Controlled mobility for sustainable wireless sensor networks

Kansal, Aman; Rahimi, Mohammad; Estrin, Deborah; Kaiser, William J.; Pottie, G.J.; Srivastava, Mohit

doi:10.1109/sahcn.2004.1381896

Cited by 67 publications

(35 citation statements)

References 22 publications

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“…Furthermore, moving the sink closer to sensor nodes helps conserve power by reducing the bridging distance between the node and the sink [22]. This also increases the performance of the network in terms of communication timeliness and data loss by efficiently utilising available bandwidth [23].…”

Section: Fig (1)mentioning

confidence: 99%

Unmanned Ground Vehicle for Data Collection in Wireless Sensor Networks: Mobility-aware Sink Selection

Aldabbas¹,

Abuarqoub²,

Hammoudeh³

et al. 2016

Open Autom. Control Syst. J.

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Abstract:Several recent studies have demonstrated the benefits of using the Wireless Sensor Network (WSN) technology in largescale monitoring applications, such as planetary exploration and battlefield surveillance. Sensor nodes generate continuous stream of data, which must be processed and delivered to end users in a timely manner. This is a very challenging task due to constraints in sensor node's hardware resources. Mobile Unmanned Ground Vehicles (UGV) has been put forward as a solution to increase network lifetime and to improve system's Quality of Service (QoS). UGV are mobile devices that can move closer to data sources to reduce the bridging distance to the sink. They gather and process sensory data before they transmit it over a long-range communication technology. In large-scale monitored physical environments, the deployment of multiple-UGV is essential to deliver consistent QoS across different parts of the network. However, data sink mobility causes intermittent connectivity and high reconnection overhead, which may introduce considerable data delivery delay. Consequently, frequent network reconfigurations in multiple data sink networks must be managed in an effective way. In this paper, we contribute an algorithm to allow nodes to choose between multiple available UGVs, with the primary objective of reducing the network reconfiguration and signalling overhead. This is realised by assigning each node to the mobile sink that offers the longest connectivity time. The proposed algorithm takes into account the UGV's mobility parameters, including its movement direction and velocity, to achieve longer connectivity period. Experimental results show that the proposed algorithm can reduce end-to-end delay and improve packet delivery ratio, while maintaining low sink discovery and handover overhead. When compared to its best rivals in the literature, the proposed approach improves the packet delivery ratio by up to 22%, end-to-end delay by up to 28%, energy consumption by up to 58%, and doubles the network lifetime.

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Section: Fig (1)mentioning

confidence: 99%

Unmanned Ground Vehicle for Data Collection in Wireless Sensor Networks: Mobility-aware Sink Selection

Aldabbas¹,

Abuarqoub²,

Hammoudeh³

et al. 2016

Open Autom. Control Syst. J.

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show abstract

“…When multiple tours are allowed (e.g., multiple data mules), the problem of minimizing the total travel distance collectively done by all tours becomes the multiple traveling salesman problem (mTSP), which is also NP-complete and does not have any efficient approximation algorithms [4]. Existing solutions for mule planning are all heuristic schemes [9], [12], [16], [25], [28].…”

Section: A Related Workmentioning

confidence: 99%

Topology dependent space filling curves for sensor networks and applications

Ban

Goswami

Zeng

et al. 2013

2013 Proceedings IEEE INFOCOM

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Abstract-In this paper we propose an algorithm to construct a "space filling" curve for a sensor network with holes. Mathematically, for a given multi-hole domain R, we generate a path P that is provably aperiodic (i.e., any point is covered at most a constant number of times) and dense (i.e., any point of R is arbitrarily close to P). In a discrete setting as in a sensor network, the path visits the nodes with progressive density, which can adapt to the budget of the path length. Given a higher budget, the path covers the network with higher density. With a lower budget the path becomes proportional sparser. We show how this density-adaptive space filling curve can be useful for applications such as serial data fusion, motion planning for data mules, sensor node indexing, and double ruling type in-network data storage and retrieval. We show by simulation results the superior performance of using our algorithm vs standard space filling curves and random walks.

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“…Information collection and delivery can explicitly use mobile nodes, such as data mules [15], [21], [32], [16], [28]. This is motivated by the observation that nodes around static sinks suffer from unbalanced traffic and energy consumption.…”

Section: A Double Rulings With Mobile Nodesmentioning

confidence: 99%

Double Rulings for Information Brokerage in Sensor Networks

Sarkar

Zhu

Gao

2009

IEEE/ACM Trans. Networking

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Abstract-We study the problem of information brokerage in sensor networks, where information consumers (sinks, users) search for data acquired by information producers (sources). In-network storage such as geographical hash table (GHT) has been proposed to store data at rendezvous nodes for consumers to retrieve. In this paper, we propose a double rulings scheme which stores data replicas on a curve instead of one or multiple isolated sensors. The consumer travels along another curve which is guaranteed to intersect the producer curve. The double rulings is a natural extension of the flat hashing scheme such as GHTs. It has improved query locality, i.e., consumers close to producers find the data quickly, and structured aggregate queries, i.e., a consumer following a curve is able to retrieve all the data. Further, by the flexibility of retrieval mechanisms we have better routing robustness (as multiple retrieval paths are available) and data robustness against regional node failures. We show by simulation that the double rulings scheme provides reduced communication costs and more balanced traffic load on the sensors.

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Controlled mobility for sustainable wireless sensor networks

Cited by 67 publications

References 22 publications

Unmanned Ground Vehicle for Data Collection in Wireless Sensor Networks: Mobility-aware Sink Selection

Unmanned Ground Vehicle for Data Collection in Wireless Sensor Networks: Mobility-aware Sink Selection

Topology dependent space filling curves for sensor networks and applications

Double Rulings for Information Brokerage in Sensor Networks

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