Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks

It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve network wide characteristics. Studies have shown that Small World properties, primarily low average path length and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small world properties show a degree distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the average path length and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on average path length, clustering coefficient and connectivity.Comment: Accepted for publication: Special Issue on Security and Performance of Networks and Clouds (The Computer Journal

Control and optimization approaches for energy-limited systems: applications to wireless sensor networks and battery-powered vehicles

This dissertation studies control and optimization approaches to obtain energy-efficient and reliable routing schemes for battery-powered systems in network settings. First, incorporating a non-ideal battery model, the lifetime maximization problem for static wireless sensor networks is investigated. Adopting an optimal control approach, it is shown that there exists a time-invariant optimal routing vector in a fixed topology network. Furthermore, under very mild conditions, this optimal policy is robust with respect to the battery model used. Then, the lifetime maximization problem is investigated for networks with a mobile source node. Redefining the network lifetime, two versions of the problem are studied: when there exist no prior knowledge about the source node’s motion dynamics vs. when source node’s trajectory is known in advance. For both cases, problems are formulated in the optimal control framework. For the former, the solution can be reduced to a sequence of nonlinear programming problems solved on line as the source node trajectory evolves. For the latter, an explicit off-line numerical solution is required. Second, the problem of routing for vehicles with limited energy through a network with inhomogeneous charging nodes is studied. The goal is to minimize the total elapsed time, including traveling and recharging time, for vehicles to reach their destinations. Adopting a game-theoretic approach, the problem is investigated from two different points of view: user-centric vs. system-centric. The former is first formulated as a mixed integer nonlinear programming problem. Then, by exploiting properties of an optimal solution, it is reduced to a lower dimensionality problem. For the latter, grouping vehicles into subflows and including the traffic congestion effects, a system-wide optimization problem is defined. Both problems are studied in a dynamic programming framework as well. Finally, the thesis quantifies the Price Of Anarchy (POA) in transportation net- works using actual traffic data. The goal is to compare the network performance under user-optimal vs. system-optimal policies. First, user equilibria flows and origin- destination demands are estimated for the Eastern Massachusetts transportation net- work using speed and capacity datasets. Then, obtaining socially-optimal flows by solving a system-centric problem, the POA is estimated

Scaling Laws for Vehicular Networks

Equipping automobiles with wireless communications and networking capabilities is becoming the frontier in the evolution to the next generation intelligent transportation systems (ITS). By means of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, information generated by the vehicle-borne computer, vehicle control system, on-board sensors, or roadside infrastructure, can be effectively disseminated among vehicles/infrastructure in proximity or to vehicles/infrastructure multiple hops away, known as vehicular networks (VANETs), to enhance the situational awareness of vehicles and provide motorist/passengers with an information-rich travel environment. Scaling law for throughput capacity and delay in wireless networks has been considered as one of the most fundamental issues, which characterizes the trend of throughput/delay behavior when the network size increases. The study of scaling laws can lead to a better understanding of intrinsic properties of wireless networks and theoretical guidance on network design and deployment. Moreover, the results could also be applied to predict network performance, especially for the large-scale vehicular networks. However, map-restricted mobility and spatio-temporal dynamics of vehicle density dramatically complicate scaling laws studies for VANETs. As an effort to lay a scientific foundation of vehicular networking, my thesis investigates capacity scaling laws for vehicular networks with and without infrastructure, respectively. Firstly, the thesis studies scaling law of throughput capacity and end-to-end delay for a social-proximity vehicular network, where each vehicle has a restricted mobility region around a specific social spot and services are delivered in a store-carry-and-forward paradigm. It has been shown that although the throughput and delay may degrade in a high vehicle density area, it is still possible to achieve almost constant scaling for per vehicle throughput and end-to-end delay. Secondly, in addition to pure ad hoc vehicular networks, the thesis derives the capacity scaling laws for networks with wireless infrastructure, where services are delivered uniformly from infrastructure to all vehicles in the network. The V2V communication is also required to relay the downlink traffic to the vehicles outside the coverage of infrastructure. Three kinds of infrastructures have been considered, i.e., cellular base stations, wireless mesh backbones (a network of mesh nodes, including one mesh gateway), and roadside access points. The downlink capacity scaling is derived for each kind of infrastructure. Considering that the deployment/operation costs of different infrastructure are highly variable, the capacity-cost tradeoffs of different deployments are examined. The results from the thesis demonstrate the feasibility of deploying non-cellular infrastructure for supporting high-bandwidth vehicular applications. Thirdly, the fundamental impact of traffic signals at road intersection on drive-thru Internet access is particularly studied. The thesis analyzes the time-average throughput capacity of a typical vehicle driving through randomly deployed roadside Wi-Fi networks. Interestingly, we show a significant throughput gain for vehicles stopping at intersections due to red signals. The results provide a quick and efficient way of determining the Wi-Fi deployment scale according to required quality of services. In summary, the analysis developed and the scaling laws derived in the thesis provide should be very useful for understanding the fundamental performance of vehicular networks

Fine-grained performance analysis of massive MTC networks with scheduling and data aggregation

Abstract. The Internet of Things (IoT) represents a substantial shift within wireless communication and constitutes a relevant topic of social, economic, and overall technical impact. It refers to resource-constrained devices communicating without or with low human intervention. However, communication among machines imposes several challenges compared to traditional human type communication (HTC). Moreover, as the number of devices increases exponentially, different network management techniques and technologies are needed. Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. This thesis provides an overview of the most common IoT applications and the network technologies to support them. We describe the most important challenges in machine type communication (MTC). We use a stochastic geometry (SG) tool known as the meta distribution (MD) of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the wireless nodes’ locations, to provide a fine-grained description of the per-link reliability. Specifically, we analyze the performance of two scheduling methods for data aggregation of MTC: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements. Finally, the impact on the fraction of MTDs that communicate with a target reliability when increasing the aggregators density is investigated

Drone-Assisted Wireless Communications

In order to address the increased demand for any-time/any-where wireless connectivity, both academic and industrial researchers are actively engaged in the design of the fifth generation (5G) wireless communication networks. In contrast to the traditional bottom-up or horizontal design approaches, 5G wireless networks are being co-created with various stakeholders to address connectivity requirements across various verticals (i.e., employing a top-to-bottom approach). From a communication networks perspective, this requires obliviousness under various failures. In the context of cellular networks, base station (BS) failures can be caused either due to a natural or synthetic phenomenon. Natural phenomena such as earthquake or flooding can result in either destruction of communication hardware or disruption of energy supply to BSs. In such cases, there is a dire need for a mechanism through which capacity short-fall can be met in a rapid manner. Drone empowered small cellular networks, or so-called \quotes{flying cellular networks}, present an attractive solution as they can be swiftly deployed for provisioning public safety (PS) networks. While drone empowered self-organising networks (SONs) and drone small cell networks (DSCNs) have received some attention in the recent past, the design space of such networks has not been extensively traversed. So, the purpose of this thesis is to study the optimal deployment of drone empowered networks in different scenarios and for different applications (i.e., in cellular post-disaster scenarios and briefly in assisting backscatter internet of things (IoT)). To this end, we borrow the well-known tools from stochastic geometry to study the performance of multiple network deployments, as stochastic geometry provides a very powerful theoretical framework that accommodates network scalability and different spatial distributions. We will then investigate the design space of flying wireless networks and we will also explore the co-existence properties of an overlaid DSCN with the operational part of the existing networks. We define and study the design parameters such as optimal altitude and number of drone BSs, etc., as a function of destroyed BSs, propagation conditions, etc. Next, due to capacity and back-hauling limitations on drone small cells (DSCs), we assume that each coverage hole requires a multitude of DSCs to meet the shortfall coverage at a desired quality-of-service (QoS). Hence, we consider the clustered deployment of DSCs around the site of the destroyed BS. Accordingly, joint consideration of partially operating BSs and deployed DSCs yields a unique topology for such PS networks. Hence, we propose a clustering mechanism that extends the traditional Mat\'{e}rn and Thomas cluster processes to a more general case where cluster size is dependent upon the size of the coverage hole. As a result, it is demonstrated that by intelligently selecting operational network parameters such as drone altitude, density, number, transmit power and the spatial distribution of the deployment, ground user coverage can be significantly enhanced. As another contribution of this thesis, we also present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first-order statistics of received signal-to-interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta-distribution. As a result, our new design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. Finally, for drone-assisted IoT sensor networks, we develop a comprehensive framework to characterize the performance of a drone-assisted backscatter communication-based IoT sensor network. A statistical framework is developed to quantify the coverage probability that explicitly accommodates a dyadic backscatter channel which experiences deeper fades than that of the one-way Rayleigh channel. We practically implement the proposed system using software defined radio (SDR) and a custom-designed sensor node (SN) tag. The measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework

Distance Distributions and Proximity Estimation Given Knowledge of the Heterogeneous Network Layout

Today's heterogeneous wireless network (HWN) is a collection of ubiquitous wireless networking elements (WNEs) that support diverse functional capabilities and networking purposes. In such a heterogeneous networking environment, proximity estimation will play a key role for the seamless support of emerging applications that span from the direct exchange of localized traffic between homogeneous WNEs (peer-to-peer communications) to positioning for autonomous systems using location information from the ubiquitous HWN infrastructure. Since most of the existing wireless networking technologies enable the direct (or indirect) estimation of the distances and angles between their WNEs, the integration of such spatial information is a natural solution for robustly handling the unprecedented demand for proximity estimation between the myriads of WNEs. In this paper, we develop an analytical framework that integrates existing knowledge of the HWN layout to enable proximity estimation between WNE supporting different radio access technologies (RATs). In this direction, we derive closed-form expressions for the distance distribution between two tagged WNEs given partial (or full) knowledge of the HWN topology. The derived expressions enable us to analyze how different levels of location-awareness affect the performance of proximity estimation between WNEs that are not necessarily capable of communicating directly. Optimal strategies for the deployment of WNEs, as means of maximizing the probability of successful proximity estimation between two WNEs of interest, are presented, and useful guidelines for the design of location-aware proximity estimation in the nowadays HWN are drawn

On the Fundamentals of Stochastic Spatial Modeling and Analysis of Wireless Networks and its Impact to Channel Losses

With the rapid evolution of wireless networking, it becomes vital to ensure transmission reliability, enhanced connectivity, and efficient resource utilization. One possible pathway for gaining insight into these critical requirements would be to explore the spatial geometry of the network. However, tractably characterizing the actual position of nodes for large wireless networks (LWNs) is technically unfeasible. Thus, stochastical spatial modeling is commonly considered for emulating the random pattern of mobile users. As a result, the concept of random geometry is gaining attention in the field of cellular systems in order to analytically extract hidden features and properties useful for assessing the performance of networks. Meanwhile, the large-scale fading between interacting nodes is the most fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel losses in general, and the inevitability of random networks in real-life situations, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the large-scale fading. Therefore, in exact closed-form notation, we generically derived the large-scale fading distributions between a reference base-station and an arbitrary node for uni-cellular (UCN), multi-cellular (MCN), and Gaussian random network models. In fact, we for the first time provided explicit formulations that considered at once: the lattice profile, the users’ random geometry, the spatial intensity, the effect of the far-field phenomenon, the path-loss behavior, and the stochastic impact of channel scatters. Overall, the results can be useful for analyzing and designing LWNs through the evaluation of performance indicators. Moreover, we conceptualized a straightforward and flexible approach for random spatial inhomogeneity by proposing the area-specific deployment (ASD) principle, which takes into account the clustering tendency of users. In fact, the ASD method has the advantage of achieving a more realistic deployment based on limited planning inputs, while still preserving the stochastic character of users’ position. We then applied this inhomogeneous technique to different circumstances, and thus developed three spatial-level network simulator algorithms for: controlled/uncontrolled UCN, and MCN deployments

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications