Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Non-cooperative power control game in D2D underlying networks with variant system conditions

In this paper, the problem of power control using a game theoretic approach based on sigmoid cost function is studied for device-to-device (D2D) communications underlying cellular networks. A non-cooperative game, where each D2D transmitter and a cellular user select their own transmit power level independently, is analyzed to minimize their user-serving cost function and achieve a target signal to interference-plus-noise-ratio (SINR) requirement. It is proved analytically that the Nash equilibrium point of the game exists and it is unique under certain constraints. Numerical results verify the analysis and demonstrate the effectiveness of the proposed game with variant system conditions, such as path loss exponents, target SINR, interference caused by the cellular user, pricing coefficients, and sigmoid control parameter

Resource Allocation for Interference Management in Wireless Networks

Interference in wireless networks is a major problem that impacts system performance quite substantially. Combined with the fact that the spectrum is limited and scarce, the performance and reliability of wireless systems significantly deteriorates and, hence, communication sessions are put at the risk of failure. In an attempt to make transmissions resilient to interference and, accordingly, design robust wireless systems, a diverse set of interference mitigation techniques are investigated in this dissertation. Depending on the rationale motivating the interfering node, interference can be divided into two categories, communication and jamming. For communication interference such as the interference created by legacy users(e.g., primary user transmitters in a cognitive radio network) at non-legacy or unlicensed users(e.g.,secondary user receivers), two mitigation techniques are presented in this dissertation. One exploits permutation trellis codes combined with M-ary frequency shift keying in order to make SU transmissions resilient to PUs’ interference, while the other utilizes frequency allocation as a mitigation technique against SU interference using Matching theory. For jamming interference, two mitigation techniques are also investigated here. One technique exploits time and structures a jammer mitigation framework through an automatic repeat request protocol. The other one utilizes power and, following a game-theoretic framework, employs a defense strategy against jamming based on a strategic power allocation. Superior performance of all of the proposed mitigation techniques is shown via numerical results

Optimal decision making in cognitive radio networks

Cognitive Radio Networks are being researched upon heavily in the various layers of the communication structure. The task of bringing software in the physical layer of communication system led to the concept of a smart radio being able to learn, adapt and make intelligent decisions in an autonomous manner by use of a Software Defined Radio. This work provides novel concepts in the areas of spectrum sensing, learning of ongoing transmissions through Reinforcment learning, use of a game theoretic concept such as Zero-sum game for resilience of authorized users in cases of jamming, and decision making of user transmissions through Markov Decision processes. This is highly applicable in dynamic radio environments such as emergency communications required during natural disasters, large scale events and in mobile wireless communications. Such applications come under the "Internet of Things"

Modeling Security and Resource Allocation for Mobile Multi-hop Wireless Neworks Using Game Theory

This dissertation presents novel approaches to modeling and analyzing security and resource allocation in mobile ad hoc networks (MANETs). The research involves the design, implementation and simulation of different models resulting in resource sharing and security’s strengthening of the network among mobile devices. Because of the mobility, the network topology may change quickly and unpredictably over time. Moreover, data-information sent from a source to a designated destination node, which is not nearby, has to route its information with the need of intermediary mobile nodes. However, not all intermediary nodes in the network are willing to participate in data-packet transfer of other nodes. The unwillingness to participate in data forwarding is because a node is built on limited resources such as energy-power and data. Due to their limited resource, nodes may not want to participate in the overall network objectives by forwarding data-packets of others in fear of depleting their energy power. To enforce cooperation among autonomous nodes, we design, implement and simulate new incentive mechanisms that used game theoretic concepts to analyze and model the strategic interactions among rationale nodes with conflicting interests. Since there is no central authority and the network is decentralized, to address the concerns of mobility of selfish nodes in MANETs, a model of security and trust relationship was designed and implemented to improve the impact of investment into trust mechanisms. A series of simulations was carried out that showed the strengthening of security in a network with selfish and malicious nodes. Our research involves bargaining for resources in a highly dynamic ad-hoc network. The design of a new arbitration mechanism for MANETs utilizes the Dirichlet distribution for fairness in allocating resources. Then, we investigated the problem of collusion nodes in mobile ad-hoc networks with an arbitrator. We model the collusion by having a group of nodes disrupting the bargaining process by not cooperating with the arbitrator. Finally, we investigated the resource allocation for a system between agility and recovery using the concept of Markov decision process. Simulation results showed that the proposed solutions may be helpful to decision-makers when allocating resources between separated teams

Spectrum Matching in Licensed Spectrum

Spectrum sharing is one of the promising solutions to meet the spectrum demand in 5G networks that results from the emerging services like machine to machine and vehicle to infrastructure communication. The idea is to allow a set of entities access the spectrum whenever and wherever it is unused by the licensed users. In the proposed framework, different spectrum provider (SP) networks with surplus spectrum available may rank the operators requiring the spectrum, called spectrum users (SUs) hereafter, differently in terms of their preference to lease spectrum, based for example on target business market considerations of the SUs. Similarly, SUs rank SPs depending on a number of criteria, for example based on coverage and availability in a service area. Ideally, both SPs and SUs prefer to provide/get spectrum to/from the operator of their first choice, but this is not necessarily always possible due to conflicting preferences. We apply matching theory algorithms with the aim to resolve the conflicting preferences of the SPs and SUs and quantify the effect of the proposed matching theory approach on establishing preferred (spectrum) provider-user network pairs. We discuss both one-to-one and many-to-one spectrum sharing scenarios and evaluate the performance using Monte Carlo simulations. The results show that comprehensive gains in terms of preferred matching of the provider-user network pairs can be achieved by applying matching theory for spectrum sharing as compared to uncoordinated spectrum allocation of the available spectrum to the SUs

Enlace de retorno satelital DVB-RCS2 : modelagem de fila e otimização de alocação de recursos baseada em teoria dos jogos

Tese (doutorado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2022.É esperado que satélites tenham um papel fundamental no futuro dos sistemas de comunicação, integrando-se às infraestruturas terrestres. Esta dissertação de mestrado propõe três contribuições principais: primeiramente, se apresenta um arcabouço de simulação capaz de prover detalhes da performance de redes de comunicação satelital em cenários realistas. Este arcabouço aplica uma metodologia orientada a eventos, modelando a rede de comunicação como um sistema baseado em eventos discretos (DES), focando no enlace de retorno do protocolo DVB-RCS2. Três diferentes cenários simulados demonstram os possíveis usos das saídas do simulador para entender o comportamento dinâmico da rede e alcançar um ponto ótimo de operação do sistema. Cada cenário explora uma característica diferente do simulador, enquanto cobre um grande território de usuários, que em nosso caso estudo o país de escolha foi o Brasil. Em um segundo tópico, este trabalho introduz um novo algoritmo modificado do método de alocação de timeslots baseado em teoria dos jogos, aplicando-se no protocolo DVB-RCS2. Este procedimento considera a eficiência espectral do terminal como um parâmetro de peso para o problema de otimização convexa resultante da solução da barganha de Nash. Este novo método garante o cumprimento dos requisitos de Qualidade de Serviço (QoS) enquanto provê uma medida de justiça maior; os resultados mostram uma melhoria de 5% na medida de justiça, com uma diminuição de 75% no desvio padrão de justiça entre os quadros, também alcançando um aumento de 12% na satisfação individual média pela alocação de capacidade aos terminais. Por final, apresentamos uma modelagem alternativa para o enlace de retorno do DVB-RCS2 usando cadeias de Markov, predizendo parâmetros tradicionais de fila como a intensidade de tráfego, tempo médio de espera, dentre outros. Utilizamos dados coletados de uma série de simulações usando o arcabouço orientado a eventos para validar o modelo de filas como uma aproximação numérica útil para o cenário real de aplicação. Nós apresentamos o algoritmo de alocação de controle do parâmetro alfa (GTAC) que consegue controlar o tempo médio de espera de um RCST na fila, respeitando um limiar de tempo enquanto otimiza a taxa média média de transmissão de dados dos terminais.Satellite networks are expected to play a vital role in future communication systems, with complex features and seamless integration with ground-based infrastructure. This dissertation proposes three main contributions: firstly, it presents a novel simulation framework capable of providing a detailed assessment of a satellite communication’s network performance in realistic scenarios, employing an event-driven methodology and modeling the communications network as a DES (discrete event system). This work focuses on the return link of the Digital Video Broadcast Return Channel via Satellite (DVB-RCS2) standard. Three different scenarios demonstrate possible uses of the simulator’s output to understand the network’s dynamic behavior and achievable optimal system operation. Each scenario explores a different feature of the simulator. The simulated range covers a large territory with thousands of users, which in our case study was the country of Brazil. In the second theme, this work introduces a novel algorithm modification for the conventional game theory-based time slot assignment method, applying it to the DVB-RCS system. This procedure considers the spectral efficiency as a weighting parameter. We use it as an input for the resulting convex optimization problem of the Nash Bargaining Solution. This approach guarantees the fulfillment of Quality of Service (QoS) constraints while maintaining a higher fairness measure; results show a 5% improvement in fairness, with a 73% decrease in the standard deviation of fairness between frames, while also managing to reach a 12.5% increase in average normalized terminal BTU allocation satisfaction. Lastly, we present an alternative queuing model analysis for the DVB-RCS2 return link using Markov chains, developed to predict traditional queue parameters such as traffic intensity, average queue size, average waiting time, among others. We used data gathered from a series of simulations using the DES framework to validate this queuing model as a useful numerical approximation to the real application scenario, and, by the end of the scope, we present the alpha allocation algorithm (GTAC) that can maintain the average waiting time of a terminal in the queue to a threshold while optimizing the average terminal throughput