Optimal Sensing and Actuation Policies for Networked Mobile Agents in a Class of Cyber-Physical Systems

The main purpose of this dissertation is to define and solve problems on optimal sensing and actuating policies in Cyber-Physical Systems (CPSs). Cyber-physical system is a term that was introduced recently to define the increasing complexity of the interactions between computational hardwares and their physical environments. The problem of designing the ``cyber\u27\u27 part may not be trivial but can be solved from scratch. However, the ``physical\u27\u27 part, usually a natural physical process, is inherently given and has to be identified in order to propose an appropriate ``cyber\u27\u27 part to be adopted. Therefore, one of the first steps in designing a CPS is to identify its ``physical\u27\u27 part. The ``physical\u27\u27 part can belong to a large array of system classes. Among the possible candidates, we focus our interest on Distributed Parameter Systems (DPSs) whose dynamics can be modeled by Partial Differential Equations (PDE). DPSs are by nature very challenging to observe as their states are distributed throughout the spatial domain of interest. Therefore, systematic approaches have to be developed to obtain the optimal locations of sensors to optimally estimate the parameters of a given DPS. In this dissertation, we first review the recent methods from the literature as the foundations of our contributions. Then, we define new research problems within the above optimal parameter estimation framework. Two different yet important problems considered are the optimal mobile sensor trajectory planning and the accuracy effects and allocation of heterogeneous sensors. Under the remote sensing setting, we are able to determine the optimal trajectories of remote sensors. The problem of optimal robust estimation is then introduced and solved using an interlaced ``online\u27\u27 or ``real-time\u27\u27 scheme. Actuation policies are introduced into the framework to improve the estimation by providing the best stimulation of the DPS for optimal parameter identification, where trajectories of both sensors and actuators are optimized simultaneously. We also introduce a new methodology to solving fractional-order optimal control problems, with which we demonstrate that we can solve optimal sensing policy problems when sensors move in complex media, displaying fractional dynamics. We consider and solve the problem of optimal scale reconciliation using satellite imagery, ground measurements, and Unmanned Aerial Vehicles (UAV)-based personal remote sensing. Finally, to provide the reader with all the necessary background, the appendices contain important concepts and theorems from the literature as well as the Matlab codes used to numerically solve some of the described problems

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented

5G Outlook – Innovations and Applications

5G Outlook - Innovations and Applications is a collection of the recent research and development in the area of the Fifth Generation Mobile Technology (5G), the future of wireless communications. Plenty of novel ideas and knowledge of the 5G are presented in this book as well as divers applications from health science to business modeling. The authors of different chapters contributed from various countries and organizations. The chapters have also been presented at the 5th IEEE 5G Summit held in Aalborg on July 1, 2016. The book starts with a comprehensive introduction on 5G and its need and requirement. Then millimeter waves as a promising spectrum to 5G technology is discussed. The book continues with the novel and inspiring ideas for the future wireless communication usage and network. Further, some technical issues in signal processing and network design for 5G are presented. Finally, the book ends up with different applications of 5G in distinct areas. Topics widely covered in this book are: • 5G technology from past to present to the future• Millimeter- waves and their characteristics• Signal processing and network design issues for 5G• Applications, business modeling and several novel ideas for the future of 5

Development of incremental strategies for wireless sensor network

Adaptive filter plays an important role in the field of digital signal processing and wireless communication. It incorporates LMS algorithm in real time environment because of its low computational complexity and simplicity. The LMS algorithm encompasses RLS (recursive least square), GN (Gaussian Newton), LMF (least mean fourth) and XE-NLMF algorithms, which provides faster convergence rate and low steady state error when compared to LMS. The adaptive distributed strategy is based on the incremental mode of co-operation between different nodes, which are distributed in the geographical area. These nodes perform local computation and share the result with the predefined nodes. The resulting algorithm is distributed, co-operative and able to respond to the real time change in environment. By using incremental method, algorithms such as RLS,GN, DCT-LMS and DFT-LMS produces faster convergence and better steady state performance than that of the LMS when simulated in the presence of Gaussian noise. Higher Order error algorithm like LMF, XE-NLMF and variable XE-NLMF algorithm produce better convergence and steady state performance under Gaussian and non-Gaussian noise. A spatial-temporal energy conservation argument is used to evaluate the steady state performance of the entire network. A topology named as CLMS (convex LMS) was presented which combined the effect of both fast and accurate filtering at the same time. Initially CLMS have parallel independent connection, the proposed topology consists of series convex connection of adaptive filters, which achieves similar result with reduced time of operation. Computer simulations corroborate the results