Abstract. Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensors nodes at MacDill Air Force Base in Tampa, Florida, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.
Abstract-We present a fast, local clustering service, FLOC, that partitions a multihop wireless network into nonoverlapping and approximately equal-sized clusters. Each cluster has a clusterhead such that all nodes within unit distance and some nodes within distance m of the clusterhead belong to the cluster. We show that, by asserting a stretch factor m ! 2, FLOC achieves locality of clustering and fault-local self-stabilization: The effects of cluster formation and faults/changes at any part of the network are contained within at most m þ 1 units. Through simulations and experiments with actual deployments, we analyze the trade-offs between clustering time and the quality of clustering and suggest suitable parameters for FLOC to achieve a fast completion time without compromising the quality of the resulting clustering.
BackgroundHospitals today are introducing new mobile apps to improve patient care and workflow processes. Mobile device adoption by hospitals fits with present day technology behavior; however, requires a deeper look into hospital device policies and the impact on patients, staff, and technology development. Should hospitals spend thousands to millions of dollars to equip all personnel with a mobile device that is only used in a hospital environment? Allowing health care professionals to use personal mobile devices at work, known as bring-your-own-device (BYOD), has the potential to support both the hospital and its employees to deliver effective and efficient care.ObjectiveThe objectives of this research were to create a mobile app development guideline for a BYOD hospital environment, apply the guideline to the development of an in-house mobile app called TaskList, pilot the TaskList app within Boston Children’s Hospital (BCH), and refine the guideline based on the app pilot. TaskList is an Apple operating system (iOS)-based app designed for medical residents to monitor, create, capture, and share daily collaborative tasks associated with patients.MethodsTo create the BYOD guidelines, we developed TaskList that required the use of mobile devices among medical resident. The TaskList app was designed in four phases: (1) mobile app guideline development, (2) requirements gathering and developing of TaskList fitting the guideline, (3) deployment of TaskList using BYOD with end-users, and (4) refinement of the guideline based on the TaskList pilot. Phase 1 included understanding the existing hospital BYOD policies and conducting Web searches to find best practices in software development for a BYOD environment. Phase 1 also included gathering subject matter input from the Information Services Department (ISD) at BCH. Phase 2 involved the collaboration between the Innovation Acceleration Program at BCH, the ISD Department and the TaskList Clinical team in understanding what features should be built into the app. Phase 3 involved deployment of TaskList on a clinical floor at BCH. Lastly, Phase 4 gathered the lessons learned from the pilot to refine the guideline.ResultsFourteen practical recommendations were identified to create the BCH Mobile Application Development Guideline to safeguard custom applications in hospital BYOD settings. The recommendations were grouped into four categories: (1) authentication and authorization, (2) data management, (3) safeguarding app environment, and (4) remote enforcement. Following the guideline, the TaskList app was developed and then was piloted with an inpatient ward team.ConclusionsThe Mobile Application Development guideline was created and used in the development of TaskList. The guideline is intended for use by developers when addressing integration with hospital information systems, deploying apps in BYOD health care settings, and meeting compliance standards, such as Health Insurance Portability and Accountability Act (HIPAA) regulations.
PDIs were identified often through hospital-initiated follow-up contact. Most PDIs were related to appointments. Hospitals caring for children may find this information useful as they strive to optimize their processes for follow-up contact after discharge.
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