A Privacy-aware Stackelberg Game Approach for Joint Pricing, Investment, Computation Offloading and Resource Allocation in MEC-enabled Smart Cities

Ultra-dense and highly heterogeneous network (HetNet) deployments make the allocation of limited wireless resources among ubiquitous Internet of Things (IoT) devices an unprecedented challenge in 5G and beyond (B5G) networks. The interactions among mobile users and HetNets remain to be analyzed, where mobile users choose optimal networks to access and the HetNets adopt proper methods for allocating their own network resource. Existing works always need complete information among mobile users and HetNets. However, it is not practical in a realistic situation where important individual information is protected and will not be public to others. This paper proposes a distributed pricing and resource allocation scheme based on a Stackelberg game with incomplete information. The proposed model proves to be more practical by solving the problem that important information of either mobile users or HetNets is difficult to acquire during the resource allocation process. Considering the unknowability of channel gain information, the follower game among users is modeled as an incomplete information game, and channel gain is regarded as the type of each player. Given the pricing strategies of networks, users will adjust their bandwidth requesting strategies to maximize their expected utility. While based on the sub-equilibrium obtained in the follower game, networks will correspondingly update their pricing strategies to be optimal. The existence and uniqueness of Bayesian Nash equilibrium is proved. A probabilistic prediction method realizes the feasibility of the incomplete information game, and a reverse deduction method is utilized to obtain the game equilibrium. Simulation results show the superior performance of the proposed method.

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Joint computation offloading and resource allocation in vehicular edge computing based on an economic theory: walrasian equilibrium

Peer-to-Peer Networking and Applications ◽

10.1007/s12083-021-01141-2 ◽

2021 ◽

Author(s):

Runhua Wang ◽

Feng Zeng ◽

Xiaoheng Deng ◽

Jinsong Wu

Keyword(s):

Economic Theory ◽

Resource Allocation ◽

Edge Computing ◽

Walrasian Equilibrium ◽

Computation Offloading

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Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Complex & Intelligent Systems ◽

10.1007/s40747-021-00434-6 ◽

2021 ◽

Author(s):

Karan Bajaj ◽

Bhisham Sharma ◽

Raman Singh

Keyword(s):

High Speed ◽

Smart Cities ◽

Fog Computing ◽

Edge Computing ◽

Computation Offloading ◽

Performance Parameters ◽

Iot Applications ◽

Time Sensitivity ◽

Implementation Analysis ◽

Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

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