Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In this paper we analyze and study the impact of cloudlets in interactive mobile cloud applications. To study the impact we propose the design of cloudlet network and service architectures. Our study focuses on file editing, video streaming and collaborative chatting which are representative enterprise application scenarios. Initial simulation results show the performance gains of using cloudlets over using clouds in terms of data transfer delay and system throughput. When not more than two cloudlet wireless hops are used to transfer data, the cloudlet-based approach outperforms the cloud-based approach for all three application scenarios. With more cloudlet wireless hops under mobility, the cloud-based approach can give a better performance for some of the data transfers even though the cloudlet-based can outperform the cloud-based approach for most of the flows. In such scenarios, we suggest that an adaptive scheme should be used. For example, a scheme making an intelligent decision on either the cloudlet network or the cloud network, whichever gives minimum delay, can be used.

Impact of Cloudlets on Interactive Mobile Cloud Applications

Summary Ad hoc networks are characterized by multi-hop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols plays an important role. We compare the performance of two prominent on-demand routing protocols for mobile ad hoc networks: Dynamic Source Routing (DSR), Ad Hoc Ondemand distance Vector Routing (AODV). A detailed simulation model with MAC and physical layer models is used to study the interlayer interactions and their performance implications. We demonstrate that even though DSR and AODV share similar on-demand behavior, the differences in the protocol mechanisms can lead to significant performance differentials. In this paper we examine two on demand routing protocols AODV and DSR based on packet delivery ratio, normalized routing load, normalized MAC load, average end to end delay by varying the number of sources, speed and pause time.

/pdf/performance-comparison-of-mobile-ad-hoc-network-routing-1gg050vrbm.pdf

Performance Comparison of Mobile Ad-hoc Network Routing Protocol

https://dept-info.labri.fr/~desbarat/PER/sujets/Autefage3-article.pdf

A novel mobility model based on semi-random circular movement in mobile ad hoc networks

Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols plays an important role. We compare the performance of two prominent on-demand routing protocols for mobile ad hoc networks: dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV). A detailed simulation model with medium access control (MAC) and physical layer models is used to study the interlayer interactions and their performance implications. We demonstrate that even though DSR and AODV share similar on-demand behavior, the differences in the protocol mechanisms can lead to significant performance differentials. In this paper, we examine both on-demand routing protocols AODV and DSR based on packet delivery ratio, normalized routing load, normalized MAC load, average end-to-end delay by varying the node density, network loading, and mobility variations for reference point group mobility and random waypoint models. This framework aims to evaluate the effect of mobility models on the performance of mobile ad hoc networks (MANETs) routing protocols. Our results show that the protocol performance may vary drastically across mobility models and performance rankings of protocols may vary with the mobility models used. This effect can be explained by the interaction of the mobility characteristics with the connectivity graph properties.

/pdf/reference-point-group-mobility-and-random-waypoint-models-in-3ekm1liv46.pdf

Reference point group mobility and random waypoint models in performance evaluation of MANET routing protocols

Summary A mobile ad hoc network (MANET) is a network consisting of a set of wireless mobile nodes that communicate with each other without centralized control or established infrastructure. The mobility model represents the moving behavior of each mobile node (MN) in the MANET that should be realistic. It is a crucial part in the performance evaluation of MANET. In this paper, we have studied the effects of various random mobility models on the performance of AODV. For experimental purposes, we have considered three mobility scenarios: Random Waypoint, Random Walk with Reflections and Random Walk with Wrapping. Experimental results illustrate that performance of the routing protocol varies across different parameters like number of nodes, packet delivery ratio and end to end delay.

/pdf/effect-of-random-mobility-models-pattern-in-mobile-ad-hoc-2una546j5r.pdf

Effect of Random Mobility Models Pattern in Mobile Ad hoc Networks

In this paper the focus is on the performance study of four routing protocols, namely AODV, AODVUU, RAODV and AOMDV. We call these protocols AODV family of protocols as all these protocols consider AODV as the base routing protocol upon which these protocols are improved. Even though AODV and AODVUU are not different protocols, we wanted to see if there is any improvement in using the AODVUU implementation for a sensor network environment. We have investigated whether a multiple path algorithm like AOMDV would result in more data delivery as compared to single path solutions like AODV in a sensor network. Also, the reverse route discovery mechanisms employed in RAODV is checked for a sensor network. There is a need to understand the versatile behavioral aspects of these routing protocols in a wireless sensor network with varying traffic loads and the number of sources. All these protocols are simulated using NS-2 over IEEE 802.15.4. We also claim that our work is the first of its kind to study and compare the performance of all these four routing protocols from a sensor network point of view by extensively using various performance metrics like packet delivery ratio, average network delay, network throughput and normalized routing load.

Performance analysis of AODV, AODVUU, AOMDV and RAODV over IEEE 802.15.4 in wireless sensor networks

A characteristic feature of ad hoc networks is the infrastructure less and seamless connectivity of the wireless mobile nodes. Mobility plays an important role in the connectivity of these nodes. In this paper a performance comparison of five important mobility models; Community Model, Group Force Mobility Model (GFMM), Reference Point Group Mobility (RPGM), Manhattan Mobility Model and Random Waypoint-Steady State (RWP-SS) Mobility Model has been analyzed. Community Model, GFMM and RPGM Mobility Models are pure group mobility models, while Manhattan Mobility Model can be considered as a pseudo Group Mobility Model. We have included RWP-SS to give a whole picture of how group mobility models stand against a random model like RWP-SS. From our analysis we deduce that group mobility models hold inherent advantage over mobility models like Random Waypoint models. Among the group mobility models, Community model has good performance when compared to other mobility models. Energy Consumption of these mobility models has also been analyzed. Various Metrics like Packet Delivery Ratio, Average Network Delay, Network Throughput, Routing Overhead and Number of Hops have been considered. The results obtained in our paper colligates with the theoretical results in [20]. We also claim that our work is the first to compare these five different mobility models together.

Simulation Based Performance Comparison of Community Model, GFMM, RPGM, Manhattan Model and RWP-SS Mobility Models in MANET

In this paper, we successfully demonstrate the role of Bluetooth Low Energy (BLE) in automating the Proximity based solutions for Internet of Things (IoT) by considering a real world use case scenario. Devices in IoT must be low powered in order to extend their lifetime. BLE compatible AltBeacon is aptly suited for this purpose. We have developed an application to show how various disparate technologies are integrated together to form IoT paradigm. We use a fixed AltBeacon node which allows an application running on a smart phone to determine its proximity with respect to the AltBeacon node. Once the application finds that it is within the proximity of the AltBeacon node then we can control various IP enabled devices through RESTful Service. We have proposed a software framework which can be used to automate IoT based on the proximity. This framework can be extended to control any of the IP enabled devices for Home Automation and Industrial Systems. From our experiments we determined that 1.5m is an optimum range for proximity.

S. Gowrishankar

Papers

Effect of Random Mobility Models Pattern in Mobile Ad hoc Networks

Performance analysis of AODV, AODVUU, AOMDV and RAODV over IEEE 802.15.4 in wireless sensor networks

Simulation Based Performance Comparison of Community Model, GFMM, RPGM, Manhattan Model and RWP-SS Mobility Models in MANET

Role of BLE in proximity based automation of IoT: A practical approach

Regular Expression Operations