Sensing Services in Cloud-Centric Internet of Things:A Survey,taxonomy and challenges Burak Kantarci,Senior Member,IEEE,and Hussein T.Mouftah,Fellow,IEEE
Abstract—Virtually interconnected objects with unique identi-?ers and computing,communication and sensing functionalities form the Internet of Things(IoT)architecture.In the cloud-centric IoT concept,IoT objects can provide sensing services based on a cloud-inspired business model.Smart mobile devices have a great potential to improve the performance of IoT applications by enabling access to their built-in sensor data through cloud servers based on the pay-as-you-go fashion.Such an architecture requires effective sensing service provider search techniques accompanied with effective recruitment algorithms for sensing service providers.Moreover,reliability and trustworthi-ness of sensing service provisioning is a crucial challenge.This paper provides a brief survey of the state of the art in sensing services over cloud-centric IoT,and presents recent research that addresses the challenges that are mentioned above.Moreover,the paper aims at de?ning a taxonomy of the surveyed schemes while reporting open issues,existing challenges and possible research directions.
Index Terms—Cloud computing,Cloud-centric IoT,Internet of Things,smart phone sensing,Sensing as a Service
I.I NTRODUCTION
The Internet of Things architecture interconnects billions of objects that are uniquely identi?able and that have communica-tions,computing and sensing functionalities[1].As shown in Fig.1,the cloud-centric IoT paradigm enables offering sensing resources and instruments as a service through cloud platforms based on the pay as you go fashion[2].
Cloud-centric IoT can leverage the ef?ciency of several applications including but not limited to pervasive healthcare, future transportation systems,smart city,and public safety which is a featured application area of the smart city.In a pervasive healthcare application,the cloud platform can enable ef?cient management of body area sensors[3]whereas cloud-based processing of metering data of smart homes can lead to advanced pro?ling and decisions on energy consumption[4]. Furthermore,smart city denotes inter-connected objects(e.g., sensors,RFID tags and so on)that control,manage and reg-ulate everyday life in an intelligent manner[5],and research reports that distributed cloud services can help manage and control IoT objects deployed in a smart city environment[6], [7].As a featured component of the smart cities,intelligent transportation systems can be enhanced by interconnection of in-vehicle smart phones,roadside sensors and/or cameras where sensing data is collected,aggregated and analyzed in
a cloud platform for regular reports or emergency alerts[8],
[9].As another featured component of smart city,public
B.Kantarci is with the Department of Electrical and Computer En-gineering at Clarkson University,Potsdam,NY,USA,13699.email: bkantarc@https://www.doczj.com/doc/968947058.html,.
H.T.Mouftah is with the School of Electrical Engineering and Computer Science of the University of Ottawa,Ottawa,ON,K1N6N5,Canada.e-mail: mouftah@site.uottawa.ca safety management can take advantage of cloud-centric IoT architecture[10],[11].
The cloud platform in a cloud-centric IoT architecture provides storage resources for aggregated sensing data,as well as computing resources for data analytics and data mining for information retrieval and knowledge discovery on sensing data received via IoT objects,namely the built-in sensors of mobile smart devices[12].Related work reports that built-in sensors of smart mobile devices can be utilized more effectively if they are integrated into the IoT architecture,and furthermore,integration of the smart device sensors introduce tremendous energy savings and reduction in communication overhead when compared to the conventional wireless sensor network(WSN)-based data collection and processing[13], [14].To the best of our knowledge,Sheng et al.introduced this cloud-inspired service model for the?rst time under the name Sensing as a Service(S2aaS)[15].
An aggregation framework for WSNs to provide sensing and actuation clouds as a service is an important challenge to transform the existing sensing infrastructure.Furthermore, since sensing is provided as a service over the cloud,sensors need to be virtualized to enable access by the users who need to utilize the physical sensor hardware in an isolated manner so that other users can utilize the same sensing hardware concurrently without any interference.In resource constrained environments,ef?cient communication protocols are emergent to enable data exchange by achieving feasible data rates. Furthermore,participatory sensing is a major application in cloud-centric IoT,and it introduces several challenges some of which can be listed as reliability,trustworthiness,user incentives and effective service provider recruitment.This paper presents a survey on the state of the art in sensing services in cloud-centric IoT along with open issues and future directions.Based on the surveyed content,the paper aims at proposing a taxonomy of existing studies with respect to several criteria such as context-awareness,energy ef?ciency, mobility-awareness,reliability and trustworthiness.
The paper is organized as follows.Section II presents the S2aaS concept while Section III introduces the current state of the art.Section IV introduces open issues and challenges along with a classi?cation of surveyed schemes.Finally,Section V summarizes the paper with concluding remarks.
II.S ENSING AS A S ERVICE:F UNDAMENTALS AND
A PPLICATIONS
Smart mobile devices have a great potential to accelerate the cloud-centric IoT applications by providing their built-in sensors as a service based on the pay-as-you-go fashion [15].Perera et al.de?ne S2aaS as a four-layer business model consisting of the sensing instruments,data publishers,cloud platform and the end-users who request and receive service
Fig.1.An overview of the cloud-centric IoT architecture[12] [16].Fig.2illustrates a sample S2aaS architecture which conforms with this four-layer business model,and uses social network services as the data publishers[11].
In order to exchange sensor and actuator data over the Internet,a Service Oriented Architecture(SOA)which utilizes Constrained Application Protocol(CoAP)has been proposed [13].CoAP is an application layer software protocol that has been developed for communication between resource-constrained environments,and it is intended for Machine-To-Machine communication applications in the IoT[17].CoAP is con?gured to run on the User Datagram Protocol(UDP) layer whereas the SOA monitors,con?gures and visualizes the sensor and actuator data via a web interface.Moreover, collaboration between services running on various sensors is managed by the SOA by recon?guring the UDP parameters. Related research reports that coupling of SOA ad CoAP can improve throughput between IoT sensors and the cloud servers up to several KHz[17].
One of the emerging approaches that would accelerate S2aaS is reported to be the integration of social networks into the sensing services in the cloud-centric IoT[15].One of the reasons of this hypothesis is that mobile device users follow community behavior and move in communities[18]. Furthermore social network services can be utilized to collect and publish sensing information[19].Social networks are expected to connect services and applications over the cloud in the close future[20]as introduced under the name,Internet of Social Things by related work[21],[22].As a S2aaS and social network integration example,CenceMe application uses built-in sensors in the smart phones to sense users body position and publish the information on their social networks[23]. III.E XISTING METHODOLOGIES TO ADDRESS SENSING AS
A SERVICE CHALLENGES IN CLOUD-CENTRIC I O T Emergent issues that need to be addressed in S2aaS are reported by the authors in[15].This paper takes a closer
look Fig.2.Four layers of S2aaS architecture
at the current solutions to those issues.
A.GPS-less mobile phone sensing and energy ef?ciency Sensing scheduling is handled based on a?ve-way hand-shake mechanism.The?ve steps are periodic location update messages sent by the sensing provider,transmission of the requested task to the sensing provider,sensing task con?rma-tion by the provider,sensing task scheduling by the server,and sensed data upload to the sensing server.Due to the location updates,sensing service providers consume signi?cant battery power by turning on their GPSs.On the other hand,location information obtained by WiFi or cellular signalling introduces precision problems.Therefore,in order to assist GPS-less sensing,probabilistic coverage models and advanced mobility prediction techniques are needed.
Energy-constrained Maximum Coverage Sensing Schedul-ing(EMCSS)and Fair Maximum Coverage Sensing Schedul-ing(FMCSS)are two recently proposed approaches that aim to address GPS-less sensing[24].
EMCSS aims at maximum coverage under battery limita-tions,and the objective function aims at maximum number of sensors selected out of the sensors set,S that will lead to maximum coverage of the destination regions.The size of the sensors set is ensured to be not greater than the available sensing budget(i.e.,total available battery power allocated for S2aaS).A greedy heuristic for EMCSS initiates an empty set of virtual sensors and adds virtual sensors based on their contribution to the improvement of the objective function. EMCSS keeps adding virtual sensors unless the sensing budget (i.e.,total battery drain)is exceeded.
The objective of FMCSS is formulated as a discrete math-ematics problem with the target of maximum submodular set cover on a matroid[25].FMCSS has been shown to address the trade-off between coverage and fairness among sensing service providers.The authors have re-formulated the objective
Fig.3.An ilustration of the Context-Aware Sensor Search and Selection and Ranking Model(CASSARAM)[28]
function by aiming at?nding the largest set covering a matroid.Similar to the greedy heuristic of EMCSS,the greedy heuristic of FMCSS adds virtual sensors incrementally to the set of sensing service providers based on their incremental contribution to the coverage over the matroid.
Correlation of sensing tasks can also help improve energy performance of GPS-less sensing.Furthermore,the authors in[26]have studied energy ef?cient data uploading problem for crowdsensing environments by classifying sensing tasks as delay tolerant and delay intolerant.The authors in[27]have proposed piggyback crowdsensing in order to exploit the times when smart phone users place phone calls or use smart phone apps so that energy required for sensing is reduced.
B.Effective sensor search techniques
Performance of sensing service is directly related to the set of sensing service providers.Moreover,the size of the sensing service provider candidates introduces the scalability problem. Reliability,sensing accuracy,residual battery,battery usage ef?ciency,current and location are the selection criteria that are used most often in selection of sensing service providers.In [28],Context-Aware Sensor Search and Selection and Ranking Model(CASSARAM)has been proposed.Fig.3illustrates the following four steps of CASSARAM:1)Selection of the requirements:Number of sensing service providers requested and the end user requirements are received as the input, and a query based on the user requirements is formed;2) Searching eligible sensing service providers:The user query runs on an ontology of sensor descriptions,and the output is a list of eligible sensing service providers that can handle the point-based(i.e.,non-negotiable)requirements;3)Indexing the devices based on proximity-based(i.e.,negotiable)user requirements:User requests are prioritized,and4)Ranking the providers:Sensing service providers are sorted based on the likelihood scores obtained through weighted user priorities and proximity-based requirements.
C.Effective user incentives
Effective data collection via S2aaS also calls for effective incentive mechanisms ensuring user privacy and trustworthi-ness of the crowdsensed data.In[29],the authors classify the incentives as user-centric or https://www.doczj.com/doc/968947058.html,er centric incentives apply to the case where sensing service providers make their own sensing plans and send their status to the cloud platform in order to join participatory sensing.On the other hand,platform-centric incentives apply to the case where the cloud platform recruits the sensing service providers and make sensing plans for them in a centralized manner.In any case, incentivized users denote sensing service providers that need to be compensated for providing their built-in sensing resources. The authors in[29]have proposed a game theory-based platform-centric incentive,and two user-centric incentives based on local search-based auction.Auction-based user-centric incentives introduce the risk of untruthful bidding of the users who aim at increasing their income by reporting their sensing costs higher than actual.MSensing auction has been proposed to overcome truthfulness problem and maximize end user’s utility.MSensing consists of a two-step auction procedure where in the?rst step,winners are selected based on their marginal contribution to the total value of the sensing tasks,and in the second step,sensing service providers are compensated based on their sensing costs,as well as the value of the tasks that are collaboratively sensed.Therefore,it can be adopted by a cloud-centric IoT framework where S2aaS forms the front-end.
Recruitment of sensing service providers by incentivizing the mobile smart device users introduce trustworthiness prob-lem.This problem has been studied in the context of user reputation-awareness and accurate sensing[30],[31],and user privacy and data integrity[32].
D.S2aaS over social networks
Crowdsensing over social networks is still in its infancy although there are a few proposals bridging the two paradigms. The authors in[33]have presented a social network ar-chitecture to ease context-aware mobile S2aaS applications. In[34],CrowdHelp which is a bridging system between social networks and S2aaS has been introduced for mobile triaging of patients.In[35]another application of disaster management via crowdsensed data through social networks has been presented whereas cooperative S2aaS via social networks has been proposed in[36].
Two mobile social network-aware frameworks,namely Mo-bile Social Network-Aware Crowdsourcing(MSNAC)and Trustworthy and Mobile Social Network-Aware Crowdsourc-ing(T-MSNAC)for a cloud centric IoT architecture have been proposed in[37].In the corresponding schemes,every sensing service provider is considered to be a node in a mobile social network topology where the cloud platform does not have access to the mobile interaction values between social network nodes.The cloud platform aims at predicting the interaction values based on co-location,proximity and con-nectedness over the social network topology.Future locations of the sensing service providers are forecasted based on the estimated interaction matrix of the social network.Estimated future locations help the cloud platform recruit sensing service providers that would provide valuable sensing data for the requested service.Mobile social network-aware S2aaS runs over the cloud-centric IoT architecture presented in Fig.2.
Furthermore,mobile smart device users move in communities
towards socially attractive destinations.Social attractiveness is
de?ned in[38].
Given I as the interaction rate matrix in a region,0
into an X×Y grid,then each sub-grid-xy into x′×y′cells. Social attractiveness of a sub-grid-xy for smart device user-i is
de?ned as the ratio of the total interaction of the smart device
user-i with other smart device users in sub-grid-xy to total
social attractiveness of the other sub-grids.It is worthwhile
mentioning that several other de?nitions of interaction can be
considered.The study in[37]uses co-location and distance
between the users on an M×N sub-grid.Thus,shorter
distance denotes higher interaction;hence the inverse of the
ratio of the distance between user-i and user-j to maximum
possible distance is used to formulate user interaction.Other
interaction de?nitions are also possible such as social force
model-based interactions[39]or interactions based on human
behavior models[40].
MSNAC adopts the MSensing auction in[29]and en-
hances it as follows:The participants of the auction are
marked as winners as long as they introduce positive marginal
contribution to the platform utility.It is worthwhile noting
that marginal contribution stands for the difference between
the marginal value introduced by the collaboratively sensing
tasks that are also contributed by the corresponding user;and
payment made to him/her.As mobile device users(i.e.,sensing
service providers)are assumed to select destinations based on
social attractiveness,it is likely that a sensing service provider
is forecasted to be out of range of a particular task before the
completion of the auction.Therefore,when a sensing task is
forecasted to be out of range of a mobile device user due to
the mobility of the mobile device user,the corresponding user
is not selected as one of the winners of the auction.In[37],
MSN-aware S2aaS has been shown to increase platform utility
under heavily arriving sensing task requests.As sensing ser-
vice provider recruitment introduces trustworthiness problem,
when reputation-awareness is incorporated into mobile social
network-awareness,the platform utility has been shown to be
improved by up to the order of55%whereas disinformation
probability is degraded by70%if reputation-awareness is
incorporated.
E.Reliability and Trustworthiness of sensing services
1)Under stationary sensing service providers:In[41], [11],MSensing auction have been adopted and extended by in-corporating reputation-awareness.The proposed framework is called Trustworthy Sensing for Crowd Management(TSCM). TSCM recruits the users based on their reputation.A user’s instantaneous reputation is de?ned as the running average ratio of the positive sensing readings to total sensing readings.Once all sensing data are collected for a set of tasks,for each task,an outlier detection algorithm[42]is run on the set of sensor readings,and the outliers are marked as negative readings whereas the rest are marked as positive readings. Overall reputation(,i,e,trustworthiness)of a user is updated via weighted sum of past and current reputation values at the end of every recruitment procedure.
Every sensing task has a pre-de?ned value for the platform. User reputations are updated and stored by the cloud platform, and these records are used as key parameters in sensing service provider recruitment.The contribution of a sensing service provider is a function of the value of the sensing tasks handled by the corresponding provider,and the reputation of the sensing provider.The difference introduced to the reputable value of the winners set,W by the addition of user-i is de?ned as the marginal reputable contribution of user-i.Reputable value of a set of users,W,is the total value of the sensing tasks handled by the users in W while the sum is normalized by the average reputation of the users in W.
As TSCM adopts MSensing[29],it recruits the sensing service providers based on an auction procedure.Upon arrival of any set of sensing tasks,sensing service providers join the auction by reporting their sensing costs as their bids. The auction consists of winner selection and payment deter-mination steps.In the winner selection step,sensing service provider bids are scaled by their reputation.By doing so,the platform ensures that the bid of a sensing service provider with high reputation is close to the actual sensing cost if the provider has a high reputation,and vice versa.In case the user has a low reputation,its scaled bid will be higher than the actual cost and the user will less likely to be recruited. This proactive approach helps avoid recruitment of malicious users who aim at being recruited as sensing service providers and aim at disinformation at the cloud platform,as well as at the end user side.The providers whose modi?ed bids are less than their reputable marginal contributions are selected as the winners of the auction,and they are added to the winners list(i.e.,utility improvement of the platform is positive), W in descending order of the improvement in platform’s utility.In the payment step,for each selected sensing service provider,w the algorithm seeks maximum hypothetical bid which would not impact recruitment of the provider w before the other providers(w v)whose compensations have not been determined so far.
Utility of the end user(and the cloud platform,as well), utility of the sensing service provider and disinformation probability are the metrics that have been used to assess the performance of TSCM.Platform utility stands for the difference between total value of the sensing tasks and the compensation made to the sensing service providers.Similarly, utility of a sensing service provider is the difference between received compensation and total sensing costs for the sensing tasks that the provider has participated in.Disinformation probability is a metric that is related to trustworthiness,and it denotes the proportion of the tasks for which a sensing service provider has been compensated for altered sensing data.Based on the compensation paid to the sensing service providers, TSCM runs in two modes,namely the aggressive mode and non-aggressive mode.The aggressive mode uses the reputable value of the participatory sensing data(?R w
v
)whereas the non-aggressive mode takes the raw value of participatory sensing tasks.
2)Under mobility of sensing service providers:In[43], TSCM has been modi?ed to predict the location of the sensing service provider at the time of data collection.Is the mobile device user who is providing sensing wanders off,in order to prevent the cloud platform and the end user from making payments for non-received data,provider location is estimated by using the triangulation method[44].The cloud platform keeps track of previous locations and velocities of the sensing service providers.Assuming that the users follow random waypoint mobility model,the platform estimates the location of the providers by the end of the data collection process by running a triangulation method.This approach is called Mobility-Aware Crowdsensing(MACS)[43].For each sensing service provider,MACS re-computes the set of sensing tasks that are expected to be still within his/her range based on his/her estimated location by the end of data collection. Maliciously altered sensing data can lead to severe conse-quences while mobility-unaware crowdsensing results in un-necessary payments to sensing providers.Therefore,Mobility-Aware Trustworthy Crowdsensing(MATCS)has been pro-posed in order to jointly address these two issues[43].MATCS recruits the sensing service providers based on their future locations,as well as their reputable contributions to the set of sensing tasks.MATCS also adopts the non-aggressive payment mode of TSCM.In[43],it is reported that under heavily arriving sensing tasks,mobility-awareness can improve the platform utility by70%whereas by incorporation of trustworthiness,further improvement by20%can be achieved in platform utility of mobility-aware sensing service provision-ing.
In[37],the authors have shown that in case of mobility of a social network of sensing service providers,and in presence of malicious providers(i.e.,smart phone users), when reputation-awareness is incorporated into mobile social network-awareness,the platform utility can be improved by 55%whereas disinformation probability is degraded by70%.
IV.O PEN ISSUES AND CHALLENGES
Figure4summarizes the schemes that have been visited and studied in detail in the paper.Energy-ef?ciency and reliability are considered as direct objectives whereas context-awareness and mobility-awareness are considered as the indirect objec-tives.It is worthwhile noting that MSNAC and T-MSNAC aim at forecasting future locations of the sensing service providers based on the interactions with their social networks; hence they can also be considered as context-aware as the context denotes mobile device user interactions and locations. As seen in the?gure,all schemes need improvement and to be complemented by one of the direct/indirect objectives.For instance,T-MSNAC is aware of the context as it maintains a user interaction map accompanied with the sensing service providers trajectories.Furthermore,it aims at reliable S2aaS, and adopts the reputation-based sensing service provider re-cruitment principle in TSCM.On the other hand,battery drain of the sensing service providers is not a major concern for T-MSNAC.Similarly,EMCSS and FMCSS introduce promising results in terms of energy savings through GPS-less
sensing Fig.4.Taxonomy and summary of the schemes that have been studied in detail in the paper
however they need improvement by a reputation-based ap-proach so that sensing data reliability is improved.Therefore, holistic schemes addressing all direct/indirect objectives and having multiple foci are emergent.Moreover,social networks can play a key role in handling the big sensing data.Context-awareness is reported to be the core component of ubiqui-tous mobile system development[45],and context-analysis in S2aaS is mainly based on mining crowdsensed data[46].
As for the sensing service provider trajectory estimation;the triangulation method is not a strong predictor for real-world user mobility as human mobility demonstrates regularity to some extent[47].Therefore incorporating more sophisticated clustering solutions such as Density-Based Spatial Clustering of Applications with Noise(DBSCAN)[48]could improve the accuracy and provide a better?t for real-time mappings.
V.C ONCLUSION
We have provided a brief overview of the state of the art and challenges in sensing services in a cloud-centric IoT architecture.Given that searching and recruitment of sensing service providers is a major challenge in this?eld,context-aware sensor search framework has been presented.Besides,as mobile devices utilize GPS for localization,sensing by turning off the GPS as much as possible can introduce energy savings while energy-aware sensing activity scheduling is another way of reducing energy consumption.Effective incentives are required to improve accuracy of the crowdsensed data whereas reputation-aware provider recruitment mechanisms can improve reliability of the crowdsensed data.The paper has also introduced reliability solutions considering random waypoint and social mobility of the sensing service providers.
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