University course timetabling with probability collectives

The Naval Postgraduate School currently uses a time consuming manual process to generate course schedules for students and professors. Each quarter, the process of timetabling approximately 2000 students into nearly 500 courses takes up to 8 weeks. This thesis introduces an automated timetabling algorithm using Probability Collectives (PC) theory. PC Theory is an agent based approach that utilizes Collective Intelligence (COIN) to solve optimization problems by using a collection of agents attempting to achieve a single goal. The algorithm was tested on a set of data provided by the organizers of the 2007 International Timetabling Competition. The algorithm provided valid timetables for every problem instance and successfully scheduled between 70% and 91.6% of all student course requests.http://archive.org/details/universitycourse109454289US Navy (USN) author.Approved for public release; distribution is unlimited

Cross-layer design of multi-hop wireless networks

MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated

On Adjacent Channel Interference-Aware Radio Resource Management for Vehicle-to-Vehicle Communication

Safety applications play an essential role in supporting traffic safety and efficiency in next generation vehicular networks. Typical safety applications require vehicle-to-vehicle (V2V) communication with high reliability and low latency. The reliability of a communication link is mainly determined by the received interference, and broadly speaking, there are two types of interferences: co-channel interference (CCI) and adjacent channel interference (ACI). The CCI is cross-talk between transmitters scheduled in the same time-frequency slot, whereas ACI is interference due to leakage of transmit power outside the intended frequency slot. The ACI is typically not a problem in cellular communication since interference is dominated by CCI due to spectrum re-usage. However, ACI is a significant problem in near-far situations, i.e., when the channel gain from the interferer to receiver is high compared to the channel gain from the intended transmitter. The near-far situation is more common in V2V broadcast communication scenario due to high dynamic range of the channel gain and penetration loss by intermediate vehicles. This thesis investigates the impact of ACI on V2V communication and methods to mitigate it by proper radio resource management (RRM), i.e., scheduling and power control.In [Paper A], we first study ACI models for various transmission schemes and its impact on V2V communication. We propose a problem formulation for a) optimal scheduling as a Boolean linear programming (BLP) problem and b) optimal power control as a mixed Boolean linear programming (MBLP) problem. The objective of the problem formulation is to maximize the connectivity among VUEs in the network. Near-optimal schedules and power values are computed by solving first a) and then b) for smaller size instances of the problem. To handle larger-size instances of the problem, heuristic scheduling and power control algorithms with less computational complexity are proposed. We also propose a simple distributed block interleaver scheduler (BIS), which can be used as a baseline method.In [Paper B], we formulate the joint scheduling and power control problem as an MBLP to maximize the connectivity among VUEs. A column generation method is proposed to address the scalability of the network, i.e., to reduce the computational complexity of the joint problem. Moreover, the scheduling problem is observed to be numerically sensitive due to the high dynamic range of channel values and adjacent channel interference ratio (ACIR) values. Therefore, a novel method is proposed to reduce the sensitivity and compute a numerically stable optimal solution at the price of increased computational complexity.In [Paper C], we extend the RRM problem formulation to include various objectives, such as maximizing connectivity/throughput and minimizing age of information (AoI). In order to account for the fairness, we also formulate the problem to improve the worst-case throughput, connectivity, and AoI of a link in the network. All the problems are formulated as MBLP problems. In order to support a large V2V network, a clustering algorithm is proposed whose computational complexity scale well with the network size. Moreover, a multihop distributed scheduling scheme is proposed to handle zero channel state information (CSI) case

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

A slotted-CDMA based wireless-ATM link layer : guaranteeing QoS over a wireless link.

Thesis (M.Sc.)-University of Natal, Durban, 2000.Future wireless networks will have to handle varying combinations of multimedia traffic that present the network with numerous quality of service (QoS) requirements. The continuously growing demand for mobile phones has resulted in radio spectrum becoming a precious resource that cannot be wasted. The current second-generation mobile networks are designed for voice communication and, even with the enhancements being implemented to accommodate data, they cannot efficiently handle the multimedia traffic demands that will be introduced in the near future. This thesis begins with a survey of existing wireless ATM (WATM) protocols, followed by an examination of some medium access control (MAC) protocols, supporting multimedia traffic, and based on code division multiple access (CDMA) physical layers. A WATM link layer protocol based on a CDMA physical layer, and incorporating techniques from some of the surveyed protocols, is then proposed. The MAC protocol supports a wide range of service requirements by utilising a flexible scheduling algorithm that takes advantage of the graceful degradation of CDMA with increasing user interference to schedule cells for transmission according to their maximum bit error rate (BER) requirements. The data link control (DLC) accommodates the various traffic types by allowing virtual channels (VCs) to make use of forward error correction (FEc) or retransmission techniques. The proposed link layer protocol has been implemented on a Blue Wave Systems DSP board that forms part of Alcatel Altech Telecoms' software radio platform. The details and practicality of the implementation are presented. A simulation model for the protocol has been developed using MIL3 's Opnet Modeler. Hence, both simulated and measured performance results are presented before the thesis concludes with suggestions for improvements and future work

Learning Automata based Shiftable Domestic Load Scheduling in Smart Grid: Accuracy and Fairness

Master's thesis in Information- and communication technology IKT590 - University of Agder 2016In this thesis, investigation is carried out on scheduling of shiftable loads which involves partly selection of loads within the power budget of operator. Domestic shiftable loads are scheduled along multiple timeslots with the considerations of the accuracy of scheduling in terms of optimization of capacity and of the fairness between appliances in terms of frequency of usage in smart grids. Since the scheduled load can not be over the capacity, the global optimal point is a combination of loads which are most close or equal to but not over the capacity. This optimization problem is shown to be NP hard, and has been formulated as a potential game. To solve this problem in a distributed manner, Learning Automata (LA) based methods are proposed. Although the LA based methods do not favour any participants of scheduling which can serve as a fair selection in the long run, the fairness among the loads in finite time is still worth studying. To make the scheduling process fair in short time, virtual coin game is employed into the scheduling. Simulations have been performed by implementing two LA methods, namely BLA and LR−I, under different number of timeslots, with and without consideration of coin game to evaluate and compare the results. Simulation results show that the accuracy in terms of the closeness of the converged result to the global optimal point achieved by both LA based scheduling methods is high and the fairness of the system is increased by applying the virtual coin game

Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated