Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because: i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world; and ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries.

Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant implications if SFAs treat emails of user interest as spam on a large scale. This is particularly important during national elections. To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. We analyzed the biases in the SFAs towards the left and the right candidates and further studied the impact of the interactions (such as reading or marking emails as spam) of email recipients on these biases. We observed that the SFAs of different email services indeed exhibit biases towards different political affiliations.

Scalability and performance requirements are driving tenants to increasingly move their applications to public clouds. Unfortunately, cloud providers do not provide a view of their networking infrastructure to the tenants, rather only provide some generic service level agreements (SLAs). Tenants are, therefore, forced to plan the deployments of their applications based on these SLAs. This limits the performance that the tenants can achieve. Keeping this in view, we present a detailed network measurement study of the largest public cloud, Amazon Web Services (AWS). We collected network data to characterize the availability and latency of AWS over a period of 100 days and studied various temporal trends across several geographical locations of AWS throughout the world. We performed our study at all three levels of cloud hierarchy: inside availability zones (AZs), across AZs, and across regions. Our results show that network behavior varies significantly over time at different geographical locations, levels of hierarchy, and temporal granularities. For example, while we observed high availability at monthly granularity, it deteriorates at daily and hourly granularities. This and many other such observations that we present have significant implications for cloud tenants. We further implemented our measurement approach on Google Cloud Platform (GCP) to demonstrate that it can be deployed on any cloud platform and present some preliminary comparative observations from this implementation. Based on our observations, we present several recommendations that tenants can use to better deploy their applications.

This paper presents an algorithmic framework for the distributed on-line source seeking, termed as DoSS, with a multi-robot system in an unknown dynamical environment. Our algorithm, building on a novel concept called dummy confidence upper bound (D-UCB), integrates both estimation of the unknown environment and task planning for the multiple robots simultaneously, and as a result, drives the team of robots to a steady state in which multiple sources of interest are located. Unlike the standard UCB algorithm in the context of multi-armed bandits, the introduction of D-UCB significantly reduces the computational complexity in solving subproblems of the multi-robot task planning. This also enables our DoSS algorithm to be implementable in a distributed on-line manner. The performance of the algorithm is theoretically guaranteed by showing a sub-linear upper bound of the cumulative regret. Numerical results on a real-world methane emission seeking problem are also provided to demonstrate the effectiveness of the proposed algorithm.

IoT deployments often require communication between devices that employ heterogeneous wireless technologies. Traditionally, expensive gateways are used to relay packets between heterogeneous nodes. Recent cross-technology communication offers a low bandwidth alternative, which is only feasible when communication between such nodes is limited to simple binary commands. In contrast, our work capitalizes on the increasing presence of multi-standard radio chips in mainstream IoT devices, to provide a new perspective on how to enable direct communication between heterogeneous nodes. We design Scylla---a software control layer---that allows multiple wireless stacks to coexist on top of a single radio chip, thereby simultaneously offering multiple communication interfaces. Uniquely, Scylla achieves near stack-native performance and requires no changes to the standards.

We propose dynamic reconfiguration of the radio interface in IoT platforms to support multiple link layers simultaneously. This allows us to tackle the increasing link layer heterogeneity in IoT devices, thus bringing a multitude of benefits: extensible and vender agnostic multihop deployments, rapid integration of the new “things” into an existing network, as well as seamless integration of the IoT with the traditional wireless Internet.

In a continuous effort to develop and incorporate innovative teaching styles in the Mechanical Engineering curriculum at King Fahd University of Petroleum and Minerals (KFUPM), a new lab course titled Advanced Manufacturing Lab (ME-407) has been introduced, wherein the senior students are exposed to Computer Aided Drafting (CAD) and computer aided manufacturing (CAM). The main objective of this course is to inculcate the knowledge in the area of CAM software, its application in computerized numerical controlled (CNC) machining, metrological study and data generation for statistical analysis. For doing so, a comprehensive project is being developed which integrates all the above elements. This paper outlines a proposed strategy to accomplish these targets and a set of carefully selected activities to follow when this new course starts.