Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Social-Loc: Improving indoor localization with social sensing

Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming in-creasingly popular for mobile applications. While GPS pro-vides accurate outdoor locations, accurate indoor localiza-tion schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In or-der to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both so-cial encounter and non-encounter events to cooperatively calibrate the estimation errors. We have fully implemented Social-Loc on the Android platform and demonstrated its performance on two underlying indoor localization systems: Dead-reckoning and WiFi fingerprint. Experiment results show that Social-Loc improves user’s localization accuracy of WiFi fingerprint and dead-reckoning by at least 22 % and 37%, respectively. Large-scale simulation results indicate Social-Loc is scalable, provides good accuracy for a long du-ration of time, and is robust against measurement errors

Toward an object-based semantic memory for long-term operation of mobile service robots

Throughout a lifetime of operation, a mobile service robot needs to acquire, store and update its knowledge of a working environment. This includes the ability to identify and track objects in different places, as well as using this information for interaction with humans. This paper introduces a long-term updating mechanism, inspired by the modal model of human memory, to enable a mobile robot to maintain its knowledge of a changing environment. The memory model is integrated with a hybrid map that represents the global topology and local geometry of the environment, as well as the respective 3D location of objects. We aim to enable the robot to use this knowledge to help humans by suggesting the most likely locations of specific objects in its map. An experiment using omni-directional vision demonstrates the ability to track the movements of several objects in a dynamic environment over an extended period of time

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Parking Assistance System for Indoor Environment

In the last decade, indoor location based applications, such as touristic guide in museums or shopping guidance in supermarkets, have been developed rapidly requiring suitable and accurate indoor positioning. However, location sensing in indoor environments is a challenging task and an intensively researched topic. Fortunately, wireless technologies can help us derive location information. In this paper, we propose and introduce our positioning and navigation system for indoor parking garage environment, called iParking, which has been under development. The iParking system collects real-time parking lot occupancy data, and tracks and navigates vehicles entering the parking garage to a preselected, e.g., the closest to the favorite shop, free parking lot. The driver’s smartphone is used as the navigation interface. The system is built on a background Wi-Fi infrastructure making the deployment and maintenance economical. Currently, we have been implementing a prototype of our iParking system in a parking garage of a shopping mall for demonstration purposes