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A recent development emanating from RFID technology is Near Field Communication (NFC). Basically, NFC is a popular short range (

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this article, we propose a machine learning-based approach that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. Our approach predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of our approach through both real test-bed experimentation and simulation. Our evaluation reveals that our approach achieves substantial reduction in total energy consumption compared to other state-of-the-art alternatives while experiencing marginal performance degradation in a few cases. more...

Popular translators such as Google, Bing, etc., perform quite well when translating among the popular languages such as English, French, etc.; however, they make elementary mistakes when translating the low-resource languages such as Bengali, Arabic, etc. Google uses Neural Machine Translation (NMT) approach to build its multilingual translation system. Prior to NMT, Google used Statistical Machine Translation (SMT) approach. However, these approaches solely depend on the availability of a large parallel corpus of the translating language pairs. As a result, a good number of widely spoken languages such as Bengali, remain little explored in the research arena of artificial intelligence. Hence, the goal of this study is to explore improvized translation from Bengali to English. To do so, we study both the rule-based translator and the corpus-based machine translators (NMT and SMT) in isolation, and in combination with different approaches of blending between them. More specifically, first, we adopt popular corpus-based machine translators (NMT and SMT) and a rule-based machine translator for Bengali to English translation. Next, we integrate the rule-based translator with each of the corpus-based machine translators separately using different approaches. Besides, we perform rigorous experimentation over different datasets to report the best performance score for Bengali to English translation till today by revealing a comparison among the different approaches in terms of translation performance. Finally, we discuss how our different blending approaches can be re-used for other low-resource languages. more...

Crowd management is a considerable challenge in many countries including Saudi Arabia, where millions of pilgrims from all over the world visit Mecca to perform the sacred act of Hajj. This holy ritual requires large crowds to perform the same activities during specific times, which makes crowd management both critical and difficult. Without proper crowd management and control, the occurrence of disasters such as stampedes, suffocation, and congestion becomes highly probable. At present, the internet of things (IoT) and its enabling technologies represent efficient solutions for managing and controlling crowd, minimizing casualties, and integrating different intelligent technologies. Moreover, IoT allows intensive interaction and heterogeneous communication among different devices over the internet, thereby generating big data. This paper proposes an intelligent IoT approach for crowd management with congestion avoidance in the Mina area, which is located in the holy city of Mecca. The approach implements a learning mechanism that classifies pilgrims based on the collected data and exploits the advantages of both IoT and cloud infrastructures to monitor crowds within a congested area, identify evacuation paths for pilgrims and guide the pilgrims to avoid congestion in real time. Moreover, the approach attempts to maximize crowd safety based on realistic scenarios by controlling and adapting pilgrim movements according to the characteristics of the possible hazards, pilgrim behavior, and environmental conditions. We evaluated our proposed approach by performing simulations based on real data sets and scenarios. more...

Mass gatherings (such as Hajj/Umrah), owing to their immensity, often present a variety of difficulties to the attendees. To have a comprehensive understanding of the difficulties as well as their potential remedies, surveying a good number of attendees is unavoidable and this can be facilitated through engaging data collectors. A crucial part here is identifying the integrity of the data collectors, which is yet to be explored in the literature to the best of our knowledge. To address this gap, in this study, we first perform a mass-scale data collection over Hajj/Umrah pilgrims through online (n = 236) and in-person (n = 752) surveys, where we cover a substantial part (n = 712) through paid data collectors (n = 53). We critically investigate data collection activities of the data collectors through focused group discussions involving expert reviewers. We explore two computing techniques to unveil the integrity of the data collectors from two different perspectives. Our study finds out influential (religious and socio-geographical) aspects that impact the data collection process. Besides, we find that the collaborative participation of expert reviewers is obligatory to scrutinize the data collectors’ integrity. Additionally, our explored computing techniques, namely conflict analysis and learning-based analysis, can identify up to 74% and 99% of the unreliable data collectors respectively. We observe that, although these computing-based filtering can indicate the integrity up to a certain level, human-in-the-loop is unavoidable for concluding on the integrity. To the best of our knowledge, this is the first study of its kind (i.e., integrity analysis of the data collectors) in the literature. more...

With the advent of pervasive usage of online platforms, online abusive behavior has become an indispensable part of our life demanding great attention from the research community. Accordingly, the research community is spending its effort on the demanding task, however, perhaps having much less effort on emails, even though emails are identified as a prominent source of exchanging online abusive behaviors. To fill in this gap in the literature, we conduct an in-depth study to investigate online abusive behavior having a special focus on emails. To do so, we perform a mixed-method user study consisting of formative interviews (n=15) and a survey (n=65) over user’s experience, coping strategies, etc., pertinent to online abuse in Bangladesh, especially focusing on abuse over emails. We also dig into users’ perspectives to analyze strengths and challenges associated with different types of abuse detection systems for online platforms, especially for emails. One of the noteworthy findings of our study is that there exists a significant demand for abuse detection systems over emailing platforms even after having a lesser frequency of abuse occurring over emails. Our findings also highlight a certain level of user preference for an automated abuse detection system potentially considering its more control and fewer privacy concerns to users, however, being challenged due to having the limitation of lesser ability to detect implicit abuse. We also identify several limiting factors associated with a human-moderator-based abuse detection system, including less comfort, less trust in different types of moderators, inhumane demands to the moderators, and time delay in detecting abuses. These findings point to opportunities for design interventions for hybrid abuse detection systems, which is the most preferred system to the users, to overcome all the limitations of automated and human-moderator-based systems. more...

Keeping firearms inside an arsenal with adequate safety is one of the prime concerns of the research community considering limitless damage in case of falling the firearms to the stain accused or rogue peoples in the society. In this regard, a remotely-operated, low-cost, and high-accuracy solution for maintaining safety in storing the firearms inside an arsenal is of utmost importance specially from the perspective of developing countries. Such a solution is yet to be proposed in the literature to the best of our knowledge. Therefore, in this study, we propose a new solution that can provide safety of firearms inside an arsenal in real time using low-power COTS sensor-based safety mechanism. Here, we propose judicious placing of RFID tags and RFID antennas, and develop a software module to retain the hardware related components inter-operable with each other. Besides, we develop a lock-and-key technique that exhibits a high standard of safety for the firearms inside the arsenal. Our solution also presents a time-attendance management system. Hardware part of our real implementation comprises a motor unit and a RFID reader, which is a low-frequency reader and connected to a host computer machine. All these components combined together provide several functionalities such as displaying ID, registering name, time of drawing firearms from the arsenal and so on. We present the implemented solution to real professional users. User evaluation of the proposed system confirms efficacy of our solution in real usage. more...

In recent years, cognitive radio networks (CRNs) have been widely investigated to solve the well-known spectrum scarcity problem through enhancing spectrum utilization. Another technique of enhancing spectrum utilization, which has already been well accepted, is to utilize multiple radios on a single node. Simultaneous usage of both these techniques is, therefore, expected to enhance the spectrum utilization further in road to improving overall network performance. However, little research efforts have been spent on investigating performance of the simultaneous usage through incorporating multiple radios in each node of a CRN. Existing studies in this regard propose several protocols for multi-radio cognitive radio networks (MRCRNs). However, none of them focuses on enhancing throughput in the network to the best of our knowledge. Nonetheless, increased network throughput should be a direct consequence of enhanced spectrum utilization through exploiting multiple radios in CRNs, even though an existing study (Khan et al., in: 2015 IEEE 11th international conference on wireless and mobile computing, networking and communications (WiMob), IEEE, pp 370–377, 2015) reports getting decreased network throughput while introducing multiple radios in each node of a CRN. Thus, a specialized treatment to the multiple radios in each node of a CRN is needed for enhancing network throughput. Accordingly, in this study, we propose a feedback-based multi-radio exploitation approach for MRCRNs, where information obtained from lower layers (Physical layer and Data Link layer) is incorporated in the process of decision making in an upper layer (Application layer) to enhance network throughput. We implement our proposed approach in the network simulator ns-3 to evaluate different performance metrics including network throughput, average end-to-end delay, and average packet drop ratio. We compare the performance against that of existing multi-radio exploitation approaches for CRNs. Our simulation results reveal that our proposed feedback-based approach always achieves substantially increased network throughput compared to the existing approaches, in parallel to achieving improved delay and packet drop-ratio in most of the cases. more...

Detecting count of human beings accurately in a closed indoor environment is crucial in diverse application areas including search and rescue, surveillance, customer analytics, abnormal event detection, human gait characterization, congestion analysis, and many more. Moreover, it has significant importance in preventing any intrusion in a secured indoor space such as a bank vault. Sensor-based technologies (for example camera, Passive Infrared sensor (PIR), etc.) are becoming more popular day by day to ensure enhanced security in such closed indoor setting. However, sensors used in these technologies have to be deployed in visible places. Thus, there exist possibilities of damaging the sensors by intruders. Therefore, this paper proposes a novel methodology to detect human count in such closed indoor setting, which can be deployed in any hidden place being oblivious to intruders. Here, we perform human counting based on different environmental gaseous parameters (Carbon Dioxide, Liquefied Petroleum Gas or LPG, Nitrogen Dioxide, and Sulfur Dioxide) and two weather parameters (temperature and humidity). We conduct numerous experiments under closed controlled settings and perform the counting task using different machine learning algorithms such as Bagging, RandomForest, IBK, and J48. We also propose a novel Deep Neural Network based framework for this task and evaluate its performance on the collected data sets. Using this method, we achieve 95% accuracy in detecting the number of humans present even in the unseen test case scenario. more...

Exploration of potential security threats and vulnerabilities of a real-time system specifically designed for detecting missing rail blocks in the context of developing countries is yet to be explored. Therefore, in this paper, we propose a new threat called power attack through exploiting vulnerability pertinent to the energy sources of the real-time system for detecting missing rail blocks. Consequently, we present both theoretical and mathematical modeling of attack model to effectively launch the power attack. In addition, we perform comparison of our proposed power attack with other attacks such as man-in-the-middle attack and jamming attack. Furthermore, we perform extensive experimentation using both ns-2 simulator and real deployment to investigate the applicability and the effectiveness of our exposed attack model for the real-time system for detecting missing rail blocks. more...

Today's Internet of Things (IoT) and its enabling technologies offer efficient solutions for utilizing available resources and integrating different techniques. In high-risk environments, IoT has already shown to have significant potential to provide safe, reliable, and efficient solutions. This paper proposes a hybrid emergency evacuation approach using IoT and combines it with cloud computing. The proposed approach attempts to utilize the advantages of both infrastructures (IoT and cloud) to calculate evacuation paths in real time during emergency evacuation. It attempts to maximize the safety of the suggested evacuation paths by adapting to the characteristics of the hazard, evacuees’ behavior, and environmental conditions. Our approach covers two emergency navigators: A Localized Emergency Navigator (LEN) and a High-Risk Emergency Navigator (HREN). Depending on some defined emergency factors, our approach decides which navigator to execute. It also handles diversified evacuation issues, such as when people get locked in a safe, however, a dead-end area of a building under emergency. Performance evaluation shows that the proposed approach achieves better outcomes in terms of survival rate and evacuation efficiency compared to the traditional algorithm. more...

Derailment due to uprooted or faulty rail blocks is an epoch-making problem in many developing countries such as Bangladesh, India, Kenya, etc. Such derailments result in both death tolls and loss of property. Low-resource settings and the paucity of networking infrastructure in remote rail areas of developing countries pose a challenge in devising an automated real-time system for detecting uprooted or faulty rail blocks to stop such derailments. As a remedy, in this paper, we propose a new networking paradigm to facilitate devising such an automated system leveraging a real-time communication between train and rail track, considering the aforementioned concerns. Here, we present a novel ad-hoc network architecture, node deployment topology, and light-weight network protocols for enabling the communication. Our proposed paradigm offers a low-cost and lightweight solution for the intended purpose worth of adopting in developing countries. Nonetheless, the paradigm exhibits a near-to-perfect performance, which we evaluate through both simulation and real experimental evaluation, yet maintaining the low-cost and lightweight status. more...

One of the most challenging problems during disasters involving crowds is pedestrian evacuation. It is very important in such situations to improve survival rates by getting all or most of the people out in the shortest possible time. Technological intervention through augmenting the evacuation process using an unmanned aerial vehicle (UAV) exhibits great potential in improving survival rates, but the exploration of this method is still in its infancy. Therefore, this study explores the potential of utilizing UAVs for crowd management during emergency evacuations. We conducted a rigorous study, using a simulation model featuring four UAVs and differing numbers of pedestrians, with use of two UAV guidance approaches: partial guidance and full guidance. The experimental results suggest that exploiting UAVs in crowd evacuation and following the partial guidance approach can lead to a more efficient evacuation process. more...

Quantum error correction comes from the marriage of quantum mechanics with the theory of error-correcting codes, which is still at the rudimentary stage tackling only 1 qubit to date. As, unlike the classical world, the extent of error in the quantum world is substantially high and unavoidable, scaling up the quantum error correction from 1 qubit to higher is of utmost significane. Therefore, to achive the scaling up, we present a general quantum error correction code that can be adapted for any number of quantum errors (bit-flip error to be specific in our case). This generalized code for bit-flip error correction realizes a delicate blending of using controlled-NOT gates with the notion of quantum entanglement. We augment the blending with majority voting in the final stage of the error correction. We demonstrate efficacy of our proposed error correction code through real experimentation in IBM Qiskit. Our experimental results up to 3-qubit bit-flip errors in the available Qiskit infrastructure confirm a total escape from errors using our proposed error correction code. more...

Network Intrusion Detection System (NIDS) is an important tool for network administrators to detect security breaches in a network. However, due to the diversity of attacks and imbalanced datasets having less number of data pertinent to attack events, current machine learning based NIDS applications often do not perform well. Therefore, it is important to synthesize data in a probabilistic manner that is similar to original attack event related data. Accordingly, in this paper, we propose a new paradigm of the synthesizing task based on Variational Laplace AutoEncoder (VLAE) and Deep Neural Network, and exploit the paradigm to develop a new intrusion detection model. Here, we go beyond the existing VLAE model through incorporating class labels as an input. We term the enhanced model as Conditional Variational Laplace AutoEncoder (CVLAE). We employ the CVLAE to learn latent variable representations of network data features and to synthesize data in a probabilistic manner. We use a Deep Neural Network (DNN) classifier, trained on the original and synthesized data, and classify the attack samples. We evaluate our model on the benchmark NSL-KDD dataset. We demonstrate efficacy of our proposed model through showing that our method achieve higher precision in minority attacks than other methods in our experimentation. more...

Parkinson’s disease is a common and highly threatening neurodegenerative disease, which has no confirmed well-adopted diagnosis method to date. All research efforts in this regard focus on diagnosing in controlled environment such as laboratories. Going beyond this common limitation, in this paper, we exploit the Electroencephalogram (EEG) signal to effectively come up with a portable Parkinson’s disease diagnosis system that can go in-the-wild. To do so, we collect EEG signals from both Parkinson’s patients and healthy people in different emotional states. Our statistical analysis finds a substantial differences in the signals in several emotional states for Parkinson’s patients and healthy people. Accordingly, we develop a machine learning based model to diagnose Parkinson’s disease that results in 97% accuracy. more...

Securing information transmission is critical today. However, with rapidly developing powerful quantum technologies, conventional cryptography techniques are becoming more prone to attacks each day. New techniques in the realm of quantum cryptography to preserve security against powerful attacks are slowly emerging. What is important though now is the fidelity of the cryptography, because security with massive processing power is not worth much if it is not correct. Focusing on this issue, we propose a method to enhance the fidelity of quantum cryptography using maximally entangled qubit pairs. For doing so, we created a graph state along a path consisting of all the qubits of ibmqx4 and ibmq_16_melbourne respectively and we measure the strength of the entanglement using negativity measurement of the qubit pairs. Then, using the qubits with maximal entanglement, we send the modified encryption key to the receiver. The key is modified by permutation and superdense coding before transmission. The receiver reverts the process and gets the actual key. We carried out the complete experiment in the IBM Quantum Experience project. Our result shows a 15% to 20% higher fidelity of encryption and decryption than a random selection of qubits. more...

Wireless nanonetworks are not a simple extension of traditional communication networks at the nano-scale. Owing to being a completely new communication paradigm, existing research in this field is still at an embryonic stage. Furthermore, most of the existing studies focus on performance enhancement of nanonetworks via designing new channel models and routing protocols. However, the impacts of different types of nano-antennas on the network-level performances of the wireless nanonetworks remain still unexplored in the literature. Therefore, in this paper, we explore the impacts of different well-known types of antennas such as patch, dipole, and loop nano-antennas on the network-level performances of wireless nanonetworks. We also investigate the performances of nanonetworks for different types of traditional materials (e.g., copper) and for nanomaterials (e.g., carbon nanotubes and graphene). We perform rigorous simulation using our customized ns-2 simulation to evaluate the network-level performances of nanonetworks exploiting different types of nano-antennas using different materials. Our evaluation reveals a number of novel findings pertinent to finding an efficient nano-antenna from its several alternatives for enhancing network-level performances of nanonetworks. Our evaluation demonstrates that a dipole nano-antenna using copper material exhibits around 51% better throughput and about 33% better end-to-end delay compared to other alternatives for large-size nanonetworks. Furthermore, our results are expected to exhibit high impacts on the future design of wireless nanonetworks through facilitating the process of finding the suitable type of nano-antenna and suitable material for the nano-antennas. more...

Basic arithmetic operations are essential skills needed in our life, and this is no different for the visually impaired. However, working arithmetic out on paper is always a challenge for visually impaired people. This situation is exacerbated by low-resource settings due to a paucity of low-cost and easy-to-use solutions. As a remedy to this situation, we propose a low-cost and easy-to-use arithmetic learning framework and draw a contrast between the conventional means of solving arithmetic problems and our proposed framework. Our proposal is engendered from comprehensive studies, both qualitative and quantitative, over the challenges faced by visually impaired children from two low-income countries. These studies are conducted in three phases—exploratory, descriptive, and explanatory—involving six visually impaired children and sixteen visually impaired grownups. User evaluation of our framework, in disguise of a tutorial session, confirms its acceptability and adaptability, along with its effectiveness in evoking interest in arithmetic. We believe that our study and proposed framework will help in breaking barriers to similar challenges in other developing regions across the border. more...

Nowadays, a major problem in faster page loading through optimizing websites is the absence of having images in their intended sizes. Accordingly, while loading a webpage, most of the time overhead pertains to image-related tasks such as image loading and resizing, which can be optimized substantially through the pre-availability of smaller-size images. Therefore, in this study, we propose a strategy to enable faster and efficient image retrieval from a cloud via the necessary pre-processing of images beyond conventional online processing. We also extend the abilities of cloud file sharing from the conventional “only storing images” to pre-processing along with security reinforcing. Here, we perform the first task of resizing images to several dimensions for covering diversified remote user devices available nowadays. Then, we perform encoding and encryption of images using the P-Fibonacci transform of Discrete Cosine Coefficients (PFCC) algorithm. Afterward, we resize images using the Bicubic interpolation method to both JPEG and Progressive-JPEG (PJPEG) formats by adding a new middleware named iBuck for faster and smooth retrieval. We leverage necessary algorithms and image types after careful evaluation of their performances in our intended framework. Our evaluation covers both objective and subjective evaluations including QoE metrics such as MSE, SSIM, etc. Furthermore, we conduct detailed experimentation over real setups to evaluate the performance of our implemented middleware in a cloud file sharing environment with both custom and standard data sets. Our evaluation reveals substantial performance improvement using our proposed framework compared to that using conventional alternatives. more...

Successful defense against eavesdropping on near-field communication (NFC) vastly depends on careful analysis of eavesdropping and adopting a suitable defense mechanism based on the analysis. However, such an analysis and such an adoption of a defense mechanism are yet to be attempted in the literature, even though eavesdropping is considered as one of the foremost security breaches on NFC. As a remedy to this situation, we propose an analytical model for analyzing the notion of eavesdropping, and also propose pragmatic defense mechanisms based on the model. In our formulation of the analytical model, we attempt to consider all the parameters related to eavesdropping. Based on the formulated model, we analyze individual impacts of the parameters. Our analysis reveals a key finding: the maximum possible distance of eavesdropping on near-field communication is significantly controlled by both conductivity and permeability of the material over which the wave propagates. Exploiting this finding, we propose to judiciously utilize coating with materials having high conductivity and high permeability to defend against eavesdropping. To further enhance the defense level against eavesdropping, we propose another defense mechanism using active jamming. We perform a set of real experiments to confirm effectiveness of both the mechanisms. more...

This paper presents a novel hybrid dynamic spectrum access approach, combining classical and stochastic flavors being augmented with new genetic operators, for multi-channel single-radio cognitive radio networks. Existing classical and stochastic approaches exhibit different advantages and disadvantages depending on network architecture. Our proposed approach exploits a delicate balance between the two different approaches for extracting advantages from both of them while limiting their disadvantages. Additionally, in our proposed approach, we boost up extent of the exploitation through designing new genetic operators. Furthermore, we provide a thorough performance evaluation of our approach using a widely used discrete event simulator called ns-2 . Here, we also simulate several existing approaches that are based on graph theory, game theory, heuristic, genetic algorithm, agent-based learning, and online learning. Simulation results demonstrate significant performance improvement using our proposed intelligent dynamic spectrum access approach over these state-of-the-art ones based on various standard metrics. more...

Automatic extractive text summarization finds the subset of the data, which represent the most salient information of the entire document. Now-a-days in the era of Internet, it is a demand of the users to understand the huge amount of texts within a very short time, as there are lots of redundant unnecessary textual information in the webpages. Here comes the value of automatic extractive text summarization. Internet users get the idea of entire content of the document by reading summary generated by the summarizer within a very short time and also can decide whether to read the entire content or not. There are several methods for generating extractive summary such as clustering based, optimization based, information and itemset based, and word embedding based methods. These methods fail to determine the redundancy generated from the summary sentences. Therefore, in this paper, we propose a novel approach for generating extractive summary by considering semantic relationships among the sentences of the document.Here, we extract the Predicate Argument Structure (PAS) from each sentence and measure semantic distance among the sentences is measured from the PAS to PAS semantic relationship of the sentences. Then, we apply modified page rank algorithm to rank the sentences. Subsequently, we employ Maximum Marginal Relevance (MMR) method to rerank the sentences. Accordingly, we propose top n sentence selection algorithm is proposed to select summary sentences. To evaluate the performance of our proposed method, we adopt Rouge-1, Rouge-2 evaluation metrics for computing precision, recall, and f-score of the summary. Furthermore, we also employ cosine simillarity measurement based approach to determine redundancy in the summary sentences. Our evaluation results demonstrate that the proposed approach achieves improved performance compared to the other existing methods available for generating an the extractive summary. more...

Nowadays, SMS based communication is common in almost every sector. Private and government organizations deliver and receive millions of SMSs every year. This huge amount of SMSs can be used later in several useful ways. However, the storage space required for storing these huge amount SMSs creates so much complexity for the SMS service providing organizations that they cannot save the details of every SMS. Therefore, in this paper, we introduce a template-oriented SMS storage system based on a graph-theoretic approach, which reduces storage space requirements significantly. We achieve the storage space reduction through considering the fixed parts of SMSs as graph nodes and variable parts as graph edges. We perform experimentation on real SMS data collected from different telecom operators of Bangladesh. Our experimentation confirms that our proposed system substantially reduces the storage space requirement. To the best of our knowledge, we are the first to propose such a space-saving system for storing SMSs. more...

Abuse in cyber space is a problem requiring immediate attention. Unfortunately, despite advances in Natural Language Processing techniques, there are clear limitations in detecting instances of cyber abuse today. Challenges arising due to different languages that teens communicate with today, and usage of codes along with code mixing and code switching make the design of a comprehensive approach very hard. Existing NLP based approaches for detecting cyber abuse thus suffer from a high degree of false negatives and positives. In this paper, we investigate a new approach to detect instances of cyber abuse. Our approach is motivated by the premise that abusers tend to have unique facial expressions while engaging in an actual abuse episode, and if we are successful, such an approach will be language-agnostic. Here, using only four carefully identified facial features without any language processing, and realistic experiments with 15 users, our system proposed in this paper achieves 98% accuracy for same-user evaluation and up to 74% accuracy for cross-user evaluation in detecting instances of cyber abuse. more...

Lying is a (practically) unavoidable component of our day to day interactions with other people, and it includes both oral and textual communications (e.g. text entered via smartphones). Detecting when a person is lying has important applications, especially with the ubiquity of messaging via smart-phones, coupled with rampant increases in (intentional) spread of mis-information today. In this paper, we design a technique to detect whether or not a person's textual inputs when typed via a smartphone indicate lying. To do so, first, we judiciously develop a smartphone based survey that guarantees any participant to provide a mix of true and false responses. While the participant is texting out responses to each question, the smartphone measures readings from its inbuilt inertial sensors, and then computes features like shaking, acceleration, tilt angle, typing speed etc. experienced by it. Subsequently, for each participant (47 in total), we glean the true and false responses using our own experiences with them, and also via informal discussions with each participant. By comparing the responses of each participant, along with the corresponding motion features computed by the smartphone, we implement several machine learning algorithms to detect when a participant is lying, and our accuracy is around 70% in the most stringent leave-one-out evaluation strategy. Later, utilizing findings of our analysis, we develop an architecture for real-time lie detection using smartphones. Yet another user evaluation of our lie detection system yields 84%-90% accuracy in detecting false responses. more...

Demand for fast data sharing among smart devices is rapidly increasing. This trend creates challenges towards ensuring essential security for online shared data while maintaining the resource usage at a reasonable level. Existing research studies attempt to leverage compression based encryption for enabling such secure and fast data transmission replacing the traditional resource-heavy encryption schemes. Current compression-based encryption methods mainly focus on error insensitive digital data formats and prone to be vulnerable to different attacks. Therefore, in this paper, we propose and implement a new Huffman compression based Encryption scheme using lightweight dynamic Order Statistic tree (HEliOS) for digital data transmission. The core idea of HEliOS involves around finding a secure encoding method based on a novel notion of Huffman coding, which compresses the given digital data using a small sized "secret" (called as secret_intelligence in our study). HEliOS does this in such a way that, without the possession of the secret intelligence, an attacker will not be able to decode the encoded compressed data. Hence, by encrypting only the small-sized intelligence, we can secure the whole compressed data. Moreover, our rigorous real experimental evaluation for downloading and uploading digital data to and from a personal cloud storage Dropbox server validates efficacy and lightweight nature of HEliOS. more...

Identifying physical traits and emotions based on system-sensed physical activities is a challenging problem in the realm of human-computer interaction. Our work contributes in this context by investigating an underlying connection between head movements and corresponding traits and emotions. To do so, we utilize a head movement measuring device called eSense, which gives acceleration and rotation of a head. Here, first, we conduct a thorough study over head movement data collected from 46 persons using eSense while inducing five different emotional states over them in isolation. Our analysis reveals several new head movement based findings, which in turn, leads us to a novel unified solution for identifying different human traits and emotions through exploiting machine learning techniques over head movement data. Our analysis confirms that the proposed solution can result in high accuracy over the collected data. Accordingly, we develop an integrated unified solution for real-time emotion and trait identification using head movement data leveraging outcomes of our analysis., 9 pages, 7 figures, NSysS 2019 more...

Microscopic traffic simulators have become efficient tools to conduct different analytic studies on roads, vehicles, behavior of drivers, and critical intersections, which lead towards a well-planned traffic solution. Devising a realistic and sustainable traffic solution requires replication of the real traffic scenario in a simulator. For example, to simulate the traffic stream of less developed countries, we need to simulate non-lane based heterogeneous traffic stream, i.e., motorized and non-motorized vehicles, with their on road behaviors such as irregular pedestrian, illegal parking, violation of laws pertaining lanes, etc. However, most of the existing traffic simulators are unable to mimic the unstructured road traffic stream of less developed countries with their diversified behaviors. We also need to represent the corresponding geographical maps as a list of nodes and edges with relevant geographic information such as length and width of the edges to feed the simulator. However, we lack a Geographic Information System (GIS) protocol to construct an effective simulator readable road network from authoritative polygonal GIS maps. State-of-the-art GIS protocol often converts the geographical maps using polyline data collected from less-credible OpenStreetMap. Although polyline data has length information (for example, length of a street), it lacks width information limiting its capability of being an effective simulator readable road network map. Therefore, in this work, we propose a new microscopic traffic simulator to handle unstructured road traffic stream of less developed countries with their diversified behaviors. To feed the simulator, we also propose a novel parsing algorithm to construct an effective simulator-readable road network from GIS maps through extracting both length and width information of the edges and develop parser based on the algorithm. Our parsing algorithm realizes a delicate blending of geometry with computation over road points to eventually generate the simulator-readable road network. Our simulator receives the network topology generated by our parser, traffic routes, and traffic demand flow rates as input, visualizes the traffic flows, and provides relevant traffic statistics. To evaluate sustainability of our proposed parser and simulator in real-life scenarios, we evaluate our parser by parsing GIS street maps of four different areas resulting in 86% accuracy on an average and evaluate our simulator after necessary calibration using real traffic data resulting in 99% accuracy in terms of travel time. more...

Recent emergence of new vulnerabilities is an epoch-making problem in the complex world of website security. Most of the websites are failing to keep updating to tackle their websites from these new vulnerabilities leaving without realizing the weakness of the websites. As a result, when cyber-criminals scour such vulnerable old version websites, the scanner will represent a set of vulnerabilities. Once found, these vulnerabilities are then exploited to steal data, distribute malicious content, or inject defacement and spam content into the vulnerable websites. Furthermore, a combination of different vulnerabilities is able to cause more damages than anticipation. Therefore, in this paper, we endeavor to find connections among various vulnerabilities such as cross-site scripting, local file inclusion, remote file inclusion, buffer overflow CSRF, etc. To do so, we develop a Finite State Machine (FSM) attacking model, which analyzes a set of vulnerabilities towards the road to finding connections. We demonstrate the efficacy of our model by applying it to the set of vulnerabilities found on two live websites. more...

Multicast is an efficient method for sending data to multiple destinations in a single transmission. Conventional multicast routing often suffers from scalability issues. Here, the number of forwarding states maintained in the Network layer generally increases with the number of concurrently active multicast groups. Consequently, large-size routing tables get generated, which in turn causes degradation in router performance. There exist only a limited number of studies on aggregated multicast to address this issue. The existing studies such as AM (Aggregated Multicast), STA (Scalable Tree Aggregation for Multicast), and STS (Shared-Tree Selection for Aggregated Multicast) attempt to perform tree aggregation, however, still retain high number of forwarding states in the routers. Besides, they mostly ignore the effect of tree aggregation ratio in terms of network performance metrics (for example delay and throughput). Hence, the research on aggregated multicast is still at an elementary stage. To this extent, in this study, we propose a novel aggregated multicast approach to reduce Network layer forwarding states. Our approach proposes new methods for selecting aggregated multicast trees, refining search range, and replacing stale trees in process of the aggregation. We show the effect of our proposed tree aggregation in terms of network performance metrics (delay and throughput). We also show that performance of our approach is better than previous approaches by comparing different performance metrics such as the number of trees, the number of forwarding states, delay, and throughput. Ns-3 simulation results confirm that our approach can reduce up to 92% forwarding states and 34% delay compared to conventional multicast, and reduce up to 88% forwarding states and 29% delay compared to the STS method. more...

Most of the research studies available to date on communication protocols for Body Area Nanonetworks (BANs) using molecular communication deals with the protocol stack’s lower layers (such as physical, MAC layer), even though the upper layers also (such as transport layer, application layer) play a significant role on the overall performance of the network. Besides, the applicability of classical upper layer protocols for molecular communication in BANs have scopes to be investigated in this context. Therefore, we center our study in developing a specialized transport layer protocol with a view to achieving a delicate balance among throughput, delivery ratio, energy consumption, end-to-end delay etc. network-level performance metrics for molecular communication in BANs. To do so, we exploit a notion of conjugate congestion control mechanism from the perspective of both the sender and receiver to attain the desired performance level as well as produce limited complexity and overhead. We evaluate and compare the performance of our proposed protocol with the existing alternatives through rigorous ns-2 simulation. The results demonstrate that compared to the other transport layer protocols, our proposed one provides a better balance among different performance metrics. more...

Recent boost in development and usage of wearable devices, in particular for emerging alert generation, portrays a necessity for availing an open platform to enable future expansions and enhancements. However, to the best of our knowledge, little effort has been spent in the literature in this regard. Consequently, in this paper, we propose a rich, yet simple, independent open platform for wearable devices that can generate emergency alarms. We present detailed design and development of the platform, which we name as Supersavior. We implement a prototype of Super-savior using low-power COTS hardware. We devise and implement an algorithm to predict the location of the user based on GSM locations provided. Afterwards, we perform real experimentation and user evaluation of the prototype by letting a group of people use it for extended periods. The experimentation reveals several system-level measures indicating effectiveness of the device in terms of both accuracy and marginal operational overheads. We compare these measures along with offered functionality of Super-savior against that of a few currently-available alternatives. Finally we analyze and present data from field experiments to demonstrate functionality of the device. more...

Computing clusters are evaluated using different performance metrics, which often appear to be conflicting while being attempted to be optimized. For such conflicting cases along with frequently having an existence of heterogeneous environment, it is difficult for the cluster administrators to efficiently schedule machines, i.e., to select the right number and right combination of machines. In this paper, we develop a technique through which cluster administrators can select the right set of machines to enhance energy efficiency and cluster performance. To do so, first, we perform extensive laboratory experiments for a period of more than one year. Based on empirical analyses of data collected from the experiments, we formulate a many-objective optimization problem for clusters and integrate a greedy approach with Non-dominated Sorting Genetic Algorithm (NSGA-III) to solve this problem. We demonstrate that our approach mostly performs better than existing approaches in the literature through both real experimentation and simulation. more...

Nowadays, Internet-based technologies possess immense processing power, capacity, flexibility, and are gradually moving towards a service- oriented functionality in order to build new distributed storage systems in the cloud. Several distributed systems are currently running in different geographically located data centers for successful deployment of modern web and social services such as Facebook, Twitter, ringID, etc. Both cache and backend servers in such distributed systems must be functional and reliable for incoming workloads by means of efficient allocation of capacity along with proper configuration and tuning of multiple system resources. To address these challenges, in this paper, we propose a test-driven automated architecture for load testing, named as 'svLoad' to compare the performance of cache and backend servers. Here, we designed test cases considering diversified real scenarios such as different protocol types, same or different URLs, with or without load, cache hit or miss, etc. using tools namely JMeter, Ansible, and some custom utility bash scripts. To validate the efficacy of our proposed methodology, we conduct a set of experiments by running these test cases over a real private cloud development setup using two open source projects - Varnish as the cache server and OpenStack Swift as the backend server. Our focus is also to find out bottlenecks of Varnish and Swift by executing load requests, and then tune the system based on our load test analysis. After successfully tuning the Swift, Varnish, and network system, based on our test analysis, we were able to improve the response time by up to 80%. more...

The ability of smart devices to recognize their owners or valid users gains attention with the advent of widespread highly sensitive usage of these devices such as storing secret and personal information. Unlike the existing techniques, in this paper, we propose a very lightweight single-time user identification technique that can ensure a unique authentication by presenting a system near-to-impossible to breach for intruders. Here, we have conducted a thorough study over single-time usage data collected from 33 users. The study reveals several new findings, which in turn, leads us to a novel solution exploiting a new machine learning technique. Our evaluation confirms that the proposed solution operates with only 5% False Acceptance Rate (FAR) and only 6% False Rejection Rate (FRR) over the data collected from 33 users. We further evaluate the performance through comparing its performance with some existing machine learning techniques. Finally, we perform a real implementation of our proposed solution as a mobile application to conduct a rigorous user evaluation over 27 participants using three different devices in order to show how the solution works in practical situations. Outcomes of the user evaluation demonstrate as low as 0% FAR after letting intruders to mimic the actual user, which ensures extremely low probability of being breached. Moreover, we let 2 users to continuously use our application over 25 days in different states during their operation. Outcomes of this evaluation demonstrate as low as 1% FRR confirming the usability of our technique in long-term usage. more...

In the context of developed countries, a considerable amount of research studies have been done to make Human-Computer Interaction (HCI) as natural and intuitive as possible for the public computing devices. Given the proliferation of public computing devices in the developing regions, a context-aware HCI design, focusing the low-literate and novice users from these regions, has the utmost importance. Taking the hygiene issue with the usage of touch-based public computing devices into account, in-air hand gestures are considered as one of the most natural forms of non-verbal interaction in this regard. In this paper, we present Duity -- a low-cost finger-count based hand gesture recognition system -- which reliably tackles the hurdle faced by novice and low-literacy populations. Exploiting the reflective property of visible light spectrum, we develop a mechanism that can reliably count fingers and thus identify different gestures. The system is fine-tuned to such an extent that varying lighting conditions and environmental setups can not perturb the accurate identification of the gestures. Our claims are supported by a user evaluation, which takes multiple scenarios into accounts, confirming the robustness, usability, and acceptability of Duity. more...

Microscopic traffic simulators have become efficient tools to conduct different analytic studies on roads, vehicles, behavior of drivers, and critical intersections, which lead to- wards a well-planned traffic solution. Devising a realistic and sustainable traffic solution requires replication of the real traffic scenario in a simulator. For example, to simulate the traffic streams of developing and under developed countries, we need to simulate non-lane based heterogeneous traffic stream, i.e., motorized and non-motorized vehicles, road traffic behaviors such as irregular pedestrian, illegal parking, violation of laws pertaining lanes, etc. However, most of the existing traffic simulators are unable to mimic the unstructured road traffic streams of less developed countries with their diversified behaviors. Therefore, in this work, we propose a new microscopic traffic simulator to handle non-lane based heterogeneous traffic stream and on road traffic behaviors that generally occurred in the road networks of cities in less developed countries. Our simulator receives network topology, traffic routes, and traffic demand flow rates as input, visualizes the traffic flows, and provides traffic statistics. To evaluate sustainability of our proposed simulator in real-life scenarios, we calibrate the simulator using real traffic data. Our evaluation reveals 99% accuracy in terms of travel time. more...

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases. more...

With growing real-time traffic and limited spectrum resources, the communication system must adopt reliable and efficient spectrum sharing techniques for providing improved services to end users. Cooperative cognitive radio network (CCRN) offers a promising solution based on the principle of cooperative diversity where primary users offer transmission opportunities to secondary users in exchange for their relay services. However, existing CCRN models ignore the overhead of cooperation formation and the potential interference introduced by secondary users’ transmissions. This may hurt successful packet delivery, and therefore, they cannot be applied to real-time systems. In this paper, we propose an interference aware reliable communication system for real-time applications exploiting the flexibility of a cooperative cognitive network. We formulate the communication system as a Markov decision process and quantify the impact of cooperation overhead and secondary interference on successful packet delivery through its actions and reward functions. We present a distributed algorithm for use in a realistic multi-user setting where a user applies the algorithm to determine cooperation gain, select cooperation pairs, and schedule cooperative transmissions. The simulation results show that the proposed model improves packet reception rate with low cooperation overhead compared to an existing cooperation model. more...

With the advent of multiple radio interfaces on a single device, wireless mesh networks start to achieve significant improvement in network capacity, latency, and fault tolerance. The improvement is achieved through concurrent transmissions over different channels utilizing the multiple radio interfaces. However, the introduction of different channels over multiple radios on single mesh node compels to retrospect different issues such as interference, channel diversity, and channel switching from novel perspectives. Due to these novel perspectives, conventional channel assignment techniques proposed for single-radio wireless mesh networks are not generally applicable to the multi-radio cases. Consequently, we have to reconsider the different issues while making a tradeoff among all the available channel assignment options to extract the best performance from a multi-radio wireless mesh network. There are a number of research studies that propose various channel assignment techniques to extract the best performance. In this paper, we present a comprehensive survey on these studies. First, we point out various design issues pertinent to the techniques presented in the studies, and adopt the issues as the basis of our further discussion. Second, we briefly describe several important already-proposed channel assignment techniques. Third, we present a number of channel assignment metrics that are exploited by the already-proposed techniques. Then, depending on the considerations in these techniques, we categorize the techniques and present an exhaustive comparison among them. Nevertheless, we point out a number of real deployments and applications of these techniques in real scenarios. Finally, we identify several open issues for future research with their current status in the literature. more...

The most-frequently used radio in wireless mesh networks, i.e., 802.11, has short transmission range. Consequently, it mostly imposes multi-hop transmission and thus limits the utilization of radio bandwidth. Emerging radio technologies under the umbrella of 4G, having long transmission range, are capable of enhancing the bandwidth utilization through single-hop transmission at the expense of limiting spatial reuse over the network. Both of these excellences, i.e., the efficient bandwidth utilization and the spatial reuse, are required in case of a concurrent presence of two types of flows--base station oriented and random flows. Therefore, it is necessary to efficiently use both types of radios, i.e., 802.11 and 4G, in next-generation wireless mesh networks, as they are going to experience a mix of the two types of flows. We propose an architecture to guarantee the efficient usage of both the radios through their symbiotic coexistence. Extensive evaluation through analytical modeling, ns-2 simulation, and real testbed experiments reveals that our proposed architecture achieves up to approximately $$6\times $$6× network throughput, 95 % decreased end-to-end delay, and 98 % decreased number of base stations compared to other radio alternatives. more...

One of the crucial aspects in designing Network-on-Chip (NoC) is to ensure reliability, which is little explored for 3D NoCs in the literature till now. Therefore, in this paper, we investigate a novel fault-tolerant design for 3D NoCs that can go beyond existing fault-tolerant designs that are mostly applicable for 2D NoCs. In our design, we propose to divide a 3D NoC into 2×2×2 sub-networks or blocks. Then, we place four spare routers at the centers of four different planes. Here, we propose sparing of routers considering the fact that routers incur much lower cost compared to that of main processing elements such as processor. Later, we formulate separate mathematical models pertinent to our proposed design along with two benchmark designs. Subsequently, based on the models, we simulate performances of our proposed design along with the benchmark ones in terms of reliability and mean time to failure (MTTF). Simulation results demonstrate that our proposed design can enhance fault tolerance of 3D NoCs by significant margins compared to that of existing benchmark designs. more...

A common application of UAVs is to search an area for certain targets. Often UAVs miss targets in spite of sensing the target region with its sensors. Possible reasons of missing targets and their remedy is suggested in the paper. Designing a UAV deployment strategy optimizing search time along with the dependability of the search is the focus of this work. A strategy, where different UAVs in a group search at different heights, is proposed to increase dependability of the search. The strategy is implemented in real battlefield simulation software and a comparison is made between the proposed strategy and an existing one in respect of time, dependability and fuel consumption. Result shows that proposed strategy offers highly dependable result in reasonable time outperforming the existing strategy. more...

We present a study of improving the efficiency, complexity and performance of language translation process by a translator. The goal of this research is to develop an efficient translation system for any language by optimizing the memory consumption and also identifying the names as nouns efficiently. Although a number of researches can be found on natural language processing in different areas, those were performed keeping English as the only target language mostly. However, a good number of languages remain nearly unexplored in the research fields yet. This study basically focuses on Bengali Language as an example of the unexplored languages. Some noticeable studies on Bengali language are on Bangla keyboard layout design, English to Bangla translator, etc. so far. However, very few researches have been done to translate Bengali text to English till now. To develop an efficient translation system is very complex and expensive as it requires huge amount of time and resources. In all the languages, there are many words having multiple meanings and multiple forms and also some sentences having multiple grammatical structures to express the same meaning. Besides, the names of people may not be easily identified due to the vast diversity of names and also the tags (prefix/ suffix) attached to emphasize names. Therefore, it remains a great challenge to recognize a sentence of a particular language with accurate semantic analysis. However it is very important to have a generalised translation system which can compute various possible outputs in reasonable time and space. In this paper we focus on the correct interpretation of the names as nouns in a sentence and also the optimization of space requirement using semantic analysis. more...

In a heterogeneous computing cluster, cluster objectives are conflicting to each other. Selecting a right combination of machines is necessary to enhance cluster performance, and to optimize all the cluster objectives. In this paper, we perform empirical performance analyses of a real cluster with our year-long collected data, formulate a new many-objective optimization problem for clusters, and integrate a greedy approach with the existing NSGA-III algorithm to solve this problem. From our experimental results, we find our approach performs better than existing optimization approaches. more...

A majority of traffic in Internet today is media sharing, which is increasing at a rapid pace. Consequently, media industries face challenges due to the associated steep rise in bandwidth, computing, and storage requirements. To face the challenges, in this paper, we sketch a new methodology, named ‘Secure Processing-aware Media Storage’ (SPMS) through expanding cloud file sharing capabilities from only storing media files to also processing them along with encryption. Our proposed approach is to package application requirements and move data after processing them based on the package of requirements. Here, first, we propose to perform resizing of images and transcoding of videos according to several resolutions for covering diversified remote devices. Afterwards, we perform necessary media encryption-decryption. These tasks are performed using several middlewares. To validate efficacy of our proposed methodology, we perform a set of experiments with implementation of the middlewares over a real private cloud enabling file sharing. Experimental results confirm substantial performance improvement using our proposed methodology compared to that obtained using conventional one. more...

This paper is an outcome of a very new and challenging research ?eld of ?nding molecular communication network model. Identifying a molecular communication ?ow network is a very challenging task since there is no de?ned network model available. In this paper, we provide a novel hierarchical information ?ow model in insulin production process. We identify the model as a hierarchical network model. We validate our layered model with the standard network simulator and we ?nd the acceptability of this network model. We consider some network performance metrics for measuring our molecular network communication model. We also ?nd the communication pattern in our given network model in the light of those metrics. more...

Management of information of different types of household service provider is often difficult for a service receiver due to communication gap between provider and receiver. As a result, not only service receivers face an unexpected trouble but also service providers fail to get their jobs according to their skills. A generic platform providing two way communication between service provider and service receiver can eliminate the exhausting efforts of searching the suitable service provider and also ensures the intended job for the service provider. A feedback based rating system can improve the skills of any service provider and service receiver can eventually get better service. more...

Signage display, which is used to convey message or information, has evolved from conventional to digital display. Conventional signage which may be hand written or printed papers are being wiped out by digital displays used by industries because of its attractive features of efficient involvement of consumers. However, extensive use of digital signage displays contributes a notable amount of power consumption (about 1000W for a 14inch × 48inch display) of a region. In this literature, we have devised a novel approach for reducing power consumption of digital signage as well as satisfying human visibility by exploiting duty cycle. Our proposed technique is capable of relinquishing a significant amount (about 14.54% in comparison with existing display system) of power consumption occurred by digital display by keeping an eye on expected human vision. more...