Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Digital Object Identifier 10.1109/ACCESS.2017.DOI

Bi-objective Optimization for EnergyAware Internet of Things ServiceCompositionOSAMA ALSARYRAH1, IBRAHIM MASHAL2, AND TEIN-YAW CHUNG.1,(Member, IEEE)1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li 32003,Taiwan2Computer Science Department, Aqaba University of Technology, Aqaba 77110, Jordan

Corresponding author: Tein-Yaw Chung (csdchung@saturn.yzu.edu.tw).

This work was supported by the Ministry of Science and Technology of Republic of China, Taiwan, under contract number MOST106-2221-E-155-014.

ABSTRACT In recent years, service-oriented based Internet of Things (IoT) has received massive attentionfrom research and industry. Integrating and composing smart objects functionalities or their services isrequired to create and promote more complex IoT applications with advanced features. When many smartobjects are deployed, how do we select the most appropriate set of smart objects to compose a service byconsidering both energy and Quality of Service (QoS) is an essential and challenging task. In this work,we reduced the problem of finding an optimal balance between QoS level and the consumed energy ofthe IoT service composition to a Bi-objective Shortest Path Optimization (BSPO) problem and used anexact algorithm named pulse to solve the problem. The BSPO has two objectives, minimizing the QoSincluding execution time, network latency, and service price and minimize the energy consumption of thecomposite service. Experimental evaluations show that the proposed approach has short execution time invarious complex service profiles. Meanwhile, it can obtain good performance in energy consumption andthus network lifetime while maintaining a reasonable QoS level.

INDEX TERMS IoT services, energy efficiency, service composition.

I. INTRODUCTION

INTERNET of things (IoT) is a new technology paradigmthat allow smart objects to be connected together to

collaborate, cooperate, and communicate with each other toprovide and support smart applications [1], [2]. Currently,there are more than 8 billion connected smart objects, and thenumber will continue to increase dramatically year after year[3]. Smart objects are heterogeneous in their functionalities,communication capabilities, and resources. In general, smartobjects are resource constrained with very limited compu-tation and storage capacities when it is a battery-powereddevice, e.g., wireless sensors and mobile phones.

With the advent and rapid development of service-definedeverything, smart objects are represented as services corre-sponding to their co-hosted functions [4]–[6]. In other words,each IoT smart object provides its function through standardservices that can be directly accessed. To this end, Service-Oriented Computing (SOC) [6] are seen as the key enablerfor IoT.

Integrating and composing smart objects functions or their

services is required to create and promote more complex IoTapplications with advanced features [4]. Composing thoseservices is done by aggregating atomic services to providenew functions that none of the services could provide indi-vidually [7]. This integration must consider the quality ofservice (QoS) and energy efficiency of the composed objects.Take a large-scale complex IoT environment as an example.When a smart object has low energy, it should be replacedwith another smart object, if any, that has more energyand can provide the same functions and a good QoS level.However, this is a challenging task since IoT QoS valuesare dynamic and can substantially vary during the lifetimeof the application when network states change. Moreover,smart objects can join, leave, fail, or new services with betterquality can appear at any time. Therefore, finding a goodbalance between the energy consumption of all objects andits QoS to prolong the network lifetime is not a simple task.

In the past, several studies have discussed service discov-ery and composition and many techniques have been devel-oped for Web services and Representational State Transfer

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(REST) [8]. However, those techniques only considered anddiscussed the functional and non-functional properties ofservices. As mentioned above, due to the nature of IoT andthe smart objects, IoT services composition must considernot only QoS, but also power consumption and residualenergy level [9].

There are a few studies on IoT service composition thataddress energy consumption [10]. However, none of themconsidered energy consumption as a separated objective;they considered both QoS and energy consumption as asingle objective. Unlike previous studies, we deal with energyconsumption of IoT services separately from other QoS at-tributes. In this way, both QoS level and energy consumptionare treated equally with the same priority.

This study aims to model and develop a Bi-objective Short-est Path Optimization (BSPO) for IoT service compositionand to find an optimal balance between QoS level and theconsumed energy of the IoT service composition. The BSPOenergy-aware IoT service composition has two objectives:minimize the QoS including execution time, network latency,and service price and minimize the energy consumption ofthe composite service. BSPO derives the optimal solutionby finding a Pareto-optimal solution for QoS and energy-aware IoT service composition based on users or operatorspreferences.

The main contribution of this paper can be summarized asfollows:

1) Investigate IoT service composition by consideringthe energy consumption along with QoS of selectedservices.

2) Formulate a novel optimization problem to maximizethe QoS level and to minimize the energy consumptionof composite service.

3) Propose a BSPO model and use the pulse algorithm tosolve the formulated problem.

4) Simulate and evaluate the proposed service composi-tion scheme, and compare its performance with othergreedy algorithms.

The rest of the paper is organized as follows. Section IIreviews the related work. Section III presents the servicecomposition model. Section IV describes problem formula-tion. Section V depicts the optimization problem. Section VIexplains the pulse algorithm. Section VII shows results andperformance evaluation. Finally, Section VIII concludes thepaper.

II. RELATED WORKA. SERVICE-ORIENTED IOTIoT adopts service-oriented architecture (SOA) paradigmbecause it can provide cooperation between heterogeneoussmart objects and is very flexibile for system integration.Accordingly, smart objects can be connected and composedvia service composition approaches. Recently, many researchworks have abstracted IoT devices as a service to providean efficient and unified way of accessing and operating IoTservices [1], [11], [12].

The Web of Things (WoT) [13] has been proposed to seam-lessly integrate heterogeneous objects. Through existing Webtechnologies such as Web services and RESTful interfaces,WoT enable objects to communicate and interact. Sun et al.[14] proposed a microservice IoT framework to provide ageneric IoT architecture based on a module or multicom-ponent application instead of the monolithic applications.The framework abstracts smart objects and IoT applicationmodules as services.

Cheng et al. [15] proposed a platform for event-drivenservice-oriented IoT coordination, where SOA is adoptedto solve interoperability issues among large numbers ofheterogeneous services and physical entities in IoT. An-other service-oriented IoT architecture is presented in [16],where the authors proposed a user-centric IoT-based Service-Oriented architecture to integrates services that utilize IoTresources in an urban computing environment. In [16], usergoals are represented as an explicit task definition that iscoordination of activities. Activities consist of configurationsof abstract services that can be instantiated by orchestratingavailable service instances, including services that can beactuated through the IoT devices or composed of more thanone smart object.

Many studies also focused on wireless sensor networks(WSNs) to provide composite services by integrating itsfunctions. Zhou et al. [4] proposed a three tiers service-oriented framework. The function of each sensor is abstractedas a service within a service class. Service classes are chainedto fulfill the functional requirements and energy-efficiency.Another work adopted service-oriented WSN presented in[17].

B. SERVICE COMPOSITION AND QOS-AWARESERVICES COMPOSITIONService composition techniques are designed for relativelycomplex services when the required functions cannot besatisfied by any single service. It combines more than oneservice to fulfill the request. Traditional service compositiontechniques compose services in a specific order to meet therequired goals [9]. In the literature, many techniques havebeen proposed for service composition based on its functionsuch as semantic-based matchmaking, Logic, Graph-Theory,Petri net, and AI-Planning based [18].

QoS-aware services composition is known to be an NP-hard problem. However, this problem has already been ad-dressed by several methods. Ngoko et al. [19] proposed amixed-integer linear programming (MILP) method to solveQoS service composition. Furthermore, they considered en-ergy consumption as a QoS attribute. Yu et al. [20] for-mulated the problem as a Multi-dimensional Multi-choiceKnapsack Problem (MMKP). LlinÃas and Nagi [21] pro-posed a graph-based model to solve the problem as a Multi-Constraint Shortest Path problem (MCSP). Wu and Zhu [22]used a directed acyclic graph (DAG) to model services com-position as a path search problem. Several studies proposedPareto optimality techniques for solving the QoS-based ser-

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vices selection problem [23], [24]. For example, Chen etal. [23] proposed a services composition algorithm using apartial selection approach. Based on dominance relation, thisapproach allows us to reduce the search space by pruning un-promising candidate services in QoS. However, they adoptedservice selection by local optimization, which only selectsthe best candidate service locally for each abstract servicewithout considering the relation between tasks in the work-flow. Unlike the work cited above, we considered the QoSfor the entire workflow as an end to end service compositionand used an exact method to solve the bi-objective optimalproblem including QoS and energy consumption.

C. SERVICE COMPOSITION IN IOT CONTEXTMany studies in QoS-aware service composition and selec-tion problem are available in the literature. Bellido et al.[7] analyzed stateless compositions of RESTful services andits control-flow patterns. The researchers also presented acomparative evaluation of different QoS attributes. Dar et al.[25] addressed the problem of integrating IoT smart objectsby adopting the concepts of centralized service composi-tion (orchestration) and decentralized service composition(choreography).

Simple Additive Weighting (SAW) technique has beenused to rank candidate IoT service in QoS. Kouicem et al.[26] and Yachir et al. [27] calculated a single utility value byaggregating the QoS values, where the service compositionproblem is transformed to a single objective optimizationproblem that finds the services with the best utility value.Jin et al. [24] proposed a three phases service compositionalgorithm. The first phase pre-sorts services according touser’s preferences. The second phase applies dominance-based filtering to eliminate sub-optimal solutions and thefinal phase sorts the rest of services to select the best service.Four QoS attributes are associated with each IoT. The can-didate services are evaluated concerning user’s requirementsby aggregating each QoS rating (utility) function. However,studies above do not consider energy consumption.

In the literature, only a few studies considered both energyand QoS. For example, Khanouche et al. [10] proposedan IoT energy-centered and QoS-aware services selectionservices and composition. The proposed selection approachpreselects the services offering the QoS level required foruser’s satisfaction using a lexicographic optimization strategyand QoS constraints relaxation technique. The concept ofrelative dominance of services is also proposed. However, thepreselecting phase aggressively prunes services that donâAZtmeet the local QoS requirements or affect the quality ofend to end QoS level when considering execution time andnetwork latency.

Unlike the aforementioned studies, our work deals withthe energy efficiency of services separately from other QoSattributes and takes into account both QoS level and en-ergy consumption of IoT services. The proposed selectionapproach transformed the problem into a bi-objective opti-mization problem that aims to minimize the amount of energy

consumed and maximize the QoS of a service. We solved theoptimization problem using bi-objective shortest path algo-rithm with four online pruning techniques to reduce the com-plexity of the algorithm. In addition, to ensure a good balanceamong the candidate services in energy consumption, theservices consuming less energy are selected for each servicecomposition. Our model aims to provide high availabilityof services by minimizing the energy consumption, i.e., bymaximizing the devices battery lifetime.

To the best of our knowledge, there exists no previousstudy that considers an end to end service compositionscheme with both QoS and the energy efficiency as theprimary metrics for IoT resources.

III. IOT SERVICE COMPOSITION MODELIoT applications consist of a set of decomposed services.In our model, we define two types of services: an abstractservice (AS) represents function of a service provided byone or more IoT devices or software modules, while a can-didate service (cs) represents a real Web service that may beinvoked. All cs are distributed across available resources.

The candidate services are characterized by two types ofproperties: functional and non-functional properties. Func-tional properties indicate the actions and the functions pro-vided by a cs, while non-functional properties are definedby the QoS attributes and the energy profile of the servicerunning at a battery-powered object. Here, we consideredthree QoS attributes, namely, execution time, cost, and en-ergy consumption profile.

From a user’s perspective, when a user’s service requestarrives at a service composition broker, the service compo-sition process is invoked. The process combines a series ofatomic service components appropriately to form a compos-ite service (path) that provides an optimal balance betweenQoS and consumed energy.

Fig.1 shows a model for composing IoT services. Sup-pose an IoT service consists of n tasks, {Ti, 1 ≤ i ≤ n},which are needed by a given IoT service requested by anend user. For each task Ti a corresponding abstract ser-vice, ASi, is used to represent its functional requirement.For each ASi, 1 ≤ i ≤ n, , there exists li candidateservices, {CSi,j , 1 ≤ j ≤ li, that can meet the functionalrequirements of ASi and thus can be selected for realizingthis service component. Note that a service component maybe either an IoT service or a software component. To meetthe requirements of a specific service request, the servicecomposition path is constructed as a path from a service en-trance portal (CS0) to a service exit portal CSe by traversingonly one candidate service of each service component. Forexample, CS0→CS1,3→CS2,5→· · ·→CSm,j→CSe is acomposite service path that may provide useful service to auser. Therefore, the main focus for IoT services compositionis to select an optimal service sequence from a pool ofavailable smart objects and software components while satis-fying various QoS requirements, in addition to the amount ofconsumed energy.

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FIGURE 1. Service composition model

IV. PROBLEM FORMULATIONLet T={T1, T2, Tj , . . . ..,Tn} denote the set of tasks thatcover the composite IoT service, where n is the total numberof decomposed tasks and Tj is the jth (j = 1, 2, 3, n) sub taskof T. Let ASi={csi1, csi2, csij , csimi} be the candidate ser-vices available for task Tj , where mi represents the numberof candidate services and csij is the jth candidate service.Thus, the directed graph model shown in Fig.1 represents allpossible compositions for an IoT service.

A distributed IoT service formed by interconnecting ndifferent service components can be modeled as a di-rected graph G= (N,A) , where N={v1, . . . ,vi, . . . ,vn}denotes a set of service components with n nodesand A={eij |vi, vj∈ N } is the set of edges (links). Eachnode vi is associated with a weight ci representing itsfunctional capability of abstract service ASi, 1 ≤ i ≤n. Each edge eij∈A are associated with two nonnegativeweights QoSU ij and EP ij , where QoSU ij and EP ij de-note the utility value of QoS attributes and the consumedenergy respectively when traversing eij . Henceforth, withoutloss of generality, QoSU ij refers to QoS attributes such asexecution time, network latency and cost. The objective ofservice composition is to select one candidate service fromeach set ASj and generate an optimal IoT CompositionService Path (CSP) x={x1, x2, . . . ..,xi, . . . . . . ,xn} from theset of available compositions under multi-objective require-ments, where xi denotes the candidate service selected forsub task Ti.

A. ENERGY PROFILE MODEL (EP)Since energy consumption is a significant factor for deviceshosting the candidate service, it is considered necessary thateach candidate service provides the composer its energy

consumption variable so that the composer can select themost energy efficient one.

We defined the energy profile (EP) of csij by two vari-ables, the residual energy level RE(csij) of the device host-ing csijand the consumed energy CE(csij) which representsthe consumed energy when running csij . RE(csij) is esti-mated as follows.

RE(csij) = CDE(csij)− Eth(csij) (1)

where CDE(csij) represent current energy level of thebattery-powered device hosting csij and Eth(csij) the en-ergy threshold value under which the device cannot supportcsij anymore.

Since our model is based on service-oriented computing,the consumed energy of running csij , CE(csij), is calculatedas in Eq. (2). Here, we assumed that the energy consumptionof csij is constant since the service runs on the same plat-form, uses the same resources, and receives and sends thesame amount of data.:

CE(csij) =ECR (csij) ∗T (csij) (2)

where ECR (csij) represents the energy consumption rate,and T (csij) represent the execution time of csij . The energyprofile of csij is calculated as shown in Eq. (3) by taking theratio between the energy consumed by invoking csij and itsresidual energy.

EP (csij) = CE(csij)/ RE(csij) (3)

Thus, the smaller is EP (csij) the better is the csij as ancandidate for Ti.

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Finally, the EP of a service composition x can be calcu-lated as shown in Eq. (4), the energy consumed by composi-tion path xi is:

EP (x) =

n∑i=1

EP(xi∣∣xi ∈ Sh

)(4)

where Sh represents the set of battery-powered devices.In our model, we consider the differences in the underlineinfrastructure of the running services. In general, IoT soft-ware services are not executed on battery powered devicesin contrast to sensing and actuating services. To differentiatebetween these two types of required services we refer to theset of battery-powered devices by Sh.

B. QOS CRITERIAIn our study, we consider a set of quantitative non-functionalproperties of IoT services which can be used to describe thequality criteria of a web service. For the sake of simplicity,in this paper, we consider only negative attributes as ournon-functional properties. These values of negative attributesneed to be minimized. We included two attributes: ServiceExecution Time (T), which is collected from records ofprevious execution monitoring, and Service Cost (C), whichis directly collected from service providers.

1) Service Execution Time (T):Service execution time represents the average time expectedfor executing a candidate service. The service provider up-dates it continuously because the load of the device hostingthe service changed dynamically. Clients expect their jobs tobe completed in a minimal time when they submit requeststo the service composer. Let L(csij) be the latency oftransmitting data from csij and ET (csij) be the executiontime of service request for csij . Thus, the service executiontime T(csij) can be computed as in Eq. (5).

T (csij) = L(csij)+ET (csij). (5)

Therefore, the service execution time for composition path xcan be given as follows.

T (x) =

n∑i=1

T (xi) (6)

2) Service Cost (C):When a user submits his/her request to a service composer,the composer manages and finds the fastest composition pathfor him/her. Meanwhile, the user is also expected to pay thefairest price for running his/her tasks. Therefore, the ServiceCost is considered a valuable QoS property. In this model,we set C(csij) as the cost of executing csij . The cost isusually fixed but may be changed according to the serviceprovider’s business policy. The execution cost is registeredby the service provider.

Therefore, as given in Eq. (7), the service cost for compo-sition path x is:

C (x) =

n∑i=1

C(xi)

(7)

3) Utility FunctionUsing utility function is a helpful mechanism for evaluatingthe aggregated quality of a given composite service. In thisresearch, we calculate the utility value of a service com-position by aggregating normalized QoS attributes values.All QoS values are mapped to a single real value between0 and 1 by comparing the QoS value with the minimumand maximum available QoS value. This enables uniformevaluation of the QoS value.

We adopted a Multiple Attribute Decision-Making ap-proach, namely, the Simple Additive Weighting (SAW) tech-nique [28] for the mapping process. For a composite servicepath x the aggregated QoS values are compared with theminimum and maximum possible aggregated values. Theminimum (or maximum) possible aggregated values can beeasily estimated by aggregating the minimum (or maximum)value of each service class.

The utility function of QoS is computed as in Eq. (8).

QoSU (x) =wt∗T (x) +wc∗ C (x) (8)

where wtand wc represent the weighting factors of execu-tion time and cost respectively. The sum of weights are equalto one, i.e., wt+ wc = 1. T (x) is the normalized serviceexecution time for x that is calculated using Eq. (6). C (x) isthe normalized service cost for x that is calculated using Eq.(7).

With the above QoS utility and power profile formulas, wecan formulate the optimization problem of the service com-position as a BSPO problem. We described the bi-objectiveshortest path optimization problem in the next section.

V. BI-OBJECTIVE OPTIMIZATION PROBLEMMulti-objective service composition selects one candidateservice from each set ASj and generates an optimal IoTCSP from the set of available compositions under multi-objective and constraints. In this study, we formulated theenergy aware IoT service composition as BSPO for findingan optimal IoT CSP from the start node css∈N to the endnode cse∈N that minimizes two different (often conflicting)objective functions. The bi-objective shortest path problemcan be formally defined as in Eq. (9):

minCSP (x) = (QoSU(x),EP (x))

s.t.,x∈X

(9)

where x represents a candidate service path from theservice entrance portal from css to the service exit portal cse.QoSU(x) represents the aggregated value of the QoS values

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along all edges of a path x. EP (x) represents the aggregatedvalue of the EP over all edges in x. X is the set of all pathsfrom css to cse. The objective of Eq. (9) is to minimize theQoS utility value and the energy profile of CSP (x). Sincethe existence of a path that simultaneously minimizes bothobjectives in Eq. (9) cannot be guaranteed, we seek for aset of paths with an acceptable tradeoff between the twoobjectives.

IoT service composition is a NP-complete problem with∏nj=1 Mj possible CSP for task T, where n represents the

number of tasks and Mi represents the number of candidateservices for task Ti. To solve the above problem, in this paper,we use the pulse algorithm detailed in the next section.

VI. THE PULSE ALGORITHM: OVERVIEWWe used the pulse algorithm [29] to solve the bi-objectiveoptimization problem. The pulse algorithm optimizes a bi-objective function CSP(x) that is composed of a quality ofservice function QoSU and energy profile function EP. Apath x optimizes CSP or is Pareto-optimal if there is noother path x′ that has lower QoSU and lower EP than x. Thegoal of the pulse algorithm is then to find a series of suchPareto-optimal paths that together form an efficient set XE

by recursively examining the entire search space of the graph.The efficiency of the algorithm is achieved by aggressively

pruning partial paths. When a pulse reaches to a newly addednode, it will check if adding the node to the existing partialpath satisfies one of the adopted pruning conditions or not.The partial path will be eliminated if it meets one of thefollowing conditions:

1) It includes cycles.2) It exceeds either one or both upper bounds obtained at

the initialization phase, represented by a nadir point,before reaching the end node.

3) It is dominated by any other path in the current efficientset before reaching the end node.

4) It is dominated by any objective value stored in thelabel of the newly added node. A node is labeled byaccumulated objective values when it is traversed by afeasible path. Thus, if the partial path is dominated bythe existing label of the newly added node, it will notbe part of a Pareto optimal path.

Suppose that we have a given network with startnode vs and end node ve.The pulse algorithm sends a pulsefrom vs to ve. This pulse travels through the entire networkwhile storing the partial path p (an ordered sequence of vis-ited nodes) and its cumulative objective functions, QoSU (p)and EP (p). Every pulse that reaches the end node ve isa feasible solution that might be efficient. Once a pulsereaches the end node, it recursively backtracks to continueits propagation through the rest of the nodes in the searchfor more efficient paths from vs to ve. If the pulse is letfree, this recursive algorithm identifies all possible paths, andguarantees that an efficient set is always found.

However, the pulse algorithm does not continue exploringany partial path that will not produce an efficient solution by

Algorithm 1: Pulse algorithmInput: G directed graph; vs: start node; ve end nodeOutput: XE : true efficient set1: p←{ }2: QoSU(p)←03: EP (p)←04: initialization (G)5: pulse( vs, QoSU (p) , EP (p) , p)6: return XE

using a look-ahead mechanism that prunes aggressively vastregions of the solution space. For the initialization procedure,the algorithm starts running a mono-objective shortest pathalgorithm to get the upper bound for each objective. Thepulse algorithm is shown in Algorithm 1.

The pulse algorithm follows a depth-first search truncatedby several pruning strategies to control the pulse propagationand prunes pulses without cutting off any efficient solu-tion. The algorithm defines four pruning strategies namely,pruning by cycles, nadir point, efficient set, and label.The pulse recursive function is shown in Algorithm 2 .It takes four input parameters, current node vi, the cumu-lative QoS utility value QoSU(p), the cumulative energyprofile EP (p), and the partial path p. The pruning strategiesapplied to the pulse are shown in Lines 1–4; if the pulse is notpruned, line 5 stores the current QoSU(p) and EP (p) andline 6 adds the node vi to the partial path. In lines 7–11, the pulse propagates over all nodes vj ∈ Γ+(vi),where Γ+(vi) is the set of outgoing neighbors of vi, and addscurrent QoS utility to the cumulative one and current EP tothe cumulative EP .

Whenever the pulse function is invoked at end node ve,a partial path P becomes a complete solution x and weupdate the online efficient set XE . Note that the informationabout XE has a global scope and is not an attribute of thetraveling pulse within the recursion. Algorithm 3 presentsthe pulse function when it is invoked on end node ve. Sincea new solution has been found, the algorithm verifies if thenew solution is efficient and updates the online efficient setaccordingly.

A. PRUNING TECHNIQUES1) Pruning by Cycles:Because all weights on the arcs are nonnegative, any efficientsolution cannot contain cycles. To avoid cycles in a path,every time we invoke the pulse function at vi, the algorithmchecks whether a node has been visited or not. If vi hasalready lain on the partial path, it is pruned from P.

2) Pruning by Nadir point:Based on the idea of the nadir point which seen as the anti-ideal point in the objective space, the algorithm aims toprune as early as possible any pulse exceeding either smallestvalues (best path) of both objectives solutions. To do so,we calculate the minimum QoSU(x) (regardless of EP) andthe minimum energy profile EP(x) (regardless of QoS) fromentrance node to the end node.

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Algorithm 2: Pulse function:pulse ( vs, QoSU (p) , EP (p) , p)Input: vi, current node; QoSU (p) ,cumalative QoS utility; EP (p) ,cumulative power; p, current path.Output: void1: if isAsyclic( vi, p) then2: if checkNadirPoint( vi, QoSU (p) , EP (p)) then3: ifcheckEfficientSet( vi, QoSU (p) , EP (p)) then4: ifcheckLabels( vi, QoSU (p) , EP (p)) then5: store (QoSU (p) , EP (p))6: p← p∪{ vi}7: for vj∈Γ+(vi) do8: qos (p)←QoSU (p) +QoSU(csij)9: EP (p)←EP (p) +EP (csij)10: pulse( vj , QoSU (p) , EP (p) , p)11: end for12: end if13: end if14: end if15: end if16: return void

Algorithm 3: Pulse function for the end node :pulse ( ve, QoSU (p) , EP (p) , p)

Input: ve, current node; QoSU (p) ,cumalative QoS utility; EP (p) ,cumulative power;; p, current path.Output: void1: if CheckEfficientSet( ve, QoSU (p) , EP (p)) then2: p← p∪{ ve}3: X←mapPathToSolution(p)4: UpdateEfficentSet(X)5:end if6:return void

Assume that we have an optimal solutions x∗QoSU andx∗EP where x∗QoSU and x∗EP represent Energy profile andQoS utility objectives of the mono-objective shortest pathproblem respectively. The images for the optimal solu-tions in the objective space are Z

(x∗QoSU

)= (EP,QoSU)

and Z (x∗EP ) = (EP,QoSU). The nadir point, denoted byZN= (EP,QoSU) , which represent a vector composed ofthe worst objective values in the objective space. In otherwords, it represents an upper bound for each objective in theobjective space.

After applying the mono objective shortest path problemfor each objective, a set of an alternative optimal solutionfor each objective can be found. EP and QoSU representthe smallest values among all alternative solutions for bothobjectives x∗QoSU and x∗EP , respectively. As shown in Fig.2Z(x∗QoSU

), Z (x∗EP ), and ZN shows the minimizer victors

in the objective space and Z* represents the ideal point.Based on this, for any solution x with QoSU (x)> QoSU

orEP (x)>EP , its image z(x) is dominated and x is notefficient. Where any point falls in the dark region in fig 2. Iseather dominated by Z

(x∗QoSU

)or Z (x∗EP ).based on this

any pulse exceeding either EP and QoSU will be pruned.

3) Pruning by Efficient set:Consider the online efficient set XE at a given in-termediate stage of the algorithm. Using the lowerbound found in the initiation phase of the algorithm

FIGURE 2. Nadir point and lower and upper bounds

namely, QoSU(vi) for QoS utility and EP (vi) for en-ergy profile, we can determine whether a partial pathwill become an efficient solution or not. Given a partialpath p to node vi, if there is a solution x∈ XE such thatQoSU (p) + QoSU(vi)≥QoSU(x) andEP (p) + EP (vi)≥EP (x), we can safely prune partialpath p, because even if it spends both the minimum costand the minimum time to reach the end node, it will still bedominated by path x.

4) Pruning by label:For each node vi a fixed number of labels saves a tu-ple of QoSU and EP values. The labels at node vi aredenoted by L (vi) = {(QoSU il, EP il)| l= 1, . . . ,Q} whereQoSU il and EP ilare the cumulative QoS utility and energyprofile for a partial path to vi respectively and Q denotes thenumber of labels at vi. For an incoming pulse, the algorithmchecks if the incoming partial path p is dominated or not; thatis, if any label dominates CSP (p), the pulse is discarded bylabel pruning.

VII. PERFORMANCE STUDYIn this section, we presented the setup of our simulation.Then we analyzed the performance of the exact bi-objectivealgorithm for IoT service composition that optimizes QoSutility, i.e. network latency, response time and service cost,and consumed energy.

To show how IoT services composition can be instantiatedand invoked while keeping an optimal balance between QoSlevel and the consumed energy of the composed service, weconducted an extensive simulation under various scenarios toevaluate the performance of the proposed IoT service com-position. In these scenario we assumed that we have a smartenvironment consist of thousands of heterogeneous objectssuch as mobile devices, wireless sensors, and smart homeand smart building devices such as smart power outlets, shat-ters, elevators, access controls, air conditioning, surveillancecameras, etc. We also assumed that these devices are hetero-geneous in communication protocols. For example, wirelesssensors can be based on ZigBee, 6lowpan or Bluetooth [2].

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To provide interoperability between these heterogeneous ob-jects the functions of these objects and software componentsare abstracted as services to become accessible through SOP,COAP or REST protocols [2]. Finally, we assumed that allcandidate services are registered in IoT orchestration systemand categorized based on its services classes.

A. SIMULATION ENVIRONMENT AND METHODOLOGYWe implemented our simulation using java under 64-bitWindows seven operating system, running on Intel Core i5-2500, 3.3 GHz, and 8 GB RAM. Each scenario generateda different number of services classes AS and candidateservices cs. Each candidate service has two QoS attributes:execution time including network latency, and service cost.

Due to the absence of datasets in QoS and energy profilevalues of IoT services, we chose to evaluate the proposedalgorithm by using synthetically generated data. For instance,each service is produced with a random QoS value accordingto the values reported in the literature [10]. Based on themodel presented in [29], we specified the energy profileof IoT services. The values of each quality parameter aregenerated according to a normal distribution. The importanceor weight of each QoS attribute in the utility function isset to be fixed 1/2. The service execution time and networklatency are generated assuming a uniform distribution overthe interval [20, 1200] and [20, 800]. The cost of servicesis generated according to a uniform distribution over theinterval [10, 20].

In order to overcome the problem of QoS values fluctu-ation during service runtime in dynamic IoT environments,QoS values are randomly changed after every service itera-tion by multiplying every QoS value with a random numberin the interval [0.9, 1.1].

In order to study the energy consumption in battery oper-ated objects, we referred to the energy model presented in[29]. We assumed that every device has an initial amount ofcharge and maximum battery charge, Cinitial and cmax,where the values of Cinitial is chosen randomly in theinterval [0.7 Cmax,1.0 Cmax] and the maximum batterycharge Cmax of an object is set as 1500 mA·h. Furthermore,any object has energy lower than CThreshold becomesunable to provide its services and will not be considered inthe composition process. In this study, we set CThresholdto be 30% of cmax. After every run of the selected candi-date service, a specific amount of power is consumed andthis amount is subtracted from current battery level of thedevice hosting the service. The amount of consumed poweris chosen randomly in the interval [100 mA.s, 10000 mA.s]after every service invocation.

In our study, a service composer is used to find an optimalbalance between QoS and consumed energy and prolong thenetwork life time. In the following sections we will referto the proposed algorithm by BOSC (Bi-Objective ServiceComposition). In order to show the added value of theproposed selection approach in a large-scale IoT servicesenvironment, we compared our results with two variants of

FIGURE 3. Selection time versus number of candidate services (10, 15, 20tasks

our algorithm: QoSC, only the QoS is taken into accountin the selection process, and EPC, only the power profileis taken into account in the selection process. The resultspresented here are derived based on the average of 100simulations.

B. SIMULATION RESULTSTo evaluate the performance of the proposed model andalgorithm we considered the following metrics: (1) Selectiontime which represents the computational time of the selectionalgorithm; (2) Energy consumption of the composite servicewhich is equal to the total energy consumed by its com-ponents; (3) Composition lifetime which is the number ofcompositions that can be executed before the first candidateservice failure. A service is considered failed when its au-tonomy is no longer sufficient to be invoked; (4) Optimalitywhich is the ratio between the QoS value of the compositeservice obtained by BOSC and the optimal QoS value of thecomposite service, obtained by that of QoSC and EPC.

1) Selection Time versus Number of ServicesTo validate the scalability of BOSC, we tested the executiontime of the selection algorithm under various numbers oftasks involved in the composition process and various num-bers of available candidate services for each task.

In the first experiment, we set the number of tasks involvedin the composition to be 10, 15 and 20 tasks. We also setthe number of candidate services for each task to be between100 and 1000. Fig. 3 compares the average execution time (inmillisecond or ms) of the composition algorithm with variousnumbers of tasks and candidate services for each task. Asshown in Fig 3, the average execution time increases as thenumber of tasks of the composite service increases. As shownin the figure, the average execution time is short and suitablefor a large scale IoT environment. For example, the selectiontime does not exceed 10 ms when running 10 tasks eachwith 1000 candidate services. When increasing the numberof tasks to 20 each with 1000 candidate services, the averageexecution time increases slightly to reach less than 70 ms.However, this increase is still reasonable and acceptable.

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FIGURE 4. Energy consumption versus number of candidate services.

2) Energy Consumption Versus Number of ServicesIn the second simulation, we compared the performance ofthe three algorithms in consumed energy (in mA.s). In thesimulation, we considered a composition path consisting of10 tasks where each task has 100 to 1000 candidate services.As shown in Fig. 4, the amount of consumed energy in BOSCand EPC gets close to each other. Note that in EPC thecandidate services with the lowest energy profile are alwaysselected.

From Fig. 4, we can see that when candidate servicesare between 100 and 1000, the amount of consumed energyby BOSC is about 35% more than that by EPC in aver-age. Certainly, EPC provides the best composite service inenergy consumption. An interesting observation is that theamount of consumed power decreases when the number ofthe available services increases. This can be explained by thefact that when increasing the number of candidate servicesthe probability of selecting more services with less powerconsumption increases, and hence, the energy consumeddecreases. Another advantage of BOSC is that it saves morepower than that by QoSC. BOSC consumed energy 70% lessthan that by QoSC.

In the third experiment, we intended to show the consumedenergy under various numbers of tasks between 10 and 50tasks when the number of candidate services for each task areset to be 100. Fig. 5 shows that the energy consumption of thecomposite path increases when the number of service classesincreases. In fact, increasing the size of composite path willincrease the number of selected services which causes morepower consumption.

3) Composition Lifetime Versus Number of ConcreteServicesStudying the performance of the proposed algorithm in ser-vice composition life time is the aim of the fourth experiment.We evaluated and compared the composition lifetime byBOSC with that by EPC and QoSC. In the simulation, we

FIGURE 5. Energy consumption versus number of tasks)

FIGURE 6. Composition lifetime versus number of candidate services.

considered a composition path consisting of 10 tasks andeach task has 100 to 1000 candidate service. As shown inFig. 6, the composition life time with BOSC is slightly lessthan that by EPC when only the lowest energy profile isselected. On the other hand, EPC guarantees the lowest en-ergy consumption while reducing the QoSU of the compositepath. Indeed, BOSC can achieve a good balance betweenthe amount of consumed energy and QoSU of the composedpath. Thus, BOSC ensures a long composition life time,which provides a high availability of candidate services.

4) Optimality of the SolutionStudying the performance of BOSC in optimality of theobtained QoSU is the purpose of this experiment. In thesimulation we considered a composed path consisting of10 tasks and each task has 100 to 1000 candidate services.As shown in Fig. 7, the optimality of the proposed methodguaranteed a QoS level about 80% close to that acquired byQoSC. It is worthy of noting that BOSC does not apply anyconstraint on QoS attributes in the simulation. The results ofBOSC can be further improved if we apply some constraints

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FIGURE 7. Optimality QoS versus number of candidate services.

on QoS attributes, such as execution time and cost thresholds.This is because it helps to reduce the solution choices withlower QoSU and hence better QoS can be achieved. As wecan observe from Fig. 7, the QoS optimality level of EPCdramatically decreases, while BOSC can provide a solutionclose to optimal solutions.

VIII. CONCLUSIONService-oriented IoT has received considerable attention overthe past few years. A crucial factor for the success of IoT andits applications is to create more complex IoT applicationswith advanced features by composing smart objects functionsand services.

In this paper, a bi-objective shortest path optimizationmodel is presented to model IoT service composition whereenergy consumption and QoS are considered. The pulsealgorithm with four embedded pruning techniques, namely,pruning by cycle, nadir point, efficient set and label, isdeveloped to solve efficiently the presented problem. Resultsshow that our proposed IoT service composition schemeovercomes and surpasses other schemes that only considerQoS or power consumption individually. Experiments alsoshow that the proposed scheme works reasonably fast inselecting suitable smart objects; the average execution timeneeds less than 70 ms, which makes the proposed modelscalable for large-scale IoT environments. The amount ofconsumed energy by BOSC is about 35% more than thatconsumed by EPC on average. The composition lifetime withBOSC is 90% more than that by the QoS only scheme. Also,the acquired optimality level of the BOSC guaranteed a QoSlevel about 80% close to that obtained by QoSC. Therefore,the proposed solution provides an optimal balance betweenQoS level and consumed energy in IoT service composition.

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OSAMA ALSARYRAH is a Ph.D. candidate inthe Department of Computer Science and Engi-neering, Yuan Ze University, Taiwan. In 2007 hereceived his M.S. in computer science from theUniversity of Jordan, Jordan. His research inter-ests include future communications systems, In-ternet of Things and Smart Applications, Web ser-vices, Sensor networks and Distributed computing

IBRAHIM MASHAL is a faculty member in theComputer Science department at Aqaba Univer-sity of Technology. He earned his PhD. degreein the field of Computer Science, specializingin Internet of Things, from Yuan Ze University,Taiwan (R.O.C.) in 2016. He received his M.S.in Computer Science from the University of Jor-dan, Jordan. His research interests include Futurecommunications systems, Internet of Things andSmart Applications, Wireless mobile network, and

Sensor networks.

TEIN-YAW CHUNG (M’87) received the M.S.and Ph.D degrees in Electrical and Computer En-gineering from North Carolina State University,Raleigh, NC, USA, in 1986 and 1990, respectively.From Feb.1990 to Feb 1992, he was with NetworkService Division, IBM, RTP, NC, USA. SinceMay 1992, he has been with Yuan Ze University,Chung-Li, Taiwan and is a full Professor now. Hiscurrent research interests include overlay network,Multimedia Communication and mobile network-

ing. He is a member of IEEE.

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