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internet of things ( iot ) is a novel concept that allows a number of devices to be connected through the internet . such devices can be sensors / actuators which are able to operate and transmit data without or with minimal human intervention . iot has brought a great influence to many areas , and there have been many iot applications implemented such as healthcare , transportation , logistics , and manufacturing  @xcite . however , the development of iot is facing many challenges especially for data management  @xcite . due to the special characteristics of iot systems and services , e.g. , heterogeneous large - scale architecture , diverse and enormous data , traditional data management approaches may become intractable such that new solutions are required . recently , the concept of _ smart data pricing ( sdp ) _  @xcite has been introduced as an alterative to address network resource management issues . its major benefit is the ability to provide effective solutions from both system and economic aspects . in particular , with sdp , prices are used not only to gain profits for providers , but also to provide tools to improve system and data management . in this article , we propose to apply sdp to iot so that prices are used to incentivize sensor owners to contribute their data to iot services , improving the service quality and generating higher revenue from selling iot services to users . we first present an overview of iot including its intrinsic features , system architecture , benefits , data management , and business model . we then introduce the sdp and review some related work . moreover , we demonstrate the applications of economic models in iot by introducing a pricing scheme to optimize the sensing data buying price and iot service subscription fee for sensor owners and service users , respectively . we adopt the bundling strategy for multiple iot providers to form a coalition and offer their services as a bundle . with bundling , the profit of the providers can be improved by encouraging many users to subscribe more services . finally , we highlight a few important future research directions . though the technology development and applications of iot are tremendously growing , its definition can be diverse and fuzzy . @xcite provides one of the formal , concrete , and standardized definitions of iot : `` _ _ internet of things envisions a self - configuring , adaptive , complex network that interconnects ` things ' to the internet through the use of standard communication protocols . the interconnected things have physical or virtual representation in the digital world , sensing / actuation capability , a programmability feature and are uniquely identifiable . _ _ ''    in the iot context , things can be objects that have internet capability . the objects with unique identification can offer services in terms of data capture , communication , and actuation . thus , derived from the definition , the fundamental features of iot are as follows  @xcite :    * _ connected to the internet : _ things must be collected to the internet using wired or wireless connections . * _ uniquely : _ things are uniquely identifiable via ip addresses . * _ sensing / actuation capability : _ things are able to perform sensing / actuation tasks . * _ embedded intelligence : _ things are embedded with intelligent functions , e.g. , self - configurability . * _ interoperable communication capability : _ the iot system has a communication capability based on standard technologies .      a general architecture for iot systems can be illustrated as in figure  [ iot_architecture ] . the architecture consists of four layers .    * _ physical layer _ is composed of smart devices , e.g. , sensors and actuators , that interact and/or gather data from the physical entities , e.g. , environment and human . * _ network and communication layer _ provides data communications and networking infrastructure to transfer data collected from devices at the physical layer to the data center . * _ data center layer _ provides infrastructures to support data storage and processing to meet requirements of iot applications . * _ service layer _ is a set of software that provides services to iot users . as shown in figure  [ iot_architecture ] , the interactions among layers in the iot architecture is through data . data is first gathered at the physical layer by sensors and then transmitted to the data center through the network and communication layer . at the data center layer , the data is stored and processed to extract useful information for users in the service layer . based on the received information , the applications or users can make appropriate decisions , e.g. , sending commands to control sensors / actuators at the physical layer . @xmath0 \end{array}$ ]      with an idea to connect everything to the internet , iot has brought a variety of benefits . the major benefits are to improve system efficiency and user satisfaction , enhance flexibility , safety , and security , and finally open new business opportunity and revenue stream . because of many benefits , there are numerous applications of iot in various sectors such as education , economics , transportations , and healthcare  @xcite . we discuss one example in the following . tomtom ( www.tomtom.com ) , a well known gps manufacturer , introduces an iot service for a congestion index . the aim is to capture anonymous travel time information particularly in urban areas . data obtained from smart devices , e.g. , vehicle journey recorders , gps , and traffic cameras , will be sent to the tomtom servers through communication channels such as 3g / wifi . the data is used to extract meaningful traffic information . based on the information , the tomtom service provides travel time information and real - life driving patterns presented to the general public , industry , and policy makers showing global congestion level . thus , using the tomtom service , drivers can have more efficient and safe journeys . government agencies and authorities can make appropriate policies , rules , and regulations in controlling road traffic , reducing accidents , and constructing road infrastructure . finally , businesses can have useful information for their operations , e.g. , to open retail stores , gas stations , and repair shops at the best locations .      as shown in the iot architecture ( figure  [ iot_architecture ] ) , data is the key component , and hence data management is a main concern in iot . data management involves the following aspects .    * _ data collection : _ sensing is the first step in the data flow to obtain iot data . in the sensing process , to collect a large amount of data with high quality , a number of sensors have to be deployed which will result in high costs . sensor deployment , data gathering and preprocessing have to be optimized . alternatively , participatory sensing can be adopted . * _ data communications : _ together with a large number of devices , data communication and networking become important issues that need further analysis and optimization to meet specific requirements of iot . machine - type - communications ( mtc ) has been introduced as the solutions for cellular networks . alternatively , bluetooth low energy ( ble ) , wifi , and 6lowpan can be used for local and personal area networks . * _ data storage and processing : _ the amount of iot data is enormous which needs to be stored and processed efficiently and securely . cloud computing becomes an effective solution . * _ information trading : _ after data is processed , the useful information will be extracted to provide ( sell ) services to iot users . this is an important step in business models in terms of profitability and sustainability . market structures , incentive and pricing mechanisms have to be newly defined because of specific characteristics of iot systems , businesses , and users . the core economic benefit of iot is to generate revenues for businesses , and thus an iot business model is important . generally , a business model _ describes the rationale of how a company creates , delivers , and captures value _ there are four major components in the business model as shown in figure  [ business_model_framework ] . the _ value proposition _ of data is the core component in the business model . the value proposition specifies what to be actually delivered and the price that will be charged to the customers . the main purpose of the value proposition is for the company to demonstrate to the customers that they will gain more benefits than what they pay . accordingly , it is important in the business model to specify pricing mechanisms . to determine the price , the company needs to analyze and know the total cost incurred to operate the business and to offer iot services . the major cost is from investing in and operating _ infrastructure _ components . moreover , the company has to quantify the willingness - to - pay value of the customers . the economic and marketing techniques can be employed to understand the utility structure based on the types and segments of the customers . with the information about cost and willingness - to - pay , the company can derive an optimal price that maximizes the provider profit and user satisfaction . @xmath1 \end{array}$ ]    in the next section , we introduce and review pricing models developed for iot data management . sdp  @xcite is a new concept to enhance network performance and to support data management through using pricing incentives . for example , during a congestion period , dynamic pricing is used to defer some non - urgent users from accessing networks , improving qos performance . there are two important attributes that make sdp a suitable solution for the data management in iot . * _ flexibility : _ instead of using static pricing , e.g. , the usage - based method ( byte - counting ) that requires users to pay the same price per unit of data accessed , sdp provides the flexible and dynamic pricing mechanisms based on the demand and requirements of the users . * _ incentive : _ devices may belong to different owners who may have no interest in the joint data management . thus , the use of sdp can attract such independent device owners to participate in iot , improving the service quality .    in this section , we review related applications of sdp in iot data management . the summary of related applications is given in table  [ table_sec3_sum ] . auction - based pricing @xcite & multiple sellers ; a buyer ; and an auctioneer & the auctioneer conducts a reverse auction in which sellers submit their asking prices . the auctioneer selects the seller with the lowest asking price as the winner &    * widely applied to theoretical researches * economics : buyer s revenue maximization * system : data aggregation ; resource allocation ; and routing protocol    & nash equilibrium sealed - bid auction - based pricing @xcite & multiple buyers ; a seller ; and an auctioneer & the auctioneer conducts an auction in which buyers submit their bidding prices . the auctioneer selects the buyer with the highest bidding price as the winner &    * widely applied to theoretical researches * economics : seller s revenue maximization * system : data aggregation ; resource allocation ; and routing protocol    & competitive equilibrium cost - based pricing @xcite&a seller ( or multiple sellers ) ; and a buyer ( or multiple buyers ) & the seller calculates selling price of an item based on costs charged for the item &    * widely applied to real world market * economics : seller s profit maximization * system : data aggregation ; and packet forwarding    & optimal solution stackelberg game - based pricing @xcite & multiple sellers , i.e. , leaders ; multiple buyers , i.e. , followers ; and multiple brokers & the sellers and brokers determine their own selling prices based on profit functions &    * applied to real world market and security domain * economics : sellers and brokers profit maximization * system : relay selection    & stackelberg equilibrium      crowdsensing or participatory sensing is a concept to use independent mobile devices to collect and deliver sensing data with the aim to reduce the cost of deploying and maintaining sensor devices . for example , gigwalk ( http://www.gigwalk.com/ ) provides a marketplace for sensing tasks performed through smartphones . mobile users , who have installed the gigwalk client application , can submit their sensing data and receive rewards . however , an important issue is how to set the price that is profitable for the provider and attractive for mobile users . in  @xcite , the authors introduced a reverse auction based dynamic price ( radp ) incentive mechanism . the objective is to minimize and stabilize the incentive cost , while achieving sufficient number of participants . in this model , mobile users can sell their sensing data to the provider through their claimed bidding prices . then , the provider will select users who have the lowest bidding prices . the authors demonstrate by simulation that the incentive cost can be reduced by more than 60% compared with random selection with a fixed price . the authors in  @xcite proposed a cost - based pricing model which can minimize the cost for transmitting packets to the provider through using short - range communications , e.g. , bluetooth and wifi , with their neighbors instead of transmitting data directly to the provider . after receiving the data , the neighbors can then use the sensing data for themselves or resell it to the provider to gain revenue . to determine the selling price , each device builds a one - hop neighbor table . the device then defines the total cost of sending packets to its neighbors according to remaining energy , resource usage , and costs . the selling prices and corresponding profits are calculated as a function of the total cost , and the device will select a neighbor that has the minimal total cost . bitmesh ( https://www.bitmesh.network/ ) is one of such data forwarding services that can adopt the aforementioned pricing model . bitmess allows users to share their internet connection with peers in a local `` marketplace '' .      due to the flexibility and efficiency , cloud computing becomes a typical infrastructure to store and process a large amount of iot data collected from devices and sensors . in cloud computing , computing and storage resources can be used in an on - demand basis , reducing total cost of operation . a variety of economic models have been developed for cloud computing services to achieve the highest profits and to meet the user requirements  @xcite . for example , multiple cloud providers can cooperate and share resources to increase scalability and reliability through the federated cloud model . the strategic - proof dynamic pricing ( spdp ) scheme  @xcite was proposed to improve resource utilization . the spdp scheme is based on an auction mechanism that the payment for resource allocation is a function of the demand of users and the supply of cloud providers . such pricing scheme can be applied to practical cloud services such as zuora ( https://www.zuora.com/ ) that offer cloud resource sharing services . after data is processed , the useful information will be extracted for services to iot users . information can be treated as goods that can be sold and bought in a market . in  @xcite , the authors studied the information service pricing by formulating a hierarchical game , i.e. , a stackelberg game , among information providers , brokers , and customers . the brokers acquire information from the providers , i.e. , leaders , and sell the services to the customers , i.e. , followers . the authors also adopted the bundling strategy for selling the information services . the relationship between the information collection cost and willingness - to - pay value of the customers .    although there are some sdp schemes applied to iot systems , few works considered bundled services of multiple providers . moreover , the service quality due to variable sensors selling data to providers was ignored . thus , in the next section , we introduce a new pricing scheme for sensing data buying and iot service subscription with bundling . in this section , we introduce a smart data pricing scheme for iot services that incorporate sensing data buying and service subscription with bundling  @xcite . we first discuss the motivations and scenarios . then , we present the detail of the pricing scheme . the numerical results are presented afterwards . some works considered the bundling strategy in the smart data pricing , e.g. ,  @xcite  @xcite . additionally , multi - tier market models were proposed  @xcite and @xcite . however , the consideration of the bundling strategy in the multi - tier market model taking the unique requirements of iot services into account was not done before . we consider iot service providers who act as brokers . each provider buys sensing data from a set of sensors belonged to other owners . the provider then transfers the sensing data and processes it with the purpose to offer a value - added iot service to a set of consumers or users . the data processing and service delivery can be performed in the facility , i.e. , private cloud , of the provider or in the public cloud that the provider rents from other cloud services . figure  [ fig : servicemodel ] illustrates the iot service offered by the provider . there can be multiple providers collecting sensing data from different sets of sensors , and the providers offer the same or different services to the same set of users , i.e. , unimodal and multimodal sensing , respectively . we assume that the users need the services from different providers separately with different preferences and values . for example , the sensing data of each provider is from a different geographical area . thus , the providers do not compete each other . @xmath2 \end{array}$ ]    this iot service is inspired and is applicable to many applications and businesses . the following are some pertinent examples .    * _ placemeter : _ placemeter ( https://www.placemeter.com/ ) is a startup that allows a user to set up a video camera to capture a view from streets and cities and to stream the video data to the placemeter . the placemeter then processes it with video analytics . the users will be paid by the placemeter depending on the view and quality of the video . the video analytics can be used to extract meaningful and useful information to trace crowds and road traffic . many businesses and government agencies can benefit from the information , e.g. , for retailers to open shops and for city agencies to plan public area usage and expansion . * _ road transportation services : _ there are a few services that allow drivers to share and obtain road traffic information . some examples are google maps for mobile ( http://www.google.com/maps/about/ ) and waze ( https://www.waze.com/ ) . drivers simply allow mobile apps on their mobile phones to send related information , e.g. , moving speed , to the service provider . then , the provider translates and processes the information , and informs other drivers of the current road conditions in different areas . * _ iot search engine : _ while aforementioned examples are the services for specific purposes , recently iot search engine services have been introduced . the services provide a generic capability for users to acquire sensing data with various types . sensor owners can share , exchange , or sell their sensing data to other users . one example is thingful ( https://thingful.net/ ) that lets sensors be connected and users can browse and obtain desired sensing data . the iot search engine is able to locate , index , and make sensing data searchable .    to develop and operate the iot services economically and profitably , in the following , we introduce an effective pricing scheme .      as shown in figure  [ fig : servicemodel ] , there are three major entities in the iot service under consideration . * _ sensors _ are suppliers of sensing data . sensor owners sell sensing data to one of the providers . each sensor has a _ reservation wage _ which is a value of sensing data . in particular , the sensor ( owner ) will sell its sensing data to a provider if the provider offers to buy with a _ buying price _ greater than the reservation wage . otherwise , the sensor will not sell the data . the reservation wage can be based on the cost that incurs because of collecting and transmitting the data to the provider . * _ users _ are the consumers of iot services offered by the providers . the users can buy any iot services . each user has a _ reservation price _ which is a willingness - to - pay value of the iot service . specifically , the user will subscribe , i.e. , buy , the iot service from the provider if the provider sells the service with a _ subscription fee _ lower than the reservation price . otherwise , the user will not subscribe the service . each user has different reservation prices for different iot services . the user can also subscribe to multiple iot services simultaneously . * _ providers _ buy sensing data from a particular set of sensors , perform value - added data processing , and deliver an iot service to users . to buy sensing data from the sensors , the provider sets a buying price . likewise , to sell the iot service to the users , the provider sets a subscription fee . in addition to setting the price and fee , the providers have an option to cooperate with each other to offer iot services as a bundle . the objective of the providers is to maximize their profits . note that the reservation wage and reservation price of the sensors and users , respectively , can be determined from cost - benefit analysis and performance requirements . alternatively , the conjoint analysis that employs various marketing techniques to quantify such values can also be employed . moreover , a sensor and user can be physically co - located . for example , vehicle drivers can be data suppliers that transmit their location , traveling speed , and other information to the google map or waze . likewise , the same drivers , as consumers , can access the google map or waze services to obtain global road network conditions to determine their best driving routes . the iot service providers will maximize their profits through the pricing scheme . firstly , they have to determine the buying price offered to the sensors and the subscription fee proposed to the users . secondly , the providers decide whether the providers cooperate to offer their iot services as a bundle , the bundled subscription fee needs to be optimized . we first consider the case without bundling . the provider determines the buying price and subscription fee considering the reservation wage and reservation price of the sensor and user , respectively .    * firstly , the utility of sensor @xmath3 is determined by @xmath4 , where @xmath5 represents the buying price offered by provider @xmath6 , and @xmath7 is the reservation wage . the sensor will sell its sensing data if the utility is positive . given the buying price @xmath5 , the number of sensors @xmath8 that sell its data can be determined from @xmath9 , where @xmath10 is an indicator function that returns one if the utility of sensor @xmath3 is greater than zero , and zero otherwise . here , @xmath11 is the number of potential sensors . * secondly , the utility of user @xmath12 subscribing to provider @xmath6 is expressed as @xmath13 , where @xmath14 is the service quality , which is defined as an increasing function of the number of sensors @xmath15 participating in the iot service of provider @xmath6 , @xmath16 is the maximum reservation price , and @xmath17 is the subscription fee . we assume that the service quality varies between 0 and 1 . naturally , when there are more sensing data from more sensors , the quality of iot service tends to be better . thus , the users will appreciate more from the service . for example , if more drivers supply their driving status , it is likely that the estimated road traffic information will be more accurate . here , @xmath18 is basically a reservation price of the user . similar to the sensor , the user will subscribe the service if the utility is positive . the profit of provider @xmath6 is @xmath19 where @xmath20 is an indicator function that returns one if the utility of user @xmath12 is greater than zero , and zero otherwise . @xmath21 is the total number of potential users . thus , the optimal buying price and subscription fee are obtained from @xmath22 .      a set or a coalition of providers , denoted by @xmath23 , can cooperate to offer their iot services as a bundle with the fee @xmath24 . they can self - organize the coalitions or the third party can facilitate the formation . for example , an iot search engine can let subscribers , who pay the fee , access all sensing data such as the weather and road traffic conditions . the cooperative providers will jointly optimize the buying prices and subscription fee of the bundle . while the utility of a sensor is the same as that in the case without bundling , the utility of a user to a bundle is defined as @xmath25 . basically , the user will buy the bundle if the total reservation price of all services in the bundle is higher than the bundle subscription fee . the profit of the coalition of providers is @xmath26 similarly , the buying prices and bundle subscription fee are optimized to maximize the profit . notably , @xmath27 is the profit of every provider in coalition @xmath23 . thus , a fair profit sharing scheme is needed . the scheme must ensure that all providers gain their profit higher than that without joining a coalition , i.e. , making a bundle . from the cooperative game theory literature , the solution concepts such as a shapley value and nash bargaining solution  @xcite can be applied .      to simplify the presentation of the numerical analysis , we use the following simple setting . there are two sets of sensors , and thus two providers offering services 1 and 2 . the reservation wage of the sensors and the reservation price of users are uniformly distributed between 0 and 1 . the number of sensors in each set is 50 , and the number of users is 200 . the service quality function of a set of sensors is logarithmic , i.e. , @xmath28 , where @xmath15 is the average number of participated sensors . @xmath11 is the total number of sensors in a set . @xmath29 is a sensing quality factor to be varied . a concave quality function is reasonable in the sense that the improvement of the service quality becomes diminishing when there are more sensors . @xmath30 \end{array}$ ]    we first show the impact of the sensing data buying price and service subscription fee to a profit of one service provider . the provider buys sensing data from one set of sensors and sells a service from processing the sensing data to the users . figure  [ fig : result_profit ] shows the profit of the provider . apparently , there are an optimal sensing data buying price and service subscription fee that maximize the profit . the profit is a unimodal function . thus , numerical methods , e.g. , a simplex method , can be applied to obtain the optimal solutions . in this case , when the buying price is low , the service quality is low due to few sensors selling data , i.e. , low supply . thus , the provider can not charge a high subscription fee to the users and can not gain a high profit . if the buying price is too high , the service quality improves , but the cost increases , and the profit plunges . similarly , when the subscription fee is low , the revenue of the provider is small . however , when the subscription fee is too high , few users will purchase the service , i.e. , low demand . consequently , the revenue and profit tumble . @xmath31 \end{array}$ ]    we then consider the impact of the bundle . here , we consider a symmetric setting of both providers for simplicity . figures  [ fig : result_bundle](a ) and ( b ) show the optimal subscription fees when two providers offer their services separately and as a bundle , respectively . the locations of the markers in the figures correspond to the reservation prices of users . for selling service separately ( figure  [ fig : result_bundle](a ) ) , the users will subscribe a service if their reservation price is higher than each of subscription fee . here , the optimal sensing data buying price from both sets of sensors is 0.486 which leads to the maximum reservation price of 0.571 . the optimal service subscription fees for both providers are 0.286 . since the services are sold separately , the optimal fees are shown as vertical and horizontal straight lines for providers 1 and 2 , respectively . thus , there are four regions . users subscribe no service if none of their reservation price is higher than the fees . users subscribe one of the services if one of their reservation prices is greater than the fee . finally , users subscribe both the services if all their reservation prices are higher than the fees .    for selling service as a bundle ( figure  [ fig : result_bundle](b ) ) , the users subscribe both services if the sum of their reservation prices is higher than the subscription fee of the bundled services . here , the optimal sensing data buying price from both sets of sensors is 0.517 which results in the maximum reservation prices of 0.601 . the optimal bundled service subscription fee is 0.491 . thus , there are two regions that correspond to the users subscribing and not subscribing the bundled services .    from figures  [ fig : result_bundle](a ) and ( b ) , the profit of selling the bundled services is higher than that of selling services separately , i.e. , 38.723 versus 33.524 . this can be observed from that more users subscribe both services in figure  [ fig : result_bundle](b ) than that in figure  [ fig : result_bundle](a ) . additionally , selling services as a bundle allows providers to charge a higher subscription fee . @xmath32 \end{array}$ ]    next , we consider an asymmetric case that the sensing quality factor for the set of sensors for service 2 is varied , while that of provider 1 is fixed at one . we apply the shapley value for profit sharing between two service providers . figure  [ fig : result_sq ] shows the profits obtained by the two providers when they cooperate and do not cooperate . clearly , when they cooperate to sell their services as a bundle , the individual profits are higher than that without cooperation , i.e. , selling services separately . we can observe that the gains from the cooperation for both providers are different . this implies that the providers can tolerate the integration cost differently . here , the integration cost incurs because of the cooperation and bundling . consider the sensing quality factor at 0.7 for service 2 . the gain for provider 2 is smaller than that of provider 1 , i.e. , 0.97 versus 1.79 . thus , if the integration cost to provider 2 is more than 0.97 , then provider 2 will not want to sell its service as a bundle . likewise , if the integration cost to provider 1 is more than 1.79 , then provider 1 will not cooperate .    based on the proposed sensing data buying and service subscription with bundling , the following points can be considered for the future work .    * _ strategic sensors and users : _ sensors and users can adjust their reservation wage and reservation price , respectively , based on market conditions . in this case , an auction can be one of the suitable tools to determine the equilibrium reservation wage and reservation price . the discriminatory pricing scheme can be developed . * _ cooperation and collusion : _ to buy sensing data from sensors and to sell services to users , providers can compete or cooperate with each other . in a competitive environment , the provider will set the buying price and the subscription fee to maximize its individual profit given the strategies of other providers . a nash equilibrium solution can be adopted . alternatively , the providers can collude to maximize their profits collectively . the collusion formation and prevention in the market can be studied . * _ quality of data : _ the providers can adjust the buying price for each sensor individually to encourage it to supply high quality sensing data . in particular , the sensors can optimize data quality based on the buying price and their resource usage for collecting , processing , and transmitting data to the provider . for example , a camera as a sensor can supply higher video quality , but at the cost of more energy consumption and bandwidth usage . hence , the camera will do so only when the buying price is sufficiently high . joint pricing and performance optimization models can be developed . in this article , we have considered smart data pricing ( sdp ) for iot systems and services . we have first introduced an overview of iot including its architecture , benefits , and business models . then , we have reviewed some related work of applying sdp to iot . we have proposed a new pricing scheme for iot service providers taking into account sensing data buying and subscription with bundling . the numerical results have clearly shown that with bundling , multiple providers can form a coalition to achieve higher profit . j.  gubbi , r.  buyya , s.  marusic , and m.  palaniswami , `` internet of things ( iot ) : a vision , architectural elements , and future directions , '' _ future generation computer systems _ , vol . 1645 - 1660 , september 2013 . j.  s. lee and b.  hoh , `` sell your experiences : a market mechanism based incentive for participatory sensing , '' in _ ieee international conference on pervasive computing and communications _ , pp .  60 - 68 , 2010 . l. mei , w. li , and k. nie , `` pricing decision analysis for information services of the internet of things based on stackelberg game , '' in _ proceedings of international conference on logistics , informatics and service science ( liss ) _ , springer , pp . 1097 - 1104 , 2013 .            t. le , m. beluri , m. freda , j .- gauvreau , s. laughlin , and p. ojanen , `` on a new incentive and market based framework for multi - tier shared spectrum access systems , '' in _ proceedings of ieee international symposium on dynamic spectrum access networks ( dyspan ) _ , pp . 477 - 488 , 1 - 4 april 2014 . x. zhang and b. li , `` on the market power of network coding in p2p content distribution systems , '' _ ieee transactions on parallel and distributed systems _ , vol . 2063 - 2070 , december 2011 .
internet of things ( iot ) has emerged as a new paradigm for the future internet . in iot , enormous devices are connected to the internet and thereby being a huge data source for numerous applications . in this article , we focus on addressing data management in iot through using a smart data pricing ( sdp ) approach . with sdp , 
 data can be managed flexibly and efficiently through intelligent and adaptive incentive mechanisms . 
 moreover , it is a major source of revenue for providers and partners . 
 we propose a new pricing scheme for iot service providers to determine the sensing data buying price and iot service subscription fee offered to sensor owners and service users , respectively . 
 additionally , we adopt the bundling strategy that allows multiple providers to form a coalition and bid their services as a bundle , attracting more users and achieving higher revenue . 
 finally , we outline some important open research issues for sdp and iot . 
 iot , bundling strategy , smart data pricing , pricing mechanism , incentive .