scholarly journals Decentralized spectrum learning for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments

2020 ◽  
Vol 75 (11-12) ◽  
pp. 711-727
Author(s):  
Christophe Moy ◽  
Lilian Besson ◽  
Guillaume Delbarre ◽  
Laurent Toutain
Keyword(s):  
Learning Algorithms ◽  
Real Life ◽  
Software Radio ◽  
Battery Life ◽  
Radio Signals ◽  
Massive Number ◽  
Collision Mitigation ◽  
Unlicensed Bands ◽  
Iot Devices ◽  
Network Capability

AbstractThis paper describes the theoretical principles and experimental results of reinforcement learning algorithms embedded into IoT devices (Internet of Things), in order to tackle the problem of radio collision mitigation in ISM unlicensed bands. Multi-armed bandit (MAB) learning algorithms are used here to improve both the IoT network capability to support the expected massive number of objects and the energetic autonomy of the IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept, based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals are diminished for IoT devices that use MAB learning. Then we describe the first implementation of such algorithms on LoRa devices operating in a real LoRaWAN network at 868 MHz. We named this solution IoTligent. IoTligent does not add neither processing overhead, so it can be run into the IoT devices, nor network overhead, so that it requires no change to LoRaWAN protocol. Real-life experiments done in a real LoRa network show that IoTligent devices’ battery life can be extended by a factor of 2, in the scenarios we faced during our experiment. Finally we submit IoTligent devices to very constrained conditions that are expected in the future with the growing number of IoT devices, by generating an artificial IoT massive radio traffic in anechoic chamber. We show that IoTligent devices can cope with spectrum scarcity that will occur at that time in unlicensed bands.

scholarly journals Practical Cross-Layer Radio Frequency-Based Authentication Scheme for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4034
Author(s):  
Arie Haenel ◽  
Yoram Haddad ◽  
Maryline Laurent ◽  
Zonghua Zhang
Keyword(s):  
Internet Of Things ◽  
Radio Frequency ◽  
Real Life ◽  
Computation Time ◽  
Cost Effective ◽  
Battery Life ◽  
Cross Layer ◽  
Security Level ◽  
Cost Of Energy ◽  
Iot Devices

The Internet of Things world is in need of practical solutions for its security. Existing security mechanisms for IoT are mostly not implemented due to complexity, budget, and energy-saving issues. This is especially true for IoT devices that are battery powered, and they should be cost effective to be deployed extensively in the field. In this work, we propose a new cross-layer approach combining existing authentication protocols and existing Physical Layer Radio Frequency Fingerprinting technologies to provide hybrid authentication mechanisms that are practically proved efficient in the field. Even though several Radio Frequency Fingerprinting methods have been proposed so far, as a support for multi-factor authentication or even on their own, practical solutions are still a challenge. The accuracy results achieved with even the best systems using expensive equipment are still not sufficient on real-life systems. Our approach proposes a hybrid protocol that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, which has a measurable benefit while keeping an acceptable security level. We implemented a full system operating in real time and achieved an accuracy of 99.8% for the additional cost of energy, leading to a decrease of only ~20% in battery life.

OpenCarrier: Breaking the User Limit for Uplink MU-MIMO Transmissions With Coordinated APs

2022 ◽  
Vol 18 (2) ◽  
pp. 1-21
Author(s):  
Yubo Yan ◽  
Panlong Yang ◽  
Jie Xiong ◽  
Xiang-Yang Li
Keyword(s):  
Real Life ◽  
Wearable Devices ◽  
Node Degree ◽  
Transmission Rates ◽  
Current State ◽  
The Arts ◽  
Mimo Networks ◽  
Massive Number ◽  
Iot Devices ◽  
Very High

The global IoT market is experiencing a fast growth with a massive number of IoT/wearable devices deployed around us and even on our bodies. This trend incorporates more users to upload data frequently and timely to the APs. Previous work mainly focus on improving the up-link throughput. However, incorporating more users to transmit concurrently is actually more important than improving the throughout for each individual user, as the IoT devices may not require very high transmission rates but the number of devices is usually large. In the current state-of-the-arts (up-link MU-MIMO), the number of transmissions is either confined to no more than the number of antennas (node-degree-of-freedom, node-DoF) at an AP or clock synchronized with cables between APs to support more concurrent transmissions. However, synchronized APs still incur a very high collaboration overhead, prohibiting its real-life adoption. We thus propose novel schemes to remove the cable-synchronization constraint while still being able to support more concurrent users than the node-DoF limit, and at the same time minimize the collaboration overhead. In this paper, we design, implement, and experimentally evaluate OpenCarrier, the first distributed system to break the user limitation for up-link MU-MIMO networks with coordinated APs. Our experiments demonstrate that OpenCarrier is able to support up to five up-link high-throughput transmissions for MU-MIMO network with 2-antenna APs.

scholarly journals Spectrum Based Power Management for Congested IoT Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2681
Author(s):  
Kedir Mamo Besher ◽  
Juan Ivan Nieto-Hipolito ◽  
Raymundo Buenrostro-Mariscal ◽  
Mohammed Zamshed Ali
Keyword(s):  
Power Management ◽  
Network Performance ◽  
Channel Allocation ◽  
Battery Life ◽  
Packet Routing ◽  
Data Packets ◽  
Increasing Demand ◽  
Iot Devices ◽  
Set Up

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

scholarly journals Rural Healthcare IoT Architecture Based on Low-Energy LoRa

2021 ◽  
Vol 18 (14) ◽  
pp. 7660
Author(s):  
Ace Dimitrievski ◽  
Sonja Filiposka ◽  
Francisco José Melero ◽  
Eftim Zdravevski ◽  
Petre Lameski ◽  
...  
Keyword(s):  
Low Power ◽  
Rural Areas ◽  
Urban Areas ◽  
Well Being ◽  
Battery Life ◽  
Ultra Low Power ◽  
Term Operation ◽  
Doctor Patient Communication ◽  
Connected Health ◽  
Iot Devices

Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

scholarly journals A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 38
Author(s):  
Amr Mohamed AbdelAziz ◽  
Louai Alarabi ◽  
Saleh Basalamah ◽  
Abdeltawab Hendawi
Keyword(s):  
Medical Care ◽  
Real Life ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Admission Process ◽  
Multi Objective ◽  
Admission Time ◽  
King Faisal Specialist Hospital ◽  
Number Of Patients ◽  
Massive Number

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

scholarly journals DDoS Detection on Internet of Things using Unsupervised Algorithms

2021 ◽  
Vol 297 ◽  
pp. 01005
Author(s):  
Hailyie Tekleselassie
Keyword(s):  
Learning Algorithms ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Accuracy Score ◽  
Ddos Attacks ◽  
Knowledge Based ◽  
Ddos Detection ◽  
Iot Devices ◽  
Unsupervised Algorithms

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

Extending Battery Life for Wi-Fi-Based IoT Devices: Modeling, Strategies, and Algorithm

2021 ◽  
Author(s):  
Shyam Krishnan Venkateswaran ◽  
Ching-Lun Tai ◽  
Yoav Ben-Yehezkel ◽  
Yaron Alpert ◽  
Raghupathy Sivakumar
Keyword(s):  
Battery Life ◽  
Iot Devices

Communication Trends in Internet of Things

2018 ◽  
pp. 284-305 ◽  
Author(s):  
Bharathi N. Gopalsamy
Keyword(s):  
Internet Of Things ◽  
Short Range ◽  
Near Field ◽  
Communication Technologies ◽  
Good Choice ◽  
Battery Life ◽  
Power Requirement ◽  
Storage Security ◽  
Central Hypothesis ◽  
Iot Devices

The central hypothesis of Internet of Things is the term “connectivity”. The IoT devices are connected to the Internet through a wide variety of communication technologies. This chapter explains the various technologies involved in IoT connectivity. The diversity in communication raises the query of which one to choose for the proposed application. The key objective of the application needs to be defined very clearly. The application features such as the power requirement, data size, storage, security and battery life highly influence the decision of selecting one or more communication technology. Near Field Communication is a good choice for short-range communication, whereas Wi-Fi can be opted for a larger range of coverage. Though Bluetooth is required for higher data rate, it is power hungry, but ZigBee is suitable for low power devices. There involves always the tradeoff between the technologies and the requirements. This chapter emphasizes that the goal of the application required to be more precise to decide the winner of the IoT connectivity technology that suits for it.

A Survey on Attacks and Defences on LoRaWAN Gateways

2021 ◽  
pp. 19-38
Author(s):  
Olof Magnusson ◽  
Rikard Teodorsson ◽  
Joakim Wennerberg ◽  
Stig Arne Knoph
Keyword(s):  
Internet Of Things ◽  
Long Range ◽  
Emerging Technology ◽  
The Internet ◽  
Area Network ◽  
Wide Area Network ◽  
Replay Attacks ◽  
Radio Signals ◽  
Iot Devices ◽  
Set Up

LoRaWAN (long-range wide-area network) is an emerging technology for the connection of internet of things (IoT) devices to the internet and can as such be an important part of decision support systems. In this technology, IoT devices are connected to the internet through gateways by using long-range radio signals. However, because LoRaWAN is an open network, anyone has the ability to connect an end device or set up a gateway. Thus, it is important that gateways are designed in such a way that their ability to be used maliciously is limited. This chapter covers relevant attacks against gateways and potential countermeasures against them. A number of different attacks were found in literature, including radio jamming, eavesdropping, replay attacks, and attacks against the implementation of what is called beacons in LoRaWAN. Countermeasures against these attacks are discussed, and a suggestion to improve the security of LoRaWAN is also included.

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