Dynamic pricing for connection-oriented services in wireless networks

Dynamic pricing policy allows the network operator to charge a cost per byte based on the network usage and the traffic load, which can potentially attract greater user consumption and increase operator's revenue. In this paper, we conduct an empirical study on user attitude towards dynamic pricing for wireless data services to examine whether average revenue per user (ARPU) can be effectively enhanced. Using structural equation modelling (SEM) based on 211 datasets collected in the California Bay Area, we reach the following conclusions: 1) consumers agree that current flat–rate wireless data pricing is too high and rigid and can be more flexible; 2) dynamic pricing can increase user satisfaction of the service; 3) dynamic pricing can increase the revenue for operators. Overall, we conclude that dynamic pricing can on one hand provide a better quality of service (QoS) to users and on the other hand achieve greater revenue for the wireless operator.

Management Insights

In a city center that is usually flooded with traffic, finding a parking space is a challenge for most commuters. Searching for parking generates additional urban traffic, increases energy consumption and vehicle emissions, and wastes time. This study examines how parking systems can be used as an effective tool for reducing traffic congestion. It proposes a new, smart approach to parking using real-time dynamic pricing and route guidance in a multi-agent system to better align supply and demand, thereby reducing both congestion and the amount of time spend by drivers searching for parking. To examine the effects of dynamic parking pricing on road traffic, we conducted a multi-agent open-source framework to simulate spatiotemporal distributions of daily individual activity. By extending MATSim, we were able to model parking usage more realistically. We developed several micro-simulation scenarios to evaluate strategy performance for both on-street and off-street parking in the city center of Tunis, Tunisia. The results show that dynamic pricing policies can be used to plan and manage parking areas more efficiently, minimizing the dispersion of parking occupancy lots. Variations in parking prices were shown to increase parking lot vacancies and reduce traffic congestion. Moreover, the system can also generate increased revenue for government and private-sector parking authorities while improving driver satisfaction. Finally, the parking dynamic pricing strategy promises a convenient mobility experience, while increasing efficiency and reducing the negative externalities of urban transportation.

Dynamic and targeted bundle pricing of two independently valued products

Optimal pricing and inventory strategies for fashion products under time-dependent interest rate and demand

The platform economy evolved rapidly due to technological breakthroughs, hence new challenges emerged in terms of pricing strategies affected from different user behaviors and market demand dynamics. Traditional pricing methods are not up to this type of challenge, and it requires a model able to be more agile — in real time. The study presents “DeepPrice,” a dynamic pricing model using deep learning namely Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (DRL) to achieve the optimal platform pricing strategies in response to user behavior and market signals. The research has an experimental design for the development and testing of the DeepPrice model. The model is pre-trained by the transaction data of an important e-commerce platform for this task. CNNs learn user profiles and product properties via the encoding layer, while DRL models implement the strategy of adjusting price according to behavior actions in tensor form. Metrics such as platform revenue, user conversion rates, and customer satisfaction are used to validate the model back to the model performance. Our solution helped DeepPrice to generate incremental 20% platform revenue on average and better adjust to the market challenges. It performed better than standard pricing solutions, especially in times of high demand, and effectively personalized price-pointing for top-value customers to drive higher conversions and improve customer satisfaction. This study points to the promise of using deep learning to improve dynamic pricing in platform economies. Flexible & Scalable Solution for any IndustryDeepPrice Nevertheless, challenges regarding the computational cost of implementing personalized pricing strategies and ethical debates surrounding such strategies remain to be studied in more detail. In platform economy, the reinforcement learner has a significant potential to provide a reliable real-time pricing solution with CNN in DeepPrice to improve both profit and customer satidfaction.

In “Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and The Value of Dynamic Pricing,” Chen, Lei, and Jasin consider a dynamic pricing problem faced by a ride-hailing service provider who manages a fixed number of servers and serves price-sensitive customers within a network. Servers serve arriving customers by relocating from the requested origins to destinations within a certain travel time. The authors first propose a static pricing policy based on the optimal solution to a deterministic relaxation of the original stochastic problem. They show that the proposed static policy matches the best possible asymptotic performance of any static policy. The authors further propose a dynamic pricing policy that adaptively changes the prices in a way that reduces the impact of past demand randomness on the balance of future distributions of servers and customers across the network. They show that the dynamic pricing policy achieves significantly better asymptotical performance. The proposed policies and their performance guarantees are further extended to a case where the firm jointly decides the relocation of vacant servers

Behavior evaluation is an important research topic in trustworthy network. Up to now, most effect focuses on the validity of host's and user's identity, such as integrity measurement and access control, which could not guarantee the trustworthiness of valid user's behavior. In this paper, we proposed an unsupervised method for evaluating user's network behavior and trustworthiness grades in a local network. Firstly, we collected network behavior samples as more as possible. The occasions for sampling network behavior should be distinctly various in order to guarantee the collection of samples contains all possible behavior modes in network. Secondly, after initially preprocessed, the sample data was tagged with different trustworthiness grades. Lastly, according to the graded sample data, our method constructed a support vector machine to evaluate user's latter network behavior. We applied this method to a corporation network which contained many employee terminals. The result of behavior evaluation showed that this method is reasonable and feasible.

Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this article, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer’s true feature, but a manipulated feature according to buyers’ strategic behavior. In addition, the seller does not observe the buyers’ valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers’ strategic behavior into the online learning to maximize the seller’s cumulative revenue. We first prove that existing nonstrategic pricing policies that neglect the buyers’ strategic behavior result in a linear Ω ( T ) regret with T the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish an O ( T ) regret upper bound of our proposed policy and an Ω ( T ) regret lower bound for any pricing policy within our problem setting. This underscores the rate optimality of our policy. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an O ( T ) regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

The continuous growth in user demand for high-quality rich media services puts pressure on Wireless Mesh Network (WMN) resources. Solutions such as those which increase the capacity of the mesh network by equipping mesh routers with additional wireless interfaces provide better Quality of Service (QoS) for video deliveries, but result in higher overall energy consumption for the network. This paper presents LBIS, a distributed solution which combines the benefits of both load-balancing and interface-shifting in order to enhance QoS levels for video services delivered over multi-hop WMNs, while maintaining low energy consumption levels within the network. Simulation-based results show very good performance of our proposed mechanism in terms of QoS metrics (delay, packet loss), Peak Signal-to-Noise Ratio (PSNR) and energy consumption in mesh network topologies, and with varying video traffic loads and distributions. The results demonstrate how LBIS can increase the QoS for video deliveries by more than 30% at the cost of an insignificant increase of the overall network energy consumption compared to the WMN with multiple radio interfaces without the LBIS adaptation.

Network management is challenging. To operate, maintain, and secure a communication network, network operators must grapple with low-level vendor-specific configuration to implement complex high-level network policies. Despite many previous proposals to make networks easier to manage, many solutions to network management problems amount to stop-gap solutions because of the difficulty of changing the underlying infrastructure. The rigidity of the underlying infrastructure presents few possibilities for innovation or improvement, since network devices have generally been closed, proprietary, and vertically integrated. A new paradigm in networking, software defined networking (SDN), advocates separating the data plane and the control plane, making network switches in the data plane simple packet forwarding devices and leaving a logically centralized software program to control the behavior of the entire network. SDN introduces new possibilities for network management and configuration methods. In this article, we identify problems with the current state-of-the-art network configuration and management mechanisms and introduce mechanisms to improve various aspects of network management. We focus on three problems in network management: enabling frequent changes to network conditions and state, providing support for network configuration in a highlevel language, and providing better visibility and control over tasks for performing network diagnosis and troubleshooting. The technologies we describe enable network operators to implement a wide range of network policies in a high-level policy language and easily determine sources of performance problems. In addition to the systems themselves, we describe various prototype deployments in campus and home networks that demonstrate how SDN can improve common network management tasks.

A retailer is endowed with a finite inventory of a nonperishable product. Demand for this product is driven by a price-sensitive Poisson process that depends on an unknown parameter that is a proxy for the market size. The retailer has a prior belief on the value of this parameter that he updates as time and available information (prices and sales) evolve. The retailer's objective is to maximize the discounted long-term average profits of his operation using dynamic pricing policies. We consider two cases. In the first case, the retailer is constrained to sell the entire initial stock of the nonperishable product before a different assortment is considered. In the second case, the retailer is able to stop selling the nonperishable product at any time and switch to a different menu of products. For both cases, we formulate the retailer's problem as a (Poisson) intensity control problem and derive structural properties of an optimal solution, and suggest a simple and efficient approximated solution. We use numerical computations, together with asymptotic analysis, to evaluate the performance of our proposed policy.

At present, most of researches on network selection algorithms are focused on single access. To support seamless mobility and provide better quality of service in heterogeneous wireless networks, this paper proposes a network selection algorithm with parallel transmission based on MADM. In the algorithm, we firstly determine all available wireless networks and consider every subset of these networks as a network scheme. Then we obtain aggregation attributes of every scheme and determine the alternative network schemes. Finally, we build the decision matrix of multiple attributes and determine the optimal scheme by using GRA. Simulation results show that the proposed algorithm can obviously improve user quality of service, improve user throughput, reduce power consumption and price cost per bit.

Dynamic Pricing and Exchange Rate Pass-Through: Evidence from Transaction-Level Data

In WCDMA networks, most economic-based resource management algorithms only assumed that network users were obedient, that is, users only accepted the price declared by network, which is called as price acceptation mechanism. Many research issues argued that it is necessary to accommodate, if possible, to use self-interest behaviours of users to strengthen the technical architecture of network engineering. Thus, this paper explicitly considered the selfishness of users, investigated the price anticipation mechanism in WCDMA networks, in which users acted as price anticipators. By price anticipator it meant users anticipated the effect of their behaviours on the network resource allocation, and adopted strategy correspondingly. From the view point of game theory, we investigated equilibrium properties of the price anticipation mechanism in WCDMA networks, and, through two scenarios, illustrated the relationship between price anticipation mechanism and price acceptation mechanism from the viewpoint of network revenue. Finally, we drew the conclusion that, the network revenue generated in price anticipation mechanism was less than revenue generated in price acceptation mechanism, (the difference between those two mechanisms is called as “price as anarchy”), and the network revenue generated in those two mechanisms tends to be consistent, when the effect of individual user is negligible.