink traffic transmitted via XG-PON in small cell CRAN.

Optimal Spectral Resource Allocation and Pricing for 5G and Beyond: A Game Theoretic Approach

Optimal allocation of the available spectrum is a crucial requirement of 5G and Beyond (B5G) for achieving higher Quality of Service (QoS) and low-latency. However, in 5G and Beyond, this requirement presents a potential need for dimensioning and managing the spectral resource in the cellular services. In this article, we address the issues of spectral distribution using DAG and Vickrey Clarke Groves (VCG) mechanisms by evaluating with a derived parameter for sustainable revenue and social welfare of the entire network. In particular, we modelled a framework to optimize social welfare of the users and the revenue of the cellular operator by proposing an efficient spectral allocation and pricing mechanism.

Efficient multi-period distribution mechanism for the innovation investment system under uncertainty

In this paper, we study the problem of a venture investor who distributes the budget between several innovation projects under conditions of uncertainty. A common method for solving this problem is through bilateral negotiations with the external evaluation of projects. However, the effectiveness almost entirely depends on the evaluation quality, but external evaluation seldom reduces the knowledge asymmetry for innovation projects. We propose an iterative revelation mechanism for this problem when the investor sequentially offers possible allocations of the limited budget in the form of threshold dividing questions. The binary choices of innovators serve as a signal of internal estimates of the project implementation costs. Under perfect information, such a mechanism, regardless of the method for determining budget allocations, always produces an effective allocation in subgame-perfect Nash equilibrium. Under uncertainty, the method of offering distribution options matters – the optimal solution is found under the English auction class of mechanisms. In an efficient iterative allocation mechanism for innovation investment, the investor proposes a new allocation of the budget each round until an efficient allocation is achieved. The proposed mechanism does not necessarily need to identify the exact minimum budgets for each innovator. Another advantage of the proposed mechanism is the ability to use different processes for organizing rounds.

Deployment of Hybrid Evolutionary Algorithm-Artificial Neural Network for Optimum Real Time Frequency Allocation in a Heterogeneous Frequency Requisition Network

In heterogeneous frequency demand cellular networks, the frequency demand varies across the cells involved, in contrast to the homogeneous scenario, where the demand is the same. This paper reports the deployment of hybridized evolutionary algorithm and artificial neural network for optimum frequency allocation in heterogeneous frequency requisition mobile networks to ensure interference control. Frequency sharing and reuse among cells, due to scarcity, are fundamental in a communication network for optimum frequency utilization. Primarily, efficient frequency sharing allows cells of adequate reuse distance the utilization of the same channels, with minimized inter-cell interference. The degree of freedom from interference and efficient frequency allocation mechanism dictate the grade of service, GoS, of a communication network. A real-time frequency allocation is defined by some degree of randomness. Thus, frequency allotment problem is mostly expressed as a constrained optimization formulation. The optimal allocations are achieved at points of minimum cost metric. In this paper, the frequency allotment issue is expressed as a two-objective optimization challenge, using Key Performance Indicators (KPIs) data acquired via Drive Test as input parameters. NSGA-II, an evolutionary algorithm was first deployed on the formulated problem, then in combination with SOM, an artificial neural network technique. The hybrid algorithm was implemented in MATLAB for a heterogeneous frequency demand scenario. The results obtained from the hybrid technique show performance improvements of between 6% and 28% in terms of fitness indices for interference cost function and, between 3% and 65% for demand infringement cost function. The algorithm could be embedded in the operating system of Base Station Controllers for enhanced real-time optimal allocation of network resources.

Efficient and strategy-proof allocation mechanisms in many-agent economies

In this paper, we show that in pure exchange economies, any Pareto-efficient and strategy-proof allocation mechanism is alternately dictatorial; that is, it always allocates the total endowment to a single agent even if the receivers vary. While many studies have shown that such an allocation mechanism is dictatorial in two-agent economies, it has long remained an open question whether such a characterization can be obtained in many-agent economies.

A Framework for an Energy-Efficient Bandwidth Allocation Approach through Dynamic ONTs Grouping in Flexible GPON Access Networks

The growing demands for high speed connectivity to keep pace with bandwidth intensive applications and services have spawned the idea of developing PONs with capabilities beyond those of copper and wireless-based technologies in access network. In this article, an approach for the design of an energy efficient bandwidth allocation mechanism for the shared upstream communication link in the Fiber to the Home (FTTH) access network is presented and evaluated using Mixed Integer Linear Programming (MILP) model. In the MILP model, two objective functions for minimization of power consumption and minimization of blocking were evaluated. The results have shown that with the objective of power minimization approach, Optical Network Terminals (ONTs) are efficiently grouped to the minimum number of active networking Optical Line Terminal (OLT) switches, traffic is groomed, ports are efficiently utilized, and hence total power consumption is minimized. Results have shown that with energy efficient bandwidth allocation approach consideration, energy savings can reach up to 80% for different examined traffic loads following uniform distribution.

A Power Control Mean Field Game Framework for Battery Lifetime Enhancement of Coexisting Machine-Type Communications

Machine-type communications (MTC) enable the connectivity and control of a vast category of devices without human intervention. This study considers a hybrid coexisting wireless cellular network for traditional and MTC devices along with the need for an energy efficient power allocation mechanism for MTC devices. A model is presented for the interference and battery lifetime of MTC devices and a battery lifetime maximization problem is formulated. Conventional game designs are unable to address the demands of a densified user environment because of the dimensional difficulty presented when attempting to achieve a converged solution that would lead to a stable equilibrium. The MTC power control problem is modeled as a differential game and a mean field game (MFG) for massive number of MTC nodes estimates the power allocation policy with system utility defined in terms of the experienced interference and reliability. The formulated power control MFG is solved using a finite difference method and analyzed using extensive simulations. The solution provides an optimal power control strategy for MTC devices, enabling them to prolong their battery lives with the implemented energy efficient power allocation scheme.

Time-Efficient Allocation Mechanisms for Crowdsensing Tasks with Precedence Constraints

Crowdsensing has emerged as an efficient and inexpensive way to perform specialized tasks by leveraging external crowds. In some crowdsensing systems, different tasks may have different requirements, and there may be precedence constraints among them, such as the Unmanned Aerial Vehicle (UAV) crowdsensing systems. Moreover, minimizing the total execution time is a regular target for finishing the crowdsensing tasks with precedence constraints. As far as we know, only a few existing studies consider the precedence constraints among crowdsensing tasks, and none of them can minimize the total execution time simultaneously. To tackle this challenge, an efficient allocation mechanism for tasks with precedence constraints is first proposed, which can minimize the total execution time. Then, a case study is given to show how to fit our mechanism in the UAV crowdsensing system. Finally, the simulation results show that the proposed mechanisms have good approximate optimal ratios under different parameter settings and are efficient for the UAV crowdsensing system as well.

Incentive mechanism for sharing distributed energy resources

To improve the controllability and utilization of distributed energy resources (DERs), distribution-level electricity markets based on consumers’ bids and offers have been proposed. However, the transaction costs will dramatically increase with the rapid development of DERs. Therefore, in this paper, we develop an energy sharing scheme that allows users to share DERs with neighbors, and design a novel incentive mechanism for benefit allocation without users’ bidding on electricity prices. In the energy sharing scheme, an aggregator organizes a number of electricity users, and trades with the connected power grid. The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads. A novel index, termed as sharing contribution rate (SCR), is presented to evaluate different users’ contributions to the energy sharing. Then, based on users’ SCRs, an efficient benefit allocation mechanism is implemented to determine the aggregator’s payments to users that incentivize their participation in energy sharing. To avoid users’ bidding, we propose a decentralized framework for the energy sharing and incentive mechanism. Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme, and the incentive mechanism allocates the benefits according to users’ contributions.

An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers

Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a new server-based approach ; each server allocates resources by maximizing a per-server utility function . We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposed Per-Server Dominant Share Fairness ). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Subsequently, we show how the proposed mechanism could be implemented in a distributed fashion. Finally, we carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones.

Radio Resource Allocation and Pricing: Auction-Based Design and Applications

The “unlimited” performance and machine-centric architecture visions for future wireless networks transform the fundamental task of allocating radio resources into a complex optimization problem that is not quickly solvable. Inspired by the increasing intelligence of connected machines, and the prosperity of auctions as efficient allocation mechanisms in the economic sector, this paper provides an alternative perspective to the problem of optimal spectrum assignment for the fifth generation (5G) of wireless networks. In a systematic approach to deal with this problem, an efficacious allocation mechanism is characterized by six axioms: incentive compatibility, individual rationality, fairness, efficiency, revenue maximization, and computational manageability . The first three are incorporated into the allocation mechanism through a nonlinear spectrum pricing. By inducing incentive compatibility through these prices, revelation of the true valuations becomes the Nash Equilibrium and puts the mechanism in the class of revelation mechanisms. The latter fact triggers the realization of the last three axioms, whereby an optimization problem is formed to find the optimal mechanism in the class of revelation mechanisms, which, by the virtue of the revelation principle, is the optimal mechanism among all auction classes. Further, it is shown that the proposed mechanism is highly scalable, as the solution to the optimization problem is obtained by root-finding operations and solving almost linear system of equations. These properties make the proposed resource allocation mechanism an ideal candidate for deployment in 5G networks.

An Efficient Dynamic Allocation Mechanism for Security in Networks of Interdependent Strategic Agents

Motivated by security issues in networks, we study the problem of incentive mechanism design for dynamic resource allocation in a multi-agent networked system. Each strategic agent has a private security state which can be safe or unsafe and is only known to him. At every time, each agent faces security threats from outside as well as from his unsafe neighbors. Therefore, the agents’ states are correlated and have interdependent stochastic dynamics. Agents have interdependent valuations, as each agent’s instantaneous utility depends on his own security state as well as his neighbors’ security states. There is a network manager that can allocate a security resource to one agent at each time so as to protect the network against attacks and maximize the overall social welfare. We propose a dynamic incentive mechanism that implements the efficient allocation and is ex-ante (in expectation) individually rational and budget balanced. We present a reputation-based payment that mitigates any risk that the agents or the network manager may face to get a negative utility or to run a budget deficit, respectively, for some realizations of the network stochastic evolution. Therefore, our results provide a dynamic incentive mechanism that implements efficient allocations in networked systems with strategic agents that have correlated types and interdependent valuations, and is approximate ex-post individually rational and budget balanced.

Markets, Distributive Justice and Community: The Egalitarian Ethos of G. A. Cohen

While markets are widely lauded as efficient and attractive allocation mechanisms, their moral limits remain a source of controversy. The writings of G. A. Cohen provide an important contribution to this debate. Cohen offers two critiques of the market. One is a distributive critique, which maintains that markets fail in eliminating the influence of differential luck on people’s lives. The other is a community critique, maintaining that market relations fall short of a community of mutual caring. These critiques differ in important ways from critiques developed by Satz and Sandel, and suggest a need to assess markets beyond desperate exchanges and the adverse effects of incentives. Cohen’s work also points to how we can realize distributive justice and community. His solution utilizes the supply and demand mechanism of the market as a signaling device rather than an allocation mechanism. High wages signal the importance of a specific job, but wage differences are subsequently taxed away. This peculiar market arrangement relies on moral rather than economic incentives and only works if it is combined with a communitarian ethos. This ethos solution is evaluated in light of recent criticisms that it would compromise the freedom to pursue personal projects, that incentives may express community, and that the competition it utilizes mitigates against community. In the end, these critiques are not deemed persuasive.

SWITCHES

SWITCHES is a task-based dataflow runtime that implements a lightweight distributed triggering system for runtime dependence resolution and uses static scheduling and compile-time assignment policies to reduce runtime overheads. Unlike other systems, the granularity of loop-tasks can be increased to favor data-locality, even when having dependences across different loops. SWITCHES introduces explicit task resource allocation mechanisms for efficient allocation of resources and adopts the latest OpenMP Application Programming Interface (API), as to maintain high levels of programming productivity. It provides a source-to-source tool that automatically produces thread-based code. Performance on an Intel Xeon-Phi shows good scalability and surpasses OpenMP by an average of 32%.

Efficient and strategy-proof allocation mechanisms in economies with many goods

In this paper, we show that in pure exchange economies where the number of goods equals or exceeds the number of agents, any Pareto-efficient and strategy-proof allocation mechanism always allocates the total endowment to some single agent even if the receivers vary.