LNMP- Management architecture for IPv6 based low-power wireless Personal Area Networks (6LoWPAN)

Mukhtar, Hamid; Kang-Myo, Kim; Chaudhry, Shafique Ahmad; Akbar, Ali Hammad; Kim, Ki-Hyung; Yoo, Seongwoo

doi:10.1109/noms.2008.4575163

Cited by 28 publications

(20 citation statements)

References 6 publications

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“…Master support control command, maintenance, and the sending down of upgrade program [4][5]. And support the partial measurement of power data.…”

Section: Multicast Routing Algorithm Simulationmentioning

confidence: 99%

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Research on the Multicast Trickle Algorithm Based on Redundant Touting Technique

Nie¹,

Meng²

2015

Advances in Intelligent Systems Research

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“…Master support control command, maintenance, and the sending down of upgrade program [4][5]. And support the partial measurement of power data.…”

Section: Multicast Routing Algorithm Simulationmentioning

confidence: 99%

“…Therefore, multicast trickle algorithm can adapt to the no connection, lossy and low-power network status. The network does not need the complex code and status [4][5].…”

Section: Micro-power Wireless Communication Rpl Multicast Routingmentioning

confidence: 99%

Research on the Multicast Trickle Algorithm Based on Redundant Touting Technique

Nie¹,

Meng²

2015

Advances in Intelligent Systems Research

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“…In addition to the aforementioned protocols, there are several other works and standards which were proposed such as dual addressing scheme (DAS) [66], ZigBeebased [67], tree-based routing algorithm (ETRA) [68], the LoWPAN Network Management Protocol (LNMP) management architecture [69] and a configurable tiny TCP/IP protocol stack featuring Session Initiation Protocol (SIP) [70]. Moreover, the protocol in [71] designed a new method to facilitate wireless connectivity based on the IEEE 802.15.14.…”

Section: Ip-enabled Techniquesmentioning

confidence: 99%

Mobility management for IoT: a survey

Ghaleb

Subramaniam

Zukarnain

et al. 2016

J Wireless Com Network

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Internet of Thing (IoT) or also referred to as IP-enabled wireless sensor network (IP-WSN) has become a rich area of research. This is due to the rapid growth in a wide spectrum of critical application domains. However, the properties within these systems such as memory size, processing capacity, and power supply have led to imposing constraints on IP-WSN applications and its deployment in the real world. Consequently, IP-WSN is constantly faced with issues as the complexity further rises due to IP mobility. IP mobility management is utilized as a mechanism to resolve these issues. The management protocols introduced to support mobility has evolved from host-based to network-based mobility management protocols. The presence of both types of solutions is dominant but depended on the nature of systems being deployed. The mobile node (MN) is involved with the mobility-related signaling in host-based protocols, while network-based protocols shield the host by transferring the mobility-related signaling to the network entities. The features of the IoT are inclined towards the network-based solutions. The wide spectrum of strategies derived to achieve enhanced performance evidently displays superiority in performance and simultaneous issues such as long handover latency, intense signaling, and packet loss which affects the QoS for the real-time applications. This paper extensively reviews and discusses the algorithms developed to address the challenges and the techniques of integrating IP over WSNs, the attributes of mobility management within the IPv4 and IPv6, respectively, and special focus is given on a comprehensive review encompassing mechanisms, advantages, and disadvantages on related work within the IPv6 mobility management. The paper is concluded with the proposition of several pertinent open issues which are of high research value.

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“…All these are non-IP based network management systems. The LowPAN Network Management protocol (LNMP) [14] and EmNets Network Management Protocol (EMP) [15] are IP based sensor network management protocols. The LNMP uses query processing and EMP uses periodic reporting to fetch information from the sensor network.…”

Section: Related Workmentioning

confidence: 99%

Resource Monitoring for Wireless Sensor Networks using ANFIS

Nagesha

Manvi

2016

Journal of Applied Computer Science Methods

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Wireless sensor networks (WSNs) are usually a resource constrained networks which have limited energy, bandwidth, processing power, memory etc. These networks are now part of Internet by the name Internet of Things (IoT). To get many services from WSNs, we may need to run many applications in the sensor nodes which consumes resources. Ideally, the resources availability of all sensor nodes should be known to the sink before it requests for any further service(s) from the sensor node(s). Hence, continuous monitoring of the resources of the sensor nodes by the sink is essential. The proposed work is a framework for monitoring certain important resources of sensor network using Adaptive-Neuro Fuzzy Inference System (ANFIS) and Constrained Application Protocol (CoAP). The ANFIS is trained with these resources consumption patterns. The input to ANFIS is the resources consumption levels and the output is the resources consumed levels that needs to be sent to the sink which may be individual or combinations of resources. The trained ANFIS generates the output periodically which determines resources consumption levels that needs to be sent to the sink. Also, ANFIS continuously learns using hybrid learning algorithm (which is basically a combination of back propagation and least squares method) and updates its parameters for better results. The CoAP protocol with its observe option is used to transport the resource monitoring data from the sensor nodes to the cluster head, then from the cluster head to the sink. The sensor nodes runs coap server, the cluster head runs both coap client and server and the sink runs coap client. The performance of the proposed work is compared with LoWPAN network management protocol (LNMP) and EmNets Network Management Protocol (EMP) in terms of bandwidth and energy overheads. It is observed that proposed work performs better when compared to the existing works.

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LNMP- Management architecture for IPv6 based low-power wireless Personal Area Networks (6LoWPAN)

Cited by 28 publications

References 6 publications

Research on the Multicast Trickle Algorithm Based on Redundant Touting Technique

Research on the Multicast Trickle Algorithm Based on Redundant Touting Technique

Mobility management for IoT: a survey

Resource Monitoring for Wireless Sensor Networks using ANFIS

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