| 1. |
- Maleki, Neda, et al.
(författare)
-
DeltaBin : An Efficient Binary Data Format for Low Power IoT Devices
- 2023
-
Ingår i: <em>2023 International Conference on Computer, Information and Telecommunication Systems (CITS), Genoa, Italy, 2023</em>. - Genoa, Italy : IEEE Press. - 9798350336108 - 9798350336092
-
Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) notion is quickly influencing t he architectures of data-driven systems d ue to the ever-increasing rapid technological progress in all sectors. The IoT involves the collection and exchange of data from a large number of interconnected devices or sensors. The collected data is structured and transmitted in a variety of different data formats such as JSON, CBOR, BSON, or simply a binary format. The data format used by an IoT device can have a significant i mpact on t he efficiency of its data transmission. In general, using a more compact and efficient data format can help to reduce t he amount of data that needs to be transmitted, which can improve the overall speed and performance of the device. For example, using a binary data format rather than a text-based format can often result in smaller data sizes and faster transmission times. Similarly, using a binary format in a more compressed form can further help to reduce the size of the data being transmitted, which can further improve the efficiency of the transmission. In this paper, we propose Delta Binary (i.e., DeltaBin) to reduce the binary data format by transmitting only changed data. We assess DeltaBin using a real IoT deployment scenario.
|
|
| 2. |
- Maleki, Neda, et al.
(författare)
-
DynaSens : Dynamic Scheduling for IoT Devices Sustainability
- 2022
-
Ingår i: 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 20222022. - : IEEE. - 9781665485982
-
Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) have shown numerous potential applications that can enhance our quality of life. IoT is becoming a core technology to bring smart homes, smart cities, and smart industries into reality. However, with potential benefits comes a challenge of sustainability, and one major concern is to minimize energy consumption. In a citywide area, managing the operation of such large-scale IoT networking is one of the complex tasks. One of the ways is to utilize dynamic sensing scheduling where the IoT device goes to the sleep mode and prevents unnecessary data transmission. In this paper, we propose a dynamic sensing (DynaSens) algorithm for an IoT-based waste management system. This algorithm helps to reduce the waste bin overflowing, thus, provides better sanitation, and it is also helpful in reducing the fuel cost of waste collection vehicles. Our work utilizes measured values such as current consumption, LiDAR measurement time, and LoRa transmission time as the input data for the simulation experiment to evaluate energy consumption. We also assessed DynaSens using a real dataset obtained from a recycling house. We use Pycom LoPy4 micro-controller as a development board. For a number of garbage-thrown scenarios, DynaSens enables longer battery longevity by reducing the repeated execution of the same tasks. © 2022 IEEE.
|
|
| 3. |
- Maleki, Neda, et al.
(författare)
-
Unraveling Energy Consumption Patterns : Insights Through Data Analysis and Predictive Modeling
- 2023
-
Ingår i: 15th International Conference on Applied Energy.
-
Konferensbidrag (refereegranskat)abstract
- Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87.
|
|
| 4. |
- Musaddiq, Arslan, et al.
(författare)
-
Industry-Academia Cooperation : Applied IoT Research for SMEs in South-East Sweden
- 2023
-
Ingår i: Internet of Things. GIoTS 2022. - Cham : Springer. - 9783031209352 - 9783031209369 ; , s. 397-410
-
Konferensbidrag (refereegranskat)abstract
- This paper presents the activities of the Applied IoT Lab at the Department of Computer Science and Media Technology, Linnaeus University (LNU), Kalmar, Sweden. The lab is actively engaged in IoT-based educational programs, including a series of workshops and pilot cases. The lab is funded by the European Union and two Swedish counties – Kalmar and Kronoberg. The workshops and pilot cases are part of the research project named IoT Lab for Small and Medium-sized Enterprises (SMEs). One of the lab’s main objectives is to strengthen and support local companies with IoT. The project IoT Lab for SMEs also aims to spread knowledge and inspire the local community about the possibilities of using IoT technologies by organizing open lab days, in-depth lectures, and seminars. This paper introduces Applied IoT Lab at LNU, its educational programs, and industry-academic cooperation, including workshops and a number of ongoing pilot cases.
|
|
| 5. |
- Musaddiq, Arslan, et al.
(författare)
-
Integrating Object Detection and Wide Area Network Infrastructure for Sustainable Ferry Operation
- 2023
-
Ingår i: <em>2023 IEEE International Conference on Imaging Systems and Techniques (IST)</em>, Copenhagen, Denmark. - : IEEE. - 9798350330830 - 9798350330847
-
Konferensbidrag (refereegranskat)abstract
- Low-Power Wide-Area Network (LPWAN) technologies offer new opportunities for data collection, transmission, and decision-making optimization. Similarly, a wide range of use cases of computer vision and object detection algorithms can be found across different industries. This paper presents a case study focusing on the utilization of LPWAN infrastructure, specifically the Helium network, coupled with computer vision and object detection algorithms, to optimize passenger ferry operation. The passenger ferry called M/S Dessi operates between Kalmar and Färjestaden in Sweden during the summer season. By implementing an Edge-computing solution, real-time data collection and communication are achieved, enabling accurate measurement of passenger flow. This approach is superior to traditional methods of collecting passenger data, such as manual counting or CCTV surveillance. Real-time passenger data is invaluable for traffic planning, crowd prediction, revenue enhancement, and speed and fuel optimization. The utilization of the Helium network ensures reliable and long-distance data transmission, extending the system’s applicability to multiple ferries and distant locations. The proposed approach can be utilized to integrate passenger ferries that operate in close proximity to urban areas into society’s digital transformation efforts. This study highlights the potential of LPWAN, computer vision, and object detection in enhancing passenger ferry operations, contributing to enhanced efficiency and sustainability.
|
|
| 6. |
- Musaddiq, Arslan, et al.
(författare)
-
Internet of Things for Wetland Conservation using Helium Network : Experience and Analysis
- 2022
-
Ingår i: 12th International Conference on the Internet of Things, IoT 2022, Delft 7 - 10 November 2022. - New York, NY, USA : ACM Digital Library. - 9781450396653 ; , s. 143-146
-
Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT), as a new paradigm of connected things or objects to the Internet, allows us to monitor the environment by collecting data in a wide spatial and temporal window. Especially the utilization of IoT has increased significantly since the development of the Long Range Wide Area Network (LoRaWAN). However, deploying LoRa gateways, maintaining network infrastructure, operational cost, and quality of service are challenging. Helium has emerged as one of the largest networks in terms of coverage for IoT devices to solve such problems. Helium is decentralized, cryptocurrency incentives-based network infrastructure replacing traditional service providers. However, due to network incentives, currently, it contains more hotspots compared to active users. This paper presents our experience and analysis of deploying IoT devices for real-world applications using the Helium network. We present experiences from the IoT device’s deployment for wetland conservation in southern Sweden.
|
|
