8th ACM MobiHoc 2018 Workshop on Pervasive Wireless Healthcare Workshop (MobileHealth 2018)

Monday, Jun 25, 2018

8am - 9am    Registration (E-VI 1st Floor Lobby)
9am - 9:10am    Opening

Session Chair: Syed Hassan Ahmed and Zilong Ye

Room: E-VI Room 289

Abstract: Today’s modern healthcare delivery systems have been mainly designed to work with the onslaught of an illness, and, logging of information into Electronic Health Record Systems (EHRs), related to symptoms, diagnosis, treatments, etc., is initiated with the illness. There is hardly any data collected during the wellness phase of a person. Furthermore, access to the data stored in such systems, for the purpose of analyses, involves complicated manual processes, and the related access policies make it extremely difficult to perform analytics. In this age of wireless sensors, advance networking and data sciences, it is fitting to envision the design of an integrated cyber physical system that allows ubiquitous, but anonymized, collection of wellness and sickness data, and facilitate the development of models and analytics to predict wellnesses and illnesses using vast amounts of data. Towards achieving this vision, we will present a smart healthcare data collection and management environment consisting of a set of intelligent sensor objects, with wireless communication capabilities, installed in clinics, hospital rooms, and mobile platforms, or embedded in human bodies (such as knee transplants or skin patches). We will elaborate on challenges and research opportunities to realize such a system.

Bio: Ashfaq Khokhar is currently serving as Professor and Palmer Department Chair of Electrical and Computer Engineering at Iowa State University. Previously, he served as chair of the Department of Electrical and Computer Engineering at Illinois Institute of Technology from 2013-2016. Before that, he was a Professor and Director of Graduate Studies in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago. Khokhar was named an Institute of Electrical and Electronics Engineer (IEEE) Fellow in 2009. Khokhar’s research centers on health care data mining, content-based multimedia modeling, retrieval and multimedia communication, high-performance algorithms, and context-aware wireless sensor networks. He earned his bachelor’s degree in electrical engineering from the University of Engineering and Technology in Lahore, Pakistan in 1985; his master’s degree in computer engineering from Syracuse University in 1988; and his Ph.D. in computer engineering from the University of Southern California in 1993.

10am - 10:30am    Break
10:30am - 12:30pm    Technical Session

Session Chair: Zilong Ye

Room: E-VI Room 289

LESLA: A Smart Solution for SDN-enabled mMTC E-health Monitoring System

Bizhu Wang, Yan Sun, Chunjing Yuan and Xiaodong Xu

Interactive Predictive Analytics for Enhancing Patient Adherence in Remote Health Monitoring

SangSeo Yoo, Apostolos Kalatzis, Navid Amini and Mohammad Pourhomayoun

Near Field Communication-Based System For Bedside Contraindication Verification

Maali Alabdulhafith and Srinivas Sampalli

(Invited paper) Predicting Glucose Levels in Patients with Type 1 Diabetes Based on Physiological and Activity Data

M. Vahedi, K. MacBride, W. Wunsik, Y. Kim, C. Fong, A. Padilla, A. Zhong, S. Kulkarni, B. Jiang, S. Arunachalam, M. Pourhomayoun

12:30pm - 12:35pm    Closing

Session Chair: Syed Hassan Ahmed and Zilong Ye

Room: E-VI Room 289

6th ACM MobiHoc 2018 Workshop on the Frontiers of Networks: Theory and Algorithms

Monday, Jun 25, 2018

8am - 8:45am    Registration (E-VI 1st Floor Lobby)
8:45am - 9am    Opening remarks

Room: E-VI Mong Auditorium (Room 180)

9am - 10am    Session I (30 min talks)

Room: E-VI Mong Auditorium (Room 180)

Approximate optimality of separation in cache-aided wireless interference networks

Suhas Diggavi (University of California, Los Angeles)

Timely-Throughput Optimal Scheduling with Prediction

Longbo Huang (Tsinghua University)

10am - 10:30am    Break
10:30am - 12pm    Session II (30 min talks)

Room: E-VI Mong Auditorium (Room 180)

Learning and Games in Spectrum Sharing

Jean Walrand (University of California, Berkeley)

Towards an Economic Internet: from communicating data to exchanging value on networks

Bhaskar Krishnamachari (University of Southern California)

Transparency and Control in Platforms & Networked Markets

Adam Wierman (Caltech)

12pm - 1:30pm    Lunch (on your own)
1:30pm - 2:30pm    Session III (30 min talks)

Room: E-VI Mong Auditorium (Room 180)

Optimal Maxing, Ranking, and Preference Learning

Alon Orlitsky (University of California, San Diego)

An Online Learning Approach to Network Application Optimization with Guarantee

John Lui (The Chinese University of Hong Kong)

2:30pm - 3:00pm    Break
3:00pm - 4:30pm    Session IV (30 min talks)

Room: E-VI Mong Auditorium (Room 180)

Cache-aware load balancing of data center applications

Aaron Archer (Google Research)

A Simple Steady-State Analysis of Load Balancing Algorithms in the Sub-Halfin-Whitt Regime

Lei Ying (Arizona State University)

Accuracy vs. Learning Rate of Replacement Algorithms in Cache Systems

Srinivas Shakkottai (Texas A&M University)

4:30pm     Closing remarks

Room: E-VI Mong Auditorium (Room 180)

4th ACM MobiHoc 2018 Workshop on Experiences with the Design and Implementation of Smart Objects

Monday, Jun 25, 2018

8am - 9am    Registration (E-VI 1st Floor Lobby)
9am - 9:10am    Opening

Room: E-VI Room 134

Abstract: In this talk we discuss ASTRO, a platform for autonomous data-driven mobile sensing missions via networked drones. ASTRO objective is to develop (i) high-resolution distributed mobile laser-spectroscopy gas sensing to identify, localize, and track health and environmental hazards in real-time and (ii) automated mobile radio-frequency spectrum analysis and usage via distributed diverse-spectrum virtual arrays. To demonstrate our system we will discuss design principles with a mobile or hiding wireless transmitter “spectrum cheater”.

Bio: Riccardo Petrolo is currently Postdoctoral Fellow at Rice University (USA) in the Rice Networks Group. He received the Bs.C. and Ms.C. degrees from University "Mediterranea" of Reggio Calabria, Italy in 2010 and 2013 respectively. In 2016, he received the Ph.D. degree from Lille 1 University, France. During this period, Dr. Petrolo served as Research Assistant at Inria Lille – Nord Europe, and he participated to the development of the EU FP7 VITAL project, one of the first Operating Systems for Smart Cities. In 2015, Dr. Petrolo was Visiting Researcher in the GTA group at UFRJ, Rio de Janeiro, Brazil. His current research interests include Internet of Things, Networking, Robotic Networks, Semantic interoperability, Edge computing, and Smart Cities.

10am - 11am    Session 1

Room: E-VI Room 134

(invited paper) mF2C: Towards a Coordinated Management of the IoT-fof-cloud Continuum

Xavi Masip-Bruin, Eva Marin-Tordera, Ana Juan-Ferrer, et al.

Abstract: Fog computing enables location dependent resource allocation and low latency services, while fostering novel market and business opportunities in the cloud sector. Aligned to this trend, we refer to Fog-to-cloud (F2C) computing system as a new pool of resources, set into a layered and hierarchical model, intended to ease the entire fog and cloud resources management and coordination. The H2020 project mF2C aims at designing, developing and testing a first attempt for a real F2C architecture. This document outlines the architecture and main functionalities of the management framework designed in the mF2C project to coordinate the execution of services in the envisioned set of heterogeneous and distributed resources.

Abstract: The ability of fine-tuning the performance of Bluetooth Low Energy (BLE) communication is essential to create low-power wireless applications with heavy user interaction, such as smart thermostats or door locks. One of the key challenges when designing such applications is finding the right trade-off between a system's responsiveness and energy-efficiency. Although there exists research works that improve the performance of BLE communication, all these approaches focus on connection-based BLE. Most BLE-based applications, however, spend the majority of their time in connection-less device discovery, waiting for approaching users. The energy-efficiency and timeliness in this state are defined by parameters that are often statically set at compile time. Although supported by the BLE specifications, how to dynamically adapt these parameters to user behavior is still an open question. In this paper, we tackle this challenge and design a strategy to improve the energy-efficiency and responsiveness of BLE device discovery. Towards this goal, we model the device discovery process and identify its key parameters. We further design an adaptive advertising strategy that allows smart objects to adapt their device discovery parameters to the user behavior. We implement this adaptive strategy and measure its performance in a real-world application, the Nuki Smart Door Lock. Our experiments show that a smart lock using our strategy consumes 48% less energy while reducing the device discovery time by up to 63% compared to the use of static parameters. Furthermore, we discuss how nearby BLE devices can be used to inform the lock about approaching user devices and hence to improve its responsiveness in low-power phases even further.

Abstract: Electric vehicles (EVs) are rapidly becoming more common and ownership is set to rise globally in coming years. The potential impacts of increased EVs on the electrical grid have been widely investigated and in its current state, existing grid infrastructure will struggle to meet the high demands at peak charging hours. The limited range of electric cars compounds this issue. We therefore propose CognitiveCharge, a novel approach to predictive and adaptive disconnection aware opportunistic energy discovery and transfer for the smart vehicular charging. CognitiveCharge detects and reacts to individual nodes and network regions which are at risk of getting depleted by using implicit predictive hybrid contact and resources congestion heuristics. CognitiveCharge exploits localised relative utility based approach to adaptively offload the energy from parts of the network with energy surplus to depleting areas with non-uniform depletion rates. We evaluate CognitiveCharge using a multi-day traces for the city of San Francisco, USA and Nottingham, UK to compare against existing infrastructure across a range of metrics. CognitiveCharge successfully eliminates congestion at both ad hoc and infrastructure charging points, reduces the time that a vehicle must wait to charge from the point at which it identifies as being in need of energy, and drastically reduces the total number of nodes in need of energy over the evaluation period.

11am - 11:30am    Break
11:30am - 12:30pm    Session 2

Room: E-VI Room 134

(invited paper) Dead Reckoning Using Time Series Regression Models

João B. Pinto Neto, Nathalie Mitton, Miguel Elias M. Campista and Luís Henrique M. K. Costa.

Abstract: Connected car technology promises to drastically reduce the num- ber of accidents involving vehicles. However, this technology de- pends on the vehicle precise location to work. The adoption of Global Positioning System (GPS) as a navigation device has limi- tations that compromise geolocation information in certain envi- ronments due to line-of-sight technology. This work introduces the Time Series Dead Reckoning System (TedriS ) as a solution for Dead Reckoning navigation in situations where GPS fails. TedriS is a simple, easy-to-implement and efficient dead reckoning naviga- tion system that uses Time Series Regression Models (TSRM) and the vehicle’s rear wheel speed sensor data from Control Area Net- work (CAN) to estimate its absolute position. The estimate position process is carried out in a training phase and in a predicting phase. In the training phase, a novel technique applies TSRM and stores the relationship between GPS and CAN data that will be used in the predicting phase. TedriS evaluation performance was performed using traces collected in the Campus of Federal University of Rio de Janeiro (UFRJ) - Brazil and with experiments with a robot carried out at French Research Institute in Computer Science (INRIA) - France. The results show an accuracy compatible with Dead Reckoning navigation state-of-art systems.

Abstract: Visible light positioning, or VLP, has emerged as a low-cost approach to enabling a variety of indoor location-based services for indoor smart spaces. However, a survey of existing approaches to VLP reveals some challenges to compare one system to the another. Advances in key areas are expected to enable new levels of performance at low cost. These include innovations at the source (LEDs, LDs/LIDAR, and ToF sensors), at the receiver (diversity receivers and AoA sensors), and in the design of the overall end-to-end VLP system. Again, comparing these improvements from one system to the another is difficult due to varying assumptions and operating conditions. In this paper we classify VLP techniques in an attemp to reconcile the wide range of characteristics. We also propose a new concept called an active zone in recognition that best performance is needed primarily in a subset of the volume of an indoor space. Finally, we show the performance of a base VLP system under the new metric and conclude with how our VLP testbed can be used to verify quantify the region we call the {\it active zone}.

Bluetooth-Based Context Modelling

Eric Vance and Ani Nahapetian

Abstract: We explores the feasibility of using only Bluetooth received signal strength of a smart device to infer user context. Devices that use Bluetooth wireless communication constantly transmit their wireless signal. Adversaries or non-malicious applications can leverage that information to extrapolate the activities in which a user is engaged and the location of certain devices. We explore the accuracy of classification of a range of activities, including walking, typing, writing, and using a mouse. This classification is carried out by simply using the RSSI of a user-worn smart watch.

12:30pm - 1:30pm    Lunch (on your own)
1:30pm - 2:30pm    Session 3

Room: E-VI Room 134

(invited paper) Analysis of LoRaWAN v1.1 Security

Ismail Butun, Nuno Pereira and Mikael Gidlund

Abstract: LoRa and the LoRaWAN specification is a technology for Low Power Wide Area Networks (LPWAN) designed to allow connectivity for connected objects, such as remote sensors. Several previous works revealed various weaknesses regarding the security of LoRaWAN v1.0 (the official 1st draft) and these led to improvements included in LoRaWAN v1.1, released on Oct 11, 2017. In this work, we provide a first look into the security of LoRaWAN v1.1. We present an overview of the protocol and, importantly, present several threats to this new version of the protocol. Besides, we propose our own ramification strategies for the mentioned threats, to be used in developing next version of LoRaWAN. The threats presented were not previously discussed, they are possible even within the security assumptions of the specification and are relevant for practitioners implementing LoRa-based applications as well researchers and the future evolution of the LoRaWAN specification.

Movement Path Modelling for Node Mobility Handling

Yuhui Yao, Yan Sun and Chris Phillips

Abstract: Mobility plays an important role when analysing natural phenomenon. With regard to the electronic engineering and computer science, further understanding of mobility can inspire the better development of artificial systems and intelligent algorithms. In order to intelligently handle mobility, appropriate representation of movement behaviour is essential. As a classical mobility model, the random walk establishes a theoretical basis for analytical study. In literature, the random walk model and its variants have been mathematically analysed. However, to the best of our knowledge, no existing research has provided a path model for correlated random walks in two-dimensional Euclidean space. In this paper, the concept of on-path certainty is proposed to describe movement path and the spatial distribution of on-path certainty is experimentally solved and analysed. As a result, functional relationships of the proposed path model are revealed and discussed for further research.

Heterogeneous data reduction in WSN: Application to Smart Grids

Jad Nassar, Karen Miranda, Nicolas Gouvy and Nathalie Mitton

Abstract: The transformation of existing power grids into Smart Grids (SGs) aims to facilitate grid energy automation for a better quality of service by providing fault tolerance and integrating renewable energy resources in the power market. This evolution towards a smarter electricity grid requires the ability to transmit in real time a maximum of data on the network usage. A Wireless Sensor Network (WSN) distributed across the power grid is a promising solution, given the reduced cost and ease of deployment of such networks. These advantages come up against the unstable radio links and limited resources of WSN. In order to reduce the amount of data sent over the network, and thus reduce energy consumption, data prediction is a potent solution of data reduction. It consists on predicting the values sensed by sensor nodes within certain error threshold, and resides both at the sensors and at the sink. The raw data is sent only if the desired accuracy is not satisfied, thereby reducing data transmission. We focus on time series estimation with Least Mean Square (LMS) for data prediction in WSN, in a Smart Grid context, where several applications with different data types and Quality of Service (QoS) requirements will exist on the same network. LMS proved its simplicity and robustness for a wide variety of applications, but the parameters selection (step size and filter length) can directly affect its global performance, choosing the right ones is then crucial. Having no clear and robust method on how to optimize these parameters for a variety of applications, we propose a modification of the original LMS that consists of training the filter for a certain time with the data itself in order to customize the aforementioned parameters. We consider different types of real data traces for the photo voltaic cells monitoring. Our simulation results provide a better data prediction while minimizing the mean square error compared to an existing solution in literature.

1st ACM MobiHoc 2018 Workshop on Mobile IoT Sensing, Security, and Privacy

Monday, Jun 25, 2018

8am - 9:15am    Registration (E-VI 1st Floor Lobby)
9am - 9:10am    Opening

Room: E-VI Room 176


Human sensing encompasses a range of sensing tasks including (but not limited to) recognizing gestures and activities of daily living, monitoring vitals, and even identifying mood. It is the key enabler of a countless number of applications in diverse domains, such as health care monitoring, sleep monitoring, fitness tracking, immersive gaming, and elderly care. Due to the innumerable ways in which human sensing can improve the quality and experience of life in today’s smart environments, coupled with the ever improving sensing hardware and novel sensing modalities, human sensing is attracting more and more interest from both academia and industry.

Over the past few years, a new class of human sensing systems has spawned that use WiFi signals to perform human sensing. The fundamental principle that enables human sensing using WiFi signals is that when a user moves in a wireless channel, his/her movements cause the wireless channel metrics (WCMs), such as channel state information, received signal strength, and angle-of-arrival of the signal, to change. The patterns of change in WCMs are unique for different human movements. By learning these patterns of change for any given movement, a WiFi-based human sensing system can recognize that movement.

In this talk, I will present our work on recognizing human gestures using WiFi. I will also describe our attempts to address some of the practical challenges facing WiFi based human sensing, such as sensitivity of these systems to positions and orientations of users, and simultaneous movements of multiple users. I will conclude with a discussion about further challenges that need to be addressed before WiFi based human sensing systems can be used in real-world settings.

Bio: Muhammad Shahzad is an Assistant Professor at the Department of Computer Science, North Carolina State University. He received his PhD from Michigan State University in 2015. His research interests are in the areas of ubiquitous sensing, RFID systems, and network measurements. To date, his work on WiFi based human sensing has involved recognition of macro human movements such as gestures and activities and micro human movements such as keystrokes and gait. Dr. Shahzad and his team won the Spectrum Sharing Radio Challenge (2016). He was also the recipient of the Fitch Beach Award (2015) and Human Competitive Award (2009).


The 4G/5G mobile networked systems are providing "anywhere, anytime" Internet access for billions of users. In the foreseen future, they are also envisioned to serve new applications (virtual/augmented reality, self-driving cars, drones, and more). However, the current systems still rely on the "black-box" operations for the network infrastructure. Consequently, the end devices lack intelligence on the underlying network.

In this talk, I will present a data-driven approach to enhancing end intelligence in the 4G/5G systems. I will show how software-hardware cooperation enables fine-grained network data that were not possible in existing solutions. I will next demonstrate how to leverage these data to construct analytics in order to catalyze end intelligence. Last, I will describe how the enhanced end intelligence, in return, helps the 4G/5G infrastructure with more provable reliability and efficiency. These results make a case for the "Knowledge Plane" for the next-generation mobile Internet.

Bio: Yuanjie Li received his Ph.D. in Computer Science from UCLA in 2017. His research interests include networked systems, mobile computing, and network security. He is a recipient of ACM MobiCom’17 and MobiCom'16 Best Community Paper Awards, UCLA Dissertation Year Fellowship in 2016, and Outstanding Chinese Oversea Graduate Student Award in 2016. His work has resulted in an open-source community tool (MobileInsight), which has been used by 268 universities and companies since its release in 2016.

10:50am - 11am    Break
11am - 12:30pm    Technical Session

Room: E-VI Room 176

Exploiting Multi-Vendor Vulnerabilities as Back-Doors to Counter the Threat of Rogue Small Unmanned Aerial Systems

Lanier Watkins, Juan Ramos, Gaetano Snow, Jessica Vallejo, William H. Robinson, Aviel D. Rubin, Joshua Ciocco, Felix Jedrzejewski, Jinglun Liu, Chengyu Li

Abstract: A recent trend for many malicious actors, such as: (1) terrorists in Iraq and Syria, (2) lone wolf domestic terrorists, (3) drug cartels, or (4) espionage-minded corporations, has been to use commercialoff- the-shelf (COTS) small unmanned aerial systems (sUAS) (i.e., drones) that can circumvent ground-based defenses to attack or spy on targets, to transport contraband, or to steal information. Because of the low cost of COTS sUAS and the prior success of these uses, this trend is increasing at an alarming rate, leading to the need to counter the malicious usage of sUAS (i.e., rogue sUAS). Researchers, the armed forces, and technologists have all proposed disparate solutions to this problem. There are no comprehensive and compact solutions capable of effectively tracking, identifying, and actively neutralizing the threats associated with rogue sUAS. Thus, we have developed a mobile cyber solution, using rigorous penetration testing across the top sUAS COTS vendors. Based on the market share of these top vendors, our approach is applicable to approximately 90% of all COTS sUAS.We demonstrate that hard-topatch vulnerabilities (i.e., vulnerabilities that exist across all the top vendors of sUAS) can be used as back-doors to counter the threat of rouge sUAS. Our solution can be launched from a standard laptop or Android mobile device with an external antenna, and is capable of tracking, identifying, and disrupting all Parrot and 3DR sUAS, as well as almost all DJI sUAS (i.e., renders them incapable of video flight) within a 300-meter radius.

Impacts and Detection of Network Layer Attacks on IoT Networks

Prasesh Adina, Raghav H. Venkatnarayan, and Muhammad Shahzad

Abstract: With the advent of the Internet of Things (IoT), wireless sensor and actuator networks, subsequently referred to as IoT networks (IoTNs), are proliferating at an unprecedented rate in several newfound areas such as smart cities, health care, and transportation, and consequently, securing them is of paramount importance. In this paper, we present several useful insights from an exploratory study of the impacts of network layer attacks on IoTNs. We envision that these insights will guide the design of future frameworks to defend against network layer attacks. We also present a preliminary such framework and demonstrate its effectiveness in detecting network layer attacks through experiments on a real IoTN test-bed.

A2G2V: Automated Attack Graph Generator and Visualizer

Alaa T. Al Ghazo, Mariam Ibrahim, Hao Ren, Ratnesh Kumar

Abstract: The Internet of Things (IoT) and Cyber-Physical Systems (CPS) technologies have increased the complexity of systems and also exposed them to additional vulnerabilities. Attack-graphs are graphical representations that provide a complete view of how interdependencies among atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Their manual construction is tedious, error-prone, and time consuming. This paper presents a model-based Automated Attack- Graph Generator and Visualizer (A2G2V). Given the networked system description (its components, connectivity, services it supports, their vulnerabilities and protections), the attack graph enlists set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise a certain system-level security. The proposed A2G2V tool extends an existing formal methods tool (a model-checker) by integrating with it an architecture description tool, our own code (for parsing counterexamples, encoding those for specification relaxation, iterating till all attack sequences are revealed), and also a graph visualization tool.

1st ACM MobiHoc 2018 Workshop on Networking and Cybersecurity for Smart Cities (SmartCitySecurity 2018)

Monday, Jun 25, 2018

8am - 1:30pm    Registration (E-VI 1st Floor Lobby)
1:30pm - 1:45pm    Opening

Session Chair: Syed Hassan Ahmed (University of Central Florida)

Room: E-VI Room 176

Abstract: Cascading power outages can inflict devastating consequences on the economic and operational security of city infrastructure, and can even result in fatalities. Service loss of a single transmission line can cause the subsequent shutdown of topologically neighboring, or electrically dependent, transmission lines as power is rerouted to meet customer demand. When apparent power demand exceeds the carrying capacity of a neighboring transmission line, that line will shut down, and a chain-reaction of propagating transmission line outages will commence, leading to a widespread outage. When a transmission line fails, the downstream network of distribution lines that deliver power to individual homes and businesses will shut down, causing loss of power to critical appliances, such as refrigeration systems, which require constant power to prevent food spoilage. The Great Blackout of 2011 on September 8 affected seven million California residents, and spread into Arizona and parts of Baja California in Mexico. Caused by the unintentional mistake of a single technician who shut down a 500 kV transmission line between the Hassayampa and North Gila substations in Arizona, the outage propagated through five major power grids within 11 minutes, which resulted from the sequential overload of networked transmission lines. The outage cost homeowners and restaurants between $12 and $18 million in damages from the spoilage of perishable food. During the Northeast blackout of 2003, nearly 100 people died, with one death resulting from loss of air conditioning that was required to keep the skin grafts of a burn victim adequately cooled, thereby illustrating the need for Smart Cities to maintain power to critical appliances as load is shed during propagating outages. The Northeast blackout affected 45 million people in the US, 10 million people in Ontario, and was caused by a software error that failed to re-distribute load to other high-capacity transmission lines after a set of 345 kV transmission lines in Walton Hills Ohio ground faulted by sagging into underlying trees. US electricity consumers lose an incredible $79 billion dollars each year from combined power outages, with a single large outage costing in the order of $10 billion dollars. In this talk, we present an LPWAN fog-computing framework called MIST, designed to monitor and measure local consumer power demand and perform real-time load- shedding decisions at the network edge. Should a localized failure occur, the fog network reacts to mitigate failure propagation by shedding load in a structured way to reduce consumer loss by powering off load in priority order. The MIST framework consists of "smart" electrical receptacles with micro controller measurement devices to monitor and predict the transient coverage of a transmission line outage and cooperatively sheds power to halt propagation. MIST receptacles use the LoRa chirp spread spectrum radio modulation technology for LPWAN, which uses the license-free sub-gigahertz (915 MHz in US) band. Current power shedding techniques employed by energy companies do not distinguish among user home appliances, and can result in a total power loss to selected users. The proposed MIST fog- computing architecture will benefit Smart Cities by providing a means by which energy companies can selectively shed power at the very edge of a grid by powering off customer appliances in order of increasing criticality, thereby mitigating the propagation of a failure and reducing consumer costs that result from power loss.

Bio: Christopher Paolini is an Assistant Professor in the Department of Electrical and Computer Engineering at San Diego State University. Chris is the recipient of grants from the Department of Energy and NASA, and five NSF Office of CyberInfrastructure awards, most recently the current NSF CC* Grant 1659169 “CC* Storage: Implementation of a Distributed, Shareable, and Parallel Storage Resource at San Diego State University to Facilitate High-Performance Computing for Climate Science”. Christopher Paolini's current research interests include Internet of Things device development, machine learning, embedded systems, cloud computing, big data analytics, deep learning, software engineering, numerical chemical thermodynamics, numerical chemical kinetics, numerical geochemistry, high performance computing, scientific computing and numerical modeling, high speed (100gbps) networking, cyber infrastructure development, and cybersecurity. Chris received a B.S. degree in Computer Science in 1991, M.S. degree in Computer Science in 1998, and his Ph.D. degree in Computational Science in 2007, all from San Diego State University.

2:30pm - 2:45pm    Break
2:45pm - 4:15pm    Paper presentations (30 min talks)

Room: E-VI Room 176

Wireless Mediation of Multiple Equi-Priority Events in Time-Critical Industrial Applications

Syed Fahad Hassan, Aamir Mahmood, Syed Ali Hassan, Mikael Gidlund

EnergyChain: Enabling Energy Trading for Smart Homes using Blockchains in Smart Grid Ecosystem

Shubhani Aggarwal, Rajat chaudhary, Gagangeet Singh Aujla, Anish Jindal, Amit Dua, Neeraj Kumar

A Real Time Power Update Scheme for the Smart Grid Using TVWS

Apoorva Deshpande, Mahasweta Sarkar, Hrishikesh Adigal, Reza Sabzehgar, Mohammad Rasouli

4:15pm - 4:25pm    Closing

Session Chair: Syed Hassan Ahmed (University of Central Florida)

Room: E-VI Room 176