A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance against that of four other related algorithms to study the performance improvements the algorithm brings to LoRaWAN in terms of DER and EC. LoRaSim was used to evaluate the algorithms’ performances in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the proposed algorithm outperforms the currently existing implementations of the ADR in terms of both DER and EC. However, the proposed algorithm is slightly outperformed by the native ADR in terms of EC. This was expected as the algorithm was mainly built to improve DER. The proposed algorithm builds on the existing algorithms and the ADR and significantly improves them in terms of DER and EC (excluding the native ADR), which is a significant step towards an ideal implementation of LoRaWAN’s ADR.

Research on the Benefit-Sharing Model of Collaborative Innovation Mechanism in Power Innovation Park

Currently, innovation is no longer limited to a single enterprise. The joint realization of collaborative innovation by multiple parties has become the main driving force for innovation and development. The Power Innovation Park is an emerging innovation and entrepreneurship model. It is guided by power grid companies, and the school, government, and enterprise are combined in the park. The Power Innovation Park urgently needs theoretical guidance in terms of the resource allocation mechanism, support incentive mode, and benefit-sharing mode, so this paper takes the park as an example to study the benefit-sharing mode of collaborative innovation. In this paper, the transformation methods of the Power Innovation Park are divided into two types: cooperation between members within the park and dominated by external enterprises in the park. This paper applies cooperative game theory and HJB equation to study the benefit-sharing model of each member in the Power Innovation Park under two different result transformation modes. Research has shown that an appropriate method of transformation of results can maximize the overall and individual interests of the park and that individual interests are affected by many factors. Finally, this paper puts forward policy recommendations to promote the collaborative innovation development of the innovation park through the research results.

Double Auction-Based Two-Level Resource Allocation Mechanism for Computation Offloading in Mobile Blockchain Application

It is increasingly popular that platforms integrate various services into mobile applications due to the high usage and convenience of mobile devices, many of which demand high computational capacities and energy, such as cryptocurrency services based on blockchain. However, it is hard for mobile devices to run these services due to the limited storage and computational capacity. In this paper, the problem of computation offloading that requires sufficient computing resources with high utilization in large-scale users and multiprovider MEC system was investigated. A mechanism based on the combinatorial double auction, G-TRAP, is proposed in this paper to solve the above problem. In the mechanism, resources are provided both in the cloud and at the edge of the network. Mobile users compete for these resources to offload computing tasks by the rule that the edge-level resources will be allocated at first while cloud-level resources could be the supplement for the edge level. Given that the proof-of-work (PoW), the core issue of blockchain application, is resource-expensive to implement in mobile devices, we provide resource allocation service to users of blockchain application as experimental subjects. Simulation results show that the proposed mechanism for serving large-scale users in a short execution time outperforms two existing algorithms in terms of social utility and resource utilization. Consequently, our proposed system can effectively solve the intensive computation offloading problem of mobile blockchain applications.

Growth and long-run sustainability

From any state of economic and environmental assets, the maximin value defines the highest level of utility that can be sustained forever. Along any development path, the maximin value evolves over time according to investment decisions. If the current level of utility is lower than this value, there is room for growth of both the utility level and the maximin value. For any resource allocation mechanism (ram) and economic dynamics, growth is limited by the long-run level of the maximin value, which is an endogenous dynamic sustainability constraint. If utility reaches this limit, sustainability imposes growth to stop, and the adoption of maximin decisions instead of the current ram. We illustrate this pattern in two canonical models, the simple fishery and a two-sector economy with a nonrenewable resource. We discuss what our results imply for the assessment of sustainability in the short and the long run in non-optimal economies.