Topology Control for Resource Management in Microwave Backhaul Networks

<p>Microwave backhaul networks are the dominant technology used to connect together access and core networks for their flexibility and cost-effectiveness in deployment. Unfortunately, microwave backhaul networks are susceptible to interference and are statically managed leading to poor Quality of Service (QoS) in the form of high delays and loss as well as being inefficient on energy. The use of Software Defined Networking (SDN) is proposed to address these problems by dynamically managing resources to work around the interference and remove static allocations. Two new algorithms, CUT and OptiCUT were designed to compute an optimal topology, to minimise loss and delay while at the same time reducing power consumption.</p>

Topology Control for Resource Management in Microwave Backhaul Networks

<p>Microwave backhaul networks are the dominant technology used to connect together access and core networks for their flexibility and cost-effectiveness in deployment. Unfortunately, microwave backhaul networks are susceptible to interference and are statically managed leading to poor Quality of Service (QoS) in the form of high delays and loss as well as being inefficient on energy. The use of Software Defined Networking (SDN) is proposed to address these problems by dynamically managing resources to work around the interference and remove static allocations. Two new algorithms, CUT and OptiCUT were designed to compute an optimal topology, to minimise loss and delay while at the same time reducing power consumption.</p>

Contact ability based topology control for predictable delay-tolerant networks

In predictable delay tolerant networks (PDTNs), the network topology is known a priori or can be predicted over time, such as space planet networks and vehicular networks based on public buses or trains. Due to the intermittent connectivity, network partitioning, and long delays in PDTNs, most of the researchers mainly focuses on routing and data access research. However, topology control can improve energy effectiveness and increase the communication capacity, thus how to maintain the dynamic topology of PDTNs becomes crucial. In this paper, a contact ability based topology control method for PDTNs is proposed. First, the contact ability is calculated using our contact ability calculation model, and then the PDTNs is modeled as an undirected weighted contact graph which includes spatial and contact ability information. The topology control problem is defined as constructing a minimum spanning tree (MST) that the contact ability of the MST is maximized. We propose two algorithms based on undirected weighted contact graph to solve the defined problem, and compare them with the latest method in terms of energy cost and contact ability. Extensive simulation experiments demonstrate that the proposed algorithms can guarantee data transmission effectively, and reduce the network energy consumption significantly.

A tale of two graph models: a case study in wireless sensor networks

Designing and reasoning about complex systems such as wireless sensor networks is hard due to highly dynamic environments: sensors are heterogeneous, battery-powered, and mobile. While formal modelling can provide rigorous mechanisms for design/reasoning, they are often viewed as difficult to use. Graph rewrite-based modelling techniques increase usability by providing an intuitive, flexible, and diagrammatic form of modelling in which graph-like structures express relationships between entities while rewriting mechanisms allow model evolution. Two major graph-based formalisms are Graph Transformation Systems (GTS) and Bigraphical Reactive Systems (BRS). While both use similar underlying structures, how they are employed in modelling is quite different. To gain a deeper understanding of GTS and BRS, and to guide future modelling, theory, and tool development, in this experience report we compare the practical modelling abilities and style of GTS and BRS when applied to topology control in WSNs. To show the value of the models, we describe how analysis may be performed in both formalisms. A comparison of the approaches shows that although the two formalisms are different, from both a theoretical and practical modelling standpoint, they are each successful in modelling topology control in WSNs. We found that GTS, while featuring a small set of entities and transformation rules, relied on entity attributes, rule application based on attribute/variable side-conditions, and imperative control flow units. BRS on the other hand, required a larger number of entities in order to both encode attributes directly in the model (via nesting) and provide tagging functionality that, when coupled with rule priorities, implements control flow. There remains promising research mapping techniques between the formalisms to further enable flexible and expressive modelling.

Contact Ability Based Topology Control for Predictable Delay-tolerant Networks

In predictable delay tolerant networks (PDTNs), the network topology is known a priori or can be predicted over time such as space planet networks and vehicular networks based on public buses or trains. Due to the intermittent connectivity, network partitioning, and long delays in PDTNs, most of the researchers mainly focuses on routing and data access research. However, topology control can improve energy effectiveness and increase the communication capacity, thus how to maintain the dynamic topology of PDTNs becomes crucial. In this paper, a contact ability based topology control method for PDTNs is proposed. First, the contact ability is calculated using our contact ability calculation model, and then the PDTNs is modeled as an undirected weighted contact graph which includes spatial and contact ability information. The topology control problem is defined as constructing a Minimum Spanning Tree(MST) that the contact ability of the MST is maximized. We propose two algorithms based on undirected weighted contact graph to solve the defined problem, and compare them with the latest method in terms of energy cost and contact ability. Extensive simulation experiments demonstrate that the proposed algorithms can guarantee data transmission effectively, and reduce the network energy consumption significantly.