Mobility in wireless sensor networks – Survey and proposal

Silva, Ricardo; Silva, Jorge Sá; Boavida, Fernando

doi:10.1016/j.comcom.2014.05.008

Cited by 97 publications

(47 citation statements)

References 37 publications

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“…Furthermore, this solution is not appropriate to be applied in large-scale networks due to its static tree-based structure networks [85]. Moreover, adding a new entity leads to increase the signaling and the end-to-end delay, especially in the interdomain mobility.…”

Section: Finally Thismentioning

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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Section: Finally Thismentioning

confidence: 99%

Mobility management for IoT: a survey

Ghaleb

Subramaniam

Zukarnain

et al. 2016

J Wireless Com Network

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“…The target trajectories in Fig. 8 are depicted by blue dashed lines for the time interval [ 1,30] s and by blue crossed lines after t = 30 s. When sensors do not move, the violet estimated trajectories indicate that the split of targets cannot be followed, while target ρ = 1 cannot be tracked after a while since is moving away from the immobile sensors. When the kinematic rules in Section 3.3 are employed, informative sensors follow closely the targets as depicted by the black dashed sensor trajectories.…”

Section: Simulationsmentioning

confidence: 99%

“…The black dashed curves show the trajectories of some of the moving sensors during the tracking process using the kinematic rules in Section 3.3. Violet colored curves correspond to estimated trajectories using immobile sensors, while the green star points to the position where the splitting of the targets takes place phase (t =[31, 42] s), there are more sensors originally located close to the trajectories of the targets, compared to the first tracking phase (t = [1,30] s) which makes the tracking error much smaller than the first 30 s, though the performance when using stationary sensors is still worse than tracking using our sensor mobility-based tracking scheme.…”

Section: Simulationsmentioning

confidence: 99%

“…One characteristic exploited more and more in sensor networks is sensor mobility and the design of kinematic rules that control sensor movement. Sensor mobility adds extra flexibility to a sensor network making it capable of covering larger areas, as well as being more energy efficient and robust [1]. Mobile sensors have been extensively utilized in target tracking applications to enable sensors to closely follow the moving target(s) and provide accurate target location estimates [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Ren

Maroulas

Schizas

2016

EURASIP J. Adv. Signal Process.

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The problem of distributed tracking of multiple targets is tackled by exploiting sensor mobility and the presence of sparsity in the sensor data covariance matrix. Sparse matrix decomposition relying on norm-one/two regularization is integrated with a kinematic framework to identify informative sensors, associate them with the targets, and enable them to follow closely the moving targets. Coordinate descent techniques are employed to determine in a distributed way the target-informative sensors, while the modified barrier method is employed to minimize proper error covariance matrices acquired by extended Kalman filtering. Different from existing approaches which force all sensors to move, here, local updating recursive rules are obtained only for the target-informative sensors that can update their location and follow closely the corresponding targets while staying connected. Simulations advocate that the proposed scheme outperforms alternative tracking schemes while accurately tracks multiple targets by imposing mobility only on the target-informative sensors.

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“…These solutions suffer heavy packet loss due to the connection termination in PMIPv6-HO. In [29], a solution based on network of proxies (NOP) is presented. NoP overlay on WSN and relives the SNs from signaling and processing tasks.…”

Section: Related Workmentioning

confidence: 99%

Advanced Prediction Based Mobility Support for 6LoWPAN Wireless Sensor Networks

Khan¹,

Mir²

2017

IJITCS

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Abstract-Wireless Sensor Nodes (SNs), the key elements for building Internet of Things (IOT) have been deployed widely in order to get and transmit information over the internet. IPv6 over low power personal area network (6LoWPAN) enabled their connectivity with IPV6 networks. 6LoWPAN has mobility and it can find an extensive application space only if provides mobility support efficiently. Existing mobility schemes are focused on reducing handoff (HO) latency and pay less attention towards packet loss and signaling cost. In time critical applications under IOT, packet loss and excessive signaling cost are not acceptable. This paper proposes a scheme based on advanced mobility prediction for reducing extra signaling cost and packet loss that incurs due to connection termination in traditional schemes such as Proxy Mobile IPv6 (PMIPv6) handover. In our proposed scheme 6LoWPAN WSN architecture with IPv6 addressing is presented. Based on this architecture the mobility algorithm is proposed for reducing signaling cost, packet loss by buffering mechanism and HO latency in particular. In the algorithm layer 2 (L2) and layer 3 (L3) HO is performed simultaneously with prior HO prediction with no Care of Address (CoA) configuration which also reduces signaling cost to some extent. The proposed scheme is analyzed theoretically and evaluated for different performance metrics. Data results showed significant improvements in reducing packet loss, signaling cost and HO latency when compared to standard PMIPv6 in time critical scenarios.

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Mobility in wireless sensor networks – Survey and proposal

Cited by 97 publications

References 37 publications

Mobility management for IoT: a survey

Mobility management for IoT: a survey

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Advanced Prediction Based Mobility Support for 6LoWPAN Wireless Sensor Networks

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