Reward Sharing Research Articles

Blockchain-based Decentralized Reward Sharing: The Case of Mining Pools

We introduce a reciprocity protocol , an innovative approach to coordinating and sharing rewards in blockchains. Inherently decentralized and implementable, it puts emphasis on incentives rather than forcing specific sharing rules from the outset. Analyzing the non-cooperative game the protocol induces, we identify a robust, strict, and Pareto-dominant symmetric equilibrium. In it, even self-centered participants show extensive systemic reciprocity. Thus, despite a setting that is generally unfavorable to reciprocal behavior, the protocol enables users to build trust between themselves by taking on a role akin to a social contract.

On Reward Sharing in Blockchain Mining Pools

On Reward Sharing in Blockchain Mining Pools

Microservice Deployment in Edge Computing Based on Deep Q Learning

The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increased node loads. In this article, we first model the invocation relationships among microservices as an undirected and weighted interaction graph to characterize the communication overhead. Then, we propose a multi-objective microservice deployment problem (MMDP) in edge computing. MMDP aims to minimize the communication overhead while achieving load balance between edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, to solve MMDP and obtain the optimal deployment strategy. In addition, to improve the scalability of the services, we propose an Elastic Scaling algorithm (ES) based on heuristics to deal with the dynamic pressure of requests. Finally, we conduct a series of experiments in Kubernetes to evaluate the performance of our approach. Experimental results indicate that, compared with interaction-aware strategy and Kubernetes default strategy, RSDQL has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure.

ARETE: On Designing Joint Online Pricing and Reward Sharing Mechanisms for Mobile Data Markets

Although data has become an important kind of commercial goods, there are few appropriate online platforms to facilitate the trading of mobile crowd-sensed data so far. In this paper, we present the first architecture of mobile crowd-sensed data market, and conduct an in-depth study of the design problem of online data pricing and reward sharing. To build a practical mobile crowd-sensed data market, we have to consider four major design challenges: data uncertainty, economic-robustness (arbitrage-freeness in particular), profit maximization, and fair reward sharing. By jointly considering the design challenges, we propose an online query-b A sed c R owd-sens E d da T a pricing m E chanism, namely ARETE-PR, to determine the trading price of crowd-sensed data. Our theoretical analysis shows that ARETE-PR guarantees both arbitrage-freeness and a constant competitive ratio in terms of profit maximization. Based on some fairness criterions, we further design a reward sharing scheme, namely ARETE-SH, which is closely coupled with ARETE-PR, to incentivize data providers to contribute data. We have evaluated ARETE on a real-world sensory data set collected by Intel Berkeley lab. Evaluation results show that ARETE-PR outperforms the state-of-the-art pricing mechanisms, and achieves around 90 percent of the optimal revenue. ARETE-SH distributes the reward among data providers in a fair way.

The Impact of Reward and Knowledge Sharing Practices on Employee Performance: A Comparative Analysis between Awash and Dashen Bank S.C.

Employee performance is measured by the quality of work, productivity, ability to work independently with little or no direction or follow-up to complete tasks, job knowledge, employees’ interpersonal relationships and so on. Reward systems have long been used as a strategy to motivate employees and increase their performance. Several studies have also suggested that knowledge sharing increases learning which improves employees’ job performance. Yet, in Ethiopia, several organizations haven’t done a comprehensive study on which type of reward system or knowledge sharing practice best fit them. The purpose of this study is to examine the reward system and knowledge sharing practices of two banks, Awash and Dashen, in Adama Ethiopia, and analyze the differences in their employee performance. To achieve this objective this study uses instruments based on intrinsic and extrinsic reward system and tacit and explicit knowledge sharing and their impact on employee performance. The target population are employees of Awash and Dashen bank in Adama, Ethiopia. An empirical study using qualitative data and survey method is employed to conduct this study. A convenient sample is collected from 120 employees in each bank. The theoretical and practical implications of the study are also discussed.

Proposal and Evaluation of Reward Sharing Method Based on Safety Level

Proposal and Evaluation of Reward Sharing Method Based on Safety Level

Contribution Deciding and Reward Sharing Based on Reward Occurring Conditions in Multi-Agent Reinforcement Learning

Contribution Deciding and Reward Sharing Based on Reward Occurring Conditions in Multi-Agent Reinforcement Learning