EIHDP: Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems

Mobile patient monitoring systems monitor and treat chronic diseases by collecting health data from wearable sensors through mobile devices carried out by patients. In the future, these systems may be hosted by a third-party service provider. This would open a number of security and ID privacy issues. One of these issues is the inference attack. This attack allows a single service provider from inferring the patient’s identity by collecting a number of contextual information about the patient such as the pattern of interaction with the service provider. Thus a security and ID privacy mechanisms must be deployed. In this paper, we propose a framework called Secure and Privacy-Preserving Data Collection (SPDC) that allows the patient to encrypt the data and then upload the encrypted data on different service providers rather than one while allowing an anonymous linkage for the patient’s data which are scattered across different service providers. In this framework, each patient is allowed to select the service providers involved in the data collection, assigns one as the home while the others consider foreign. The patient uses the foreign to upload data while the home is responsible for anonymously collecting the patient’s data from multiple foreign service providers and deliver them to the healthcare provider. This framework also shows a novel mechanism to conduct anonymous authentication across different distributed service provides. The framework has been analyzed against the specified design requirements and security threats.

A service chain (SC) comprises a series of service functions realized as virtualized network functions (VNFs). Each VNF has specific resource, bandwidth, and location requirements. This study assumes multiple SCs to be placed on geo-distributed heterogeneous edge servers owned by multiple edge service providers (ESPs). Each ESP selectively hosts SCs to earn profit. We consider the problem to maximize the total profit (i.e., social welfare) of all ESPs by organizing ESPs into a set of disjoint federations called federation structure. We formulate the problem of finding an optimal federation structure, identify the difficulties in solving the problem, and present two time-efficient heuristics as our approach. Simulation results show that elaborate federation structures do bring in higher social welfare compared with simple all-in or all-out ESP organization. The proposed heuristics can also approximate the optimal result in many cases.

In this paper, we present some interesting issues of resource allocation in network virtualization. We also propose RAiNV, a resource allocation framework to handle those issues. Sharing of resources is in the heart of network virtualization. The key idea of network virtualization is to replace conventional Internet service provider (ISP) with two separate business entities: infrastructure providers (InP) and service providers (SP). The InPs are the owners of resources. They are responsible for maintaining a certain level of quality of service (QoS) at the physical layer. The SPs lease resources from InPs. They offer various services to end users. It is more realistic to think about a scenario with multiple InPs and multiple SPs. A single service provider (SP) can integrate resources from multiple InPs. Similarly, a single InP can distribute resources to multiple SPs. As a result, optimal resource sharing becomes a challenging task. Resource allocation covers all the virtual network (VN) provisioning matters. Major parts include resource advertisement, request, negotiation, etc. The RAiNV aims to ease dealing with multiple InPs. Its performance is evaluated using obtained numerical results through simulations.

In the Internet of Things (loT) era, edge computing is a promising paradigm to improve the quality of service for latency sensitive applications by filling gaps between the loT devices and the cloud infrastructure. Highly geo-distributed edge computing resources that are managed by independent and competing service providers pose new challenges in terms of resource allocation and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose a novel blockchain-based model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori using smart contracts in Ethereum. The smart contract in the proposed model acts as the auctioneer and replaces the trusted third-party to handle the auction. The blockchain-based auctioning protocol increases the transparency of the auction-based resource allocation for the participating edge service and infrastructure providers. The design of sealed bids and bid revealing methods in the proposed protocol make it possible for the participating bidders to place their bids without revealing their true valuation of the goods. The truthful auction design and the utility-aware bidding strategies incorporated in the proposed model enables the edge service providers and edge infrastructure providers to maximize their utilities. We implement a prototype of the model on a real blockchain test bed and our extensive experiments demonstrate the effectiveness, scalability and performance efficiency of the proposed approach.

Congestion-dependent pricing is a form of traffic management that would ensure the efficient allocation of bandwidth between users and applications. As the unpredictability of congestion prices creates revenue uncertainty for network providers and cost uncertainty for users, it has been suggested that longer-term financial agreements such as forward contracts could be used to manage these risks. In a network managed by a single service provider, long-term forward contracts are beneficial for both the provider and the users. We investigate whether forward contracts would be adopted by multiple service providers in a future Internet with congestion-dependent pricing. We develop a novel game-theoretic model of a multi-provider communication network with two complementary segments. Service on the upstream segment is provided by a single Internet Service Provider (ISP) and priced dynamically to maximize profit, while several smaller ISPs sell connectivity on the downstream network segment, with the advance possibility of entering into forward contracts with their users for some or all of their capacity. We show that the equilibrium forward contracting levels are necessarily asymmetric, with one downstream provider entering into fewer forward contracts than the other competitors, thus ensuring a high subsequent downstream price level. In practice, network providers will choose the extent of forward contracting strategically based not only on their risk tolerance, but also on the market structure in the interprovider network and their peers' actions.

Network virtualization is a new technology that aims at allowing multiple virtual networks (VNs) to coexist in the same equipment and to hide the heterogeneity of network infrastructure. The critical issue for a given infrastructure provider (InP), is how to provide customized and on demand resources for multiple service providers (SPs) with different Quality of Service (QoS) requirements. The should also fairly distribute the network physical resources, such as bandwidth of each physical link, buffer spaces, and processing cycles at each node. In this paper, we propose a new framework based on game theory, for both link and node dynamic allocation between multiple infrastructure providers (InPs) and service providers (SPs). Our approach focuses on provisioning and managing the physical resources in a virtualized network infrastructure. We propose a two-stage approach based on non-cooperative games. The first one is the resource negotiation game where the SP requests link and node resources from multiple InPs. The InP may reject the SP's request when it can potentially cause network congestion. The second stage of the proposal concerns dynamic resource provisioning and consists of two non cooperative games; the node allocation game and the link allocation game. The objective of both games is to allocate physical resources for different isolated VNs that are sharing the same physical substrate network. In the node allocation game, the proportional share mechanism is used. Every SP assigns a weight and submits a bid to each physical node and thereafter it receives a share proportional to its bid. In the link allocation game we investigate the case when multiple SPs compete for a portion of the available physical network capacity. Simulation results show that the proposed approach achieves high resource utilization, improves the network performance, and fairly distributes the link and node resources between multiple SPs.

A HIERARCHICAL NETWORK GAME WITH A LARGE NUMBER OF PLAYERS

We consider a hierarchical network game with multiple links, a single service provider, and a large number of users with multiple classes, where different classes of users enter the network and exit it at different nodes. Each user is charged by the service provider a fixed price per unit of bandwidth used on each link in its route, and chooses the level of its flow by maximizing an objective function that shows a tradeoff between the disutility of the payment to the service provider and congestion cost on the link the user uses, and the utility of its flow. The service provider, on the other hand, wishes to maximize the total revenue it collects. We formulate this problem as a leader-follower (Stackelberg) game, with a single leader (the service provider, who sets the price) and a large number of Nash followers (the users, who decide on their flow rates). We show that the game admits a unique equilibrium, and obtain the solution in analytic form. A detailed study of the limiting case where the number of followers is large reveals a number of interesting and intuitive properties of the equilibrium, and answers the question of whether and when the service provider has the incentive to add additional capacity to the network in response to an increase in the number of users on a particular link.

Improving continuity of care for frequent users of emergency departments: service user and provider perspectives.

Besides the computation limitation, the requirement of storing the entire blockchain is another challenge for blockchain mining in mobile environments, and thus has hindered the development of blockchain-powered mobile applications. Storage outsourcing to a cloud service provider (CSP) is a viable solution. An individual miner can store his blockchain in the cloud and then validate transactions by querying the CSP. However, validation outsourcing to a remote CSP incurs delay and damages a miner's winning probability in the mining competitions. To shorten such an unwanted delay, miners can also cache the unspent transaction output (UTXO) set in a nearby edge service provider (ESP) for fast transaction validations, which definitely brings extra costs. In this paper, we consider a two-layer outsourcing paradigm to solve storage shortage for mobile miners. Due to the delay-cost tradeoff when selecting service providers, we can model interactions among miners as a non-cooperative game and formulate a Nash equilibrium problem to investigate the effects of outsourcing on miners' utilities. We also study the access probability of UTXOs with different generation times. This will guide miners on how to select unspent transaction outputs if they decide only to cache the partial UTXO set in the edge. We further extend our game by modeling multiple mining rounds as a one-shot game to see how the cache update frequency affects miners' strategies. Numerical evaluation is conducted to show the feasibility of storage outsourcing and to validate the proposed models and theoretical results.

The proliferation of cloud computing allows scientists to deploy computation and data intensive applications without infrastructure investment, where large generated datasets can be flexibly stored with multiple cloud service providers. Due to the pay-as-you-go model, the total application cost largely depends on the usage of computation, storage and bandwidth resources, and cutting the cost of cloud-based data storage becomes a big concern for deploying scientific applications in the cloud. In this paper, we propose a novel algorithm that can automatically decide whether a generated dataset should be 1) stored in the current cloud, 2) deleted and re-generated whenever reused or 3) transferred to cheaper cloud service for storage. The algorithm finds the trade-off among computation, storage and bandwidth costs in the cloud, which are three key factors for the cost of storing generated application datasets with multiple cloud service providers. Simulations conducted with popular cloud service providers' pricing models show that the proposed algorithm is highly cost-effective to be utilised in the cloud.

Mobile edge computing (MEC) and 5G networks can provide ultra-low latency connections. Combining the two, caching services at the network edge can greatly reduce the delay of virtual reality (VR) and augmented reality (AR) services, enhancing the Quality of Service (QoS) for users. In this paper, we investigate an efficient collaborative service caching scheme between multiple service providers (SPs) with a game-theoretical approach. We model SPs as players who care about nothing but their profits and can form coalitions by sharing edge server resources as well as costs with other members. More than one coalition can be formed in an edge server. Our algorithm guarantees to reach a Nash equilibrium, where no one has the incentive to deviate. Simulation results show that through the proposed collaboration scheme, SPs can reach a higher profit compared to several baselines as well as previously proposed schemes.

The ongoing quest for transportation electrification with the massive proliferation of EV charging stations (EVCSs) will deepen the interaction and require the further coordination of coupled power and transportation networks (PTN). The individually-owned EVCSs located in an urban transportation network (UTN) will compete using price signals to maximize their respective payoffs. In this paper, a multi-agent deep reinforcement learning (MA-DRL) method is proposed to model the pricing game in UTN and determine the optimal charging prices for a single EVCS. The EVCS charging demand is first analyzed using a modified user equilibrium traffic assignment problem (UE-TAP) with elastic traveling demands and different charging prices. The price competition problem is then formulated as a game with incomplete information in which the market environment is complex due to nonlinear traffic assignments. Thus, the MA-DRL approach is proposed to learn the charging pricing strategies of multiple EVCSs and approximate the Nash Equilibrium (NE) of the pricing game using the incomplete information. The proposed solution will determine the optimal pricing strategies for an EVCS in UTN. The case studies on a 24-node Sioux-Falls network, and the real-world Xi'an and Hangzhou cities are conducted to verify the effectiveness and scalability of the proposed approach.

This work proposes a novel dynamic and adaptive yet fair spectrum allocation policy to be adopted by a service provider (SP). Here, we consider a scenario where a single SP owns multiple access networks (ANs) of different (not necessarily competing) technologies. We conceive that an SP employs an entity, called local spectrum controller (LSC), which manages the common pool of spectrum and is responsible for distributing the spectrum to individual ANs in a fair manner. Since the available spectrum is insufficient to satisfy the aggregate demand from all ANs simultaneously, LSC has to resort to a dynamic spectrum allocation technique. We have modeled the problem as an n-player cooperative bankruptcy game and have solved the problem with the help of Shapley value. We have proposed a suitable utility function for the AN with respect to its received spectrum and have identified possible objectives of LSC (i.e., SP) namely, maximizing equality of distribution (MED) and maximizing proportional fairness (MPF). We have proposed a novel equal utility distribution (EUD) algorithm that maximizes our MED objective. Finally, we have studied performances of EUD, Shapley value and max-min fairness algorithm with respect to the above objectives and have compared with nucleolus solution concept.

The recent developments of smart mobile phones and 3G networks enable users to enjoy video programs by subscribing to data plans. Due to phone-to-phone communication technologies and ubiquity of mobile phones, data-plan subscribers are able to redistribute the video content to non-subscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized re-distribution behavior to protect or maximize his/her own profit. In this paper, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.