Commodity Search Based on the Hybrid Breadth-Depth Algorithm in the Crowd Intelligence Based Transaction Network

Abstract

Crowd intelligence based transaction network (CIbTN) is a new generation of e-commerce. In a CIbTN, buyers, sellers, and other institutions are all independent and intelligent agents. Each agent stores the commodity information in a local node. The agents interconnect through a circle of friends and construct an unstructured network. To conduct the commodity search task in a network more efficiently and in an energy-saving manner when a buyer presents a commodity demand, a hybrid breadth-depth search algorithm (HBDA) is proposed, which combines the search logic of the breadth-first search algorithm and the depth-first search algorithm. We defined the correlation degree of nodes in a network, optimized the rules of search and forwarding paths using the correlation degree between a node and its neighboring nodes in the circle of friends, and realized the HBDA based on the PeerSim simulation tool and Java. Experimental results show that, in general, the proposed HBDA has a better search success rate, search time, commodity matching degree, and search network consumption over the two blind search algorithms. The HBDA also has good expansibility, thus allowing it to be used for commodity search efficiently with a high success rate in large-scale networks.