Decentralized cellular networks have emerged to increase network accessibility by distributing infrastructure ownership over independent entities. Unlike the centralized setting, these architectures can allow users to connect to any untrusted base station without prior subscription. However, verification of the service is necessary in the absence of trust for commensurate payments by the user. Further, any method of verification must be non-intrusive and reliably agreed upon by the involved parties. To this end, we describe two-sided measurements where both the users and the providers independently assess the cellular service. We find that reconciling measurements from different layers of the cellular stack for a diverse set of matching observations is challenging but not impossible. Hence, new use cases such as a decentralized slicing marketplace, and contract-free roaming can be enabled by two-sided measurements. We envision applying two-sided measurements to real-time, on-demand network slicing and present an architecture that is capable of offering, as well as verifying, such slices in a scalable manner.

Air pollution is a public health emergency in large cities. The availability of commodity sensors and the advent of Internet of Things (IoT) enable the deployment of a city-wide network of 1000's of low-cost real-time air quality monitors to help manage this challenge. This needs to be supported by an IoT cyber-infrastructure for reliable and scalable data acquisition from the edge to the Cloud. The low accuracy of such sensors also motivates the need for data-driven calibration models that can accurately predict the science variables from the raw sensor signals. Here, we offer our experiences with designing and deploying such an IoT software platform and calibration models, and validate it through a pilot field deployment at two mega-cities, Delhi and Mumbai. Our edge data service is able to even-out the differential bandwidths from the sensing devices and to the Cloud repository, and recover from transient failures. Our analytical models reduce the errors of the sensors from a best-case of 63% using the factory baseline to as low as 21%, and substantially advances the state-of-the-art in this domain.

We are motivated by the problem of impromptu or as-you-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations, these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.

We present a centralized integrated approach for: 1) enhancing the performance of an IEEE 802.11 infrastructure wireless local area network (WLAN), and 2) managing the access link that connects the WLAN to the Internet. Our approach, which is implemented on a standard Linux platform, and which we call ADvanced Wi-fi Internet Service EnhanceR (ADWISER), is an extension of our previous system WLAN Manager (WM). ADWISER addresses several infrastructure WLAN performance anomalies such as mixed-rate inefficiency, unfair medium sharing between simultaneous TCP uploads and downloads, and inefficient utilization of the Internet access bandwidth when Internet transfers compete with LAN-WLAN transfers, etc. The approach is via centralized queueing and scheduling, using a novel, configurable, cascaded packet queueing and scheduling architecture, with an adaptive service rate. In this paper, we describe the design of ADWISER and report results of extensive experimentation conducted on a hybrid testbed consisting of real end-systems and an emulated WLAN on Qualnet. We also present results from a physical testbed consisting of one access point (AP) and a few end-systems.