Mobile Anchor Assisted Error Bounded Sensing inSensor Networks: An Implementation Perspective

Qingquan Zhang‡, Lingkun Fu∗, Ting Zhu¶, Yu Gu†, Ping Yi§, Jiming Chen∗‡Lemko Corporation, Schaumburg, IL, 60195, USA

∗State Key Laboratory of Industrial Control Technology, Zhejiang University, China¶Department of Computer Science, Binghamton University, USA

†Singapore University of Technology and Design, Singapore§School of Information Security Engineering, Shanghai Jiao Tong University, China

Abstract—Energy constraint is a critical hurdle hinderingthe practical deployment of long-term wireless sensor networkapplications. Turning off (that is, duty cycling) sensors couldreduce energy consumption, however, this would occur at thecost of low sensing fidelity due to sensing gaps introduced.Existing techniques focus mainly on scheduling a network withstatic anchors. Few methods provides a rigorous approach toconfining sensing errors within desirable bounds while seeking tooptimize the tradeoff between energy consumption and accuracyof predictions. In this work, we propose a sensing schedulingscheme, called MAS, to support mobile anchors in sensornetworks. Within a node, we use a sensing probability boundto control tolerable sensing errors. While communicating withthe mobile anchor, nodes trigger additional sensing activities toaccommodate the QoS requirement in mobile communication. Wevalidated the concept by constructing a lab-grade mobile anchorthat fully supports 4G-LTE communications for monitoringapplications. We further conducted simulations to investigatesystem performance. The simulation results demonstrated thatthe MAS achieved enhancement performance compared to severalother sensing schemes.

I. INTRODUCTION

Wireless Sensor Networks (WSNs) have been used in manymonitoring applications. Due to the small form factor and lowcost of sensor nodes (for example, the Mica series), they arenormally equipped with limited power sources. If workingcontinuously, a sensor node can typically sustain for only a fewdays. However, long-term applications [1], [2] are normallyrequired to operate for weeks or even months. The discrepancybetween limited resources and stringent requirements makes itnecessary to develop scheduling protocols to turn sensors onand off (duty cycle) to conserve energy.

Research on sensor node scheduling is not new [3], [4],[5], [6], [7], [8], [9]. Most of the projects focus on how toefficiently select or deploy a minimum set of sensor nodesto provide full/partial spatiotemporal coverage. These projectsdetermine the sensing activities of nodes based on coveragerequirements in space and/or time. None of them focuses onhow to schedule sensing activities based on sensing error,and hence fail to provide a rigorous approach to confine dataaccuracy within desirable bounds while seeking to optimize thetradeoff between energy consumption and predicting accuracy.Moreover, in most of the previous research projects, the datacollection scenarios lie mainly into the creation of routingpaths, through which the sensing data is routed back to the datacenter, or in the utilization of static anchor nodes which act as

data sinks. It is reasonable to assume that anchors are static asin conventional situations because that makes the system de-sign easier. However, a sensor system on demand, especially ina public catastrophe, such as an earthquake, requires mobilityof both sensor and sink nodes due to the unavailability of staticrouting infrastructure. Clearly, existing scheduling techniquescan only partially reconcile the conflict between system designand performance in realistic applications.

In this work, we take a completely different approach toaddress the issues in the mobile anchor scenario in which theanchors are no longer static. Compared to the static anchornodes scenario, mobile anchors possess a set of advantages.They can provide data routing supports to either static ornon-static sensor networks, and are robust to environmentalchanges, and infrastructure destruction. Furthermore, recentadvances in mobile technology make it possible to use aportable 4G-LTE base station as the primary mobile data an-chor. Equipped with this kind of base station, sensor networkscan establish a routing backbone through the mobile anchornodes with the flexibility to overcome territorial constraints. Itis very possible that in the near future we could witness largescale deployment of wireless sensor systems with the supportof mobile anchor nodes.

Although mobile anchor based designs have apparent ad-vantages, they pose a major challenge: mobile anchors willinevitably bring the Quality-of-Service (QoS) into consider-ation in synchronizing the communication between sensorsnodes, either mobile or static, and mobile anchors. Comparedto static anchor node design, the scheduling scheme has toincorporate the impact of the QoS requirement. To the best ofour knowledge, there has been little investigation of the mobileanchor node design and no related sensor system design.Unlike previous research that focuses mainly on theoreticaldesign, the driving idea of this work is error-bounded controlwith QoS consideration - a holistic approach toward the energyand performance tradeoff in wireless sensor networks. In thiswork, we propose a generic error-based scheduling algorithmwhich addresses the QoS requirement in wireless networks.We also build a prototype of such a system, based on 4G-LTEwireless technology to verify the performance of the proposedalgorithm.

The rest of this paper is organized as follows: we presentthe overview for our design in Section II. Sections III and IVdescribe the details of mobile anchor design. Section V ad-dresses QoS control. The performance evaluation is presented

34

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Sleep

Sleep

(a) Stage I

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Registration, Sensing

and Data Transfer

(b) Stage II

34

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De-registration

Registration, Sensing

and Data Transfer

(c) Stage III

34

1

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Sleep

De-Registration

Registration, Sensing,

and Data Transfer

(d) Stage IV

Fig. 1: The Overview of our system design

in Section VI, and Section VII concludes the paper.

II. OVERVIEW AND OBJECTIVES

This section presents an overview of our Mobile AnchorError Bounded Sensing System (MAS).

In this section, we overview the main scheme of our designusing a walk-through example. The key concept in MAS is toexploit the past detection errors between the ground data andoutput of embedded prediction models and the mobility man-agement information of mobile anchors to infer the operationalcycles of sensor nodes. A brief description below presents theon-going error control process.

Figure 1a, Stage I: Each sensor node executes its local errorcontrol procedure (IES) [10] independently. In our example,node 1−4 in Figure 1a are in dormant mode. At this moment,the mobile anchor has not reached the territory boundary. Thelocal error controller handles error control and duty cyclemanagement.

Figure 1b, Stage II: The mobile anchor moves into the com-munication boundary of sensor node 1. Then, sensor node 1wakes up to operate sensing, establishes registration processeswith the anchor, and follows with the data transfer. Figure 1blists the operational modes of nodes 1−4 at this stage. Nodes2 − 4 will continuously use their internal data correlation topredict the output. Previous literature has pointed out that it isreasonable to assume that this correlation is relatively stablefor a period of time [11]. The mobile anchor broadcasts theQoS requirement and its mobility information, for example,the moving speed, to sensor node 1. Upon receiving the QoSrequirement and the mobility information, the sensor nodeincorporates them into its scheduling decision.

Figure 1c, Stage III: As the mobile anchor moves out of thecommunication territory of sensor node 1, the mobile anchortriggers the de-registration process. Meanwhile, the mobileanchor is in the communication territory of sensor node 2.Therefore, sensor node 2 should switch into operational mode,initiating the registration process as described in stage II. Thelocal error control layer in sensor node 1 has completed a fullregistration and deregistration process in which all importantinformation, for example, the tick time (contact duration) ti,is stored and processed.

Figure 1d, Stage IV: The mobile anchor is out of thecommunication territory of sensor node 2 and starts to havecontact with sensor node 3. Sensor node 1 updates its localerror control layer with the mobility information, and operateswith refurbished scheduling cycles. In this example, becausemobile anchor is leaving sensor node 2’s territory, it also

initiates the deregistration process as well. Sensor node 3 willexperience the same process as node 1 and 2 did. After themobile anchor leaves the sensing coverage area, the node caneither remain turned off if the detection error from its localerror control layer is not large enough to trigger the duty cyclecontrol process, or be switched on if otherwise.

As illustrated by this walk-through, MAS exploits themobility information from the mobile anchor to transmit thesensing data and to update its local error control layer.

III. MOBILE ANCHOR NODE SYSTEM ARCHITECTURE

The mobile anchor system that we developed, as shownin Fig 2, is a man-portable cellular system equipped withLTE compatible Remote Radio Unit (RRU), called Node1.Node1 is a stand alone distributed cellular switching systembased on the proprietary architecture design principles. TheRRU and the base band unit on board form an intergratedBase Station Controller (BSC), allowing the mobile anchorsystem to establish local wide-band connections with othermobile sensors. Each Node1 provides the interface supportfor the cellular LTE, UMTS, GSM base stations. The func-tionality of traditional base station controller is integratedinto the Node1 platform. The DMA server for the Node1is typically co-located with the BTS. The Node1 server is aCentOS based platform that is completely interconnected overIP. The Node1 operating system and supplemental softwarecomponents consist of the following: CentOS Release 5.4,Apache Jakarta Tomcat 5.5.12 or later and MySQL Version14.12, or above. This integrated linux-based server systemensures the big data computation and communication can behandled in a manner suitable for QoS quality control. Aswe can see from Fig 2, the modular based system designmakes the mobile anchor scalable, which means that if thecurrent sensor system data volume or message packets pendingfor processings exceed single Node1’s capacity, another nodecan be stacked or introduced for parallel operations. Thisfeature is highly desirable because the scale of sensor networkscan increase exponentially in some practical applications, forexample, farm land monitoring or underwater surveillance.

As the system grows, additional network communicationprocessing capacity can be added by introducing additionalNode1s. This enables the system to meet the demands ofscaling mobile sensor network applications without requiring alarge, up front expense that goes under utilized. Because of thiskey property of implementing a portable mobile anchor, wecan experimentally verify the scheduling algorithms proposedother than by simulations.

Our distributed architecture for mobile anchors takes thebest effort to fulfill the requirement for 4G-LTE networks with

Fig. 2: Mobile Anchor architecture

a minimum cost. Following the design of the mobile anchor,we will discuss the details of design of the sensor nodes.

IV. LOCAL ERROR CONTROL

The design of the error control function is motivated by theobservation that a sensor node should be able to run programsindependently even in isolation. Therefore, the data detectedand stored locally should also be fault-tolerant, which goal isachieved by local error control.

As in [12], we refer to the error control as IES, whichsupports routine applications that include the duty cycle con-trol and local error prediction. A sensor uses its local errorpredictor, detailed in [13], to predict the environment statuswithout performing actual sensing operation. When data isobtained through actual sensing, a node compares predictedsensing values with the actual sensing values, and then storesthe prediction errors into the local error data library. Basedon the accuracy of the local error predictor, the duty cyclecontroller adjusts the sensing frequency through error-boundcontrol, which serves to confine the system prediction errorwithin a user-specified bound.

The duty cycle controller receives and analyzes the pre-diction errors from the local error predictor. The secondstep is to optimize the duty cycle based on the QoS profilerequirement. In this design, we separate the controller into theerror analyzer and the duty cycle adapter, two processes thatcan run collaboratively. Error analyzer predicts the potentialrisk from using prediction model at each cycle so that thepotential statistical error caused by the prediction is smallerthan the error tolerance.

A large prediction error ei imposes a higher resamplingfrequency, leading to high energy consumption. The criticalissue is to reduce this prediction error ei especially under theQoS constraints required in mobile communication. Therefore,it is important to note that the determination of the lower boundsensing is based not only on the prediction error, but also onthe QoS requirement. For our purposes, we need an onlinemethod to improve error control under QoS constraints, whichis achieved through the QoS Control described in the nextsection.

Fig. 3: Prototype of videosensors

Fig. 4: The mobile anchorsystem

V. QOS CONTROL

In this section, we present the design of network QoScontrol. In order to support online data transmission betweennodes and mobile anchors, the node system should maintainthe link quality set from the mobile anchors. In feedback basedcommunication system, for example, GSM or LTE systems,the profile sent to the mobile anchors has to contain its sensoridentification and the signal strengths received from the mobileanchors. With the possibility of having more than one mobileanchor, signal strengths received from different mobile anchorsare needed to carry out the hand-off procedure, a process inwhich sensor nodes switch their connection to different mobileanchors. For our purpose, it is not necessary to fully understandthe payload aspects of the profiles, which assumes that it ispossible to quantify exactly how much the signal strength anindividual sensor node will derive and how much overheadthe profile transmission will cost. But it is important for usto maximize the utilization of the communication bandwidthto conserve energy. Based on the preceding description of thegeneration of profiles, it is clear that, an optimal decision inscheduling is to synchronize the profile generation and thesensing and data transmission. Let λd and λp be, respectively,the sensing rate and the profile generation rate. In the 3GPPstandards, two mechanisms have been defined for profile com-munication. The entities generating profiles should comparethe data rate, λd, with the profile rate, λp in determining thetransmission process. In one way, if λd > λp, the profilesshould be stored in a buffer and then be dequeued andpiggybacked into the data at transceivers before transmission.As expected, there is only one profile in the buffer at anytime because the sensing data packets are generated at a fasterrate. On the other hand, if λd < λp, the profile is transmittedseparately from the sensing data transmission because there islittle overlap between them. Based on the description of thegeneration of mobile profiles, it is obvious that λd should beadjusted to λp to reduce the frequency of data transmissionsand thus the energy consumption of the radio. If λd > λp,the QoS management feature can be enabled, and the systemperformance is governed by the data sensing rate. However, forλd < λp, the profile generation heavily dominates the radiotransmission and can result in significant degradation of energyconsumption because most of the packets in transmission areprofile data. Therefore, it is preferred that the sensing systemoperates in the first scenario when interacting with mobileanchors. Assuming that the profile can be properly generated,the metrics used to quantify the performance include: systemregistration delay and feedback interval.

A. Mobility Consideration

Profiles contain control signals, but do not carry infor-mation in a messaging sense. Sending feedback information

too frequently reduces efficiency. On the other hand, sendingfeedback information too infrequently degrades effectiveness.The selection of the feedback interval is important in deter-mining the sensing interval. To meet both sensing error andQoS constraints, the accurate estimates of interval is important.In QoS constraint, the mean number of profiles requested bythe mobile anchor, defined as Np, is transferred from sensornodes. If we define the average feedback interval If , therate of transmission is rp = 1/If . Thus, we can approximateNp = rp × tc. Based on this, rp = Np/tc, from which we canderive an estimation of rp. Therefore, we propose a mobilitymodel to estimate the rp.

To facilitate the discussion, we define Sa to be the travelingdistance by a mobile anchor from the time of contact with onesensor node until disconnection. Define Va as the velocity ofthe mobile anchor during interaction with sensor nodes. Then,

Sa =

∫ tc

0

Va(t) · dt (1)

The tc value relies on both Sa and Va. As shown, Va is adynamic variable, which is determined by the characteristics ofmobile anchors, for example, a base station on an automobileis tied to the power engine of the car. Therefore, a realisticassumption is that Va uniformly distributes between Vmin andVmax. Sa, transverse distance, relies on the mobility path ofthe mobile anchor while in contact with one sensor node.If mobile anchor simply does not change its direction whilemoving, then a maximum path distance Smax can be assumed.Therefore, we can derive the probability density function of tcas follows [14]:

ftc (t) =1

2

(1

β+

1

α

)[µ (t)− µ (t− β)]

+

[α2β − βt2

2t2 (α2 − αβ)

][µ (t)− µ (t− β)]

(2)

in which, β = Smax

Vmax, α = Smax

Vmin, and µ (t) =

{1 t ≥ 00 t < 0

Using the derived probability density function of tc, theexpected value of the connection time is given by

E (tc) =αβ

2 (α− β)ln

(α

β

)(3)

With the expected tc, the feedback interval and the rate oftransmitting profiles can be estimated. A simulation programhas been written to verify the accuracy of this model.

VI. EVALUATION

A. System Implementation

Our proposed complete architecture has been implementedin our newly constructed mobile anchor, shown in Figure 4.This system architecture includes a portable anchor device,a man-portable or auto-assisted Node1, and sensor nodesconnected to a workstation with LTE dongle, or simply justa smart-phone. For demonstration purpose, we use Lenovonetbooks on which a video sensor has been installed. Eachsensor system can be used as an individual subsystem, whichis powered, controlled and metered separately. Sensor nodesare divided into several groups according to space proximity

1 5 9 13 17 21 25 29 33 37 410

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Setup Time

Fig. 5: The average setup time

as planned. Both random and controlled movement patternsfor the sensor nodes are created to emulate the movementchange of the mobile anchor in real environment and thenstore the image sensing data into local storage files. Thesesensing data are then broadcast back to the mobile anchorto verify both the transmission rate and the miss ratio underdifferent velocity and distance conditions. The experimentalresults collected allow further analysis to optimize the overallscheduling system.

We evaluate the setup time between sensor nodes andmobile anchors at different distances. Figure 5 shows theaverage time taken to establish the connection between thetwo entities. When conducting this experiment, the power ofantennas have been decreased 20db, thus the correspondingcommunication range may decrease 100 times, a level whichwe can mimic the real effects in a lab environment. Fromthe results we can see that the setup time does not changedramatically across different distances except at the edge ofthe communication border. The average time it takes to setupthe connection at the border surges to 61 seconds. This is dueto the signal strength which degrades enough that the sensornode’s profile information can not be transmitted back to themobile anchor correctly. Duplication of profile information andretransmission cause degradation of the authentication processrequired by the 3GPP standards. This is an important factorthat should be considered into scheduling control.

To evaluate the performance of our proposed schedul-ing approach, we have developed a simulation programthat uses historical soil temperature data collected fromthe Wisconsin-Minnesota Cooperative Extension AgriculturalWeather Page [15] where soil temperature was monitoredcontinuously for over 10 years.

B. Metrics and baseline

In order to evaluate the scheduling quality of a sensornetwork, we define metrics as follows:

• Error Rate: this metric is defined as the error ratethat the prediction system produces during the sameobservation window.

• Energy Consumption: this metric is defined as the totalenergy consumed by the network during the operationperiod.

The sensing schemes proposed are assessed using theabove metrics with respect to different system parameters, suchas the error tolerance. Through these examples, comparisonbetween different benchmarks and our proposed MAS is usedto demonstrate the performance of our design.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

0.1

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Error Tolerance

Pre

ditio

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IESMAEBS50% Fixed Probability

Fig. 6: Error performance

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.65

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Error Tolerance

Ave

rage

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rgy

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ptio

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IESMAEBS50% Fixed Probability

Fig. 7: Energy consumption

We present the results of three different experiment sce-narios. In the first scenario, a sensor switches on and offrandomly with a probability of 50 percent, and the sensornodes send information to the mobile anchor during its dutycycle only. In the second scenario, the sensor nodes use thedefault scheduling algorithm without QoS constraints, in orderto be active at every cycle. The data is sent in the same manneras in scenario one. In the third scenario, however, sensorsperforms according to our designed MAS scheduling algorithm.

Figure 6 illustrates the error performance of IES andMAS. With error tolerances, we can see that both approachescan satisfy the error performance requirement. However, underthe MAS scheme, the error rate is at least 20 percent lessthan with the stand-alone IES. This is due to the up-tunedadjustment of the duty cycle for QoS support.

Figure 7 demonstrates the energy consumption for bothschemes. Comparing the 25 percent error rate improvementin MAS to the fixed rate approach, the additional energyconsumption is small, as the maximum difference between thetwo schemes is only 5 percent.

We can also see that energy conservation could reach above70 percent level when error tolerance et is relatively high butcould be acceptable in real application. The MAS’s energyconservation is less than that of random cases when et is low,implying that sensors have a higher chance of switching on,especially when the mobile anchor establishes the connectionwith QoS constraints.

From the above three figures, we can conclude that theMAS method provides QoS support to the stand-alone IESscheme with slightly more energy consumption. This costcan be traded for the reduction in message communication,which has been considered important in certain monitoringapplications [1].

Based on these comparisons, we conclude that MAS canprovide a practical implementation of mobile anchor assistedsensor network with 4G-LTE capability. The results alsoconfirm that the error-bounded scheduling limitation, whichis missing in many approaches, is achieved in our approach.

VII. CONCLUSIONS

In this paper, we have presented a stochastic sensingalgorithm to reduce energy consumption. Our approach usesthe data correlation between nodes to reduce the error ratefor prediction of model performance. Observed correlationsbetween nodes have been used to estimate the neighboringnodes’ errors, and to adjust their operation accordingly. Wepropose a sensing scheduling scheme with mobile anchors,called MAS and based on 4G-LTE technology, to support

mobile anchors in sensor networks. While communicating withthe mobile anchors, sensor nodes trigger additional sensingactivities to accommodate the QoS requirement in mobilecommunication. We validated the concept by constructing alab-grade mobile anchor node which fully supports 4G-LTEcommunications for monitoring applications.

VIII. ACKNOWLEDGMENT

This work was supported in part by the National Sci-ence Foundation grant CNS-1217791, National Key BasicResearch Program of China2013CB329603, National Nat-ural Science Foundation of China (Grant No. 61271220,No.61170164No.60932003), Singapore-MIT International De-sign Center IDG31000101 and iTrust Cyber Physical SystemProtection project, NSFC under grant 61222305, 111 Programunder grant B07031 and 863 High-Tech Project under grant2011AA040101-1 and the Fundamental Research Funds forthe Central Universities under Grants 2013QNA5013 and2013FZA5007.

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