Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes.

This paper presents a new human activity recognition method that uses a camera mounted on a mobile robot. We assume that the robot's camera captures images of a person and recognizes his/her activities based on skeletal and visual features extracted from the images. A key issue encountered with this method for activity recognition is that it requires the robot to position itself so that it has an adequate field of view of the activities being conducted. For example, if the robot is directly behind a person while observing that person making tea, it will be difficult for the robot to distinguish that activity from other similar activities such as preparing a meal or washing dishes. Our method employs deep reinforcement learning to control the movements of the mobile robot that is observing the activities in order to maximize its recognition accuracy while minimizing its energy consumption related to its movement. We propose effective action- and state-space designs that can achieve early training convergence and highly accurate activity recognition by: (i) incorporating the confidence of the activity recognition output when evaluating the quality of the current state (position), (ii) incorporating the costs of subsequent actions when estimating values for those actions, and (iii) designing an effective action space that accelerates reinforcement learning by restricting the movement space of the robot to the circumference of a circle with a predefined radius centered on the person.

Currently, over nine billion things are connected in the Internet of Things (IoT). This number is expected to exceed 20 billion in the near future, and the number of things is quickly increasing, indicating that numerous data will be generated. It is necessary to build an infrastructure to manage the connected things. Cloud computing (CC) has become important in terms of analysis and data storage for IoT. In this paper, we consider a cloud broker, which is an intermediary in the infrastructure that manages the connected things in CC. We study an optimization problem for maximizing the profit of the broker while minimizing the response time of the request and the energy consumption. A multiobjective particle swarm optimization (MOPSO) is proposed to solve the problem. The performance of the proposed MOPSO is compared with that of a genetic algorithm and a random search algorithm. The results show that the MOPSO outperforms a well-known genetic algorithm for multiobjective optimization.

Participatory sensing enables to broadly collect information from mobile devices with embedded sensors. However, users have to use limited energy on the mobile devices as well as have to pay more communication fee when reporting sensory data to service providers. It is important to give them incentives to send information in participatory sensing systems. In this paper, we propose a novel incentive mechanism for participatory sensing based on the evolutionary algorithm. Our proposed algorithm is evaluated by extensive simulations and its performance is compared with state-of-the-art algorithms. The simulation results demonstrate that our algorithm successfully increases the number of participating users while it can also uniformly expand the coverage of a sensing area.