Advances in wireless sensor networks have enabled the monitoring of daily activities of elderly people. The goal of these monitoring applications is to learn normal behavior in terms of daily activities and look for any deviation, i.e., anomalies, so that alerts can be sent to relatives or caregivers. However, human behavior is very complex, and many existing anomaly detection systems are too simplistic which cause many false alarms, resulting in unreliable systems. We present Holmes, a comprehensive anomaly detection system for daily in-home activities. Holmes accurately learns a resident's normal behavior by considering variability in daily activities based not only on a per day basis, but also considering specific days of the week, different time periods such as per week and per month, and collective, temporal, and correlation based features. This approach of learning complicated normal behaviors reduces false alarms. Also, based on resident and expert feedback, Holmes learns semantic rules that explain specific variations of activities in specific scenarios to further reduce false alarms. We evaluate Holmes using data collected from our own deployed system, public data sets, and data collected by a senior safety system provider company from an elderly resident's home. Our evaluation shows that compared to state of the art systems, Holmes reduces false positives and false negatives by at least 46% and 27%, respectively.

Cities are deploying tens of thousands of sensors and actuators and developing a large array of smart services. The smart services use sophisticated models and decision-making policies supported by Cyber Physical Systems and Internet of Things technologies. The increasing number of sensors collects a large amount of city data across multiple domains. The collected data have great potential value, but has not yet been fully exploited. This survey focuses on the domains of transportation, environment, emergency and public safety, energy, and social sensing. This article carefully reviews both the data sets being collected across 14 smart cities and the state-of-the-art work in modeling and decision making methodologies. The article also points out the characteristics, challenges faced today, and those challenges that will be exacerbated in the future. Key data issues addressed include heterogeneity, interdisciplinary, integrity, completeness, real-timeliness, and interdependencies. Key decision making issues include safety and service conflicts, security, uncertainty, humans in the loop, and privacy.

Abstract-The populations of large cities around the world are growing rapidly. Cities are beginning to address this problem by implementing significant sensing and actuation infrastructure and building services on this infrastructure. However, as the density of sensing and actuation increases and as the complexities of services grow there is an increasing potential for conflicts across Smart City services. These conflicts can cause unsafe situations and disrupt the benefits that the services were originally intended to provide. Although some of the conflicts can be detected and avoided during designing the services, many can still occur unpredictably during runtime. This paper carefully defines and enumerates the main issues regarding the detection and resolution of runtime conflicts in smart cities. In particular, it focuses on conflicts that arise across services. This issue is becoming more and more important as Smart City designs attempt to integrate services from different domains (transportation, energy, public safety, emergency, medical, and many others). Research challenges are identified and then addressed that deal with uncertainty, dynamism, real-time, mobility and spatiotemporal availability, duration and scale of effect, efficiency, and ownership. A watchdog architecture is also described that oversees the services operating in a Smart City. This watchdog solution detects and resolves conflicts, it learns and adapts, and it provides additional inputs to decision making aspects of services. Using data from a Smart City dataset, an emulated set of services and activities using those services are created to perform a conflict analysis. A second analysis hypothesizes 41 future services across 5 domains. Both of these evaluations demonstrate the high probability of conflicts in smart cities of the future.

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An increasing number of monitoring systems have been developed in smart cities to ensure that a city's realtime operations satisfy safety and performance requirements. However, many existing city requirements are written in English with missing, inaccurate, or ambiguous information. There is a high demand for assisting city policy makers in converting human-specified requirements to machine-understandable formal specifications for monitoring systems. To tackle this limitation, we build CitySpec, the first intelligent assistant system for requirement specification in smart cities. To create CitySpec, we first collect over 1,500 real-world city requirements across different domains from over 100 cities and extract city-specific knowledge to generate a dataset of city vocabulary with 3,061 words. We also build a translation model and enhance it through requirement synthesis and develop a novel online learning framework with validation under uncertainty. The evaluation results on real-world city requirements show that CitySpec increases the sentence-level accuracy of requirement specification from 59.02% to 86.64%, and has strong adaptability to a new city and a new domain (e.g., F1 score for requirements in Seattle increases from 77.6% to 93.75% with online learning).