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Current era is witnessing data explosion being generated from a wide range of resources including RFID (Radio-frequency identification), sensors, web logs, social media, IoT (Internet of Things) devices and many more. Pace at which data is being generated routinely in all the task performed by us has overwhelmed the proficiency and working of present infrastructure and analytical solutions available. Data has become the driving force of economy and has been treated as an asset for an organization. It contains truth or facts that can be interpreted and manipulated to gain insight for knowledge discovery. To excel out in competition enterprises are escalating their big data projects for knowledge discovery to gain valuable insights. These projects require scalable architectures for storage and data processing. Data-centric technologies are gaining impetus which can be provisioned as service to the organizations. Cloud computing is an effective and promising solution for refined analytical application. Cloud computing model supports resources to be provisioned as service. Herein paper we examine the requirements for provisioning Big Data Knowledge Discovery as a service. In addition, we explore the prevalent big data frameworks accessible and provisioned as a service via cloud. We also explore the state-of-the- art progress in this arena with open challenges and research prospects.

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves $$99.5 \%$$ detection and $$0.5 \%$$ false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks.

Disk failures cause data loss and system malfunctions have various severe impacts on the performance and availability of data centers. In data center environments, failures of hard disk drive (HDD) are a very costly & rare occurrence. Therefore, to reduce the rate of failures of this occurrence, different vendors of hard disk as performed a cost saving measure. For the prediction of hard drive failure there are different attributes such as Self-Monitoring and reporting Technology (SMART) collected during normal operations by different HDD manufacturing companies. Now days by using simply threshold values prediction of disk drives failure is done by using SMART attributes. Replacement of healthy disks is very expensive for high false positive rate results. HDD health statistics can be representing by SMART attributes such as the number of command timeout, scan errors and reallocation counts of a Hard disk. The goal of this study is to apply Machine Learning techniques to accurately and proactively predict hard disk drive failures because Machine Learning techniques are best way to solve the learning and rare event-based problem. In evaluating how a method produces the highest accuracy model, we implemented Random Forest algorithms, Decision tree & Naive Bayes algorithm.

Agriculture plays a crucial role in Indian economy. Crop yield is main component of food security as human population is increasing in a drastic way. One of the most important problems of agriculture is crop yield prediction. Agriculture yield depends on the various factors such as weather situation (rain, humidity, temperature etc.), information about pesticides. Apart from these factors exact information about the crop yield history is an essential concept for making predictions and controlling agriculture risk. Earlier yield prediction was performed by considering the farmer's experience on a particular field and crop. In this study machine learning is used to predict four popular yields which are mostly cultivated all over India. Once the crop yield is site specifically predicted, the inputs such as fertilizers could be applied variably according to the expected crop and soil needs. In our study we use Machine Learning approaches to develop a trained model to identify the patterns among data and it is used for crop prediction. In this study the prediction of four most cultivated yields in India is considered by applying machine learning. These crops include: Maize, Potatoes, Rice (Paddy) and wheat.

Enterprise thrives on software applications that are built to fulfil the core business requirements. A single business application can offer a cluster of capabilities to generate value from processing huge amount of data often termed as Big Data. The time-based requirements of these applications are satisfied frequently by applying monolithic approaches with increased complexity and less scalability. Traditional approaches for Big Data Analytics suffer from overpriced, excessive and irrelevant data transfer owing to the constricted coupling amongst computing resources and data processing logic. Service-oriented approach came into existence as a new paradigm to enable applications to be rendered as service for better flexibility and scalability. Service orientation architecture avoids monolithic style but web services, one of its major implementation encourages monolith development of software application. Thus building a scalable, robust, resilient, cost-effective and optimum solution is one of the major requirements for outsized data. New software development style Microservices offer low degree of coupling and smaller size. This work reviews the existing and prevalent approaches like monolithic architecture in this area along with their drawbacks. This work also proposes a generic microservice model µBIGMSA for handling Knowledge Discovery in Big Data. Reference applications are implemented using proposed model. The effectiveness of the proposed model is evaluated by comparing the reference application with the monolithic application using various software metrics.

The Internet of Medical Things (IoMT) is a kind of connected infrastructure of smart medical devices along with software applications, health systems and services. These medical devices and applications are connected to healthcare systems through the Internet. The Wi-Fi enabled devices facilitate machine-to-machine communication and link to the cloud platforms for data storage. IoMT has the ability to make accurate diagnoses, with fewer mistakes and lower costs of care. IoMT with smartphone applications permits the patients to exchange their health related confidential and private information to the healthcare experts (i.e., doctors) for the better control of diseases, and also for tracking and preventing chronic illnesses. Due to insecure communication among the entities involved in IoMT, an attacker can tamper with the confidential and private health related information for example an attacker can not only intercept the messages, but can also modify, delete or insert malicious messages during communication. To deal this sensitive issue, we design a novel blockchain enabled authentication key agreement protocol for IoMT environment, called BAKMP-IoMT. BAKMP-IoMT provides secure key management between implantable medical devices and personal servers and between personal servers and cloud servers. The legitimate users can also access the healthcare data from the cloud servers in a secure way. The entire healthcare data is stored in a blockchain maintained by the cloud servers. A detailed formal security including the security verification of BAKMP-IoMT using the widely-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is performed to demonstrate its resilience against the different types of possible attack. The comparison of BAKMP-IoMT with relevant existing schemes is conducted which identifies that the proposed system furnishes better security and functionality, and also needs low communication and computational costs as compared to other schemes. Finally, the simulation of BAKMP-IoMT is conducted to demonstrate its impact on the performance parameters.

As we all know that the technology is projected to be next to humans very soon because of its holistic growth. Now-a-days, we see a lot of applications that are making our lives comfortable such as smart cars, smart homes, smart traffic management, smart offices, smart medical consultation, smart cities, etc. All such facilities are in the reach of a common man because of the advancement in Information and Communications Technology (ICT). Because of this advancement, new computing and communication environment such as Internet of Things (IoT) came into picture. Lot of research work is in progress in IoT domain which helps for the overall development of the society and makes the lives easy and comfortable. But in the resource constrained environment of Wireless Sensor Network (WSN) and IoT, it is almost inconceivable to establish a fully secure system. As we are moving forward very fast, technology is becoming more and more vulnerable to the security threats. In future, the number of Internet connected people will be less than the smart objects so we need to prepare a robust system for keeping the above mentioned environments safe and standardized it for the smooth conduction of communication among IoT objects. In this survey paper, we provide the details of threat model applicable for the security of WSN and IoT based communications. We also discuss the security requirements and various attacks possible in WSN and IoT based communication environments. The emerging projects of WSNs integrated to IoT are also briefed. We then provide the details of different architectures of WSN and IoT based communication environments. Next, we discuss the current issues and challenges related to WSN and IoT. We also provide a critical literature survey of recent intrusion detection protocols for IoT and WSN environments along with their comparative analysis. A taxonomy of security and privacy-preservation protocols in WSN and IoT is also highlighted. Finally, we discuss some research challenges which need to be addressed in the coming future.

Big Data is rapidly gaining impetus and is attracting a community of researchers and organization from varying sectors due to its tremendous potential. Big Data is considered as a prospective raw material to acquire domain specific knowledge to gain insights related to management, planning, forecasting and security etc. Due to its inherent characteristics like capacity, swiftness, genuineness and diversity Big Data hampers the efficiency and effectiveness of search and leads to optimization problems. In this paper we explore the complexity imposed by big search spaces leading to optimization issues. In order to overcome the above mentioned issues we propose a hybrid algorithm for Big Data preprocessing ACO-clustering algorithm approach. The proposed algorithm can help to increase search speed by optimizing the process. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency.

In an Internet of Things (IoT) enabled e-health system, smart health devices sense the health data continuously and share the collected data with the neighboring controller device (i.e., personal server) via some wireless communication mechanism (i.e., bluetooth and zigbee), and finally the data is stored on some health server (i.e., a server over the cloud). The health data is then accessible to healthcare service providers (for example, doctors, nursing staff, relatives of patient) for tracking and monitoring of health conditions of the patients for their better treatment at the earliest. In an IoT enabled health system, the smart healthcare devices communicate over public channel, which causes various types of threats and attacks on the ongoing communication. Therefore, we need a powerful privacy-preserving security mechanism to secure the communication happens in an IoT enabled e-health system as the health data is strictly private and confidential. In this paper, we propose a new privacy-preserving access control and key management scheme for the secure communication of IoT enabled e-health system (SPCS-IoTEH). We also conduct informal security analysis of the proposed SPCS-IoTEH to show its robustness against various types of active and passive attacks. The performance of SPCS-IoTEH is also shown to be better than other existing competing schemes.

Background: Myocardial infarction, or heart attack, is caused by a blockage of a coronary artery, which prevents blood and oxygen from accessing the heart properly. Arrhythmias are a form of CVD that refers to irregular variations in the normal heart rhythm, such as the heart beating too quickly or too slowly. Arrhythmias include Atrial Fibrillation(AF),Premature Ventricular Contraction(PVC), Ventricular Fibrillation(VF), and Tachycardia are just a few examples of arrhythmias. It aggravates if not detected and treated on time i.e., on-time /proper diagnosis of arrhythmias may minimize the risk of death. It is very labor-intensive to externally evaluate ECG signals, due to their small amplitude. Furthermore, the analysis of ECG signals is arbitrary and can differ between experts. As a consequence, a computer-aided diagnostic device that is more objective and reliable is needed. Methods: In the recent era, Machine Learning based approaches to detect arrhythmias has been established proficiently. In this view, we proposed a hybrid Deep Learning-based model to detect three types of arrhythmias on MIT-BIH arrhythmia databases. In particular, this paper makes two-fold contributions. First, we translated 1D ECG signals into 2D Scalogram images. When one-dimensional ECG signals are turned into two-dimensional ECG images, noise filtering and feature extraction are no longer necessary. This is notable since certain ECG beats are ignored by noise filtering and feature extraction. Then, based on experimental evidence, we suggest combining two models, 2D-CNN-LSTM, to detect three forms of arrhythmias: Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). Results: The experimental findings indicate that the model attained 99\% accuracy for "normal sinus rhythm," 100\% accuracy for "cardiac arrhythmias," and 99\% accuracy for "congestive heart failures," with an overall classification accuracy of 98.6\%. The sensitivity and specificity were 98.33\% and 98.35\%, respectively. The proposed model, in particular, will aid doctors in correctly detecting arrhythmia during routine ECG screening. Conclusion: As compared to the other State-of-the-art methods our proposed model outperformed and will greatly minimise the amount of intervention required by doctors.

Internet of things (IoT) is one of the fastest-growing technologies. With the deployment of massive mobile and faster networks, almost every daily-use item is worked upon to be connected to a network. Such a massive network will exchange gargantuan data every second and give way to security threats. In this paper, we aim to classify the different categories of attack and non-attack data packets which could be used as a standard to apply the redemption techniques and enhance the security features to ensure the data integrity and a foolproof IoT network and propose an ANN-based multi-class malware detection scheme to classify different types of attacks.

Energy which drives the whole world is also required for the propitious technology called wireless sensor network (WSN), which is the driving force of many helpful applications in many areas like health, environment, and industry monitoring, mainly for detecting the threats. The ease of portability, availability, accuracy, and advancement in wireless communication has made it popular among the masses. Sensing nodes are set up in a geographical area of interest, and then, these nodes sense the environmental physical phenomenon and accumulate the information and transfer it to the sink (base station) for application specific action. One of the major concerns with WSN is the network lifetime because of the battery-powered sensing devices. It is one of the most challenging problems which is related with all the issues present in the WSN. The recharge of the sensing devices and prolonging the network lifetime can be achieved through energy harvesting techniques. The primary goal is to enhance the lifetime of the battery-powered sensors and prolong the network lifetime. Numerous of routing protocols have been proposed by the researchers like many versions of LEACH protocols, RPL, 6LowPAN, etc. Here, we propose optimized form of LEACH routing protocol which improves the lifetime of the network, and the simulation results show that the proposed work has improved the network lifetime and stability of the protocol compared with the other existing techniques.

Network lifetime play a vital role in setting up an energy efficient wireless sensor network. The lifetime of the network can be improved by efficient deployment and scheduling of the sensor nodes inside the network. In this paper, on the basis of mathematically calculated upper bound lifetime of the network, the sensor nodes are efficiently deployed by using Simulated Annealing and Particle Swarm Optimization with pre-specified sensing range of the sensor nodes and on the second fold the sensor nodes are efficiently scheduled by applying the Simulated Annealing and Dempster Shafer Theory. The overall objective of this paper is to find the optimized location and schedule for sensor nodes. The comparative study shows that the simulated annealing and particle swarm optimization algorithms performs better for sensors deployment. Simulated annealing and Dempster Shafer theory achieves the goal to provide the different schedules with high efficiency.

The sinkhole attack in an edge-based Internet of Things (IoT) environment (EIoT) can devastate and ruin the whole functioning of the communication. The sinkhole attacker nodes ( S H A s) have some properties (for example, they first attract the other normal nodes for the shortest path to the destination and when normal nodes initiate the process of sending their packets through that path (i.e., via S H A ), the attacker nodes start disrupting the traffic flow of the network). In the presence of S H A s, the destination (for example, sink node i.e., gateway/base station) does not receive the required information or it may receive partial or modified information. This results in reduction of the network performance and degradation in efficiency and reliability of the communication. In the presence of such an attack, the throughput decreases, end-to-end delay increases and packet delivery ratio decreases. Moreover, it may harm other network performance parameters. Hence, it becomes extremely essential to provide an effective and competent scheme to mitigate this attack in EIoT. In this paper, an intrusion detection scheme to protect EIoT environment against sinkhole attack is proposed, which is named as SAD-EIoT. In SAD-EIoT, the resource rich edge nodes (edge servers) perform the detection of different types of sinkhole attacker nodes with the help of exchanging messages. The practical demonstration of SAD-EIoT is also provided using the well known NS2 simulator to compute the various performance parameters. Additionally, the security analysis of SAD-EIoT is conducted to prove its resiliency against various types of S H A s. SAD-EIoT achieves around 95.83 % detection rate and 1.03 % false positive rate, which are considerably better than other related existing schemes. Apart from those, SAD-EIoT is proficient with respect to computation and communication costs. Eventually, SAD-EIoT will be a suitable match for those applications which can be used in critical and sensitive operations (for example, surveillance, security and monitoring systems).

Advanced technology market desires more durability and reliability from wireless technologies, but during the transmission different factors, i.e., node failure, delay, data traffic, noise, distortion, etc., reduce the quality of communication. To overcome from such factors, some relatable parameters like signal-to-noise ratio (SNR), Error Vector Magnitude (EVM), Bit Error Rate (BER), etc., have been defined to describe the performance of a signal in the transmission. ZigBee is a wireless personal area network standard, which is designed for monitoring and control applications, i.e., home and industrial automation, grid monitoring, asset tracking, etc., because it is less expensive and more efficient than other wireless technologies. In this work, Error Vector Magnitude (EVM) is considered as a performance measuring metric for the quality of digitally modulated transmission in ZigBee wireless network and it is minimized for the improvement in reliability and efficiency of the transmission. However, by simulating the ZigBee sensor network in 2.4 GHz frequency by Offsetting the Quadrature Phase Shift Keying (OQPSK) modulation with half-sine pulse shaping filter minimizes the root mean square value of EVM is to 0.1% which is more efficient than Binary Phase Shift Keying (BPSK) with Root Raised Cosine (RRC) filter.

Network lifetime and connectivity are the key parameters to guarantee quality of service (QoS). As a result it becomes our top most priority to preserve the energy of sensor nodes which have limited initial energy at the time of deployment and it is impossible to recharge the battery due to out of reach deployment. Heterogeneous wireless sensor network consists of more than one type of sensor nodes which differs from one another in terms of initial energy, computing power, performance, etc. In order to reduce unwanted energy dissipation and congestion during long distance data transmission, fixed number of relay nodes are selected and placed as a router to maintain connectivity. Relay nodes receive aggregated data from cluster head and send it back to base station . In this paper, we have proposed an (EDAC) Energy and Density Aware clustering scheme to optimize the selection and placement of cluster heads (CH) and relay Nodes (RN) to enhance the connectivity and network lifetime of network. The proposed centralized protocol enables the base station to select the cluster head and relay node on the basis of residual energy, distance from base station and relay node, node density , overlapping ratio of nodes, etc. The results of simulation using Matlab proves that our proposed algorithm, EDAC increases the overall connectivity , 100% coverage ratio by 80.21% and network lifetime by 131.61%.

Big data is spreading its span in almost every walk of science and engineering. Both public and private sector enterprises have been collecting and deploying enormous amount of domain-specific information to gain insights about areas like security, marketing, forecasting, fraud-detection, strategic planning etc.. This big data potential is unquestionably noteworthy; but to explore it fully and sensibly it requires new ideas and original learning techniques to address challenges associated with it. With the universe being getting more knowledge-based and computerized, an enormous range of applications shows interest in machine learning (ML) techniques. Machine learning is one of the most sought after field to handle big data challenge. With this paper we endow with a literature analysis related to the up-to-the-minute progress in researches on big data processing deploying Machine Learning as an analytical tool. We will review machine learning techniques with a focus on the promising learning methods like transfer learning, active learning, deep learning, representation learning, distributed, kernel-based learning and parallel learning. Also we will be reviewing the challenges in big data machine learning.

Wireless sensors collect the data from their physical environment and send them to the monitoring station to carry out operations accordingly. The entire architecture of the Wireless Sensor System depends on these sensor nodes. The lifetime of these sensors is limited and they die once the energy of the batteries of the sensors is consumed. Since the energy sources are irreplaceable, the main aim is to increase the lifetime of the sensor nodes to the maximum. Various routing protocols and algorithms' have been proposed over time to increase the lifetime of the sensor nodes. Since the lifetime of Wireless Sensor Network is greatly dependent on the lifetime of these sensor nodes, researches have been carried out to develop a routing technique which increases the Wireless Sensors lifetime with never ending demand for development in Science and Technology, Wireless Sensor Networks have proved to be an emerging and a prominent technology for communication and data transfer in various military, geographical, medical, agriculture and monitoring fields worldwide. The ease with which it is portable, available, accurate has made it popular in wireless communication and research field. The Wireless Sensor Network consists of large number of different sensor nodes which are deployed randomly in an area or region. These sensors collect. Various hierarchical routing techniques have been developed and discussed. In this research paper, we present a proposed optimized form of TEEN routing protocol. The result simulations of our proposed model show that our optimized protocol has enhanced the network lifetime and has also increased the stability period. Apart from it, various comparisons have been shown to demonstrate its performance.

One of the major issues in WSN is coverage lifetime due to the constraint of Battery power. Prolonging the network lifetime while covering the sufficient area is an important issue in WSN research. In a dense WSN the excessive use of sensors causes some problems such as congestion. Some researchers have applied the notion of extra nodes for connectivity purpose and develop the methodology that broadcast the status(on/off) information of sensor nodes to their one hop neighbors, but these approaches are able to increase the messaging and density of the WSN. In this paper we are providing an analytical method for increasing the lifetime of the WSN by maintaining the disjoint groups of sensors. An individual group can provide the whole area coverage at a time. So we can make only one group active at a time in WSN. The tessellation form after placing a group of sensor is regular rectangle. The analytical and simulation results show that we can increase the coverage lifetime by more than two times of the original lifetime.

Wireless Sensor network consists of large number of sensor nodes, which are deployed densely. A key challenge of wireless sensor network is that their sensor nodes contain less amount of battery power. Consequently, efficient utilization of battery power inside the network is a main design consideration. In the querying or monitoring application like critical condition in a particular region, we need the network that respond in few seconds. For the purpose of response in less time, we require energy efficient routing of information inside the network. In this paper, we are proposing an energy efficient clearance routing method to collect/route the query towards a particular region inside the network.

Many optical packet switch (OPS) architectures are proposed and demonstrated. In the design of these architectures, most of the attention is paid on the network layer parameters like high through put (low packet loss probability) and low latency, etc. and to achieve these goals. The structure of the architectures becomes very complex. In real scenarios, these architectures may not work efficiently because of physical layer constraints. Hence, cross layer optimization needs to be considered. This paper addresses the major physical layer constraints, and it has been found that the optical packet switch architecture can work efficiently within the bounded regimes which can be called as an operation window.

In this paper, optical loop buffer based architecture is discussed; with its advantages over other architectures. In general the performance of the switch is measured at physical and network layer. The physical layer analysis deals with power budget analysis, however, at the network layer; the performance is measured in terms of packet loss probability and average delay. To obtain more realistic performance at the network layer the QoS parameters need to be included. In this paper, a QoS parameter which is known as priority of the incoming packets is included and corresponding results are presented, and it has been found that even at the load of 0.7, packet loss probabilities on the order of 10 -5 can be achieved.

In wireless communication fading of channels is the serious cause of the received degraded signals. The effect of fading can be minimized by using various time and space domain techniques. However, space domain techniques are preferred over the others due to its advantages. In this paper, comparison of the wireless MIMO system under Almouti's and maximum ratio combining schemes is presented. Basic idea in these schemes is to transmit and receive more than one copy of the original signals. Using two transmitter antennas and one receiver antenna, the scheme provides the nearly same diversity order as the maximal-ratio receiver combining (MRRC) with one transmitter antenna, and two receiver antennas. Results for one transmitter and four receivers under MRRC is also presented and compared. Finally, results are presented while varying the average transmitted power.

In this paper, an all-optical regenerator based, photonic packet switch architecture, which consists of the fiber loop for the storage of the contending packets, is considered. In the loop buffer, the available buffer space may not be fully utilized due to the limited re-circulation count of the data placed on buffer. This limit can be counteracted by placing a pool of regenerators inside the buffer. As optical regenerators are costly devices, hence they should be placed optimally in the buffer. The simulations results are presented by consider Prioritized and non - prioritized traffic. It is shown in the results that regeneration of data is essential if prioritized traffic has to be considered.

RSSI method is used to measure the distance between beacon nodes and unknown node. This technique is not more relevant because the radio frequency (RF) signals are mostly affected with the noise available in the environment. Therefore, the accurate distance evaluation cannot possible between the known nodes and unknown nodes. RSSI method is having many accuracy challenges which need improvements. Similarly, IRSSI method has also some faults regarding the calculation of model parameter. In this paper, a New Received Signal Strength Indicator (NRSSI) method is proposed. The new proposed RSSI method is achieved with improvement of the parameter values and introduction of the noise factor (or thermal noise). The thermal noise influences the RF signal severely, therefore, it is fused as a part of noise in RSSI method to reduce the measurement error effectively. In this paper, the NRSSI method improves the accuracy of the distance estimation for unknown nodes than RSSI and IRSSI.

Underwater sensor network (UWSN’s) localizations play a very crucial role in order to monitor or to track the underwater critters or any kind of underwater application. Many different methods for terrestrial sensor network localization have been developed, but for underwater sensor localization, it is still having many big challenges. The main reason is RF signals, as in underwater it will propagate very less. This paper deals with the design challenges that are faced by the UWSN localization, in which the locations of the unknown sensor node will be estimated by using the acoustic signals that is sent by the sensor nodes and after locating the UWSN. It will be then helpful for tracking and monitoring the underwater critters like fishes which is one of the major sources for countries economy. Hence, this UWSN localization will help the fisherman’s to find the location of the aquatic creatures in well advanced and as a result will take a step forward toward in growing the economy of the country.

All Denial of Service Attacks (DoS) is malignant. Their occurrence in the network affects the entire network performance. Dropping attack either Packet Dropping or Datagram Dropping is DoS in nature. Both these attacks occur corresponding to Transport layer of Mobile Ad hoc Network (MANET) stack which affects the entire functionality of transport layer. In this paper we have discussed a novel kind of Datagram Chunk Dropping Attack occurring in the MANET multimedia transmission. If multimedia transmission is datagram oriented, the datagram are transferred in the form of certain data streams called Chunks. In this attack an intermediate intruder node drops some data streams decreasing the throughput of the network in turn affecting the Quality of Service (QoS). Thus the detection and prevention of this attack becomes very crucial. The Datagram Chunk Dropping Attack detection and prevention technique i.e. Cluster Based Datagram Chunk Dropping Detection and Prevention Technique (CBDCDDPT) is proposed. The network performance parameter i.e. throughput shows considerable amount of improvement by the use of proposed technique.

Wireless Sensor Networks (WSNs) are prone to various attacks in which Blackhole a kind of Denial of Service (DoS) attack is very difficult to detect and defend. In blackhole attack, an intruder captures and re-programs a set of nodes in the network to block the packets they receive instead of forwarding them towards the base station. As a result any information that enters the blackhole region is captured and not able to reach destination causing high end-to-end delay and low throughput. Previously little amount of work is done for detection and prevention of the Blackhole attack in the WSN making its detection and prevention very crucial as per network performance is concerned. In this paper initially the affect of Blackhole attack is measured on the network parameters followed by the proposal of a novel technique for the detection and prevention of Blackhole attack in WSN.

Constrained resources, the ad-hoc nature of deployment and the vulnerability of wireless media are some of the most challenging characteristics of Wireless Sensor Network (WSN) which pose a need for unique security solutions. WSNs are susceptible to various attacks, in which Misdirection a kind of Denial of Service (DoS) attack is very difficult to detect and defend. In Misdirection Attack, the intruder misdirects the incoming packets to a node other than the intended node which introduces high end-to-end delay (sometimes infinite) in the network. The performance of the network (i.e. throughput) is also degraded. Thus the detection and prevention of this attack becomes very crucial. In this paper we have proposed a novel Cluster Based Intrusion Detection and Prevention Technique for Misdirection Attack. The network parameters calculated by the use of this technique shows considerable amount of improvement in throughput while introducing small amount of delay.

In Wireless Sensor Networks (WSNs) there has been an extensive research going on to extend the life time of the network. Energy efficiency is one of the major concern in WSN's that impacts the network lifetime. There is a compulsive need to find out new and improved ways to reduce the energy consumption associated with different aspects in WSN due to the fact that energy is a critical and important resource in the design of WSN. In this paper, we focus on an important distribution aspect of WSN, task allocation with energy efficiency that helps in improving the utility of the network. Most previous sensor network lifetime enhancement techniques focused on balancing power distribution, based on assumption of uniform battery capacity allocation among homogenous nodes. This work aims to provide solution to the task assignment problem in heterogeneous WSN that also incorporates the log for data recovery in the event of failure. It has been calculated that the proposed method increases the throughput by 9.14%.

In Wireless Sensor Networks (WSNs), the coverage means how well a sensor network will monitor the total area of the region. Node deployment is one of the important issues that need to be solved. The number of sensor nodes has direct impact on the cost of the Wireless Sensor Networks. So, keeping this in mind, the proper deployment of nodes can reduce the complexity of problems, energy consumption can be reduced and thus extends the lifetime of the Wireless Sensor Networks. In this paper, we are considering the tactical environment, so the deployment of sensor node becomes random. We are forming the tessellation of the deployment as Rhombus other than Triangle, Square and Hexagon that are taken ideally for deploying the sensor nodes and after applying the proposed method the life time of the given network increases.

Lifetime of the network plays a vital role in working wireless sensor networks. Network lifetime can be improved by the efficient use of present sensor nodes in wireless sensor nodes. In this paper, our objective is to deploy and schedule the sensor nodes in such a manner that improves the lifetime and provides a way to distribute the tasks among the sensor nodes that completes the task on the basis of priority and accuracy. Thus, the overall objective of this paper is to calculate the optimised location of sensor nodes contain a fix sensing range and schedule the sensor nodes that maximises the lifetime. The upper bound of network lifetime can be pre-calculated mathematically, we are applying this knowledge to find the locations of sensor nodes that improves the lifetime. Further, the nodes are scheduled and task is distributed among the sensor nodes that achieve this upper bound. In this paper, we are applying teaching learning algorithm to find the deployment location and schedule followed by an algorithm for task distribution. The comparative study shows that our method outperforms as compared to naive methods.

Wireless Sensor Network (WSN) has a great potential to be deployed in wide range of applications like consumer, industrial and defense sectors. WSNs are susceptible to various attacks, in which misdirection a kind of Denial of Service (DoS) attack is very difficult to detect and defend. In misdirection attack, the intruder misdirects the incoming packets to a node other than the intended node. Due to this attack, high end- to- end delay (sometimes infinite) is introduced in the network and performance of the network (i.e. throughput) is degraded. In this paper we have done a topological analysis of WSN in the presence of misdirection attack and presented an algorithm for the prediction of delay and throughput. We observed that WSN performs better for tree network topology as compared to mesh topology.

Background: The present epidemiological study was planned and conducted to determine the prevalence of bronchial asthma in school going children of Agra, and to assess the precipitating factors in the causation of bronchial asthma mainly on the basis of the questionnaire and spirometry wherever possible. Methods: A total of 2175 school children in the age group 6-12 years from the two schools located in Agra were surveyed. These schools were selected on the basis of the surrounding environment and the socioeconomic status of their students. A detailed history was taken from students using a validated questionnaire. A total 153 cases of bronchial asthma were identified on the basis of the questionnaire. Out of which only 79 cases were subjected to spirometry. Spirometry could not be done in 74 students as their parents did not give consent. Results: The prevalence of bronchial asthma was found to be 7%. There were 61.4% male and 38.6% female patients having the disease. The study revealed that the upper classes had the largest number of students suffering from bronchial asthma. There is a higher prevalence of bronchial asthma in nonvegetarian children. Conclusion: This survey shows that the prevalence rates of bronchial asthma in school population (6-12 years) of Agra are almost similar to those seen in other parts of Northern India.

Hepatodiaphragmatic interposition of small/large intestine or Chilaiditi syndrome is a rare condition in which colon or small intestine is interposed (temporally or permanently) between liver and diaphragm. The syndrome is usually discovered by chance during the study of another event. The condition is often asymptomatic; however abdominal pain, abdominal distension, vomiting, anorexia, and constipation have previously been reported in clinical presentation of this rare syndrome. We report a 60-year-old female who presented in department of TB and Chest Diseases, S.N. Medical college, Agra with complaints of breathlessness, cough, constipation, and recurrent abdominal pain.

Due to unattended nature of Wireless Sensor network makes it's vulnerable to security issues. The sensor nodes in the wireless sensor networks collect the information from the environment and send the information to a sink node so it's a main concern to preserve the anonymity of the sink node from the attacker. The attacker can conceal the identity of the sink node by performing the traffic analysis inside the network. In this paper we are proposing a method that can preserve the identity of the sink node. The simulation results demonstrate that the proposed method can hide the location information of the sink node effectively and efficiently.