Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

Wireless Body Area Networks: A Survey

Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced.

Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

Annals of mathematics studies

Internet of Things (IoT) is a concept that envisions all objects around us as part of internet. IoT coverage is very wide and include variety of objects like smart phones, tablets, digital cameras, sensors, etc. Once all these devices are connected with each other, they enable more and more smart processes and services that support our basic needs, economies, environment and health. Such enormous number of devices connected to internet provides many kinds of services and produce huge amount of data and information. Cloud computing is a model for on-demand access to a shared pool of configurable resources (e.g. compute, networks, servers, storage, applications, services, and software) that can be easily provisioned as Infrastructure (IaaS), software and applications (SaaS). Cloud based platforms help to connect to the things (IaaS) around us so that we can access anything at any time and any place in a user friendly manner using customized portals and in built applications (SaaS). Hence, cloud acts as a front end to access Internet of Things. Applications that interact with devices like sensors have special requirements of massive storage to storage big data, huge computation power to enable the real time processing of the data, and high speed network to stream audio or video. In this paper, we describe how Internet of Things and Cloud computing can work together can address the Big Data issues. We also illustrate about Sensing as a service on cloud using few applications like Augmented Reality, Agriculture and Environment monitoring. Finally, we also propose a prototype model for providing sensing as a service on cloud.

Cloud computing for Internet of Things & sensing based applications

Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.

A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments

In this work a framework for modeling power systems using hybrid input/output automata (HIOA) is proposed. The system is assumed to consist of several distinct components. Some of them drive the continuous dynamics while others exhibit event-driven discrete dynamics. Such behavior is characterized by interactions between continuous dynamics and discrete events. Therefore the power systems are an important example of hybrid systems. This hybrid modeling process is applied to a simple power system.

Hybrid systems modeling for power systems

The use of sensor networks for healthcare, well-being, and working in extreme environments has long roots in the engineering sector in medicine and biology community. With the growing needs in ubiquitous communications and recent advances in very-low-power wireless technologies, there has been considerable interest in the development and application of wireless networks around humans. With the maturity of wireless sensor networks, body area networks (BANs), and wireless BANs (WBANs), recent efforts in promoting the concept of body sensor networks (BSNs) aim to move beyond sensor connectivity to adopt a system-level approach to address issues related to biosensor design, interfacing, and embodiment, as well as ultra low-power processing / communication, power scavenging, autonomic sensing, data mining, inferencing, and integrated wireless sensor microsystems. As a result, the system architecture based on WBAN and BSN is becoming a widely accepted method of organization for ambulatory and ubiquitous monitoring systems. This review paper presents an up-to-date report of the current research and enabling applications and addresses some of the challenges and implementation issues.

An overview of body sensor networks in enabling pervasive healthcare and assistive environments

Fault diagnosis is a challenging task in the control of Hybrid Systems. In this work we introduce the notion of diagnosabilit y of Hybrid Systems in the framework of Hybrid Input Output Automata (HIOA). We present a methodology for detection of faults imposing the conditions for a Hybrid System to be diagnosable. This approach is applicable to a wide rage of systems since Hybrid Systems involve both continuous and discrete dynamics. The states of the Hybrid System model reflect the normal and the failed status of the system components. The faults in our setting are modeled as either discrete or continuous (detrimental) state changes.

Diagnosability of hybrid systems

This paper addresses the fault tolerant control problem for an omni-directional mobile platform with four mecanum wheels moving on a well-known flat and constrained workspace with static obstacles. As a fault, we consider the case where a wheel cannot be actuated and hence it rotates freely around its drive shaft owing to the friction with the flat surface. Depending on the multitude of the faults, a robust motion control scheme is developed that achieves any desired configuration within the operational workspace, avoids collisions with the obstacles and does not violate the workspace boundaries despite the presence of dynamic model uncertainties. The challenge with respect to the current state of the art in fault tolerant control for such mobile platforms, where only one faulty wheel has been considered (i.e., the platform still retains its full actuation capabilities), lies in completely compensating up to two faulty wheels (i.e., the model becomes underactuated in this way) despite the dynamic model uncertainty and the presence of static obstacles in the workspace. Navigation Functions are innovatively incorporated with adaptive control techniques to deal with the parametric uncertainty in the robot dynamics, extending thus greatly the current state of the art in robust motion planning and collision avoidance by studying second order dynamics with parametric uncertainty. Finally, an extensive experimental study clarifies the proposed method and verifies its efficiency in various faults.

Fault tolerant control for omni-directional mobile platforms with 4 mecanum wheels

This paper describes the design of a Fault Tolerant Control scheme for an omni-directional mobile robot with four mecanum wheels. We consider actuation faults in which the wheels are not able to receive commands, but still can rotate freely due to the friction with the ground. A Non-linear Model Predictive Controller is employed, in order to appropriately exploit the inherited actuation redundancy of the omni-directional robot, and provide on the fly a unified accommodation solution for multiple combinations of actuation faults. A basic Fault Detection and Isolation scheme is responsible for identifying and isolate the faulty wheels. Accordingly, the control input vector and consequently the dynamic model of the vehicle are reformulated depending on the identified faults. Hence, the predictive control scheme is dynamically updated and calculates the optimal actuation solution, given the faults occurred. The proposed scheme, is able to guide the vehicle to any goal configuration within the workspace, while simultaneously satisfying state (e.g obstacle avoidance) and input (e.g motor limits) constraints. The efficacy of the proposed scheme is evaluated via a set of realistic simulation scenarios, where an omni-directional mobile robot executes a way point tracking mission with obstacle avoidance, inside a restricted workspace and in the presence of different combinations of actuation faults.

George K. Fourlas

Papers

Hybrid systems modeling for power systems

An overview of body sensor networks in enabling pervasive healthcare and assistive environments

Diagnosability of hybrid systems

Fault tolerant control for omni-directional mobile platforms with 4 mecanum wheels

Model Predictive Fault Tolerant Control for Omni-directional Mobile Robots