Improving the Data Delivery Latency in Sensor Networks with Controlled Mobility

Sugihara, Ryo; Gupta, Rajesh K.

doi:10.1007/978-3-540-69170-9_26

Cited by 95 publications

(60 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The project has extended the work on controllably mobile nodes case [7], multiple data mules case [8], and arbitrarily mobile nodes case [9]. While these formulations are similar in spirit to ours, we also generalize the problem to include a precise mobility model with acceleration constraints and stronger guarantees on the optimality, as demonstrated in our previous study [10].…”

Section: Related Workmentioning

confidence: 95%

“…However, simultaneously optimizing them is an NP-hard problem, which is implied by the NP-hardness of the simplified path selection problem [10]. Consequently, in previous studies, the problem has been simplified in various ways by employing assumptions that restrict the capabilities of sensor nodes and data mule.…”

Section: Problem Framework For Optimal Control Of Data Mulementioning

confidence: 99%

“…We call it the 1-D DMS problem and have presented algorithms in [16]. As for the path selection subproblem, we formulated it as an independent problem in [10], which we briefly describe later.…”

Section: Problem Framework For Optimal Control Of Data Mulementioning

confidence: 99%

See 2 more Smart Citations

Optimizing Energy-Latency Trade-Off in Sensor Networks with Controlled Mobility

Sugihara

Gupta

2009

Ieee Infocom 2009

View full text Add to dashboard Cite

Abstract-We consider the problem of planning path and speed of a "data mule" in a sensor network. This problem is encountered in various situations, such as modeling the motion of a data-collecting UAV (Unmanned Aerial Vehicle) in a field of sensors for structural health monitoring. Our specific context here is use of a data mule as an alternative or supplement to multihop forwarding in a sensor network. While a data mule can reduce the energy consumption at each sensor node, it increases the latency from the time the data is generated at a node to the time the base station receives it. In this paper, we introduce the "data mule scheduling" or DMS framework that enables data mule motion planning to minimize the data delivery latency. The DMS framework is general; it can express many previously proposed problem formulations and problem settings related to data mules. We design algorithms for DMS and extend to the more general case of combined data mule and multihop forwarding to enable a flexible trade-off between energy consumption and data delivery latency. Using DMS, we can calculate the optimal way for node-to-node forwarding and data mule motion plan. Our implementation and simulation results using ns2 show nearly monotonic decrease of data delivery latency when each node can use more energy, thus vastly increasing the flexibility in the energy-latency trade-off for sensor network communications.

show abstract

Section: Related Workmentioning

confidence: 95%

Section: Problem Framework For Optimal Control Of Data Mulementioning

confidence: 99%

See 1 more Smart Citation

Optimizing Energy-Latency Trade-Off in Sensor Networks with Controlled Mobility

Sugihara

Gupta

2009

Ieee Infocom 2009

View full text Add to dashboard Cite

show abstract

“…However, when used in WSN with a mobile sink, these heuristic solutions become impractical as the number of nodes increases [4]. Moreover, typical speed of mobile sink is slow and therefore it increases data collection latency in a large network [5,23]. In this paper, we propose a rendezvous based technique in which a subset of sensor nodes is selected as Rendezvous Points (RPs).…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization using Neighborhood based Weighted Rendezvous Technique for Wireless Sensor Networks

Ruthvic¹,

Ravi²,

Shenoy³

2015

IJCA

View full text Add to dashboard Cite

Establishing an energy efficient wireless sensor network is a challenging task. A significant amount of research work has already been carried out in this direction to study energy optimization in WSN by taking the advantage of mobile sink or mobile agents. Many approaches using mobile sink have demonstrated that energy usage can be optimized significantly in the phenomenon area from where the sink would collect the sensed readings from the sensor nodes via single or multi hop communication. But, the slow mobility speed of the sink will tend to increase the data collection latency in the sensor network, especially in delay bound WSN applications. To overcome this problem, several rendezvous based techniques have been proposed, in which sink is allowed to visit a subset of the sensor nodes to collect the data via single hop communication. This subset of nodes, called rendezvous points (RPs), is considered as data collection points. All other nodes send their sensed data using the shortest path to these RPs. In this paper a simple neighbourhood based rendezvous technique is proposed. In our approach a subset of sensor nodes from the network is designated as rendezvous points (RPs) set to receive and buffer the data from their nearest source nodes. The selected RP set is such that they take care of denser part of the network where energy consumption is more so that the energy hole problem can be minimized and help optimize the energy consumption in the network. A shortest sink tour is then constructed using only RPs, using which mobile sink makes its tour and collects the buffered data from the RPs within a given deadline. In this paper we explain the NBWRP (Neighbourhood based Weighted Rendezvous Planning) algorithm to compute the RPs. We also evaluate and compare its performance with static sink WSN and WSN with random sink mobility. Our results show that the algorithm achieves better WSN lifetime compared to static sink case and random movement strategy. General TermsWireless Sensor Networks, Mobile base station.

show abstract

“…The sink meets all sensor nodes exactly once during the tour. In similar methods presented in [5,6], the authors model the sink tour as a "label covering tour" that relaxes the requirement of exact one-time-visits of the sink to each sensor's neighborhood.…”

Section: Introductionmentioning

confidence: 99%

Optimal Stop Points for Data Gathering in Sensor Networks with Mobile Sinks

Park¹,

Moon²,

Yoo³

et al. 2012

WSN

View full text Add to dashboard Cite

Given a wireless sensor network (WSN) in which a mobile sink is used to collect data from the sensor nodes, this paper addresses the problem of selecting a set of stop points that results in low energy usage by the sensor nodes. This paper assumes an approach in which a mobile sink travels along a fixed path and uses a stop-and-collect protocol since this has previously been shown to be an efficient WSN data collection method. The problem of selecting an optimal set of stop points is shown to be an NP-hard problem. Then, an Integer Linear Programming (ILP) formulation is used to derive an optimal algorithm that can be used for small problem instances. Next, a polynomial-time Tabu-search-based heuristic algorithm is proposed. Simulations are used to compare the energy consumption values, computation times and expected network lifetimes when using the optimal ILP algorithm, the proposed heuristic algorithm and several other possible heuristic algorithms. The results show that the proposed heuristic algorithm results in near-optimal energy usage values with low computation times, thereby making it suitable for large-sized WSNs.

show abstract

Improving the Data Delivery Latency in Sensor Networks with Controlled Mobility

Cited by 95 publications

References 13 publications

Optimizing Energy-Latency Trade-Off in Sensor Networks with Controlled Mobility

Optimizing Energy-Latency Trade-Off in Sensor Networks with Controlled Mobility

Energy Optimization using Neighborhood based Weighted Rendezvous Technique for Wireless Sensor Networks

Optimal Stop Points for Data Gathering in Sensor Networks with Mobile Sinks

Contact Info

Product

Resources

About