Topology control for wireless sensor networks

Pan, Jianping; Hou, Y. Thomas; Chen, Lin; Shi, Yi; Shen, Sherman

doi:10.1145/939014.939015

Cited by 86 publications

(141 citation statements)

References 0 publications

Supporting

Mentioning

140

Contrasting

Unclassified

Order By: Relevance

“…Currently, an extensive body of research has focused on the area of low-energy radios. Different assumptions about the radio characteristics, including energy dissipation in the data transmitting, receiving and fusion pattern, will change the advantages of different protocols [3]. Figure 1 describes the radio model of transmitting n messages and the size of each message is k − bit, so the total amount of transmitting messages is n × k bits.…”

Section: Related Workmentioning

confidence: 99%

“…Communication is a prime factor of energy dissipation for the sensors in WSN, therefore, we focus on the communication-related activities of the sensors which use battery as their energy supplier [3]. We assume that each sensor within the network has the same data transmitting rate denoted by r , so the minimal energy dissipation of transmitting messages with the total amount of n × k bits at rate r over the Euclid distance d is formulated in (1).…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the total communication-related energy dissipation of relaying n ×k bits messages at rate r over distance d at i − th node is shown in (3).…”

Section: Related Workmentioning

confidence: 99%

“…In the scene with multiple Source-Sink pairs, AEMRP-LB can adaptively adjust the available residual energy of shared nodes so as to make use of them reasonably and make them provide different qualities of services for the Interests with different importance. 3. A series of well-designed experiments are undertaken to compare AEMRP-LB with MERP [19] and Direct Diffusion [23].…”

mentioning

confidence: 99%

See 3 more Smart Citations

An Adaptive Energy-aware Multi-path Routing Protocol with Load Balance for Wireless Sensor Networks

Tao

Yang

2010

Wireless Pers Commun

View full text Add to dashboard Cite

Using the available but limited resources of sensors more efficiently has been the recent interest in designing the routing protocols for Wireless Sensor Network (WSN). The major concern includes energy, storage and computing resources. Accordingly, we propose an adaptive energy-aware Multi-path routing protocol with load balance (AEMRP-LB). It introduces a concept named direction-angle to overcome the deficiency of broadcast and takes into account the tradeoff between the residual energy and hop count to establish multiple node-disjoint paths. The traffic load is balanced over the selected paths by using a weighted traffic scheduling algorithm considering their transmitting capacity, and then AEMRP-LB uses the advantages of Multi-path Source Routing to report the acquired data as saving the computing and storage resources of the sensors. In the scene with multiple Source-Sink pairs, AEMRP-LB can adaptively adjust the available residual energy of shared nodes so as to make reasonable use of them. The simulation results show that AEMRP-LB outperforms the two comparative schemes, and the sensors consume the energy in a more equitable way which ensures a more graceful degradation of service with time.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Therefore, the total communication-related energy dissipation of relaying n ×k bits messages at rate r over distance d at i − th node is shown in (3).…”

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Energy-aware Multi-path Routing Protocol with Load Balance for Wireless Sensor Networks

Tao

Yang

2010

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

“…Although there is active research on maximizing network lifetime (see, e.g., [1,3,9,19]) or network capacity (see, e.g., [6,10,13,15,20]), most of these efforts consider a sensor network under a given physical topology. Indeed, the location problems for base stations have been very difficult to analyze (shown to be NP-complete in [2]) and only very special cases have been investigated for optimal placement, e.g., single-hop communication between sensor node and base station [11] or special grid topology [2].…”

Section: Introductionmentioning

confidence: 99%

Algorithm design for a class of base station location problems in sensor networks

2007

Self Cite

View full text Add to dashboard Cite

Base station placement has significant impact on sensor network performance. Despite its significance, results on this problem remain limited, particularly theoretical results that can provide performance guarantee. This paper proposes a set of procedure to design (1 − ε) approximation algorithms for base station placement problems under any desired small error bound ε > 0. It offers a general framework to transform infinite search space to a finite-element search space with performance guarantee. We apply this procedure to solve two practical problems. In the first problem where the objective is to maximize network lifetime, an approximation algorithm designed through this procedure offers 1/ε 2 complexity reduction when compared to a stateof-the-art algorithm. This represents the best known result to this problem. In the second problem, we apply the design procedure to address base station placement problem when the optimization objective is to maximize network capacity. Our (1 − ε) approximation algorithm is the first theoretical result on this problem.

show abstract

Estimating distances via connectivity in wireless sensor networks

Huang

Anderson

et al. 2012

Wireless Communications

View full text Add to dashboard Cite

Distance estimation is vital for localization and many other applications in wireless sensor networks.In this paper, we develop a method that employs a maximum-likelihood estimator (MLE) to estimate distances between a pair of neighboring nodes in static wireless sensor networks using their local connectivity information, namely the numbers of their common and non-common one-hop neighbors.We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log-normal (shadowing) model as special cases. Under the log-normal model, we numerically study the bias and standard deviation associated with our method and show that for long distances our method outperforms the method based on received signal strength (RSS); we investigate the impact of the log-normal model uncertainty; we provide a Cramér-Rao lower bound (CRLB) analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on applying the proposed method based on realistic measurement data and also in connectivity-based sensor localization, the superiority of the proposed method is confirmed.

show abstract

Topology control for wireless sensor networks

Cited by 86 publications

References 0 publications

An Adaptive Energy-aware Multi-path Routing Protocol with Load Balance for Wireless Sensor Networks

An Adaptive Energy-aware Multi-path Routing Protocol with Load Balance for Wireless Sensor Networks

Algorithm design for a class of base station location problems in sensor networks

Estimating distances via connectivity in wireless sensor networks

Contact Info

Product

Resources

About