A Multilevel Scheduling MAC Protocol for Underwater Acoustic Sensor Networks(UASN)

Underwater acoustic sensor networks (UASNs) have attracted great attention in recent years and utilizes as a part of oceanic applications. This network has to deal with propagation delay, energy constraints and limited bandwidth which are strenuous for designing a Medium Access Control (MAC) protocol for underwater communication. There also exists an idle channel listening and overhearing problem which sets down the energy into starvation in the contention-based MAC protocols. Alternatively, lengthy time slots and time synchronization equated by schedule-based MAC protocols, outcomes the variable transmission delay and degrades the network performances. To iron out these problems, we propose a cluster-based MAC protocol, tagged as Multilevel Scheduling MAC (MLS-MAC) protocol for UASN in the paper. The cluster head is a decision maker for packet transmission and aids to inflate the lifetime of sensor nodes. To reinforce the channel efficiency, the multilevel scheduling in data phase is initiated with two queues depending on the applications fixed by the cluster head. The simulation result shows that the MLS-MAC has increased the network throughput and has decreased energy consumption

A systematic review on energy efficiency in the Internet of Underwater Things (IoUT): recent approaches and research gaps

Due to the advancement of wireless communications, Internet of Things (IoT) becomes a promising technology in today’s digital world. For the enhancement of underwater applications such as ocean exploration, deep-sea monitoring, underwater surveillance, diver network monitoring, location and object tracking, etc., Internet of underwater things (IoUT) has been introduced. However, underwater communication suffers from energy consumption due to fluctuations of the underwater environment and operational factors according to the distributions of objects or vehicles in shallow and deep water. The IoT quality of service (QoS) in underwater communication networks is critically affected by the different energy factors related to networking and the physical layer. Network topology and routing protocol are two important major factors affecting the power consumption of IoUT nodes and vehicles. The clustering approach is considered the best choice for IoUT, however it may suffer from various influences related to the underwater environment. The optimisation-based AI technologies in clustering approaches enable to achieve of energy efficiency for IoUT applications. This paper provides a systematic review of different energy efficiency methodologies for IoUT, and classified them according to the strategies used, in addition to the research gaps in clustering-based approaches, and future directions

Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks

In this paper, a cluster-based approach is used to address the distributed fusion estimation problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of random deception attacks. At each sampling time, measured outputs of the signal are provided by a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local processor which gathers the measured outputs of its sensors and, in turn, the local processors of all clusters are connected with a global fusion center. The proposed cluster-based fusion estimation structure involves two stages. First, every single sensor in a cluster transmits its observations to the corresponding local processor, where least-squares local estimators are designed by an innovation approach. During this transmission, deception attacks to the sensor measurements may be randomly launched by an adversary, with known probabilities of success that may be different at each sensor. In the second stage, the local estimators are sent to the fusion center, where they are combined to generate the proposed fusion estimators. The covariance-based design of the distributed fusion filtering and fixed-point smoothing algorithms does not require full knowledge of the signal evolution model, but only the first and second order moments of the processes involved in the observation model. Simulations are provided to illustrate the theoretical results and analyze the effect of the attack success probability on the estimation performance.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

Euclidean distance geometry and applications

Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in Euclidean space that realizes the given distances. We survey some of the theory of Euclidean distance geometry and some of the most important applications: molecular conformation, localization of sensor networks and statics.Comment: 64 pages, 21 figure

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Swarm Robotic Systems with Minimal Information Processing

This thesis is concerned with the design and analysis of behaviors in swarm robotic systems using minimal information acquisition and processing. The motivation for this work is to contribute in paving the way for the implementation of swarm robotic systems at physically small scales, which will open up new application domains for their operation. At these scales, the space and energy available for the integration of sensors and computational hardware within the individual robots is at a premium. As a result, trade-offs in performance can be justified if a task can be achieved in a more parsimonious way. A framework is developed whereby meaningful collective behaviors in swarms of robots can be shown to emerge without the robots, in principle, possessing any run-time memory or performing any arithmetic computations. This is achieved by the robots having only discrete-valued sensors, and purely reactive controllers. Black-box search methods are used to automatically synthesize these controllers for desired collective behaviors. This framework is successfully applied to two canonical tasks in swarm robotics: self-organized aggregation of robots, and self-organized clustering of objects by robots. In the case of aggregation, the robots are equipped with one binary sensor, which informs them whether or not there is another robot in their line of sight. This makes the structure of the robots’ controller simple enough that its entire space can be systematically searched to locate the optimal controller (within a finite resolution). In the case of object clustering, the robots’ sensor is extended to have three states, distinguishing between robots, objects, and the background. This still requires no run-time memory or arithmetic computations on the part of the robots. It is statistically shown that the extension of the sensor to have three states leads to a better performance as compared to the cases where the sensor is binary, and cannot distinguish between robots and objects, or robots and the background

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

SHIFTING GROUNDS: SCIENTIFIC AND TECHNOLOGICAL CHANGE AND INTERNATIONAL REGIMES FOR THE OCEAN AND OUTER SPACE

Emerging planetary-scale environmental problems, such as climate change and space debris, indicate a growing need for effective governance regimes for domains beyond the borders of territorial nation-states. This dissertation addresses the basic question: what explains patterns of success and dysfunction in regimes for non-terrestrial spaces? Under what conditions can global commons regimes function to achieve their goals? The answer depends in a fundamental way on scientific knowledge and technological capability, which create, define, and describe the problems, interests, and practices that shape the formation and features of governance regimes, and thus create the conditions for their effective functioning. This project employs and extends recent revivalist geopolitical approaches examining the influences of material factors (geography, ecology, and technology), and applies them to explain important features of regimes for the ocean and orbital space. This approach claims that geography, ecology, and technology together constitute an influencing context, which creates specific problem structures and constrains possible solution sets, and thereby sets conditions for regime performance. In contrast, recent post-modernist and constructivist approaches discount the importance and influence of material contexts in shaping politics, and are incapable of explaining important aspects of regimes. Rationalist (interest-centered) approaches to theorizing regimes employ thin treatments of the material context, limiting their ability to explain regime content and effectiveness. The explanatory traction of material-contextual factors is demonstrated by a detailed examination of regime formation, content and effectiveness over four periods of ocean governance across five centuries, and orbital space over the last sixty years. These cases demonstrate that successful regime formation must foreground scientific uncertainty, ecological dynamics, and the balance of technological capability. To the extent that global commons regimes ignore the existence and dynamism of these material structures, they are more likely to fail to achieve their goals. Greater consideration of material contexts produces a strengthened International Relations theory of regimes. These findings also suggest ways to improve regime design, outlined in the concluding chapter

Estimation and control of non-linear and hybrid systems with applications to air-to-air guidance

Issued as Progress report, and Final report, Project no. E-21-67

Integrated design optimization methods for optimal sensor placement and cooling system architecture design for electro-thermal systems

Dynamic thermal management plays a very important role in the design and development of electro-thermal systems as these become more active and complex in terms of their functionalities. In highly power dense electronic systems, the heat is concentrated over small spatial domains. Thermal energy dissipation in any electrified system increases the temperature and might cause component failure, degradation of heat sensitive materials, thermal burnouts and failure of active devices. So thermal management needs to be done both accurately (by thermal monitoring using sensors) and efficiently (by applying fluid-based cooling techniques). In this work, two important aspects of dynamic thermal management of a highly dense power electronic system have been investigated. The first aspect is the problem of optimal temperature sensor placement for accurate thermal monitoring aimed toward achieving thermally-aware electrified systems. Strategic placement of temperature sensors can improve the accuracy of real-time temperature distribution estimates. Enhanced temperature estimation supports increased power throughput and density because Power Electronic Systems (PESs) can be operated in a less conservative manner while still preventing thermal failure. This work presents new methods for temperature sensor placement for 2- and 3-dimensional PESs that 1) improve computational efficiency (by orders of magnitude in at least one case), 2) support use of more accurate evaluation metrics, and 3) are scalable to high-dimension sensor placement problems. These new methods are tested via sensor placement studies based on a 2-kW, 60Hz, single-phase, Flying Capacitor Multi-Level (FCML) prototype inverter. Information-based metrics are derived from a reduced-order Resistance-Capacitance (RC) lumped parameter thermal model. Other more general metrics and system models are possible through application of a new continuous relaxation strategy introduced here for placement representation. A new linear Programming (LP) formulation is presented that is compatible with a particular type of information-based metric. This LP strategy is demonstrated to support the efficient solution of finely-discretized large-scale placement problems. The optimal sensor locations obtained from these methods were tested via physical experiments. The new methods and results presented here may aid the development of thermally-aware PESs with significantly enhanced capabilities. The second aspect is to design optimal fluid-based thermal management architectures through enumerative methods that help operate the system efficiently within its operating temperature limits using the minimum feasible coolant flow level. Expert intuition based on physics knowledge and vast experience may not be adequate to identify optimal thermal management designs as systems increase in size and complexity. This work also presents a design framework supporting comprehensive exploration of a class of single-phase fluid-based cooling architectures. The candidate cooling system architectures are represented using labeled rooted tree graphs. Dynamic models are automatically generated from these trees using a graph-based thermal modeling framework. Optimal performance is determined by solving an appropriate fluid flow distribution problem, handling temperature constraints in the presence of exogenous heat loads. Rigorous case studies are performed in simulation, with components having variable sets of heat loads and temperature constraints. Results include optimization of thermal endurance for an enumerated set of 4,051 architectures. In addition, cooling system architectures capable of steady-state operation under a given loading are identified. Optimization of the cooling system design has been done subject to a representative mission, consisting of multiple time-varying loads. Work presented in this thesis clearly shows that the transient effects of heat loads are expected to have important impacts on design decisions when compared to steady-state operating conditions