Over five billion people are active cellular subscribers, spending over a trillion dollars a year on communications. Despite this, hundreds of millions of people are still not connected. Implicit in these networks is a top-down design, with large nationscale telecommunication firms deciding when and where coverage will be available. This is enforced by the large capital investment required to run cellular systems; base stations can cost upwards of US$100,000 and require expensive related core infrastructure. Recent technological innovations have enabled much cheaper cellular equipment; a base station now costs around US$10,000 and requires none of the other related systems.This reduction in cost is enabling new models of cellular telephony. Small organizations are suddenly capable of being service providers. In this work we ask, "How successful would bottom-up cellular networks be?" Essentially we argue for and demonstrate a local cellular network, utilizing existing infrastructure (e.g., power, network, and people) to operate at much lower cost, with less required capital, bringing coverage to areas not traditionally able to support cellular deployments. This network also provides sustainable employment and revenue to local entrepreneurs and services for the local community.We demonstrate the value of this concept by conducting an ongoing six-month long field deployment in rural Papua, Indonesia, in partnership with local NGOs. This network is currently live, with 187 subscribers sustainably providing US$830 per month in revenue (US$368 in profit) for the operator and employment for three different credit sellers in the village. We also show that this network provides a valuable service to the community through usage logs and user interviews.

Abstract-The GSM network is the largest network on Earth, providing vital communications service to billions of people. Yet hundreds of millions of people live outside coverage of existing cellular providers. Recently, researchers have demonstrated a new model of cellular connectivity, community cellular, that has the potential to bring coverage to extremely rural populations. Although the total capital costs for these networks (

The smartphone has been touted as the technology of the 21st century. Global smartphone adoption rates are growing rapidly, up to over 24% in 2014, with usage increasing 25% in the last year. However, rural areas are often the last places to benefit from these technological trends. Utilizing cellular network registration logs, we explore the adoption and usage of smartphones in an extremely remote community in Indonesia. We found that 16% of the phones in the area were smartphones (compared to between 14-24% in Indonesia). This shows that smartphone adoption in rural Indonesia is similar to the rest of the country. We also explored usage in the network, and found that smartphone users were more likely to text, especially to other smartphone users.

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Modern enterprises almost ubiquitously deploy middlebox processing services to improve security and performance in their networks. Despite this, we find that today's middlebox infrastructure is expensive, complex to manage, and creates new failure modes for the networks that use them. Given the promise of cloud computing to decrease costs, ease management, and provide elasticity and faulttolerance, we argue that middlebox processing can benefit from outsourcing the cloud. Arriving at a feasible implementation, however, is challenging due to the need to achieve functional equivalence with traditional middlebox deployments without sacrificing performance or increasing network complexity.In this paper, we motivate, design, and implement APLOMB, a practical service for outsourcing enterprise middlebox processing to the cloud. Our discussion of APLOMB is data-driven, guided by a survey of 57 enterprise networks, the first large-scale academic study of middlebox deployment. We show that APLOMB solves real problems faced by network administrators, can outsource over 90% of middlebox hardware in a typical large enterprise network, and, in a case study of a real enterprise, imposes an average latency penalty of 1.1ms and median bandwidth inflation of 3.8%.

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