- Open Access
Evaluation of topological planarity and reliability for interference reduction in radio sensor networks
© Staniec and Debita; licensee Springer. 2012
- Received: 30 October 2011
- Accepted: 22 February 2012
- Published: 22 February 2012
One of the key factors in distributed sensor networks is their reliability in packets delivery even in the event of failure of one or a few nodes. Another factor--often underestimated--is the intra-network interference experienced by individual nodes from other transmitting counterparts. Common network spanning algorithms, however, neither posses the reliability feature nor keep the interference at minimum. It can be demonstrated that by enforcing a planar network topology combined with the requirement of minimum-pathloss between connections, one can achieve a gain in signal-to-noise and interference ratio (SNIR) by a few decibels with respect to standards spanning methods. The use of directional antennas and controlled duty cycle improves the situation even further--by carefully controlling the beamwidth, one may adjust the SNIR to a desired level at a high range of dynamics.
- sensor network
- directional antennas
- duty cycle
- radio interference
A wireless sensor network (WSN) begins with an unorganized deployment of physical sensors connected to transmit/receive modules. Such a system of sensors can only be discussed in terms of a network if logical and physical connections have been established between these modules--a process called hence forth the network spanning. There are multiple spanning methods, of which two are particularly common, namely the ZigBee native method (or Minimum Spanning Tree, MST) and Stojmenovič algorithm (or Local MST algorithms, LMST), as described in [1–3]. The article provides a comparative analysis of these two methods with one which combines two features not encountered in the other algorithms, i.e., planarity and reliability (referred to from now on as a planar algorithm). The reliability should be an invaluable feature of sensor networks, particularly in situations where the lives of humans or living stock are dependent of it, e.g., networks for early fire or flood prevention. It is therefore crucial to ensure uninterrupted network operation even upon failure of some of the network elements scattered across a given area. However, current popular network spanning methods are deficient as regards assurance of the network reliability (see [4–9]) and the topological planarity. The latter feature, particularly when combined with the use directional antenna, can greatly contribute to the minimization of the intra-network radio interference as will be demonstrated in the article.
The purpose of this article is therefore to present a quantitative analysis of the extent to which some improvements incorporated to the existing network spanning methods can enhance the distributed sensor network operation. These improvements are the planar network topology and reliability. All results presented here will refer to the performance of the ZigBee standard based on IEEE 802.15.4 specification  operating in the 2.4 GHz ISM (Industrial, Scientific, Medical).
The organization of the article is as follows: Section 2 gives an introduction to standard network spanning methods (MST and LMST) and the reliable planar algorithm. Section 3 describes three basic parameters that will be investigated, i.e., signal-to-noise and interference ratio (SNIR), the antenna beamwidth, and duty cycle (DC). Section 4 serves as an introduction to the simulation environment used to generate results. Sections 5-7 provide results of simulations indicating how some operational mesh network parameters react to changes in the parameters mentioned in Section 3. All outcomes are presented in a manner allowing to compare the network performance in LMST, MST, and the reliable planar algorithm.
In the following sections, it will be shown quantitatively that if the topological planarity condition is maintained during the mesh networks design stage (taking as an example technology the IEEE 802.15.4, or ZigBee), a remarkably reduced intra-network radio interference level should be expected (apart from increased reliability) in comparison to standard LMST and MST methods.
The last aspect subject to investigations is the DC expressed in percentage and understood in the following manner: a notation DC = p% is equivalent to the restraint that during the time period T a given device will transmit only for p% of T duration. Such a bound on DC has been officially imposed on ZigBee devices operating at 868 MHz (DC < 1%)  but none such limitation exists for the other two frequency bands. There is also a broad publication coverage on this aspect although mainly from energy-saving viewpoint (as in [21–24]), whereas in this article attention is laid on its effect on the SNIR improvement in the network.
In , it was demonstrated how both directional ARP and limited DC can lead to reduction of intra-network interference and thus improve the network overall performance whereas in the investigations to follow the aim was to verify the extent to which the interference can be further reduced in the WSN if the planar topology is enforced by the spanning method in the network formation stage.
For the purpose of comparison between the ZigBee, Stojmenovič, and the planar network spanning algorithm, a software simulator has been created comprising two main components:
Matlab scripts to perform the network spanning algorithms on the random scenarios generated by the C++ application (the component described below);
a C++ Builder application for (a) generating random node distribution scenarios for each population of nodes M (i.e., from 10, 20 up to 100). These serve as input files to the Matlab scripts and (b) calculating SNIR based on the output files generated by the Matlab scripts.
In order to study the behavior of the ZigBee network, multiple scenarios were generated with a variable number of sensor nodes (M = 10-100), each located at 1.5 m above the ground and transmitting with the power 0 dBm and 0 dBi gain. The nodes were Monte-Carlo distributed over a square area of 500 × 500 m2.
Lastly, it is noteworthy that even for a "needle" ARP (i.e., with θ3dB = 0°) the average SNIR is still a few decibels higher in the planar network than in the other networks. This is due to the feature implemented by the authors in the planar algorithm (recall Section 2) which allows links to be established only between nodes with the lowest pathloss (i.e., with the highest signal). Hence, even in the absence of any interference due to infinitely thin ARP (only the noise term remains in the denominator of Equation 2), the average desired signal S will be expectedly higher in planar networks.
Both these features (i.e., the planarity and the minimum-pathloss neighbor selection) combined in the planar spanning algorithm, make the network operate at an energetic optimum as opposed to the two other methods where candidate nodes are selected on the "first-to-respond" basis where it is not guaranteed that "the first" will be equivalent to "the closest" at all times (and indeed is not, as stems from simulations and measurements). An immediate advantage to be taken from the least-pathloss approach is an extended sensor network longevity since less energy is now needed to transfer data to the closest neighbor instead of a remote one.
It can be noticed from the definition of planar GG provided in Section 2 that the condition prohibiting the presence of nodes within a circle formed around any edge l (Figure 1a) implies that the angles between successive connections will (on average) also tend to be wider than in networks without this constraint. This, in turn, will affect the number of interferers (ΣI) "seen" by a statistical node within the radio visibility range (and subtended within θ3dB angle).
It has been demonstrated that two most popular network spanning methods, namely Stojmenovič and ZigBee algorithms, possess some significant drawbacks, namely they do not assure reliability and allow for energetically non-optimal transmissions to distant neighbors. Moreover, the latter feature--as was also shown in simulations--gives rise to excessive interference in the radio network. The authors have posed a thesis that the planar GG, after little modifications for reliability and a least-pathloss neighbor selection, should be an optimal algorithm of choice for forming mesh ad hoc networks. The justification of this assertion is provided throughout Sections 5-7 by investigating SNIR as a function of the transmit/receive antenna directivity. It was demonstrated (Section 5) that SNIR in reliable planar networks is improved by 4-5 dB, at the beamwidth wider by up to 50° than in ZigBee or Stojmenovič cases. An explanation of this effect is given in Section 6 where it is demonstrated that the average number of interfering nodes affecting a statistical node is lower by 9% in reliable planar networks than in Stojmenovič- and ZigBee-spanned networks. Lastly, it was shown in Section 7 that when a DC is imposed on network devices, an additional gain of 4.15 dB in SNIR is obtained for the reliable planar topology over the non-planar topologies.
This paper has been written partially as a result of realization of the project entitled: "Detectors and sensors for measuring factors hazardous to environment - modeling and monitoring of threats". The project financed by the European Union via the European Regional Development Fund and the Polish state budget, within the framework of the Operational Programme Innovative Economy 2007÷2013. The contract for refinancing No. POIG.01.03.01-02-002/08-00.
This paper has been written partially as a result of realization of the project entitled: " Research on topology, distribution of sinks and packets routing with respect to reliability and energetic optimization in wireless sensor networks". The contract for refinancing No. B10089.
- Stojmenovič I: Handbook of Sensor Networks--Algorithms and Architectures. John Wiley and Sons, Ottawa; 2004.Google Scholar
- Shooman ML: Reliability of Computer Systems and Networks--Fault Tolerance, Analisys and Design. John Wiley & Sons; 2002.View ArticleGoogle Scholar
- Pióro M, Medhi D: Routing, Flow, and Capacity Design in Communication and Computer Networks. Morgan Kaufmann Publishers, San Francisco; 2004.MATHGoogle Scholar
- Staniec K, Debita G: Interference mitigation in WSN by means of directional antennas and duty cycle control. Wirel. Commun. Mobile Comput 2011., 11: doi:10.1002/wcm.1089Google Scholar
- Debita G, Staniec K: Reliable mesh network planning with minimization of intrasystem interference. In Proceedings of Broadband Communication, Information Technology & Biomedical Applications BROADBAND-COM'09. Wrocław, Poland; 2009:243-244.Google Scholar
- Frank H, Frisch I: Analysis and design of survivable networks. IEEE Trans Commun Technol 1970, 18(5):501-518. 10.1109/TCOM.1970.1090419MathSciNetView ArticleGoogle Scholar
- Narsingh D: Graph Theory with Applications to Engineering and Computer Science. Prentice-Hall, Inc; 1974.MATHGoogle Scholar
- Ford LR, Fulkerson DR: Maximal flow through a network. Can J Math 1956, 8: 399-404. 10.4153/CJM-1956-045-5MathSciNetView ArticleMATHGoogle Scholar
- Debita G: Methods for optimal planning of mesh networks with consideration of reliability criteria. Doctoral dissertation, Wroclaw University of Technology, Poland; 2010.Google Scholar
- IEEE Std 802.15.4™-2006 Part 15.4: Wireless Medium Access Control (MAC) and Physical layer (PHY) Specification for Low-Rate Wireless Personal Area Networks (WPANs).Google Scholar
- Saunders SR: Antennas and Propagation for Wireless Communications Systems. 2nd edition. John Wiley & Sons, Ltd., Chichester; 2007.Google Scholar
- Jasani H, Yen K: Performance improvement using directional antennas in ad hoc networks. Int J Comput Sci Netw Secur 2006, 6(6):180-188.Google Scholar
- Choudhury RR, Vaidya NH: Impact of directional antennas on ad hoc routing. Proceedings of IFIP Personal and Wireless Communications (PWC) 2003, Venice, Italy; 2003:590-600.Google Scholar
- Ramanathan R, Redi J, Santivanez C, Wiggins D, Polit S: Ad hoc networking with directional antennas: a complete system solution. IEEE J Sel Areas Commun 2005, 23(3):496-506.View ArticleGoogle Scholar
- ElBatt T, Anderson T, Ryu B: Performance evaluation of multiple access protocols for ad hoc networks using directional antennas. In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC). Volume 2. New Orleans, Louisiana, USA; 2003:982-987.Google Scholar
- Munari A, Rossetto F, Zorzi M: Cooperative cross layer MAC protocols for directional antenna ad hoc networks. ACM Mobile Comput Commun Rev 2008, 12(2):12-30. 10.1145/1394555.1394558View ArticleGoogle Scholar
- Tran-Xuan C, Koo I: An RSS-based localization scheme using direction calibration and reliability factor information for wireless sensor networks. KSII Trans Internet Inf Syst 2010, 4(1):45-61.Google Scholar
- Dai HN: Throughput and delay in wireless sensor networks using directional antennas. Proc 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) 2009, 421-426.View ArticleGoogle Scholar
- Bashir Y, Jalel BO: Towards a classification of energy aware MAC protocols for wireless sensor networks Wirel Commun. Mobile Comput 2009, 9(12):1572-1607.Google Scholar
- Jenn D, Loke Y, Matthew TCH, Choon YE, Siang OC, Yam YS: Distributed phased arrays and wireless beamforming networks. Int J Distrib Sensor Netw 2009, 5(4):283-302. 10.1080/15501320701863635View ArticleGoogle Scholar
- Jurdak R, Lopes CV, Baldi PA: A survey, classification and comparative analysis of medium access control protocols for ad hoc networks. IEEE Commun Surv Tutor 2004, 6(1):2-16.View ArticleGoogle Scholar
- Wei Y, Heidemann J, Estrin D: Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Trans Netw 2004, 12(3):493-506. 10.1109/TNET.2004.828953View ArticleGoogle Scholar
- Miller MJ, Vaidya NH: A MAC protocol to reduce sensor network energy consumption using a wakeup radio. IEEE Trans Mobile Comput 2005, 4(3):228-242.View ArticleGoogle Scholar
- Moshaddique AA, Riazul Islam SM, Kwak K: Energy saving mechanisms for MAC protocols in wireless sensor networks. Int J Distrib Sensor Netw 2010, 2010: Article ID 163413, 16.Google Scholar
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