7,836 research outputs found
Optimal Status Updating with a Finite-Battery Energy Harvesting Source
We consider an energy harvesting source equipped with a finite battery, which
needs to send timely status updates to a remote destination. The timeliness of
status updates is measured by a non-decreasing penalty function of the Age of
Information (AoI). The problem is to find a policy for generating updates that
achieves the lowest possible time-average expected age penalty among all online
policies. We prove that one optimal solution of this problem is a monotone
threshold policy, which satisfies (i) each new update is sent out only when the
age is higher than a threshold and (ii) the threshold is a non-increasing
function of the instantaneous battery level. Let denote the optimal
threshold corresponding to the full battery level , and denote
the age-penalty function, then we can show that is equal to the
optimum objective value, i.e., the minimum achievable time-average expected age
penalty. These structural properties are used to develop an algorithm to
compute the optimal thresholds. Our numerical analysis indicates that the
improvement in average age with added battery capacity is largest at small
battery sizes; specifically, more than half the total possible reduction in age
is attained when battery storage increases from one transmission's worth of
energy to two. This encourages further study of status update policies for
sensors with small battery storage.Comment: 15 pages, 6 figure
Isomorph-Free Branch and Bound Search for Finite State Controllers
The recent proliferation of smart-phones and other wearable devices has lead
to a surge of new mobile applications. Partially observable Markov decision
processes provide a natural framework to design applications that
continuously make decisions based on noisy sensor measurements. However,
given the limited battery life, there is a need to minimize the amount of
online computation. This can be achieved by compiling a policy into a
finite state controller since there is no need for belief monitoring or
online search. In this paper, we propose a new branch and bound technique
to search for a good controller. In contrast to many existing algorithms
for controllers, our search technique is not subject to local optima. We
also show how to reduce the amount of search by avoiding the enumeration of
isomorphic controllers and by taking advantage of suitable upper and lower
bounds. The approach is demonstrated on several benchmark problems as well
as a smart-phone application to assist persons with Alzheimer's to wayfind
Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework
123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
One-Shot Parameter Identification of the Thevenin's Model for Batteries: Methods and Validation
Parameter estimation is of foundational importance for various model-based
battery management tasks, including charging control, state-of-charge
estimation and aging assessment. However, it remains a challenging issue as the
existing methods generally depend on cumbersome and time-consuming procedures
to extract battery parameters from data. Departing from the literature, this
paper sets the unique aim of identifying all the parameters offline in a
one-shot procedure, including the resistance and capacitance parameters and the
parameters in the parameterized function mapping from the state-of-charge to
the open-circuit voltage. Considering the well-known Thevenin's battery model,
the study begins with the parameter identifiability analysis, showing that all
the parameters are locally identifiable. Then, it formulates the parameter
identification problem in a prediction-error-minimization framework. As the
non-convexity intrinsic to the problem may lead to physically meaningless
estimates, two methods are developed to overcome this issue. The first one is
to constrain the parameter search within a reasonable space by setting
parameter bounds, and the other adopts regularization of the cost function
using prior parameter guess. The proposed identifiability analysis and
identification methods are extensively validated through simulations and
experiments
An energy management system for a smart office environment
The evolution of the electricity grid towards the smart grid paradigm is fostering the integration of distributed renewable energy sources in Smart Buildings: a combination of local power generation, battery storage and controllable loads can greatly increase the energetic self-sufficiency of a Smart Building, enabling it to operate in islanded mode or to participate in an Automatic Demand Response framework, thus taking advantage of time-variable tariffs to achieve economical savings. This paper proposes an energy management system specifically tailored for a Smart Office building, which relies on actual data and on forecasting algorithms to predict the future patterns of both local energy generation and power loads. Performance is compared to the optimal energy usage scheduling, which would be obtained assuming the exact knowledge of the future energy production and consumption trends, showing gaps below 10% w.r.t. the optimum
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