Finding Mobile Applications in Cellular Device-to-Device Communications: Hash Function and Bloom Filter-Based Approach

The rapid growth of mobile computing technology and wireless communication have significantly increased the mobile users worldwide. We propose a code-based discovery protocol for cellular device-to-device (D2D) communications. To realize proximity based services such as mobile social networks and mobile marketing using D2D communications, each device should first discover nearby devices, which have mobile applications of interest, by using a discovery protocol. The proposed discovery protocol makes use of a short discovery code that contains compressed information of mobile applications in a device. A discovery code is generated by using either a hash function or a Bloom filter. When a device receives a discovery code broadcast by another device, the device can approximately find out the mobile applications in the other device. The proposed protocol is capable of quickly discovering massive number of devices while consuming a relatively small amount of radio resources. We analyze the performance of the proposed protocol under the random direction mobility model and a real mobility trace. By simulations, we show that the analytical results well match the simulation results and that the proposed protocol greatly outperforms a simple non-filtering protoco

An Efficient Service Discovery Algorithm for Counting Bloom Filter-Based Service Registry

The Service registry, the yellow pages of Service-Oriented Architecture (SOA), plays a central role in SOA-based service systems. The service registry has to be scalable to manage large number of services along with their requirements on storage and discovery. Based on our previous work on feature-based services quantification, we characterize services according to their diverse functional and non-functional requirements, and represent them as string formats which can be stored, probed, and indexed by efficient data structures, such as hash table and Bloom filter. Then, we propose a comprehensive service-storage solution using the counting Bloom filter (CBF). The application of CBF enables us to structure candidate services into separate groups, resulting in an accelerated services discovery process. The contributions of this research work include a new approach to manage large number of services based on quantified service features, and a storage architecture design to support service discovery. Experimental results strongly support these claims.This is a manuscript of a proceeding published as S. Cheng, C. K. Chang and L. -J. Zhang, "An Efficient Service Discovery Algorithm for Counting Bloom Filter-Based Service Registry," 2009 IEEE International Conference on Web Services, 2009, pp. 157-164, doi: 10.1109/ICWS.2009.121. Posted with permission. © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works