The exponential growth of communication devices such as Internet of Things (IoT) devices, Augmented Reality/Virtual Reality (AR/VR) devices, sensors/actuators, mobile devices, and many others have led to a substantial increase in the quantity of traffic generated. This heavy traffic may cause significant delays for delay-sensitive applications and burden the fronthaul in Cloud-RAN architecture. Supporting all types of communications with the C-RAN architecture will be expensive and inefficient for telecom network operators (TNOs) and devices. Consequently, a fog computing-based RAN has evolved to reduce the heavy burden on fronthaul and ensure timely delivery of requested content for delay-sensitive applications. Fog computing resolves this problem by bringing computational resources and networking closer to users or devices, which is particularly useful for delay-sensitive applications. The cloud has extensive computing and resource capabilities, but its latency is more significant than fog computing-based RAN. In this paper, we examine a market in which TNOs lease third-party deployed fog access points (F-APs) in order to optimize the utilization of fog computing-based RAN architecture and F-APs resources. By employing a fog computing paradigm and efficiently using available fog node resources, the fronthaul infrastructure can relieve some of its burdens. Reducing service latency for end users is one of the most promising advantages of the fog paradigm. A user agrees to utilize fog resources in this endeavor and vice versa. Users and fog nodes do not provide usable utility and cost functions, so we implement a market controller to regulate the market. To maximize the social welfare of the network for participating users and fog nodes, we devise a two-sided auction method to assign computational resources and appropriate compensation.

A leading cause of mortality of hospitalized patients are hospital acquired infections (HAI). Unclean hands of healthcare personnel (HCP) are the most common factor contributing to HAI, but their strict compliance to hand hygiene protocols is difficult to supervise. In this work, we propose CleanHands: a simple, low-cost and scalable monitoring and alerting system to ensure adequate thoroughness of disinfection. CleanHands uses a combination of low-cost Bluetooth low energy (BLE) beacon tags and mobile phones for HCP tracking. It integrates infection control models and state-following algorithms for alarming in the event of noncompliance to hand hygiene. Our preliminary experiments in a mockup, small scale intensive care unit (ICU) facility shows promising results with less than 5% false positives.

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.