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This paper considers collection and processing of data by solar-powered servers operating in an Internet of Things (IoT) network. Specifically, these servers aim to cooperatively maximize the amount of data collected from devices and computed over multiple time slots. To achieve this aim, they must consider computation deadline, time-varying energy arrivals at sensor devices and other servers. To this end, this paper outlines a mixed integer linear program (MILP), which can be used to optimize t...

In online reinforcement learning, operators predict the return by weighting the successors’ estimated value. However, due to the lack of uncertainty quantification, weights assigned by operators are affected by the potentially biased estimations. As a result, the partial order of estimated values is ineffective. To increase the probability of outputting an optimal partial order, this paper introduces the hedonistic expected value (HEV), an upper bound of the return's expectation to quanti...

Data collection is a fundamental operation in energy harvesting industrial Internet of Things networks. To this end, we consider a hybrid access point (HAP) or controller that is responsible for charging and collecting L bits from sensor devices. The problem at hand is to optimize the transmit power allocation of the HAP over multiple time frames. The main challenge is that the HAP has causal channel state information to devices. In this article, we outline a novel two-step reinforcement learnin...

Purpose: User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination as pre- and post-estimators of the actual relevance. They also assume that User...

Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy uti...

Risk Awards 2022: New machine learning tool tackles an age-old, old age problem

Abstract We consider a Radio Frequency (RF)-charging network where sensor devices harvest energy from a solar-powered Hybrid Access Point (HAP) and transmit their data to the HAP. We aim to optimize the power allocation of both the HAP and devices to maximize their Energy Efficiency (EE), which is defined as the total received data (in bits) for each Joule of consumed energy. Unlike prior works, we consider the case where both the HAP and devices have causal knowledge of channel state informati...