Considering Complex Search Techniques in DHTs Under Churn

Abstract-Traditionally complex queries have been performed over unstructured P2P networks by means of flooding, which is inherently inefficient due to the large number of redundant messages generated. While Distributed Hash Tables (DHTs) can provide very efficient look-up operations, they traditionally do not provide any methods for complex queries. By exploiting the structure inherent in DHTs we can perform complex querying over structured P2P networks by means of efficiently broadcasting the search query. This allows every node in the network to process the query locally, and hence is as powerful and flexible as flooding in unstructured networks, but without the inefficiency of redundant messages. While there have been various approaches proposed for broadcasting search queries over DHTs, the focus has not been on validation under churn. Comparing blind search methods for DHTs through simulation we see that churn, in particular nodes leaving the network, has a large impact on query success rate. In this paper we present novel results comparing blind search over Chord and Pastry while under varying levels of churn. We further consider how different data replication strategies can be used to enhance the query success rate

An XCAST Multicast Implementation for the OverSim Simulator

The development of hybrid multicast simulation models is required for analyzing proposed hybrid multicast architectures such as those from the IRTF Scalable Adaptive Multicastw Research Group. However most network layer simulators don't scale to the number of nodes needed for analyzing large overlays, and most overlay simulators don't have multicast routing models needed for analyzing hybrid approaches. In this work we have extended the OverSim simulator and INET framework which run on OMNET++ to include a multi-destination multicast routing protocol (XCAST). This paper describes our implementation experience

Application Layer Multicast Extensions to RELOAD

Native multicast deployment is relatively slow and linked with a number of issues. However, there are a number of native multicast regions. Application Layer Multicast (ALM) can be used in areas of the network where there is no native multicast available. The SAM (Scalable Adaptive Multicast) Research group within the IRTF is investigating hybrid approaches to multicast, involving native deployments were available and ALM in other regions. SAM is using a P2P overlay to connect the nodes. Here we describe a protocol and API extensions to RELOAD for constructing Scalable Adaptive Multicast (SAM) sessions using hybrid combinations of ALM, native multicast, and multicast tunnels. The Automatic Multicast Tunneling (AMT) relay and gateway elements are employed for interoperation between native regions and ALM regions

Towards Optimising WLANs Power Saving: Novel Context-aware Network Traffic Classification Based on a Machine Learning Approach

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on Machine Learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed, Context-aware Listen Interval (CALI) power saving modes. A real-world dataset is recorded, based on nine smartphone applications’ network traffic, reflecting different types of network behaviour and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. Comparative results show that more than 99% of accuracy can be achieved. Our study indicates that ML classifiers are suited for classifying smartphone applications’ network traffic based on levels of interaction in the background

Performance evaluation of OnehopMANET

When used together, Peer-to-Peer overlays and MANET complement each other well. While MANET provides wireless connectivity without depending on any pre-existing infrastructure, P2P overlays provide data storage/retrieval functionality. However, both systems face common challenges: maintaining connectivity in dynamic and decentralized networks. In this paper we evaluate the performance of OnehopMANET[1] as a structured P2P over MANET system that uses cross-layering with a proactive underlay. We compare the performance of OnehopMANET with two recent structured P2P over MANET systems (MA-SP2P and E-SP2P) that use the same underlay protocol (OLSR) and that have been shown to outperform other proposals. Through simulation we show that OnehopMANET achieves a better performance in terms of file discovery delay, lookup fail rate and total traffic load for all the simulated scenarios

OnehopMANET: One-hop structured p2p over mobile ad hoc networks

There are many common characteristics between P2P (Peer to Peer) overlay networks and MANET (mobile ad hoc networks). Previous work has shown that when used together, the two approaches complement each other and performance synergies can be exploited. While MANET provide wireless connectivity without depending on any pre-existing infrastructure, P2P overlays provide data storage/retrieval functionality. On the other hand, both approaches face common challenges: maintaining connectivity in dynamic and decentralized networks. This paper proposes One hop MANET as a structured P2P over MANET the uses cross-layering with a proactive underlay. Unlike previous work, One hop MANET uses a P2P overlay that is capable of achieving lookups in a single hop. Through simulation we show that this approach offers performance benefits when compared with approaches which employ a multi-hop P2P overlay

Design and evaluation of a peer-to-peer MANET crosslayer approach: OneHopOverlay4MANET

Peer-to-Peer overlay networks can be deployed over Mobile Ad hoc Networks (MANET) to address content discovery issues. However, previous research has shown that deploying P2P systems straight over MANET do not exhibit satisfactory performance. Bandwidth limitation, limited resources and node mobility are some of the key constraints. OneHopOverlay4MANET exploits the synergies between MANET and P2P overlays through cross-layering. It combines Distributed Hash Table (DHT) based structured P2P overlays with MANET underlay routing protocols to achieve one logical hop between any pair of overlay nodes. In this paper, we present OneHopOverlay4MANET and evaluate its performance when combined with different underlay routing protocols. We evaluate OneHopOverlay4MANET with two proactive underlay (OLSR and BATMAN) and with three reactive underlay routing protocols (DSR, AODV and DYMO). Through simulation we show that the use of OLSR in OneHopOverlay4MANET yields the best performance. In addition, we compare the performance of the proposed system over OLSR to two recent structured P2P over MANET systems (MA-SP2P and E-SP2P) that adopted OLSR as the routing protocol. As simulation result shows, better performance can be achieved using OneHopOverlay4MANET

Towards Improved Vehicle Arrival Time Prediction in Public Transportation: Integrating SUMO and Kalman Filter Models

Accurate bus arrival time prediction is a key component for improving the attractiveness of public transport. In this research, a model of bus arrival time prediction, which aims to improve arrival time accuracy, is proposed. The arrival time will be predicted using a Kalman Filter (KF) model, by utilising information acquired from social networks. Social Networks feed road traffic information into the model, based on information provided by people who have witnessed events and then updated their social media accordingly. In order to accurately assess the efficiency of KF model, we simulate realistic road scenarios using the traffic simulator Simulation in Urban Mobility (SUMO). SUMO is capable of simulating real world road traffic using digital maps and realistic traffic models. This paper discusses modelling a road journey using Kalman Filters and verifying the results with a corresponding SUMO simulation. As a second step, SUMO based measures are used to inform the KF model. Integrating the SUMO measures with the KF model can be seen as an initial step to verifying our premise that realtime data from social networks can eventually be used to improve the accuracy of the KF prediction. Furthermore, it demonstrates an integrated experimental environment

Structured Peer-to-Peer Overlay Deployment on MANET: A Survey

There are many common characteristics between Peer-to-Peer (P2P) overlay networks and Mobile Ad-hoc Networks (MANET). Self-organization, decentralization, dynamicity and changing topology are the most shared features. Furthermore, when used together, the two approaches complement each other. P2P overlays provide data storage/retrieval functionality, and their routing information can complement that of MANET. MANET provides wireless connectivity between clients without depending on any pre-existing infrastructure. The aim of this paper is to survey current P2P over MANET systems. Specifically, this paper focuses on and investigates structured P2P over MANET. Overall, more than thirty distinct approaches have been classified into groups and introduced in tables providing a structured overview of the area. The survey addresses the identified approaches in terms of P2P systems, MANET underlay systems and the performance of the reviewed systems