Ted Scully

Wireless LAN Load-Balancing with Genetic Algorithms

In recent years IEEE 802.11 wireless local area networks (WLANs) have become increasingly popular. Consequently, there has also been a surge in the number of end-users. The IEEE 802.11 standards do not provide any mechanism for load distribution and as a result user quality of service (QoS) degrades significantly in congested networks where large numbers of users tend to congregate in the same area. The objective of this paper is to provide load balancing techniques that optimise network throughput in areas of user congestion, thereby improving user QoS. Specifically, we develop micro-genetic and standard genetic algorithm approaches for the WLAN load balancing problem, and we analyse their strengths and weaknesses. We also compare the performance of these algorithms with schemes currently in use in IEEE 802.11 WLANs. The results demonstrate that the proposed genetic algorithms give a significant improvement in performance over current techniques. We also show that this improvement is achieved without penalising any class of user.

Coalition calculation in a dynamic agent environment

We consider a dynamic market-place of self-interested agents with differing capabilities. A task to be completed is proposed to the agent population. An agent attempts to form a coalition of agents to perform the task. Before proposing a coalition, the agent must determine the optimal set of agents with whom to enter into a coalition for this task; we refer to this activity as coalition calculation. To determine the optimal coalition, the agent must have a means of calculating the value of any given coalition. Multiple metrics (cost, time, quality etc.) determine the true value of a coalition. However, because of conflicting metrics, differing metric importance and the tendency of metric importance to vary over time, it is difficult to obtain a true valuation of a given coalition. Previous work has not addressed these issues. We present a solution based on the adaptation of a multi-objective optimization evolutionary algorithm. In order to obtain a true valuation of any coalition, we use the concept of Pareto dominance coupled with a distance weighting algorithm. We determine the Pareto optimal set of coalitions and then use an instance-based learning algorithm to select the optimal coalition. We show through empirical evaluation that the proposed technique is capable of eliciting metric importance and adapting to metric variation over time.

Wireless LAN load balancing with genetic algorithms

In recent years IEEE 802.11 wireless local area networks (WLANs) have become increasingly popular. Consequently, there has also been a surge in the number of end-users. The IEEE 802.11 standards do not provide any mechanism for load distribution and as a result user quality of service (QoS) degrades significantly in congested networks where large numbers of users tend to congregate in the same area. The objective of this paper is to provide load balancing techniques that optimise network throughput in areas of user congestion, thereby improving user QoS. Specifically, we develop micro-genetic and standard genetic algorithm approaches for the WLAN load balancing problem, and we analyse their strengths and weaknesses. We also compare the performance of these algorithms with schemes currently in use in IEEE 802.11 WLANs. The results demonstrate that the proposed genetic algorithms give a significant improvement in performance over current techniques. We also show that this improvement is achieved without penalising any class of user.

Economic optimisation for a building with an integrated micro-grid connected to the national grid

This paper proposes a novel operating cost optimisation method for a building with an integrated micro-grid (MG) connected to the National Power Grid (NPG). The MG consists of a photovoltaic system (PVS) and a lead-acid battery bank (BB). The optimisation utilised a twenty-four hour forecast of building energy consumption and the corresponding electrical prices from the NPG. A piecemeal decision algorithm (PDA) and a particle swarm optimisation (PSO) algorithm were used to generate a charge/discharge rates schedule for the BB. The building energy consumption model was developed using empirical data and employee work schedules. Electricity prices were predicted using a real time pricing (RTP) model based on data from the single electricity market operator (SEM-O)[1]. The PVS and BB were modelled based on specifications from manufacturers, and weather data from the Cork Institute of Technology (CIT). The simulation results demonstrate that the building operating costs can be reduced by up to 23 % per day for a single charge/discharge rates schedule, or by up to 30 % per day for a multiple charge/discharge rates schedule.

Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms

Abstract This study analysed the performance of a range of machine learning algorithms when applied to the prediction of electricity and on-farm direct water consumption on Irish dairy farms. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, commercial Irish dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed for their predictive power of monthly electricity and water consumption, respectively. These variables were related to milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions. A CART decision tree algorithm, a random forest ensemble algorithm, an artificial neural network and a support vector machine algorithm were used to predict both water and electricity consumption. The methodology employed backward sequential variable selection to exclude variables, which added little predictive power. It also applied hyper-parameter tuning with nested cross-validation for calculating the prediction accuracy for each model on unseen data (data not utilised for model development). Electricity consumption was predicted to within 12% (relative prediction error (RPE)) using a support vector machine, while the random forest predicted water consumption to within 38%. Overall, the developed machine-learning models improved the RPE of electricity and water consumption by 54% and 23%, respectively, when compared to results previously obtained using a multiple linear regression approach. Further analysis found that during the January, February, November and December period, the support vector machine overpredicted electricity consumption by 4% (mean percentage error (MPE)) and water consumption by 21% (MPE), on average. However, overprediction was greatly reduced during the March – October period with overprediction of electricity consumption reduced to 1% while the overprediction of water consumption reduced to 8%. This was attributed to a phase shift between farms, where some farms produce milk all year round, some dry off earlier/later than others and some farms begin milking earlier/later resulting in an increased the coefficient of variance of milk production making it more difficult to model electricity and water accurately. Concurrently, large negative correlations were calculated between the number of dairy cows and absolute prediction error for electricity and water, respectively, suggesting improvements in electricity and water prediction accuracy may be achieved with increasing dairy cow numbers. The developed machine learning models may be utilised to provide key decision support information to both dairy farmers and policy makers or as a tool for conducting macro scale environmental analysis.

Application of multiple change point detection methods to large urban telecommunication networks

An integral enabler of the smart city vision is the ability to effectively model collective population behaviour. The realisation of sustainable smart mobility is underpinned by the effective modelling of the spatial movements of the population. Furthermore, it is also crucial to identify significant deviations in collective behaviour over time. For example, a change in urban mobility patterns would subsequently impact traffic management systems. This paper focuses on the issue of modelling the collective behaviour of a population by utilizing mobile phone data and investigates the ability to identify significant deviations in behaviour over time. Mobile phone data facilitates the inference of real social networks from their call data records (CDR). We use this data to model collective behaviour and apply change-point detection algorithms, a category of anomaly detection, in order to identify statistically significant changes in collective behaviour over time. The result off the empirical analysis demonstrate that modern change point detection can accurately identify change points with an R2 value of 0.9633.

On the Importance of Spatial Perception for the Design of Adaptive User Interfaces

Automatically changing interfaces, such as Adaptive User Interfaces, are employed in state of the art software products, applications for mobile devices and web sites. While previous work has reported that some adaptive approaches to user interfaces achieve a low user satisfaction score, it has also been shown that they obtain a higher user performance score when compared with their static equivalent. This phenomenon has not been further investigated by previous research. The motivation of this paper is to further investigate this issue and consequently propose design principles for automatically changing interfaces. This is achieved by a fourfold contribution: a literature review of the current state of the art research of interface adaptation methods, the identification of the importance of spatial properties of interface elements (such as position, orientation and size) based on the review, a second literature review of human capabilities and limits that affect the perception and interpretation of spatial changes and lastly design principles for Adaptive User Interfaces are proposed that are derived based on the two literature reviews.

Genetic optimisation for a stochastic model for opportunistic maintenance planning of offshore wind farms

The cost incurred from operations and maintenance activity for offshore wind turbines contributes significantly to the overall lifecycle cost for offshore wind farms. This work addresses the issue of identifying a near-optimal schedule for the planning of operations and maintenance activity for the turbines in an offshore wind farm. Significant cost savings can be realized by scheduling maintenance tasks at times of predicted low power production. This research implements a rolling horizon stochastic model and applies meta-heuristic optimization techniques to identify a near-optimal schedule for the opportunistic maintenance of wind farms. Empirical evaluation of the proposed approach produces schedules that achieve a cost saving in the range of 13-21% over standard techniques.

Promoting Cooperation and Fairness in Self-interested Multi-Agent Systems

The issue of collaboration amongst agents in a multi-agent system (MAS) represents a challenging research problem. In this paper we focus on a form of cooperation known as coalition formation. The problem we consider is how to facilitate the formation of a coalition in a competitive marketplace, where self-interested agents must cooperate by forming a coalition in order to complete a task. Agents must reach a consensus on both the monetary amount to charge for completion of a task as well as the distribution of the required workload. The problem is further complicated because different subtasks have various degrees of difficulty and each agent is uncertain of the payment another agent requires for performing specific subtasks. These complexities, coupled with the self-interested nature of agents, can inhibit or even prevent the formation of coalitions in such a real-world setting. As a solution, an auction-based protocol called ACCORDis proposed.ACCORDmanages real-world complexities by promoting the adoption of cooperative behaviour amongst agents. Through extensive empirical analysis we analyse the ACCORDprotocol and demonstrate that cooperative and fair behaviour is dominant and any agents deviating from this behaviour perform less well over time.

Forming Coalitions in Self-interested Multi-agent Environments Through the Promotion of Fair and Cooperative Behaviour

The issue of collaboration amongst agents in a multi-agent system (MAS) represents a challenging research problem. In this paper we focus on a form of cooperation known as coalition formation. The problem we consider is how to facilitate the formation of a coalition in a competitive marketplace, where self-interested agents must cooperate by forming a coalition in order to complete a task. Agents must reach a consensus on both the monetary amount to charge for completion of a task as well as the distribution of the required workload. The problem is further complicated because different subtasks have various degrees of difficulty and each agent is uncertain of the payment another agent requires for performing specific subtasks. These complexities, coupled with the self-interested nature of agents, can inhibit or even prevent the formation of coalitions in such a real-world setting. As a solution, a novel auction-based protocol called ACCORD is proposed here. ACCORD manages real-world complexities by promoting the adoption of cooperative behaviour amongst agents. Through extensive empirical analysis we analyse the ACCORD protocol and demonstrate that cooperative and fair behaviour is dominant and any agents deviating from this behaviour perform less well over time.