Qatar Foundation Annual Research Conference Proceedings Volume 2018 Issue 3

The Internet has become part of almost every organizational culture where security threats increase with the increase use of the Internet. Therefore, security practice has become increasingly important for almost every organization where is the same case of the Qatar Meteorology Department (QMD) in the state of Qatar. The aim of this research is to evaluate the current security level of the QMD by examining the current security practices that present in the organization and the security awareness level among the organization employees. After that, provide the organization with security policy and awareness program recommendations to enhance the organization security practice. Furthermore, the importance of this research is its contribution to enhance the organization security level. In order to achieve the research objectives, a mixture of different methodologies has been used to collect the fundamental data includes: survey questionnaires, interviews, and field observation. For the data collection process to success, a number of strategies have been used in each method to ensure achieving the most benefits of each the used method. These methods satisfied in collecting the essential primary data. Furthermore, a number of literatures were reviewed in order to understand the research subject further. Based on the collected data, a number of analysis methods have been used to draw a conclusion of the organizational security level where the findings illustrate the needs for security policies and awareness programs in order to enhance the organization security level. Thus, a number of security policies and awareness program recommendations have been established. The research findings and the provided recommendations can support the organization to enhance its security level as much as possible since no system is completely secure. Furthermore, this research presents valuable information about the organization current security level and provides recommendations to enhance this security level.

We live in the digital age where most of our activities and services are carried out over the internet. Items such as music, movies, products etc. are being consumed over the web by millions of users. The number of such items is large enough that it is impossible for a user to experience everything. This is where recommender systems come into play. Recommender systems are employed to play a crucial role of filtering and ranking items to each user based on their individual preferences. Recommender systems essentially assist the user in making decisions to overcome the problem of information overload. These systems are responsible for understanding a user's interests and inferring their needs over time. Recommender systems are widely employed across the web and in many cases, are the core aspect of a business. For example, on Quora, a question-answering website, the entire interface relies on the recommender system for deciding what content to display to the user. The content ranges from homepage question ranking, topics recommendation and answer ranking. The goal of a recommender system is to assist users in selecting items based on their personal interest. By doing so, it also increases the number of transactions thereby creating a win-win situation for both the end users and the web service. Recommender systems is a relatively new and exciting field that promises a huge potential in the future. It has originated from the field of information retrieval and search engines where the task was: given a query retrieve the most relevant documents. In the recommender system domain, the user should be able to discover items that he/she would not have been able to search for directly. One main challenge in recommender systems is cold-start. It is defined as the situation when a new user/item joins the system. We are interested in item cold start and in this case the recommender system needs to learn about the new item and decide which users should it recommend to. In this work, we propose a new approach to tackle the cold-start problem in recommender system using word embeddings. Word embeddings are semantic representations of the words in a mathematical form like vectors. Embeddings are very useful since they are able to capture the semantic relationship between words in the vocabulary. There are various methods to generate such a mapping which include: neural networks, dimensionality reduction on word co-occurrence matrix, probabilistic models etc. The underlying concept behind these approaches is that words that share common contexts in the corpus have close proximity in the semantic space. Word2vec is a popular technique by Mikolov et al. that has gained tremendous popularity in the natural language processing domain. They came up with two versions, namely: continuous skip-gram and continuous bag-of-words model (CBOW). They were able to overcome the problem of sparsity in text and demonstrate its effectiveness in a wide range of NLP tasks. Our dataset is based on a popular website called Delicious which allows users to store, share and discover bookmarks on the web. For each bookmark, users are able to generate tags that provide meta information about the page such as the topics discussed, important entities. For example, a website about research might contain tags like science, biology, experiment. The problem now becomes: Given a new bookmark with tags, compute which users to recommend this new bookmark. For item cold start situation, a popular technique is to use content based approaches and find items similar to the new item. The new item can then be recommended to users of the computed similar items. In this paper, we propose a method to compute similar items using word embeddings of the tags present for each bookmark. Our methodology involves representing each bookmark as a vector by combining the word embeddings of its tags. There are various possible aggregation mechanisms and we chose to use the average in our experiments since it is intuitive and easy to compute. The similarity between two bookmarks can be computed by taking the cosine similarity between their corresponding embedding vectors. The total number of bookmarks in the dataset is around 70,000 with around 54,000 tags. The embeddings are obtained from the GloVe project where the training is performed on Wikipedia data based on aggregated global word-word co-occurrence statistics. The vocabulary of these embeddings are fairly large, containing about 400 k words and each word is stored in the form of a 300-dimension vector. The results were evaluated manually and the results look promising. We found that the bookmarks recommended were highly relevant in terms of the topics being discussed. Some example topics being discussed in the bookmarks were: social media analysis, movie reviews, vacation planner, web development etc. The reason that embeddings perform well is that they are to capture the semantic information of bookmarks using tags which is useful in cold start situations. Our future work would involve using other aggregation combinations such as weighting the tags differently based on their importance. A more suitable method of evaluation would be to measure the feedback (ratings/engagement) from users in a live recommender system and compare along with other approaches. In this work, we demonstrate the feasibility of using word embeddings to tackle the item cold start problem in recommender systems. This is an important problem that can deliver a positive impact in improving the performance of recommender systems.

Mental stress occurs when a person perceives abnormal demands or pressures that influence the sense of well-being. These high demands sometimes exceed human capabilities to cope with. Stressors such as workload, inflexible working hours, financial problem, or handling more than one task can cause work-related stress which in turn leads to less productive employees. Lost productivity costs global economy approximately US $ 1 trillion per year [1]. A survey conducted among 7000 workers in U.S. found that 42% had left their job to escape the stressful work environment [2]. Some people can handle stress better than others, therefore the stress symptoms can vary. Stress symptoms can affect the human body and make him down both physically and mentally. Hopelessness, anxiety, and depression are examples of emotional symptoms, while headaches, over-eating, sweaty hands, and dryness of mouth are physical signs of the stress. There are also behavioral cues for stress like aggression, social withdrawal, and loss of concentration [3]. When the thread is perceived, a survival mechanism called «fight or flight response» will be activated to help the human body to adapt the situation quickly. In this mechanism, the central nervous system (CNS) asks adrenal glands to release cortisol and adrenaline hormones, which boost glucose levels in the bloodstream, quicken the heartbeat, and raise blood pressure. If CNS does not succeed to return to normal state, the body reaction will continue which in turn increases the possibility of having heart stroke or attack [4]. There are several techniques used to explore physiological and physical stress measures, for example, electrocardiogram (ECG) measures the heart»s electrical activity, electroencephalography (EEG) records the brain»s electrical activity, electrodermal activity (EDA) or galvanic skin response (GSR) measures the continuous variations in the skin»s electrical characteristics, electromyography (EMG) records electrical activity in muscles, photoplethysmography (PPG) estimates the skin blood flow, and Infrared (IR) tracks eye activities. On the other hand, prolonged ongoing worrying can lead to chronic stress. This type of stress is most harmful and has been linked to cancer, and cardiovascular disease (CVD) [5]. Therefore, several approaches were proposed in an attempt to identify stress triggers and amount of stress. Some of these methods used instruments such as questionnaires to assess affective states, but these techniques usually suffer from memory and response biases. However, stress detection via the analysis of various bio-signals are deemed more valuable and thus have been the focus of modern day research. In particular, various bio-signals are collected from participates. These bio-signals are then subjected to advanced signal processing algorithms in an attempt to extract salient features for classification by machine learning algorithms. In our project, we are interested in exploring new machine learning techniques which wearable devices to record various bio-signals. The goal is the development of an automatic stress detection system based on the analysis of bio-signals through the use of signal processing and machine learning. The outcome of this research will allow users to be notified when their bodies enter a state of unhealthily stress levels so that they may take preventative action to avoid unnecessary consequences.

In this research work, we propose a framework of experiential learning to enhance student work skills and experience. This research main to contribute to the development and expansion of local industry, through the conduct of long-term fundamental research that contributes to the science base and understanding needs of national economy through industrial by providing an adapted method, enhance the teaching contents and pedagogical organization to be more accurate and adapted to the competency requirements of local employers.

Wikipedia is the most influential popular information source on the Internet, and is ranked as the fifth most visited website [1] (Alexa, 2017). The English-language Wikipedia is a prominent source of online health information compared to other providers such as MedlinePlus and NHS Direct (Laurent and Vickers, 2009). Wikipedia has challenged the way that traditional medical encyclopaedia knowledge is built by creating an open sociotechnical environment that allows non-domain experts to contribute to its articles. Also, this sociotechnical environment allows bots – computer scripts that automatically handle repetitive and mundane tasks – to work with humans to develop, improve, maintain and contest information in Wikipedia articles. The contestation in Wikipedia is unavoidable as a consequence of its open nature, which means that it accepts contradictory views on a topic and involves controversies. The objective of this research is to understand the impact of controversy on the relationship between humans and bots in environments that are managed by the crowd. This study analyses all the articles under the WikiProject Medicine, and includes 36,850 Wikipedia articles. Medical articles and their editing history have been harvested from the Wikipedia API covering all edits from 2001 till 2016. The data includes the revisions ID, username, timestamp, and comment. The articles under the WikiProject Medicine contain 6,220,413 edits and around 1,285,936 human and bot editors. To measure controversies, we studied reverted and undone edits. A revert on Wikipedia occurs when an editor, whether human or bot, restores the article to an earlier version after another editor's contribution. Undone edits are reverted single edits from the history of a page, without simultaneously undoing all constructive changes that have been made since the previous edit. Reverted and undone edits that occur systematically indicate controversy and conflict (Tsvetkova et al., 2017). To measure the relationship between humans and bots, we focused on both positive and negative relationships. A positive relationship is when an editor, such as a human, endorses another editor, such as a bot, by reverting or undoing a recent edit to the other editor's contribution. A negative relationship is when an editor, such as human, discards another editor, such as a bot, by reverting or undoing the other editor's contribution. Our results show that there is a relationship between controversial articles and the development of a positive relationship between humans and bots. The results demonstrate that bots and humans could behave differently in controversial environments. The study highlights some of the important features of building health-related knowledge on Wikipedia. The contribution of this work is to build on previous theories that consider web-based systems as social machines. These theories recognise the joint contribution of humans and machines to activities on the web, but assume a very static type of relationships that is not sensitive to the environment in which humans and machines operate in. Understanding the interactions between humans and bots is crucial for designing crowdsourced environments that are integrative to their human and non-human population. We discuss how our findings can help set up future research directions and outline important implications for research on crowd. References: Laurent, M. R & Vickers, T. J (2009) ‘Seeking Health Information Online: Does Wikipedia Matter.?’ J Am Med Inform Assoc, 16(4), 471-479. Tsvetkova, M, García-Gavilanes, R, Floridi, L and Yasseri, T. (2017) ‘Even good bots fight: The case of Wikipedia.’ PLoS One, 12(2): e0171774. [1] https://www.alexa.com/topsites

As urban data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. We are interested in developing a new generation of deep learning based computational technologies that predict traffic congestion and crowd management. In this work, we are mainly interested in efficiently predicting future traffic with high accuracy. The proposed deep learning solution allows the revealing of the latent (hidden) structure common to different cities in terms of dynamics. The data driven insights of traffic analytics will help shareholders, e.g., security forces, stadium management teams, and travel agencies, to take fast and reliable decisions to deliver the best possible experience for visitors. Current traffic data sources in Qatar are incomplete as sensors are not yet permanently deployed for data collection.The following topics are being addressed:Predictive Crowd and Vehicles Traffic Analytics: Forecasting the flow of crowds and vehicles is of great importance to traffic management, risk assessment and public safety. It is affected by many complex factors, including spatial and temporal dependencies, infrastructure constraints and external conditions (e.g. weather and events). If one can predict the flow of crowds and vehicles in a region, tragedies can be mitigated or prevented by utilizing emergency mechanisms, such as conducting traffic control, sending out warnings, signaling diversion routes or evacuating people, in advance. We propose a deep-learning-based approach to collectively forecast the flow of crowds and vehicles. Deep models, such as Deep-Neural-Networks, are currently the best data-driven techniques to handle heterogeneous data and to discover and predict complex data patterns such as traffic congestion and crowd movements. We will focus in particular on predicting inflow and outflow of crowds or vehicles to and from important areas, tracking the transitions between these regions. We will study different deep architectures to increase the accuracy of the predictive model, and explore ways on how to integrate spatio-temporal information into these models. We will also study how deep models can be re-used without retraining to handle new data and better scale to large data sets. What-If Scenarios Modeling: Understanding how congestion or overcrowd at one location can cause ripples throughout a transportation network is vital to pinpoint traffic bottlenecks for congestion mitigation or emergency response preparation. We will use predictive modeling to simulate different states of the transportation network enabling the stakeholder to test different hypotheses in advance. We will use the theory of multi-layer networks to model and then simulate the complex relationship between different but coexisting types of flows (crowd, vehicles) and infrastructures (roads, railways, crossings, passageways, squares…). We will propose a visual analytic platform that will provide necessary visual handles to generate different cases, navigate through different scenarios, and identify potential bottleneck, weak points and resilient routes. This visualization platform connected to the real-time predictive analytic platform will allow supporting stakeholder decision by automatically matching the current situation to the already explored scenarios and possible emergency plans. Safety and Evacuation Planning based on Resilience Analytics: Determining the best route to clear congestion or overcrowded areas, or new routes to divert traffic and people from such areas is crucial to maintain high security and safety levels. The visual analytic platform and the predictive model will enable the test and set up of safety and evacuation plans to be applied in case of upcoming emergency as detected by the predictive analytic platform. Overall, the proposed approach is independent of the type of flows, i.e., vehicles or people, or infrastructures, as long as proper sensors (magnetic loops, video camera, GPS tracking, etc.) provide relevant data about these flows (number of people or vehicles per time unit along a route of some layer of the transportation network). The proposed data-driven learning models are efficient, and they adapt to the specificities of the type of flows, by updating the relevant parameters during the training phase.

Secure Multi-Party Computation (SMPC) enables parties to compute a pub- lic function over private inputs. A classical example is the millionaires problem, where two millionaires want to figure out who is wealthier without revealing their actual wealth to each other. The insight gained from the secure computa- tion is nothing more than what is revealed by the output (in this case, who was wealthier but not the actual value of the wealth). Other applications of secure computation include secure voting, on-line bidding and privacy-preserving cloud computations, to name a few. Technological advancements are making secure computations practical, and recent optimizations have made dramatic improve- ments on their performance. However, there is still a need for effective tools that facilitate the development of SMPC applications using standard and famil- iar programming languages and techniques, without requiring the involvement of security experts with special training and background. This work addresses the latter problem by enabling SMPC application de- velopment through programs (or repurposing existing code) written in a stan- dard programming language such as ANSI C. Several high-level language (HLL) platforms have been proposed to enable secure computation such as Obliv-C [1], ObliVM [2] and Frigate [3] These platforms utilize a variation of Yao's garbled circuits [4] in order to evaluate the program securely. The source code written for these frameworks is then converted into a lower-level intermediate language that utilizes garbled circuits for program evaluation. Garbled Circuits have one party (garbler) who compiles the program that the other party (evaluator) runs, and the communication between the two parties happens through oblivi- ous transfer. Garbled circuits allow two parties to do this evaluation without a need for a trusted third party. These frameworks have two common characteristics: either define a new language [2] or make a restricted extension of a current language [1]. This is somewhat prohibitive as it requires the programmer to have a sufficient under- standing of SMPCs related constructs and semantics. This process is error-prone and time-consuming for the programmer. The other characteristic is that they use combinational circuits, which often require creating and materializing the entire circuit (circuit size may be huge) before evaluation. This introduces a restriction on the program being written. TinyGarble [5], however, is a secure two-party computation framework that is based on sequential circuits. Compared with the frameworks mentioned earlier, TinyGarble outperforms them by orders of magnitude. We are developing a framework that can automatically convert a HLL pro- gram (in this case ANSI C) into an hardware definition language, which is then evaluated securely. The benefit of having such transformation is that it does not require knowledge of unfamiliar SMPC constructs and semantics, and per- forms the computation in a much more efficient manner. We are combining the efficiency of sequential circuits for computation as well as the expressiveness of a HLL like ANSI C to be able to develop a secure computation framework that is expected to be effective and efficient. Our proposed approach is two-fold: first, it offers a separation of concern between the function of computation, written in C, and a secure computation policy to be enforced. This leaves the original source code unchanged, and the programmer is only required to specify a policy file where he/she specifies the function/variables which need secure computations. Secondly, it leverages the current state-of-the-art framework to generate sequential circuits. The idea is to covert the original source code to Verilog (a Hardware Definition Language) as this can then be transformed into standard circuit description which TinyGarble [5] would run. This will enable us to leverage TinyGarbles efficient sequential circuits. The result would be having the best of both worlds where we have HLL that would be converted and evaluated as a sequential circuit. References [1] S. Zahur and D. Evans, “Obliv-c: A language for extensible data-oblivious computation,” IACR Cryptology ePrint Archive, vol. 2015, p. 1153, 2015. [2] C. Liu, X. S. Wang, K. Nayak, Y. Huang, and E. Shi, “Oblivm: A pro- gramming framework for secure computation,” in 2015 IEEE Symposium on Security and Privacy, SP 2015, San Jose, CA, USA, May 17-21, 2015, pp. 359–376, 2015. [3] B. Mood, D. Gupta, H. Carter, K. R. B. Butler, and P. Traynor, “Frigate: A validated, extensible, and efficient compiler and interpreter for secure com- putation,” in IEEE European Symposium on Security and Privacy, EuroS&P 2016, Saarbrúcken, Germany, March 21-24, 2016, pp. 112–127, 2016. [4] A. C. Yao, “Protocols for secure computations (extended abstract),” in 23rd Annual Symposium on Foundations of Computer Science, Chicago, Illinois, USA, 3-5 November 1982, pp. 160–164, 1982. [5] E. M. Songhori, S. U. Hussain, A. Sadeghi, T. Schneider, and F. Koushanfar, “Tinygarble: Highly compressed and scalable sequential garbled circuits,” in 2015 IEEE Symposium on Security and Privacy, SP 2015, San Jose, CA, USA, May 17-21, 2015, pp. 411–428, 2015.

One of the most prominent areas of energy consumption in residential units is for heating, ventilation and air-conditioning (HVAC) systems. The conventional systems for HVAC depend on the wired thermostats that are deployed at fixed locations and hence, are not convenient and do not respond to the dynamic nature of the thermal envelope of the buildings. Moreover, it is important to note that the distribution of the spatial temperature is not uniform. The current environment control strategies are based on the maximum occupancy numbers for the building. But there are always certain areas of a building which are used less frequently and are cooled needlessly. Having the real-time occupancy data and mining on it to predict the occupancy patterns of the building will help in energy effective strategy development for the regulation of HVAC systems through a central controller. In this work, we have deployed a network of multiple wireless sensors (humidity, temperature, CO2 sensors etc.), computational elements (in our case, a raspberry pi, to make it cost effective) and camera network with an aim to integrate the data from the multiple sensors in a large multifunction building. The sensors are deployed at multiple locations in such a way that the non-uniform spatial temperature distribution is overcome, and these sensors capture the various environmental conditions at a temporal and much finer spatial granularity. The pi camera is connected to a raspberry pi which is fixed at an elevation. The detection is performed using the OpenCV library and the python programming. This system can detect the occupancy with an accuracy of up to 90%. For occupancy detection and counter, a linear SVM is trained sampling on positive and negative images and the evaluation on test images or video feed makes use of non-maximum suppression (NMS Algorithm) to ignore redundant, overlapping HOG (Histogram Oriented Gradient) boxes. The data collected by the sensors is sent to the central controller on which the video processing algorithm is also running. Using the multiple environmental factors data available to us, models are developed to predict the usage in the building. These models help us to define the control parameters for the HVAC systems in adaptive manner in such a way that these parameters not only help in reducing the energy used in a building, but also help to maintain the thermal comfort. The control parameters are then sent as IR signals to AC systems that are controlled by IR remotes or as wireless signals to AC systems controlled by wireless thermostats. In comparison to the conventional temperature controller, our system will avoid overcooling of areas to save energy and predict the occupancy in the buildings so that the temperature is brought within the comfort zone of humans before over-occupancy takes place. Our system also has benefits of using wireless sensors that operate on low power, but the tradeoff between the power and the communication frequency should be well maintained. Our system additionally has two features: firstly, it can provide the live video streaming for remote monitoring using a web browser for the user interface and secondly, sending automatic notifications as messages in case of anomalies like abnormally high temperatures or high carbon dioxide concentration in a room. These two features can be used as cost-effective replacement for traditional systems in the applications of CCTV, burglary systems respectively. Keywords: wireless sensors, Air conditioning, opencv, NMS Algorithm, Histogram Oriented Gradient, thermal comfort.

E-learning is the use of Internet technologies or computer-assisted instruction, to enhance and improve knowledge and performance, because knowledge is the basic right for a human right that should be accessible by everyone regardless of the status of they disabilities. E-learning technologies offer learners control over content, learning sequence, pace of learning, time, and often media, allowing them to tailor their experiences to meet their personal learning objectives. this paper explores how adaptive e-learning for person with disabilities focusing intellectual disabilities in the Higher Education (HE) can show the important of making the technology with digital content have accessible to student with disabilities instead of face-to-face education with respect to ‘electronic’ vs. ‘traditional’ learning methods, this way of adapting E-Learning can be considered as its competent substitute complement, and examine the current situation progress in Qatar HE, because ubiquitous technologies have become a positive force of transformation and a crucial element of any personal development/empowerment and institutional framework for inclusive development. Keywords: e-learning, person with disabilities, intellectual disabilities, and learning methods.

I. Background In power grid, circuit breakers in switchgears are often the last line of defense when big systems must be protected from faults [1], sudden switchgear failures could cause long outages, huge economic losses and even present threats to the public safety. Based on field experience, the major causes of switchgear failure are loose or corroded metal connections, degraded cable insulation and external agents (e.g. dust, water) [2]. Due to ohmic loss at these weak points, the causes of switchgear failure are always accompanied with an increased thermal signature over time. With continuous thermal monitoring inside the switchgear, adequate data can be collected to make timely failure predication and prevention, especially for equipment deployed in harsh environment. II. Objective This paper presents the design of a passive radio frequency identification (RFID) sense tag, which measures temperature at critical spots of the switchgear and wirelessly (EPC C1G2 standard) transmits the data to the base station for real-time analysis. Compared with infrared imaging [2], surface acoustic wave (SAW) sensing system or fiber bragg grating (FBG) sensing system [1][3], no cables for power and communication are necessary, which avoids potential side effects like arcing in the grid after the addition of the sensor. The use of standard CMOS process results in a cost effective solution and the proposed passive wireless sensor can be easily retrofitted to existing switchgears with simple bolted connections. III. Passive Tag Design and Measurement Results Fig. 1 shows the proposed passive tag with temperature sensing capability. The power management unit in the chip harvests the incoming RF wave (860 ∼ 960 MHz) and sustains all the other on-chip building blocks (sensor, clock, memory, baseband, modem). The energy harvesting efficiency is in the range of 15%∼25% based on the operating mode of the tag. With 10 uW system power, the effective reading distance of this tag is 4.5 m ∼ 6 m. The on-chip temperature sensor adopts PNP bipolar as the sensing device, which has a temperature sensitivity of ∼2 mV/oC [4]. By using a triple-slope analog-to-digital converter (A/D), temperature-sensitive voltages are digitized and transmitted back to the reader after modulation by the modulator. Because there's no battery or other energy sources on the device, the power consumption of the tag, especially the sensor should be in the order of sub-mA to maintain the tag sensitivity. In this work, a passive integrator instead of an active one is used in the A/D, where its caused nonlinearity error is compensated by adding an additional nonlinear current into the temperature signal. The overall power consumption of the sensor is 0.75 uW and achieves 10 bit sensing resolution (0.12oC/LSB) within 10 ms conversion time, corresponding to a resolution FoM of 1.08x102 pJ√K2, which is among the most energy-efficient embedded sensor designs. Fig.2(a) shows the micro-photo of the designed passive RFID sense tag. Fig.2(b) shows its ceramic package, which can be readily installed on the spot locations with bolted connection. By designing the antenna with an additional ground layer, this tag is able to work in switchgear with full metal enclosure [5]. The measured tag sensitivity is -12 dBm. By measuring and calibrating multiple samples, the overall sensing precision of the tag is +/-1.5oC, which is enough for switchgear thermal monitoring, as shown in Fig.3(a). Thanks to the designed on-chip supply protection circuit, the sensor performance does not degrade much with the reading power or reading distance (received power ∝ or ∝ 1/distance2), as shown in Fig.3(a).IV. Conclusion The combination of passive RFID tags with sensors enables a lot of new applications and it help to bring embedded intelligence to the legacy power grid. The designed passive sense tag is of low-cost and with robust sensing performance after optimizing the tag at the system level and the use of low-power circuit design techniques. By re-designing the tag package, it can also be used in other applications like cold supply chain or highly flammable goods monitoring. Acknowledgement This work is in collaboration with Land Semiconductor Ltd., Hangzhou China and thanks Mr. Qibin Zhu and Mr. Shengzhou Lin for helping the measurement. Reference [1] G.-M. Ma et al., “A Wireless and Passive Online Temperature Monitoring System for GIS Based on Surface-Acoustic-Wave Sensor,” IEEE Trans. on Power Delivery, vol.31, no.3, pp. 1270 - 1280, June 2016. [2] Top Five Switchgear Failure Causes, Netaworld. [Online]. Available: http://www.netaworld.org/sites /default/files/public/neta-journals/NWsu10-NoOutage-Genutis.pdf, accessed Oct. 2017. [3] Fundamentals of Fiber Bragg Grating (FBG) Optical Sensing, NI White Papers. [Online]. Available: http://www.ni.com/white-paper/11821/en/, accessed Oct. 2017. [4] B. Wang, M.-K. Law and A. Bermak, “A Precision CMOS Voltage Reference Exploiting Silicon Bandgap Narrowing Effect,” IEEE Trans. on Electron Devices, vol. 62, no.7, pp.2128-2135, July 2015. [5] Chong Ryol Park and Ki Hwan Eom, “RFID Label Tag Design for Metallic Surface Environments,” Sensors, vol. 11, no. 1, pp. 938 - 948, Jan. 2011.