Crowdsourcing Access Network Spectrum Allocation Using Smartphones

Abstract

The hundreds of millions of deployed smartphones provide an unprecedented opportunity to collect data to monitor, debug, and continuously adapt wireless networks to improve performance. In contrast with previous mobile devices, such as laptops, smartphones are always on but mostly idle, making them available to perform measurements that help other nearby active devices make better use of available network resources. We present the design of PocketSniffer, a system delivering wireless measurements from smartphones both to network administrators for monitoring and debugging purposes and to algorithms performing realtime network adaptation. By collecting data from smartphones, PocketSniffer supports novel adaptation algorithms designed around common deployment scenarios involving both cooperative and self-interested clients and networks. We present preliminary results from a prototype and discuss challenges to realizing this vision.

Cooperative content delivery exploiting multiple wireless interfaces: methods, new technological developments, open research issues and a case study

“…Appropriate knowledge of the heterogeneous network topology allows the design of more efficient interferenceavoidance algorithms and improved allocation of limited network resources for CCD. The work in [60] considers the process of local topology discovery via crowdsourcing, where crowdsourcing is used for the task of detecting and reporting the existence of conflicting neighbors to access points that are deployed by independent users. A method is proposed in [60], in which, each user periodically reports information about the set of neighboring access points observed.…”

“…The work in [60] considers the process of local topology discovery via crowdsourcing, where crowdsourcing is used for the task of detecting and reporting the existence of conflicting neighbors to access points that are deployed by independent users. A method is proposed in [60], in which, each user periodically reports information about the set of neighboring access points observed. • Scenario 3: Over-the-top (OTT) content refers to video, audio, television and other services provided over the internet rather than through a dedicated and managed network such as IPTV.…”

Benchmarking resource usage for spectrum sensing on commodity mobile devices

“…Regardless, it is always too expensive or impractical to deploy laboratory-grade devices at any significant spatial granularity covering arbitrary areas. Recently, there have been propositions to scale sensing at finer spatial granularities using crowd-sourcing mechanisms [19]. Initial prototypes have shown an early promise using commodity mobile devices [16,23,17] as spectrum sensors.…”

PLOMaR: An ontology framework for context modeling and reasoning on crowd-sensing platform

“…Through experimental instrumentations and direct modification of the operating system's source code, researchers can use the provided JSON scheme and API functions to extract information of interest in order to study user behaviors and their correlations with device performance. Some examples include crowd-sensing network utilization in order to compute better resource allocation [7] and assisting access points to make channel assignment decisions with mobile clients [8]. Researchers can instrument participants' devices with functions that record parameters either periodically, or triggered by events.…”