research article data dissemination protocol for...

Research ArticleData Dissemination Protocol for Mobile Sink inWireless Sensor Networks

Suraj Sharma1 and Sanjay Kumar Jena2

1 International Institute of Information Technology Bhubaneswar 751003 India2National Institute of Technology Rourkela 769008 India

Correspondence should be addressed to Suraj Sharma surajatnitrklgmailcom

Received 25 November 2013 Revised 2 March 2014 Accepted 21 March 2014 Published 27 April 2014

Academic Editor Geyong Min

Copyright copy 2014 S Sharma and S K Jena This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

In wireless sensor networks the sensor nodes find the route towards the sink to transmit the data The sensor node transmits thedata directly to the sink or it relays the data through neighbor nodes The nodes near to the sink transmit more data than othernodes It results in the small lifetime of the network To prolong the lifetime of the network we use the mobile sink approachThe mobile sink makes the network dynamic It is a challenging task to find the route in the dynamic network In this paper wehave proposed a distributed tree-based data dissemination (TEDD) protocol with mobile sink The protocol is validated throughsimulation and compared with the existing protocols using some metrics such as energy consumption average end-to-end delayand throughput The experiment results show that the proposed protocol outperforms the existing protocols

1 Introduction

Sensor network is a multihop network which consists ofhundreds of sensor nodes The main resource constraint ofthe sensor node is the energy Generally sensor networks aredeployed in the unattended and hostile environment such aswildlife detection continuous environment monitoring andmilitary So it is impossible to replace or recharge the batteryThemain goal of the proposed paper is to develop the energy-efficient protocol to prolong the lifetime of the network

In the sensor network the work of the sensor node is notonly to sense environmental data but also to relay those datato the sink Sink is a resource-rich node whose responsibilityis to collect the sensed data from the sensor nodes and sendit to the user via the Internet Sensor node has constraints oflimited communication range which does not allow directcommunication between source and sink It relays the data tothe sink in the multihop manner

The sensor nodes close to the sink transmit more datathan the other nodes in the network That is why theydepleted their energy and died This may result in thepartition of the network This situation is called ldquocrowdedcenter effectrdquo [1] or ldquoenergy hole problemrdquo [2] The energyhole problem can be overcome by using the mobile sink in

the network The mobile sink moves across the network andcollects the data from the sensor nodesThemovement of thesink may be random controlled or predefined The mobilesink makes the network dynamic So the data disseminationprotocols for network with static sink are unsuitable for thenetwork with the mobile sink It is a challenge to developenergy-efficient data dissemination protocols for the mobilesink

In this paper we have proposed a tree-based energy-efficient data dissemination protocol In this protocol anysensor node can disseminate the data to the sink via tree

The rest of the paper is organized as follows Related workis discussed in Section 2 We describe the working princi-ple and algorithm of the proposed model in Section 3 InSection 4 we discussed the experimental setup energymodelfor sensor nodes mobility model for sink and performancemetrics to evaluate the protocol The simulation result andanalysis are described in Section 5

2 Related Work

A number of data dissemination protocols have beeninvented for the mobile sink These data disseminationprotocols are broadly classified as single-hop or multihop

Hindawi Publishing CorporationJournal of Computational EngineeringVolume 2014 Article ID 560675 10 pageshttpdxdoiorg1011552014560675

2 Journal of Computational Engineering

In the single-hop data dissemination protocol distancebetween the source and the sink is one-hop Khan et al[3] and Sudarmani and Kumar [4] have proposed cluster-based approach where a cluster head sends aggregated datato the mobile sink In flat structure schemes [5ndash7] mobilesink broadcasts the small beacon packets periodically whileit moves to the new location The sensor nodes in its rangereceive the beacon and transmit data to the sink In all single-hop schemes data deliveries are more reliable but increasethe latency (delay) These types of schemes are suitable forthe delay tolerant applications [8ndash10]

The multihop data dissemination protocols have beenproposed by many researches [11ndash16] Virtual grid-basedgeographical-based cluster-based flat proactive routing-based and tree-based protocols are the examples of multihopdata dissemination protocols In virtual grid-based protocols[11 17] network is partitioned among the number of gridswhich consists of a limited number of sensor nodes and ahead node The head node is responsible for relaying thedata to the mobile sink Lee et al [18] have proposed a grid-based two-tier data disseminationmodel which discovers therouting path from each source to the sink in mobile sinkenvironment In geographical routing the sink informs itscurrent position to the network so that source node candisseminate the data to the sink [19 20] In cluster-baseddata dissemination strategy [3 4] the network is divided intoa number of clusters and each cluster is associated with acluster head Sink informs its present position to the nearestcluster head Each cluster head aggregates the data and sendsit to the sink Intanagonwiwat et al [21] have proposed aflat multihop routing protocol called directed diffusion Thesink node broadcasts its interest to the network The nodewhich satisfies the interest will send the data to the sinkThe routing path is constructed by setting gradients towardsthe sink In tree-based data dissemination approach a treeis constructed to disseminate the data to the sink The treestructure frequently changes according to the new positionof the sink Kim at al [12] have proposed a tree-based routingprotocol formobile sink In this protocol a tree is constructedto disseminate the data to mobile sink via an access nodeThe access node is the node which can send the data directlyto the sink and the location of the sink known by the accessnode only The dissemination tree is reconstructed when thenetwork needs to elect the new access node Zhang et al [22]provide the solution to reconfigure the tree when the sourceand the sink change their positions However it uses morecontrol packets as the tree size increases Hwang and Eom[14] have proposed an adaptive reversal tree (ART) protocolIt uses an adaptive reversal algorithm to make the reverselink to the sink node It uses a dynamic method to managethe mobile sink Hwang and Eom [13] have proposed anothertree-based protocol called Distributed Dynamic Shared TreeThe shape of the tree dynamically changes according to thesink location Formaking a sink-oriented tree the sink selectsa root node It maintains a robust connectivity with the sinkAnother tree-based protocol which supportsmultiplemobilesink has been proposed by Carneiro Viana et al knownas SUPPLE [15] This protocol allows each source to sendits generated data to the target set The target sets are the

nodes called storing nodes This scheme efficiently selectsthose well distributed storing nodesThey store the incomingdata until the sink comes in its trajectory Faheem andBoudjithave proposed a multipoint relay-based data disseminationprotocol called SN-MPR [16] In this scheme the tree isconstructed with the sink as a root

In this paper we have discussed tree-based protocols indetail as they increase energy efficiency and decrease thelatency due to their connectivity capability Although there isthe drawback of the above tree-based protocols which is thehigh mobility management cost the mobility managementcost depends on the affected area due to sink mobility Tosolve this problem we proposed a tree-based protocol Thestructure of the tree depends on the nodes instead of themobile sink Source nodes can send their data to the sinkthrough relay nodes

3 Proposed Model

We proposed an energy-efficient data dissemination protocolwhich generates a tree T from the sensor network It can berepresented as a graph 119866(119881 119864) where 119881 are the sensor nodesand 119864 are the links between them The tree construction isindependent of the sink position This method reduces thetraffic and prolongs the lifetime of the network

31 TEDD Tree-Based Efficient Data Dissemination ProtocolTEDD is an energy-efficient data dissemination protocol withmobile sink Initially it creates the tree in the network witha root node There are two categories of the nodes in thenetwork one is relay node (119877119873) and the other is nonrelaynode (119899119900119899-119877119873) The relay node is responsible to relay thedata from the nodes to its next relay nodeThe nonrelay nodecan only communicate its data to a relay node So it is aunidirectional communication between nonrelay and relaynodes However the communication is bidirectional betweentwo relay nodes The tree topology changes when the role ofthe node changes from relay to nonrelay or from nonrelay torelay node To rotate the responsibility of the relay node eachnodersquos residual energy is considered

The sink is mobile and collects the data from the sourcenodes through the gateway node The gateway node maybe the relay node or the nonrelay node The gateway nodeis selected by the sink based on the criteria mentioned inSection 32The sink periodically transmits a small beacon tomake the connection alive with the gateway node If the sinkmoves out from the range of the current gateway node thenit elects another node as the gateway nodeThe rotation of thegateway node can overcome the problem of the energy hole[2]

32 TEDDWorking Principle Let 119899 be the number of sensornodes which are randomly deployed in the network Allnodes are homogeneous and static in nature Each nodepossesses its id and knows the residual energy In thebeginning the initiator node triggers the neighbor discoveryphase by broadcasting the NBR DET control packet At the

Journal of Computational Engineering 3

RN nodeNon-RN node

(a) Initial view of tree construction

RN nodeNon-RN node

(b) Final view of tree construction

Figure 1 Tree construction steps shown in (a) and (b)

RN nodeNon-RN node

(a) Link reversal process

Gateway RNGateway non-RN

Sink

RN nodeNon-RN node

(b) Sink mobility management and gateway nodeselection

Figure 2 Link reversal and sink mobility management shown in (a) and (b)

end each node acquires neighbor list119873119861119877(119909) and candidaterelay node list 119877119873(119909)

The initiator node triggers the tree construction bybroadcasting T MSG Figure 1 illustrated the construction ofthe tree in the network There are two types of nodes in thenetwork as shown in Figure 1 relay node and nonrelay node

After the tree construction it is required to reverse thecommunication link The nonrelay node only communicatesto its parent relay node and the relay node communicates toits neighbor relay node The link between nonrelay and relaynodes is unidirectional and between relay node and relaynode is bidirectional as shown in Figure 2(a)

The source nodes can send their data to the sink bymanaging the mobility of the sink The interface betweennetwork and sink is the gateway node The gateway nodeis selected by the sink if that node is in the range Mobilesink periodically broadcasts the small signal called beaconto notify the neighbor sensor nodes The nodes that receivethe beacon send their response to become the gateway nodebased on their residual energy The sink selects one of themand declares it as the gateway node Among the responsesreceived by the sink it prefers relay node as the gateway If thegateway is a nonrelay node then its parent relay node will be

the gateway node and set the link as shown in Figure 2(b)Thegateway node sends the RREQ packet to relay nodes to makethe path towards the gateway node for data transmissionThedata dissemination starts as soon as routing path constructionis over as shown in Figure 3 The proposed protocol consistsof various phases like neighbor discovery tree constructionrelay node selection and data dissemination

321 Neighbor Discovery It is the initial phase of the pro-posed protocol in which each node finds its neighbor nodesAs illustrated in Algorithm 1 the initiator node broadcaststhe NBR DET packet It includes the node id of the sender119894119889119909and the willingness to be the relay node ⟨NBR DET

119894119889119909119882119868119871119871

119909⟩ The willingness is decided by the sender node

itself based on its residual energy 119864119903 If 119864

119903ge 119864Threshold

119882119868119871119871119909will be true otherwise false In the protocol we

assumed that the threshold energy is the half of the nodersquosinitial energy Any node 119909 that receives the NBR DET packetdoes the following operations

(i) It checks for existence of the sender node id if it is notfound it includes the sender node id in the neighborlist119873119861119877(119909)


Data structure for any sensor node 119909119873119861119877(119909) neighbor set of node 119909 initialized to 120601119877119873(119909) the set of neighbors of node 119909 which are willing to be the relay node initialized to 120601119882119868119871119871

119909 either true or false depends on the willingness of node 119909 to become a relay node

119873119861119877 119863119864119879 119878119864119873119879119909 set to true when the sensor node 119909 sends NBR DET packet initialized to false

Node 119909 receives following packet from node 119910NBR DET ⟨NBR DET 119894119889

119910119882119868119871119871

119910⟩

if (119910 notin 119873119861119877(119909)) then119873119861119877(119909) larr 119873119861119877(119909) cup 119910if (119882119868119871119871

119910== 119905119903119906119890) then

119877119873(119909) larr 119877119873(119909) cup 119910end ifif (119873119861119877 119863119864119879 119878119864119873119879

119909== 119891119886119897119904119890) then

119873119861119877 119863119864119879 119878119864119873119879119909larr 119905119903119906119890

119897 119903119887(NBR DET 119894119889119909119882119868119871119871

119909) ⊳ Broadcast NBR DET packet

elseDrop the packet

end ifelse

Drop the packetend if

Algorithm 1 Neighbor discovery

(ii) It checks for the willingness to be a relay node if thisis found to be true it includes sender node 119894119889 in therelay node list 119877119873(119909)

(iii) It checks if the NBR DET packet is broadcasted by thereceiver node if it is not broadcasted then it broad-casts the packet with format ⟨NBR DET 119894119889

119909119882119868119871119871

119909⟩

and makes119873119861119877 119863119864119879 119878119864119873119879119909as true

Neighbor discovery phase is over as soon as each nodebroadcasts its NBR DET packet At the end each node getsthe partial view of the network in the form of neighborinformation

322 Tree Construction and Relay Node Selection Aftergetting the neighbor list each node has the neighbor infor-mation such as 119894119889 and the willingness to become the relaynode The tree construction and relay node selection phaseare initiated by using the neighbor information As depictedin Algorithm 2 the initiator node starts the tree constructionby broadcasting the T MSG control packet The node receivesthe following packets during the tree construction and relaynode selection phase

T MSG In the process of tree construction T MSG con-trol packet is used The format of the packet is ⟨T MSG119894119889119910 119875119886119903119890119899119905(119910)⟩ Here 119894119889

119910is the sender node id and119875119886119903119890119899119905(119910)

is its parent node id Any node 119909 that receives the T MSGpacket performs the following operations

(i) If the senderrsquos parent node id is the same as thereceiver node id then it includes the sender 119894119889 in thechildren list 119862ℎ119894119897119889119903119890119899(119909) and includes the receiver 119894119889in the relay node list 119877119873nodes

(ii) If it has not selected any parent and sender belongs tothe list of relay node 119877119873nodes then it selects sendernode as its parent

(iii) If 119879 119872119878119866 119878119864119873119879 is false then it broadcasts T MSGpacket with modified parameter to the network

T ERR Timeout occurs to the node when the time durationexpires for the tree construction phase Any node119910 checks forits parent node if it does not exist then node 119910 broadcasts anerror message T ERR to its neighbor nodesThe receiver nodeperforms the following operation

(i) It initiates tree construction by broadcasting T MSG ifit belongs to the tree otherwise it drops the packet

In this way the rest of the nodes that do not belong to the treewill get the opportunity to connect with the tree

323 Data Dissemination Data can be generated by thenonrelay nodes or relay nodes The responsibility of relaynode is to forward the data to the next relay node Anynode can sense the data from the environment and send itto the next relay node Node 119909 receives the following packetduring the data dissemination phase from node 119910 as shownin Algorithm 3

DATA Each node in the network senses the environmentgenerates the data and sends it towards the next relay nodewith the format⟨DATA 119894119889

119910 119904119890119888 119899119900

119910⟩ Here 119894119889

119910is the 119894119889 of

sender node 119910 and 119904119890119888 119899119900119910is the data sequence number of

the node119910 Any node that receives the DATA packet performsthe following actions

(i) If receiver node is a relay node and it receives theduplicate data then it drops the data packet


Data structure for any sensor node 119909119862ℎ119894119897119889119903119890119899(119909) children set of node 119909 initialized to 120601119875119886119903119890119899119905(119909) parent of node 119909 initialized to 120601119877119873nodes set of relay nodes in the network119875119886119903119890119899119905 119878119890119897119890119888119905119890119889

119909 set to true once the sensor node 119909 selects its parent initialized to false

119879 119872119878119866 119878119864119873119879119909 set to true once the sensor node 119909 sends T MSG packet initialized to false

119877119873(119909) the set of neighbors of node 119909 which are willing to be the relay node initialized to 120601

Node 119909 receives following packets from node 119910 isin 119873119861119877(119909)T MSG ⟨T MSG 119894119889

119910 119875119886119903119890119899119905(119910)⟩

If (119894119889119909isin 119875119886119903119890119899119905(119910)) then

119862ℎ119894119897119889119903119890119899(119909) larr 119862ℎ119894119897119889119903119890119899(119909) cup 119894119889119910

119877119873nodes larr 119877119873nodes cup 119909 ⊳ node 119909 declares itself as a relay nodeDrop the packet

else if (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119909== 119891119886119897119904119890 and 119910 isin 119877119873(119909)) then

119875119886119903119890119899119905(119909) larr 119910119875119886119903119890119899119905 119878119890119897119890119888119905119890119889

119909larr 119905119903119906119890

if ((119879 119872119878119866 119878119864119873119879119909== 119891119886119897119904119890)) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890

119897 119903119887(T MSG 119894119889119909 119875119886119903119890119899119905(119909)) ⊳ Broadcast T MSG packet

elseDrop the packet

end ifelse


⊳ Timeout occurs to the node 119910 when the time duration expires for the tree constructionphase and 119879119868119872119864119874119880119879

119910become 119905119903119906119890

if (119879119868119872119864119874119880119879119910== 119905119903119906119890) then

if (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119910== 119891119886119897119904119890) then

119897 119903119887(T ERR 119894119889119910) ⊳ Broadcast T ERR packet

end ifend ifT ERR ⟨T ERR 119894119889

119910⟩

If (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119909== 119905119903119906119890) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end if

Algorithm 2 Tree construction and relay node selection


Sink

RN nodeNon-RN node

Figure 3 Path construction for gateway node and data transmis-sion

(ii) If receiver node is a gateway node then it forwardsthe data packet to the sink otherwise it forwardsthe DATA packet to its next relay node

(iii) It adds the sender 119894119889 and data sequence number to thelist 119878119890119899119889 119863119886119905119886(119909)

4 Simulation Model

41 Experimental Setup and Simulator The simulation isperformed using the network simulator NS-2 version 234 InNS-2 we concentrated in the network layer more specificallyon routing protocol Our aim is to simulate the proposedprotocol (TEDD) and the existing protocols such as SUPPLE[15] SN-MPR [16] and ART [14] to examine the energy con-sumption end-to-end delay and throughput of the network


Data structure for any sensor node 119909119878119890119899119889 119863119886119905119886(119909) node 119909 adds the pair of 119894119889 and 119904119890119888 119899119900 after receiving the DATA packet initialized to 120601

Node 119909 will receive following packet from node 119910 isin 119873119861119877(119909)DATA ⟨DATA 119894119889

119910 119904119890119888 119899119900

119910⟩

if (119909 isin RN119899119900119889119890) thenif (⟨119894119889

119910 119904119890119902 119899119900

119910⟩ notin 119878119890119899119889 119863119886119905119890(119909)) then

if (119909 == 119866119886119905119890119908119886119910) then119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900

119910

Forward DATA packet towards the sinkelse

119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900119910

Forward DATA packet to its neighbor relay node towards gatewayend if

elseDrop the packet

end ifelse


Algorithm 3 Data dissemination

In the simulation we use the specification of MICAz [23]a popular sensor mote to make the simulation supportto the real hardware parameters of the sensor networksThe MICAz mote transceiver power range is from minus24 dbmto 0 dbm and outdoor communication range is from 75mto 100m Our simulation follows the power consumptionmodel of the MICAz motes that require about 10mJ fortransmitting about 05mJ for receiving and about 004mJin idle mode The transceiver in the simulation has an 80mradio range at 24GHz frequency which is the case with theradio transceiver of a MICAz mote The initial energy ofeach sensor node is 10 J at the time of deployment For faircomparison between the proposed protocol and the existingprotocol we set simulation parameters equivalent to SUPPLE[15] SN-MPR [16] and ART [14] The simulation runs withup to 200 sensor nodes with energy constraint and a mobilesink with no constraint The nodes are randomly deployedin the 1000 times 1000meter2 area The simulation also includesIEEE 80211 as the underlying MAC protocol The sensornodersquos energy model and sink mobility model are discussedin Sections 42 and 43 In NS-2 we use omnidirectionalantenna and two-ray ground model for radio propagationEach sensor node senses the environment and generates dataof 64 bytes at each 120579 interval (here 120579 = 1 sec) and the sizeof the control packet is 32 bytes We performed extensivesimulations up to the duration of 200 seconds

42 Energy Model Each sensor node constantly calculatesits residual energy based on the energy model The energyconsumption in the sensor nodes depends on the variousradio interface mode and processing costThe energy model-ing in the sensor network is based on the theoretical energyconsumption In the energy model we consider the energyconsumption due to transmission of the packet (1) reception

of the packet (2) and energy spent by nodes in the idle mode(3) The total energy consumption (4) of a sensor node isthe sum of transmission receiving and idle mode energyconsumption Consider the following

119864Transmission = 119864119883119879

lowast 119905 (bits) + 119864119883119875

(d2) (1)

119864Receiving = 119864119883119877

lowast 119905 (bits) + 119864119883119860

lowast 119905 (bits) (2)

119864Sleep = 119864119883119868

lowast 119905 (sec) (3)

119864Total = 119864Transmission + 119864Receiving + 119864Sleep (4)

In (1) (2) and (3) 119864119883119879

refers to energy consumption perbit for transmission 119864

119883119875(d2) is the energy consumed for

finding the next hop forward node 119864119883119877

is the energyconsumption per bit for receiving and119864

119883119860refers to the

energy consumption per bit for aggregating the received datapacket 119864

119883119868is the energy consumption per second in ideal

mode In the proposed protocol the sensor nodes are of twotypes relay node or nonrelay node A nonrelay node will notconsume energy in aggregation (119864

119883119860) since it only receives

the control packets The energy consumption is calculated inthe joule per node to find the total energy consumption Theconsumption of energy is measured in each phase such as theneighbor detection tree construction relay node selectionsink mobility management and data dissemination phase

43 Mobility Model In the simulation to show the impactof the sink mobility we considered two mobility modelsGaussian-Markov model [24] and random waypoint model[25]

431 Gaussian-MarkovModel TheGaussian-Markov modelhas been initially proposed for PCS [24] and also used in


the ad hoc networks It is a mobility model which generatesthe next position depending on the previous position andconsidering the parameters like speed and direction

If at time 1199051the initial position of the sink is 119875(119883

1 1198841)

then the next position is determined with the followingequations

119883119899= 119883119899minus1

+ 119878119899minus1

Cos (119863119899minus1

)

119884119899= 119884119899minus1

+ 119878119899minus1

Sin (119863119899minus1

)

(5)

Here 119878119899minus1

and 119863119899minus1

are speed and direction (119883119899minus1

119884119899minus1

)

and (119883119899 119884119899) are the old and new positions of the sink

respectivelyTheGaussian-Markovmodel is used to calculatethe (119899)th position direction and speed from the (119899 minus 1)thposition direction and speed The equations for speed (119878

119899)

and direction (119863119899) are as follows

119878119899= 120572119878119899minus1

+ (1 minus 120572) 1198781015840radic(1 minus 1205722)119878119909

119899minus1

119863119899= 120572119863119899minus1

+ (1 minus 120572)1198631015840radic(1 minus 1205722)119863119909

119899minus1

(6)

where 1198781015840 and 119863

1015840 are the values representing the mean ofthe speed and direction as 119899 rarr infin 119878119909

119899minus1and 119863119909

119899minus1are

random variables from a Gaussian distribution The level ofrandomness is obtained by varying the value of 120572 from 0 to 1that is 0 le 120572 le 1

To restrict the sink within the bounded area we considerthe boundary value119875max that is [119883max 119884max]The calculationof the next position takes place from the previous nonbound-ary position Sink keeps the earlier position in the memoryas long as it does not get the valid subsequent position sothis model generates the relative motion of the sink For theexperiment we consider the sink Pause time (120575) as 5 sec Theextensive simulations are performed for the protocol with thespeed 119878 = (5 10 15 20 25 30)metersec

432 Random Waypoint Model Random waypoint modelis a ldquobenchmarkrdquo mobility model for ad hoc networksto evaluate the performance of the routing protocol Weconsider the random waypoint model for the sink mobilityin wireless sensor networks In the network simulator (NS-2) setdest tool from the CMU monarch group widely usedrandom waypoint model It randomly generates the nextposition in between 119875min and 119875max It then travels towardsits next position with constant speed or random speed Thesimulation is performed with the speed of 119878 = (5 10 15 2025 and 30)metersec When the sink node reaches the nextposition it pauses for a duration called the Pause time (120575)here we consider (120575) = 5 sec

Unlike the Gaussian-Markov model the random way-point model does not consider the previous position tocalculate the next position Hence it does not generate therelative motion In the simulation we have analyzed theimpact of relative motion and random motion of the sink invarious data dissemination protocols with the pause time (120575)and the speed (119878)

44 Performance Metrics

441 Energy Consumption Energy consumption at eachnode is consideredThe total communication energy includesneighbor discovery tree construction mobile sink man-agement and data transmission In the experiment weconsider the control packet and the control plus data packetcommunication The goal is to minimize the control packetoverhead to manage the mobile sink Due to the less controloverhead total communication energy also decreases whichprolongs the lifetime of the network This metric indicateshow efficiently a protocol works in the network

442 Average End-to-End Delay Average end-to-end delayismeasured as the average time between sending and success-fully receiving a packet Here the sender is the sensor nodeand the receiver is the sink We can say the average time apacket takes to reach the sink It considers all types of delayssuch as queuing delay route discovery delay and interfacedelay

443 Throughput (Packet Delivery Ratio) Throughput ismeasured as the ratio of packet received at the sink to thepacket sent by the sensor node Throughput defines thesuccessful delivery of the data packet Protocol with betterthroughput is considered as the consistent protocol Thismetric also indicates the degree of reliability and robustnessof the routing path

5 Simulation Result

The performance of the proposed protocol TEDD is evalu-ated and the result is compared with the tree-based mod-els such as SUPPLE [15] SN-MPR [16] and ART [14]In the simulation we have used a mobile sink and 200randomly deployed sensor nodes Each experiment has beenperformed with the varying sink speed from 5metersecto 30metersec We observed the impact of sink speed inenergy consumption end-to-end delay and throughput Inaddition we observed the impact of mobility models likeGaussian-Markov and random waypoint model in energyconsumption

51 Energy Consumption

511 Average EnergyConsumption of Control Packet Figure 4illustrated the average energy consumption of control packetin the network with varying sink speed To construct a treeand manage the sink mobility the sensor node transmits thecontrol packets The tree reconstruction and sink manage-ment cost much less in the proposed protocol (TEDD) ascompared to the other protocols

In ART [14] the whole network should know the currentposition of the sink The tree is rebuilt with the nearest nodeas root The tree reconstruction cost of ART depends on theaffected area

In SN-MPR [16] the root of the tree is the sink Like ARTSN-MPR also rebuilt the tree when the sink moves However


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)

(a) Gaussian-Markov mobility model

5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD

(b) Random waypoint mobility model

Figure 4 Average energy consumption for control packet with changing sink speed Result with different mobility model is shown in (a) and(b)

03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


Figure 5 Average energy consumption for data and control packet with changing sink speed Result with different mobility model is shownin (a) and (b)

the new position of the sink is only known to the selectednodes So the control overhead of the SN-MPR is less thanthat of the ART

In SUPPLE [15] the tree is constructed and storing nodesare selected The storing nodes temporarily store the datafrom the nodes When the sink comes in the range thestoring node transmits the data Unlike the above protocolsthe SUPPLE does not depend on the movement of the sinkSo control packet overhead is only due to tree formation andstoring node selection

In TEDD the new position of the sink should be knownonly to the one-hop neighbors which leads to the less controlpacket overhead

512 Average Energy Consumption of Data and ControlPacket The total energy consumption at each node for data

and control packet is shown in Figure 5 Although in theproposed protocol the average distance between source andsink is the same as ART and SN-MPR due to the lesscontrol packet overhead the proposed protocol (TEDD)outperforms the existing protocols

In SUPPLE the average distance between the sourceand the storing nodes is 1198992 where 119899 is the number ofsensor nodes The distance between the storing nodes tothe sink is one-hop Although the average distance is lessit consumes more energy than the proposed protocol Sinceeach storing node stores the data of all the sensor nodes itincreases the traffic of the network hence it raises the energyconsumption

513 Impact of Mobility Model in Energy Consumption Theaverage energy consumption due to control packet and data


5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD

(a) Average end-to-end delay

05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD

(b) Data delivery ratio

Figure 6 Result with changing sink speed is shown in (a) and (b)

plus control packet by using two different mobility modelsfor sink is illustrated in Figures 4 and 5 respectively Theresults shown in Figures 4(a) and 5(a) were obtained byusing the Gaussian-Markov mobility model To observe theimpact of mobility we use another mobility model called therandom waypoint model shown in Figures 4(b) and 5(b)It can be observed from Figures 4 and 5 that the cost ofthe tree reconstruction in SN-MPR and ART protocols withthe random waypoint model is higher than the Gaussian-Markov model This is due to the fact that the affected areais more in the random waypoint model in comparison withthe Gaussian-Markov model

Although the proposed protocol (TEDD) is not affectedby the different mobility models the current position of thesink does not have to disseminate throughout the networkIt only affects the one-hop neighbors at a time and the treeconstruction is independent of the sink position

The energy consumption in the SUPPLE protocolremains unchanged since there is no effect in the networkwith different mobility models

52 Average End-to-End Delay Delay mainly depends on thetime to find the valid path between source and sink Delayincreases if the data generation rate is more than the datareception rate Figure 6(a) presents the average end-to-enddelay with various sink speed using the Gaussian-Markovmobility model

The time required to reconstruct the tree based on thenew position of the sink causes the delay in ART and SN-MPR In SN-MPR the affected area is less than that in ARTSo ART causes more end-to-end delay than SN-MPR

In SUPPLE the sensor data is temporarily stored in thestoring nodes The storing nodes wait for the sink to come inthe trajectory It causesmore end-to-end delay than the aboveprotocols

The proposed protocol TEDD overcomes all the draw-backs of SUPPLE ART and SN-MPR because it requiresless cost and time to manage the mobility of the sink It

can be seen from Figure 6(a) that TEDD outperforms theabovementioned protocols in terms of average end-to-enddelay

53 Throughput (Data Delivery Ratio) Figure 6(b) showsthe data delivery ratio with respect to different sink speedsThroughput represents the success ratio of the data deliverySUPPLE performed well because the distance between sinkand storing node is one-hop SN-MPR also performed welldue to less affected area and recovery technique The successratio for ART decreases as the sink speed rises The highersink speed increases the frequency of link failure whichcauses data loss However the proposed protocol (TEDD)is robust that is the link is always maintained between thesource and the sink so the throughput is very high

54 Conclusion In this paper we proposed a distributedrobust and efficient tree-based data dissemination protocolcalled TEDD The proposed protocol can effectively and effi-ciently manage the sink mobility We simulated the proposedprotocol with two different mobility models The results arecompared with the existing protocols such as SUPPLE SN-MPR and ART It was observed that TEDD outperformedthe above protocols due to its unique method to handle themobility of the sink

Conflict of Interests

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

References

[1] L Popa A Rostamizadeh R Karp C Papadimitriou andI Stoica ldquoBalancing traffic load in wireless networks withcurveball routingrdquo in Proceedings of the 8th ACM InternationalSymposium on Mobile Ad Hoc Networking and Computing pp170ndash179 September 2007


[2] J Li and P Mohapatra ldquoAnalytical modeling and mitigationtechniques for the energy hole problem in sensor networksrdquoPervasive andMobile Computing vol 3 no 3 pp 233ndash254 2007

[3] N M Khan I Ali Z Khalid G Ahmed A A Kavokin and RRamer ldquoQuasi centralized clustering approach for an energy-efficient and vulnerability-aware routing in wireless sensornetworksrdquo inProceedings of the 1st ACM InternationalWorkshopon Heterogeneous Sensor and Actor Networks HeterSanet 2008pp 67ndash72 ACM May 2008

[4] R Sudarmani and K R S Kumar ldquoEnergy-efficient clusteringalgorithm for heterogeneous sensor networkswithmobile sinkrdquoEuropean Journal of Scientific Research vol 68 no 1 pp 60ndash712012

[5] L Song and D Hatzinakos ldquoDense wireless sensor networkswith mobile sinksrdquo in Proceedings of the IEEE InternationalConference on Acoustics Speech and Signal Processing (ICASSPrsquo05) pp 677ndash680 IEEE March 2005

[6] L Song and D Hatzinakos ldquoArchitecture of wireless sensornetworks with mobile sinks sparsely deployed sensorsrdquo IEEETransactions on Vehicular Technology vol 56 no 4 pp 1826ndash1836 2007

[7] D Puthal B Sahoo and S Sharma ldquoDynamic model forefficient data collection in wireless sensor networks withmobilesinkrdquo International Journal of Computer Science and Teleologyvol 3 no 1 pp 623ndash628 2012

[8] P Juang H Oki Y Wang M Martonosi L S Peh and DRubenstein ldquoEnergyefficient computing for wildlife trackingdesign tradeoffs and early experiences with zebranetrdquo SIGOPSOperation System Review vol 36 no 5 pp 96ndash107 2002

[9] S Farrell V Cahill D Geraghty I Humphreys and PMcDonald ldquoWhen TCP breaks delay- and disruption-tolerantnetworkingrdquo IEEE Internet Computing vol 10 no 4 pp 72ndash782006

[10] L Selavo A Wood Q Cao et al ldquoLuster Wireless sensornetwork for environmental researchrdquo in Proceedings of the 5thACM International Conference on Embedded Networked SensorSystems pp 103ndash116 ACM New York NY USA November2007

[11] H Luo F Ye J Cheng S Lu and L Zhang ldquoTTDD two-tier data dissemination in large-scale wireless sensor networksrdquoWireless Networks vol 11 no 1-2 pp 161ndash175 2005

[12] H S KimT FAbdelzaher andWHKwon ldquoMinimum-energyasynchronous dissemination to mobile sinks in wireless sensornetworksrdquo in Proceedings of the 1st International Conferenceon Embedded Networked Sensor Systems pp 193ndash204 ACMNovember 2003

[13] K I Hwang J In and D S Eom ldquoDistributed dynamicshared tree for minimum energy data aggregation of multiplemobile sinks in wireless sensor networksrdquo Proceedings of the3rd European conference on Wireless Sensor Networks SpringerBerlin Germany vol 3868 pp 132ndash147 2006

[14] K I Hwang and D S Eom ldquoAdaptive sink mobility manage-ment scheme for wireless sensor networksrdquo in Proceedings ofthe 3rd International Conference on Ubiquitous Intelligence andComputing Lecture Notes in Computer Science pp 478ndash487Springer Berlin Germany 2006

[15] A Carneiro Viana T Herault T Largillier S Peyronnet andF Zaıdi ldquoSupple A flexible probabilistic data disseminationprotocol for wireless sensor networksrdquo in Proceedings of the13th ACM International Conference on Modeling Analysis andSimulation of Wireless and Mobile Systems pp 385ndash392 ACMOctober 2010

[16] Y Faheem and S Boudjit ldquoSN-MPR A multi-point relay basedrouting protocol for wireless sensor networksrdquo in Proceedingsof the IEEEACM International Conference on Green Computingand Communications amp International Conference on CyberPhysical and Social Computing pp 761ndash767 IEEE ComputerSociety December 2010

[17] N C Wang Y F Huang J S Chen and P C Yeh ldquoEnergy-aware data aggregation for grid-based wireless sensor networkswith a mobile sinkrdquoWireless Personal Communications vol 43no 4 pp 1539ndash1551 2007

[18] E Lee S Park F Yu Y Choi M S Jin and S H KimldquoA predictable mobility-based data dissemination protocol forwireless sensor networksrdquo in Proceedings of the 22nd Inter-national Conference on Advanced Information Networking andApplications pp 741ndash747 IEEE Computer Society March 2008

[19] G Wang T Wang W Jia M Guo and J Li ldquoAdaptive locationupdates for mobile sinks in wireless sensor networksrdquo Journalof Supercomputing vol 47 no 2 pp 127ndash145 2009

[20] A Munari W Schott and S Krishnan ldquoEnergy-efficientrouting in mobile wireless sensor networks using mobilitypredictionrdquo in Proceedings of the IEEE 34th Conference on LocalComputerNetworks (LCN rsquo09) pp 514ndash521 IEEEOctober 2009

[21] C Intanagonwiwat R Govindan and D Estrin ldquoDirecteddiffusion a scalable and robust communication paradigm forsensor networksrdquo in Proceedings of the 6th Annual InternationalConference on Mobile Computing and Networking (MOBICOMrsquo00) pp 56ndash67 ACM Boston Mass USA August 2000

[22] W Zhang G Cao and T La Porta ldquoDynamic proxy tree-baseddata dissemination schemes for wireless sensor networksrdquoWireless Networks vol 13 no 5 pp 583ndash595 2007

[23] CrossbowTechnology IMicaz datasheet Technical report SanJose Calif USA httpwwwopenautomationnetuploadsprod-uctosmicazdatasheetpdf

[24] B Liang and Z J Haas ldquoPredictive distance-based mobilitymanagement for PCS networksrdquo in Proceedings of the 18thAnnual Joint Conference of the IEEE Computer and Communi-cations Societie pp 1377ndash1384 IEEE March 1999

[25] J Broch D AMaltz D B Johnson Y C Hu and J Jetcheva ldquoAperformance comparison of multi-hop wireless ad hoc networkrouting protocolsrdquo in Proceedings of the 4th Annual ACMIEEEInternational Conference on Mobile Computing and Networkingpp 85ndash97 ACM 1998

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



In the single-hop data dissemination protocol distancebetween the source and the sink is one-hop Khan et al[3] and Sudarmani and Kumar [4] have proposed cluster-based approach where a cluster head sends aggregated datato the mobile sink In flat structure schemes [5ndash7] mobilesink broadcasts the small beacon packets periodically whileit moves to the new location The sensor nodes in its rangereceive the beacon and transmit data to the sink In all single-hop schemes data deliveries are more reliable but increasethe latency (delay) These types of schemes are suitable forthe delay tolerant applications [8ndash10]

The multihop data dissemination protocols have beenproposed by many researches [11ndash16] Virtual grid-basedgeographical-based cluster-based flat proactive routing-based and tree-based protocols are the examples of multihopdata dissemination protocols In virtual grid-based protocols[11 17] network is partitioned among the number of gridswhich consists of a limited number of sensor nodes and ahead node The head node is responsible for relaying thedata to the mobile sink Lee et al [18] have proposed a grid-based two-tier data disseminationmodel which discovers therouting path from each source to the sink in mobile sinkenvironment In geographical routing the sink informs itscurrent position to the network so that source node candisseminate the data to the sink [19 20] In cluster-baseddata dissemination strategy [3 4] the network is divided intoa number of clusters and each cluster is associated with acluster head Sink informs its present position to the nearestcluster head Each cluster head aggregates the data and sendsit to the sink Intanagonwiwat et al [21] have proposed aflat multihop routing protocol called directed diffusion Thesink node broadcasts its interest to the network The nodewhich satisfies the interest will send the data to the sinkThe routing path is constructed by setting gradients towardsthe sink In tree-based data dissemination approach a treeis constructed to disseminate the data to the sink The treestructure frequently changes according to the new positionof the sink Kim at al [12] have proposed a tree-based routingprotocol formobile sink In this protocol a tree is constructedto disseminate the data to mobile sink via an access nodeThe access node is the node which can send the data directlyto the sink and the location of the sink known by the accessnode only The dissemination tree is reconstructed when thenetwork needs to elect the new access node Zhang et al [22]provide the solution to reconfigure the tree when the sourceand the sink change their positions However it uses morecontrol packets as the tree size increases Hwang and Eom[14] have proposed an adaptive reversal tree (ART) protocolIt uses an adaptive reversal algorithm to make the reverselink to the sink node It uses a dynamic method to managethe mobile sink Hwang and Eom [13] have proposed anothertree-based protocol called Distributed Dynamic Shared TreeThe shape of the tree dynamically changes according to thesink location Formaking a sink-oriented tree the sink selectsa root node It maintains a robust connectivity with the sinkAnother tree-based protocol which supportsmultiplemobilesink has been proposed by Carneiro Viana et al knownas SUPPLE [15] This protocol allows each source to sendits generated data to the target set The target sets are the

nodes called storing nodes This scheme efficiently selectsthose well distributed storing nodesThey store the incomingdata until the sink comes in its trajectory Faheem andBoudjithave proposed a multipoint relay-based data disseminationprotocol called SN-MPR [16] In this scheme the tree isconstructed with the sink as a root

In this paper we have discussed tree-based protocols indetail as they increase energy efficiency and decrease thelatency due to their connectivity capability Although there isthe drawback of the above tree-based protocols which is thehigh mobility management cost the mobility managementcost depends on the affected area due to sink mobility Tosolve this problem we proposed a tree-based protocol Thestructure of the tree depends on the nodes instead of themobile sink Source nodes can send their data to the sinkthrough relay nodes

3 Proposed Model

We proposed an energy-efficient data dissemination protocolwhich generates a tree T from the sensor network It can berepresented as a graph 119866(119881 119864) where 119881 are the sensor nodesand 119864 are the links between them The tree construction isindependent of the sink position This method reduces thetraffic and prolongs the lifetime of the network

31 TEDD Tree-Based Efficient Data Dissemination ProtocolTEDD is an energy-efficient data dissemination protocol withmobile sink Initially it creates the tree in the network witha root node There are two categories of the nodes in thenetwork one is relay node (119877119873) and the other is nonrelaynode (119899119900119899-119877119873) The relay node is responsible to relay thedata from the nodes to its next relay nodeThe nonrelay nodecan only communicate its data to a relay node So it is aunidirectional communication between nonrelay and relaynodes However the communication is bidirectional betweentwo relay nodes The tree topology changes when the role ofthe node changes from relay to nonrelay or from nonrelay torelay node To rotate the responsibility of the relay node eachnodersquos residual energy is considered

The sink is mobile and collects the data from the sourcenodes through the gateway node The gateway node maybe the relay node or the nonrelay node The gateway nodeis selected by the sink based on the criteria mentioned inSection 32The sink periodically transmits a small beacon tomake the connection alive with the gateway node If the sinkmoves out from the range of the current gateway node thenit elects another node as the gateway nodeThe rotation of thegateway node can overcome the problem of the energy hole[2]

32 TEDDWorking Principle Let 119899 be the number of sensornodes which are randomly deployed in the network Allnodes are homogeneous and static in nature Each nodepossesses its id and knows the residual energy In thebeginning the initiator node triggers the neighbor discoveryphase by broadcasting the NBR DET control packet At the


RN nodeNon-RN node


RN nodeNon-RN node



RN nodeNon-RN node



Sink

RN nodeNon-RN node









119894119889119909119882119868119871119871












119910119882119868119871119871

119910⟩


119910== 119905119903119906119890) then

119877119873(119909) larr 119877119873(119909) cup 119910end ifif (119873119861119877 119863119864119879 119878119864119873119879

119909== 119891119886119897119904119890) then

119873119861119877 119863119864119879 119878119864119873119879119909larr 119905119903119906119890

119897 119903119887(NBR DET 119894119889119909119882119868119871119871


elseDrop the packet

end ifelse





119909119882119868119871119871

119909⟩

and makes119873119861119877 119863119864119879 119878119864119873119879119909as true














119910 119904119890119888 119899119900

119910⟩ Here 119894119889

119910is the 119894119889 of










119910 119875119886119903119890119899119905(119910)⟩

If (119894119889119909isin 119875119886119903119890119899119905(119910)) then

119862ℎ119894119897119889119903119890119899(119909) larr 119862ℎ119894119897119889119903119890119899(119909) cup 119894119889119910



119875119886119903119890119899119905(119909) larr 119910119875119886119903119890119899119905 119878119890119897119890119888119905119890119889

119909larr 119905119903119906119890

if ((119879 119872119878119866 119878119864119873119879119909== 119891119886119897119904119890)) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end ifelse



119910become 119905119903119906119890

if (119879119868119872119864119874119880119879119910== 119905119903119906119890) then

if (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119910== 119891119886119897119904119890) then



119910⟩

If (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119909== 119905119903119906119890) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end if



Sink

RN nodeNon-RN node




4 Simulation Model





119910 119904119890119888 119899119900

119910⟩

if (119909 isin RN119899119900119889119890) thenif (⟨119894119889

119910 119904119890119902 119899119900

119910⟩ notin 119878119890119899119889 119863119886119905119890(119909)) then

if (119909 == 119866119886119905119890119908119886119910) then119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900

119910


119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900119910


elseDrop the packet

end ifelse






119864Transmission = 119864119883119879

lowast 119905 (bits) + 119864119883119875

(d2) (1)

119864Receiving = 119864119883119877

lowast 119905 (bits) + 119864119883119860


119864Sleep = 119864119883119868

lowast 119905 (sec) (3)


In (1) (2) and (3) 119864119883119879
















1 1198841)


119883119899= 119883119899minus1

+ 119878119899minus1

Cos (119863119899minus1

)

119884119899= 119884119899minus1

+ 119878119899minus1

Sin (119863119899minus1

)

(5)

Here 119878119899minus1

and 119863119899minus1


119884119899minus1

)



119899)


119878119899= 120572119878119899minus1


119899minus1

119863119899= 120572119863119899minus1


119899minus1

(6)

where 1198781015840 and 119863


119899minus1and 119863119909

119899minus1are









5 Simulation Result







5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD



03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD











5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










RN nodeNon-RN node


RN nodeNon-RN node



RN nodeNon-RN node



Sink

RN nodeNon-RN node









119894119889119909119882119868119871119871












119910119882119868119871119871

119910⟩


119910== 119905119903119906119890) then

119877119873(119909) larr 119877119873(119909) cup 119910end ifif (119873119861119877 119863119864119879 119878119864119873119879

119909== 119891119886119897119904119890) then

119873119861119877 119863119864119879 119878119864119873119879119909larr 119905119903119906119890

119897 119903119887(NBR DET 119894119889119909119882119868119871119871


elseDrop the packet

end ifelse





119909119882119868119871119871

119909⟩

and makes119873119861119877 119863119864119879 119878119864119873119879119909as true














119910 119904119890119888 119899119900

119910⟩ Here 119894119889

119910is the 119894119889 of










119910 119875119886119903119890119899119905(119910)⟩

If (119894119889119909isin 119875119886119903119890119899119905(119910)) then

119862ℎ119894119897119889119903119890119899(119909) larr 119862ℎ119894119897119889119903119890119899(119909) cup 119894119889119910



119875119886119903119890119899119905(119909) larr 119910119875119886119903119890119899119905 119878119890119897119890119888119905119890119889

119909larr 119905119903119906119890

if ((119879 119872119878119866 119878119864119873119879119909== 119891119886119897119904119890)) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end ifelse



119910become 119905119903119906119890

if (119879119868119872119864119874119880119879119910== 119905119903119906119890) then

if (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119910== 119891119886119897119904119890) then



119910⟩

If (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119909== 119905119903119906119890) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end if



Sink

RN nodeNon-RN node




4 Simulation Model





119910 119904119890119888 119899119900

119910⟩

if (119909 isin RN119899119900119889119890) thenif (⟨119894119889

119910 119904119890119902 119899119900

119910⟩ notin 119878119890119899119889 119863119886119905119890(119909)) then

if (119909 == 119866119886119905119890119908119886119910) then119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900

119910


119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900119910


elseDrop the packet

end ifelse






119864Transmission = 119864119883119879

lowast 119905 (bits) + 119864119883119875

(d2) (1)

119864Receiving = 119864119883119877

lowast 119905 (bits) + 119864119883119860


119864Sleep = 119864119883119868

lowast 119905 (sec) (3)


In (1) (2) and (3) 119864119883119879
















1 1198841)


119883119899= 119883119899minus1

+ 119878119899minus1

Cos (119863119899minus1

)

119884119899= 119884119899minus1

+ 119878119899minus1

Sin (119863119899minus1

)

(5)

Here 119878119899minus1

and 119863119899minus1


119884119899minus1

)



119899)


119878119899= 120572119878119899minus1


119899minus1

119863119899= 120572119863119899minus1


119899minus1

(6)

where 1198781015840 and 119863


119899minus1and 119863119909

119899minus1are









5 Simulation Result







5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD



03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD











5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














119910119882119868119871119871

119910⟩


119910== 119905119903119906119890) then

119877119873(119909) larr 119877119873(119909) cup 119910end ifif (119873119861119877 119863119864119879 119878119864119873119879

119909== 119891119886119897119904119890) then

119873119861119877 119863119864119879 119878119864119873119879119909larr 119905119903119906119890

119897 119903119887(NBR DET 119894119889119909119882119868119871119871


elseDrop the packet

end ifelse





119909119882119868119871119871

119909⟩

and makes119873119861119877 119863119864119879 119878119864119873119879119909as true














119910 119904119890119888 119899119900

119910⟩ Here 119894119889

119910is the 119894119889 of










119910 119875119886119903119890119899119905(119910)⟩

If (119894119889119909isin 119875119886119903119890119899119905(119910)) then

119862ℎ119894119897119889119903119890119899(119909) larr 119862ℎ119894119897119889119903119890119899(119909) cup 119894119889119910



119875119886119903119890119899119905(119909) larr 119910119875119886119903119890119899119905 119878119890119897119890119888119905119890119889

119909larr 119905119903119906119890

if ((119879 119872119878119866 119878119864119873119879119909== 119891119886119897119904119890)) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end ifelse



119910become 119905119903119906119890

if (119879119868119872119864119874119880119879119910== 119905119903119906119890) then

if (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119910== 119891119886119897119904119890) then



119910⟩

If (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119909== 119905119903119906119890) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end if



Sink

RN nodeNon-RN node




4 Simulation Model





119910 119904119890119888 119899119900

119910⟩

if (119909 isin RN119899119900119889119890) thenif (⟨119894119889

119910 119904119890119902 119899119900

119910⟩ notin 119878119890119899119889 119863119886119905119890(119909)) then

if (119909 == 119866119886119905119890119908119886119910) then119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900

119910


119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900119910


elseDrop the packet

end ifelse






119864Transmission = 119864119883119879

lowast 119905 (bits) + 119864119883119875

(d2) (1)

119864Receiving = 119864119883119877

lowast 119905 (bits) + 119864119883119860


119864Sleep = 119864119883119868

lowast 119905 (sec) (3)


In (1) (2) and (3) 119864119883119879
















1 1198841)


119883119899= 119883119899minus1

+ 119878119899minus1

Cos (119863119899minus1

)

119884119899= 119884119899minus1

+ 119878119899minus1

Sin (119863119899minus1

)

(5)

Here 119878119899minus1

and 119863119899minus1


119884119899minus1

)



119899)


119878119899= 120572119878119899minus1


119899minus1

119863119899= 120572119863119899minus1


119899minus1

(6)

where 1198781015840 and 119863


119899minus1and 119863119909

119899minus1are









5 Simulation Result







5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD



03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD











5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















119910 119875119886119903119890119899119905(119910)⟩

If (119894119889119909isin 119875119886119903119890119899119905(119910)) then

119862ℎ119894119897119889119903119890119899(119909) larr 119862ℎ119894119897119889119903119890119899(119909) cup 119894119889119910



119875119886119903119890119899119905(119909) larr 119910119875119886119903119890119899119905 119878119890119897119890119888119905119890119889

119909larr 119905119903119906119890

if ((119879 119872119878119866 119878119864119873119879119909== 119891119886119897119904119890)) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end ifelse



119910become 119905119903119906119890

if (119879119868119872119864119874119880119879119910== 119905119903119906119890) then

if (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119910== 119891119886119897119904119890) then



119910⟩

If (119875119886119903119890119899119905 119878119890119897119890119888119905119890119889119909== 119905119903119906119890) then

119879 119872119878119866 119878119864119873119879119909larr 119905119903119906119890


elseDrop the packet

end if



Sink

RN nodeNon-RN node




4 Simulation Model





119910 119904119890119888 119899119900

119910⟩

if (119909 isin RN119899119900119889119890) thenif (⟨119894119889

119910 119904119890119902 119899119900

119910⟩ notin 119878119890119899119889 119863119886119905119890(119909)) then

if (119909 == 119866119886119905119890119908119886119910) then119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900

119910


119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900119910


elseDrop the packet

end ifelse






119864Transmission = 119864119883119879

lowast 119905 (bits) + 119864119883119875

(d2) (1)

119864Receiving = 119864119883119877

lowast 119905 (bits) + 119864119883119860


119864Sleep = 119864119883119868

lowast 119905 (sec) (3)


In (1) (2) and (3) 119864119883119879
















1 1198841)


119883119899= 119883119899minus1

+ 119878119899minus1

Cos (119863119899minus1

)

119884119899= 119884119899minus1

+ 119878119899minus1

Sin (119863119899minus1

)

(5)

Here 119878119899minus1

and 119863119899minus1


119884119899minus1

)



119899)


119878119899= 120572119878119899minus1


119899minus1

119863119899= 120572119863119899minus1


119899minus1

(6)

where 1198781015840 and 119863


119899minus1and 119863119909

119899minus1are









5 Simulation Result







5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD



03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD











5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












119910 119904119890119888 119899119900

119910⟩

if (119909 isin RN119899119900119889119890) thenif (⟨119894119889

119910 119904119890119902 119899119900

119910⟩ notin 119878119890119899119889 119863119886119905119890(119909)) then

if (119909 == 119866119886119905119890119908119886119910) then119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900

119910


119878119890119899119889 119863119886119905119886(119909) larr 119878119890119899119889 119863119886119905119886(119909) cup 119910 119904119890119888 119899119900119910


elseDrop the packet

end ifelse






119864Transmission = 119864119883119879

lowast 119905 (bits) + 119864119883119875

(d2) (1)

119864Receiving = 119864119883119877

lowast 119905 (bits) + 119864119883119860


119864Sleep = 119864119883119868

lowast 119905 (sec) (3)


In (1) (2) and (3) 119864119883119879
















1 1198841)


119883119899= 119883119899minus1

+ 119878119899minus1

Cos (119863119899minus1

)

119884119899= 119884119899minus1

+ 119878119899minus1

Sin (119863119899minus1

)

(5)

Here 119878119899minus1

and 119863119899minus1


119884119899minus1

)



119899)


119878119899= 120572119878119899minus1


119899minus1

119863119899= 120572119863119899minus1


119899minus1

(6)

where 1198781015840 and 119863


119899minus1and 119863119909

119899minus1are









5 Simulation Result







5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD



03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD











5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












1 1198841)


119883119899= 119883119899minus1

+ 119878119899minus1

Cos (119863119899minus1

)

119884119899= 119884119899minus1

+ 119878119899minus1

Sin (119863119899minus1

)

(5)

Here 119878119899minus1

and 119863119899minus1


119884119899minus1

)



119899)


119878119899= 120572119878119899minus1


119899minus1

119863119899= 120572119863119899minus1


119899minus1

(6)

where 1198781015840 and 119863


119899minus1and 119863119909

119899minus1are









5 Simulation Result







5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD



03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD











5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r

SUPPLESN-MPR

ARTTEDD

cont

rol p

acke

ts (J

)


5 10 15 20 25 300

00501

01502

02503

03504

04505

Sink speed (ms)

Aver

age e

nerg

y co

nsum

ptio

n fo

r co

ntro

l pac

kets

(J)

SUPPLESN-MPR

ARTTEDD



03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD


03

035

04

045

05

055

06

065

Aver

age e

nerg

y co

nsum

ptio

n fo

r da

ta an

d co

ntro

l pac

kets

(J)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD











5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










5 10 15 20 25 30Sink speed (ms)

0

001

002

003

004

005

006

007Av

erag

e dat

a del

iver

y de

lay (s

)

SUPPLESN-MPR

ARTTEDD


05055

06065

07075

08085

09095

1

Dat

a del

iver

y ra

tio (

)

5 10 15 20 25 30Sink speed (ms)

SUPPLESN-MPR

ARTTEDD















References





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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