Industrial control systems (ICS), such as smart grid systems, are frequently composed of hundreds of devices distributed over a large geographic area. While mobile applications have been used with good success in managing ICSs, traditional methods of distributing applications (e.g., app stores) are not well suited to the task of discovering, distributing, and building human machine interfaces (HMIs) for ICS, as the highly individualized and often proprietary individual components of ICSs have vastly different interfaces leading to a need to download hundreds of applications. We propose the No Effort Rapid Development (NERD) middleware framework to address the challenges of in-field HMI discovery, provisioning, communication, and co-evolution with related ICSs. Middleware services offer the ability to simplify on-demand HMI distribution and operation of ICSs. NERD leverages existing ICS device-markers (e.g., QR-codes or RFID tags) or Bluetooth low-energy protocols for rapid cyber-physical discovery and provisioning of HMIs in the field. Device-markers and Bluetooth low-energy protocols have a very limited data capacity and transmission speed, and to achieve on-device storage of HMIs, we propose using a compact data-driven domain-specific language that emphasizes data sources and sinks between the HMI and IC.

Industrial Control Systems (ICS), such as wastewater treatment systems, are frequently composed of hundreds of devices distributed over a large geographic area. While mobile applications have been used with good success in managing ICSs, traditional methods of distributing applications (e.g. app stores) are not well suited to the task of distributing ICS mobile applications, as the highly individualized and often proprietary individual components of ICSs have vastly different interfaces leading to a need to download hundreds of applications. We propose the No Effort Rapid Development (NERD) framework to address the challenges of in-field human-machine interface (HMI) discovery, provisioning, and co-evolution with related ICSs. Mobile cloud services offer the ability to simplify on-demand HMI distribution and operation of ICSs. NERD leverages existing ICS device-markers (e.g. QR-codes or RFID tags) for rapid cyber-physical discovery and provisioning of HMIs in the field. Device-markers have a very limited data capacity, and to achieve on-device storage of HMIs we propose using a compact data-driven domain specific language that emphasizes data sources and sinks between the HMI and ICS cloud based services. Our approach seamlessly links controls in the physical domain with specific resources in the digital domain, such as device-specific interfaces or ICS-wide control interfaces. We provide a quantitative evaluation of NERD and show how NERD can simplify the process of in-field discovery and provisioning of HMIs and related applications.

Medication administration is one pathway by which Adverse Drug Events (ADE) can occur. While Electronic Medical Administration Record (eMAR) systems help reduce the number of ADEs, current eMAR implementations suffer from workarounds that defeat safety and verification mechanisms meant to limit the number of potential ADEs that occur during medication administration. In this paper, we introduce Multi-Element ChipLess (MECL) RFID tags which enable real-time event notifications through event signatures. Event signatures correspond to the physical configuration of different RFID elements in a chipless RFID tag. Augmenting physical objects, such as a pill container, with MECL-RFID can allow caregivers to detect the moment a particular pill container is opened or closed. We present the fundamentals behind real-time event detection using MECL-RFID and propose a cyber-physical intervention system that can be used to reduce ADEs through realtime event monitoring and notifications sent to clinicians administering medication. We also present a prototype MECL-RFID to demonstrate potential future improvements to eMAR systems that minimize ADEs.

In 3GPP LTE, the physical layer is divided into data and signaling, where the signaling (or control information) enables efficient data exchange/resource scheduling. The LTE uplink contains a physical channel known as the Physical Uplink Control Channel (PUCCH), which carries uplink control information such as message acknowledgements, scheduling requests, and channel status information from user equipment (UE) to LTE base stations (eNodeB). The PUCCH is located on the edges of the system bandwidth in a static location. The static allocation of the PUCCH presents a dilemma: an adversary can disrupt the uplink channel with minimal effort and only needs to know the PUCCH's spectrum allocation. In this paper we (i) take a closer look at the purpose and specification of the PUCCH, (ii) we propose various strategies to be used for the detection of interference specifically on the PUCCH, and (iii) we outline strategies for mitigating 'protocol-aware' interference on the PUCCH. Some of the mitigation strategies, such as control information duplication, can be implemented with minimal changes to LTE eNodeBs and UEs, while other countermeasures require augmentations to both eNodeB and UE hardware or software.

Determining what physical activity the user is performing using mobile devices is an elusive task even though consumer off the shelf (COTS) mobile devices carried by most users contain a vast array of physical sensors. When conducting work improvement studies, construction crew members are typically observed by eye to record information about the physical activities being performed. Another approach to capturing crew member activities is utilizing sensors within COTS mobile devices carried by crew members. This paper presents CTrack: an approach for capturing telemetry from the array of sensors on-board COTS mobile devices. The resulting sensor telemetry is fused together and can be used to make informed decisions when characterizing crew member activities. Additionally, this paper presents results from field testing CTrack and discusses the current benefits and limitations of fusing multiple telemetry sources to characterize crew member activities.

Smart phone-powered data collection systems are rapidly becoming an effective method of gathering field data. One major challenge of using smart phones to collect data is the ability to link smart phone metadata, such as location at a specific time, back to the user -- thereby violating the privacy of that individual. A promising approach to helping ensure user privacy is through geographical k-anonymity, which attempts to ensure that every gathered data reading is geographically indistinguishable from k-1 other readings. The approach helps prevent precise localization of the user or reverse engineering of reported data by leveraging the user's known location. This paper presents a dynamic tessellation algorithm for k-anonymity that provides better privacy preservation and data reporting precision than previous static algorithms for k-anonymity. The paper presents empirical results from a real world data set that demonstrate the improvements in privacy provided by the algorithm.