EFFICIENT AGGREGATE COMPUTATIONS IN LARGE-SCALE DENSE WSN

Nuno Pereira

Abstract— This work addresses scenarios where evena small area may contain several tens of nodes, in thiscontext, we addresses the problem obtaining aggregatequantities (e.g., the minimum, maximum or median ofthe values proposed by all nodes) with a time-complexitythat is independent of the number of nodes, or growsvery slowly as the number of nodes increases. This isachieved by co-designing the distributed algorithms forobtaining aggregate quantities and the underlying com-munication system. In this paper, we summarize theresults of the research developed around this problem.These results are significant because often networks ofnodes that take sensor readings are designed to be largescale, dense networks and it is exactly for such scenariosthat the proposed distributed algorithms for obtainingaggregate quantities excel. The implementation and testof these distributed algorithms in a hardware platformdeveloped has shown that aggregate quantities in large-scale, dense wireless sensor systems can be obtained ef-ficientlly.

Keywords—Medium Access Control (MAC), Data Pro-cessing, Wireless Sensor Networks, Cyber-Physical Sys-tems, Data aggregation.

Motivation and Objectives

Microprocessors are everywhere. Nowadays, we canfind computing capabilities in everyday physical objectsas diverse as mobile phones, digital personal assistants,gaming platforms, household appliances or cars, just toname a few examples.

Computing-enabled physical objects often have todeal with physical processes and tightly integrate com-puting with the physical world via sensors and actua-tors. The integration of physical processes and comput-ing is not a new problem. Embedded systems, whichhave been in place long ago, often combine physicalprocesses with computing. However, with the massivedeployment of networked embedded computing devices,we are observing the next step in the evolution of em-bedded computing. The term Cyber-Physical Systems(CPS) has been used to describe these pervasive com-puting systems, where emphasis is put on the physical,real-time and embedded aspects [1].

Such large-scale, sensor-rich networked systems willgenerate an enormous amount of sensor data [2], andhandling such amounts of data introduces significantchallenges. One approach to deal with the amountof data generated in these systems is to perform in-network data aggregation. Instead of collecting datafrom all nodes to a central point, in-network data ag-gregation applies a data-reduction function to the datatraveling through the network such that the total num-ber of messages transmitted is reduced [3].

Despite the previous research developed in the fieldof data aggregation, its performance is limited by thefact that, nodes in the same radio broadcast range can-not transmit in parallel, hence the time-complexity stilldepends on the number of sensor nodes. If we envisionscenarios where even a small area may contain severaltens of sensor nodes, the advantages of typical dataaggregation solutions found in the literature are signif-icantly impaired.

It is in this context that the research work describedaddresses the problem of performing scalable and ef-ficient aggregate quantities (e.g., the minimum, maxi-mum or median of the values proposed by all nodes)in dense networks. Here “efficient” means that thedesired computation should be performed while con-suming very little resources (such as energy, commu-nication links, memory and processor) and “scalable”means that the consumption of resources should in-creases slowly or not at all as the number of sensorreadings to be processed and/or the number of embed-ded computer nodes increases.

To illustrate this concept, consider a large-scale densenetworked sensor system, whose nodes have a commonsensing goal to measure temperature. Now consider theproblem of computing a simple aggregate quantity: theminimum (MIN) sensed temperature among the nodesat some given moment. Computing MIN seems triv-ial, but for dense and large-scale systems, it poses animportant problem: communicating sensor data indi-vidually makes the time-complexity of computing MINa function of the number of nodes. This is true for anydata aggregation mechanism employed.

This research work aims at being able to validate andexplore the following hypothesis:

Is it possible to efficiently obtain aggregate quantitieswith a time-complexity that is independent of thenumber of sensor nodes?

In other words, and taking the example of MIN, weaim at computing MIN with a time-complexity that isequivalent to the time of transmitting a single message,even if tens or thousands of nodes share the same radiobroadcast range.

Obtaining scalable and efficient aggregate quanti-ties in large-scale dense networked sensor systems re-quires tight integration between the data aggregationtechniques and communication mechanisms. This is akey observation underlying this research work, where

Fig. 1. Data Aggregation by Exploiting a Prioritized MAC.

the approach to obtain scalable and efficient aggregatequantities in large-scale dense networked sensor sys-tems is co-designing (i) distributed algorithms to obtainaggregate quantities and (ii) the underlying communi-cation services.

The main objective of this thesis is to demonstratethat is possible to obtain aggregate quantities efficientlyby co-designing distributed algorithms for data aggre-gation with the underlying communication services.The approach to achieve this includes developing aprioritized MAC protocol and design distributed algo-rithms that exploit this MAC protocol. The next sub-section presents a summary of the contributions madeby this research work.

Research Approach

This research work explores mechanisms for obtain-ing aggregate quantities that are efficient, even in verydense networks. The efficiency of traditional data ag-gregation mechanisms results from applying data re-duction functions to data coming from different sources,and from exploiting the opportunities for parallel trans-missions. In the extreme case where all nodes are inthe same broadcast domain, nodes cannot transmit inparallel and there are no opportunities for traditionaldata aggregation techniques to apply a data reductionfunction.

The novel approach explored in this thesis is based onthe adaptation to wireless media of a family of mediumaccess control (MAC) protocols. This family of proto-cols is known as dominance or binary countdown pro-tocols [4] and is already present in wired networkingsolutions: the Controller Area Network (CAN) tech-

nology [5]. We then design distributed algorithms thatexploit the MAC protocol to efficiently obtain aggre-gate quantities.

Dominance/binary countdown protocols can be ex-ploited to efficiently obtain a range of aggregate quan-tities. Let us briefly exemplify, to give further intuition,the case of MIN, which can be obtained with a time-complexity that is equivalent to the time of transmit-ting a single message. This is illustrated in Figure 1,where all nodes are in the same broadcast domain. Sup-pose that the temperature values are coded as n-bitintegers. Starting with the most significant bit first,let each node send the temperature reading bit by bit.Consider also that, for each transmitted bit, nodes readthe resulting value in the channel (something straight-forward in a wired medium) and the channel imple-ments a logical AND of the transmitted bits. Further-more, if a node reads ’0’ and is transmitting a ’1’, itstops transmitting. Then, at the end of the transmis-sion of the n bits, the “observed” value in the channelwill correspond to the MIN. It is as if all temperaturereadings were transmitted in parallel at the same time,and the resulting value of this non-destructive collisionis a useful aggregate quantity.

It is based on this concept that the novel dis-tributed algorithms developed in this work are designedupon. To accomplish this proposal, first it is neces-sary to design a MAC protocol that implements domi-nance/binary countdown in wireless environments, andthen develop the algorithms to exploit that MAC proto-col. However, designing and implementing such MACprotocol for wireless media is not trivial. For this rea-son, a substantial part of this work is focused on the

development of such MAC protocol.First, the problem is tackled assuming that all nodes

belong to a single broadcast domain (SBD). Nodes arein a SBD when (i) a wireless broadcast made by onenode reaches all other nodes in the same broadcastdomain and (ii) if a node transmits a packet, then itcan be correctly received by another node in the samebroadcast domain only if the transmission of the packetdoes not overlap in time with another packet trans-mission. Note that, in this research work, there is noassumption about regular propagation patterns of theradio signals.

Achieving dominance in the wireless domain is chal-lenging. To begin with, it is not possible to directlytranslate the behavior of wired protocols, as these re-quire that nodes are able to transmit and receive atthe same time. This is not possible in common radiotransceivers, because the transmitted energy is muchhigher than the received energy. For this reason, dom-inance in wireless systems was achieved using a sim-ple principle: when the transmitted bit is dominant, apulse of a carrier wave is transmitted and there is noneed to sense the medium. Conversely, when the bit totransmit is recessive, nothing has to be effectively sent,instead only the medium state has to be sensed.

Although the concept and approach sounds simple,a number of difficulties must be solved when proposingthe design of a correct dominance protocol for wire-less networks. These include achieving proper synchro-nization between the nodes, defining the parameters ofthe protocol such that clock inaccuracies, time-of-flightand other real-world effects are dealt with and how toperform reliable carrier detection. These aspects areaddressed in this work and an implementation of dom-inance protocols in wireless media − WiDom (short forwireless dominance) − is presented.

This work also deals with the case of networks withmultiple broadcast domains (MBD). Considering MBDis important because it will be difficult to make theSBD assumption hold in a large number of networksdeployed in the real-world. Nodes are in a MBD net-work if it holds that a wireless broadcast made by onenode cannot reach all nodes in the network. Such net-works (MBD networks) suffer from the well known hid-den node problem. This is a challenge that needs tobe solved when considering the extention of WiDom toMBD.

While a significant effort of this research work is putinto designing novel distributed algorithms to obtainaggregate quantities in a SBD, local aggregation be-tween nodes in geographic proximity can be used asan intermediate step to compute aggregated quantitiesamong all nodes in a multihop network; hence the solu-tion to the problem of computing aggregated quantitiesin a SBD forms a relevant building block for large-scaledata aggregation in multihop networks. This challengeis also tackled in this work.

A final note on the MAC protocol that imple-ments dominance/binary countdown in wireless media(WiDom). One important property of WiDom is that itallows enforcing static priorities. Therefore, it enables,for the first time, static priority scheduling over wirelessmedia. This is also a relevant characteristic in emergingembedded systems because these systems deal with thephysical world, therefore one important requirement tobe met is that their data services are able to meet tim-ing constraints [1]. The research approach also takesthis property into account, and a response-time analy-sis for the proposed MAC protocol is also developed.

Contributions Developed

In this work we reasoned on the design and im-plementation of a prioritized MAC protocol (WiDom)which enforces strict priorities over wireless channelsand on the design of algorithms that efficiently ex-ploit this MAC protocol to obtain, in a SBD, the min-imum (MIN), maximum (MAX) and interpolation ofsensor values with a time-complexity that is indepen-dent of the number of sensor nodes (it depends onlyon the sensor value range). These techniques also en-able to efficiently obtain estimates of the number ofnodes (COUNT) and the MEDIAN. For MBD, thetime-complexity of the proposed distributed algorithmsdeveloped also depends on the network diameter.

Overview

Adaptation of Dominance Protocols to Wire-less Media. This research work introduced an adapta-tion of a dominance protocols for wireless media, whichexisted previously only for wired media. The imple-mentation of a dominance protocol for wireless mediawas named WiDom and was initially proposed underthe assumption of a SBD [6], [7]. WiDom can be ex-ploited to efficiently obtain aggregated quantities, andit is also useful to provide pre-runtime guarantees forsporadic messages streams. A schedulability analysisfor WiDom was developed accordingly.

Extension of WiDom to Support MultipleBroadcast Domains. To cope with larger geo-graphical areas, networks with multiple broadcast do-mains (MBD) need to be considered. An extension ofWiDom for wireless networks with MBD was also pro-posed. The proposed solution is the first prioritizedand collision-free MAC protocol designed to success-fully deal with hidden nodes without relying on out-of-band signaling [8].

Improving the Reliability of WiDom in SBD.The techniques employed to solve the hidden nodeproblem in [8] can also be adapted to improve the re-liability of the protocol in a SBD. The proposed solu-tion has a result that is similar to a cooperative relay-ing scheme, where several nodes can participate in thetransmission of the priority bits [9].

Scalable and Efficient Aggregate Quantities.

By exploiting dominance protocols it is straightforwardto demonstrate that, in a SBD, the minimum value(MIN) can be obtained with a time-complexity thatis O(npriobits), where npriobits is the number of bitsused to represent the sensor data (the same techniquecan be applied to obtain the maximum value (MAX);).techniques to efficiently compute more complex aggre-gate quantities such as the number of nodes (COUNT),MEDIAN and interpolation by exploiting dominanceprotocols were also implemented in the wireless do-main [10]. Finally, the techniques employed to obtainaggregate quantities in a SBD were also extended formultihop networks [11]. The algorithms proposed forMBD have a time-complexity that only depends on thenetwork diameter and on the value range of the sensorreadings.

Discussion

There are two important sets of contributions in thisresearch: (i) the development and implementation ofWiDom and the support for static priority schedulingover wireless links; and (ii) the algorithms for efficientdata aggregation based on WiDom. Let us now reviewand briefly discuss each of them.

WiDom Development and Implementation.

WiDom was proposed, a novel wireless MAC proto-col inspired in dominance/binary countdown protocolswhich existed previously only for wired media [4]. Thisachievement is non-trivial. Firstly, implementations ofdominance protocols for a wired medium are based ona wired–AND behavior of the bus, where the domi-nant signal overwrites the recessive signal. Secondly,these implementations require that nodes are able tomonitor the medium while transmitting. Clearly thisdoes not easily extend to the case of wireless channels.Moreover, due to non-idealities of transceivers and thenature of the wireless medium, it was not obvious howa dominance protocol could be achieved.

WiDom supports a large number of priorities. Al-though this number of priorities introduces overhead,the application developer has the freedom to choosethe number of priority levels required, and thus possi-bly reduce the overhead introduced. Nevertheless, sucha large number of priorities can be supported by otherprioritized protocols (see e.g., [12], [13]) but at the costof an overhead several orders of magnitude higher.

The initial design of WiDom was created under theassumption of a SBD. An extension of WiDom forwireless networks with MBD was also developed. Insuch scenario, the hidden node problem must be dealtwith. The proposed solution is the first prioritizedand collision-free MAC protocol designed to success-fully deal with hidden nodes without relying on out-of-band signaling.

The idea of retransmitting priority bits used to solvethe hidden node problem can also be adapted to im-

prove the reliability of WiDom in a SBD. In the pres-ence of several nodes in the same broadcast domain,various nodes can cooperate in the transmission of thepriority bits. This simple modification of the protocolcan result in a substantial gain in the reliability, as thenumber of nodes increases.

WiDom was implemented and evaluated experimen-tally using Commercial-Off-The-Shelf (COTS) technol-ogy. The implementation of WiDom using COTS tech-nology suffered however from a significant overhead.Therefore, a platform with better characteristics to im-plement dominance protocols was also studied and de-veloped. This platform is the proof of concept thathighly scalable aggregate computations in wireless net-works are competitive in practice.

The experimental evaluation of WiDom shows thatthe probability that a message is transmitted collision-free, correctly prioritized and received (neither lost norcorrupted) by all other nodes is high and this reliabilityjustifies the study of schedulability analysis techniquesfor sporadic messages in wireless networks; WiDom isan enabling technology allowing schedulability analy-sis (for example to exercise in practice the analysisproposed by [14]) in wireless multihop networks withmultiple broadcast domains. For the case of SBD, aresponse-time analysis for WiDom was developed andtested as well.

Efficient Data Aggregation.

The research on efficient data aggregation in thiswork is motivated by scenarios where even a smallbroadcast domain may contain several tens of sensornodes. In these scenarios, the advantages of data ag-gregation solutions found in previous research are lost,since it is neither possible to apply data reduction func-tions to data coming from different sources nor is itpossible to exploit the opportunities for parallel trans-missions. In this thesis was demonstrated that it ispossible to exploit a dominance-based MAC protocolto efficiently compute aggregate quantities with a time-complexity that is equivalent to the time of transmit-ting a single message, even if hundreds of nodes are inthe same broadcast domain.

Concretely, the present work demonstrated that, ina single broadcast domain (SBD), the minimum value(MIN) can be obtained with a time-complexity thatis O(npriobits), where npriobits is the number of bitsused to represent the sensor data. In this case, themessage complexity (and thus, the time-complexity) isindependent of the number of sensor nodes. The sametechnique can be applied to obtain the maximum value(MAX).

Based on these techniques (of obtaining MIN orMAX), more elaborate aggregated quantities can beobtained. In this work, useful examples such as thenumber of nodes (COUNT) and MEDIAN were alsoaddressed.

Often, it is required to know how physical quantities(such as temperature) vary over an area. Clearly, thephysical location of each node must be known then.For such systems, an algorithm that produces an in-terpolation of the sensor data as a function of spacecoordinates was proposed. The resulting interpolationis a compact representation of sensor data at a momentand is obtained efficiently.

The algorithms (to obtain aggregate quantities) wereinitially designed with the assumption of a SBD net-work. Nevertheless, in practice, most networks arenot SBD networks. Therefore, solutions for MBD net-works were also studied and proposed. An algorithmfor computing the MIN (or MAX) of sensor readings ina multihop network was proposed. That algorithm hasthe particularly interesting property of having a time-complexity that does not depend on the number of sen-sor nodes; only on the network diameter and the rangeof the value domain of sensor readings matter. Othermore sophisticated algorithms were demonstrated alsofeasible for MBD networks.

These results are significant because often networksof nodes that take sensor readings are designed to belarge scale, dense networks and it is exactly for such sce-narios that the proposed algorithms (designed in closearticulation with the MAC protocols) excel. The imple-mentation of these algorithms in the hardware platformdeveloped shows that such highly scalable aggregatecomputations in wireless networks are indeed competi-tive in practice.

Discussion on Important Assumptions

There are two scenarios in this research work thatare important to fully grasp the relevance of the con-tributions presented.

Dense Wireless Networks

The algorithms designed in this research are devel-oped for large-scale, dense networks. In particular, itis in the presence of many nodes in the same broad-cast domain that the advantages of an algorithm whosecomplexity does not depend on the number of nodes be-comes evident. Note that, it was show that we can seeadvantages with as few as two nodes [10].

Some may object accepting such scenario. Indeed,there are a few results which might advise againstdeploying dense networks. For instance, previous re-sults on the capacity of ad-hoc wireless networks [15]show that, under certain assumptions, the capacity pernode approaches zero as the number of nodes increases.However, several facts show that this is not the casein the context of WSN [16], [17], and other solutionscan be devised (see, for example, the results reportedin [18]).

One reason to search for other solutions is that datain sensor network is correlated, and this can be exploredin several ways (e.g., [16], [18]). In this work, the spa-

tial correlation between sensor readings was exploitedto perform a weighted-average interpolation and selectonly a subset of nodes.

A second reason differentiating WSN is that the com-munication pattern is often from several source nodesto a sink. This enables several techniques such as in-network data aggregation or other techniques such asantenna sharing [17].

Another question is why deploy a dense sensor net-work if we know that we will be gathering a lot of re-dundant data? Deploying a dense sensor network mightbe convenient for several reasons. First, when consid-ering the case where the individual cost of each node isnegligible, then deploying redundant nodes might notbe a primary factor in the cost of the system. Redun-dant nodes are useful for fault-tolerance (under certainfault assumptions) and noise immunity. Deploying re-dundant nodes also allows for a very fine spatial resolu-tion in the sampling of the phenomena being observed.More importantly, deploying a dense network allows abetter resolution of how the physical world is perceived;for example, when we are interested in high-resolutionsampling in a certain region of interest, but we do notknow in advance where that region is. This is expectedto be essential in forthcoming innovative applicationsin cyber-physical systems.

Timeliness Guarantees in Wireless and Reliability

An important contribution of this research workis also a MAC protocol that supports static priorityscheduling in wireless networks. This contribution en-compasses its full impact, when assuming that it is rele-vant to analyze the problem of providing timing require-ments within a hard real-time context.

It is often indicated that wireless links are unreliable,thus it is not meaningful to approach the problem froma guaranteed timeliness perspective.

It is true that designing a protocol with an upperbound on queuing time is not sufficient to guaranteethat hard real-time deadlines are satisfied in practice.However, it is a necessary step towards that goal. It isimportant to note that part of this problem is a tech-nological one. The reliability of wireless networks hasevolved noticeably over the last few years; it is safe toassume this evolution will continue. The experimen-tal evaluation performed in this research work suggeststhat deadline misses due to noise are rare, so this pro-vides evidence that it is still useful to consider hardreal-time bounds on queueing delays. Obviously, anykind of guarantees are subject to some assumptions.

For the case of WiDom in SBD networks, there is arange of techniques originally developed for the CANbus that can be applied, and this is a very interest-ing research path to explore. These techniques in-clude the schedulability analysis as presented in [6], [7],which are easily extendable to consider acknowledge-ments and retransmissions, and also other techniques

such as stochastic approaches to model faults [19], forexample. Moreover, the scheme to improve the reliabil-ity of WiDom in a SBD results in a substantial gain inthe reliability, as the number of nodes increases. Thisfact alone can enable hard real-time deadlines to besatisfied with a very high probability in practice.

Even if not employing a guaranteed framework, be-ing able to enforce strict priorities is useful in general.One recent example can be found in [13], where theauthors built a streaming audio application employinga prioritized MAC protocol previously proposed in theliterature [20]. It was found that, although the over-head of this MAC protocol is high, it still offers betterthroughput than normal CSMA/CA protocols becausesuch prioritized MAC protocols eliminate the overheadof back-off after collisions. The scalability of the workreported in [13] is partially limited to the small numberof priorities supported by the MAC protocol adoptedand it is exactly in this point that WiDom stands out.Compared to that MAC protocol, WiDom offers a muchhigher number of priorities for a given similar overhead.

Future Work

There are several opportunities that can be identifiedfor further research:• development of other mechanisms to obtain other ag-gregate quantities or perform other distributed compu-tations;• enclosing in a query processing system;• integration with other communication protocols;• power management schemes/duty cycling;• improvement of current implementations;• further development of radio hardware;• maximize the number of parallel transmissions ofWiDom-MDB.The following paragraphs briefly discuss each one ofthese possible future research topics.

The fact that MIN can be obtained efficiently by ex-ploiting WiDom can serve as a building block for othercomputations like COUNT, MEDIAN or Interpolation.It is possible to foresee that other computations mayeventually be devised out of similar ideas and this is aresearch topic to be explored.

The computations enabled by WiDom could beencapsulated in a query processing system, similarTinyDB [21]. The query processing would receive thequery specifications and map these into primitives thatexploit WiDom adequately. Eventually, queries thatcannot be more efficiently carried out by exploitingWiDom could be mapped into other primitives thatdo not work by directly exploiting WiDom.

One interesting possibility is to integrate WiDom inother communication protocols. For example, a TDMAprotocol can benefit from WiDom by allowing severalnodes to share the same timeslot. Inside that timeslot,contention is resolved using WiDom. This combinationwould allow reducing the TDMA cycle. Another similar

example is to integrate WiDom in the IEEE 802.15.4beacon-enabled mode, where one timeslot could be re-served for WiDom. In this case, during that times-lot, all nodes are allowed to access the medium usingWiDom. Both examples can improve the schedulabilityof the system.

WiDom is energy efficient as it can avoid packet col-lisions and enable very efficient computations, but itlacks energy efficiency in the sense that it requires thatnodes constantly monitor the medium. However, thisdoes not need to be the case. WiDom is compatiblewith power management schemes proposed in WSN,such as coordinating activity/sleep schedules betweenthe nodes (e.g. [22]). This is an important topic forfurther research.

At this point, the implementations of WiDom (anddistributed algorithms) available are considered onlyas proof-of-concept prototypes. This means that theimplementations were not developed for general use andthey require some effort of understanding the details ofthe implementation in order to be used. This is not atopic for research, but it is important to consider thisfact in order to asess the amount of effort needed toexperiment with WiDom and/or further develop it.

The development of an efficient implementation ofWiDom is still an important subject for future work.While a platform specifically designed for implement-ing WiDom efficiently in provides an indication that theoverhead of the protocol can be low such that the algo-rithms based on exploiting WiDom can be very com-petitive [10], there is still a lot of work to be done inthis aspect. The platform presented is a prototype de-veloped from components commercially available at thetime. The development and research of better radiosfor the execution of WiDom is a topic that would ben-efit the algorithms for obtaining aggregate quantitiespresented and would also enable low overhead staticpriority scheduling in wireless systems.

Finally, the proposed protocol for WiDom in a net-work with MBD [8], does not maximize the number ofparallel transmissions. This is a problem that can bedifficult to solve, but, in practice, strategies as planningthe priorities in the nodes such that multihop compet-ing is avoided can provide interesting solutions.

Conclusion

This paper presented the research efforts developedaround the following hypothesis: Is it possible to com-pute aggregate quantities with a time-complexity that isindependent of the number of sensor nodes?

To achieve this goal, WiDom was designed in closearticulation with the data aggregation mechanisms. Byexploiting the properties of a prioritized MAC protocol,the formulated hypothesis can be supported in SBDnetworks. Effectively, the time-complexity of the algo-rithms for obtaining aggregate quantities in SBD net-works only depends on the sensor value range. In the

case of a MBD network, the time-complexity of thealgorithms developed also depends on the network di-ameter.

One important part of this research was dedicatedto make this approach effective in wireless networks.This included the design and implementation of a pri-oritized MAC protocol in wireless media. Several im-plementations [6], [7], [8], [10] have shown that suchapproach is viable. While evidence was presented thatthe approach is feasible and the algorithms based onexploiting WiDom can be very competitive, there is abig gap to fulfill until such mechanisms are useful inmore general settings. Filling this gap involves a widerange of aspects, from development of a framework toease the use of such techniques by application develop-ers, maturation of radio hardware or integration withother communication protocols.

This research work has demonstrated innovativemechanisms for obtaining certain aggregate quantitiesin dense wireless sensor networks. The work includedthe development of a prioritized MAC protocol that en-ables these mechanisms and can also efficiently schedulesporadic messages. Despite the dificulties yet to over-come, these constitute an attractive set of solutions foremerging Cyber Physical Systems.

Acknowledgment

This research work was partially developed at theReal-Time Computing Systems Research Centre (CIS-TER), from the School of Engineering of the Polytech-nic Institute of Porto (ISEP).

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Nuno Pereira is a researcher at CIS-TER/ISEP and a Lecturer at the Com-puter Engineering Department of ISEP,the School of Engineering of the Polytech-nic of Porto. He received a licentiate de-gree in Computer Engineering from theSchool of Engineering of the PolytechnicInstitute of Porto and an MSc Degree fromthe University of Minho in 2002 and 2005respectively, and has also recently pre-sented his doctoral thesis. Nuno worked

in a number of research projects related to several aspects ofreal-time systems and has authored and co-authored more than20 research papers in international refereed conferences and jour-nals.