EDIC RESEARCH PROPOSAL 1

Reactive Low-Power Wireless Communication forSkin Sensor Network

Camilo Rojas

CSEM and LAMS, I&C, EPFL

Abstract—The WiseSkin project aims at creating a smartartificial skin capable of reproducing the sense of tactility. Theskin embeds a wireless sensor network that senses and propagatesthe pressure, slip and temperature towards the user. Such anapplication requires a bounded end-to-end latency (~10s of ms)and distributed measurement of slip; in addition to reliability,low power and scalability (10s to 100s of nodes in a hand); andshould be able to operate under variable traffic and link quality.

The research focus of this thesis consists in the design of aMAC and a routing protocol for WiseSkin, that will enable it toreproduce a natural tactile feeling.

We analyze the requirements and trade-offs of real-timequality of service in WiseSkin and propose to study the useof adaptable MACs with support for dynamic routing protocols.The goal is providing latency bounds while coping with variabletraffic. We plan to address the coexistence with a second routingprotocol to enable local estimation of slip.

Index Terms—Wireless Sensor Networks, Routing, MAC,Smart Skin, Real-Time QoS, Distributed Sensing

I. INTRODUCTION

THE WiseSkin project aims at creating a smart artificialskin capable of tactile sensing, that could serve as cover

Proposal submitted to committee: January 30th, 2015; Can-didacy exam date: February 6th, 2015; Candidacy examcommittee: Rachid Guerraoui, Jean-Yves Le Boudec, Jean-Dominique Decotignie.

This research plan has been approved:

Date: ————————————

Doctoral candidate: ————————————(name and signature)

Thesis director: ————————————(name and signature)

Thesis co-director: ————————————(if applicable) (name and signature)

Doct. prog. director:————————————(B. Falsafi) (signature)

EDIC-ru/30.01.2015

for robots or robotic prosthesis used by amputees, and providetactile feedback to the user. At the moment, most roboticsystems are not able to emulate the sense of touch in humanskin. Furthermore, commercially available robotic prosthesislack the mechanisms to deliver tactile feedback to the user[1], which severely complicates the control of the force andhence, the usability of the prosthesis. This deficiency oftenresults in the patient abandoning the prosthesis [1].

WiseSkin is composed of tiny (smaller than 1cm x 1cm x2mm) wireless sensor nodes embedded in a flexible syntheticskin. Each node comprises a micro-controller, a radio, anantenna and one or a few pressure sensors. The nodes arecapable of sensing the pressure applied over the skin andtransmitting the information wirelessly between each other orto a base station, and then to the user. Adjacent nodes maycollaborate to derive slip information from pressure. Figure1 displays a diagram of the system with 16 nodes, futureiterations are expected to have 100s of nodes in a single hand.

The communication system of WiseSkin is a dense wirelesssensor network (WSN) focused on collecting data about pres-sure, slip (elaborated from synchronized information from 2 ormore sensors) and temperature. The system’s ability to reactto tactile stimulus and allow the user to feel it realistically andrespond accordingly imposes a series of requirements, such asa sensor-to-sink latency bound smaller than 20 to 50 ms fordetecting touch with a sampling and update rate up to 1000Hz per sensor in case of slip, distributed measurement of slip,support for networks of 10s to 100s of nodes in the palm ofa hand, capacity to handle total data volumes of 24 kbps -2.4 Mbps and a low power consumption [2]. Additionally, thesystem should be able to overcome the fast transition fromlow traffic, when the skin is inactive, to traffic surges, that cangenerate congestion, upon stimuli.

As a part of the multidisciplinary WiseSkin project, thisPhD thesis tackles the challenge of designing a medium accesscontrol (MAC) and routing protocol that enables WiseSkin toeffectively emulate the sense of touch.

The research will focus on two identified priorities: thelow latency and the distributed measurement of slip. Whileprevious approaches to the design of a low latency protocolfocus on minimizing the latency [3], WiseSkin requires latencybounds that guarantee a real-time tactile feeling. Additionally,these proposals have focused on congestion control [4], butthis option is not appropriate for WiseSkin, as it comeswith the risk of discarding significant tactile events over theskin. Note that the distributed measurement of slip requirescorrelating information over time.

EDIC RESEARCH PROPOSAL 2

Multiple MAC and routing schemes aiming at providingquality of service (QoS) guarantees can be found in theliterature. Nevertheless, they are not tailored to handle theparticular traffic characteristics of the skin, alternating betweenperiods of ”silence” and intense activity, nor the performancetargets imposed by WiseSkin.

A promising approach is a MAC layer able to adapt to vary-ing traffic conditions, by switching between different MACprotocols, such as the Self-Adapting MAC Layer suggestedin [5]. This layer can include protocols that are specializedin different tasks, such as low energy consumption or lowlatency, and they can be designed to be compatible, in orderto avoid the global state problem. A considerable researcheffort must be undertaken to overcome several shortcomingsof this approach, such as ensuring the compatibility betweendifferent MACs, reducing the latency and energy overheadof the switching, providing latency bounds and supportingdynamic routing.

Our proposal aims to deliver a MAC and a routing protocolthat can leverage expected properties of the system, such asspatial and temporal correlation of the traffic, while copingwith sudden changes in the network conditions (ex. skinbending, traffic load, etc.). The final proposal must meet thesecommunication requirements in order for WiseSkin to deliver arealistic experience to the user of the smartskin, which involvesproviding QoS guarantees for latency, energy and reliability.It is also desirable for the protocols to support scalability onthe density of the network, to allow for the development offuture versions.

This research is also expected to contribute to the fieldsof industrial automatic control, robotics, haptic interfaces andbody wireless sensor networks.

The remaining sections of the document are organized asfollows: section II presents the WiseSkin system and thechallenges it poses in terms of communication system, sectionIII reviews previous approaches to the problem of real-timequality of service in WSNs and their take-home messagefor WiseSkin and section IV explains the principle behindadaptable MAC layers and discusses their feasibility for thisproject. Finally, section V details our research proposal foraddressing the exposed communication challenges.

II. THE WISESKIN ARTIFICIAL SKIN

The smart prosthetics domain has shown significant ad-vances over the past years, due to new technologies of sensors,actuators and batteries. Even though functional and highlydexterous prosthesis exist on the market, their use by amputeesremains very limited. The lack of sensory functions is oftenthe cited reason [1].

WiseSkin’s goal of realistically reproducing the tactile senseimplies providing to the user (a human owner of a prostheticarm or a robot) with all the information that composes thissense (i.e. pressure, slip and temperature) [6]. The proposedsystem, depicted in Figure 1, is a smart skin composed by anetwork of tiny (smaller than 1cm x 1cm x 2mm) wirelesssensor nodes embedded in a polymer. The nodes will sensethe tactile parameters and the information will be sent to a

collection point (a 2.4 GHz radio is considered). In the caseof a human user, the information will be delivered via anarray of actuators that will stimulate the nervous endings onthe stump that correspond to the region of the hand beingtouched. The system aims to effectively port the touch feelingfrom the hand to the nervous system of the person. Studiesperformed by Antfolk et al [7] show that this type of tactilefeedback is successful in enhancing the perception of a naturaltactile feeling. A robot user can forward the tactile signalselectronically to the corresponding processing unit.

Figure 2 shows a transversal cut of the skin. The con-ductive layer covering both faces of the skin is expected tobehave like a waveguide encapsulating the network [6], andprovide power to each node through wires, while protectingthe communication network from external electromagneticinterference. This approach promises reliability and scalabilitysuperior to the wired counterparts of WiseSkin (ex. [8] and[9]), by eliminating the risk of broken wires and the burdenof packaging them.

Some of the main challenges for the communication systemare: ensuring a delay short enough to emulate the real skin andminimizing the energy consumption [6].

Given its complexity, WiseSkin is a multidisciplinary projectthat is composed of 5 parallel research lines handled bydifferent work groups: 1) miniature skin sensors; 2) conformal,stretchable power distribution system; 3) ULP radio; 4) com-munication protocols and 5) system integration. Our researchline and the focus of this thesis plan is 4) communicationprotocols.

Antfolk et al detail the possible use cases for WiseSkinand the expected application traffic they will generate. Theyalso provide an estimate of the power consumption of thecommunication system in two use scenarios: continuous sam-pling and event driven. The first refers to the case where thenetwork wakes up periodically, senses and then transmits thedata. The latter refers to the case when a single node worksas an activation switch, that wakes up the entire network incase of stimulation.

The power consumption estimation of the entire Wise-Skin system has two components: the communication system(WSN) and the sensory feedback system (array of actuators forskin stimulation). The communication system alone consumes17.2% of the WiseSkin power budget for the continuoussampling scenario and 3.5% for the event-driven scheme.

A. Challenges for the Communication SystemThe document by Antfolk et al [6] was selected as one of

the basis of this research proposal because it formulates theproblem and the requirements that a communication systemfor WiseSkin must address.

The paper presents the use of wireless communications, anew approach to the design of a smart skin, expected to havetwo key advantages over the wired option: robustness (as wiresbreak after repetitive bending) and scalability (as wires wouldhave to be packed in the skin). As such, the system providesan experimental playground for developing new technologieswith a key role in the fields of automatic control, robotics,haptic interfaces and body wireless sensor networks.

EDIC RESEARCH PROPOSAL 3

Node = Pressure sensor(s) + Microcontroller + Radio

Actuator

Control Unit

Stump&

Miniature Sensor Node

Low Power Radio

Cosmetic cover Waveguide

Polymer

Waveguide Attachment

Figure 1. WiseSkin concept for prosthetics

Fig. 1. Diagram of a prosthetic hand with the WiseSkin system. The bluedots represent the pressure sensitive nodes. They use wireless communicationsto transmit the tactility information to a control unit, which uses an array ofactuators to convey the signals to the nervous (or control) system of the human(or robot) user.

Node = Pressure sensor(s) + Microcontroller + Radio

Actuator

Control Unit

Stump&

Miniature Sensor Node

Low Power Radio

Cosmetic cover Waveguide

Polymer

Waveguide Attachment

Figure 1. WiseSkin concept for prosthetics

Fig. 2. Transversal view of the WiseSkin system. The nodes obtain the powerthrough wires connected to the waveguide faces and communicate wirelessly.

The estimation of the power consumption provided byAntfolk et al is based on a simplified wireless sensor networkmodel and relies on several assumptions that are determinantfor the calculation, detailed as follows:

• The MAC protocol is highly abstracted by assuminga wakeup delay and perfect synchronization: A MACprotocol is a distributed scheme that coordinates theaccess of the different nodes to the channel. Thereforeit defines the waiting time before a node is able totransmit and has a high impact on the end-to-end delayof the sensed data (hereafter called latency metric). Italso determines the transmission and listening (active andidle) schedules of the radios, which are the largest energyconsumers in the network [6]. Depending on its design,the power consumption of the network (hereafter calledpower metric) can vary several orders of magnitude.

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Fig. 3. Measurement of the scattering parameters of the WiseSkin propa-gation medium: forward transmission (s21) and reflection (s11) coefficients[10].

• The network topology is predefined and invariable: Thisimplies that the routing is assumed to be static. Nev-ertheless, the characterization of the channel (Figure 3)suggests that the quality can change considerably atfrequencies around 2.4 GHz. Even with the networkoperating at constant frequency, the bending of the skincould be responsible for channel modifications and thetemporary breaking of the links. In this scenario, a staticrouting scheme would render the nodes isolated fromthe network for certain hand positions. This drawbackjustifies the consideration of dynamic routing protocolsin order to allow the nodes the use of alternative routesto convey time sensitive data.

Therefore, the power consumption in [6] should be regardedas a lower bound rather than an approximation. Consequently,the consumption of the communication system can be expectedto represent a proportion of WiseSkin’s power budget that ishigher than the document’s projection (up to ~17%) and theprotocol design could have an impact on the battery life of theentire prosthetic device.

One of the strengths of the monograph is that it characterizesthe use case scenarios for WiseSkin and provides the number,frequency, length and type-distribution of daily grasps. Thisdescription reveals valuable information about the input trafficand has to be handled by the network and contributes to closethe gap between the application requirements and the designspecifications for the communication system.

For example, it is reasonable to expect the stimulation ofa particular region of the skin to be accompanied by thesimultaneous stimulation of the neighboring regions, whichimplies a network traffic that could be temporally and spatiallycorrelated. It can also be expected to observe very low andpossibly periodic traffic (due to the control traffic) whenthe skin is not stimulated. However, upon stimuli, severalnodes will try to transmit simultaneously and more than once,generating traffic surges. If confirmed, such phenomena are tobe taken into consideration in the design of the communicationstack.

Antfolk et al also mention low latency to have paramountimportance for the user experience. Failing to deliver a human-like delay on the tactile sensations would negatively affect the

EDIC RESEARCH PROPOSAL 4

feeling of body ownership [11], compromising the purpose ofthe project. This makes the provision of low latency boundsone of the main design priorities.

If the traffic effectively displays surges upon stimulationof the skin, a possible approach to reduce the latency is toensure a high probability of arrival of the most importantpackets (hereafter called reliability metric) and suppressionof the redundant ones in order to avoid congestion. Anotherstrategy is to try transmitting all the packets on a best-effortbasis and assume that, even if some of them will be lost dueto congestion, the redundancy of their content will ensurethe important information will arrive with high probability.Addressing the reliability of the network is directly correlatedwith the latency, thus turning the reliability metric into thesecond highest design priority.

The previous requirements demand a reactive protocol,which must be able to provide latency-bounds in face ofvariable traffic.

The scalability of the network is also a desirable feature,supporting a future increase in the number of nodes (hereaftercalled scalability metric).

The correlation of pressure values from different nodes canalso yield valuable information about the tactility profile, suchas the slip. This feature can be enabled by allowing the nodesto quickly exchange data with the neighbors. Another optionwould be synchronizing the nodes and correlating the time-stamped data upon collection at the sink. The detection ofslip is necessary to perform basic actions such as detectinga glass that is slipping off the hand; thus becoming a mainrequisite for the communication system.

WiseSkin exposes the problem of defining a MAC androuting protocol capable of providing a natural user expe-rience, which requires a low latency and the detection ofslip. It also uses four design metrics (in order of decreasingpriority): latency, reliability, energy and scalability. The trade-offs between the design metrics will be explained in detail inthe next section.

III. REAL-TIME QOS SUPPORT IN WIRELESS SENSORNETWORKS

The domain of Wireless Sensor Networks promises todeliver a large number of connected devices able to collectinformation from the environment, while operating for longperiods of time. The applications for these systems are asvaried as the natural phenomena to be monitored.

In order to maximize the energy autonomy, cost and size,such devices are constrained to operate with scarce energyand hardware resources. Additionally, some of the applicationswill have particular requirements, including communicationreliability or latency.

The review from Li et al [3] focuses on WSNs for ap-plications that need real-time QoS guarantees and providesan overview of the mechanisms for supporting them. Thisrequirement is typical of networks that must react dependingon the sensed values, such as the control systems.

The authors classify the RT guarantees in Hard Real-Time(HRT) and Soft Real-Time (SRT). The first one requires a

deterministic end-to-end delay bound, while the second onecan tolerate a certain level of lateness (e.g. meeting 96% ofthe deadlines).

The paper lays particular emphasis on addressing the prob-lem of real-time QoS and includes medium access control,routing, data processing, cross layer designs and data aggre-gation.

Finally, the article provides a discussion of the challengesand open issues in the domain. It draws special attention tothe provision of real time guarantees in face of the stochasticnature of the wireless channel, without wasting resources dueto over-sizing.

A. Lessons for WiseSkin

The review [3] dates back to 2007 and therefore provides anoutdated view of the state of the art in terms of the mentionedprotocols. Nevertheless, it was chosen as a guide for thismonograph because it provides a view of the problem thatis based on the requirements for enabling real-time QoS. Thereview also covers the mechanisms that can be implementedat different system levels, such as data processing, cross layerdesigns and MAC, among others.

More recent reviews tend to limit the analysis of real-time QoS to congestion control, thus ignoring other importantdimensions such as the synergy between MAC and routing.The protocols mentioned in the 2012 review [4] (specialized inrouting for QoS in WSNs), that are not focused on congestioncontrol and not included in [3], still rely on discarding packetswithout regards of their content (Tandem Queue Model andSAR). The protocols in the 2012 review [12] (specialized inMACs for mission-critical WSNs), that do not require specifichardware, aim towards reducing the latency or the congestion,but not on providing latency guarantees; and they all datebefore 2007 (S-MAC-AL, TMAC, DSMAC, funneling-MAC,etc.).

Considering alternative approaches is especially importantfor the WiseSkin communication system, because simplyavoiding congestion could force the system to reduce the sens-ing accuracy or to discard packets to the level of hamperingthe user experience.

The approach of Li et al is based on the distinction betweenHRT and SRT bounds. The authors of the paper highlightthat HRT is only possible when both the MAC and routingprotocols are HRT. This statement underlines the importanceof a synergy between the real time guarantees provided at eachlevel of a protocol stack.

They also clarify that implementing HRT in a real WSNdeployment is very resource expensive. For example, theEarliest Deadline First (EDF) protocol employed in cellularnetworks uses a star topology, multiple resource-rich central-ization points and a network synchronization mechanism for aTDMA scheme. These resources are not typically availablein a WSN, and WiseSkin is no exception, thus renderingthe approach unfeasible for our purposes. For this reason,providing SRT guarantees becomes the preferred option overHRT, as long as they do not significantly impact the userexperience.

EDIC RESEARCH PROPOSAL 5

The HRT vs. SRT perspective is demonstrated to be suitablefor defining the complex trade-offs encountered in the domain.The authors make a comprehensive analysis, including themechanisms that current protocols implement to tackle thesetrade-offs. This analysis highlights a series of challenges thatwill also have to be addressed by WiseSkin’s protocol stack:

• Energy vs. reliability trade-off: The reliability metric canbe improved by probing the channel more frequentlyto assess its quality, or by retransmitting a packet aftera failure. Either case typically implies transmitting andreceiving packets, thus carrying an energy overhead. TheCollection Tree Protocol (CTP) is a common benchmarkon reliability and focuses on improving reliability at theexpense of the energy consumption [13]

• Energy vs. latency trade off: One of the most effectivemechanisms for improving the energy metric consists ofwakening up the nodes less often. This implies howevera deterioration of the latency metric as the transmittingnodes will have to wait for the wake-up of the receiver.

• Latency vs. reliability: The mechanisms to improve thereliability typically involve a traffic overhead (e.g. send-ing of acknowledgments and packet retransmissions).They increase the channel occupation and increase thelikelihood of congestion, with the consequent deteriora-tion of the latency metric.

Additionally, the protocols must cope with low-quality links,memory and bandwidth constraints and scalability. A thoroughunderstanding of these trade-offs is a complex undertaking,giving the typically extremely large search space [14]. On theother hand it helps satisfy the application requirements, with-out falling in overly conservative bounds and, consequently,low resource utilization.

The modeling of the performance metrics can shed somelight in our understanding of the trade-offs. An approachthat has proven successful for similar problems in roboticsis the multi-level modeling described in [15]. It consists inusing multiple models with increasing levels of abstraction: (1)the multi-agent physical system represented in an embodiedsimulation, (2) each simulated agent represented as a Proba-bilistic Finite State Machine, (3) the average dynamics of theentire system represented as a single Probabilistic Finite StateMachine.

IV. ADAPTABLE MAC LAYERS

The publication by Sha et al [5] proposes the Self-AdaptingMAC Layer (SAML) to address the problem of varying per-formance targets and network conditions during the operationof low power wireless sensor networks. This MAC layer canswitch between different MAC protocols and choose the bestfit to satisfy the required performance based on the perceivedconditions. It consists of two modules: a MAC selection engineand a re-configurable MAC.

The MAC selection engine chooses the MAC to be used. Ituses a classifier with a model calibrated via supervised learn-ing that takes as inputs: the QoS priorities of the application,the traffic pattern and the interference level. This procedure isperformed by a node designated as network coordinator and

announced periodically to the other nodes. A node wantingto join the network would use a default baseline MAC thatsupports the initial formation of the network.

The Re-configurable MAC (RMA) contains several MACprotocols and can switch between them at run-time. The designof this module is driven by the need of a reliable switchingbetween the MACs, while minimizing the memory footprintand the time-overhead of the swap. The first is reduced by thesharing of components between the multiple MACs and thelatter by operating with a simple star topology.

The SAML internals are transparent to the application,the only input required being the relative importance of thethree following performance parameters: reliability, delay andenergy.

The switching procedure operates as follows: upon receptionof a MAC change request, the coordinator waits for thetransmission of all the packets buffered in the MAC layerand starts the new MAC. The successful transmission afterthe change is the confirmation of a successful transition.Otherwise, the coordinator rolls back to the previous MACand tries the transition up to 30 times before giving up.

Sha et al report the implementation and test of 3 adaptableMAC schemes: CDMA/TDMA, Receiver/Sender initiated andswitching between 5 MACs (BoX-MAC, pure TDMA, RI-MAC, adaptive TDMA and ZigBee MAC). Their proposalis tested by measuring the energy consumption and delayof key operations, such as switching the MAC in a node(respectively, 2.94 µJ and 3.5ms). Additionally, it is tested ina real-world case study, where two nodes monitor vital signsand transmit periodic traffic with occasional surges duringthree days. The study revealed that SAML saves 31.6% of theenergy consumed by each node in a pure TDMA WSN and stillachieves the QoS reliability requirement of the application.

A. Feasibility for WiseSkin

According to Sha et al, SAML is the first MAC architectureto switch MACs during run-time for low power wirelesssensor networks. The protocol does not aim at improvingthe performance by tailoring the mechanisms that are alreadyknown to have a high impact, such as the wake up scheduleor the reception signaling, in a particular use case. Insteadit takes an out-of-the-box approach: it renounces the ”one-protocol-fits-all” vision for the design of a MAC and focusesthe efforts on making the switching feasible.

As explained in section II, it is reasonable to expect thetraffic in WiseSkin to depend on the manipulation activity ofthe hand. Thus switching from an almost-no-traffic condition(hand resting) to heavy traffic in all the network (grabbinga bottle) is a probable scenario. The changes could also besudden or progressive, and impose contradictory targets to theMAC. In a low and constant traffic scenario, saving energycan have a greater importance than reducing the latency, but atraffic surge can switch the priorities and make us prefer usingas much energy as needed to serve it with low latency.

WiseSkin requires a communication stack capable of detect-ing the network conditions and of rapidly adapting to changesin the traffic. The SAML addresses this need by using the

EDIC RESEARCH PROPOSAL 6

MAC selection engine to estimate the network state and thereconfigurable MAC module to switch to a more appropriateMAC.

SAML involves a considerable latency for switching andensuring a coherent global state in the MAC. Extrapolatingfrom the graphs provided in the paper, a node requires 120 msto join the network and switch to a consistent MAC, whichalready exceeds the latency target for WiseSkin.

Moreover, the proposal was tested in a star topology, whichis the simplest conceivable option. If the SAML must beused in applications that require more complex topologies, thelatency and energy cost of the switching will become evenmore important limitations. Supporting a multi-hop networktopology and eventually dynamic routing entails the non-trivialchallenge of maintaining a consistent MAC protocol across thenetwork. A possible approach is to use compatible MACs todismiss the requirement of having a consistent layer acrossthe network before proceeding to forward the traffic, as it hasbeen done in [16].

The steps considered to build on top of the contributionof Sha et al are to explore different switching architectures,network topologies, routing schemes and consistency mech-anisms, and to define a common framework to design MACprotocols compatible with the switching approach.

V. DISCUSSION AND THESIS PLAN

We have learned from Antfolk et al [6] that the commu-nication system for WiseSkin requires a MAC and a routingprotocol capable of providing latency, reliability, energy andscalability guarantees in the presence of highly variable andunpredictable traffic. The review of the state of the art fromLi et al [3] highlighted the complexities of the WiseSkincommunications challenge and exposed good practices fortackling them, notably: understanding the performance trade-offs before choosing the resources that the design shouldleverage on and considering the synergy between the MAC androuting layers to provide performance guarantees. The workof Sha et al [5] presents the valuable alternative direction ofan adaptable MAC, which will provide flexibility to cope withthe expected traffic model.

Within this context, the work during the first year of myPhD has focused on developing a deeper understanding of theproblem of developing communication protocols for WiseSkin.This included becoming familiar with the development ofcommunication protocols for embedded systems in C andC++, simulating WSNs with OMNeT++ [17], and analyzingthe issue from three perspectives: traffic filtering, routing andMAC.

Regarding the traffic filtering, I have investigated a mecha-nism to reduce the spatially and temporally correlated trafficin a WSN that senses a pressure field and implemented itwith WiseMAC as the MAC protocol and static routing. Theworking principle is to allow the nodes to decide if they shouldreport their data based on the snooping of the neighbor’spackets. If their information is redundant they should abstainof propagating it. The simulation in OMNeT++ revealedthat the algorithm is successful in reducing the number of

generated messages and the end-to-end latency, but at theexpense of reducing the accuracy of the field measurementsand of a high energy consumption caused by snooping. Theresults also provided a quantitative notion of the dependencybetween the energy, latency and reliability, for the assumedWiseSkin traffic model.

A significant feature of the traffic filtering research has beenthe study of the communication mechanisms of the animalskin, in order to extract features that could be used in thedesign of the communication protocol. For example, the lateralinhibition is a property of the sensory systems in animalsthat allows a neuron to inhibit the transmission of signalsby the adjacent ones, thus creating a contrast in stimulationthat improves the sensory perception [18]. Implementing thismechanism in WiseSkin could reduce the traffic and, con-sequently, the energy and latency. Another example is theadaptation feature of the sensory neurons, which reduces theintensity of constant stimulus over time. This explains whywe do not actively feel an arm resting over a table during theentire time that is in this position, but we feel it during thefirst contact. Emulating this mechanism can provide furthertraffic reductions.

With respect to routing, I have developed a protocol basedon the Collection Tree Protocol (CTP) [13] for the WiseNodeplatform [19] and compatible with WiseMAC [20]. The proto-col is currently in the simulation phase of the embedded codeand will subsequently be tested in a real-world deployment.CTP is commonly used as benchmark for reliability in theWSNs literature. The aim of this project was the deeper un-derstanding of the sources of unreliability in the transmissionof packets and of the mechanisms that CTP uses to cope withthem.

Additionally, I have deployed a real WSN to perform along term experimental study of the causes of packet deliveryfailure in WiseNode based networks at the MAC level.

We will use simulations and more abstract models to aidthe protocol design and understand the trade-offs betweenthe different performance metrics. The simulation environmentwill continue to be OMNeT++ and some of the more abstractrepresentations that will be considered are the multi-levelmodeling framework [15] and analytical models (inspired in[21]).

It is worth clarifying that producing and validating generalmodels for WSNs is not a main objective of this thesis. Theimportance of modeling will be justified to the extent to whichit provides tools for taking design decisions.

As we gain a better understanding of the problem, we willbe able to take more accurate decisions during the protocoldesign. The proposed direction is based on continuing the workof the Self-Adapting MAC Layer. The design will focus on thefollowing feature improvements: a faster protocol switchingmechanism, a pool of candidate MAC protocols tailored to theWiseSkin system needs, and the dedicated support for dynamicrouting protocols. This direction is motivated by consideringthe concept of an adaptable MAC a promising approach forsystems where the traffic conditions might change abruptly.

At routing level, we will investigate the problem of sup-porting a dynamic routing protocol with an adaptable MAC in

EDIC RESEARCH PROPOSAL 7

presence of fluctuating links. At the beginning, we will focuson the issue of ensuring compatibility with the MAC layer andon ensuring a SRT latency bound for the sensor data collection.At a later stage, we will address the coexistence with a secondrouting protocol that supports the local comparison of pressurevalues to estimate the slip between two nodes.

The above road map will not be regarded as a sequentialwork plan distributed during the thesis duration. It is thoughtas an iterative process where each ”loop” should provideincremental improvements, both in the understanding of theproblem and in the quality of the solution.

This thesis aims at building on top of promising approachesthat tackle the problem of creating a MAC protocol forvarying traffic conditions (e.g. SAML). The novelty of theresearch proposal resides in delivering an adaptable MAC anda dynamic routing protocol that can provide, together, latencybounds in a WSN.

Additionally, the application of our communication systemin WiseSkin will imply addressing particular problems largelyunexplored in the WSNs domain, such as coping with themechanical bending of the transmission medium during trafficsurges and the distributed calculation of features of the sensedfield (ex. the slip over the skin derived from the readings ofdifferent nodes).

This thesis will aim to overcome the communication proto-cols challenges imposed by the use of WSNs in a new domain:smart prosthetic skins.

REFERENCES

[1] E. Biddiss, D. Beaton, and T. Chau, “Consumer design priorities forupper limb prosthetics,” vol. 2, no. 6, pp. 346–357.

[2] J. Farserotu, J. Baborowski, J.-D. Decotignie, P. Dallemagne, C. Enz,F. Sebelius, B. Rosen, C. Antfolk, G. Lundborg, A. Bjorkman, T. Kniel-ing, and P. Gulde, “Smart skin for tactile prosthetics,” in 2012 6thInternational Symposium on Medical Information and CommunicationTechnology (ISMICT), pp. 1–8.

[3] Y. Li, C. S. Chen, Y.-Q. Song, and Z. Wang, “Real-time QoS supportin wireless sensor networks: a survey,” in 7th IFAC International Con-ference on Fieldbuses & Networks in Industrial & Embedded Systems -FeT’2007.

[4] R. A. Uthra and S. V. K. Raja, “QoS routing in wireless sensor networks- a survey,” vol. 45, no. 1, pp. 9:1–9:12.

[5] M. Sha, R. Dor, G. Hackmann, C. Lu, T.-s. Kim, and T. Park, “Self-adapting MAC layer for wireless sensor networks,” in Real-Time SystemsSymposium (RTSS), 2013 IEEE 34th, pp. 192–201.

[6] C. Antfolk, V. Kopta, J. Farserotu, J.-D. Decotignie, and C. Enz, “TheWiseSkin artificial skin for tactile prosthetics: A power budget investi-gation,” in 2014 8th International Symposium on Medical Informationand Communication Technology (ISMICT), pp. 1–4.

[7] C. Antfolk, M. DAlonzo, B. Rosn, G. Lundborg, F. Sebelius, andC. Cipriani, “Sensory feedback in upper limb prosthetics,” vol. 10, no. 1,pp. 45–54.

[8] J. Kim, M. Lee, H. J. Shim, R. Ghaffari, H. R. Cho, D. Son, Y. H.Jung, M. Soh, C. Choi, S. Jung, K. Chu, D. Jeon, S.-T. Lee, J. H. Kim,S. H. Choi, T. Hyeon, and D.-H. Kim, “Stretchable silicon nanoribbonelectronics for skin prosthesis,” vol. 5.

[9] R. Dahiya, P. Mittendorfer, M. Valle, G. Cheng, and V. Lumelsky,“Directions toward effective utilization of tactile skin: A review,” vol. 13,no. 11, pp. 4121–4138.

[10] C. Rojas, V. Kopta, C. Antfolk, J.-D. Decotignie, C. Enz, andJ. Farserotu, “Wiseskin communication system: A novel approach forhigh density wireless sensor networks,” nano-Tera Annual Meeting.

[11] H. H. Ehrsson, B. Rosn, A. Stockselius, C. Ragn, P. Khler, andG. Lundborg, “Upper limb amputees can be induced to experience arubber hand as their own,” vol. 131, no. 12, pp. 3443–3452.

[12] P. Suriyachai, U. Roedig, and A. Scott, “A survey of MAC protocolsfor missioncritical applications in wireless sensor networks,” IEEECommunications Surveys Tutorials, vol. 14, no. 2, pp. 240–264, 2012.

[13] O. Gnawali, R. Fonseca, K. Jamieson, M. Kazandjieva, D. Moss, andP. Levis, “CTP: An efficient, robust, and reliable collection tree protocolfor wireless sensor networks,” vol. 10, no. 1, pp. 1–49.

[14] R. Hoes, T. Basten, C.-K. Tham, M. Geilen, and H. Corporaal, “Quality-of-service trade-off analysis for wireless sensor networks,” vol. 66, no. 3,pp. 191–208.

[15] K. Lerman, A. Martinoli, and A. Galstyan, “A review of probabilisticmacroscopic models for swarm robotic systems,” in Swarm Robotics,ser. Lecture Notes in Computer Science, E. ahin and W. M. Spears,Eds. Springer Berlin Heidelberg, 2005, no. 3342, pp. 143–152.

[16] J. Rousselot and J.-D. Decotignie, “When ultra low power meets highperformance: The wisemac high availability protocol,” in Proceedingsof the 8th ACM Conference on Embedded Networked Sensor Systems,ser. SenSys 1́0. New York, NY, USA: ACM, 2010, pp. 441–442.

[17] A. Varga et al., “The omnet++ discrete event simulation system,” inProceedings of the European Simulation Multiconference (ESM2001),vol. 9. sn, 2001, p. 185.

[18] I. University, “Lateral inhibition,” Jan. 2015. [Online]. Available:http://www.indiana.edu/p̃1013447/dictionary/lat i.htm

[19] CSEM, “WiseNode fact sheet,” Centre Suisse dElectronique etde Microtechnique, Switzerland, Technical Report, 2007. [Online].Available: http:csnej106.csem.chdetailedpdfe WiseNode4211007.pdf

[20] A. El-Hoiydi and J.-D. Decotignie, “WiseMAC: an ultra low powerMAC protocol for the downlink of infrastructure wireless sensor net-works,” in Ninth International Symposium on Computers and Commu-nications, 2004. Proceedings. ISCC 2004, vol. 1, pp. 244–251 Vol.1.

[21] K. Langendoen and A. Meier, “Analyzing MAC protocols for low data-rate applications,” vol. 7, no. 2, pp. 19:1–19:40.