Net Utility Function Research Articles

Game Theory-Based IoT Efficient Power Control in Cognitive UAV

With the help of network densification, network coverage as well as the throughput can be improved via ultra-dense networks (UDNs). In tandem, Unmanned Aerial Vehicle (UAV) communications have recently garnered much attention because of their high agility as well as widespread applications. In this paper, a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal. Further, the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation. The quality of service (QoS) related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’ net utility function. Moreover, an energy efficiency non-cooperative game theory power allocation with pricing scheme (EE-NGPAP) is proposed to obtain an efficient power control within IoT wireless nodes. Further, uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through simulation. Simulation results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction, which is regarded to be apt with the 5G networks’ vision. Finally, the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.

Joint Customized Price and Power Control for Energy-Efficient Multi-Service Wireless Networks via S-Modular Theory

In this paper, the problem of joint utility-based customized price and power control in multi-service wireless networks is addressed via S-modular theory. Each user is associated with a generic two-variable net utility function. The latter takes a different form based on the type of the requested service, and depends on both user’s uplink transmission power and the price he is willing to pay to achieve his quality of service prerequisites. The joint customized price and power control problem is formulated as a two-variable optimization problem and confronted as a non-cooperative distributed game. The S-modular theory is adopted to solve the corresponding optimization problem. The existence of the game’s Nash equilibrium point with respect to both user’s uplink transmission power and price is analytically shown, while game convergence is also proven. A distributed and iterative algorithm for computing the two-variable Nash equilibrium point is also presented. The optimal price and uplink transmission power are both determined simultaneously in a distributed manner while the control intelligence and the decision making process lie at the user. The performance of the proposed approach is evaluated via modeling and simulation and its superiority compared to other state of the art approaches is illustrated especially in terms of energy efficiency.

Effort Application in an Arranged Marriage: A Game-Theoretic Analysis

In this note we study a simple game model of effort application by two individuals (1 and 2) who wish to have an arranged marriage. We first specify the net utility function of individual i as a function of this individual’s own effort and the effort applied by individual j,i≠j, in three distinct cases. Next, we compute the ith individual’s best response function. Finally, we analyze effort application by two identical pairs of individuals who are located in two different nations. In the first (second) nation, arranged marriages are uncommon (common) and hence it is relatively difficult (straightforward) for the two individuals to apply and coordinate effort. We explain why the net utility of the pair wishing to have an arranged marriage in the first nation is likely to be much lower than the corresponding net utility of the pair in the second nation.

A Time-Adaptive Heuristic for Cognitive Cloud Offloading in Multi-RAT Enabled Wireless Devices

We introduce the concept of cognitive cloud offloading where all viable wireless interfaces of a multiple radio enabled device are used for computation offloading. We propose a time and wireless adaptive heuristic for offloading computationally intensive applications to a remote cloud with goals of reducing the energy consumption on the mobile device, execution time of the application, and efficient use of the multiple radio interfaces available at the device. The proposed algorithms simultaneously determine: 1) execution place of each application component (mobile/cloud); 2) amount of the associated data to be sent via each available interface of the multiple radio access technology device; and 3) scheduling order of the application components. We define a net utility function that trades off mobile device resources (battery, CPU, and memory) with realtime communication costs, such as latency and communication energy, subject to constraints that ensure queue stability of radio interfaces. Simulations using real data from an HTC smartphone running multi-component applications with Amazon EC2 as the cloud, and two radios, LTE and WiFi, show that cognitive cloud offloading provides higher net utility in comparison to the best-interface protocol. Scalability of the proposed heuristic is further analyzed using various levels for component dependency graphs and energy-delay trade-off factors.

Optimising the strategic management of wide-area environmental cleanup campaigns

This paper presents a fundamental analysis of a wide-area environmental cleanup campaign's net-present value or net-utility gain. It is designed to support the site-specific optimisation strategies of US Department of Homeland Security. The analysis is subject to the highly imperfect market condition of 'full competition'. This is where the executive managers can control: 1) the production rate and cleanliness performance of the cleanup process; 2) the supply-side investments in the cleanup-production system to lower (or shift to the right) the variable cleanup-cost function; 3) the demand-side investments in knowledge creation and dissemination or public communication that increase (or shift to the right) the contaminated property's value or cleanup-benefit function for society. The net-utility gain function that is derived under these conditions is then analysed quantitatively to determine optimum values of production rate and cleanliness level, as well as the optimum levels of investments in containment or decontamination, cleanup-production system, and public communication. The net-utility gain function also used to analyse qualitatively six widely contrasting historical case studies. It therefore represents a basic strategic theory for guiding the management of environmental cleanup campaigns.

Bid strategies of primary users in double spectrum auctions

Extensive research in recent years has shown that dynamic spectrum sharing is a promising approach to address the artificial spectrum scarcity problem by improving spectrum utilization. This new communication paradigm, however, requires a well-designed spectrum allocation mechanism. This paper designs a double spectrum auction framework that allows unlicensed secondary users to obtain selected idle spectra assigned to licensed primary users. This is a win-win game because primary users can earn extra revenue and secondary users can obtain spectra they desperately need. The competition among primary users in the auction framework is studied combining game theory with a double spectrum auction in a non-cooperative game with the Nash Equilibrium (NE) as the best solution. Primary users use the prices obtained from the NE as their bid strategies to participate in the auction. In this auction sellers and buyers bid privately and confidentially, which means that the secondary users do not actually know the price and the spectrum size offered by the primary users, then a new net utility function was developed for the primary users with an iterative algorithm to find the Nash equilibrium point. Simulations demonstrate that this design effectively improves spectrum utilization.

Adaptive Two-Loop Power Tracking Control in CDMA Systems with the Utility Optimization

An adaptive two-loop power tracking control scheme is proposed for direct sequence code-division multiple-access (DS-CDMA) cellular systems in a wireless network. The scheme includes an outer-loop controller and an inner-loop controller. An adaptive searching algorithm is designed for determining an optimal target signal to interference and noise ratio (SINR) for each user in the outer loop. The utility optimization can be achieved by using the adaptive algorithm through the net utility function based on the quality of service (QoS) for the purpose of resource management of wireless communication networks. A prediction control is designed to compensate for the effect of round-trip time-delay, channel fading, multiple access interference (MAI), and noise in the inner loop. The optimal target SINR tracking can be achieved by using the prediction control through a robust pole placement. Combining these two controllers in the outer loop and inner loop, utility optimization and target SINR tracking can be achieved at the same time. The stability and convergence of the proposed scheme are discussed. Several simulation results are given to confirm the performance of the proposed method.

Distributed utility maximization for network coding based multicasting: a shortest path approach

One central issue in practically deploying network coding is the adaptive and economic allocation of network resource. We cast this as an optimization, where the net-utility-the difference between a utility derived from the attainable multicast throughput and the total cost of resource provisioning-is maximized. By employing the MAX of flows characterization of the admissible rate region for multicasting, this paper gives a novel reformulation of the optimization problem, which has a separable structure. The Lagrangian relaxation method is applied to decompose the problem into subproblems involving one destination each. Our specific formulation of the primal problem results in two key properties. First, the resulting subproblem after decomposition amounts to the problem of finding a shortest path from the source to each destination. Second, assuming the net-utility function is strictly concave, our proposed method enables a near-optimal primal variable to be uniquely recovered from a near-optimal dual variable. A numerical robustness analysis of the primal recovery method is also conducted. For ill-conditioned problems that arise, for instance, when the cost functions are linear, we propose to use the proximal method, which solves a sequence of well-conditioned problems obtained from the original problem by adding quadratic regularization terms. Furthermore, the simulation results confirm the numerical robustness of the proposed algorithms. Finally, the proximal method and the dual subgradient method can be naturally extended to provide an effective solution for applications with multiple multicast sessions

Searching for a Mate: Theory and Experimental Evidence

We provide a theoretical framework for studying mate search and selection based on a two-sided matching model. Guided by the model, we study dating behavior using data from an experimental dating market, where we generate random matching of subjects and create random variation in the number of potential partners. Male selectivity is invariant to group size, while female selectivity is strongly increasing in group size. This difference implies that the female net utility function of dating is more concave in the number of dates. We also report the valuation of various attributes by men and women; in accordance with the common stereotype, females put greater weight on intelligence relative to males, while males put relatively greater weight on physical appearance. Further, we find that males are less attracted to females with intelligence or ambition greater than their own.

Pricing and Congestion Management in a Network With Heterogeneous Users

This note presents an economic model for a communication network with utility-maximizing elastic users who adapt to congestion by adjusting their flows. Users are heterogeneous with respect to both the utility they attach to different levels of flow and their sensitivity to delay. Following Kelly et al. (1998), we introduce dynamic rate-control algorithms, based on the users' utility functions and delay sensitivities, as well as tolls charged by the system, and examine the behavior of these algorithms. We show that allowing heterogeneity with respect to delay sensitivity introduces a fundamental nonconvexity into the congestion-cost functions. As a result, there are often multiple stationary points of the aggregate net utility function. Hence, marginal-cost pricing-equating users' marginal utilities to their marginal costs-may identify a local maximum or even a saddle point, rather than a global maximum. Moreover, the dynamic rate-control algorithm may converge to a local rather than global maximum, depending on the starting point. We present examples with different user utility functions, including some in which the only interior stationary point is a saddlepoint which is dominated by all the single-user optimal allocations. We also consider variants of the dynamic algorithm and their performance in a network with heterogeneous users. Our results suggest that applying a rate-control algorithm such as TCP (Transmission Control Protocol), even when augmented by some form of implicit or explicit pricing, may have unexpected and perhaps undesirable effects on the allocation of flows among heterogeneous delay-sensitive users.