An Energy Aware and Secure MAC Protocol for Tackling Denial of Sleep Attacks in Wireless Sensor Networks

Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful energy awareness is essential when working with these devices. Indeed,the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection c ould give room for energy drain attacks such as denial of sleep attacks which have a higher negative impact on the life span ( of the sensors than the presence of security features. This thesis, therefore, focuses on tackling denial of sleep attacks from two perspectives A security perspective and an energy efficiency perspective. The security perspective involves evaluating and ranking a number of security based techniques to curbing denial of sleep attacks. The energy efficiency perspective, on the other hand, involves exploring duty cycling and simulating three Media Access Control ( protocols Sensor MAC, Timeout MAC andTunableMAC under different network sizes and measuring different parameters such as the Received Signal Strength RSSI) and Link Quality Indicator ( Transmit power, throughput and energy efficiency Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty cycle protocols Sensor MAC ( Timeout MAC ( and TunableMAC in addition to creating a novel MAC protocol that is also more resilient to denial of sleep a ttacks than existing protocols. The main contributions to knowledge from this thesis are the developed framework used for evaluation of existing denial of sleep attack solutions and the algorithms which fuel the other contribution to knowledge a newly developed protocol tested on the Castalia Simulator on the OMNET++ platform. The new protocol has been compared with existing protocols and has been found to have significant improvement in energy efficiency and also better resilience to denial of sleep at tacks Part of this research has been published Two conference publications in IEEE Explore and one workshop paper

Energy efficiency control of direct expansion air conditioning systems

The dynamic mathematical models for direct expansion air conditioning (DX A/C) systems with respect to indoor carbon dioxide (CO2) concentration, relative humidity and air temperature and the coupling effects among them have been built in this thesis. To reduce the energy cost and improve the energy efficiency for DX A/C systems while maintaining both indoor air quality (IAQ) and thermal comfort at acceptable levels, a hierarchical control structure is proposed in this thesis. This control structure includes two levels. The upper level is an open loop optimal controller to generate the optimal setpoints of indoor CO2 concentration, relative humidity and air temperature for the lower level controller. The lower level designs a closed-loop model predictive control (MPC) controller to optimize the transient processes reaching the setpoints where the energy efficiency improvement and energy cost savings are achieved. In Chapter 2, the control objective is to improve both IAQ and thermal comfort as well as energy efficiency for a DX A/C system. The details of a hierarchical control structure in this chapter are as follows: In the upper layer, an energy-optimised open loop controller is proposed based on an optimization of energy consumption of the DX A/C system and given reference points of indoor CO2 concentration, relative humidity and air temperature to generate a unique and optimised steady state for the lower layer controller. In the lower layer, the closed-loop MPC controller is proposed such that the indoor CO2 concentration, relative humidity and air temperature follow the steady state computed by the upper layer, whereas the energy efficiency is improved. To facilitate the MPC design, the nonlinear DX A/C control system is linearized around the optimised steady state. In Chapter 3, the control objective is to lower the energy cost and consumption of a DX A/C system while maintaining both IAQ and thermal comfort at comfort levels. To achieve this purpose, an autonomous hierarchical control (AHC) structure is designed and described below. The upper level is an open loop nonlinear optimal controller, which optimizes the predicted mean vote (PMV) index and the energy cost for the DX A/C system under a time-of-use (TOU) price structure of electricity according to the changing environment over a 24-hour period, to generate the tradeoff setpoints of indoor CO2 concentration, relative humidity and air temperature for the lower level controller. The lower layer is formed as a closed-loop MPC to track the trajectory reference points calculated by the optimization layer. This AHC strategy means the upper controller can adaptively and automatically set the setpoints and the lower layer adaptively and optimally tracks them, minimizing energy consumption and costs. In addition, in this chapter, the volumes of outside air allowed to enter the DX A/C system are regarded as varying with the changing circumstance over a day and are optimized by the AHC. Moreover, a supply fan to steer the pressure swing absorption with a built-in proportional-integral (PI) controller is proposed to lower the indoor CO2 concentration such that it would reduce the complexity of computation for the AHC and the cost of hardware. In Chapter 4, the control objective is to reduce energy cost, improve energy efficiency, and reduce communication resources, computational complexity and conservativeness, as well as peak demand for a multi-zone building multi-evaporator air conditioning (ME A/C) system while maintaining multi-zones’ thermal comfort and IAQ at comfort levels. To realize this objective and to consider the interaction effects between rooms, we present an autonomous hierarchical distributed control (AHDC) method. The upper level is an open loop nonlinear optimizer, which only collects measurement information and solves a distributed steady state optimization problem to adaptively and automatically generate time-varying and optimised reference points of indoor CO2 concentration, relative humidity and air temperature for the lower-layer controllers, by minimizing the demand and energy costs of a multi-zone building ME A/C system under the TOU price structure of electricity according to the changing circumstance during the day. The lower level also uses local information to track the trajectory references calculated by the upper-layer distributed controller, via distributed MPC controllers. The proposed hierarchical control strategy is distributed in two layers since they use only local information from the working zone and its neighbours. To validate the performance of these hierarchical control strategies for DX A/C systems, simulation tests are performed in this thesis. In Chapter 2, simulations are provided to show that the closed-loop regulation of the MPC controller and the energy-optimised open loop controller can maintain indoor CO2 concentration, relative humidity and air temperature at their desired setpoints with small deviations and reduce the effect of indoor cooling and pollutant loads. The simulations also demonstrate that the controllers are superior to conventional controllers in terms of energy efficiency. In Chapter 3, the simulation tests show that the AHC strategy can reduce more energy consumption and cost than the baseline strategy. In addition, the tests demonstrate that the AHC scheme is not sensitive to the physical parameters of the DX A/C system. In Chapter 4, to show the performance of the two-layer distributed control strategies, a case study is given. The simulation tests demonstrate that the AHDC strategy is capable of shifting demand from peak hours to off-peak hours and reducing the energy cost for a multi-zone building ME A/C system while maintaining multi-zones’ IAQ and thermal comfort at comfort levels.Electrical, Electronic and Computer EngineeringPhDUnrestricte

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Multi-Criteria Performance Evaluation and Control in Power and Energy Systems

The role of intuition and human preferences are often overlooked in autonomous control of power and energy systems. However, the growing operational diversity of many systems such as microgrids, electric/hybrid-electric vehicles and maritime vessels has created a need for more flexible control and optimization methods. In order to develop such flexible control methods, the role of human decision makers and their desired performance metrics must be studied in power and energy systems. This dissertation investigates the concept of multi-criteria decision making as a gateway to integrate human decision makers and their opinions into complex mathematical control laws. There are two major steps this research takes to algorithmically integrate human preferences into control environments: MetaMetric (MM) performance benchmark: considering the interrelations of mathematical and psychological convergence, and the potential conflict of opinion between the control designer and end-user, a novel holistic performance benchmark, denoted as MM, is developed to evaluate control performance in real-time. MM uses sensor measurements and implicit human opinions to construct a unique criterion that benchmarks the system\u27s performance characteristics. MM decision support system (DSS): the concept of MM is incorporated into multi-objective evolutionary optimization algorithms as their DSS. The DSS\u27s role is to guide and sort the optimization decisions such that they reflect the best outcome desired by the human decision-maker and mathematical considerations. A diverse set of case studies including a ship power system, a terrestrial power system, and a vehicular traction system are used to validate the approaches proposed in this work. Additionally, the MM DSS is designed in a modular way such that it is not specific to any underlying evolutionary optimization algorithm

Distributed control for a multi-evaporator air conditioning system

An autonomous hierarchical distributed control (AHDC) strategy is proposed for a building multi-evaporator air conditioning (ME A/C) system in this paper. The objectives are to minimize peak demand and energy costs, and to reduce communication resources, computational complexity and conservativeness while maintaining both thermal comfort and indoor air quality (IAQ) in acceptable ranges. The building consists of multiple connected rooms and zones. The proposed control strategy consists of two layers. The upper layer is an open loop optimizer, which only collects local measurement information and solves a distributed steady state resource allocation problem to autonomously and adaptively generate reference points, for low layer controllers. This is achieved by optimizing the demand and energy costs of a multi-zone building ME A/C system under a time-of-use (TOU) rate structure, while meeting the requirements of each zone’s thermal comfort and IAQ within comfortable ranges. The lower layer also uses local information to track the trajectory references, which are calculated by the upper layer, via a distributed model predictive control (DMPC) algorithm. The control strategy is distributed at both layers because they use only local information from the working zone and its neighbors. Simulation results are provided to illustrate the advantages of the designed control schemes.http://www.elsevier.com/locate/conengprac2020-09-01hj2019Electrical, Electronic and Computer Engineerin

Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings.

This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the “Autonomous Cycle of Data Analysis Tasks” concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.post-print4413 K

μGIM - Microgrid intelligent management system based on a multi-agent approach and the active participation of end-users

[ES] Los sistemas de potencia y energía están cambiando su paradigma tradicional, de sistemas centralizados a sistemas descentralizados. La aparición de redes inteligentes permite la integración de recursos energéticos descentralizados y promueve la gestión inclusiva que involucra a los usuarios finales, impulsada por la gestión del lado de la demanda, la energía transactiva y la respuesta a la demanda. Garantizar la escalabilidad y la estabilidad del servicio proporcionado por la red, en este nuevo paradigma de redes inteligentes, es más difícil porque no hay una única sala de operaciones centralizada donde se tomen todas las decisiones. Para implementar con éxito redes inteligentes, es necesario combinar esfuerzos entre la ingeniería eléctrica y la ingeniería informática. La ingeniería eléctrica debe garantizar el correcto funcionamiento físico de las redes inteligentes y de sus componentes, estableciendo las bases para un adecuado monitoreo, control, gestión, y métodos de operación. La ingeniería informática desempeña un papel importante al proporcionar los modelos y herramientas computacionales adecuados para administrar y operar la red inteligente y sus partes constituyentes, representando adecuadamente a todos los diferentes actores involucrados. Estos modelos deben considerar los objetivos individuales y comunes de los actores que proporcionan las bases para garantizar interacciones competitivas y cooperativas capaces de satisfacer a los actores individuales, así como cumplir con los requisitos comunes con respecto a la sostenibilidad técnica, ambiental y económica del Sistema. La naturaleza distribuida de las redes inteligentes permite, incentiva y beneficia enormemente la participación activa de los usuarios finales, desde actores grandes hasta actores más pequeños, como los consumidores residenciales. Uno de los principales problemas en la planificación y operación de redes eléctricas es la variación de la demanda de energía, que a menudo se duplica más que durante las horas pico en comparación con la demanda fuera de pico. Tradicionalmente, esta variación dio como resultado la construcción de plantas de generación de energía y grandes inversiones en líneas de red y subestaciones. El uso masivo de fuentes de energía renovables implica mayor volatilidad en lo relativo a la generación, lo que hace que sea más difícil equilibrar el consumo y la generación. La participación de los actores de la red inteligente, habilitada por la energía transactiva y la respuesta a la demanda, puede proporcionar flexibilidad en desde el punto de vista de la demanda, facilitando la operación del sistema y haciendo frente a la creciente participación de las energías renovables. En el ámbito de las redes inteligentes, es posible construir y operar redes más pequeñas, llamadas microrredes. Esas son redes geográficamente limitadas con gestión y operación local. Pueden verse como áreas geográficas restringidas para las cuales la red eléctrica generalmente opera físicamente conectada a la red principal, pero también puede operar en modo isla, lo que proporciona independencia de la red principal. Esta investigación de doctorado, realizada bajo el Programa de Doctorado en Ingeniería Informática de la Universidad de Salamanca, aborda el estudio y el análisis de la gestión de microrredes, considerando la participación activa de los usuarios finales y la gestión energética de lascarga eléctrica y los recursos energéticos de los usuarios finales. En este trabajo de investigación se ha analizado el uso de conceptos de ingeniería informática, particularmente del campo de la inteligencia artificial, para apoyar la gestión de las microrredes, proponiendo un sistema de gestión inteligente de microrredes (μGIM) basado en un enfoque de múltiples agentes y en la participación activa de usuarios. Esta solución se compone de tres sistemas que combinan hardware y software: el emulador de virtual a realidad (V2R), el enchufe inteligente de conciencia ambiental de Internet de las cosas (EnAPlug), y la computadora de placa única para energía basada en el agente (S4E) para permitir la gestión del lado de la demanda y la energía transactiva. Estos sistemas fueron concebidos, desarrollados y probados para permitir la validación de metodologías de gestión de microrredes, es decir, para la participación de los usuarios finales y para la optimización inteligente de los recursos. Este documento presenta todos los principales modelos y resultados obtenidos durante esta investigación de doctorado, con respecto a análisis de vanguardia, concepción de sistemas, desarrollo de sistemas, resultados de experimentación y descubrimientos principales. Los sistemas se han evaluado en escenarios reales, desde laboratorios hasta sitios piloto. En total, se han publicado veinte artículos científicos, de los cuales nueve se han hecho en revistas especializadas. Esta investigación de doctorado realizó contribuciones a dos proyectos H2020 (DOMINOES y DREAM-GO), dos proyectos ITEA (M2MGrids y SPEAR), tres proyectos portugueses (SIMOCE, NetEffiCity y AVIGAE) y un proyecto con financiación en cascada H2020 (Eco-Rural -IoT)