An optimal network coding based backpressure routing approach for massive IoT network

Malathy, S.; Porkodi, V.; Sampathkumar, A.; Hindia, M. H. D. Nour; Dimyati, Kaharudin; Tilwari, Valmik; Qamar, Faizan; Amiri, Iraj Sadegh

doi:10.1007/s11276-020-02284-5

Cited by 37 publications

(16 citation statements)

References 56 publications

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“…3. NET and TAP Layers-The problem of routing data packets through diverse platforms inherent to NET layer of the proposed architecture can be solved by applying optimum routing schemes, monitoring and managing network condition through load balancing, traffic control, traffic policing, and traffic shaping Malathy et al (2020).…”

Section: Expected Challenges and Future Research Directions Challengesmentioning

confidence: 99%

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

et al. 2021

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Social distancing has been regarded as a key method by the authorities worldwide to manage the pandemic of COVID-19. Digital technologies play a crucial role to support the social, professional and economic activities when people are forced to stay locked-down in their homes. Internet of things (IoT) technologies have a track of providing high quality remote health care and automation services which could guarantee social distancing while maintaining health and well-being of populations. In this paper, we propose an end-to-end IoT architecture to support the social distancing in the event of pandemic. The architecture comprises of the major use cases of IoT in relevance with the COVID-19. Furthermore, we also present a short-term and long-term strategy to mange the social distancing methodology using the proposed IoT architecture. The challenges associated with each layer of architecture have been highlighted and design guidelines have been presented to deal with them.

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Section: Expected Challenges and Future Research Directions Challengesmentioning

confidence: 99%

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

et al. 2021

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“…These intelligent things can include anything, such as a person, device, sensor, and any smart object, which are permitted to communicate with each other with or without human interaction 7,8 . Developing IoT applications is affected by various factors, such as the heterogeneity of adopted communication protocols, utilizing multiple programming languages, a lack of general frameworks or guidelines to simplify high‐level implementation and low‐level communication, and the high complexity of distributed computing 9,10 …”

Section: Introductionmentioning

confidence: 99%

“…adopted communication protocols, utilizing multiple programming languages, a lack of general frameworks or guidelines to simplify high-level implementation and low-level communication, and the high complexity of distributed computing. 9,10 Over the last decades, with the fast growth of Internet-connected objects, a novel wireless communication technology emerged called low-power wide-area network (LPWAN). In fact, this technology has been strongly suggested as the primary network for IoT applications.…”

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confidence: 99%

The role of low‐power wide‐area network technologies in Internet of Things: A systematic and comprehensive review

Mousavi

Khademzadeh

Rahmani

2021

Int J Communication

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Summary With the quick progress of wireless technologies, the Internet of Things (IoT) has been recognized as the main part of daily people's lives that makes their life more convenient by utilizing a diverse range of smart devices. Over the last decade, various useful and forthcoming application fields have been developed by taking advantage of the IoT concept. In this regard, LPWAN technologies such as LoRa, Sigfox, and NB‐IoT play an important role in advancing. These technologies are suitable wireless communication protocols for battery‐powered IoT objects that enable long‐distance communication in low‐power devices at a low operation cost. Some review papers have investigated the LPWAN technology in the IoT from different perspectives. However, the lack of detailed and systematic study outlining the role of these technologies in different IoT applications is very clear. Consequently, this gap inspired us to write the current study. The current works are classified into three main classes, including smart city, home automation, and smart healthcare. Besides, the involved models in the smart city group are categorized into five subgroups, including smart monitoring, smart metering, air pollution, smart agriculture, and smart parking. The selected works are reviewed, and their main features, including main idea, coverage range, sensor types, processing board, frequency band, communication protocols, power consumption analysis, and achievement, are specified. Generally, our main aim is to describe the challenging problems of applying LPWAN to various IoT applications, specify the efficient models, and recommend some hints for upcoming studies.

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“…[4]. The wireless networks may be categorized into two designs, i.e., infrastructure-and non-infrastructure-based networks [5]. In an infrastructure-based network, wireless nodes are communicating by using access points (which can be relay nodes or base stations (BSs)), which are connected to a fixed network structure, such as switches or routers [2].…”

Section: Introductionmentioning

confidence: 99%

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

2020

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Vehicle Ad-Hoc Network (VANET) is a dynamic decentralized network that consists of various wireless mobile vehicles with no individual user management. Several routing protocols can be used for VANETs, for example, the Location-Aided Routing (LAR) protocol that utilizes location information provided by the Global Positioning System (GPS) sensors. It can help to reduce the search space for the desired route—limiting the search space results in fewer route discovery messages. However, two essential aspects are ignored while applying the LAR protocol in the VANET-based environment. Firstly, the LAR does not exploit the fact that nodes in VANET do not have pure random movement. In other words, nodes in LAR predict the position of destination node by ignoring the fact that the pre-defined constraint on the destination node navigation is met. Secondly, the nodes in the conventional LAR (or simply stated as LAR) protocol use the location information of the destination node before selecting the route location, which is most likely to expire because of the fast movement of the nodes in the VANET environment. This study presents an estimation based on a heuristic approach that was developed to reject weak GPS location data and accept accurate ones. The proposed routing protocol stated as Rectangle-Aided LAR (RALAR) is based on a moving rectangular zone according to the node′s mobility model. Additionally, the proposed RALAR protocol was optimized by using the Genetic Algorithm (GA) by selecting the most suitable time-out variable. The results were compared with LAR and Kalman-Filter Aided-LAR (KALAR), the most commonly utilized protocols in VANET for performance metrics using Packet Delivery Ratio (PDR), average End-to-End Delay (E2E Delay), routing overhead and average energy consumption. The results showed that the proposed RALAR protocol achieved an improvement over the KALAR in terms of PDR of 4.7%, average E2E delay of 60%, routing overhead of 15.5%, and 10.7% of energy consumption. The results proved that the performance of the RALAR protocol had outperformed the KALAR and LAR protocol in terms of regular network performance measures in the VANET environment.

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An optimal network coding based backpressure routing approach for massive IoT network

Cited by 37 publications

References 56 publications

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

The role of low‐power wide‐area network technologies in Internet of Things: A systematic and comprehensive review

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

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