Recent advances in the areas of Internet of Things (IoT), Big Data, and Machine Learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as smart devices prevail more in our daily life. Ensuring Quality of Service (QoS) for delay-sensitive applications is a must, and fog computing is seen as one of the primary enablers for satisfying such tight QoS requirements, as it puts compute, storage, and networking resources closer to the user.In this paper, we first introduce FOGPLAN, a framework for QoS-aware Dynamic Fog Service Provisioning (QDFSP). QDFSP concerns the dynamic deployment of application services on fog nodes, or the release of application services that have previously been deployed on fog nodes, in order to meet low latency and QoS requirements of applications while minimizing cost. FOGPLAN framework is practical and operates with no assumptions and minimal information about IoT nodes. Next, we present a possible formulation (as an optimization problem) and two efficient greedy algorithms for addressing the QDFSP at one instance of time. Finally, the FOGPLAN framework is evaluated using a simulation based on real-world traffic traces.

A neighbor discovery protocol is proposed for initializing an ad-hoc deployable autonomous underwater system in which the nodes (floating sensors, crawlers, vehicles) have no knowledge of the system topology upon deployment. The goal is to establish multi-hop, minimum-power acoustic communication links over a given coverage area. The protocol uses distributed power control and random access to provide connectivity within a finite power budget and without global synchronization. Physical laws of acoustic propagation are taken into account, namely the distance-and frequency-dependent path loss, as well as large-scale fading which is modeled via log-normal distribution. Simulation results quantify the energy consumption, time to completion, and system reliability in the presence of fading. The key features of the protocol are simplicity of implementation and efficient use of power.

Wireless ad-hoc sensor network is gaining popularity in all organization and it is basic means for communication. Wireless ad-hoc sensor network is vulnerable to Denial of Service (DOS) attack.DOS attack make the network resources is unavailable to users. In DOS attack it makes the node to consume more battery power and degrades the network performance. Various techniques are used for detection and prevention of DOS attack such as spread spectrum, packet leash, lightweight Secure Mechanism and energy weight monitoring system but DOS attack cannot fully prevented using this techniques. This paper reviews various types of DOS attacks and its Detection techniques.

Abstract-A neighbor discovery protocol is proposed for initializing an ad-hoc deployable autonomous underwater system in which the nodes (floating sensors, crawlers, vehicles) have no knowledge of the system topology upon deployment. The goal is to establish multi-hop, minimum-power acoustic communication links over a given coverage area. The protocol uses distributed power control and random access to provide connectivity within a finite power budget and without global synchronization. Physical laws of acoustic propagation are taken into account, namely the distance-and frequency-dependent path loss, as well as large-scale fading which is modeled via log-normal distribution. Simulation results quantify the energy consumption, time to completion, and system reliability in the presence of fading. The key features of the protocol are simplicity of implementation and efficient use of power.

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Abstract-Quantitative evaluation of the quality of a speaker's pronunciation of the vowels of a language can contribute to the important task of speaker accent detection. Our aim is to qualitatively and quantitatively distinguish between native and non-native speakers of a language on the basis of a comparative study of two analysis methods. One deals with relative positions of their vowels in formant (F1-F2) space that conveys important articulatory information. The other method exploits the sensitivity of trained phone models to accent variations, as captured by the log likelihood scores, to distinguish between native and non-native speakers.

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