Jason B. Ernst

A survey of QoS/QoE mechanisms in heterogeneous wireless networks

Heterogeneous Wireless Networks (HWNs) are an important step in making connectivity ubiquitous and pervasive. Leveraging the increasing variety of connectivity options available to devices solves many problems such as capacity, spectrum efficiency, coverage and reliability. Anytime decisions are made for selection, handover, scheduling or routing many performance metrics along with energy efficiency and cost for access must be considered. The increased number of choices in an HWN makes the problem more difficult than traditional homogeneous networks since each Radio Access Technology (RAT) has unique characteristics. For instance, Bluetooth networks have low range and speed but are cheap compared to 4G networks. These types of observations can be factored into decision making in HWNs. Quality of Service and Experience should be considered so that the best possible configuration of connectivity, price and user application is made. All of this should occur autonomously. This paper provides a survey of recent works in HWNs with these ideas in mind. Existing approaches are categorized by function. Limitations and strengths of solutions are highlighted and comparisons between approaches are made to provide a starting point for further research in the area.

The design and evaluation of fair scheduling in wireless mesh networks

In this paper we address the problem of scheduling in wireless mesh networks. First, we provide a comparison of existing scheduling algorithms and classify them based on the degree of fairness, the scheduling techniques and their implementation frameworks. Then we propose a fair scheduling approach using multiple gateways. The proposed scheduling approach consists of four important steps, namely, requirement tables, requirement propagation, clique generation and schedule generation. Simulation experiments are conducted to compare the performance of fair scheduling with the method that does not use fair scheduling. The simulation results confirm that the proposed scheduling has better performance with respect to the metrics used for performance evaluation.

Performance evaluation of mixed-bias scheduling schemes for wireless mesh networks

Typically, peripheral nodes in a multi-hop wireless network experience poor performance from starvation, congestion, queue build-up and contention along the path towards internet gateways. We propose three adaptive methods for scheduling based on mixed-bias scheduling which aim to prioritise mesh routers near the gateways to ensure they can handle their own traffic and peripheral traffic. We also give an overview of the mixed-bias approach for scheduling. We then evaluate the performance of each technique in comparison with each other and the IEEE 802.11 distributed coordination function. Each solution is evaluated based on average packet delivery ratio and average end-to-end delay. Two experiments were performed to examine the performance. First, we studied the effect of varying the inter-arrival rate of the packets. Second we examined the effect of changing the number of sources. In all experiments, the proposed approaches perform at least as well or better than IEEE 802.11 DCF.

Creature Learning to Cross a CA Simulated Road

Agent-based models approximate the behaviour of simple natural and man-made systems. We present a simple cognitive agent capable of evaluating if a strategy has been applied successfully and capable of applying this strategy again with small changes to a similar but new situation. We describe some experimental results, present our conclusions, and outlines future work.

Simulated Naïve Creature Crossing a Highway

We present a simple cognitive agent, a “Simulated Naive Creature”, capable of evaluating if a strategy has been applied successfully. The agents are born as “tabula rasa”, i.e. a “blank slate”. They are provided with a mechanism to reason and plan toward their goal, but they have no built-in knowledge-base of their environment. Our simulation shows that the performance of the agents is affected by the conditions of the environment (e.g. the traffic density on the highway and by the crossing point location on the highway) and by their fears and desires.

Fair Scheduling with Multiple Gateways in Wireless Mesh Networks

This paper addresses the problem of scheduling in Wireless Mesh Networks (WMNs). The paper first discusses existing scheduling algorithms and classifies them based on the technique they use and their implementation frameworks.Then detailed discussions of the proposed scheduling method are presented. Simulation experiments are conducted to compare the performance of fair scheduling with the method that does not use fair scheduling. The simulation results confirm that the proposed scheduling method has better performance with respect to the metrics used for performance evaluation.

Algorithms and Implementation for Interconnection Graph Problem

The Interconnection Graph Problem (IGP) is to compute for a given hypergraph H= (V, R) a graph G= (V, E) with the minimum number of edges |E| such that for all hyperedges N? Rthe subgraph of Ginduced by Nis connected. Computing feasible interconnection graphs is basically motivated by the design of reconfigurable interconnection networks. This paper proves that IGP is NP-complete and hard to approximate even when all hyperedges of Hhave at most three vertices. Afterwards it presents a search tree based parameterized algorithm showing that the problem is fixed-parameter tractable when the hyperedge size of His bounded. Moreover, the paper gives a reduction based greedy algorithm and closes with its experimental justification.

Naïve creature learns to cross a highway in a simulated CA-like environment

We present a model of simple cognitive agents, called “creatures”, and their learning process, a type of “social observational learning”, that is each creature learns from the behaviour of other creatures. The creatures may experience fear and/or desire, and are capable of evaluating if a strategy has been applied successfully and of applying this strategy again with small changes to a similar but new situation. The creatures are born as “tabula rasa”; i.e. without built-in knowledge base of their environment and as they learn they build this knowledge base. We study learning outcomes of a population of such creatures when they are learning how to safely cross various types of highways. The highways are implemented as a modified Nagel-Schreckenberg model, a CA based highway model, and each creature is provided with mechanism to reason to cross safely the highway. We present selected simulation results and their analysis.

Co-channel interference modelling between RATs in heterogeneous wireless networks

Co-channel interference models are increasingly becoming important in wireless networking. This is especially true in heterogeneous networks where a single device may cause unintended interactions between multiple radios using difference radio access technologies, even though they are located on the same device. Currently simulation tools provide varying levels of modelling of this phenomenon. This paper tries to quantify how well popular wireless simulation tools capture these effects using qualitative techniques while providing quantitative evaulation of the effects of interference between two radio access technologies - Bluetooth and Wi-Fi. Each tool is compared according to level of interference modelling offered, the technique for interference modelling and the types of RATs which are supported. The effect of co-channel interference between Bluetooth and Wi-Fi is evaluated with respect to throughput using devices equipped with both Blutooth and Wi-Fi radios.

Cross-Layer Mixed Bias Scheduling for Wireless Mesh Networks

In this paper we propose a mixed bias approach which makes use of cross layer optimization. The cross-layer parameters are based on conditions in the network from multiple layers and are used to determine resource and time allocation for nodes in the network. Unlike existing proposals, we propose to bias against several parameters such as link quality and queue size in addition to node distance. We also propose a combined mixed bias approach which takes into account multiple parameters together. The scheme is evaluated using simulation experiments. The performance results are reported in this paper.