Quality of service support for multimedia applications in mobile ad hoc networks

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Stable Clustering through Mobility Prediction for Large-scale Multihop Intelligent Ad Hoc Networks

Abstract — In this paper we present a framework for dynamically organizing mobile nodes (MNs) in large-scale mobile ad hoc networks (MANETs), with the eventual aim to support Quality of Service (QoS). Our dynamic, distributed clustering approach is based on intelligent mobility prediction that enables each MN to anticipate the availability of its neighbors. We present a scalable way to predict the mobility, and thus availability, of MNs, achieved with the introduction of geographically-oriented virtual clusters. We name the proposed model as the (p, t, d)-clustering model that facilitates the formation of stable clusters. Simulation results demonstrate the performance advantages of our approach. Keywords- Ad-hoc networking; Mobility prediction; Hierarchical clustering, QoS

Quality of Service Support for Multimedia Applications in Mobile Ad Hoc Networks - A Cross-Layered Approach

Definition: Quality of Service (QoS) is characterized as a set of service requirements to be met by the network while transporting a packet stream between a given source-destination pair, while a Mobile Ad hoc NETwork (MANET) is defined as an “autonomous system of mobile routers (and associated hosts) connected by wireless links – the union of which form an arbitrary graph”

Stable Clustering Through Mobility Prediction for Large-scale Multihop Intelligent Ad Hoc Networks

In this paper we present a framework for dynamically organizing mobile nodes (MNs) in large-scale mobile ad hoc networks (MANETs), with the eventual aim to support Quality of Service (QoS). Our dynamic, distributed clustering approach is based on intelligent mobility prediction that enables each MN to anticipate the availability of its neighbors. We present a scalable way to predict the mobility, and thus availability, of MNs, achieved with the introduction of geographically-oriented virtual clusters. We name the proposed model as the (p, t, d)-clustering model that facilitates the formation of stable clusters. Simulation results demonstrate the performance advantages of our approach

Two-way Admission Control and Resource Allocation for Quality of Service Support

This paper proposes a new QoS-aware medium access control (MAC) protocol in mobile ad hoc networks (MANETs). This takes the unique challenges of MANETs into consideration, and works in conjunction with the location-based forwarding strategy. This novel protocol is based on the legacy IEEE 802.11, and thus can be relatively easily integrated into existing systems. It is adaptive and network-aware depending on the type and intensity of traffic, and relative mobility patterns of nodes. In addition, it makes use of the pointcoordination-function (PCF) of IEEE 802.11 in a distributed fashion for the first time in multihop MANETs. Our strategy enables two-way admission control for improved performance, whereby the forwarder-node selection algorithm allows previous hop nodes to perform implicit admission control using locally available information, while a selected forwarder-node performs explicit admission control depending on its current load. Analytical results confirm the performance improvement of our strategy. I

Stable clustering through mobility prediction for large-scale multihop intelligent ad hoc networks

In this paper we present a framework for dynamically organizing mobile nodes (MNs) in large-scale mobile ad hoc networks (MANETs), with the eventual aim to support quality of service (QoS). Our dynamic, distributed clustering approach is based on intelligent mobility prediction that enables each MN to anticipate the availability of its neighbors. We present a scalable way to predict the mobility, and thus availability, of MNs, achieved with the introduction of geographically-oriented virtual clusters. We name the proposed model as the (p, t, d)-clustering model that facilitates the formation of stable clusters. Simulation results demonstrate the performance advantages of our approach

CLUSTER-BASED LOCATION-SERVICES FOR SCALABLE AD HOC NETWORK ROUTING

Abstract: We propose a location-service to assist location-based routing protocols, realised through our Associativity-Based Clustering protocol. The main goal of our scheme, which employs hierarchical principles, is to minimize the control traffic associated with location-management. In location-based routing protocols, the control traffic is mainly due to location-updates, queries and responses. Our scheme employs a novel geographically-oriented clustering scheme in order to minimize control traffic without impairing performance. In our location management scheme, nodes are assigned home-zones, and are required to send their location-updates to their respective home-zones through a dominating-set. This strategy, unlike similar location-management approaches, minimizes inevitable superfluous flooding by every node, and prevents location updates and queries from traversing the entire network unnecessarily, hence conserving bandwidth and transmission power. We evaluate our proposed scheme through simulations, and the results indicate that our protocol scales well with increasing node-count

Effective Management Through Prediction-based Clustering for Next Generation Ad Hoc Networks

Abstract—A framework for a proactive network management with the eventual aim to support Quality of Service (QoS) provisioning in ad hoc networks is proposed in this paper. This process is facilitated through our novel hierarchical clustering approach. This clustering approach is dynamic and distributed, and enables each mobile node (MN) to anticipate the availability of its neighbors through a scalable intelligent mobility prediction algorithm. With the formation of stable clusters, our clustering algorithm enables adaptability, autonomy, economy, scalability and survivability requirements in managing ad hoc networks by adopting policy-based management technique with mobile agent concepts. Initial results demonstrate the stability improvement of our approach. Keywords – Ad-hoc networking; Mobility prediction; Hierarchical clusteringe; Ad-hoc management; QoS provisioning