Fog-centric Localization for Ambient Assisted Living Kriti Bhargava, Gary McManus, Stepan Ivanov Telecommunications Software &Systems Group Waterford Institute of Technology Waterford, Ireland (kbhargava, gmcmanus, sivanov) @tssg.org Abstract—Ambient Assisted Living (AAL) is a novel discipline that aims at improving the quality of life for all generations, especially the elderly, with the help of information and communication technologies. Behavioral tracking AAL systems necessitate the monitoring and understanding of daily activities and preferences of the user for design of customized, context- aware services and detection of behavior anomalies. Localization of the user is, therefore, key to facilitate real-time activity monitoring in AAL applications. Although several localization techniques have been proposed to date, majority of them incur a high operational cost owing to dependency on dense sensor deployments for ambient intelligence or use of expensive hardware such as GPS receivers. In this paper, we propose a low-cost Wireless Sensor Networks (WSN) system, comprising of a single wearable device and a cloud gateway, for outdoor localization in the context of AAL. With the inception of the Fog Computing paradigm, we consider the implementation of a light-weight data mining technique, Iterative Edge Mining (IEM), on the wearable device for on-board activity recognition. IEM is based on the classification of signal distributions to enable real-time mobility tracking as the user moves around an environment. Given the topology information and the activity sequence generated by the algorithm, we estimate the user location by associating the distance covered over time with the orientation values. Alerts are signaled locally upon detection of behavior anomalies and transmitted to the gateway node using a delay-tolerant communication framework. As such, IEM runs autonomously on the sensor node without interaction with external objects, thereby, improving the responsiveness as well as the operational cost of our system. We evaluate the performance of IEM in terms of localization accuracy in an outdoor environment. Keywords—ambient assisted living; localization; fog computing; edge mining; wireless sensor network I. INTRODUCTION With advancing age, the elderly often experience physical disabilities and require support with mobility and the activities of daily living. Moreover, they may develop some form of Dementia, a chronic syndrome that causes deterioration in the cognitive function beyond what might be otherwise expected with ageing. This, in turn, leads to challenging behavioral and psychological changes such as repetition, aggression, agitation and psychosis. Alzheimer's is the most prevailing form of Dementia that affects the short-term memory, orientation and intellectual capacity of an individual [1]. It may result in loss of identity, thereby, increasing distress for the patient as well as the caregivers. Wandering is a common symptom for Alzheimer patients that poses serious threat to their safety and may lead to traumatic experiences. Personalized monitoring and care of the elderly is, therefore, important to assist them with daily activities and ensure their well-being. Ambient Assisted Living (AAL) is a recent trend that combines Information and Communication Technologies (ICT) with the social environment with a view to improve the quality of life for all generations, primarily the ageing population with cognitive disabilities [2]. An important aspect of AAL is localization of the user to enable activity monitoring for safe and independent living and minimize the risk of wandering [3]. AAL solutions have the potential to not only allow patients to restore their usual routine but also to reduce the burden on caregivers. Although a few activity tracking systems have been proposed for AAL, their implementation is constrained due to the high operational cost incurred by use of expensive hardware such as GPS modules or dense sensor deployments and cloud infrastructure required for ambient sensing, communication and data analysis. Meanwhile, owing to the growth in ICT, there has been a tremendous improvement in the design and computational capabilities of small devices that constitute edge of the network in the Internet of Things (IoT). A new networking paradigm, Fog Computing, proposes a partial migration of intelligence away from the cloud towards the network edges [4]. That is, Fog Computing aims at facilitating localized data processing and event detection at the end-user terminals. The concept has gained importance owing to its ability to efficiently utilize the in-network resources while minimizing dependency on the cloud infrastructure. It not only reduces the operational cost but also improves the responsiveness of the system for alert generation. Over the past few years, numerous interpretations of fog nodes within IoT have been discussed. While some approaches propose the use of computational resources at edge devices such as network switches [5], others suggest the use of free computation slots on user mobile phones [6]. Recent studies have further brought down the concept of Fog Computing to wireless, battery-operated sensor devices that sit at the edge of Wireless Sensor Networks (WSN). Edge Mining is a novel approach that suggests the implementation of light-weight data mining tasks on the sensor devices [7]. While resource-intensive network learning is performed on the cloud, minor computations carried out at the sensor nodes enable real-time event detection. Furthermore, Edge Mining algorithms improve the energy efficiency of WSN by reducing packet transmissions to the cloud gateway via localized data reduction and, in turn, increase operational time of the system.