research article a distributed task allocation strategy...

Research ArticleA Distributed Task Allocation Strategy for CollaborativeApplications in Cluster-Based Wireless Sensor Networks

Feng Wang,1,2,3 Guangjie Han,1,2,4 Jinfang Jiang,1 and Hao Qiu1

1 Department of Information & Communication Systems, Hohai University, Changzhou 213022, China2Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis,Guangdong University of Petrochemical Technology, Maoming 525000, China

3 School of Information Science & Engineering, Changzhou University, Changzhou 213164, China4Changzhou Key Laboratory of Photovoltaic System Integration & Production Equipment Technology, Changzhou 213022, China

Correspondence should be addressed to Guangjie Han; [email protected]

Received 25 March 2014; Accepted 19 May 2014; Published 8 June 2014

Academic Editor: Lei Shu

Copyright © 2014 Feng Wang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Balancing energy consumption and prolonging network lifetime have been hot topics in energy limited wireless sensor networks(WSNs). It is generally known that multiple sensor nodes collaborating with each other to perform tasks are an important meansof energy balancing. In addition, a reasonable task allocation strategy is also an efficient method for task performing and energysaving. Furthermore, in order to save energy consumption, many WSNs are organized into clusters. Therefore, in this paper, wepropose a distributed task allocation strategy for collaborative applications (DTAC) in cluster-based WSNs. In DTAC, the serviceabilities of sensor nodes are first evaluated and then the sensor nodes are classified into different ranking domains. Based on theservice abilities of cluster members, the service ability of each cluster is calculated. A complex task can be first allocated to severalclusters, and then scheduled on collaborative cluster members. Simulation results demonstrate that, compared with current relatedworks, DTAC is much more efficient for complex task processing in terms of reducing energy consumption, shortening executiontime, and balancing network loads.

1. Introduction

1.1. Background and Motivation. In recent years, WSNs havebeen widely used in many applications including military,industrial, household, medical, marine, and other fields,especially in natural disaster monitoring, early warning,rescuing, and other emergency situations [1]. With thedevelopment ofWSNs, the related applications present higherreal-time requirements. At the same time, the amounts ofcalculation and communication also become bigger, whichintroduces high energy consumption and short networklifetime. However, it is generally known that sensor nodesin WSNs are small with limited processing and computingresources [2]. It is impossible for a single sensor node toindependently accomplish a complex task. Therefore, howto assign a complex task with a large amount of calculationto appropriate sensor nodes has become a pressing issue inenergy limited WSNs.

There are two kinds of network structures for WSNs:flat and hierarchical networks. Compared with flat networks,hierarchical networks, for example, cluster-based WSNs, aremuchmore energy efficient, where sensor nodes are classifiedinto several clusters andmanaged by cluster heads.Therefore,we research on the task allocation topic in cluster-basedWSNs.

Many task allocation methods have been proposed forWSNs [3–6]. Most of them only concentrate on reducingenergy consumptions of sensor nodes. However, balancingall the node energy consumptions in network is consideredto be a better choice for prolonging network lifetime ratherthan just reducing energy consumptions of several sensornodes. In addition, a task allocation inWSNsmust satisfy thefollowing requirements.

(i) Real-time requirement: most applications of WSNshave real-time requirements. Thus task allocations

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 964595, 16 pageshttp://dx.doi.org/10.1155/2014/964595

2 International Journal of Distributed Sensor Networks

in these applications need to be completed in time.In order to ensure the real-time requirement of taskallocation, the task processing speeds of sensor nodesshould be considered.

(ii) Heterogeneous network requirement: heterogeneousnetwork is a new trend in the development of WSNs,where sensor nodes have different initial energy levelsand different abilities of computation, communica-tion, processing, and storing.Therefore, in the processof task allocation, the heterogeneity of sensor nodesshould be taken into account.

(iii) Flexibility requirement: due to different hardwareconfigurations of sensor nodes and dynamic environ-ment parameters, randomly deployed sensor nodessense different data information. The sensory data isdynamically changed over time. Therefore, an effi-cient task allocation method should be dynamicallyadjusted according to different environment parame-ters.

(iv) Scalability requirement: WSNs are always deployedin unattended and unreliable environments, wheresensor nodes are easily attacked and died out, whichultimately changes the network structure. Therefore,a task allocation method should be suitable for anykinds of WSNs with dynamic network structure.

Based on the above-mentioned requirements, it isobserved that research on a realistic task allocationmodel forWSNs is still at its infancy. In this paper, we proposed a dis-tributed task allocation strategy for collaborative applicationsin cluster-based WSNs. Through simulations, it is found thatour task allocation strategy outperforms traditional relatedworks in terms of reducing energy consumption, shorteningexecution time, and balancing network loads.

1.2. Paper Organization. The rest of the paper is organizedas follows. Section 2 introduces related works about taskallocation in WSNs. Section 3 gives system models includingnetwork model, task model, and energy consumption model.Section 4 presents overview of DTAC. Section 5 investi-gates details of DTAC. Section 6 presents simulation results.Finally, Section 7 concludes the paper.

2. Related Work

2.1. Existing Task Allocation Strategies. In recent years,numerous studies have been conducted for task allocationand scheduling. For example, in [7], Giannecchini et al.proposed an online task schedulingmechanism called CoRAlto allocate network resources to tasks of periodic applicationsin WSNs. Given the amount of available resources and thetask set, CoRAl can determine the sampling frequency for allthe tasks so that the frequencies of the tasks on each sensor areoptimized subject to the previously evaluated upper-boundexecution frequencies. Simulation results show that CoRAlcan efficiently assign the available resources among all theactive tasks. However, CoRAl does not addressmapping tasks

to sensor nodes. In addition, CoRAl fails to explicitly discussenergy consumptions of sensor nodes.

In [8], an energy-constrained task mapping and schedul-ing called EcoMapS is proposed, which incorporates channelmodeling, concurrent task mapping, communication andcomputation scheduling, and sensor failure handling algo-rithm. First, a channel model is presented for single-hopWSNs. Then, based on this channel model, communicationand computation are jointly scheduled in the initializationphase. Furthermore, a quick recovery algorithm is executedin the quick recovery phase in case of sensor node failures.EcoMapS efficiently minimizes the schedule length subjectto the energy consumption constraint in single-hop clusteredWSNs. However, EcoMapS does not provide the guaranteeof execution deadline; thus it is not suitable for real-timeapplications.

Tian et al. developed an application-independent taskmapping and scheduling solution in [9], which not onlyprovides real-time guarantee, but also implements dynamicvoltage scaling mechanism to further optimize energy con-sumption. Using a novel multihop channel model and acommunication scheduling algorithm, computation tasksand associated communication events are scheduled simul-taneously with a dynamic critical-path scheduling algorithm.The critical-path in a DAG is the one with the largest sumof computation and communication costs. However, the pro-posed solution is not suitable for heterogeneousWSNs, whereconstituent components are not known priori. Consequently,for each node and edge in the DAG, there is generally morethan one possible cost for executing communication task, sothat the critical-path may not be distinct.

In [10], a novel task allocation strategy called balancedenergy-aware task allocation (BEATA) is proposed for col-laborative applications running on heterogeneous networkedembedded systems aiming at making the best trade-offbetween energy saving and schedule length. The trade-off isachieved by the defined energy-adaptive window (EAW), inwhich a sensor node with lower energy consumption andearlier finish time is chosen for task processing. Choosingprocessing nodes without considering residual energy bringsabout a consequence that some nodes are chosen frequentlyand died early.Therefore, BEATA cannot well balance energyconsumptions of sensor nodes.

Another similar task allocation algorithm which con-centrates on both energy saving and time constraint isproposed in [11]. The proposed algorithm is a two-phase taskallocation technique based on queuing theory. In the firstphase, tasks are equally assigned to sensor nodes to measurethe service capabilities of them. In the second phase, tasksare specifically allocated to some sensor nodes according totheir measured capabilities in such a way to reduce the totalcompletion times of all the tasks in network. However, theresidual energy of sensor nodes dynamically changes withthe number of processing tasks, which results in dynamiccapabilities of sensor nodes. Therefore, how to dynamicallyupdate capabilities of sensor nodes needs further research.

Allocating tasks to sensor nodes should consider notonly reducing processing time and energy consumption,but also improving reliability of the entire task execution.

International Journal of Distributed Sensor Networks 3

Therefore, a novel task allocation algorithm based on A∗algorithm is proposed to optimize reliability and cost of taskexecution [12]. In order to further save energy consumption,a greedy A∗ algorithm is proposed based on the traditionalA∗ algorithm. Comparedwith centralized execution, bothA∗and greedyA∗ algorithms can distribute energy consumptionmore uniformly across sensor nodes. Relatively speaking,the greedy A∗ algorithm is more scalable than A∗ algorithmwith increasing number of sensor nodes. However, thesensor nodes conducting task allocation consumemuchmoreenergy than ordinary sensor nodes do. That is to say, theproposed algorithm does not work well in energy balancing.

In order to achieve a fair energy balance among sensornodes andmaximize the overall network lifetime, an auction-based strategy for distributed task allocation is proposedin [13]. The real-time distributed task allocation problemis formulated as an incomplete information reverse auctiongame. The task sensing and allocating node is called auc-tioneer. The task processing node is a bidder. Then, basedon winner determination protocol (WDP), an energy anddelay efficient distributed winner determination protocol(ED-WDP) is proposed to select winner nodes to performtasks. To the best of our knowledge, this is the first work thatconsiders a completely distributed framework for incentivecompatible reverse auction-based task allocation with aneffective distributed winner protocol for WSN applications.

In the above-mentioned relatedworks, we can see that theusage of resources is usually highly related to the executionof tasks which consumes a certain amount of energy. Exceptreducing and balancing energy consumption or shortingtask execution time, parallel processing among sensor nodesis considered to be a promising solution for efficient taskallocation. In [14], a self-adapted task scheduling strategyis proposed to achieve parallel processing of tasks. First, amultiagent-based architecture for WSNs is proposed and amathematical model of dynamic alliance is constructed fortask allocation. Then, an effective discrete particle swarmoptimization algorithm is proposed for the dynamic alliance,in which the task allocation is conducted based on the energyconsumption of sensor nodes, the amount of network loads,and the execution time of tasks.

In [15], a dynamic task allocation is proposed for multi-hop WSNs with low node mobility. The proposed algorithmincorporates a fast task reallocation algorithm to quicklyrecover from network disruptions, such as node or linkfailures. In addition, an evolutional self-learning mechanismbased on a genetic algorithm continuously adapts the systemparameters in order to meet the desired application delayrequirements, while also achieving a sufficiently long networklifetime. Since the algorithm runtime incurs considerabletime delay while updating task assignments, an adaptive win-dow size is introduced to limit the delay periods and ensurean up-to-date solution based on node mobility patterns. Tothe best of our knowledge, this is the first study for mobilemultihop WSNs.

2.2. Comparing DTACwith Existing Task Allocation Strategies.From the above-mentioned related works, we can find that

existing task allocation strategies always only take one influ-encing factor, such as energy consumption or network load,into account for task allocation. However, other contributingfactors, such as the residual energy level or the reliabilityof a sensor node, cannot be neglected. Therefore, a noveldistributed and energy efficient task allocation strategy isproposed in this paper. Compared with the existing taskallocation strategies, the contributions of DTAC are listed asfollows.

(i) We layer the subtasks to different layers based ondirected acyclic graph (DAG) task model. All thesubtasks in the same layer can be processed at thesame time, which can promise efficient task parallelprocessing to reduce execution time.

(ii) In each cluster, all cluster members are classifiedinto different ranking domains based on their serviceabilities.The ranking domain of each sensor node canbe dynamically adjusted to guarantee the success rateof task processing.

(iii) We study the evaluation of service ability on eachcluster. Based on the service abilities of clusters,complex tasks can be allocated to different clustersto balance energy consumption and prolong networklifetime.

(iv) In the process of task allocation, an improved contractnet consultation mechanism is proposed to narrowthe scope of bidding, which can significantly reducecommunication overheads.

3. System Models

3.1. Network Model. A cluster-based WSN is studied inthis paper, where sensor nodes are classified into severalclusters according to communication range. Each cluster iscomposed of one cluster head and several cluster members.Cluster heads have much more residual energy and longercommunication range than cluster members. Each clustermember can only directly communicate with its cluster head,while one cluster head can not only directly communicatewith its neighbor cluster heads, but also contact other clusterheads or the base station with multihop communication.The cluster-based WSN is considered to be heterogeneous,where sensor nodes have different initial energy levels anddifferent processing speeds for tasks. However, the energyconsumption of data transmission is assumed to be the same.

3.2. Task Model. A complicated task consists of severalsubtasks, which can be denoted by a directed acyclic graph𝐺 = (𝑇, 𝐸, 𝑤, 𝑙) [3]. As shown in Figure 1, a DAG consists ofa set of vertices 𝑇 representing the tasks needs to be executedand a set of directed edges 𝐸 representing dependenciesamong tasks. Each task has an execution deadline 𝑡dl. In orderto ensure the smooth completion of the complex task, eachsubtask must be completed before its execution deadline. Ifthere is a directed edge 𝑒

𝑖𝑗from a vertex 𝑡

𝑖to another one

𝑡𝑗, the execution of subtask 𝑡

𝑗needs the output result of the

execution of subtask 𝑡𝑖. Therefore, the subtask 𝑡

𝑗needs to


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Figure 1: A DAG task model.

be processed after the completion of 𝑡𝑖. The weight value

𝑤𝑖of a vertex refers to the amount of computations of

the task. The weight value 𝑙𝑖of a directed edge refers to

the communication traffic between two tasks. For the sakeof simplicity, we assume that the amount of computationsapproximately equals the amount of transmitted data, whichfollows a normal distribution.

For an edge 𝑒𝑖𝑗, 𝑡𝑖is called an immediate predecessor

of 𝑡𝑗. 𝑡𝑗is called an immediate successor of 𝑡

𝑖. A task

without immediate predecessors is an entry task and a taskwithout immediate successors is an exit task. If one task 𝑡

𝑗is

scheduled on one node while its immediate predecessor 𝑡𝑖is

scheduled on another node, a communication between thetwo nodes is required.Therefore, 𝑡

𝑗cannot start its execution

until the communication is completed and the result of 𝑡𝑖is

received. If both 𝑡𝑗and 𝑡𝑖are assigned on the same node, the

communication latency is considered to be zero. 𝑡𝑗can start

execution after 𝑡𝑖is finished. In addition, we assume that a

subtask can only be assigned on one sensor node, but eachsensor node can process several subtasks.

3.3. Energy Consumption Model. The energy consumption ofa sensor node consists of two parts: energy consumption oftask processing and communication overheads. When a task𝑡𝑖is processed by node 𝑗. The energy consumption of task

processing is calculated by

𝐶comp (𝑖, 𝑗) = 𝑃𝑐(𝑗) × 𝑡

𝑗

𝑖, (1)

where 𝑃𝑐(𝑗) is the average energy consumption per second. 𝑡𝑗

𝑖

is the processing time on node 𝑗, which is calculated by

𝑡𝑗

𝑖=𝑤𝑖

𝑓𝑗

, (2)

where 𝑤𝑖is the amount of computation for task 𝑡

𝑖. 𝑓𝑗is the

task processing speed of node 𝑗.The communication overheads also consist of two parts:

energy consumption of transmitting and receiving data.

The energy consumption of transmitting and receiving 𝑙-bit data over a distance 𝑑 is defined as 𝐶

𝑡𝑥(𝑙, 𝑑) and 𝐶

𝑟𝑥(𝑙),

respectively [16],

𝐶𝑡𝑥(𝑙, 𝑑) = {

(𝜉elec + 𝜉𝑓𝑠 ∗ 𝑑4

) ∗ 𝑙, 𝑑 ≥ 𝑑0

(𝜉elec + 𝜉amp ∗ 𝑑2

) ∗ 𝑙, 𝑑 < 𝑑0

𝐶𝑟𝑥(𝑙) = 𝜉elec ∗ 𝑙,

(3)

where 𝜉elec, 𝜉𝑓𝑠, and 𝜉amp are hardware-related parameters[17]. 𝑑

0is a defined threshold. When 𝑑 ≥ 𝑑

0, the energy

consumption of transmitting greatly increases. Therefore,the communication overheads of node 𝑗 are given as𝐶comm(𝑖, 𝑗) = 𝐶

𝑡𝑥(𝑙, 𝑑) + 𝐶

𝑟𝑥(𝑙). Thus, the total energy

consumption of node 𝑗 is obtained as follows:

𝐶total (𝑉𝑗) = ∑

𝑖∈𝑛

𝐶comp (𝑖, 𝑗) + ∑𝑖∈𝑛

𝐶comm (𝑖, 𝑗) , (4)

where 𝑛 is the number of processed tasks.

4. Overview of DTAC

4.1. Definitions

Task Source Cluster.Acluster sensing complex tasks is definedas a task source cluster.The cluster head in task source clusteris responsible for complex task allocation.

Collaborative Cluster. If a task source cluster does not haveenough resources to complete a complex task, it turns toneighbor clusters asking for collaboration to complete the restof subtasks. The neighbor cluster providing collaboration iscalled collaborative cluster.

The task allocation consists of two phases: (1) interclusterallocation and (2) intracluster allocation. A complex taskcan be divided into several subtasks. Intercluster allocationassigns each complex task in DAG to several collaborativeclusters. Intracluster allocation schedules the subtasks oncluster members. In order to short task execution time,balance network loads, reduce energy consumption, andprolong network lifetime, the parallel processing of task isachieved by defining ranking domain.

4.2. Ranking Domain. In order to achieve the parallel-processing capability of a complex task, we propose a conceptof ranking domain. A complex task can be divided intocertain subtasks which can be assigned to different sensornodes at the same time. Ranking domain is proposed toclassify sensor nodes into different levels based on theirservice abilities.The sensor node with a higher service abilitycan be divided into a higher level ranking domain.Thus it canbe selected with higher priority to perform processing tasks.The ranking domain classification is implemented basedon sensor nodes’ reliability and ability. Here, node trust isproposed to represent the reliability of a sensor node andresidual energy level denotes the ability. Next, the calculationof reliability and ability is introduced.

Trust management models have been recently suggestedas an effective security mechanism for WSNs [18], which can


be used to evaluate the reliability of a sensor node. In thispaper, node trust is defined as

𝐷𝑖=𝑁𝑖

success𝑁𝑖

total, (5)

where 𝑁𝑖success is the number of tasks successfully processed.𝑁𝑖

total is the total number of tasks cumulatively processed.Residual energy is always considered to represent the

ability of a sensor node. However, except the residual energy,the rate of energy consumption of a sensor node shouldalso be taken into account. Therefore, the remaining energyratio is proposed in this paper, which is the proportion ofremaining energy accounting for the initial energy level. Theremaining energy ratio of a sensor node is defined as

𝑅𝑖=𝐸𝑖

residual𝐸𝑖

initial, (6)

where 𝐸𝑖residual and 𝐸𝑖

initial are the residual and initial energy ofnode 𝑖, respectively.

Hence, the node service ability can be calculated by

𝑃𝑉𝑖= 𝛼𝐷𝑖+ 𝛽𝑅𝑖= 𝛼

𝑁𝑖

reliability

𝑁𝑖

total+ 𝛽

𝐸𝑖

residual𝐸𝑖

initial, (7)

where 𝛼 + 𝛽 = 1, 𝛼 ∈ [0, 1], 𝛽 ∈ [0, 1], and 𝑃𝑉𝑖∈ [0, 1].

Based on the service abilities, cluster members can beclassified into three ranking domains {𝐺

1, 𝐺2, 𝐺3}. First, the

threshold Th of remaining energy ratio is defined. If theremaining energy ratio of a sensor node is less than thethreshold (𝑅

𝑖< Th), the sensor node does not have enough

energy for task processing and thus is divided into the thirdranking domain 𝐺

3. Otherwise, all the sensor nodes with

enough service abilities are sorted in order from the highestvalue to the lowest value, and the selection rate 𝛼 is defined,which is the percentage of sensor nodes in the first rankingdomain. 𝛼 can be dynamically adjusted according to theactual network parameters. Based on the sum number of thesensor nodes with enough service abilities and the selectionrate 𝛼, a part of sensor nodes can be selected as the firstranking domain nodes. The rest of sensor nodes are thesecond ranking domain nodes. However, if the number of thesensor nodeswith enough service abilities is less than or equalto 2, all the sensor nodes are divided into the first rankingdomain.

4.3. Service Ability Evaluation of Cluster. Based on the serviceabilities of cluster members, the service ability of a cluster 𝐶

𝑖

can be calculated as follows.First, the remaining energy ratio of a cluster is defined as

𝑅𝐶𝑖=𝐸𝐶𝑖

residual − 𝐶𝐶𝑖

𝐸𝐶𝑖

initial

× 100%, (8)

4th layer

3rd layer

1st layer

2nd layer

5th layer

6th layer

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150

130

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Figure 2: Task layering based on DAG task model.

where𝐸𝐶𝑖residual is the current residual energy of the cluster. It isthe sum of residual energy of all the cluster members, whichcan be calculated by

𝐸𝐶𝑖

residual = ∑

𝑉𝑗∈𝐶𝑖

𝐸𝑉𝑗

residual. (9)

𝐶𝐶𝑖

is the current energy consumption of the cluster,which can be calculated by

𝐶𝐶𝑖= ∑

𝑉𝑗∈𝐶𝑖

𝐶𝑉𝑗

total. (10)

𝐸𝐶𝑖

initrial is the initial energy level of the cluster, which canbe calculated by

𝐸𝐶𝑖

initial = ∑

𝑉𝑗∈𝐶𝑖

𝐸𝑉𝑗

initial. (11)

Then, a threshold Th𝐶of the remaining energy ratio is

defined. If 𝑅𝐶𝑖

≥ Th𝐶, the cluster can be a collaborative

cluster. Otherwise, the cluster is considered to be inefficientfor task processing.

Based on the remaining energy ratio of a cluster, thenumber of sensor nodes in the first and second rankingdomains, and the service abilities of cluster members, theservice ability of a cluster is defined as follows:

𝑃𝐶𝑖= 𝑔1× ∑

𝑉𝑗∈𝐺1

𝑃𝑉𝑗×𝑅𝐶𝑖−ThTh

, (12)

where 𝑔1is the number of sensor nodes in the first ranking

domain.We can conclude that the higher 𝑃𝐶𝑖is the easier and

𝐶𝑖can be selected as a collaborative cluster.

5. Details of DTAC

In this section, the details of DTAC are presented. DTACconsists of two steps.The first step is task layering.The second


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The number of times of simulations83 166 249 332 415 498

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Figure 3: Average residual energy and the number of sensor nodes.

step is task allocation. A complex task is first allocated toseveral clusters, which is called intercluster allocation. Then,in each cluster, subtasks are scheduled on different clustermembers, which is called intracluster allocation.

5.1. Task Layering. As shown in Figure 2, based on DAG, acomplex task can be layered to several layers as follows. (1)Anentry task is assigned in the first layer. Its immediate successortask is assigned in the second layer. (2) For a subtask, if itsimmediate predecessor task is assigned in the 𝑖th layer, thenthe subtask can be assigned in the (𝑖 + 1)th layer. (3) For thetasks in the same layer, they are placed in an arrangementaccording to the order of their execution deadline 𝑡dl fromsmall to large. The task with smaller 𝑡dl is placed in the frontof the layer; therefore, it can be preferentially allocated to

a sensor node with higher processing ability and completedwithin the deadline. After task layering, we can find that thetasks in the same layer do not have immediate predecessoror successor correlation. Therefore, they can be allocated todifferent nodes and concurrently processed, which ultimatelyreduces significant execution time.

5.2. Task Allocation. When a task source cluster senses acomplex task, it first checks the number of sensor nodes inthe first ranking domain. If the number of sensor nodes in thefirst ranking domain is greater than or equal to the numberof subtasks, all the subtasks are allocated to the sensor nodesin the first ranking domain. Otherwise, the source clusterwill ask neighbor clusters for collaboration based on theirservice abilities. If the collaborative clusters are selected,


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83 166 249 332 415 498The number of times of simulations

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(c) 𝑁task = 27

Figure 4: Network equilibrium and the number of sensor nodes.

the task allocation can be completed according to two steps:(1) intercluster allocation and (2) intracluster allocation.

5.2.1. Task Intercluster Allocation. Task allocation beginsfrom the first layer and then schedules the tasks in the nextlayer step by step. Only after the tasks in the upper layer areall scheduled, the tasks in the current layer can be allocated.We assume there are 𝑛 subtasks in the 𝑖th layer, which aredenoted by 𝑇

1, 𝑇2, . . . , 𝑇

𝑛. The number of sensor nodes in

the first ranking domain of the source cluster is ℎ. The taskallocation can be conducted as follows.

(i) If 𝑛 ≤ ℎ, all the tasks are allocated to the sensor nodesin the first ranking domain of the source cluster.

(ii) Otherwise, the first ℎ tasks 𝑇1, 𝑇2, . . . , 𝑇

ℎare allocated

to the sensor nodes in the first ranking domain of

the source cluster. Then, the source cluster asksfor neighbor clusters’ collaboration by broadcastingcollaboration request.

(iii) The neighbor clusters which receive the collaborationrequest first check their remaining energy ratio. Fora neighbor cluster 𝐶

𝑖, if 𝑅𝐶𝑖

≥ Th𝐶, it replies the

collaboration request and becomes a collaborativecluster. The reply message includes the service abilitylevel 𝑃

𝐶𝑖and the number of the sensor nodes 𝑔

𝑖in the

first ranking domain.(iv) Once receiving the reply message, the source cluster

places the collaborative clusters in an order accordingto their service abilities from small to large.

(v) The rest of tasks 𝑇ℎ+1

, 𝑇ℎ+2

, . . . , 𝑇𝑛are allocated to the

collaborative clusters according to the placed order. If


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Figure 5: Task execution time and the number of sensor nodes.

the number of collaborative clusters is still not enoughfor task allocation, the source cluster will continuallyask for neighbor clusters’ collaboration to process therest of tasks until all the tasks are completed.

5.2.2. Task Intracluster Allocation. If a cluster receives severalsubtasks, the cluster head schedules the subtasks to its clustermembers. In this paper, intracluster task allocation is imple-mented based on a contract net protocol [19]. In the negotia-tion process, the cluster head is a bidder which is responsiblefor task bidding. However, in conventional contract netprotocol, the bidding is completed by broadcasting whichwastes a large amount of energy. Therefore, in this paper, theranking domain is proposed to reduce energy consumptionand communication overheads by narrowing the scope of

the bidding. That is, the cluster head first chooses the sensornodes in the first ranking domain as objects of bidding. Onlywhen the sensor nodes in the first ranking domain fail tocomplete the tasks, the sensor nodes in the second rankingdomain are chosen as objects of bidding. Compared with thebidding with broadcast style, the improved bidding in thispaper is much more energy efficient.

If a sensor node cannot reply the task processed resultsin time, the cluster head needs to replay bidding informationto the sensor node. However, it is impossible for cluster headto repeat the bidding information without limit. Therefore,the maximum number of times of the replay is defined. If thenumber of relays gets to the defined maximum number, thecluster head will give up repeating the bidding regardless ofwhether the task is completed or not.


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Figure 6: Average residual energy and 𝛼.

6. Simulation

We use Matlab to evaluate the performance of DTAC. In oursimulations, sensor nodes are randomly deployed in a squareterrain (500m ∗ 500m) with a base station. The base stationis deployed at the center of the deployed area (250, 250)

and assumed to have unlimited energy. Other simulationparameters are shown in Table 1.

We performed two different sets of simulations. In thefirst set experiment, we study two parameters’ impact onthe performance of DTAC in terms of average residualenergy, network equilibrium, and task execution time. The

parameters are (1) the number of sensor nodes and (2) 𝛼

which is the ratio of sensor nodes in the first rankingdomain. In the second set experiment, we compare theperformance ofDTAC, EECNP [20], andED-WDP [13], sincethe three algorithms are all proposed based on the contractnet protocol [19].

6.1. Performance of DTAC. First, we simulate the perfor-mance of DTAC with different number of sensor nodes interms of average residual energy, network equilibrium, andtask execution time. Network equilibrium is used to evaluate


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Figure 7: Network equilibrium and 𝛼.

the network load balancing, which is denoted by the varianceof sensor nodes’ remaining energy.

6.1.1. Average Residual Energy and the Number of SensorNodes. We set the number of sensor nodes ranges from 125to 1000. As shown in Figure 3, the residual energy decreasesas the radio range increases. This is because more sensornodes consume more resources, which leads to less residualenergy. In addition, it is obvious that when the numberof tasks (𝑁task) grows from 9 to 27, the residual energydecreases since processing more tasks needs higher energyconsumption. Furthermore, with the increase of the numberof simulation times, the residual energy of sensor nodes alsodecreases. However, the less the number of sensor nodes is,

the faster the decrease of the residual energy is. Because,when the number of sensor nodes is small, they always needother nodes’ collaboration to process tasks.The collaborationprocess itself also consumes large amounts of energy, whichthus greatly reduces the average remaining energy of thewhole network. While more sensor nodes can provide moreof total energy, thus in this case, the average residual energydecreases slightly.

6.1.2. Network Equilibrium and the Number of Sensor Nodes.As shown in Figure 4, we can see that more sensor nodesresult in lower variance of sensor nodes’ remaining energy.It means that, with the same number of tasks, more sensornodes can have better network load balancing. In addition,


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Figure 8: Task execution time and 𝛼.

the variance of remaining energy increases as the numberof tasks increases. When the number of tasks increases, lesssensor nodes result in higher variance of remaining energy.That is to say, more tasks need more sensor nodes to balancethe network load. In order to balance the network load, wecan dynamically adjust the density of sensor nodes accordingto the amount of tasks.

6.1.3. Task Execution Time and the Number of Sensor Nodes.As shown in Figure 5, we can conclude that the numberof sensor nodes has little effect on the task execution time.In DTAC, all the tasks can be processed concurrently. It isunnecessary to deploy as many sensor nodes as possible fortask processing. For example, in Figure 5, 125 sensor nodes

are enough to complete tasks. In this case, more sensor nodessuch as 500 or 1000will not change the average task executiontime. In addition, we can find the execution time decreasesas the number of tasks increases. This is because the numberof tasks in the same layer grows as the total number of tasksincreases. The tasks in the same layer can be handled at thesame time; thus the total execution time of tasks rapidlyreduces. That is to say, DTAC is well suitable for processingmany complex tasks.

6.1.4. Average Residual Energy and 𝛼. In DTAC, the rankingdomain is proposed for task parallel processing. The rankingdomain is classified based on 𝛼, which is the predefined ratioof sensor nodes in the first ranking domain.Therefore, in this



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Figure 9: Average residual energy.

section, we simulate the performance of DTACwith differentvalues of 𝛼, which is set as 𝛼 = 0.1, 𝛼 = 0.3, 𝛼 = 0.5, 𝛼 = 0.7,and 𝛼 = 0.9.

Figure 6 shows that when the number of tasks is small(𝑁task = 9), the effect of 𝛼 is not distinct, since thesensor nodes in the first ranking domain are enough for taskprocessing. Even increasing 𝛼 can provide more candidatesensor nodes, the number of actual used sensor nodesremains unchanged, and thus the average residual energystays almost the same. However, we also find that, with theincreasing number of tasks, greater𝛼 results in higher averageresidual energy. This is because when there are many tasks

waiting for processing, increasing 𝛼 provides more sensornodes in the first ranking domain.The tasks can be completedin the process of intracluster allocation.Therefore, the energyconsumption for intercluster allocation is saved.

6.1.5. Network Equilibrium and 𝛼. Figure 7 shows that whenthe number of tasks is small (𝑁task = 9), the effect of 𝛼 isnot distinct. However, with the increasing number of tasks,greater 𝛼 results in better network load balancing. Sincegreater 𝛼 provides more sensor nodes for task allocation,the same amount of tasks can be scheduled on more sensor


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Figure 10: Network equilibrium.

nodes, which thus balances the energy consumption of sensornodes.

6.1.6. Task Execution Time and 𝛼. Figure 8 shows that whenthe number of tasks remains the same, the effect of 𝛼 isnot distinct. If the value of 𝛼 is small, the bidding needsto be implemented among several collaborative clusters.Otherwise, the bidding only needs to be implemented withinthe source cluster. The difference is the execution time ofintercluster and intracluster bidding. However, comparedwith the task processing time, the bidding time can be almostignored. In addition, we can find that the task processing timeis almost the small under different 𝛼. That is to say, 𝛼 greatly

impacts the average energy consumption and network loadbalancing but has little influence on the task execution time.However, the task execution time decreases as the numberof tasks increases. This is because the number of tasks in thesame layer grows as the total number of tasks increases. Thetasks in the same layer can be handled at the same time; thusthe total execution time of tasks rapidly reduces.

6.2. Comparison of DTAC, EECNP, and ED-WDP. In thissection, we compare the performance of DTAC with thatof other two schemes: EECNP [20] and ED-WDP [13]. Thenumber of sensor nodes is 250. 𝛼 is set as 0.3.


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Figure 11: Task execution time.

6.2.1. Average Residual Energy. As shown in Figure 9, it isobvious that the performance of DTAC is better than thatof the other two algorithms. Since DTAC can assign severaltasks at the same time, EECNP and ED-WDP can only assigna task at a time. Therefore, for the same amount of tasks,DTAC is much more energy efficient. In addition, the energyefficiency of DTAC becomes more apparent with the increaseof the number of simulation times. However, the energyconsumption of DTAC is also increased as the number oftasks increases since much more energy is consumed for taskbidding and communications.

6.2.2. Network Equilibrium. As shown in Figure 10, ED-WDPworks worst. DTAC outperforms EECNP and ED-WDP.Since ED-WDP always chose the optimal sensor nodes to

perform tasks, the frequently chosen nodes consume moreenergy than other nodes do; therefore the energy consump-tion of different sensor nodes is significantly different. WhileDTAC and EECNP are both taking the residual energy intoaccount, thus they can work better in terms of balancingenergy consumption. Compared with EECNP, DTAC canuse more sensor nodes in neighbor collaborative clusters toallocate tasks. Therefore, DTAC works better than EECNP.

6.2.3. Task Execution Time. As shown in Figure 11, EECNPworks worst. DTAC outperforms EECNP and ED-WDP.Since EECNP allocates tasks one by one, ED-WDP canallocate tasks while processing them. DTAC can provide taskparallel processing. Therefore, DTAC is the best one with theshortest execution time.


Table 1: Simulation parameters.

Parameter ValueNumber of sensor nodes 125 to 1000Packet size 25 bitData transmission rate 100 kbpsInitial energy of sensor nodes 2 JEnergy consumption of calculation 4 nJ/bitCommunication radius 75m𝑑0

150m𝜉elec 50 nJ/bit𝜉𝑓𝑠

10 pJ/bit/m2

𝜉amp 0.0013 pJ/bit/m4

7. Conclusion

In order to reduce average energy consumption of sensornodes, balance network loads, shorten task execution time,and prolong network lifetime, this paper studied a complextask allocation problem and proposed a task allocationstrategy called DTAC. DTAC consists of two steps: tasklayering and task allocation. Task allocation is implementedby intercluster allocation and intracluster allocation. In orderto ensure parallel processing of task, the ranking domain isproposed based on sensor nodes’ service abilities to classifythe sensor nodes into three levels. Sensor nodes with higherranking domain can be preferentially selected to performtasks. Based on the service abilities of cluster members, theservice abilities of clusters can be calculated. Then, the tasksource cluster can choose several collaborative clusters tocomplete complex tasks according to the service abilities ofthem. Simulation results show that the proposed algorithmis much more energy efficient than related works. Also, it isquite suitable for complex task processing.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgments

The work is supported by “Natural Science Foundation ofJiangsu Province of China, no. BK20131137,” “Science & Tech-nology Pillar Program (Social Development) of ChangzhouScience and Technology Bureau, no. CE20135052,” and“Jiangsu Province Ordinary University Graduate InnovationProject, no. CXZZ13 02.”

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