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Abstract: Road surface monitoring is an important problem in providing smooth road infrastructure to the commuters. The key to road condition monitoring is to detect road potholes and bumps, which affect the driving comfort and transport safety. This paper presents a smartphone based sensing and crowdsourcing technique to detect the road surface conditions. The in-built sensors of the smartphone like accelerometer and GPS 1 have been used to observe the road conditions.

Abstract: Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Today’s fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving behavior of drivers. Understanding the driver behavior is an essential component in Intelligent Driver Assistance Systems. One of potential cause of traffic fatalities is aggressive driving behavior. However, drivers are not fully aware of their aggressive actions.

Abstract: We study a lossy source coding problem for a memoryless remote source. The source data is broadcast over an arbitrarily varying channel (AVC) controlled by an adversary. One output of the AVC is received as input at the encoder, and another output is received as side information at the decoder. The adversary is assumed to know the source data non-causally, and can employ randomized jamming strategies arbitrarily correlated to the source data. The decoder reconstructs the source data from the encoded message and the side information. We prove upper and lower bounds on the adversarial rate distortion function for the source under randomized coding. Furthermore, we present some interesting special cases of our general setup where the above bounds coincide, and thus, provide their complete rate distortion function characterization.

Abstract: We consider secure computation of randomized functions by two users, where both the users (Alice and Bob) have inputs, Alice sends a message to Bob over a rate-limited, noise-free link, and then Bob produces the output. We study this problem when privacy is required only against Bob, i.e., from the message, Bob must not learn any information about Alice’s input other than what can be inferred by his own input and output. We give a single-letter expression for the optimal rate. We also explicitly characterize securely computable randomized functions when input has full support, which leads to a much simpler expression for the optimal rate. Recently, Data (ISIT 2016) studied the other two cases (first, when privacy is required against both the users; and second, when privacy is required only against Alice) and obtained single-letter expressions for optimal rates in both the scenarios. Yassaee, Gohari, and Aref (IEEE Transactions on Information Theory 2015) studied the case when there is no privacy requirement and obtained a single-letter expression for the optimal rate, when Alice and Bob interact for arbitrary but finite number of rounds, and both of them may produce potentially different outputs.

Abstract: The essential requirement for a 5G wireless communication system is the realization of energy efficient as well as spectrally efficient modulation schemes. Double Spatial Modulation (DSM) is a recently proposed high rate Index Modulation (IM) scheme, designed for use in Multiple Input Multiple Output (MIMO) wireless systems. The aim of this scheme is to increase the spectral efficiency of conventional Spatial Modulation (SM) systems while keeping the energy efficiency intact. In this paper, the impact of imperfect channel knowledge on the performance of DSM system under Rayleigh, Rician and Nakagami-m fading channels has been quantified. Later, a modified low complexity decoder for the DSM scheme has been designed using ordered block minimum mean square error (OB-MMSE) criterion. Its performance under varied fading environments have been quantified via Monte Carlo simulations. Finally, a closed form expression for the pairwise error probability (PEP) for a DSM scheme under conditions of perfect and imperfect channel state information has been derived. This is employed to calculate the upper bound on the average bit error probability (ABEP) over aforementioned fading channels.

Abstract: In this paper, we propose two new signal constellation designs employing Gaussian and Eisenstein Integers for Enhanced Spatial Modulation (ESM). ESM is a novel technique which was propounded by Cheng et al. The advantage of ESM over other Spatial Modulation (SM) schemes lies in its ability to enhance spectral efficiency while keeping the energy efficiency intact. This is done by activating either one or two antennas judiciously depending upon the required trade-off. In ESM, information radiated from the antennas depends upon index of the active transmit antenna combination(s) and also on the set of constellation points chosen, which may include points from multiple constellations. In this paper, we propose signal constellations based on multiplicative groups of Gaussian and Eisenstein integers. The set comprising of Gaussian and Eisenstein integers serves as primary and secondary constellation points for Gaussian Enhanced Spatial Modulation (GESM) scheme.

Abstract: Majority of urban commuters prefer cabs due to easy accessibility. A big fraction of these cabs are managed by web-based cab operators, who provide the service to commuters through a mobile application. Sometimes, the drivers of these cabs swindle the customers by over-charging them for the journey. Œis happens when the cab drivers start the billing process before the commuter boards the cab and/or, when they end the billing process much later than when the commuter has deboarded the cab. Such cases can be prevented by detecting and recording the actual boarding and deboarding time of the commuter. Our proposed system handles such cases of fraud, by detecting the boarding and deboarding events of commuter in the cab. It suggests to do so by using the pressure values recorded by the barometer sensor of the smartphone of the commuter, and of a barometer device installed in the cab or using the barometer sensor of smartphone of the cab driver. Œe core of our mechanism is a comparison of the sequence of barometer values across two devices: the commuter’s and the driver’s. Since barometer sensor is passive and uses very low power, it makes overall system economical in terms of power consumption. Further the proposed system necessitates minimal amount of data to be sent to the server for processing.Œe proposed system is tested in varied environmental conditions and good average accuracy is observed in all the cases.

Abstract: Making transport network of a city smarter is one of the major tasks when it comes about making a city smart. Public transport becomes a major focus area as it facilitates a major fraction of commuters in any city on a day to day scenario. So efficiently tracking the position of a public transport for e.g. tram cars, buses can give valuable information that can further be used to regulate city traffic. The aim of this paper is to track the position of a bus at a bus stop using Bluetooth Low Energy (BLE) beacons and calculate estimated time of arrival (ETA) at the subsequent bus stop based on current location traffic congestion. Bluetooth Low energy beacons will be used for bus detection and getting an accurate estimate on traffic congestion. This proposed approach is further implemented using Raspberry pi on which various algorithms keep a track of nearby beacons and determine real time traffic congestion. Also speech notification about the bus details is generated for the commuters on the bus-stop.And initial results suggests that the proposed approach can be used with a good accuracy in different environments.

Abstract: This paper is a submission to the sub-task Acoustic Scene Clas-sification of the IEEE Audio and Acoustic Signal Processing challenge: Detection and Classification of Acoustic Scenes and Events 2017. The aim of the sub-task is to correctly detect 15 different acoustic scenes, which consist of indoor, outdoor, and vehicle categories. This work is based on log mel-filter bank features and deep learning. In this short paper, the impact of different parameters while applying a basic Deep Neural Net-work (DNN) architecture is first analyzed. The accuracy gains obtained by the different types of deep learning architectures such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) are then reported. It has been observed that the overall best scene classification accuracy was obtained with CNN.

Abstract: This paper presents a comprehensive study of the state-of-the-art acoustic signals for vehicular feature extraction and classification. Acoustic signals can be used as powerful means to analyze and monitor traffic. Various other sensors are also used for analysis, but they have certain limitations which are overcome by acoustics. The acoustic classification process includes acoustic data input, sensing unit, segmentation, feature extraction, classification, and outputs decision. Multi sensor real time environment consists of an acoustic classification system as its part for traffic monitoring and surveillance. In this paper, a comprehensive review of various acoustic features along with acoustic classifiers and the datasets used is presented. Different challenges related to acoustic vehicle recording data are also addressed.

Abstract: Traffic congestion occurs when the number of the vehicles increases more than the existing space of the road. This deleterious problem is increasing at an alarming rate in the whole world. For any effective Intelligent Transportation System, early detection of traffic congestion is very important to take corrective action. Several techniques have been developed to detect traffic congestion, most of which are infrastructure based. Even though these techniques are widely used, but they have many downsides as well. They require large capital input for installation as well as for maintenance. In this paper, we propose an efficient and cost-effective method using smartphones to determine the traffic state of the road. The acoustic data collected from commuter’s smartphone is segmented into fixed size frames. Various time and frequency based features such as (MFCC, Delta & Delta-Delta, ZCR, STE, and RMS) are extracted from each frame and used for detecting traffic state as ’busy street’ or ’quiet street’. We have compared the accuracy of two classifiers Support Vector Machines and Neural Network by using acoustic data collected from 320 different recording sessions. Experiments have shown that feature set having features MFCC, STE and RMS, results in better classification accuracy of 91.8% with Neural Network and 93% with SVM. Furthermore, various relevant factors affecting the classification accuracy are also tested like frame size, window functions, overlapping size and different combination of features. The frame size of 8192 and hamming window function proved to be more efficient than others.

Abstract: The traffic and number of vehicles on roads are increasing with an unstoppable pace, which in turn leads to the problem of traffic congestion. We propose the use of Acoustics to determine

Abstract: Diabetes Mellitus is a metabolic disorder, which makes high blood glucose level over an extended period. Approximately 1.5 to 5 million deaths were reported every year due to diabetes. In this work, a minimally invasive blood glucose measurement technique using a microstrip split-ring resonator sensor is formulated. This method is based on the shift in resonant frequency of the split-ring resonator with respect to the change in dielectric constant of the surrounding medium. The variation in dielectric constant of blood with change in glucose concentration is the fundamental reason for frequency shift. Microstrip resonator sensors are widely used for determining the dielectric permittivity of solvents. Several configurations of microstrip resonators are used for finding the dielectric constant. A Split Ring Resonator (SRR) model has been fabricated for performance at 2.45 GHz for the dielectric permittivity measurement of glucose-water solutions of various concentrations. This is extended to find glucose level in blood using the same concept. Different blood samples are studied and effective prediction of glucose is possible with this method. The dielectric permittivity is measured using Keysight E 5080 A Vector Network Analyzer.

Abstract: Resonators can be energized using different feeding mechanisms. The effect of U-shaped Microstrip feed electromagnetically coupled to an open loop resonator is presented in this paper. The proposed feeding mechanism induces a band-stop response for the resonator. The band-stop filter optimized by simulation is fabricated and tested using Keysight E 5080 A Vector Network Analyzer. The measured results shows the filter to have resonance at 2.7 GHz with 430 MHz bandwidth. A simplified equivalent circuit model for the proposed design is also presented. The results obtained by simulation, equivalent circuit and experiment are in consensus with each other. This feeding technique can be used to energize other type of resonators as well.

Abstract: A high isolation Diplexer with Pentagonal Open Loop Resonators (P-OLR) is presented in this paper. The Diplexer contains two resonators providing pass bands in the desired frequency, thus attaining significant size reduction. The P-OLR offers several design freedoms, such as the pass band frequency can be adjusted by properly tuning its geometrical parameters and its bandwidth can be controlled through changing the coupling coefficients between the T-shaped feed and the resonator. To verify the designed method a P-OLR diplexer operating at the Wi-Fi bands is successfully designed and fabricated on FR4 epoxy. The maximum insertion loss of two pass bands is 1.5 dB and the isolation between channels at the pass bands higher than 25 dB.

Abstract: Effective utilization of resonating structure in RFID tags is studied in this paper. Petal shaped semicircular resonating structure is direct coupled with the strip line. For enhanceing the characteristics, the single base resonator is modified.Useing ANSYS HFSS the resonator structure is optimized for performance analysis. Validation of the optimized resonator is done,both the measured and simulated results are found to be in compromise with each other.

Abstract: OceanNet project uses a hierarchical point-tomulti- point backhaul network using long range Wi-Fi technology to provide extended offshore connectivity for fishing vessels. This work is aimed to understand the interrelationship between the design parameters and the reason for considering specific parameter values for the OceanNet solution. The research work explores the communication systems theoretical limits on received power with respect to the variation due to transmitted power, distance, antenna type, frequency, and modulation schemes. These different models that explain the interrelationship are simulated to gain deeper understanding to the design of communication systems. This paper discusses the variation in received power and transmitted power with respect to change in distance for a given frequency, comparison of channel bandwidth with channel capacity, analysis of signal to noise ratio and its impact on different modulation schemes. We also analyse different antenna types used for the deployment and how the performance is affected due to congestion in the nodes. This paper also details the results and analysis of the simulation performed based on fishing vessel scenario.

Abstract: Antenna can be considered as an essential component for different wireless communication systems. When designing an antenna for these applications, one important factor that needs to be considered is the amount of the electromagnetic radiation absorbed by the human body which is measured as Specific Absorption Rate (SAR). The amount of energy that is radiated to the human body depends on the various parameters such as size, material properties and the structure of antenna. A solution to reduce the radiation towards human ear by providing null in the specified direction of the radiation pattern is proposed in this work. The proposed design comprises of an Open Loop Resonator (OLR) structure along with the coplanar waveguide fed loop antenna. The property of the open loop resonator leads to the reduction of electromagnetic waves towards the human body without affecting the performance of the system.

Abstract: In many developing nations such as India the majority of the country’s population lives in rural areas. The main challenge of the twenty first century is to enable connectivity in rural areas. Rural Connectivity enables residents to get access to health care services, medical facilities, education services, etc., through the internet. Use of wireless backhaul technologies has the potential to speed up the process of providing connectivity with reasonable bandwidth and optimum coverage. This paper studies all the existing wireless technologies, such as WiMAX, Long range Wi-Fi, Cognitive Radio, LTE and 2G/3G, with respect to their performance in cost, transmission range, vendor support, data rate, bandwidth requirement, latency and spectrum licensing expense. A utility function is evaluated based on these parameters and thereby, a low cost technology for internet access is proposed for providing affordable rural connectivity.

Abstract: Our research center has developed a cost-effective marine communication network architecture to provide affordable Internet access to marine fishermen. This solution uses a multi-level infrastructure network based on long range Wi-Fi and TDMA for backhaul. Directional antennas are used to increase the range. Standard Wi-Fi connectivity is provided on the boats to which the fishermen connect using their smartphones. The various angular and linear movements of the boat due to the oceanic waves will impact the alignment of the directional antenna to the base station thereby affecting the link quality. In this work, the extent of such movements is modeled theoretically, and the values are compared with the actual results obtained from field trials for sea states 4 and 5. It is also shown that the signal strength fluctuations are higher when the sea is rougher. Finally, future work planned for further analysis based on experimental modeling is also described.

Abstract: Marine fishermen are unable to economically communicate either with other boats over the ocean or with the people on the shore due to the shortcomings in the available cellular and satellite communication. Project OceanNet bridges this gap to deploy a heterogeneous wireless communication system to provide internet access to the fishermen’s smart phones in the boat helping them to reach out for help in critical situations. This work involves designing a wireless multi-hop backhaul network to extend the coverage and to improve the connectivity through an ad hoc mesh network between the access routers in different boats. Selecting the best path from the fishermen’s end devices in the heterogeneous network can increase the throughput. The design and implementation of an elimination algorithm is dicussed to spectacle the improvement of the throughput and coverage. The algorithm is based on the analysis of the quality of various types of links using Signal-to-noise ratio(SNR) and Expected Transmission Count(ETX) metrics. The performance analysis in hardware test bed prove that the packet delivery ratio is substantially improved by the proposed path selection algorithm in the network.

Abstract: Delay tolerant networks (DTN) are characterized by lack of end-to-end communications and stable infrastructures. This paper deals with DTN networks consisting of a number of heterogeneous mobile fishing vessels where some nodes, referred to as adaptive nodes, are capable of communicating through long-range Wi-Fi whereas other nodes are having simple Wi- Fi access network. The nodes form different clusters consisting of adaptive nodes and access nodes. Message routing in this heterogeneous network happens through adaptive nodes if the source and destination nodes belong to different clusters. Real data from field study reflects that mobile nodes in this network follow Gaussian-Markov mobility model and may have high intermeeting arrival time based on deployment and node density. Our proposed DTN routing protocol incorporates simple encounterbased message forwarding and achieves lower latency and high delivery probability in the range of 90–98% for most of the scenarios. The proposed protocol is verified through a realistic mobile ad-hoc wireless simulator.

Abstract: A cost-effective marine communication network infrastructure to provide internet access to fishermen as far away as 60 km from the shore has been designed and developed by our research centre. The marine communication infrastructure is based on Long Range Wi-Fi technology for backhaul with an onshore base station situated on top of a tall structure and client stations and mobile base stations on the boats. Standard Wi-Fi network is used for access so that the fishermen can connect to the network using the smart phones and tablets they own already. This paper presents the study of the impact of the rocking movements of the boat caused by the waves on the stability of the backhaul links in this network. The study is done on the basis of data collected during several field trials conducted over the Arabian Sea in Kerala. The wave motion effects for the different planes corresponding to pitch and roll have been calculated from the collected data. Different statistical parameters like mean, median, standard deviation, etc., have been computed for the data sets obtained and correlation of the computed data with parameters such as received signal strength, transmission rates and the sea states has been done. This will lead to better understanding of the extent of misalignment between the antennas on the boat and the base station due to rocking movements of the boat triggered by the waves and its effect on the deterioration of backhaul link quality.

Abstract: A novel cost-effective network architecture for providing In- ternet connectivity to marine shermen has been success- fully prototyped by our research center. A pilot deploy- ment is in progress in a coastal Indian village. This will improve the quality of life of the nancially constrained ma- rine shermen who spend 5-7 days offshore on average for a single shing trip; it will also help in their safety and se- curity. The architecture employs multiple long range Wi-Fi (LR Wi-Fi) based infrastructure networks stitched together as backhaul. The access network consists of Ethernet and Wi-Fi mesh. The shermen connect to the on board Wi-Fi access point cum router using their smart phones and are able to use all the apps and services on their smart phone. While the primary infrastructure network uses onshore base stations, the secondary infrastructure networks use boats as mobile base stations. Three eld trials were conducted over the ocean using one onshore base station and two mid-sized boats known as trawlers. The performance of both primary and secondary infrastructure networks was assessed during these eld trials. This paper describes the impressive results obtained in assessing the performance of the secondary in- frastructure network.

Abstract: Radio Frequency Identification (RFID) tag using microstrip resonator for object tracking is presented in this paper. The passive tag is realized using half wave open circuited Microstrip lines. Effective use of each resonator as 4 bit RFID tag is demonstrated. The resonators are optimized by HFSS and is validated by measurements of the realized prototypes. The tag design is completed with inclusion of an omni-directional antenna as transmitter and receiver along with the resonators. The range of the proposed tag is also measured experimentally in indoor environment.

Abstract: Fishing is a major industry that provides the livelihood for millions of families worldwide. The shing vessels are typ- ically at sea for 5-7 days at a stretch completely cut off from the land. In a low-cost oshore communication system for shermen currently being developed, a multilevel P2MP backhaul network connects clusters of boats at the shing zones. Each cluster forms a wireless mesh network with multiple potential gateways to the backhaul network. This work proposes a resilient lightweight protocol for optimized dynamic provisioning of the backhaul network based on an adaptive, demand driven algorithm. A local controller, ca- pable of running on any access router, selects the optimal subset of gateways so that the rest can be recongured as mobile base stations or switched to power saver mode. It as- signs every boat in a cluster to the most appropriate gateway maximizing the utilization of gateways. A proof-of-concept implementation has been done with a hardware test bed and the results are very encouraging.

Abstract: High proliferation of wireless devices have ushered in an unprecedented rise in the demand for wireless connectivity across the world. However, most of these devices are restricted to the ISM band for accessing network services. These ISM bands already suffer from high congestion and limited communication range. Moreover, the current policy of static spectrum usage is proving detrimental to the proper utilisation of available spectrum in the country. Use of the TeleVision White Space(TVWS) has been proposed to mitigate the current scenario of over-crowded, limited bandwidth and low radio range wireless networks. Cognitive Radio Networks (CRNs) were proposed just for this purpose. CRNs are communication networks built using cognitive radios (CR) and rely heavily on opportunistic usage of licensed bands which are otherwise severely under-utilised, without affecting the transmission of the licensed users. The current work highlights the spectral occupancy in VHF and UHF including GSM range in the North East region of India. Such a study can prove to be very useful for real deployment of CRNs for broadband access in the near future.

Abstract: In collaborative sensing, multiple secondary users (SUs) cooperate for a more accurate sensing decision to detect spectrum holes in cognitive radio networks (CRNs). This technique however can be adversely aected by malicious users (MUs) who route falsied spectrum sensing data to the fusion center (FC). This attack is known as the Spectrum Sensing Data Falsication (SSDF) attack. The task of the FC is to aggregate local sensing reports and is thereby responsible for making the nal sensing decision. In this paper we propose a detection and isolation scheme based on local outlier factor (LOF) to detect and reduce the unfavorable eects of SSDF attack. The key feature of this scheme is that for each SU a metric is calculated, which is called the local outlier factor (LOF). Based on the LOF, a decision is made about whether an SU is an attacker or not. We support the validity of the proposed scheme through extensive simulation results.

Abstract: Cognitive Radio Network (CRN) is an emerging technology that holds promises for solving the wireless spectrum scarcity problem by allowing secondary users (SU), also called unlicensed users, to coexist with primary user (PU), also called licensed users, without causing any interference to the PU’s communication. However, similar to any networking technology, CRNs are susceptible to many attacks, one of which is the jamming attack. A jamming attack can be referred to as an intentional interference attack on wireless channels with an intent to either interfere with the transmissions of SUs or to gain access to the channel for selfish utilisation of the channel. This paper presents an evolutionary game theoretic approach to mitigate such a jamming attack. The proposed evolutionary game helps normal secondary users gain fair utilization of channels even though a malicious SU (jammer) could be present in those channels. Results obtained from this study establishes the fact that the proposed algorithm works.

Abstract: An accurate detection of spectrum opportunities is a key factor in governing the efficient spectrum usage in a cognitive radio (CR) system. Energy detection based spectrum sensing has been widely used due to its ease of implementation with lower computational complexity; however, its robustness and performance are highly affected by the noise uncertainty. In the present work, a real time hardware implementable spectrum sensor has been realized and tested for an unsupervised learning based K-means clustering approach, to detect the white spaces in the spectrum. A CR network with one primary transmitter and two secondary nodes has been considered for which the data is collected over an FM band using a software defined radio peripheral, i.e. USRP B210. The whole system has been implemented with the help of MATLAB Simulink & Xilinx System Generator. The decision accuracy of the proposed algorithm is verified at different values of the signal-to-noise ratios (SNRs) and found that the classification based sensing is quite accurate even at low SNR region.

Abstract: An accurate detection of unused part of the spectrum is a key requirement in cognitive radio system. In this context, it is necessary to set an appropriate threshold between signal and noise. As noise power is instantaneous in nature, a periodic estimation of noise power is required. In this paper, we have proposed a combination methodology, that uses Rank order filtering in tandem with a gradient based approach. The performance of the same has been compared with two other wellknown existing techniques for noise floor estimation in nonfading as well as fading scenarios, where it is found that the proposed technique outperforms the other two in terms of minimum mean square error in the estimation of spectrum occupancy.

Abstract: In this paper, we analyze the performance of a twohop network in which a multiantenna source communicates with a single-antenna destination through a decode-and-forward singleantenna energy harvesting (EH) relay.We assume a novel practical system framework in which the EH relay has the option of augmenting the wireless energy harvested by drawing energy from a supplementary battery, thereby improving link performance. Assuming use of the time-switching relaying (TSR) EH protocol, the outage and throughput performance are analyzed. We optimize the fraction of time devoted to EH (the TSR parameter) so as to maximize throughput for fixed supplementary battery energy. We also optimize the TSR parameter to minimize supplementary battery energy consumption for target throughput performance. We further show that exploiting second-hop channel knowledge at the relay can dramatically reduce the energy drawn from the supplementary battery. These insights are of fundamental importance to system designers. Simulation results validate the derived analytical expressions and corroborate our findings.

Abstract: We consider a two-hop underlay cognitive relay network with an energy harvesting (EH) relay. A simple improved time-switching protocol is proposed in which energy is transmitted in subslots until the relay node is sufficiently charged. The number of charging energy subslots required is fed back to the source by the relay node after the first energy subslot. We demonstrate that the proposed EH transmission scheme can yield better secondary throughput performance as compared to that achieved by an optimal EH transmission protocol using a fixed charging duration, while at the same time eliminating the need for feedback of channel estimates. We first analyze throughput when the source does not possess first-hop channel knowledge, but the relay possesses second hop channel knowledge. We then analyze throughput performance in the important case when the second hop channel estimate is in error, or the practical case when this channel estimate is not available. We show that when the second hop channel estimate is poor, it might be beneficial to use knowledge of only its statistical value. We show that the proposed scheme is energy efficient in terms of energy required per transmitted bit. Computer simulation results validate the derived expressions.

Abstract: in this paper, we study the performance of a cluster-based multi-hop underlay cognitive radio network using an ad-hoc routing protocol (called max-link-selection protocol) with decode-and-forward relays over Rayleigh fading channels. Both peak-power and peak-interference constraints are considered in the system. At first, the end-to-end exact and asymptotic outage probabilities of the considered system are derived. By using the derived asymptotic outage, an approximate expression for the optimum number of hops that minimize the outage probability is derived for fixed-rate and fixed-distance between the source and destination nodes. Later, the exact and asymptotic expressions are derived for end-to-end symbol-error-rate (SER) and ergodic-rate of the considered system. Numerical results are presented to validate the derived analytical expressions. It is shown by analysis that with increasing number of relaying hops between the source and destination, the SER performance improves but the ergodic-rate declines

Abstract: In this paper, we investigate the performance of a three-node dual-hop cognitive radio network (CRN) with a halfduplex (HD) decode-and-forward (DF) buffer-aided relay. We derive expressions for the average rate and symbol error rate (SER) performance of an adaptive link selection based channelaware buffer-aided relay (CABR) scheme that imposes peakpower and peak-interference constraints on the secondary nodes, and compare them with those of conventional non-buffer-aided relay (CNBR) and conventional buffer-aided relay (CBR) schemes for a delay-tolerant system. For a finite-sized buffer, we analyze the performance of a modified threshold-based scheme for fixedrate transmission. We analyze the trade-offs between the delay, throughput and SER. Computer simulation results are presented to demonstrate accuracy of the derived expressions. Keywords - Underlay Cognitive Network, Adaptive Link Selection, Buffer-Aided Relay.

Abstract: In this paper, we consider a novel energy-harvesting (EH) cognitive radio framework in which the primary source beamforms excess available energy to an EH secondary transmitter (ST) so as to enable it to share the spectrum. In the first phase of transmission, the multi-antenna primary source optimally beamforms symbols to its decode and forward relay while beamforming all excess energy to a selected EH ST. In the second phase while the relay transmits symbols to a selected primary receiver, the EH ST transmits symbols using underlay principles to its secondary receiver. Assuming peak interference constraints at the STs, we derive approximate expressions for outage probability and ergodic rate of the secondary link with the optimal beamformer. Using asymptotic expressions for outage and ergodic rate, we derive useful insights into performance of the system.We show that diversity equal to the number of secondary transmitters is achieved by the considered system. Computer simulations confirm accuracy of the derived expressions.

Abstract: In this letter, we derive an expression for the secrecy outage probability of a multiuser underlay downlink in which a selected idle secondary user serves as a friendly jammer to enhance physical-layer secrecy of secondary downlink communication in the presence of a passive eavesdropper. Secondary nodes constrain their transmit powers to ensure that the interference caused to the primary network is below an interference temperature limit IP . We show that careful apportioning of IP between the secondary source and the selected jammer (which transmit simultaneously), and judicious choice of peak powers, is the key to improving secrecy performance. Computer simulation results demonstrate accuracy of the derived expressions.

Abstract: This letter investigates the performance of an analog network coding based underlay cognitive two-way energy harvesting relay network. The amplify-and-forward relay harvests energy using the power-splitting relaying (PSR) protocol. Using approximate expressions for the terminal outages, throughput, and ergodic sum-rate, we derive insights into performance. Specifically, we derive closed-form expressions for the throughput maximizing interference temperature apportioning parameter (ITAP) and the PSR parameter, and show that they can be optimized separately. We also derive a closed-form expression for the ergodic sum-rate maximizing PSR parameter, and show that an ITAP of half maximizes ergodic sum-rate. Simulations demonstrate the accuracy of the derived expressions.

Abstract: Predicting the onset of Autism from its early symptoms is a difficult proposition. In this paper we have proposed a machine learning algorithm for assisting the doctors in the diagnosis of the disease in affected individuals. The Machine learning Association Rule (AR), along with the minimum Redundancy-Maximum- Relevance (mRMR) method, is used to pull the symptoms which need to be further tested for the prediction process, from a prior database. We have used the Mutual Information Difference (MID) method for selecting the additional symptoms which can strengthen the symptom set, through the information of previously cases. The proposed system needs one or two primary symptoms as preliminary inputs (measured through sensors or through mobile applications) and then it will automatically pull the appropriate associated symptoms from the domain knowledge and the previous patterns of a patient’s database. The symptoms are further tested and confirmed. We have tested this process by collecting a dataset of 500 autism disease patients. Our experimental results show that the average accuracy of correctly predicting the diagnoses for the various types of autism, by using our technique, is around 83 %.

Abstract: —Availability of a particular doctor for a patient every time in emergency arises, may not be possible in case of remote monitoring of patients with chronic illnesses or for elderly people. It has been observed often that such patients display or experience situations that have occurred are identical to what has occurred to them in the past. Under such circumstances, the diagnoses and medications are mostly similar. In the case of unreachability of the original doctor, if the new doctor is provided with the past similar pattern with a comparison study between different symptoms parameters at that time, prior identical situations, medications and drug allergies, then it becomes easier and faster for a new doctor to arrive at a more accurate diagnoses for the patient. In this paper we have proposed a framework for remote health monitoring of patients that will continuously store and monitor patient data and mark identical or similar patterns in the readings and establish relationship between the different symptoms parameters , on a remote server, which can be sent to the new treating doctor in case the need arises.

Abstract: Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSNs), where malicious secondary users (SU) try to emulate primary users (PUs) to maximize their own spectrum usage or obstruct other SUs from accessing the spectrum. In this paper we have used a classification algorithm to monitor and classify the different types of PU and SU signal profiles. Detection of an abnormal signal profile of any SU helps us to identify a PUE attacker in the network. Although the learning phase is computation-intensive, the signal classification phase of our application does not requires extensive computational capabilities and memory and is therefore suitable for use in resource constrained cognitive sensor nodes. We have implemented and tested our application on a CWSN simulator. Since it is not based on the positional information of nodes, so it is also suitable for mobile cognitive sensor networks.

Abstract: The key to effectively controlling an epidemic situation and reducing the number of casualties, is to devise a surveillance system which is capable of generating real time alerts for apprising the concerned health authorities in a timely manner. Since rural areas also suffer from poor connectivity both in terms of transport and network, and lesser access to good medical facilities, deploying such a system while keeping the rural population in mind becomes even more important. Although there are at present a number of offline systems for the prediction of epidemics, they are not sufficient to handle the dynamics of such a situation in a real-time manner. There are also some works on the control and containment of an epidemic situation. However, the aim of this paper is predominantly epidemic detection and alert for an epidemic in an area. We have devised a framework to raise alerts for an epidemic scenario in any region where the number of villagers reported with symptoms of a particular disease crosses a threshold value. At any point it will also give the current number of infected, semi infected and not infected patients based on the symptoms.

Abstract: Patients in rural India cannot able to enquire about their health using appropriate disease related keywords, submitted as query. Lack of domain knowl‐ edge prevents the patients to refine the query using well-known feedback mech‐ anism. Moreover, due to scarcity of doctors in rural India, the health assistants who run the health centers do not have enough knowledge to treat the patients based on the imprecise query. In the paper, we propose an autonomous provisional disease diagnosis system by classifying the query, which has been expanded using semantic of the domain knowledge. First, we apply spatial distribution based nearest neighbor spacing distribution (NNSD) on the disease related medical document corpus (MDC) to find the relevant terms, mostly symptoms with respect to different diseases. We frame a symptom vocabulary (SV) with the unique terms present in different diseases, known apriori. Each query is expanded as bag of symptoms (BoS) using 5-gram collocation model and log likelihood ratio (LLR) to measure the association between the query and the terms in the MDC. The terms in the BoS may not exactly match with the symptoms in the SV but have contextual similarity. We propose a novel approach to know which symptoms in the SV are nearest in context to the corresponding terms in the BoS. The feature vector is obtained by encoding the SV with respect to (w.r.t.) each BoS, which is sparse in nature. We apply sparse representation based classifier (SRC) to classify the query into a particular disease. Proposed nearest neighbor spacing distribution based sparse representation classifier (NNSD-SRC) shows promising perform‐ ance considering MDC dataset and we validate the results with the doctors showing negligible error.

Abstract: Scarcity of doctors in rural areas of developing countries is a major problem and has serious impact in health sector of villages. Health kiosks driven by the health assistants in different remote places are the backbone of rural healthcare services. However, due to limited knowledge and experience of the health assistants, diagnosis is often ambiguous. Therefore, there is an increasing demand to develop a knowledge based decision-making system to treat the rural patients at primary level. In this paper, a graph based clinical decision support system (CDSS) has been proposed to facilitate the health assistants for provisional disease diagnosis of the patients. The graph-based knowledge base is developed by integrating the medical knowledge represented of different ontologies. We apply the modified depth first search algorithm and topological sort algorithm for achieving minimum cost in graph traversal for differential diagnosis of the diseases. Diagnosis may be performed in two modes - online and offline, in the presence of the patient and using patient records respectively.

Abstract: Solutions in the domain of mobile computing face a typical problem of fragmentation due to permissible customization to android framework. Fragmentation is a problem at the device side where the expected behavior of an application is not exhibited identically over all other devices. Presently, cross-layer design is becoming essential aspect of the app development, targeted to deliver an energy-efficient and productive solution. Fragmentation is posing an enormous challenge for development community, and solutions are designed per case basis. There are solutions developed in the domain of graphics and Web access, but as far as the fragmentation with core framework is considered, the solution is still missing. In this paper, we are proposing an intermediate background app residing between the application and the core framework. The proposed app TelePatch generates a map between the intended calls with actually supported calls. The map so obtained can be used by the interested application to obtain the services from the core framework. In our case, we have deployed TelePatch with NeSen, used for capturing the network-state parameters using telephony API of the Android.

Abstract: Gait analysis refers to identification of a person from the systemic study of the motions of his/her different body parts at the time of walking. It is one of the behavioural biometric approaches to human identification. Like other behavioural approaches, gait also suffers from low-repeatability leading to poor recognition accuracy. Therefore, multimodal systems are required for better recognition accuracy. With the advent of multimodal biometric systems there is a need for low-cost methods for individual biometric modalities so that the overall complexity of the system does not overshoot the real-time requirements. In view of this, a partial-silhouette-based approach for gait recognition is reported in this article. This approach is translation, rotation and scale invariant and is low-cost in terms of computational complexity. Experimental results and comparative performance analysis on benchmark dataset reveal the potential of the partial-silhouette-based approach.

Abstract: In this paper, we deal with the problem of supply forecasting in the context of an application based taxi hailing service. We first propose a method to optimally partition the city space using a Voronoi tessellation. The generating points of the Voronoi regions are obtained as demand density cluster centers, from the taxi demand dataset. We also identify the optimal temporal resolution to use for forecasting supply in these Voronoi regions. We use a linear time-series based algorithm to forecast supply in each Voronoi region. Using this methodology for the city of Bengaluru, India, we obtained a supply forecast accuracy of about 90 percent for the heavily used Voronoi regions. This represents a substantial improvement in the forecast accuracy compared to similar time-series based approaches, employed over rectangular geohashes

Abstract: Reaction delays are important in determining the qualitative dynamical properties of a platoon of vehicles traveling on a straight road. In this paper, we investigate the impact of delayed feedback on the dynamics of the Modified Optimal Velocity Model (MOVM). Specifically, we analyze the MOVM in three regimes – no delay, small delay and arbitrary delay. In the absence of reaction delays, we show that the MOVM is locally stable. For small delays, we then derive a sufficient condition for the MOVM to be locally stable. Next, for an arbitrary delay, we derive the necessary and sufficient condition for the local stability of the MOVM. We show that the traffic flow transits from the locally stable to the locally unstable regime via a Hopf bifurcation. We also derive the necessary and sufficient condition for non-oscillatory convergence and characterize the rate of convergence of the MOVM. These conditions help ensure smooth traffic flow, good ride quality and quick equilibration to the uniform flow. Further, since a Hopf bifurcation results in the emergence of limit cycles, we provide an analytical framework to characterize the type of the Hopf bifurcation and the asymptotic orbital stability of the resulting non-linear oscillations. Finally, we corroborate our analyses using stability charts, bifurcation diagrams, numerical computations and simulations conducted using MATLAB

Abstract: As per WHO recent estimations, the four major potentially fatal respiratory diseases (Asthma, Pneumonia, Tuberculosis and Chronic Obstructive Pulmonary Disease-COPD will account for about one in five deaths worldwide. Respiratory diseases are therefore likely to remain a major burden on society for decades to come. Both the prevention and treatment of lung diseases will need to be improved if their impact on longevity and quality of life of individuals, and their economic burden on society, are to be reduced worldwide. Early detection and effective diagnosis is the only rescue to lessen respiratory diseases fatality. Soft computing approaches are gaining importance in medical disease diagnosis because of their classification performance. In this paper we have developed the expert system which is based on artificial neural network that models human abilities in analyzing and diagnosing respiratory diseases like Asthma, Pneumonia, COPD and Tuberculosis. The objective of developing the system is to diagnosing the potential disease that one may suffer is done by providing relevant inputs through a consultation in which the patient has to answer the set of questions related to signs and symptoms. Index Terms: Disease diagnosing, respiratory diseases, expert system, inference technique.

Abstract: These days most of the people especially children are attacked by pulmonary diseases such as asthma and chronic bronchitis throughout the world. Early identification of these diseases is necessary and regular monitoring is compulsory to reduce severity of these diseases. Respiratory sound analysis plays a major role in early diagnosis. Analysis of respiratory sounds helps in the detection of these diseases, as respiratory signal carries information of the lungs which is used to detect the adventitious lung sounds. Wheeze is one of the most common symptoms for these diseases like asthma and chronic bronchitis. This paper presents an approach for lung sound classification as normal, abnormal sounds. The abnormal sounds are of types wheeze, crackles, rhonchi etc. Our primary focus is on wheeze sounds, so abnormal sounds are classified as wheeze, nonwheeze. The proposed system consists of techniques such as feature extraction and classification algorithms. Feature extraction methods commonly used here is MFCC. The MFCC’s of different sounds is classified using K-means as normal and abnormal lung sounds. The abnormal sounds features are classified using the Gaussian mixture model algorithm as wheeze and nonwheeze. We collected real time data consisting of different lung sounds under the supervision of pulmonologist. The code is developed in MATLAB environment and performs the wheeze classification satisfactorily.

Abstract: There are several resource-poor communities in the world that lack access to respiratory diseases, due to insufficiency in medical expertise and less availability of medical diagnostic devices. Respiratory diseases like tuberculosis, pneumonia and asthma are becoming major problem throughout the world. Analysis of cough sound helps in detection of respiratory diseases because cough is one of the most common symptoms among all respiratory diseases. These diseases are differentiated on basis of frequency of cough and cough pattern. The identification of wet and dry cough is an important measurement to diagnose the respiratory diseases like tuberculosis, asthma and pneumonia. Though there are existing systems in analyzing cough signal, still there is a need in developing tool which analyses cough signal and capable of detecting the disease at earlier stages as well as monitoring the recovery of patients suffering either tuberculosis or asthma. In this paper we developed a method for automatic recognition of cough from continuous speech and differentiate cough as wet or dry cough. This method uses the (DSP) Digital Signal Processing techniques to extract the sound events by using event extraction technique and classify them as cough and non cough by using K-means algorithm. The extracted coughs are classified into dry and wet by using (GMM) Gaussian Mixture Model. The software tool analysis is based on a machine learning algorithm that uses cough sound to diagnose the respiratory condition and this can be easily integrated with an expert system which provides respiratory digital health services which provide low-cost diagnostics to base populations and to connect patients with the physicians.

Abstract: Goal models play an important role by providing a hierarchic representation of stakeholder intent, and by providing a representation of lower-level subgoals that must be achieved to enable the achievement of higher-level goals. A goal model can be viewed as a composition of a number of goal refinement patterns that relate parent goals to subgoals. In this paper, we offer a means for mining these patterns from enterprise event logs and a technique to leverage vector representations of words and phrases to compose these patterns to obtain complete goal models. The resulting machinery can be quiote powerful in its ability to mine know-how or constitutive norms. We offer an empirical evaluation using both real-life and synthetic datasets.

Abstract: In this paper, we propose a framework toward formal representation and validation of goal compliance for a business process model. All the tasks, postconditions, constraints, and goals are captured using first-order logic (FOL). We have used theorem prover (Prover9) for goal entailment. An experimental validation for goal compliance is presented considering a use case on health care domain. We start with an exhaustive solution space of all possible business process models for all possible activities on a particular domain and derive a reduced solution space of goal complied process models.

Abstract: Remote and eHealthcare Systems are designed to provide healthcare solutions catering to wide variety of requirements ranging from highly personalized to domain-specific systems. Often, a smartphone is used as an aid to port data from embedded or external sensors to remote repository. A majority of smartphones are equipped with multiple network interfaces including provisions for dual subscriber identity modules (SIMs) and a variant of Android as the operating system. Android being an open source system allows customization by the vendor or chipset manufacturer. This raises a serious concern in terms of fragmentation—a form of portability issue with application deployment. For example, App developed on API 16 from MediaTek behaves or crashes over a phone of API 16 from QualComm. We have developed a mobile App called “NeSen” to assess the parameters of all prevalent networks in an area. NeSen uses only the standardized telephony framework and is tried over various smartphones from vendors including Samsung, HTC, LG, iBall, Lava, Micromax, Karbonn, Xiaomi, and Gionee having chipset from MediaTek, QualComm, SpreadTrum, and BroadComm. In this paper, using NeSen, we have conducted first ever evaluation of fragmentation in Android’s basic framework. During experimental trails, several issues concerning device fragmentation are noted.

Abstract: Traffic engineering governs the operational performance of a network and its optimization. Splitting the network traffic using multipath routing is one of the standard techniques of traffic engineering. Multipath routing maximizes network resource utilization and throughput by giving nodes a choice of next hops for the same destination along with minimizing the delay. On the other hand, Opportunistic routing minimizes operational cost and the burden of redundant route maintenance by using a constrained redundancy in route selection. POMSec: Pseudo Opportunistic, Multipath Secure routing is one such algorithm that combines the advantages of both the routing methods and additionally implements an underlying trust model to secure the communication in Smart Grid.

Abstract: Today, web applications are often considered as real-life reflections of enterprises. Patrons do invest large finances. Besides, lot of time and effort are put in towards development and maintenance of the web applications. The effectiveness of such web applications is an indicator for both further investment and refinement of existing content, services and human resources. In this work, a new approach is proposed to quantify the effectiveness of knowledge driven web application. This is named as Unique Track Measure Orientation (UTMO). The proposed UTMO computes the effectiveness from navigation of different types of information in accordance with the business value. The business value could be anything-the brand value, turnover, or cumulative profit.

Abstract: In this paper, we have proposed a methodology towards improved business process model redesign based on QoS. We have extended an existing framework that generates an exhaustive space of process models from a set of capability library. The solution space is pruned based on goal and constraints considered thereafter. An algebraic framework is deployed that permits integrated multi-dimensional assessments of QoS factors for choosing path from the reduced space towards derivation of an optimal business process model by comparing the QoS values on both quantitative and qualitative scales. The proposed methodology ensures that while deriving a solution, no possible superior business process model is left out. Further, the designs that do not satisfy the given constraints are eliminated. Eventually, the extended and improved framework provides a comprehensive, both syntactically and semantically correct, consistent and improved business process that adheres to the target business goals and constraints specific to a business house. A use case is used to describe our methodology.

Abstract: n rural India providing primary health care services to the people is a real challenge due to shortage of trained manpower. In the paper an autonomous fuzzy based health assistance system has been proposed which analyzes the basic health attributes of the persons (blood pressure, pulse rate) in order to infer whether a person is in good health or needs treatment. Here measurement error and heterogeneity in the health data set has been handled using fuzzy variables and represented as symptoms. A parameter severity_factor has been designed to measure health condition of the patient based on the combination of the symptoms, demonstrating deviation from the normal values. Another parameter disease_factor is proposed to assess chance of disease of the person based on symptoms other than blood pressure and pulse rate. A person is categorized as fit or needs treatment depending on the two factors where the separation margin is determined experimentally. The outcome is interpretable and 96.21% accuracy has been obtained using 10 fold cross validation technique.

Abstract: —A remote health infrastructure is proposed based on emerging technologies including e-health sensors and cloud. The challenge is to collect accurate health data for the use of remote diagnosis by the doctors in spite of the presence of uncertainty due to physical sensors (PS). Virtual sensors (VS) provide a layer of abstraction over the PS layer. The ”possibly erroneous” data from PSs can be filtered by adding a level of intelligence to VS. In this paper, we introduce algorithms for uncertainty reduction by virtual sensing techniques in remote health care, by approximating errors using physicians perception and fuzzy system modeling (FSM).

Abstract: In the current scenario, it is possible to provide remote healthcare facilities to people living in rural and remote areas using mobile, health sensor and cloud technologies. However, in order to support a remote health framework and to store and access data generated through this system, it is necessary to develop a standard ontology. Till date there is no standard format to hold health data generated from a remote health framework which uses sensors to measure the clinical parameters and store the data in the cloud. In this paper we propose an ontology for health data for a remote health framework, particularly focusing on primary healthcare delivery. This ontology handles health data and care for common causes of illness and death in child upto ve years in developing countries. However, it can be extended to support primary healthcare in general.In the current scenario, it is possible to provide remote healthcare facilities to people living in rural and remote areas using mobile, health sensor and cloud technologies. However, in order to support a remote health framework and to store and access data generated through this system, it is necessary to develop a standard ontology. Till date there is no standard format to hold health data generated from a remote health framework which uses sensors to measure the clinical parameters and store the data in the cloud. In this paper we propose an ontology for health data for a remote health framework, particularly focusing on primary healthcare delivery. This ontology handles health data and care for common causes of illness and death in child upto ve years in developing countries. However, it can be extended to support primary healthcare in general.In the current scenario, it is possible to provide remote healthcare facilities to people living in rural and remote areas using mobile, health sensor and cloud technologies. However, in order to support a remote health framework and to store and access data generated through this system, it is necessary to develop a standard ontology. Till date there is no standard format to hold health data generated from a remote health framework which uses sensors to measure the clinical parameters and store the data in the cloud. In this paper we propose an ontology for health data for a remote health framework, particularly focusing on primary healthcare delivery. This ontology handles health data and care for common causes of illness and death in child upto ve years in developing countries. However, it can be extended to support primary healthcare in general.

Abstract: Solutions in the domain of mobile computing face a typical problem of fragmentation due to permissible customization to android framework. Fragmentation is a problem at the device side where the expected behavior of an application is not exhibited identically over all other devices. Presently, cross-layer design is becoming essential aspect of the app development, targeted to deliver an energy-efficient and productive solution. Fragmentation is posing an enormous challenge for development community, and solutions are designed per case basis. There are solutions developed in the domain of graphics and Web access, but as far as the fragmentation with core framework is considered, the solution is still missing. In this paper, we are proposing an intermediate background app residing between the application and the core framework. The proposed app TelePatch generates a map between the intended calls with actually supported calls. The map so obtained can be used by the interested application to obtain the services from the core framework. In our case, we have deployed TelePatch with NeSen, used for capturing the network-state parameters using telephony API of the Android.

Abstract: Most NoSQL databases have been devised independently from each other with specific application requirements. This has resulted in developing separate own data model and query language for each NoSQL database. The lack of standards in data models and query languages make applications and data less portable using these databases. Further, absence of formal semantics in query languages inhibits a precise understanding of query over NoSQL databases. To handle these issues, in this paper, an ontology driven query language for NoSQL databases is proposed. The proposed query language provides an efficient and common abstraction over the operational aspects on various kinds of NoSQL databases. The language includes formal common syntax and semantics of distinct query operators of NoSQL databases and those are represented in Description Logic. Further, usefulness of proposed query operators are proved using a suitable case study.

Abstract: Effective modelling has helped in explain, formalize and understand Service Oriented Architecture (SOA) that is a complex architecture style inherently. Yet, a serious gap is still exist in modelling inter dependency between structural and behavioural characteristics of SOA. Beside, lack of precise semantics and formalization in modelling of SOA has made serious challenges in checking consistency over SOA models. In order to address these challenges, in this paper, an ontology driven meta-model has been proposed for SOA. The proposed metamodel is conformed towards an ontology driven meta-meta model called, Generalized Ontology Modelling (GOM). It can be further restricted towards distinct models of SOA based applications. The proposed meta-model is implemented using ontology editorial tool Protégé and illustrated using suitable case study.

Abstract: Heterogeneous stream formats, related contexts, vocabularies and schema structures are key difficulties to facilitate sharing and extracting knowledge from stream databases. To resolve these heterogeneities, the key challenge is how to provide common semantic representation for context-dependent data stream formats along with streaming databases. To address such issues, this paper proposes an ontology driven formal semantics of context-dependent data streams together with a universal conceptualization of streaming databases. The novelty of this work is to handle heterogeneity, large volume and availability of streaming data, such as web content, commercial broadcasting data etc. It also facilitates to recognize evolving information from semantic representation of data streams at conceptual modelling level. Besides, the proposed conceptual model is flexible to represent finite partition of stream and thus help in data stream storing and further querying. The conceptualization is implemented using an ontology editorial tool Protégé for the initial validation of proposed set of formal semantics. Several crucial properties of the proposed conceptualization are specified in order to exhibit the benefits of the proposed work. The expressiveness of proposed model is illustrated using a suitable case study.

Abstract: The traditional approach to business process modelling frequently results in large models that are difficult to change and maintain. In cloud-based environment, business dynamics are mandating that business processes normally be increasingly responsive to changes. This demands business process should be highly modular, scalable and flexible for cloud-based applications. Further, in cloud-based business environments, besides describing new capabilities, process models should also define how those capabilities can be integrated with the existing systems. In this paper, a hierarchical graph-based specification called business component graph for SaaS (BCGS) has been proposed to address those issues. The proposed BCGS, formally, realises the business components for software as a service (SaaS)-based applications. BCGS represents the complex business logic design as a set of business components and their inter-relationships. Here, business component is defined as methodical integration of business processes and business rules. This proposed integration approach facilitates high scalability and reusability of constituent elements of business components and ensures the consistency between processes and business rules. Moreover, this paper also includes the service orientation of the proposed concepts in SaaS framework. A detailed case study of BCGS also has been illustrated to show the expressiveness of the proposed concepts.

Abstract: In Service Oriented Systems (SOS), implementation of business processes is accomplished through services in distributed, loosely coupled manner based on business process requirements of the users. Consequently, importance of business process requirements analysis for development of SOS is strongly highlighted in both academia and industry. Usually, traditional requirement engineering is competent enough to specify and analysis business requirements for development of software systems efficiently. However, Service Oriented Requirement Engineering (SORE) emerging for SOS development is differ from traditional requirement engineering due to complex nature of services. Yet, a serious gap is still exist between early and detailed specification of business process requirements in SORE and further mapping towards design of SOS from set of business processes. To address this issue, in this paper, a requirements analysis framework is proposed for development of SOS systems. The contribution of the proposed work is formal representation of business process requirements for SOS based on business scenario and Cause-Effect-Dependency (CED) graph in dimensions of six aspects of services - What, Why, How, Who, When and Where (5W1H). Both early and detailed level requirements analysis in the context of SORE is facilitated by the proposed approach. Beside, traceability of proposed approach towards design of business processes for development of SOS is also exhibited in this paper. Moreover, the practical utility of the proposed approach is demonstrated using a suitable case study.

Abstract: In this paper, we study the problem of devising a signal control policy for road transportation networks that is fair and provides high throughput. In particular, we propose and study a novel Queue-Delay Backpressure algorithm with variable cycle length, that takes into account both the queue lengths and the head-of-line delay at a junction. Using a variety of simulations, we show that the proposed algorithm achieves a middle ground between maximizing throughput and minimizing the maximum delays incurred. We then optimize cycle lengths to achieve minimum weighted sum of delays. Finally, we also study the effect of explicitly considering start-up losses in headways while optimizing the cycle lengths, and conclude that it is beneficial to consider these effects while designing signal control policies.

Abstract: In post disaster situation, the existing network infrastructure might be partly or fully damaged. In that case, a very popular online social network like twitter can be an effective tool, where people can share their views and knowledge about what is actually happening in the affected areas. It is a very challenging task to analyze the situation during the golden hours of any large scale disaster due to the absence of any renowned news media. All the tweets posted related to disaster are not genuine. Hence, some filtration must be performed to discard rumor tweets. After eliminating rumors, it has been observed that the volume of genuine tweets obtained is also very large. Thus, it is non-trivial for relief or rescue teams to analyze that large number of tweets and to take any decision regarding the relief and rescue as manual processing of those large numbers of tweets take significant amount of time. It is necessary to devise a summarization technique for efficient processing and analysis of genuine information at any point of time. In this work, an FOPL based summarization technique has been adopted to summarize the genuine tweets. From results it has been analyzed that the proposed technique has achieved better ROUGH-1 variant score compared to some other existing popular baseline techniques. The generated summary achieves an average precision, recall and Fmeasure score of 0.79, 0.39 and 0.55 respectively. Keywords—Tweet; FOPL; Summarization; Genuine; Information; Twitter

Abstract: Getting the right information at the right time and place is the key for efficient disaster management. Various mobile and web applications are now being used for collecting situational information in digital form, assessing damage, coordinating relief operations and offering different location based services to the affected communities during disaster management. This article provides a thorough investigation on popular web-based and mobile applications currently being used in different countries. Subsequently, the taxonomy of essential services needed for systematic and coordinated disaster management is formulated based on literature review and the authors’ interaction with different stakeholders. An outline of a collaborative disaster management service framework is then proposed with the facility of interaction for the stakeholders through their mobile phones to avail the services in different phases of a disaster. A basic version of this framework is implemented to evaluate its effectiveness as a provider of significant actionable information to offer responsive services

Abstract: Objective: The objective of this study is to explore a log of WhatsApp messages exchanged among members of a health-care group "Doctors For You" (DFY), while providing medical relief in the aftermath of the Nepal earthquake in April 2015. The motivation is to identify the medical resource requirements during the disaster, in order to help the government agencies and other responding organizations to be better prepared to address the potential medical requirements during any upcoming disaster. Methods: A large set of WhatsApp messages exchanged among DFY members during the Nepal earthquake was collected and analyzed to identify the medical resource requirements during different phases of the relief operations. Result: The study reveals detailed phase-wise requirement of various types of medical resources, including medicines, medical equipment, and medical personnel. The data also reflects some of the problems that were faced by the medical relief workers in the earthquake-affected region. Conclusions: The insights from this study might help not only the Nepal government, but also authorities in other earthquake-prone regions of the world, to better prepare for similar disasters in future. Moreover, real-time analysis of such online data during a disaster would aid decision makers to formulate resource-mapping strategies dynamically.

Abstract: Opportunistic or Delay-tolerant networks (DTNs) may be used as a viable option for exchanging situational information during post-disaster communication in the absence of existing communication infrastructure. As opposed to traditional communication networks, situational information dissemination in DTNs depends on the degree of intermittent connectivity among the mobile nodes. Higher degree of connectivity implies smaller delay to disseminate situational information which results in better knowledge sharing. The degree of intermittent connectivity is influenced by mobility pattern of the nodes which is heterogeneous in nature. In this work, we attempt to formulate an empirical relationship between the degree of intermittent connectivity and extent of situational knowledge dissemination over DTNs in order to quantify the impact of the node mobility on the inter-shelter knowledge transfer. In the absence of prior knowledge about the mobility patterns, we consider only the broad features of the impact. We introduce two metrics, Total Encounters and Knowledge Sharing Ratio. Total Encounters depends on node density, average movement speed of the nodes and elapsed time after initial node deployment. It serves as an indicator for estimating the degree of intermittent connectivity. The extent of situational information dissemination over an entire network is measured in terms of Knowledge Sharing Ratio. The relationship between Total Encounters and Knowledge Sharing Ratio is validated using the real mobility traces of volunteers captured from a field trial carried out in a disaster affected area. This analysis also helps us to formulate a suitable strategy for deployment of volunteer nodes in a post-disaster scenario.

Abstract: IR methods are increasingly being applied over microblogs to extract real-time information, such as during disaster events. In such sites, most of the user-generated content is written informally – the same word is often spelled differently by different users, and words are shortened arbitrarily due to the length limitations on microblogs. Stemming is a common step for improving retrieval performance by unifying different morphological variants of a word. In this study, we show that rule-based stemming meant for formal text often cannot capture the arbitrary variations of words in microblogs. We propose a context-specific stemming algorithm, based on word embeddings, which can capture many more variations of words than what can be detected by conventional stemmers. Experiments on a large set of English microblogs posted during a recent disaster event shows that, the proposed stemming gives considerably better retrieval performance compared to Porter stemming.