Bouckaert, Stefan

Demeester, Piet

Liu, Wei

Mehari, Michael

Moerman, Ingrid

Tytgat, Lieven

English

Ghent University Academic Bibliography

Demo Abstract: A Proof of ConceptImplementation for Cognitive Wireless SensorNetwork on a Large-scale Wireless TestbedWei liu, Michael Mehari, Stefan Bouckaert, Lieven Tytgat, Ingrid Moerman, Piet DemeesterDepartment of Information TechnologyInternet Based Communication Networks and Services (IBCN)Ghent University - iMindsAbstract—Cognitive Radio (CR) is a promising technologyin the wireless communication field. CR is able to adapt itschannel according to the local spectrum condition. Recently CRtechnology is also applied in the field of wireless sensor network(WSN).Receiver Directed Transmission (RDT) is a cognitive solutionfor wireless sensor network developed under the FP7 CONSERNproject[2]. In RDT, the sender selects the optimum channelaccording to the location of the receiver, assuming sensors aredistributed spatially and there is sufficient diversity in spectrumacross the entire network.In this demo we focus on an implementation of RDT-basedWSN on a large-scale wireless testbed. The channel selectionof RDT relies on distributed and heterogeneous sensing. Wecompare the performance of the cognitive WSN against regularWSN. We further explore how the energy detection threshold caninfluence the performance of the RDT solution. The experimentis conducted via a set of benchmarking tools, developed in thescope of the European project CREW[1].I. INTRODUCTIONWith the growth of wireless technology, radio spectrumbecomes increasingly crowded. Optimization of spectrum al-location is needed in order to achieve better performance.Cognitive radio (CR) allows radios to switch channels atruntime and hence provides capacity for dynamic spectrumallocation. In the Receiver Directed Transmission (RDT), thechannel used for transmission is determined by the interferencemeasured at the receiver. For a large-scale network wheresensors are covered under complex interference conditions,finding one single channel that is suitable for all sensors isvery difficult. Instead of selecting a singa large scale wirelesstestbed —le channel for the whole sensor network, each sensornode select its own optimum receive channel individually. Thisis the motivation of RDT.The demo scenario is about a wireless sensor network(WSN) deployed in an office environment. Typical applica-tions of sensor networks are wireless building automationor alarm detection. Reliability is critical for such kind ofapplications. The challenge is how to realize a reliable sensornetwork operating in the already crowded ISM bands. Weconsider two types of sensor networks: one is the cognitivesensor network based on RDT solution; the other is a regularsensor network with static channel configuration.Fig. 1. Sensing engines in w-iLab.t ZwijnaardeWe implement our demo on the w-iLab.t Zwijnaardetestbed[3]. For RDT based sensor network, we use the dis-tributed and heterogeneous sensing facility in w-iLab.t Zwij-naarde as input for distributed energy detection. Based on thisinput, we further use the CREW benchmarking tools to explorethe performance of RDT based WSN under various conditions.In the remainder of the introduction part, we briefly introducethe w-iLab.t Zwijnaarde testbed and the CREW benchmarkingtools. Part II describes the demo scenario in more details andfinally we give a preview of experiment results and someconclusions in Part III.a) w-iLab.t Zwijnaarde testbed: The w-iLab.t Zwijnaardetestbed is a generic and heterogeneous wireless testbed. It islocated at a utility room where no human activity is present.A regular node in w-iLab.t Zwijnaarde consists an embeddedPC with two Wi-Fi interfaces, one Zigbee sensor and oneBluetooth dongle. A subset of the Zigbee sensors form theWSN of the demo. The floor plan of the testbed is shown inFigure 1. In addition, there are 6 USRP and 5 imec sensingengines deployed. USRP is a popular software-defined radioplatform[4]. We combine customized software application andUSRP hardware for spectrum analysis. This is referred as theUSRP sensing engine. The imec sensing engine is a powerfulcustomize designed sensing engine[5]. It uses an advancedfront-end and dedicated chips for spectrum analysis. In ourdemo, the sensing engines are configured to monitor the energylevel on all Zigbee channels in 2.4GHz ISM band. The outputof sensing engines are stored in a predefined database.b) CREW Benchmarking Tool: The CREW benchmark-ing tool is a web-based generic tool, explained further below.It has three parts, experiment configuration, experiment execu-tion and benchmarking. Users can easily configure, scheduleexperiments and benchmark the results. Benchmarking is theact of measuring and evaluating a solution under referenceconditions relative to a reference evaluation. To this enddifferent solutions are compared based on a general score, avalue between 0 and 10, that is derived from measurements.The benchmarking framework also provides advance featuresfor automatic parameter optimization.II. IMPLEMENTATIONWe first emulate an office environment by setting up 4Wi-Fi networks and 2 Bluetooth pairs. The Wi-Fi networksare present on Ch.1, Ch.5, Ch.9 and Ch.13 respectively. Theinterference sources are spread over different parts of thetestbed to ensure sufficient diversity in frequency domain.The scenario is illustrated in Figure 2. A sensor network issuperimposed on top of the office environment. Sensor nodesare highlighted in Figure 2. We consider the collecting treeprotocol(CTP) scenario[6], where the sensor at the right endis selected as the sink node, the rest of the sensors transmittowards the sink node following the tree topology, as indicatedwith arrows in Figure 2. The packet reception rate(PRR) ismeasured on each link individually. We use the averaged end-to-end PRR as the performance metric. This metric is scaledby a factor of ten. The scaled metric is the score used in thebenchmarking framework.Apart from the background environment, the RDT solutionalso requires channel assessment. Here we use the distributedand heterogeneous spectrum sensing. The 6 USRP and 5imec sensing engines are configured to scan over all 16Zigbee channels in the 2.4 GHz band. At higher level, anenergy threshold is specified. When the measured energy isabove this threshold the channel is considered busy. Next thepercentage of the channel being busy over a certain intervalis calculated. The result is referred as channel occupancy. Werank the Zigbee channels based on the channel occupancy forall sensing engines individually. Each sensor node is associatedwith one sensing engine based on distance. For a specificreceiver, the most nearby sensing engine is selected. Beforetransmission, each sensor node queries the sensing engineassociated with the targeted receiver for the best local channel.III. CONCLUSIONFor the RDT solution, we vary the energy threshold incertain range and observe the benchmarking score. The resultof an example experiment is shown in Figure 4, where theenergy threshold is varied from -85 to -55 in step of 5 dBm.We can see that the score shows a reverse “U” shape, bestperformance appears around -70 dBm, which is slightly abovethe noise floor of the testbed. For comparison, we use staticchannel configuration on the same sensor network, the channelindex is varied from 12 to 24 in step of 2. The score seemsFig. 2. Sensor Network and Office Environment−85 −80 −75 −70 −65 −60 −550246810Energy Detection Threshold (dBm) SCOREFig. 3. Score of RDT solution vs energy threshold12 14 16 18 20 22 240246810Static channel SCOREFig. 4. Score of static solution vs channel indexto increase with channel index. This is because nearby thesink node, Wi-Fi interference is located on the lower part ofthe 2.4GHz ISM band. Links nearby sink node have moreimpact on the end-to-end PRR, hence the overall score isdominant by the PRR on links nearby the sink. The best scoreof RDT solution is 10, meaning all packets on the networkare successfully received. We hence have demonstrated that theRDT solution performs better than the single channel solution,certainly when considering that the interference conditionsmay be very dynamic and RDT will always dynamically selectthe most optimal channel.ACKNOWLEDGMENTThe research leading to these results has received fundingfrom the European Union’s Seventh Framework ProgrammeFP7/2007-2013 under grant agreements n 257542 (CONSERNproject) and n 258301 (CREW project).REFERENCES[1] http://www.crew-project.eu/[2] http://kandalf.di.uoa.gr/consern/[3] S. Bouckaert et al. “Federating wired and wireless test facilities throughEmulab and OMF: the iLab.t use case” in the proceedings of TridentCom2012[4] Ettus Research. http://www.ettus.com/.[5] S.Pollin, ”An integrated reconfigurable engine for multi-purpose sensingup to 6 GHz”, in Dyspan, 3-6 May 2011[6] G.Omprakash, F.Rodrigo et al. ”Collection tree protocol” in SenSys: 114,2009

Demo abstract: a proof of concept implementation for cognitive wireless sensor network on a large-scale wireless testbed

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Demo abstract: a proof of concept implementation for cognitive wireless sensor network on a large-scale wireless testbed

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