Mervat Abu-Elkheir

Data Management for the Internet of Things: Design Primitives and Solution

The Internet of Things (IoT) is a networking paradigm where interconnected, smart objects continuously generate data and transmit it over the Internet. Much of the IoT initiatives are geared towards manufacturing low-cost and energy-efficient hardware for these objects, as well as the communication technologies that provide objects interconnectivity. However, the solutions to manage and utilize the massive volume of data produced by these objects are yet to mature. Traditional database management solutions fall short in satisfying the sophisticated application needs of an IoT network that has a truly global-scale. Current solutions for IoT data management address partial aspects of the IoT environment with special focus on sensor networks. In this paper, we survey the data management solutions that are proposed for IoT or subsystems of the IoT. We highlight the distinctive design primitives that we believe should be addressed in an IoT data management solution, and discuss how they are approached by the proposed solutions. We finally propose a data management framework for IoT that takes into consideration the discussed design elements and acts as a seed to a comprehensive IoT data management solution. The framework we propose adapts a federated, data- and sources-centric approach to link the diverse Things with their abundance of data to the potential applications and services that are envisioned for IoT.

Internet of nano-things healthcare applications: Requirements, opportunities, and challenges

Ubiquitous healthcare is becoming a reality thanks to the advances in sensing and communication technologies, which make it possible to provide monitoring and diagnosis services outside the premises of healthcare providers. The Internet of Things (IoT) is the main paradigm through which medical devices will be connected to the Internet, thereby empowering near-realtime health services and transforming a patients physical space into a smart space. Recent developments in nanotechnology are giving rise to the Internet of Nano-Things, with a new set of finegrained and highly sophisticated healthcare applications that can be run inside the human body. In this paper, we outline a vision of the ubiquitous healthcare ecosystem and its architectural requirements in order to incorporate nanonetworks. We identify some of the envisioned IoNT healthcare applications and the IoNT requirements that are necessary to support the different application categories, as well as the underlying healthcare service opportunities. In order to understand the current status of implementation, we provide a brief analysis of the major efforts targeted at IoNT performance analysis and evaluation. We finally discuss the most pressing challenges that the IoNT paradigm poses for healthcare applications and services.

Enhancing emergency response systems through leveraging crowdsensing and heterogeneous data

Robust and prompt emergency response is a crucial service that smart cities should provide to citizens, communities, and corporations. Emergency management strategies that are currently supported by cities yield pre-determined protocols that can only handle well-understood incidents. However, there are incidents whose nature, shape, scale, and timing are not as predictable. The lack of adequate data management platforms to harvest emergency-related data from the proliferation of data sources scattered around a city is a major shortfall in current emergency response and risk assessment processes. We propose an improved information infrastructure to assist emergency personnel in responding effectively and proportionally to large-scale, distributed, unstructured natural and man-made hazards such as multi-vehicle accidents, outbreaks of human or animal diseases, major weather events, large fires, and terrorist attacks. The proposed infrastructure will crowdsource the multitude of human and physical sensing resources that can generate data about incidents (e.g. smartphones, sensors, vehicles, etc.) in order to build a comprehensive understanding of emergency situations and provide situational awareness and recommendations to emergency teams on the scene. Our infrastructure consists of three components: (1) large-scale crowdsensing and data quality valuation, (2) heterogeneous data integration and analytics, and (3) decision making, alternative generation and recommendations. Leveraging crowdsensing and heterogeneous data analytics will improve the response coordination to critical incidents and real-time incident management, which will contribute to saving lives and reducing injuries, improving the quality of life, and saving resources by deploying them more effectively.

Towards prolonged lifetime for large-scale Information-Centric Sensor Networks

Information-Centric Sensor Networks (ICSNs) are a class of context-aware communication networks that provide an infrastructure for knowledge-based intelligent information service provisioning to anyone, anywhere and at any time. It provides the sensed information to the end-user based on application requirements. To cater to the needs of ICSNs applications, WSNs may need to be deployed on a very large scale. Maintaining network connectivity and longevity in such a large scale deployment of sensor nodes, while catering to the application and service requirements of the ICSN is a challenging task because sensor nodes are very resource constrained in terms of power, communication range and processing capabilities. Hence, we propose the use of cognitive nodes in the underlying WSN, which can help to achieve better management of the sensor networks resources. The cognitive nodes make use of the requested information to learn the dynamic network environment, and make decisions based on the application requirements and current network conditions. Cognitive nodes will use information stored in their knowledge base to help with resource management in the sensor network while catering to the service requirements of the ICSN. The main contribution of this work is to provide a strategy for their deployment in large-scale WSNs used in ICSN applications.

A stable matching algorithm for resource allocation for underlaying device-to-device communications

In this paper, we propose a novel deferred acceptance based resource allocation algorithm (DARA) for allocating LTE cellular user resource to D2D devices. The quality of service (QoS) constrained downlink resource block (RB) allocation problem is first formulated as a computationally expensive mixed integer nonlinear programming (MINLP) problem. We also propose a polynomial-time matching algorithm based on deferred acceptance method to find an allocation of cellular resources to D2D devices. Outcome of this algorithm is a stable matching of cellular users to the D2D devices. We compare the system sum rate obtained from DARA to that from a simple local search based algorithm (SLOC), a greedy heuristic based resource allocation algorithm (GHRA) and a random resource allocation algorithm. The simulation results show that, DARA achieves significantly better system sum rate than the rest of the algorithms while it runs in O(n2) in the worst case and O(n log n) on average.

Orchestrating access to smart city services

Smart cities are poised to provide innovative public services to citizens, communities, corporations, and governmental bodies. Realization and management of these services is assumed to take place centrally, and according to well-defined policies established by city authorities. However, the proliferation of connected devices and crowd-sensing made it possible for ad hoc services to emerge before strict policies and architectures were enforced, and with little provision of interoperability. Furthermore, many existing services in todays cities use networks of privately-owned smart devices, which confine such services to local domains because of ownership and corporate governance. We propose a resource cooperation approach that orchestrates access to connected devices across local domains in order to compose new and potentially large-scale services that are not necessarily bound by local ownership. The resource cooperation approach makes informed recruitment of network segments from local network domains in response to stakeholder queries, which will be posted to a centralized authority responsible for smart city services. Our approach aims at maximizing the utility of city-wide services while maintaining local governance and minimizing performance degradation of service provisioning within local domains. The approach will also facilitate the composition of new smart city services from existing heterogeneous resources, thus minimizing the need to deploy dedicated service infrastructures.

Improving Localization Accuracy: Successive Measurements Error Modeling

Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. The mathematical models used in current vehicular localization schemes focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, we first investigate the existence of correlation between successive positioning measurements, and then incorporate this correlation into the modeling positioning error. We use the Yule Walker equations to determine the degree of correlation between a vehicle’s future position and its past positions, and then propose a p-order Gauss–Markov model to predict the future position of a vehicle from its past p positions. We investigate the existence of correlation for two datasets representing the mobility traces of two vehicles over a period of time. We prove the existence of correlation between successive measurements in the two datasets, and show that the time correlation between measurements can have a value up to four minutes. Through simulations, we validate the robustness of our model and show that it is possible to use the first-order Gauss–Markov model, which has the least complexity, and still maintain an accurate estimation of a vehicle’s future location over time using only its current position. Our model can assist in providing better modeling of positioning errors and can be used as a prediction tool to improve the performance of classical localization algorithms such as the Kalman filter.

Regression forecasting model to improve localization accuracy

Location information with a high level of accuracy is a crucial component in many of the emerging services provided to users by wireless and mobile networks. The proposed mathematical models for localization focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, we first investigate the correlation between successive positioning measurements, and then take this correlation into consideration when modeling positioning error. We propose a p-order Gauss-Markov model to predict the future position of a mobile node from its current mobility statistics, and use the Yule Walker equations to determine the degree of correlation between a nodes future position and its past positions. Using vehicular networks as a case study, we investigate the existence of correlation for two datasets representing the mobility traces of two vehicles over a period of time. We prove the existence of correlation between successive measurements in the two datasets, and show that the time correlation between measurements can have a value up to 4 minutes. Through simulations, we validate the robustness of our model and show that it is possible to use the first-order Gauss-Markov model, which has the least complexity, and still maintain an accurate estimation of a vehicles location over time. Our model can assist in providing better modeling of positioning errors and can be used as a prediction tool to improve the performance of classical localization algorithms such as the Kalman filter.

Hybrid data management system for mHealth

Mobile and wearable sensing technology stands to provide a wealth of information to healthcare providers, and allows them to envision systems with reduced costs, automated monitoring and evaluation, and overall improved healthcare services. However, the volume of data produced by such mobile and sensing technologies needs to be managed efficiently and continuously so as to realize its full potential in providing cutting-edge services. In this paper, we propose a mHealth data management system with the aim to provide near real-time operational an analytical services, while supporting long-term and offline processes and deep analytics. The components of the system will be discussed, and the potential workflows will be outlined.

Tiered Data Integration for Mobile Health Systems

One of the most promising instantiations of the Internet of Things (IoT) are mobile health (mHealth) systems, which promise to deliver intelligent health monitoring and assisted living as well as advanced and integrated health services. To realize the full potential of these services, fragmented and heterogeneous data that is generated by different segments of the system need to be consolidated in order to support high-quality processes. This paper proposes a tiered data integration scheme for mHealth systems that works on the schema, entity, and event levels. The proposed scheme incorporates an algorithm that merges and ranks sensor streams for schema integration and event identification, and performs contextual record registration and deduplication for entity resolution. We tested the proposed integration scheme on two sets of sensor-based mHealth data related to human activity recognition. Preliminary results show that the proposed integration scheme contributes to enhancements in event identification precision compared to the classification performance of separate datasets produced within the same mHealth system.