Abstract

Demand response is a crucial tool to maintain the stability of the smart grids. With the upcoming research trends in the area of electricity markets, it has become a possibility to design a dynamic pricing system, and consumers are made aware of what they are going to pay. Though the dynamic pricing system (pricing based on the total demand a distributor company is facing) seems to be one possible solution, the current dynamic pricing approaches are either too complex for a consumer to understand or are too naive leading to inefficiencies in the system (either consumer side or distributor side). Due to these limitations, the recent literature is focusing on the approach to provide incentives to the consumers to reduce the electricity, especially in peak hours. For each round, the goal is to select a subset of consumers to whom the distributor should offer incentives so as to minimize the loss which comprises of cost of buying the electricity from the market, uncertainties at consumer end, and cost incurred to the consumers to reduce the electricity which is a private information to the consumers. Due to the uncertainties in the loss function (arising from renewable energy resources as well as consumption needs), traditional auction theory-based incentives face manipulation challenges. Towards this, we propose a novel combinatorial multi-armed bandit (MAB) algorithm, which we refer to as GLS-MAB to learn the uncertainties along with an auction to elicit true costs incurred by the consumers. We prove that our mechanism is regret optimal and is incentive compatible. We further demonstrate efficacy of our algorithms via simulations.

Abstract

In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging. We propose a neural network-based framework, FNNC, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints.

Abstract

The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketchstroke sequences as visual data. Sketch-QA involves asking a fixed question (“What object is being drawn?”) and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. Tomimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor.We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. Wealso conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.

Abstract

As the communications among the vehicles, theRoad-Side Units(RSU)and the Edge Servers(ES)take placevia wireless communication and the Internet, an adversary maytake the opportunity to tamper with the data communicatedamong various entities in an Internet of Vehicles (IoV) envi-ronment. Therefore, it demands secure communication amongthe involved entities in an IoV-based Intelligent TransportationSystem (ITS) deployment. In this work, we design a newblockchain-enabled certificate-based authentication scheme forvehicle accident detection and notification in ITS, called BCAS-VADN. In BCAS-VADN, through the authentication process, eachvehicle can securely notify accident related transactions to itsnearby Cluster Head(CH), if an accident is detected on roadseither by its own or neighbor vehicle(s). TheCHthen securelysends the transactions received from the vehicles to itsRSUand subsequently, these transactions are also received secretlyby theESs. TheESis responsible for preparing partial blockcontaining transactions and Merkle tree root, and a digital signa-ture on those information, and then forwarding to its associatedCloud Server(CS)in the Blockchain Center(BC)for completeblock creation, verification and addition of the block using thedesigned consensus process. Due to blockchain technology usage,it is shown that BCAS-VADN is not only secure against variouspotential attacks, but also maintains transparency, immutabilityand decentralization of the information. Furthermore, a compre-hensive comparative analysis reveals that BCAS-VADN achievesbetter security and more functionality attributes, and has lowcommunication and computational overheads as compared toother competitive authentication schemes in IoV. In addition,the practical demonstration using the blockchain technology hasbeen also provided.

Abstract

Agriculture is a vital area for the sustenance ofmankind engulfing manufacturing, security, traceability, andsustainable resource management. With the resources recedingexpeditiously, it is of utmost significance to innovate techniquesthat help in the subsistence of agriculture. The growth of Internetof Things (IoT) and Blockchain technology as two rapidlyemerging fields can ameliorate the state of food chain today. Thispaper provides a rigorous literature review to inspect the state-of-the-art development of the schemes that provide informationsecurity using blockchain technology. After identifying the corerequirements in smart agriculture, a generalized blockchain-based security architecture has been proposed. A detailed costanalysis has been conducted on the studied schemes. A meticu-lous comparative analysis uncovered the drawbacks in existingresearch. Furthermore, detailed analysis of the literature has alsorevealed the security goals towards which the research has beendirected and helped to identify new avenues for future researchusing artificial intelligence.

Abstract

Recent technological evolution in the Internet ofThings (IoT) age supports better solutions to magnify themanagement of the power quality and reliability concerns, andimposes the measures of a smart grid. In smart grid environment,a smart meter needs to securely access the services from a serviceprovider via insecure channel. However, since the communicationis via public channel, it imposes various security threats byan adversary. To deal to this, in this article we design a newanonymous signature-based authenticated key exchange schemefor IoT-enabled smart grid environment, called AAS-IoTSG.The dynamic smart meter addition phase is also permissiblein AAS-IoTSG after initial deployment. The security of AAS-IoTSG has been tested rigorously using formal security analysisunder the Real-Or-Random (ROR) model which is one of thebroadly-accepted standard random oracle models, formal secu-rity verification under the broadly-used Automated Validationof Internet Security Protocols and Applications (AVISPA) tooland also using informal security analysis. Finally, an exhaustivecomparative study unveils that AAS-IoTSG supports bettersecurity & functionality features and requires less communication& computation overheads as compared to the existing state-of-artauthentication mechanisms in smart grid systems

Abstract

The modern day traffic continues to evolve in termsof scale, autonomy and access to information. Every vehiclegathers information and contributes its decisions to the globalvehicular network every second. With the advent of 5G equippeddigital communication and sophisticated algorithms are in placefor the vehicles to communicate with each other in a closelymonitored, yet decentralized network, there is a need to ensurethat no form of incorrect data be propagated without priorvalidation. There is also a requirement of maintaining cache sincethere is no centralized network where all vehicles report theiractivities and gather information from, at a required speed. Thedistribution of cache is a major hurdle both in terms of validationand propagation. Cache poisoning can occur if a malicious vehicleor a compromised vehicle intentionally or unintentionally putsincorrect data and other vehicles use that data to skew their ownfuture decisions. In this paper, we explore different methodologiesto address and combat the cache poisoning scenarios and suggestan efficient and secure scheme for validation and distribution ofcache using the Directed Acyclic Graph (DAG) based ledger,which is based on Tangle™.

Abstract

The attempt to enroll citizens under the umbrella of effective governance is being accomplished by bringing digitization to the pedestal of identity. The paper unfolds the story of India’s biometric based digital identity programme,Aadhaar, in fulfilling its goals of facilitating national security, financial inclusion and entitlement delivery. Using thesocio-technical framework of ‘Good ID’,a user-centric approach todigital identity, we critique the intricacies of the design of Aadhaar as a bridge between citizen beneficiaries and the government and propose suggestions to forward the case of Aadhaar as ‘good’ digital ID.

Abstract

The periodic opening and closing of the vibrating vocal foldschanges the production system continuously during the pro-duction of voiced speech. The subglottal and supraglottalcavities have distinct structure and impedance. A couplingand decoupling of these cavities affects effective length of thevocal tract which in turn affects its impedance. This can bestudied based on the shift in the location of dominant reso-nant frequencies (DRFs) of the instantaneous system responseobtained using zero time windowing (ZTW) method. Thechange in the impedance of the vocal tract is reflected in thebandwidth of spectral resonances. Bandwidth values esti-mated using the group delay function at dominant resonancelocations, approximated by the Hilbert envelope of the nu-merator of group delay (HNGD) function. An estimate ofthis change is made by comparing the bandwidths in closedglottal phase of the glottal cycle for the oral and nasal tracts.A study of the relative difference in the bandwidth of vowelsand nasals in speech is attempted to compare the impedanceof the respective tracts. The study examines VC/CV segmentsin English for speakers in the TIMIT database. The resultsshow that the nasal segments exhibit higher bandwidth valuescompared to vowels

Abstract

Events during an emergency unfold in an unpredictable fashion which makes management of traffic duringemergencies pretty challenging. Furthermore, some vehicles would need to be evacuated faster than otherse.g., emergency vehicles or large vehicles carrying a lot more people. The Prioritized Routing Assistant forFlow of Traffic (PRAFT) enables prioritized routing during emergencies. However, the PRAFT solution doesnot compute multiple plans that can help handle better dynamic nature of emergencies. PRAFT maps theprioritized routing problem to the Minimum-Cost Maximum-Flow (MCMF) problem, hence its solution canaccommodate maximum flow while routing vehicles based on priority (maps higher priority vehicles to betterquality routes (i.e., ones with minimum cost)). We build upon the PRAFT solution to make the followingcontributions: (a) Develop a Pareto Minimum-Cost Maximum-Flow (Pareto-MCMF) algorithm which cancompute all the possible MCMF solutions. (b) Through a series of experiments performed using the wellknown traffic simulator SUMO, we could show that all the solutions generated by Pareto-MCMF indeed haveproperties similar to a MCMF solution thus providing multiple high quality options for traffic police to pickfrom depending on the situation.