Markov fluid queue model of an energy harvesting IoT device with adaptive sensing

Energy management is key in prolonging the lifetime of an energy harvesting Internet of Things (IoT) device with rechargeable batteries. Such an IoT device is required to fulfill its main functionalities, i.e., information sensing and dissemination at an acceptable rate, while keeping the probability that the node first becomes non-operational, i.e., the battery level hits zero the first time within a given finite time horizon, below a desired level. Assuming a finite-state Continuous-Time Markov Chain (CTMC) model for the Energy Harvesting Process (EHP), we propose a risk-theoretic Markov fluid queue model for the computation of first battery outage probabilities in a given finite time horizon. The proposed model enables the performance evaluation of a wide spectrum of energy management policies including those with sensing rates depending on the instantaneous battery level and/or the state of the energy harvesting process. Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing policies are obtained that maximize the information sensing rate of the IoT device while meeting a Quality of Service (QoS) constraint given in terms of first battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using a two-state CTMC-based EHP. © 2017 Elsevier B.V

A Product-Form Model for the Performance Evaluation of a Bandwidth Allocation Strategy in WSNs

Wireless Sensor Networks (WSNs) are important examples of Collective Adaptive System, which consist of a set of motes that are spatially distributed in an indoor or outdoor space. Each mote monitors its surrounding conditions, such as humidity, intensity of light, temperature, and vibrations, but also collects complex information, such as images or small videos, and cooperates with the whole set of motes forming the WSN to allow the routing process. The traffic in the WSN consists of packets that contain the data harvested by the motes and can be classified according to the type of information that they carry. One pivotal problem in WSNs is the bandwidth allocation among the motes. The problem is known to be challenging due to the reduced computational capacity of the motes, their energy consumption constraints, and the fully decentralised network architecture. In this article, we study a novel algorithm to allocate the WSN bandwidth among the motes by taking into account the type of traffic they aim to send. Under the assumption of a mesh network and Poisson distributed harvested packets, we propose an analytical model for its performance evaluation that allows a designer to study the optimal configuration parameters. Although the Markov chain underlying the model is not reversible, we show it to be.-reversible under a certain renaming of states. By an extensive set of simulations, we show that the analytical model accurately approximates the performance of networks that do not satisfy the assumptions. The algorithm is studied with respect to the achieved throughput and fairness. We show that it provides a good approximation of the max-min fairness requirements

Analysis of Stochastic Models through Multi-Layer Markov Modulated Fluid Flow Processes

This thesis is concerned with the multi-layer Markov modulated fluid flow (MMFF) processes and their applications to queueing systems with customer abandonment. For the multi-layer MMFF processes, we review and refine the theory on the joint distribution of the multi-layer MMFF processes and develop an easy to implement algorithm to calculate the joint distribution. Then, we apply the theory to three quite general queueing systems with customer abandonment to show the applicability of this approach and obtain a variety of queueing quantities, such as the customer abandonment probabilities, waiting times distributions and mean queue lengths. The first application is the MAP/PH/K+GI queue. The MMFF approach and the count-server-for-phase (CSFP) method are combined to analyze this multi-server queueing system with a moderately large number of servers. An efficient and easy-to-implement algorithm is developed for the performance evaluation of the MAP/PH/K +GI queueing model. Some of the queueing quantities such as waiting time distributions of the customers abandoning the queue at the head of the waiting queue are difficult to derive through other methods. Then the double-sided queues with marked Markovian arrival processes (MMAP) and abandonment are studied. Multiple types of inputs and finite discrete abandonment times make this queueing model fairly general. Three age processes related to the inputs are defined and then converted into a multi-layer MMFF process. A number of aggregate queueing quantities and quantities for individual types of inputs are obtained by the MMFF approach, which can be useful for practitioners to design stochastic systems such as ride-hailing platforms and organ transplantation systems. The last queueing model is the double-sided queues with batch Markovian arrival processes (BMAP) and abandonment, which arise in various stochastic systems such as perishable inventory systems and financial markets. Customers arrive at the system with a batch of orders to be matched by counterparts. The abandonment time of a customer depends on the batch size and the position in the queue of the customer. Similar to the previous double-sided queueing model, a multi-layer MMFF process related to some age processes is constructed. A number of queueing quantities including matching rates, fill rates, sojourn times and queue length for both sides of the system are derived. This queueing model is used to analyze a vaccine inventory system as a case study in the thesis. Overall, this thesis studies the joint stationary distribution of the multi-layer MMFF processes and shows the power of this approach in dealing with complex queueing systems. Four algorithms are presented to help practitioners to design stochastic systems and researchers do numerical experiments

Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам