Technologies and challenges in developing Machine-to-Machine applications: A survey

Ali, Asif; Shah, Ghalib A.; Farooq, Muhammad Omer; Ghani, Usman

doi:10.1016/j.jnca.2017.02.002

Cited by 79 publications

(39 citation statements)

References 40 publications

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Section: Related Workmentioning

confidence: 99%

“…Instead, the data rate in LPWAN is intentionally traded off for the long transmission distance. The key performance metrics defined for LPWAN are energy efficiency, scalability, and coverage [20,21].Among existing LPWAN technologies, LoRa is one of the most prominent and receiving great attention from researchers. Due to the utilization of unlicensed bands, LoRa technology is perfect for outlying regions without cellular network coverage, and for establishing private networks with specific requirements for quality and security [22].…”

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confidence: 99%

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A Long-Range 2.4G Network System and Scheduling Scheme for Aquatic Environmental Monitoring

2019

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Wireless communications for applications of inshore fishery and large area aquatic environmental monitoring are really challenging, due to the characteristics of a long monitoring period, large coverage area, and adverse transmission conditions. Recently, LPWAN (low-power wide-area network) became the new solution to address these challenges, due to its long transmission distance and low power consumption of end-nodes. In this paper, we designed a novel network system for aquatic environmental monitoring, based on long-range 2.4G technology, which consisted of a low cost dual-channel gateway and end-nodes. A DMSF (dual-channel multiple spreading factors)–TDMA (time division multiple access) MAC (medium access control) scheme for this system was proposed, which largely reduces the channel collision probability, and improves the real-time for urgent data and the average lifetime of end-nodes. We verified the applicability of the long-range 2.4G technology in an aquatic environment, by point-to-point communication experiments over lake water. The performance evaluation and analysis of DMSF–TDMA is presented through simulations, and comparison with other existing schemes. The results demonstrated the benefit of our proposed scheme, in terms of the packet delivery rate, delay, and energy consumption.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

A Long-Range 2.4G Network System and Scheduling Scheme for Aquatic Environmental Monitoring

2019

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“…e.g. the connected things interconnections with each other and with humans (Maia et al, 2016), in M2M communication, the differentiating characteristic from other communication paradigms is its capability to completely eliminate human activities in the communication cycle, and the main focus in M2M communications, is connectivity (Ali et al, 2017;Sikorski et al, 2017;Verma et al, 2016;Vrabi et al, 2017). M2M interconnects intelligent machines in a digital network using diverse communication technologies to autonomously monitor and control machines without any human intervention (Bruns et al, 2015).…”

Section: Cognitive M2m Networkmentioning

confidence: 99%

“…Some challenges created by M2M technology include congestion and overload in network, energy efficiency, heterogeneity, reliability, QoS, and ultra-scalable connectivity. To cater for millions of machines in M2M and to overcome challenges imposed by M2M, there is a need for more spectrum (Ali et al, 2017;Sikorski et al, 2017;Verma et al, 2016). Accordingly, authors in Verma et al (2016) suggest incorporation of cognitive radio technology in M2M, they argue that due to limited licensed spectrum, a secondary spectrum is needed, to prevent M2M devices from consuming more energy and degrading network performance and efficiency.…”

Section: Cognitive M2m Networkmentioning

confidence: 99%

A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN)

Oyewobi

Hancke

2017

Journal of Network and Computer Applications

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Industrial wireless sensor network (IWSN) applications are mostly timebound, mission-critical and highly delay sensitive applications therefore IWSN defines strict, stringent and unique QoS requirements such as timeliness, reliability and availability. In IWSN, unlike other sensor networks, late arrival of packets or delay or disruption to an on-going communication are considered as critical failure. Also, because IWSN is deployed in the overcrowded industrial, scientific, and medical (ISM) band it is difficult to meet this unique QoS requirements due to stiff competition for bandwidth from other technologies operating in ISM band resulting in scarcity of spectrum for reliable communication and/or disruption of ongoing communication. However, cognitive radio (CR) provides more spectral opportunities through opportunistic-use of unused licensed spectrum while ensuring minimal interference to licensed users. Similarly, spectrum handoff, which is a new type of handoff in cognitive radio, has the potential to offer increase bandwidth, reliable, smooth and interference-free communication for IWSNs through opportunistic-use of spectrum, minimal switching-delays, and efficient target channel selection strategies as well as effective link recovery maintenance. As a result, a new paradigm known as cognitive radio industrial wireless sensor network (CR-IWSN) has become the interest of recent research efforts. In this paper, we highlight and discuss important QoS requirements of IWSN as well as efforts of existing IWSN standards to address the challenges. We discuss the potential and how cognitive radio and spectrum handoff can be useful in the attempt to provide real-time reliable and smooth communication for IWSNs.Keywords: cognitive radio, industrial wireless sensor network, opportunistic use, spectrum handoff, quality of service. (Cheng et al., 2016;Silva et al., 2015;Silva and Hancke, 2016). Therefore, reliable and stable communication is of critical importance and top priority in IWSNs (Kumar S et al., 2014;Phala et al., 2016). For instance, in industrial automation; precision and timeliness are important and in industrial monitoring; availability, reliability, delay-intolerance, and robustness of communication links are key (Abu-Mahfouz and Hancke, 2011;Silva and Hancke, 2016). Yet, these requirements can be relatively difficult to achieve when operating in the ISM band due to interference and overcrowding which result in continuous switching, high latency due to switching, reduced link capacity and disruption of on-going communications. However, spectrum handoff can offer increase bandwidth, reliable, smooth and interference-free communication for IWSNs through opportunistic-use of spectrum, minimal switching-delays, and efficient target channel selection strategies as well as effective link recovery maintenance. A well-planned and designed spectrum handoff technique will provide reliable communication by reducing number of spectrum handoffs and maintaining quality communication link for IWSN systems and application...

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“…Big data can be distinguished based on the large amount of digital data that is being created at an unprecedented rate by humans, sensor networks, mobile telecommunications, the Internet of Things, and many other heterogeneous devices [1][2][3][4]. These data exist in the form of query logs [5,6]; transaction records in database [7]; images, videos, and audios; abstracts of digital manuscripts; webpages; and microblog posts [8].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Indonesian Acronym and Expansion Pairs with Supervised Learning and MapReduce

et al. 2020

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During the previous decades, intelligent identification of acronym and expansion pairs from a large corpus has garnered considerable research attention, particularly in the fields of text mining, entity extraction, and information retrieval. Herein, we present an improved approach to recognize the accurate acronym and expansion pairs from a large Indonesian corpus. Generally, an acronym can be either a combination of uppercase letters or a sequence of speech sounds (syllables). Our proposed approach can be computationally divided into four steps: (1) acronym candidate identification;(2) acronym and expansion pair collection; (3) feature generation; and (4) acronym and expansion pair recognition using supervised learning techniques. Further, we introduce eight numerical features and evaluate their effectiveness in representing the acronym and expansion pairs based on the precision, recall, and F-measure. Furthermore, we compare the k-nearest neighbors (K-NN), support vector machine (SVM), and bidirectional encoder representations from transformers (BERT) algorithms in terms of accurate acronym and expansion pair classification. The experimental results indicate that the SVM polynomial model that considers eight features exhibits the highest accuracy (97.93%), surpassing those of the SVM polynomial model that considers five features (90.45%), the K-NN algorithm with k = 3 that considers eight features (96.82%), the K-NN algorithm with k = 3 that considers five features (95.66%), BERT-Base model (81.64%), and BERT-Base Multilingual Cased model (88.10%). Moreover, we analyze the performance of the Hadoop technology using various numbers of data nodes to identify the acronym and expansion pairs and obtain their feature vectors. The results reveal that the Hadoop cluster containing a large number of data nodes is faster than that with fewer data nodes when processing from ten million to one hundred million pairs of acronyms and expansions.Information 2020, 11, 210 2 of 13 predicted to considerably increase with the increasing number of smartphone users, which is predicted to reach 6.1 billion users in 2020 [9]. Furthermore, new online trading trends have contributed to the rapid accumulation of records in databases by ecommerce companies, such as Alibaba and Amazon, which generate and store several terabytes of data every day [7]. The analysis of a large amount of data requires machine learning techniques to automate the creation of analytical models based on historical data and then use the model for learning from the data [10], discovering useful patterns [11], and performing automated decisions with little human intervention [12]. Many queries are posed by millions of users from across the globe each day on Google's search engine, which has attracted considerable attention from researchers who have analyzed the query logs using machine learning techniques to track and predict phenomena, including the spread patterns of flu symptoms in the United States [5,6]. The web search queries are considered to be less biased a...

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Technologies and challenges in developing Machine-to-Machine applications: A survey

Cited by 79 publications

References 40 publications

A Long-Range 2.4G Network System and Scheduling Scheme for Aquatic Environmental Monitoring

A Long-Range 2.4G Network System and Scheduling Scheme for Aquatic Environmental Monitoring

A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN)

Recognizing Indonesian Acronym and Expansion Pairs with Supervised Learning and MapReduce

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