Browsing by Subject "Internet of Things"

Now showing 1 - 5 of 5

Open Access
Energy efficient ip-connectivity with ieee 80211 for home m2m networks
(2014) Özçelik, İhsan Mert
Machine-to-machine communications (M2M) technology enables large-scale communication and networking of devices of various kinds including home devices and appliances. A critical issue for home M2M networks is how to efficiently integrate the already existing home consumer devices and appliances into an IP based wireless M2M network with least modifications to existing components. Due to its popularity and widespread usage in closed spaces, Wi-Fi is a good alternative as a wireless technology to enable M2M networking for home devices. This thesis addresses the energy-efficient integration of home appliances to a Wi-Fi and IP based home M2M network. Towards this goal, we first propose an integration architecture that requires least modifications in existing components. Then, we propose a novel long-term sleep scheduling algorithm to be applied together with the existing 802.11 power save mode (PSM). The proposed scheme utilizes the multicast DNS (mDNS) protocol to maintain device and service availability when devices go into deep sleep mode. We implemented our proposed architecture and algorithm as a prototype to build an M2M network of home appliances as a test-bed. We performed various experiments on this test-bed to evaluate the proper operation and energy savings of our proposal. We also did extensive simulation experiments for larger-scale scenarios. As a result of our test-bed and simulation experiments, we observed energy savings up to 70% compared to the existing infrastructure which applies no sleep mechanism, and up to 20% compared to standard 802.11 PSM scheme, while ensuring device and service availability at the same time.
Open Access
Energy management in energy harvesting wireless sensor nodes with lifetime constraints
(2016-06) Tunç, Çağlar
Advancements in the \Internet of Things (IoT)" concept enables large numbers of low-power wireless sensors and electronic devices to be connected to the Internet and outside world over a wide area wireless network without a need for human interaction. Using rechargeable batteries with energy harvesting to power these wireless sensors has been shown to preserve the self-sustainability and selfsu fficiency of a sensor node and prolong its lifetime, hence the whole network it belongs to. However, it brings the question of how to intelligently manage the energy in the battery so that the node maintains its functionalities by keeping the battery level over zero for an extended duration of time, known as the lifehorizon. We propose a risk-theoretic Markov uid queue model to compute the battery outage probability of a wireless sensor node for a given finite life-horizon. The proposed method enables the performance evaluation of a wide spectrum of energy management policies including those with adaptive sensing rate (or duty cycling). In this model, the node gathers data from the environment according to a Poisson process whose rate is to depend on the instantaneous battery level and/or the state of the energy harvesting process (EHP) which is characterized by a Continuous time Markov Chain (CTMC). Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing rate policies are obtained that maximize the information sensing rate of the sensor node while meeting lifetime constraints given in terms of battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using two-state CTMC-based EHPs.
Open Access
Fog-Based Data Distribution Service (F-DAD) for Internet of Things (IoT) applications
(Elsevier, 2019) Karataş, Fırat; Körpeoğlu, İbrahim
With advances in technology, devices, machines, and appliances get smarter, more capable and connected to each other. This defines a new era called Internet of Things (IoT), consisting of a huge number of connected devices producing and consuming large amounts of data that may be needed by multiple IoT applications. At the same time, cloud computing and its extension to the network edge, fog computing, become an important way of storing and processing large amounts of data. Then, an important issue is how to transport, place, store, and process this huge amount of IoT data in an efficient and effective manner. In this paper, we propose a geographically distributed hierarchical cloud and fog computing based IoT architecture, and propose techniques for placing IoT data into the components, i.e., cloud and fog data centers, of the proposed architecture. Data is considered in different types and each type of data may be needed by multiple applications. Considering this fact, we model the data placement problem as an optimization problem and propose algorithms for efficient and effective placement of data generated and consumed by geographically distributed IoT nodes. Data used by multiple applications is stored only once in a location that is efficiently accessed by applications needing that type of data. We perform extensive simulation experiments to evaluate our proposal and the results show that our architecture and placement techniques can place and store data efficiently while providing good performance for applications and network in terms of access latency and bandwidth consumed.
Open Access
IoT based smart office application for advanced indoor working environment and energy efficiency
(IEEE, 2017) Batı, Arda Cankat; Coşkun, Ercan; Gözüaçık, Ömer; İlhan, Giray; Şahin, Fatih Alperen; Uncuoğlu, Uygar; Güngen, Murat Alp; Telli, A.
Internet of Things is a complex network, consisting of many elements that communicate with each other constantly. The network includes various modules, sensors, and computers etc. which constantly share data with each other, and carry out independent actions. The system may include interfaces connecting the users to the system, such as mobile apps and websites. In our vision, a 'Smart Office' is an office which knows or can determine office users' needs, and acts according to this knowledge. Our main goal is to design an office which will make independent decisions to maintain optimal office environment. These decisions will be made according to manually set user preferences and sensor readings. With our smart office system, we are aiming to provide flexible and energy efficient working environment to the users.
Open Access
A temperature sensor implant for active implantable medical devices for in vivo subacute heating tests under MRI
(John Wiley and Sons, 2018) Silemek, B.; Açıkel, V.; Oto, C.; Alipour, A.; Aykut, Z. G.; Algın, O.; Atalar, Ergin
Purpose: To introduce a temperature sensor implant (TSI) that mimics an active implantable medical device (AIMD) for animal testing of MRI heating. Computer simulations and phantom experiments poorly represent potential temperature increases. Animal experiments could be a better model, but heating experiments conducted immediately after the surgery suffer from alterations of the thermoregulatory and tissue properties during acute testing conditions. Therefore, the aim of this study was to introduce a temperature sensor implant that mimics an AIMD and capable of measuring the electrode temperature after implantation of the device without any further intervention at any time after the surgery in an animal model. Methods: A battery-operated TSI, which resembled an AIMD, was used to measure the lead temperature and impedance and the case temperature. The measured values were transmitted to an external computer via a low-power Bluetooth communication protocol. In addition to validation experiments on the phantom, a sheep experiment was conducted to test the feasibility of the system in subacute conditions. Results: The measurements had a maximum of 0.5°C difference compared to fiber-optic temperature probes. In vivo animal experiments demonstrated feasibility of the system. Conclusion: An active implant, which can measure its own temperature, was proposed to investigate implant heating during MRI examinations. Magn Reson Med 79:2824-2832, 2018.