A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements

Kobo, Hlabishi Isaac; Abu‐Mahfouz, Adnan M.; Hancke, Gerhard P.

doi:10.1109/access.2017.2666200

Cited by 406 publications

(240 citation statements)

References 142 publications

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“…By using the comp. node utilization decision variable U n , the minimization can be expressed as shown in (6).…”

Section: E Minimizing Service Migrations On Subsequent Iterations -Mmentioning

confidence: 99%

“…The communication matrix C is a random number between [0.02, 0.04] which represents the bandwidth requirement (in Mbit/s) between two communicating services, s 1 and s 2 , respectively. Moreover, the bandwidth matrix B is a random number between [6,14] which represents the available bandwidth (in Mbit/s) between two comp. nodes, n 1 and n 2 , respectively.…”

Section: Evaluation Setupmentioning

confidence: 99%

“…However, there is still a large number of research challenges associated with this approach since Fog Computing is in its early stages and needs more time to evolve. One of the main challenges is the proper resource allocation, since services can be placed in a highly congested location, or even further from the end devices, which would result in a higher communication latency because current end devices and gateways are lacking in terms of processing power, storage capacity and memory [6]. Moreover, few resource management strategies are currently addressing the real-time constraints of Smart City IoT applications while minimizing resource costs and maximizing quality of service (QoS).…”

Section: Introductionmentioning

confidence: 99%

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Resource provisioning for IoT application services in smart cities

Santos¹,

Wauters²,

Volckaert³

et al. 2017

2017 13th International Conference on Network and Service Management (CNSM)

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Abstract-In the last years, traffic over wireless networks has been increasing exponentially, due to the impact of Internet of Things (IoT) and Smart Cities. Current networks must adapt to and cope with the specific requirements of IoT applications since resources can be requested on-demand simultaneously by multiple devices on different locations. One of these requirements is low latency, since even a small delay for an IoT application such as health monitoring or emergency service can drastically impact their performance. To deal with this limitation, the Fog computing paradigm has been introduced, placing cloud resources on the edges of the network to decrease the latency. However, deciding which edge cloud location and which physical hardware will be used to allocate a specific resource related to an IoT application is not an easy task. Therefore, in this paper, an Integer Linear Programming (ILP) formulation for the IoT application service placement problem is proposed, which considers multiple optimization objectives such as low latency and energy efficiency. Solutions for the resource provisioning of IoT applications within the scope of Antwerp's City of Things testbed have been obtained. The result of this work can serve as a benchmark in future research related to placement issues of IoT application services in Fog Computing environments since the model approach is generic and applies to a wide range of IoT use cases.

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“…By using the comp. node utilization decision variable U n , the minimization can be expressed as shown in (6).…”

Section: E Minimizing Service Migrations On Subsequent Iterations -Mmentioning

confidence: 99%

Section: Evaluation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resource provisioning for IoT application services in smart cities

Santos¹,

Wauters²,

Volckaert³

et al. 2017

2017 13th International Conference on Network and Service Management (CNSM)

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“…That's why the creation of an approach to such systems simulation is a topical task. 13. Shevchuk, V. P. Metrologiya intellektual'nyh izmeritel'nyh sistem [Text]: monografiya / V. P. Shevchuk, V. I.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the synergy of SDN and Neural Networks technologies can be expedient in the context of parallel computing systems' resources allocation [11,12]. Moreover, SDN is considered as a solution for Wireless Sensor Networks challenges [13]. The opportunities for SDN technology integration with existing 4G/5G mobile networks have also been discussed [14].…”

Section: Introductionmentioning

confidence: 99%

Development of stratified approach to software defined networks simulation

Shkarupylo

Skrupsky

Oliinyk

et al. 2017

EEJET

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Запропоновано стратифікований підхід до імітаційного моделювання програмно-конфі-гурованих мереж. Запропоновано імітаційні моделі мережі, активних і пасивних компо-нентів -контролера, комутатора, хоста та комунікаційних каналів. Придатність підхо-ду до цільового використання підтверджено шляхом співставлення одержаних результа-тів імітаційного моделювання із результата-ми емуляції мережі у середовищі Mininet Ключові слова: програмно-конфігурована мережа, імітаційне моделювання, дискрет-но-подійна специфікація системи, великі дані Предложен стратифицированный подход к имитационному моделированию программно-конфигурируемых сетей. Предложены ими-тационные модели сети, активных и пассив-ных компонентов -контроллера, коммута-тора, хоста и коммуникационных каналов. Пригодность подхода к целевому использо-ванию подтверждена путем сопоставления полученных результатов имитационного мо-делирования с результатами эмулирования сети в среде Mininet Ключевые слова: программно-конфигури-руемая сеть, имитационное моделирование, дискретно-событийная спецификация систе-мы, большие данные UDC 004.75 : 004.94

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