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When IoT Keeps People in the Loop:A Path Towards a New Global Utility

Vitaly Petrov†, Konstantin Mikhaylov, Dmitri Moltchanov, Sergey Andreev, Gabor Fodor,Johan Torsner, Halim Yanikomeroglu, Markku Juntti, and Yevgeni Koucheryavy

Abstract—While the Internet of Things (IoT) has made signif-icant progress along the lines of supporting individual machine-type applications, it is only recently that the importance of peopleas an integral component of the overall IoT infrastructure hasstarted to be fully recognized. Several powerful concepts haveemerged to facilitate this vision, whether involving the humancontext whenever required or directly impacting user behaviorand decisions. As these become the stepping stones to developthe IoT into a novel people-centric utility, this paper outlinesa path to materialize this decisive transformation. We begin byreviewing the latest progress in human-aware wireless network-ing, then classify the attractive human–machine applications andsummarize the enabling IoT radio technologies. We continuewith a unique system-level performance characterization of arepresentative urban IoT scenario and quantify the benefits ofkeeping people in the loop on various levels. Our comprehensivenumerical results confirm the significant gains that have beenmade available with tighter user involvement, and also corrob-orate the development of efficient incentivization mechanisms,thereby opening the door to future commoditization of the globalpeople-centric IoT utility.

I. INTRODUCTION AND VISION

The Internet of Things (IoT) has undergone a fundamentaltransformation in recent decades: departing from the legacyradio frequency identification (RFID) technology of the 1980sand the wireless sensor networks (WSNs) of the 1990s, whichwere essentially siloed “connectivity islands” with limitedinteroperability, to its present form, which is becoming in-creasingly interconnected and heterogeneous. Today’s IoT isalready a fusion of numerous networked tools and appliances,equipped with advanced computational intelligence and richcommunication capabilities. More broadly, the principles ofcontemporary IoT overlap with and permeate many adjacentdomains, including mobile and pervasive computing, as wellas robotics and cyber-physical systems – with applicationsranging from smartphone-based social networking that reducestraffic and pollution in cities to mission-critical industrial au-tomation that monitors and actuates over factory processes [1].

Beyond legacy embedded systems with constrained ap-plicability, the emerging IoT solutions are becoming moreopen and integrated by adaptively combining sensors andactuators with actionable intelligence for automatic monitoringand control [2]. However, as a multitude of interconnectedand intelligent machines communicate with each other and

V. Petrov, D. Moltchanov, S. Andreev, and Y. Koucheryavy are withthe Laboratory of Electronics and Communications Engineering, TampereUniversity of Technology, Finland.

K. Mikhaylov and M. Juntti are with the Centre for Wireless Communica-tions, University of Oulu, Oulu, Finland.

H. Yanikomeroglu is with the Department of Systems and ComputerEngineering, Carleton University, Canada.

G. Fodor is with Ericsson Research and Wireless@KTH, Sweden.J. Torsner is with Ericsson Research, Finland.This work was supported by the Academy of Finland and by the project

TAKE-5: The 5th Evolution Take of Wireless Communication Networks,funded by Tekes. †V. Petrov is the contact author: vitaly.petrov@tut.fi

autonomously adapt to changing contexts without user involve-ment, the fact that present technology is made by humansand for humans is often overlooked [3]. Indeed, modern IoTsystems are still widely unaware of the human context andinstead consider people to be an external and unpredictableelement in their control loop. Therefore, future IoT applica-tions will need to intimately involve humans, so that peopleand machines could operate synergistically. To this end, humanintentions, actions, psychological and physiological states, andeven emotions could be detected, inferred through sensorydata, and utilized as control feedback.

This concept, which is known as human-in-the-loop (HITL),becomes a logical next step toward truly social computingand communication in smart cities [4]. HITL opens the doorto next-generation people-oriented IoT platforms, which areaware of the people context, mobility, and even mood, thushaving more efficient and intuitive manipulation. As usersincreasingly interact with such human-aware HITL systems,they may also become directly influenced by the control-loopdecisions, thereby closing the loop. In fact, people may receivecontrol input from the system in the form of suggestions andincentives (or even penalties) to diverge from their defaultbehavior. Accordingly, human behavior may be impacted ineither space (for example, the users are encouraged to moveto a less congested location) or time (for example, the usersare convinced to reduce their current data demand in case thenetwork is overloaded); this is known as the “user-in-the-loop”(UIL) [5].

With UIL, often referred to as “layer 8”, the space-timeuser traffic demand may be shaped opportunistically [6] andbetter matched with the actual resource supply from thepeople-centric wireless system. While HITL involves the userwhenever human participation is desired or required and UILextends the user’s role beyond a traffic-generating and traffic-consuming black box [7], these trends must account for thefact that people are, in essence, walking sensor networks [8].Indeed, a wide diversity of user-owned companion devices,such as mobile phones, wearables, connected vehicles, andeven drones may become an integral part of the IoT infrastruc-ture. Hence, they can augment a broad range of applications,in which human context is useful, including traffic planning,environmental monitoring, mobile social recommendation, andpublic safety, among others. Therefore, we envision that –in contrast to past concepts where the user only assists thenetwork to receive better individual service – future userequipment will truly merge with the IoT architecture to form adeep-fused human–machine system that efficiently utilizes thecomplementary nature of human and machine intelligence.

Should the IoT keep people in the loop, it has the potentialto evolve into an integrated multi-tenant system-of-systemsthat may form novel, unprecedented services [9]. For instance,

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the underlying people-centric sensor and actuator networkmay act as a utility – similar to electricity and water –creating important usable knowledge from vast amounts ofdata. Facilitated by this new global utility, different IoT devicesand networks that previously had nothing to do with eachother may discover and start talking to one another, therebyaugmenting the current talk-by-design approaches. This visionrenders future IoT as a genuinely multi-user, multi-tenant, andmulti-application platform that can be materialized in the nearfuture by means of candidate radio technologies [10], whichwe systematically review below. After describing a workingpeople-centric IoT system as a case study, we also see howthese technologies respond to the representative human userinvolvement models and quantify the resultant system-widebenefits.

II. ENVISAGED PEOPLE-CENTRIC IOT APPLICATIONS

In light of the above, a contemporary perspective on theIoT expects it to soon become “the infrastructure of the infor-mation society”1. The very capable machines, ranging fromsensors to vehicles, are already assisting humans in their dailylives. Explosive growth in the population of such connectedobjects leads to complex human–machine interactions thatbecome increasingly frequent, facilitated by the HITL and UILconcepts. As these interactions intensify, many categories ofpeople-centric IoT services emerge and are expected to bedeployed over the following years:

• Intention- and mission-aware services. These servicesprimarily reflect user’s current intention or desire andassist by enabling, for example, situation-aware smartcommuting for pedestrians, cyclists, and drivers of scoot-ers, trucks, and other vehicles. This group of applicationscan help people in a variety of use cases, from highlight-ing the nearest available parking space on a vehicle’shead-up display in urban areas to status reporting on adisplay or augmented reality (AR) glasses in challengingenvironments, such as mines, construction sites, etc.

• Location- and context-aware services. Another groupof services is formed by location- and context-awareapplications, such as those communicating alerts fromenvironmental sensors (for example, “put on/take off yourmask” when entering/leaving a polluted area). Many moreof these services are envisioned to be deployed in thecoming years, such as identifying slippery floors and lowceilings, notifying about forgotten trash when a user isabout to leave the house, and many other examples.

• Condition- and mood-aware services. A deeper level ofIoT penetration into people’s lives can be achieved byintegrating city/area infrastructure with personal medicaland wellness devices. For instance, dietary restrictionscould be applied on a menu when ordering food or asquad leader may be advised to give a break to a workerwhose blood pressure has recently gone up.

Summarizing the above examples of services, we note thatdepending on the environment the set of requirements andchallenges to implement a particular application may vary

1ITU, “Internet of Things Global Standards Initiative,” tech. report, 2015.

Fig. 1. Consumer and industrial contexts of people-centric IoT applications.

considerably. To further offer a challenges-based grouping, wepropose to differentiate between two major contexts: consumerand industrial (see Fig. 1). The former is characterized by thepresence of numerous devices that are heterogeneous in termsof their communication means and ownership. Therefore, themajor challenge in this context is to provide sufficient scala-bility of the deployed connectivity solution. On the contrary,the latter context is more challenging in terms of maintainingcommunication reliability due to more difficult propagationenvironments. At the same time, the system operator has morecontrol over device population in such areas.

We continue by addressing how people-centric IoT applica-tions are to be engineered, that is, which radio technologiesneed to be employed in particular scenarios and how to ensuretheir suitability for the target operating conditions.

III. REVIEW OF WIRELESS CONNECTIVITY OPTIONS

Inspired by the above use cases, we review the contempo-rary IoT radio access technologies (RATs) and analyze themthrough the prism of their applicability for HITL applications.Fig. 2 brings together the major characteristics of the consid-ered RATs.

1) Current technology diversity: The demand for develop-ing RATs that focus specifically on the needs of machineswas commonly understood in the mid to late 2000s. Thelow power consumption, affordable cost, high communicationrange, and capability to handle massive deployments of infre-quently transmitting devices became the major requirementsfor these new solutions, which can be collectively referred toas Low-Power Wide-Area Networks (LPWANs). Developmentof such technologies progressed in parallel within leadingstandardization bodies, including IEEE, ETSI, and 3GPP.

This resulted in the sheer diversity of today’s LPWANoptions, which comprise a number of standardized solutions.Across this diversity, the paths taken by the technology devel-opers differ substantially. To offer illustrative examples, weconsider two emerging LPWAN solutions, namely, SIGFOXand LoRaWAN [11]. Both technologies operate in the sub-GHz license-exempt industrial, scientific, and medical (ISM)frequency bands and employ ALOHA-based channel accesswith frequency hopping, which places them under severe duty

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Features: 1) 8 MCSs2) Multi-channel ALOHA MAC

Band: SubGHz ISM Carrier: 433/868 MHz

Devices in a cell:up to millions

Rate: 293-50 000 bpsuplink/downlink

Tx power: up to 20 dBmLink budget: 117-156 dBRange: over 30 km

Features: 1) 8 MCSs2) Multi-channel ALOHA MAC

Chipset price: 1-5 EUR

LoRaWAN

€

Band: licensed UHFCarrier: 0.7-2.1 GHz

Devices in a cell:up to millions

Rate: 310-250K bps uplink440-226 700 bps downlink

Tx power: up to 23 dBmLink budget: < 164 dBRange: over 30 km

Features: 1) Over 7 MCSs2) OFDM / SC-FDMA

Chipset price: 2-5 EUR

NB-IoT

Band: SubGHz ISM Carrier: 868 MHz

Devices in a cell:up to millions

Rate: 100 bps uplink600 bps downlink

Tx power: 14 dBm Link budget: 156 dBRange: over 30 km

Features: 1) Single MCS2) Multi-channel ALOHA MAC

Chipset price: 2-5 EUR€

SIGFOX

Band: SubGHz ISM Carrier: 868 MHz

Devices in a cell:up to 8192

Rate: 150-7800* kbps*Single spatial stream

Tx power: up to 14 dBm Link budget: 72-115 dBRange: up to 1 km

Features: 1) Over 30 MCSs2) OFDM

Chipset price: 3-8 EUR

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Fig. 2. Key characteristics of candidate radio technologies for advanced human–machine interaction.

cycle restrictions. Topologically, both alternatives adhere to acellular-like structure.

The SIGFOX solution operates with ultra-narrow band(UNB) signals: 100 Hz using Binary Phase-Shift Keying(BPSK) modulation and 600 Hz using Gaussian FrequencyShift Keying (GFSK) modulation for uplink and downlink,respectively. Some limitations of this technology are harshrestrictions on the application payload of a radio frame (i.e.,12 bytes at most), the single modulation and coding scheme(MCS), and the limitation on the number of packets thatcan be sent to and from a device per day to 4 and 140,respectively. Today’s SIGFOX installation base exceeds 200thousand devices2.

In contrast to SIGFOX, LoRaWAN supports multiple MCSs.The mandatory MCSs are based on the Semtech’s proprietaryLoRa Spread Spectrum (SS) modulation derived from theChirp SS modulation. The number of chirps to carry a singlebit as well as the bandwidth of the channel that affects theduration of a single chirp can be adjusted to trade the on-airtime for the transmission range. There are currently severalcommercial deployments of LoRaWAN with the overall num-ber of devices in excess of 30 thousand3.

2) Machines talk Wi-Fi: The path followed by the IEEE802.11 community is substantially different. The work onIEEE 802.11ah standard (also known as Wi-Fi HaLow) hasapproached its final stage and early chipsets implementingthis technology are already announced. The solution targets the“high-end” devices with demanding performance requirementsin terms of throughput and latency. To this end, 802.11ah deliv-ers a non-backward compatible communication technology inthe sub-1 GHz (S1G) ISM band, which is based on orthogonalfrequency division multiplexing (OFDM) and features severaldozens of different channel bandwidths as well as modulationand coding options. Wi-Fi HaLow does not have any largecommercial installations yet, as the technology has recentlyentered the testing phase4.

2BusinessWire, “Verisure Securitas Direct Connects 200,000 Alarm Sys-tems to the SIGFOX Internet of Things Network in France,” February 2016.

3Internet of Business, “Actility launches LoRaWAN networks in SaudiArabia and Tunisia,” May 2017.

4Newracom, “Newracom Successfully Communicates with Wi-Fi HaLow,IEEE 802.11ah at a Distance Greater than 1 km,” June 2016.

One of the intrigues that remain today is how close therelationship between the HaLow and the conventional Wi-Fisystems will be. That is, whether HaLow continues on its ownor be merged with the traditional Wi-Fi to form dual-mode de-vices similar to Bluetooth Smart: for example, dual technologyenabled access points or Wi-Fi HaLow access points combinedwith a Wi-Fi client to be used in a smartphone.

3) 3GPP goes machine: Machine-type communication re-ceived considerable attention in 3GPP. Back in the early 2010s,3GPP focused efforts on outlining the technology named LTE-MTC or LTE-M. Addressing the need for reduced cost andenergy consumption while increasing the coverage range andthe number of served devices per cell, the said technology hasmade so far several decisive steps, with a major emphasis onthe recently standardized narrowband IoT (NB-IoT) solution.

In September 2015, following the recent activities on thecellular IoT condensed in the TR 45.820 document, thework on NB-IoT has officially commenced. The new Cat.NB1 devices can be integrated into today’s communicationnetworks and enable UEs with about 10% complexity of thatfor Cat. 1. To achieve this goal, the bandwidth is reduceddown to 180 kHz, OFDMA with 15 kHz subcarrier spacing forthe downlink, FDMA with 15 kHz subcarrier spacing and SC-FDMA with either 3.75 or 15 kHz between the subcarriers inthe uplink. NB-IoT can offer three deployment options: stan-dalone, in-band on the LTE carrier, or in the LTE guard band.The standardization process behind NB-IoT was completed inJune 2016 and the solution was included into LTE Release 13.Presently, NB-IoT features multiple trial implementations andis subject to ambitious plans to deploy over 20 commercialnetworks by the end of 20175.

IV. DEVELOPED EVALUATION METHODOLOGY

With the aim to characterize the tentative performance gainsof various user involvement mechanisms, we concentrate on arepresentative urban use case that may correspond to severalpractical IoT applications (see Fig. 1): smart commuting,context-aware alerts, personalized advertisements, etc. Thefollowing text summarizes the considered deployment and

5GSA, “As NB-IoT nears completion, GSA predicts 20 commercial net-works by late 2017,” May 2017.

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Fig. 3. Part of our representative urban IoT scenario: 300 m × 300 m out of1 km × 1 km area is displayed.

network topology, the proposed user involvement strategies,and the details of our conducted simulation study.

1) Deployment parameters: In this work, we focus on atypical urban scenario over a 1 km2 Manhattan grid deploy-ment (see Fig. 3). A total of 100 city blocks are modeled. Weconsider two types of connected machines: stationary devicesthat represent smart road signs, environmental sensors, etc.,and mobile wearable machines that offer healthcare, fitness,and well-being functionality. Following Google StreetViewdata on the density of road signs and pedestrians on a weeklybasis in Manhattan, New York City, USA6, the number ofconnected machines is set to its minimum feasible value of20 pre-deployed devices per block and 30 wearable devicesper block. This leads to 2,000 stationary and 3,000 movingmachines across the entire simulation scenario. All of themodeled devices are deployed uniformly over the sidewalks.

We also model vehicles that participate in intense downtowntraffic, where cars are driving along the streets with theconstant speed of 30 km/h. The random inter-vehicle distancefollows an exponential distribution with the mean of 3 m. Atthe intersections, we adopt the Manhattan mobility pattern:vehicles continue in the same direction with the probabilityof 0.5, while the chances to make a left or right turn areequal to 0.25. Pedestrians carrying wearable machines followa similar mobility pattern with the speed of 5 km/h. To elim-inate border effects and maintain uniform density of mobileobjects (namely, moving machines and assisting vehicles), awraparound mechanism is implemented, such that any mobiledevice leaving the area of interest on a side immediatelyappears on the opposite side of the map.

2) Simulation details: The reported performance assess-ment has been conducted with our custom-made system-levelsimulator, named WINTERsim7, which has been extensivelyutilized recently for studying various IoT scenarios [12]. Inthe present study, we focus primarily on the uplink IoT trafficby modeling connectivity between a number of machines andthe base station. To fairly compare the behavior of all thefour considered RATs (i.e., to avoid overloading SIGFOX), the

6Particularly, Midtown East between 2nd and 3rd Avenue as well as 42ndand 57th Street is taken as an example.

7WINTERsim system-level simulator: http://winter-group.net/download/

TABLE ISIMULATION SETUP AND PARAMETERS

Parameter ValueMessage payload (stationary machine) 10 BMessage payload (moving machine) 100 BInter-arrival time (stationary machine) 5 sInter-arrival time (moving machine) 60 sCity block size 80 mStreet width 25 mScenario size (100 blocks) 1050 m × 1050 mTotal number of stationary machines 2,000 / scenarioTotal number of moving machines 3,000 / scenarioBase station height 10 mCar roof height 1.5 mDeployment height of stationary machines Uniform in [0;10) mDeployment height of moving machines 1.5 mNumber of vehicles 1,000 / scenarioNumber of pedestrians 3,000 / scenarioSpeed of vehicles 30 km/hSpeed of pedestrians 5 km/hAverage inter-vehicle distance 3 mInter-vehicle distance distribution ExponentialMobility pattern of vehicles and pedestrians “Manhattan”Association rule Max received power

data transmissions are assumed to be regular and infrequent.More specifically, stationary machines communicate 10 B mes-sages every 5 s, while moving devices send a 100 B updateevery minute, which corresponds to the typical sensing-basedapplications (e.g., assessing user’s physical condition with amedical sensor).

From the connectivity perspective, machines communicatewith their serving base station by default (termed baselineconnectivity). Alternatively, if a better signal level is available,the devices may also connect to one of the femtocell relaystations deployed on the mobile vehicles across the trackingarea (termed assisted connectivity). To characterize the effectsof involving user-owned relaying cars for the “average” IoTdevice, “median-quality” connections to the base station are ofparticular interest. Therefore, in our scenario the base stationis located at a certain distance from the center of the areato ensure the baseline average SINR level of 10 dB. As theproperties of the considered LPWAN RATs vary significantly,four different simulation rounds have been completed, withthe distances to the base station ranging from 520 m for Wi-FiHaLow and up to 12 km for LoRaWAN.

Certain additional assumptions have been made to im-plement the LPWAN technologies of interest. Particularly,the hard message limitation for SIGFOX was relaxed. Then,LoRaWAN was restricted to operate exclusively over the868 MHz band. The bandwidth of Wi-Fi HaLow, in its turn,was set to 1 MHz by disregarding the 2 MHz and widerbandwidth options that offer higher data rates for the cost ofreduced coverage. Finally, only 7 key MCSs were supportedfor NB-IoT. The remaining radio technology related parame-ters were adopted from Fig. 2, while other important settingsare summarized in Table I.

In our subsequent evaluation, we focus on the two keymetrics: (i) SINR at the receiver and (ii) energy efficiencyof machines, which is defined as the amount of data that hasbeen reliably delivered from the IoT device to the base stationin relation to the total energy consumed by the machine’s radiointerface.

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Fig. 4. Average SINR values with various user involvement types.

Fig. 5. Energy-efficiency (EE) gains with various user involvement types.

3) Degrees of user involvement: As relaying vehicles in ourIoT scenario are not owned by the operator but by the privateusers, the assisted connectivity option requires certain levelsof user involvement. In this article, we study two differenttypes of such engagement in the characteristic urban IoTdeployment:

• User involvement Type 1. In this case, some of the drivingvehicles share their communication capabilities and, if in closeproximity, can opportunistically forward the IoT traffic fromthe machines to the application server. The deployed machinescompare the connection quality to the base station with thatto the nearest vehicle that is willing to assist, and transmittheir subsequent update over a better channel. In particular,the signal strength of the received beacon has been consideredhere as the selection criterion. In its turn, the assisting vehiclerelays thus received IoT data to the application server over itson-board communication equipment.

• User involvement Type 2. As the user involvement Type1 does not affect the vehicle mobility patterns, it can onlyoffer opportunistic gains. On the contrary, the user involvementType 2 suggests the car owner to temporarily park the vehicleclose to the cluster of machines suffering from inadequateconnection quality. For instance, the car owner may be offeredas a reward a discounted parking permit in the said location (orfree charging time in case of an electric vehicle). In this case,conforming vehicles are placed next to the centers of deviceclusters and start continuously forwarding the IoT data fromthe machines to the application server. Accordingly, a linkwith a better quality can be made available to the machinesfor longer intervals of time. However, the user involvementType 2 may require humans to deviate from their intendedmobility patterns and the corresponding sources of motivationshould thus be provided.

The achievable performance gains in terms of machine’s en-ergy efficiency for both types of user involvement are reported

in subsection V-A, while a discussion on the nature and theorigins of user involvement is offered in subsection V-B.

V. BENEFITS AND NATURE OF USER INVOLVEMENT

A. Achievable Performance Gains

To ease further exposition, we only account for the pro-portion of data transmitted by the machines. To this aim,we reasonably assume that the wireless connections fromthe privately-owned vehicles (which are neither battery- norpower-constrained) to the base station are reliable enough toguarantee the delivery of the relayed IoT data.

First, Fig. 4 reports on the average levels of SINR atthe receiver (either the base station or the assisting vehicle,depending on the machine’s current connectivity option).We learn that for all the IoT technologies in question theconsidered user involvement options notably outperform thebaseline alternative. In particular, noticeable SINR gains areachieved already with a few assisting vehicles. The SINRgains for the SIGFOX solution are the highest, while theother three LPWAN protocols behave similarly as the degreeof human assistance increases. The explanation behind theseresults is in the fact that SIGFOX operates in a full-powerregime with a single data rate, while other RATs allow themachines to dynamically select their transmit power and MCS.The results for the user involvement Type 2 are, on average,several dB higher than the corresponding numbers for the userinvolvement Type 1, which confirms that careful positioning ofassisting nodes is preferred over their opportunistic placement.

Further, Fig. 5 illustrates the impact of the studied userinvolvement types on the average energy efficiency of thecommunicating machines. This parameter offers insights intohow far the battery life could be extended and, consequently,how much the operating costs could be reduced. In the figure,we first observe a marginal impact of user involvement on the

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energy efficiency of SIGFOX. Albeit substantial improvementsin the SINR values are confirmed by Fig. 4, the single transmitpower level in SIGFOX limits the potential benefits of theassisting vehicles. The observed gain of only several percentis due to a slightly increased level of reliability.

On the contrary, the alternative three RATs demonstrateconsiderable energy efficiency improvements (e.g., up to 4times higher energy efficiency for NB-IoT with user involve-ment Type 2). The observed growth is not only due to betterchannel conditions (which are similar for the said technologiesin the same scenario), but also due to the flexibility of the IoTRATs themselves (specifically, more freedom in the MCS andtransmit power selection). In summary, the considered ways ofuser involvement may lead to notable improvements in the IoTservice reliability (due to higher SINR) as well as in the energyefficiency of networked machines. Hence, people in the loopassist the operator in reducing both the capital investmentsand the operating costs. We now discuss several approachesto materializing these tentative gains.

B. User Incentivization Options

A crucial component of the considered system operation isconditional on user’s willingness to share own resources whilehelping improve the IoT network performance. Even thoughthe concept of user involvement for resource provisioning hasbeen discussed for years in the context of various mobilesystems, these mechanisms have not been implemented widelyas of yet. This is in part owing to conservative human naturethat resists new models of service provisioning, especiallywhen the process requires involving own resources withoutimmediately perceived benefits.

We expect that appropriate user incentivization mechanismsneed to be natively integrated into the envisioned IoT in-frastructure. In systems with regular topologies and centralcontrol – that emerge primarily in the industrial context – therealways remains an opportunity to enable resource sharing bydesign. On the contrary, strictly enforcing resource sharing inconsumer scenarios may lead to customer dissatisfaction andtherefore clever incentivization mechanisms are required.

To develop appropriate incentivization schemes for the con-sumer context, one has to mediate between dissimilar interestsof at least five major stakeholders: (i) owners of the pre-deployed machines; (ii) owners of the wearable machines;(iii) owners of the assisting vehicles; (iv) car manufacturers,and (v) system operators. Furthermore, in order to deploythe discussed mechanisms in practice, the operators need tocarefully “moderate the dialog” between all of these parties,where the most non-trivial aspect is to engage the owners ofvehicles.

In order to make it happen, a service provider has to offer(i) a type of compensation for the car owners and (ii) acorresponding billing mechanism. For the former, there is a setof options including those directly related to the operator–userrelationships, such as decreased monthly rates and/or extendedsubscription periods, as well as priority in service, guaranteedrate during congestion, etc.

Further, a billing mechanism is a necessary component ofthe incentive-driven services. The set of requirements im-

posed on its choice includes robustness to a loss of controlconnection, high levels of security during authorization andencryption of all the exchanged billing-specific information,operational accuracy, as well as simplicity. There are two fun-damental paths to implementing an efficient billing algorithm.

In a fully centralized system, the operators become re-sponsible for all the phases, such as collection, storage, andinterpretation of information on the individual contributionsby the users. To alleviate the cases of false notification bymalicious participants, each session has to be authorized anddata needs to be collected from both endpoints. In caseof a multi-tenant deployment, a dedicated entity needs tobe empowered to account, store, and distribute the rewardscollected by the stakeholders [13].

An alternative to the above is a network-assisted decentral-ized billing solution [14]. There are numerous practical con-texts, where decentralized incentivization mechanisms havebeen deployed successfully. A famous example is peer-to-peer file sharing and streaming services. There, reciprocity-based mechanisms where the peers maintain and share theinformation about other peers’ contributions remain a popularmethod. In such schemes, network assistance is still requiredfor authentication purposes, but the actual billing process couldbe made decentralized.

VI. CONCLUSIONS

The unprecedented proliferation of the interconnected au-tonomous machines drastically increases the intensity and thedepth of human–machine interactions. On the one hand, thisintroduces a wide range of novel challenges with respect tohow the individual procedures, technologies, as well as theoverall working services and applications should be engineered– mindful of the unique capabilities and limitations of humansand machines alike. On the other hand, this brings alongan excellent opportunity to jointly engage both sides to takeadvantage of rich mutual collaboration and shield any weak-nesses with each other’s strengths. In this article, we haveconfirmed that this remains valid for people as well as forconnected machines.

Our novel analysis of an illustrative consumer IoT scenariofor different user involvement levels and over four perspectiveradio technologies demonstrates that even moderate humanassistance makes a decisive difference and unlocks signif-icant energy savings for networked machines. Importantly,our offered numerical results suggest that while each of theaddressed wireless solutions does benefit from keeping peoplein the loop, the actual gains for every system profile dependon the flexibility of the underlying access protocol. At thesame time, the costs associated with involving the humancontext may include certain incentivization-related expendi-tures. These may vary a lot in the envisaged heterogeneous,multi-tenant deployments, which calls for careful planning ofuser engagement in future people-centric IoT infrastructure.Ultimately, the rapidly maturing human-aware IoT ecosystemmay become a new global utility, thus “disappearing” in the“fabric of everyday life” [15], while enabling a plethora ofnext-generation applications and services.

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ACKNOWLEDGMENT

The authors are particularly grateful to Yngve Selen (Wire-less Access Networks Department, Ericsson Research) for theinsightful comments that allowed to improve this paper.

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