Browsing by Subject "Internet of things"

Now showing 1 - 8 of 8

Open Access
Application scheduling with multiplexed sensing of monitoring points in multi-purpose IoT wireless sensor networks
(IEEE, 2024-02) Çavdar, Mustafa Can; Körpeoğlu, İbrahim; Ulusoy, Özgür
Wireless sensor networks (WSNs) play a crucial role in Internet-of-Things (IoT) systems serving a variety of applications. They gather data from specific sensor nodes and transmit it to remote units for processing. When multiple applications share a WSN infrastructure, efficient scheduling becomes vital. In our research, we address the problem of application scheduling in WSNs. Specifically, we focus on scenarios where applications request data from monitoring points within the coverage area of a WSN. We propose a shared-data approach that reduces the network’s sensing and communication load by allowing multiple applications to use the same sensing data. To tackle the scheduling challenge, we introduce a genetic algorithm named GABAS and three greedy algorithms: LMPF, LMSF, and LTSF. These algorithms determine the order in which applications are admitted to the WSN infrastructure, considering various criteria. To assess the performance of our algorithms, we conducted extensive simulation experiments and compared them with standard scheduling methods. We also evaluated the performance of GABAS as compared to another genetic scheduling algorithm that has recently appeared in the literature. The overall experimental results show that the methods we propose outperform the compared approaches across various metrics, namely makespan, turnaround time, waiting time, and successful execution rate. In particular, our genetic algorithm proves to be highly effective in scheduling applications and optimizing the mentioned metrics.
Open Access
Design of an economical and reliable net-metering device for residential consumption measurement using IoT
(IEEE, 2020-11) Jamal, H.; Arshad, M. Nauman; Butt, Y.; Shafiq, H.; Manan, A.; Arif, A.
With the proliferation of decentralised power generation technologies and smart grid infrastructures, residential renewable energy generation (REG) has burgeoned to become intrinsic to modern power grids and is expected to play a fundamental role for the realization of netzero energy buildings to cater to the ever-escalating energy demand. The integration of domestic REG into the utility grid necessitates a medium of intelligent devices to perform net metering operations. Henceforth, we propose one such cost effective smart net-metering device for household REG systems with in-built wireless bi-directional communication based on Internet of Things (IoT) technology. Net metering operations are implemented using a microcontroller to synchronously collect, analyze and transmit data from sensors installed at the utility to consumer and REG to utility sides to both an offline display and an online server via a WiFi module interfaced to the microcontroller. We successfully implemented a prototype device for real-time energy monitoring over a sustained period of time, yielding accurate power generation and energy transfer statistics with remote-control operations. These results demonstrate the considerable potential for the prototype to be a stimulant in catalysing our eventual shift to decentralised power generation.
Open Access
Energy efficient IP-connectivity with IEEE 802.11 for home M2M networks
(Oxford University Press, 2017) Ozcelik, I. M.; Korpeoglu, I.; Agrawala, A.
Machine-to-machine communication (M2M) technology enables large-scale device communication and networking, including home devices and appliances. A critical issue for home M2M networks is how to efficiently integrate existing home consumer devices and appliances into an IP-based wireless M2M network with least modifications. Due to its popularity and widespread use in closed spaces, Wi-Fi is a good alternative as a wireless technology to enable M2M networking for home devices. This paper addresses the energy-efficient integration of home appliances into a Wi-Fi- and IP-based home M2M network. Toward this goal, we first propose an integration architecture that requires least modifications to existing components. Then, we propose a novel long-term sleep scheduling algorithm to be applied with the existing 802.11 power save mode. The proposed scheme utilizes the multicast DNS protocol to maintain device and service availability when devices go into deep sleep mode. We prototyped our proposed architecture and algorithm to build a M2M network testbed of home appliances. We performed various experiments on this testbed to evaluate the operation and energy savings of our proposal. We also did simulation experiments for larger scale scenarios. As a result of our test-bed and simulation experiments, we observed significant energy savings compared to alternatives while also ensuring device and service availability. © The British Computer Society 2017. All rights reserved.
Open Access
Energy management for age of information control in solar-powered IoT end devices
(Springer, 2021-07) Aydin, A. K.; Akar, Nail
In this paper, we propose several harvesting-aware energy management policies for solar-powered wireless IoT end devices that asynchronously send status updates for their surrounding environments to a network gateway device. For such devices, we aim at minimizing the average age of information (AoI) metric which has recently been investigated extensively for status update systems. The proposed energy management policies are obtained using discrete-time Markov chain-based modeling of the stochastic intra-day variations of the solar energy harvesting process in conjunction with the average reward Markov decision process formulation. With this approach, energy management policies are constructed by using the time of day and month of year information in addition to the instantaneous values of the age of information and the battery level. The effectiveness of the proposed energy management policies in terms of their capability to reduce the average AoI as well as improving upon the tail of the AoI distribution, is validated with empirical data for a wide range of system parameters.
Open Access
Hybrid fog-cloud based data distribution for internet of things applications
(2019-09) Karataş, Fırat
Technological advancements keep making machines, devices, and appliances faster, more capable, and more connected to each other. The network of all interconnected smart devices is called Internet of Things (IoT). It is envisioned that there will be billions of interconnected IoT devices producing and consuming petabytes of data that may be needed by multiple IoT applications. This brings challenges to store and process such a large amount of data in an efficient and effective way. Cloud computing and its extension to the network edge, fog computing, emerge as new technology alternatives to tackle some of these challenges in transporting, storing, and processing petabytes of IoT data in an efficient and effective manner. In this thesis, we propose a geographically distributed hierarchical cloud and fog computing based IoT storage and processing architecture, and propose techniques for placing IoT data into its components, i.e., cloud and fog data centers. Data is considered in different types and each type of data may be needed by multiple applications. Considering this fact, we generate feasible and realistic network models for a large-scale distributed storage architecture, and propose algorithms for efficient and effective placement of data generated and consumed by large number of geographically distributed IoT nodes. Data used by multiple applications is stored only once in a location that is easily accessed by applications needing that type of data. We performed extensive simulation experiments to evaluate our proposal. The results show that our network architecture and placement techniques can be used to store IoT data efficiently while providing reduced latency for IoT applications without increasing network bandwidth consumed.
Open Access
Markov fluid queue model of an energy harvesting IoT device with adaptive sensing
(Elsevier B.V., 2017) Tunc C.; Akar, N.
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.
Open Access
Reservation frame slotted ALOHA for multi-class IOT networks
(2019-01) Fiaz, Mahzeb
The Internet of Things (IoT) is a promising technology capable of revolutionizing our work and daily lives. ALOHA based medium access schemes are widely used in IoT applications due to their low complexity despite lower throughput figures. In this study, we aim to improve the performance of Frame Slotted Aloha (FSA) for a single hop IoT network without increasing the overall complexity. Duty cycling is a key concept for managing energy consumption of wireless networks with battery powered nodes having maximum duty cycle constraints. The goal of this study is to improve the performance of frame slotted Aloha by exploiting duty cycle patterns in these networks and using reservations in advance. We discuss the system model for a single class IoT network and study via simulations the performance of Reservation Frame Slotted Aloha (RFSA) as compared to FSA, as well as the performance implications of different system parameters related to traffic patterns. With the insight gained from this preliminary study, we next study a multi-class IoT network with nodes belonging to different classes with different duty cycle constraints. Adopting RFSA for such a network requires different schemes for allocating channel resources for each class. We propose several static and dynamic channel allocation schemes based on our traffic model and study their performance as compared to FSA. Static partitioning has better performance for low traffic loads but dynamic partitioning offers better throughput at higher traffic loads. Selection of an appropriate channel allocation scheme can vary according to the load as well as several system parameters of the network.
Open Access
Resource optimization of multi-purpose IoT wireless sensor networks with shared monitoring points
(2022-11) Çavdar, Mustafa Can
Wireless sensor networks (WSNs) have many applications and are an essential part of IoT systems. The primary functionality of a WSN is to gather data from certain points that are covered with sensor nodes and transmit the collected data to remote central units for further processing. In IoT use cases, a WSN infrastructure may need to be shared by many applications. Moreover, the data gathered from a certain point or sub-region can satisfy the need of multiple ap-plications. Hence, sensing the data once in such cases is advantageous to increase the acceptance ratio of the applications and reduce waiting times of applications, makespan, energy consumption, and traffic in the network. We call this approach monitoring point-based shared data approach. In this thesis, we focus on both placement and scheduling of the applications, each of which requires some points in the area a WSN covers to be monitored. We propose genetic algorithm-based approaches to deal with these two problems. Additionally, we propose greedy al-gorithms that will be useful where fast decision-making is required. We realized extensive simulation experiments and compared our algorithms with the methods from the literature. The results show the effectiveness of our algorithms in terms of various metrics.