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Constrained Application Protocol (CoAP) is a communication protocol at application layer in the Internet of things (IoT). It employs NSTART parameter to maintain outstanding parallel connections toward individual destinations for data transfer. In the existing implementation of CoAP, the value for NSTART is defined statically. Where the higher values of this parameter up to a threshold give better throughput in moderate traffic, the lower values for the same control the congestion in overload situations. In overload situations, higher values of NSTART result in increased overhead and thereby perform poorly in terms of throughput. The static nature of the parameter and its relevant performance issues demand to manage NSTART dynamically in CoAP. In this work, we propose an algorithm to adjust the value of NSTART dynamically based on the congestion in the network. The proposed approach is an attempt to make the client free from managing NSTART dynamically. For the proof of concept, we implement and run simulations on Cooja mote at IoT/FIT-Lab testbed and Californium CoAP client. The results show 15% improvement in throughput, 15% reduction in average exchange duration, and 48% reduction in average number of retries (average taken of three scenarios) by proposed approach in comparison with the best case of CoCoA (NSTART = 4).

A common practice in the Internet of Things (IoT) is to integrate the Constrained Application Protocol (CoAP), a lightweight application layer protocol based on UDP (User Datagram Protocol), in its architecture. While UDP does not have any provision to control congestion at the transport layer, CoAP and its other variants, such as CoCoA and CoCoA+, get around this issue by employing their own congestion control mechanism, a send-rate-based congestion control technique, at the application layer. These techniques estimate the send-rate through Round Trip Time (RTT), however, fail to map acknowledgments and re-transmitted messages, leading to inaccurate RTT in case of bursty traffic and thus performs poorly. To tackle this problem, the article proposes novel a technique to regulate the send-rate considering the current and previous congestion ratio, congestion factor, and throughput. The proposed method aims to regulate the send-rate according to congestion level in the network. The performance measurements of the proposed approach show significant increments in goodput and fairness as compared to the techniques used in CoCoA+ and CoAP in burst traffic scenarios.

Internet of Things (IoT) networks are the type of Low Power and Lossy Networks. Routing protocol for Low Power and Lossy Networks (RPL) is the standard routing protocol for IoT network. RPL uses the Destination Oriented Directed Acyclic Graph (DODAG) to get route from source to destination. DODAG formation is based on different objective functions which optimize various routing metrics to get reliable and more optimized path. Objective function Zero (OF0) which is based on minimum hop count method and Minimum Rank with Hysteresis Objective Function (MRHOF) which optimize one routing metric i.e. Expected Transmission Count (ETX) or energy are standard objective functions defined by Internet Engineering Task Force (IETF). Different routing metrics are combined so far as use of single routing metric by RPL doesn’t fulfil overall routing performance requirements. This research proposes a new modified MRHOF objective function which is modification in standard objective function MRHOF. Modified MRHOF objective function uses and optimizes three routing metrics i.e. ETX, Energy and Delay. Implementation of proposed work is done on Cooja Simulator on Contiki OS. Performance evaluation of proposed objective function is done on the basis of these parameters- Packet Delivery Ratio (PDR), Average Power Consumption and Throughput and results show that proposed objective function performs better than other objective functions -0F0 and MRHOF in terms of these parameters.

Internet of things (IoT) is the disruptive technology used in the computer automaton. Embedded electronic hardware is connected to Internet in IoT. Connecting IoT devices to the Internet, IoT security becomes the important issue. In this paper, we explain the recent case studies of attacks on IoT devices and network along with vulnerabilities behind each attack. We enlist reasons behind these attacks along with preventive measures to make the IoT devices and network more secure. The paper aims to attract attention of research community toward IoT security.

In this article, the authors have discussed about the connection between Internet of Things and growth of big data. They have also discussed short reference on the evolution, features, lifecycle, and implementation of Big Data from IoT over the cloud. Internet of Things represents a platform or environment that consists of enormous number of sensors and mediators interconnecting heterogeneous physical devices over the internet. IoT applications are available in many real-world areas such as smart city, smart workplace, smart home, smart transportation and various other ubiquitous computing areas. Using IoT applications generates tremendous amount of data for storage and management in the internet. With the time and research evolution integration of the IoT platforms and cloud comes in the market and IoT platforms data storage and management started shifting to the cloud from the internet connected physical systems for many real-world application areas. Meanwhile when this data becomes huge termed as Big Data. Handling of Big Data over the cloud develops many new areas of research and attention.

Part of speech (POS) cataloguing is the process of allocating the part of speech tag or other philological class sign to each and every word in a sentence. In many Natural Language Processing presentations such as word intellect disambiguation, information recovery, information handling, analyzing, interrogating, and machine interpretation, POS tagging is reflected as the one of the basic obligatory tool. Categorizing the uncertainties in language philological items is the puzzling objective in the procedure of emerging an effectual and correct POS Tagger. Works survey displays that, for Indian lingoes, POS taggers were established only in Hindi, Punjabi, Bengali and Dravidian languages. Some POS taggers were also established generic to the Hindi, Telugu and Bengali tongues. All scheduled POS taggers were grounded on diverse Tag-set, established by diverse organization and individuals. This paper speaks the various developments in POS-taggers and POS-tag-set for Indian language, which is very essential computational verbal tool needed for many natural language processing (NLP) presentation [15].

In commercial and academic areas of Web usage mining techniques, the interest has been increased due to the information explosion in World Wide Web. Through web user navigation patterns, proposed a approach which predict users’ future request that provide valuable information for web designers which quickly respond to their individual needs for the efficient organization of the website by mining the weblog files using Web weblog Expert and Universal Extractor. The approach is based on the combined mining to discover knowledge from web server log files which derive user navigation profiles and the contents of the retrieved web pages. The proposed algorithm implemented, and the system achieves the bandwidth of nearly 80% and the efficiency of about 75% of extracted log files, which is about 20% higher than original log files by mining Web server logs. This approach has been carried out in order to validate and facilitate better web personalization and website organization.

Mobile communication has various important issues such as congestion control, scarcity of radio spectrum, bandwidth utilization, and security. With increased number of threats in mobile devices, security has become the most important issue now-a-days. Security can be considered primarily at two levels; wireless transmission medium and mobile data level, where malicious applications execute their behavior by exploiting vulnerabilities present in mobile OS. The chapter also focuses on the latter one as these attacks can be easily performed by targeting vulnerabilities present in OS. The content is specifically on Android Operating System security as Android has become the largest installed base of mobile platform. This chapter specifies these vulnerabilities and focuses on malicious intentions of latest and sophisticated malware in detail. The chapter covers static and dynamic analysis tools as well as focuses on limitations of these methods for analyses. The results for malware samples with emulator detection capabilities show that these malware behaves more as benign samples.

Present day scenario shows a drastic increase in the growth of the malware. According to Kaspersky Security Lab report, India ranks seventh in offline threats and ninth in online threats caused by malware, among top ten countries of the world. Advancement in the evasion techniques like code obfuscation, packing, encryption or polymorphism help malware writers to avoid detection of their malwares by Anti-Virus Scanners (AVS), as AVS primarily fails to detect unknown malwares. In this paper we elucidate a malware detection method based on mining behavioral aspects of API calls, as extraction and interpretation of API calls can help in determining the behavior and functions of a program. We propose a feature selection algorithm to select unique and distinct APIs and then we have applied machine learning techniques for categorizing malicious and benign PE files.

Cross-Site Scripting (XSS) is a common attack technique that lets attackers insert the code in the output application of web page which is referred to the web browser of visitor and then the inserted code executes automatically and steals the sensitive information. In order to prevent the users from XSS attack, many client- side solutions have been implemented; most of them being used are the filters that sanitize the malicious input. However, many of these filters do not provide prevention to the newly designed sophisticated attacks such as multiple points of injection, injection into script etc. This paper proposes and implements an approach based on encoding unfiltered reflections for detecting vulnerable web applications which can be exploited using above mentioned sophisticated attacks. Results prove that the proposed approach provides accurate higher detection rate of exploits. In addition to this, an implementation of blocking the execution of malicious scripts have contributed to XSS-Me: an open source Mozilla Firefox security extension that detects for reflected XSS vulnerabilities which can be considered as an effective solution if it is integrated inside the browser rather than being enforced as an extension.

Wireless sensor network most recently used technology. Different routing protocols and topologies are used to transmit data from source to sink. A tree based routing technique is based on tree topology whereas parent-child relationship is used to forward the packet. Security is also a major issue in transmission that should be considered. A packet that is transmitted from source should be reached to destination in pre-specified time period. Some security mechanism should be applied and some parameters should be added with each data packet that is to be transmitted.

Since last thirty years, more research has been done in the area of reconstructing multi-dimensional objects from two-dimensional camera images. To capture the feature correspondences are one of the ordinary tasks among them. e.g., a same projection is addressed for multi-dimensional geometrical or textural feature of multiple images. Traditionally structure estimation reconstruction is based on either stereo image pairs or monocular image sequences. Boundaries in both of these approaches are generating to interest for measuring structure in stereo image sequences. In this paper we discuss, an Enhance matching in multi-dimension image reconstruction (EMMR) algorithm using stereo image sequences. The proposed EMMR algorithm has incrementally dense representation of feature extraction of images. We are expressly concerned this approach in the context of recognizing pineapple feature reconstruction and enhancing, dock and fault analysis. Conclusion is drained and possible future work is discussed.

This paper introduced a method for producing massive and immediate result in multi-join query (DAP Join, for short). Mostly previous research are based on single join operator while DAP join is based on multiple join operators. DAP join distinguishes itself from all previous techniques by concluding benefits of Hash Merge join, Rate Based Progressive Join and State Spilling and its own new approach. DAP join employs a new flushing technique to move N amount of data from memory to disk when memory allotment is exhausted and move N/2 amount of data when data arrival rate is slow in online environment. DAP join moves data to disk which is least useful and use Symmetric Hash Join in in-memory join makes it efficient in order to maximize overall throughput and produce early result.

A very large number of localization algorithms have been used till under various domains. But the subject of our research paper deals with the domain of VPR i.e. vehicle plate recognition system. The accuracy of the license plate recognition system depends on the performance of the localization algorithm. It is a computationally intensive process which takes a lot of time to localize the license plate. In our research paper we will discuss different types of localization algorithms and which of them should be used for a particular application. Different countries have their own types of license plates for instance some use single line horizontal license plates while others use multi line non horizontal and differently located number plates. Some of the widely used localizations algorithms which are used in the neural network recognizer are global threshold scheme, NiBlack's threshold.

This paper presents a new technique for transmitting secret information securely from one party to another by embedding this information into a video after encryption through salt cryptography. We have tried to utilize the advantages of salt cryptography which has been ignored by data hiding community. In this encryption method some random data is added to the secret keys and passwords. We will define this random data as a salt which is needed to access the encrypted data, along with the password. Alone these passwords have no use since they will be able to locate the hidden data only when mixed with proper salt. This salt is managed by a certified third party. Different salt is created for different pairs of communicating parties. The purpose of salt is to add arbitrary random data to the string being hashed, such that you increase the length of input to hash. We have also introduced the concept of Enterprise Dependent Value (EDD), which are the embedding values corresponding to the binary digits and are specific to the communicating enterprises. The effectiveness of the techniques has been shown through experimental results. The performance of the proposed technique has been compared with the other techniques of watermarking, steganography and encryption.

As information technology (I.T.) is progressing rapidly day by day a massive amount of data is emerging at a fast rate in different sectors. Data dredging provides techniques to have relevant data from a large amount of data for the task. This paper introduces an algorithm for fuzzy data dredging through which fuzzy association rules can be generated for time series data. Time series data can be stock market data, climatic observed data or any sequence data which has some trend or pattern in it. In the past many approaches based on mathematical models were suggested for dredging association rules but they were quite complex for the users. This paper emphasis on the reduction of large number of irrelevant association rules obtained providing a better platform of future prediction using fuzzy membership functions and fuzzy rules for time series data. Secondly, this paper also measures the data dispersion in time series data mainly in stock market data and shows the deviation of the stock prices from the mean of several stock price data points taken over a period of time which help the investors to decide whether to buy or sell their products. Risk investment can be predicted understanding the obtained curve in the experiment. Experiments are also carried out to show the results of the proposed algorithm.