Distributed Algorithms for Internet-of-Things-enabled Prosumer Markets: A Control Theoretic Perspective

Internet-of-Things (IoT) enables the development of sharing economy applications. In many sharing economy scenarios, agents both produce as well as consume a resource; we call them prosumers. A community of prosumers agrees to sell excess resource to another community in a prosumer market. In this chapter, we propose a control theoretic approach to regulate the number of prosumers in a prosumer community, where each prosumer has a cost function that is coupled through its time-averaged production and consumption of the resource. Furthermore, each prosumer runs its distributed algorithm and takes only binary decisions in a probabilistic way, whether to produce one unit of the resource or not and to consume one unit of the resource or not. In the proposed approach, prosumers do not explicitly exchange information with each other due to privacy reasons, but little exchange of information is required for feedback signals, broadcast by a central agency. In the proposed approach, prosumers achieve the optimal values asymptotically. Furthermore, the proposed approach is suitable to implement in an IoT context with minimal demands on infrastructure. We describe two use cases; community-based car sharing and collaborative energy storage for prosumer markets. We also present simulation results to check the efficacy of the algorithms.Comment: To appear as a chapter in "Analytics for the Sharing Economy: Mathematics, Engineering and Business Perspectives", Editors: E. Crisostomi et al., Springer, 2019 (forthcoming book

On the Efficiency of Sharing Economy Networks

Exchange of resources (or, services) over sharing economy networks is attracting increasing interest. Despite their broad applicability, many fundamental questions about their properties remain unanswered. We consider a general sharing economy model and analyze the dynamic interactions of nodes under three different approaches in a stochastic environment. First, we study a centrally designed allocation policy that yields the fair resource each node should receive based on the resources it offers to others. Next, we consider a competitive market where each node determines its allocation strategy so as to maximize the service it receives in return, and a coalitional game model where nodes may coordinate their policies. We prove there is a unique equilibrium exchange allocation for both settings, which also coincides with the central fair allocation. We also characterize the properties of the long-term equilibrium allocations, and analyze their dependence on the network graph. Finally, a dynamic decentralized algorithm is introduced that achieves this desirable operation point with minimal information exchange. The proposed policy is the natural reference point to the various mechanisms that are considered for motivating node collaboration in such networked sharing economy markets