Understanding the IoT connectivity landscape: a contemporary M2M radio technology roadmap

Andreev, Sergey; Galinina, Olga; Pyattaev, Alexander; Gerasimenko, Mikhail; Tirronen, Tuomas; Torsner, Johan; Sachs, Joachim; Dohler, Mischa; Koucheryavy, Yevgeni

doi:10.1109/mcom.2015.7263370

Cited by 242 publications

(100 citation statements)

References 8 publications

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“…Our results and findings are consistent with the results provided by Andreev et al in [41]. WiFi IEEE 802.11n remains a candidate for BBN communications for indoor scenarios, but with consideration of the above-cited limitation.…”

Section: Real Indoor Experiments Scenario: a Wireless Body-to-body Consupporting

confidence: 93%

Communication Challenges in on-Body and Body-to-Body Wearable Wireless Networks—A Connectivity Perspective

et al. 2017

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Wearable wireless networks (WWNs) offer innovative ways to connect humans and/or objects anywhere, anytime, within an infinite variety of applications. WWNs include three levels of communications: on-body, body-to-body and off-body communication. Successful communication in on-body and body-to-body networks is often challenging due to ultra-low power consumption, processing and storage capabilities, which have a significant impact on the achievable throughput and packet reception ratio as well as latency. Consequently, all these factors make it difficult to opt for an appropriate technology to optimize communication performance, which predominantly depends on the given application. In particular, this work emphasizes the impact of coarse-grain factors (such as dynamic and diverse mobility, radio-link and signal propagation, interference management, data dissemination schemes, and routing approaches) directly affecting the communication performance in WWNs. Experiments have been performed on a real testbed to investigate the connectivity behavior on two wireless communication levels: on-body and body-to-body. It is concluded that by considering the impact of above-mentioned factors, the general perception of using specific technologies may not be correct. Indeed, for on-body communication, by using the IEEE 802.15.6 standard (which is specifically designed for on-body communication), it is observed that while operating at low transmission power under realistic conditions, the connectivity can be significantly low, thus, the transmission power has to be tuned carefully. Similarly, for body-to-body communication in an indoor environment, WiFi IEEE 802.11n also has a high threshold of end-to-end disconnections beyond two hops (approximatively 25 m). Therefore, these facts promote the use of novel technologies such as 802.11ac, NarrowBand-IoT (NB-IoT) etc. as possible candidates for body-to-body communications as a part of the Internet of humans concept.

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Section: Real Indoor Experiments Scenario: a Wireless Body-to-body Consupporting

confidence: 93%

Communication Challenges in on-Body and Body-to-Body Wearable Wireless Networks—A Connectivity Perspective

et al. 2017

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“…Andreev et al [10] review long-range communications technologies to interconnect devices in an IoT network. Myers [11] presents the low-power wide-area channel access method Random phase multiple access (RPMA), employing direct-sequence spread spectrum with multiple access (DSSS) using the 2.4 GHz band.…”

Section: Related Workmentioning

confidence: 99%

Internet of THings Area Coverage Analyzer (ITHACA) for Complex Topographical Scenarios

et al. 2017

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Abstract:The number of connected devices is increasing worldwide. Not only in contexts like the Smart City, but also in rural areas, to provide advanced features like smart farming or smart logistics. Thus, wireless network technologies to efficiently allocate Internet of Things (IoT) and Machine to Machine (M2M) communications are necessary. Traditional cellular networks like Global System for Mobile communications (GSM) are widely used worldwide for IoT environments. Nevertheless, Low Power Wide Area Networks (LP-WAN) are becoming widespread as infrastructure for present and future IoT and M2M applications. Based also on a subscription service, the LP-WAN technology SIGFOX TM may compete with cellular networks in the M2M and IoT communications market, for instance in those projects where deploying the whole communications infrastructure is too complex or expensive. For decision makers to decide the most suitable technology for each specific application, signal coverage is within the key features. Unfortunately, besides simulated coverage maps, decision-makers do not have real coverage maps for SIGFOX TM , as they can be found for cellular networks. Thereby, we propose Internet of THings Area Coverage Analyzer (ITHACA), a signal analyzer prototype to provide automated signal coverage maps and analytics for LP-WAN. Experiments performed in the Gran Canaria Island, Spain (with both urban and complex topographic rural environments), returned a real SIGFOX TM service availability above 97% and above 11% more coverage with respect to the company-provided simulated maps. We expect that ITHACA may help decision makers to deploy the most suitable technologies for future IoT and M2M projects.

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“…The Internet of Things (IoT) has been regarded as the third wave of information technology [1][2][3][4][5][6][7][8][9][10][11][12]. Based on IoT technologies, the Industrial Internet of Things (IIoTs) [13] corporates machine learning and big data technology, sensor data, and machine-to-machine (M2M) communications that have existed in industrial areas for years [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Xiao

et al. 2018

J Wireless Com Network

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The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy losses to industrial applications. In this paper, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs. In an asynchronous Media Access Control protocol, when senders need to send data, they always require to wait for their corresponding receiver to wake up. Thus, the delay here is greater than in the synchronous network. However, the synchronization cost of the whole network is enormous, and it is difficult to maintain. Therefore, LS mechanism uses a partial synchronization approach to reduce synchronization costs while effectively reducing delay. In LS approach, instead of synchronizing the nodes in the entire network, only sender nodes and part of the nodes in their forwarding node set are synchronized by self-learning methods, and accurate synchronization is not required here. Thus, the delay can be effectively reduced under the low cost. Secondly, the nodes near sink maintain the original duty cycle, while the nodes in the regions away from the sink use their remaining energy and perform synchronization operations, so as not to damage the network lifetime. Finally, because the synchronization in this paper is based on different synchronization periods among different nodes, it can improve the network performance by reducing the conflict between simultaneous data transmission. The theoretical analysis results show that compared with the previous approach FFSC, LS approach can reduce the end-to-end delay by 5. 13-11.64% and increase the energy efficiency by 14.29-17.53% under the same lifetime with a more balanced energy utilization.

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Understanding the IoT connectivity landscape: a contemporary M2M radio technology roadmap

Cited by 242 publications

References 8 publications

Communication Challenges in on-Body and Body-to-Body Wearable Wireless Networks—A Connectivity Perspective

Communication Challenges in on-Body and Body-to-Body Wearable Wireless Networks—A Connectivity Perspective

Internet of THings Area Coverage Analyzer (ITHACA) for Complex Topographical Scenarios

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

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