142,900 research outputs found
DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data
With the rapid evolution of the Internet of Things, many real-world
applications utilize heterogeneously connected sensors to capture time-series
information. Edge-based machine learning (ML) methodologies are often employed
to analyze locally collected data. However, a fundamental issue across
data-driven ML approaches is distribution shift. It occurs when a model is
deployed on a data distribution different from what it was trained on, and can
substantially degrade model performance. Additionally, increasingly
sophisticated deep neural networks (DNNs) have been proposed to capture spatial
and temporal dependencies in multi-sensor time series data, requiring intensive
computational resources beyond the capacity of today's edge devices. While
brain-inspired hyperdimensional computing (HDC) has been introduced as a
lightweight solution for edge-based learning, existing HDCs are also vulnerable
to the distribution shift challenge. In this paper, we propose DOMINO, a novel
HDC learning framework addressing the distribution shift problem in noisy
multi-sensor time-series data. DOMINO leverages efficient and parallel matrix
operations on high-dimensional space to dynamically identify and filter out
domain-variant dimensions. Our evaluation on a wide range of multi-sensor time
series classification tasks shows that DOMINO achieves on average 2.04% higher
accuracy than state-of-the-art (SOTA) DNN-based domain generalization
techniques, and delivers 16.34x faster training and 2.89x faster inference.
More importantly, DOMINO performs notably better when learning from partially
labeled and highly imbalanced data, providing 10.93x higher robustness against
hardware noises than SOTA DNNs
Managing Service-Heterogeneity using Osmotic Computing
Computational resource provisioning that is closer to a user is becoming
increasingly important, with a rise in the number of devices making continuous
service requests and with the significant recent take up of latency-sensitive
applications, such as streaming and real-time data processing. Fog computing
provides a solution to such types of applications by bridging the gap between
the user and public/private cloud infrastructure via the inclusion of a "fog"
layer. Such approach is capable of reducing the overall processing latency, but
the issues of redundancy, cost-effectiveness in utilizing such computing
infrastructure and handling services on the basis of a difference in their
characteristics remain. This difference in characteristics of services because
of variations in the requirement of computational resources and processes is
termed as service heterogeneity. A potential solution to these issues is the
use of Osmotic Computing -- a recently introduced paradigm that allows division
of services on the basis of their resource usage, based on parameters such as
energy, load, processing time on a data center vs. a network edge resource.
Service provisioning can then be divided across different layers of a
computational infrastructure, from edge devices, in-transit nodes, and a data
center, and supported through an Osmotic software layer. In this paper, a
fitness-based Osmosis algorithm is proposed to provide support for osmotic
computing by making more effective use of existing Fog server resources. The
proposed approach is capable of efficiently distributing and allocating
services by following the principle of osmosis. The results are presented using
numerical simulations demonstrating gains in terms of lower allocation time and
a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication,
Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5
April 2017, http://www.iccmit.net/ (Best Paper Award
Smart Asset Management for Electric Utilities: Big Data and Future
This paper discusses about future challenges in terms of big data and new
technologies. Utilities have been collecting data in large amounts but they are
hardly utilized because they are huge in amount and also there is uncertainty
associated with it. Condition monitoring of assets collects large amounts of
data during daily operations. The question arises "How to extract information
from large chunk of data?" The concept of "rich data and poor information" is
being challenged by big data analytics with advent of machine learning
techniques. Along with technological advancements like Internet of Things
(IoT), big data analytics will play an important role for electric utilities.
In this paper, challenges are answered by pathways and guidelines to make the
current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on
Engineering Asset Management (WCEAM) 201
Profit Maximization Auction and Data Management in Big Data Markets
A big data service is any data-originated resource that is offered over the
Internet. The performance of a big data service depends on the data bought from
the data collectors. However, the problem of optimal pricing and data
allocation in big data services is not well-studied. In this paper, we propose
an auction-based big data market model. We first define the data cost and
utility based on the impact of data size on the performance of big data
analytics, e.g., machine learning algorithms. The big data services are
considered as digital goods and uniquely characterized with "unlimited supply"
compared to conventional goods which are limited. We therefore propose a
Bayesian profit maximization auction which is truthful, rational, and
computationally efficient. The optimal service price and data size are obtained
by solving the profit maximization auction. Finally, experimental results on a
real-world taxi trip dataset show that our big data market model and auction
mechanism effectively solve the profit maximization problem of the service
provider.Comment: 6 pages, 9 figures. This paper was accepted by IEEE WCNC conference
in Dec. 201
Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328Kamalinejad, P., Mahapatra, C., Sheng, Z., Mirabbasi, S., M. Leung, V. C., & Guan, Y. L. (2015). Wireless energy harvesting for the Internet of Things. IEEE Communications Magazine, 53(6), 102-108. doi:10.1109/mcom.2015.7120024Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C.-T., & Liu, X. (2016). Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects. IEEE Access, 4, 5356-5373. doi:10.1109/access.2016.2603219Grieco, L. A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di Paola, D., & Boggia, G. (2014). IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54, 32-47. doi:10.1016/j.comcom.2014.07.013Aijaz, A., & Aghvami, A. H. (2015). Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective. IEEE Internet of Things Journal, 2(2), 103-112. doi:10.1109/jiot.2015.2390775Lin, Y.-B., Lin, Y.-W., Chih, C.-Y., Li, T.-Y., Tai, C.-C., Wang, Y.-C., … Hsu, S.-C. (2015). EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art. IEEE Internet of Things Journal, 2(6), 551-561. doi:10.1109/jiot.2015.2423286Bello, O., & Zeadally, S. (2016). Intelligent Device-to-Device Communication in the Internet of Things. IEEE Systems Journal, 10(3), 1172-1182. doi:10.1109/jsyst.2014.2298837Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Kaur, N., & Sood, S. K. (2017). An Energy-Efficient Architecture for the Internet of Things (IoT). IEEE Systems Journal, 11(2), 796-805. doi:10.1109/jsyst.2015.2469676Erol-Kantarci, M., & Mouftah, H. T. (2015). Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues. IEEE Communications Surveys & Tutorials, 17(1), 179-197. doi:10.1109/comst.2014.2341600Machine-to-Machine Communications (M2M). M2M Service Requirementshttp://www.etsi.org/deliver/etsi_ts/102600_102699/102689/01.01.01_60/ts_102689v010101p.pdfKhan, M., Silva, B. N., & Han, K. (2016). Internet of Things Based Energy Aware Smart Home Control System. IEEE Access, 4, 7556-7566. doi:10.1109/access.2016.2621752Huang, S.-C., Chen, B.-H., Chou, S.-K., Hwang, J.-N., & Lee, K.-H. (2016). Smart Car [Application Notes]. IEEE Computational Intelligence Magazine, 11(4), 46-58. doi:10.1109/mci.2016.2601758Kant, K., & Pal, A. (2017). Internet of Perishable Logistics. IEEE Internet Computing, 21(1), 22-31. doi:10.1109/mic.2017.19Roopaei, M., Rad, P., & Choo, K.-K. R. (2017). Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging. IEEE Cloud Computing, 4(1), 10-15. doi:10.1109/mcc.2017.5Tröster, G. (2011). Smart Clothes—The Unfulfilled Pledge? IEEE Pervasive Computing, 10(2), 87-89. doi:10.1109/mprv.2011.32Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376. doi:10.1109/comst.2015.2444095Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 4(5), 1125-1142. doi:10.1109/jiot.2017.2683200Perera, C., Liu, C. H., Jayawardena, S., & Min Chen. (2014). A Survey on Internet of Things From Industrial Market Perspective. IEEE Access, 2, 1660-1679. doi:10.1109/access.2015.2389854Kamilaris, A., & Pitsillides, A. (2016). Mobile Phone Computing and the Internet of Things: A Survey. IEEE Internet of Things Journal, 3(6), 885-898. doi:10.1109/jiot.2016.2600569Arcadius Tokognon, C., Gao, B., Tian, G. Y., & Yan, Y. (2017). Structural Health Monitoring Framework Based on Internet of Things: A Survey. IEEE Internet of Things Journal, 4(3), 619-635. doi:10.1109/jiot.2017.2664072Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2016). Middleware for Internet of Things: A Survey. IEEE Internet of Things Journal, 3(1), 70-95. doi:10.1109/jiot.2015.2498900Luong, N. C., Hoang, D. T., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2016). Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey. IEEE Communications Surveys & Tutorials, 18(4), 2546-2590. doi:10.1109/comst.2016.2582841Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454. doi:10.1109/surv.2013.042313.00197Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017). Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions. IEEE Wireless Communications, 24(3), 17-25. doi:10.1109/mwc.2017.1600404Ahmed, E., Yaqoob, I., Gani, A., Imran, M., & Guizani, M. (2016). Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5), 10-16. doi:10.1109/mwc.2016.7721736Cao, Y., Jiang, T., & Han, Z. (2016). A Survey of Emerging M2M Systems: Context, Task, and Objective. IEEE Internet of Things Journal, 3(6), 1246-1258. doi:10.1109/jiot.2016.2582540Rajandekar, A., & Sikdar, B. (2015). A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications. IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Risteska Stojkoska, B. L., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464. doi:10.1016/j.jclepro.2016.10.006Liu, C. H., Fan, J., Branch, J. W., & Leung, K. K. (2014). Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments. IEEE Transactions on Emerging Topics in Computing, 2(4), 473-487. doi:10.1109/tetc.2014.2364915Du, R., Gkatzikis, L., Fischione, C., & Xiao, M. (2015). Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing. IEEE Journal on Selected Areas in Communications, 33(12), 2997-3010. doi:10.1109/jsac.2015.2481199Chen, Y., Chiotellis, N., Chuo, L.-X., Pfeiffer, C., Shi, Y., Dreslinski, R. G., … Kim, H. S. (2016). Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes. IEEE Journal on Selected Areas in Communications, 34(12), 3962-3977. doi:10.1109/jsac.2016.2612041Akgül, Ö. U., & Canberk, B. (2016). Self-Organized Things (SoT): An energy efficient next generation network management. Computer Communications, 74, 52-62. doi:10.1016/j.comcom.2014.07.004Ahn, J. H., & Lee, T.-J. (2018). ALLYS: All You can Send for Energy Harvesting Networks. IEEE Transactions on Mobile Computing, 17(4), 775-788. doi:10.1109/tmc.2017.2740929Mondal, S., & Paily, R. (2017). Efficient Solar Power Management System for Self-Powered IoT Node. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2359-2369. doi:10.1109/tcsi.2017.2707566Qureshi, F. F., Iqbal, R., & Asghar, M. N. (2017). Energy efficient wireless communication technique based on Cognitive Radio for Internet of Things. Journal of Network and Computer Applications, 89, 14-25. doi:10.1016/j.jnca.2017.01.003Nguyen, T. D., Khan, J. Y., & Ngo, D. T. (2017). Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications. Ad Hoc Networks, 56, 109-121. doi:10.1016/j.adhoc.2016.12.003Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-Centered and QoS-Aware Services Selection for Internet of Things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256-1269. doi:10.1109/tase.2016.2539240Afzal, B., Alvi, S. A., Shah, G. A., & Mahmood, W. (2017). Energy efficient context aware traffic scheduling for IoT applications. Ad Hoc Networks, 62, 101-115. doi:10.1016/j.adhoc.2017.05.001Song, L., Chai, K. K., Chen, Y., Schormans, J., Loo, J., & Vinel, A. (2017). QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems. IEEE Systems Journal, 11(3), 1447-1455. doi:10.1109/jsyst.2015.2465292Energy-Efficient Probabilistic Routing Algorithm for Internet of Thingshttp://www.ietf.org/rfc/rfc3561.txtMachado, K., Rosário, D., Cerqueira, E., Loureiro, A., Neto, A., & de Souza, J. (2013). A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications. Sensors, 13(2), 1942-1964. doi:10.3390/s130201942Chelloug, S. A. (2015). Energy-Efficient Content-Based Routing in Internet of Things. Journal of Computer and Communications, 03(12), 9-20. doi:10.4236/jcc.2015.312002Zhao, M., Ho, I. W.-H., & Chong, P. H. J. (2016). An Energy-Efficient Region-Based RPL Routing Protocol for Low-Power and Lossy Networks. IEEE Internet of Things Journal, 3(6), 1319-1333. doi:10.1109/jiot.2016.2593438Qiu, T., Lv, Y., Xia, F., Chen, N., Wan, J., & Tolba, A. (2016). ERGID: An efficient routing protocol for emergency response Internet of Things. Journal of Network and Computer Applications, 72, 104-112. doi:10.1016/j.jnca.2016.06.009Liu, Y., Liu, A., Hu, Y., Li, Z., Choi, Y.-J., Sekiya, H., & Li, J. (2016). FFSC: An Energy Efficiency Communications Ap-proach for Delay Minimizing in Internet of Things. IEEE Access, 1-1. doi:10.1109/access.2016.2588278Qiu, S., Haselmayr, W., Li, B., Zhao, C., & Guo, W. (2017). Bacterial Relay for Energy-Efficient Molecular Communications. IEEE Transactions on NanoBioscience, 16(7), 555-562. doi:10.1109/tnb.2017.2741669Biason, A., Pielli, C., Rossi, M., Zanella, A., Zordan, D., Kelly, M., & Zorzi, M. (2017). EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design. IEEE Access, 5, 6894-6908. doi:10.1109/access.2017.2692522Huang, Z., Lin, K.-J., Yu, S.-Y., & Hsu, J. Y. (2014). Co-locating services in IoT systems to minimize the communication energy cost. Journal of Innovation in Digital Ecosystems, 1(1-2), 47-57. doi:10.1016/j.jides.2015.02.005Kwak, J., Kim, Y., Lee, J., & Chong, S. (2015). DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems. IEEE Journal on Selected Areas in Communications, 33(12), 2510-2523. doi:10.1109/jsac.2015.2478718Abu Sharkh, M., & Shami, A. (2017). An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Vehicular Communications, 9, 199-210. doi:10.1016/j.vehcom.2017.02.004Bui, D.-M., Yoon, Y., Huh, E.-N., Jun, S., & Lee, S. (2017). Energy efficiency for cloud computing system based on predictive optimization. Journal of Parallel and Distributed Computing, 102, 103-114. doi:10.1016/j.jpdc.2016.11.011Liu, A., Zhang, Q., Li, Z., Choi, Y., Li, J., & Komuro, N. (2017). A green and reliable communication modeling for industrial internet of things. Computers & Electrical Engineering, 58, 364-381. doi:10.1016/j.compeleceng.2016.09.005Kim, J. (2015). Energy-Efficient Dynamic Packet Downloading for Medical IoT Platforms. IEEE Transactions on Industrial Informatics, 11(6), 1653-1659. doi:10.1109/tii.2015.2434773Chiu, T.-C., Shih, Y.-Y., Pang, A.-C., & Pai, C.-W. (2017). Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids. IEEE Internet of Things Journal, 4(2), 374-383. doi:10.1109/jiot.2016.2556006Gandotra, P., Jha, R. K., & Jain, S. (2017). Green Communication in Next Generation Cellular Networks: A Survey. IEEE Access, 5, 11727-11758. doi:10.1109/access.2017.2711784Li, J., Peng, M., Yu, Y., & Ding, Z. (2016). Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks. IEEE Transactions on Vehicular Technology, 65(12), 9873-9887. doi:10.1109/tvt.2016.2531184Kaiwartya, O., Abdullah, A. H., Cao, Y., Lloret, J., Kumar, S., Shah, R. R., … Prakash, S. (2018). Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things. IEEE Internet of Things Journal, 5(2), 571-580. doi:10.1109/jiot.2017.2717704Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3kaiwartya, omprakash, Abdullah, A., Cao, Y., Rao, R. S., Kumar, S., Lobiyal, D. K., … Shah, R. R. (2016). T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study. IEEE Sensors Journal, 1-1. doi:10.1109/jsen.2016.2614748Alrajeh, N. A., Khan, S., Lloret, J., & Loo, J. (2013). Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(1), 374796. doi:10.1155/2013/374796Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.272
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
- …