Healthcare applications, such as patient record, patient – doctor mapping service, various disease symptoms etc. The major challenges is processing and integrating the real time data. Following-up with analyzing the data would involve moving the data in Data Lake to a Cloud to known about the status of the patient and for tracking purpose. In this research, we recommend a cloud based information integration framework using Data Lake in Cloud. This work extends the information integration architecture by designing with Data Lake in Cloud, using mathematical model (Petri Net) to verify architecture. This approach has the ability to scale up and down in real time data, and leading to efficient way of using data resources in the Cloud.

The healthcare monitoring system is very useful for the old age people, cardiac vascular endues and many others for observing vital parameters like blood pressure, heart rate, pulse rate, body temperature etc. The proposed work utilizes 8bit X-Mega2560 microcontroller with an application created for overseers and hospital to constantly watch the patient’s parameters from their home, hospitals etc. The proposed system senses the vital parameters of the patients and sends an alert message to respective caretakers, through Wi-Fi which utilizes COAP conventions. These models are integrated to a simple arduino uno microcontroller to exhibit different functionalities. The system will reduce the cost, empowering the patients who are not able to consult the doctors for diagnosis and also minimize the patient’s transportations. This system will bridge the gap between patient and doctor in real time.

Mobile Ad hoc Networks (MANET) are prone to malicious attacks and intermediate nodes on the home network may spoof the packets being transmitted before reaching the destination. This study implements an enhanced Steganography Adaptive Neuro-Fuzzy Algorithm (SANFA) technique for securing the ambient home network against spoofing attacks. Hybrid techniques that comprises image steganography, adaptive neuro-fuzzy and transposition cipher were used for the model development. Two variant of the model: SANFA and transpose SANFA were compared using precision and convergence time as performance metrics. The simulation results showed that the transpose SANFA has lower percentage of precision transmitting in a smaller network and a higher percentage of precision transmitting in a larger network. The convergence time result showed that packet transmitted in a smaller network size took longer time to converge while packet transmitted in a larger network size took shorter period to converge.

Long Term Evolution (LTE) emerges as the promising communication technology for data and voice service in Human to Human Communication (H2H). Currently, LTE network also includes Machine to Machine (M2M) communication. Combining the legacy systems and evolving Machine type devices, increases the access rate to a particular base station leading to congestion in Access network. Congestion in Random Access Network (RAN) that occurs during the Random Access Process affects the performance of H2H communication. The objective of the paper is to analyze the existing congestion control techniques and to provide a comprehensive summary on classified poll and event based mechanisms.

In this paper, we propose a secure, lightweight acoustic pseudo-random number generator (SLA-LFSR-PRNG) that consumes less memory, CPU capacity and adopts the multi-thread parallelization to generate huge random numbers per second by taking the advantages of multi-core CPU and massively parallel architecture of GPU. The generator is based on cryptographically secure Linear Feedback Shift Register(LFSR) and extracts the entropy from sound sources. The major attraction of proposed Pseudo Random Number Generator(PRNG) is its immunity to major attacks on pseudo-random number generators. The paper presents test results of proposed PRNG subjected to NIST SP 800-22 statistical test suite and also shows the performance comparison of proposed generator on different systems.

The paper explores the architecture of mobile cloud technologies. The advantages and the problems emerging during the use of this technology are analyzed. The study analyzes the establishment of network infrastructure based on cloudlets at the second level of mobile computation clouds with hierarchic structure. At the same time, the issues of computation and meeting the demand for memory resources of mobile devices are investigated by employing this technology. The paper presents the solution of the linking problem of cloudlets located in the proximity of base stations of wireless metropolitan area networks (WMAN). The study also determines the number of communication channels among cloudlets for the purpose of effective utilization of cloudlet resources. The issue of uploading necessary program extensions to a corresponding cloudlet by using the possible number specifying the importance of cloudlets was investigated and a method was proposed.

Distributed denial of service attacks are the acts aiming at the exhaustion of the limited service resources within a target host and leading to the rejection of the valid user service request. During a DDoS attack, the target host is attacked by multiple, coordinated attack programs, often with disastrous results. Therefore, the effective detection, identification, treatment, and prevention of DDoS attacks are of great significance. Based on the research of DDoS attack principles, features and methods, combined with the possible scenarios of DDoS attacks, a Multi-Agent System-based DDoS attack detection method is proposed in this paper to implement DDoS attack detection for high-load communication scenarios. In this paper, we take the multi-layer communication protocols into consideration to carry out categorizing and analyzing DDoS attacks. Especially given the high-load communication scenarios, we make an effort to exploring a possible DDoS attack detection method with employing a target-driven multi-agent modeling methodology to detect DDoS attacks relying on considering the inherent characteristics of DDoS attacks. According to the experiments verification, the proposed DDoS attack detection method plays a better detection performance and is less relevant with the data unit granularity. Meanwhile, the method can effectively detect the target attacks after the sample training. The detection scheme based on the agent technology can reasonably perform the pre-set behaviors and with good scalability to meet the follow-further requirements of designing and implementing the prototype software.