FEAR: Fuzzy-Based Energy Aware Routing Protocol for Wireless Sensor Networks

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1 Int l J. of Communcatons, etwork and System Scences, 2011, 4, do: /jcns Publshed Onlne June 2011 ( FEAR: Fuzzy-Based Energy Aware Routng Protocol for Wreless Sensor etworks Abstract Iman M. ALMoman, Maha K. Saadeh Computer Scence department, Kng Abdullah II School for Informaton Technology (KASIT), The Unversty of Jordan, Amman, Jordan E-mal:.moman@ju.edu.jo, saadeh.maha@yahoo.com Receved Aprl 9, 2011; revsed Aprl 28, 2011; accepted May 7, 2011 Wreless Sensor etworks (WSs) are used n dfferent cvlan, mltary, and ndustral applcatons. Recently, many routng protocols have been proposed attemptng to fnd sutable routes to transmt data. In ths paper we propose a Fuzzy Energy Aware tree-based Routng (FEAR) protocol that ams to enhance exstng tree-based routng protocols and prolong the network s lfe tme by consderng sensors lmted energy. The desgn and mplementaton of the new protocol s based on cross-layer structure where nformaton from dfferent layers are utlzed to acheve the best power savng. Each node mantans a lst of ts neghbors n order to use neghbors lnks n addton to the parent-chld lnks. The protocol s tested and compared wth other tree-based protocols and the smulaton results show that FEAR protocol s more energy-effcent than comparable protocols. Accordng to the results FEAR protocol saves up to 70.5% n the number of generated control messages and up to 55.08% n the consumed power. Keywords: Energy Awareness, Fuzzy Logc, Routng Protocol, Wreless Sensor etwork, WS 1. Introducton WSs are envsaged to become a very sgnfcant enablng technology n many sectors as they are wdely used n many cvlan and mltary as well as ndustral applcatons [1,2]. Unlke tradtonal wreless communcaton networks, WS has ts own characterstcs. It conssts of small, low cost, and low power sensors. Each sensor s embedded wth a mcroprocessor and a wreless transcever to provde data processng and communcaton capabltes besdes ts sensng faclty. The sgnfcant nterest n WSs comes from ts mportance n many applcatons. In the healthcare sector, for example, WSs are used n dfferent applcatons such as patents montorng, dseases dagnoss and management, and elderly people homecare [2-5]. The use of WSs n healthcare applcatons ams to provde remote healthcare montorng by treatng and followng up patents n ther own homes. Ths out-of-hosptal treatment does not replace the role of hosptals and healthcare centers; t rather nvolves the patents to be actve partcpants n ther own healthcare. As n medcal applcatons, WSs have a sgnfcant role n mltary applcatons [6,7]. They can be used n battlefeld montorng, ntellgent gudng, and remote sensng to sense chemcal weapons and detect enemes attacks. In ndustral applcatons WSs are successfully used to montor manufacturng processes such as products qualty and montorng equpments status [2,8]. In a WS sensor nodes are scattered n the sensng feld formng a network accordng to nodes connectvty. Dependng on the underlyng network topology sensors collect dfferent envronmental data and send the observatons to the Base Staton (BS) whch s also known as the snk node. The snk node s responsble for data gatherng and processng. Also, t connects the WS to the Internet or to other networks. Usually the snk node has unconstraned capabltes n terms of data processng, data storage, and power resources. Unlke the snk node, sensors are resource-constraned devces, they have lmted processng and storage capactes, fnte battery power, and short rado range [1,9]. In addton to devces lmtatons, WSs suffer from dfferent challenges such as mult node-to-node transmsson, data redundancy and hgh unrelablty snce sensors are subject to physcal damage and falure. These lmtatons and problems present many challenges n the desgn and deployment of WSs. Many recent researches IJCS

2 404 I. M. ALMOMAI ET AL. have been carred out attemptng to solve the desgn and mplementaton ssues n WSs. Some researches attempts to address the problem of lmted energy by proposng energy-effcent solutons to prolong the network s lfetme. These solutons nclude data routng protocols [10,11]. Other researches focus on the problem of unrelable data transmsson and provde solutons to ncrease the network s relablty and data avalablty such as mult-path and mult-snk protocols [12,13]. Ths paper proposes a Fuzzy Energy Aware Routng (FEAR) protocol based on tree routng. The protocol ams to enhance the exstng Tree-based Routng (TR) protocol supported by IEEE [14] n terms of reducng the number of hops and solvng the problem of node/lnk falure. It ams to elmnate the need for addtonal control messages, as n Plus-Tree Routng (PTR) protocol [15], to buld the neghbors tables by explotng the control messages that are used for the purpose of tree constructon. In ths way control messages are used to construct the tree and at the same tme to buld the neghbors tables, consequently, the network s overhead s reduced. FEAR protocol conssts of several phases: tree constructon, data transmsson and tree reconstructon due to ether node(s) or lnk(s) falure. The rest of ths paper s organzed as follows: Secton 2 summarzes some related work. In Sectons 3 the proposed FEAR protocol s dscussed n detals and an analytcal evaluaton for ths protocol s also presented. Secton 4 explans the fuzzy system used by FEAR protocol. Secton 5 dscusses the smulaton results. Secton 6 concludes the paper and presents avenues for future work. 2. Related Work Many routng protocols have been proposed for WSs. Some protocols utlze the concept of herarchcal routng to perform energy-effcent routng n WSs and extend system s lfetme. In these protocols hgh-energy nodes can be used to process and send nformaton, whle low-energy nodes can be used to perform sensng [9,16]. A well-known herarchcal protocol s called Low- Energy Adaptve Clusterng Herarchy (LEACH) [17]. The dea s to dvde the network nto clusters and choose a cluster head for each one of them. All local cluster heads wll be used as routers to the snk. Ths clusterng wll save energy snce all data s processed locally nsde the cluster. Moreover, all transmssons are performed through cluster heads rather than nvolvng all sensor nodes. Cluster heads are changed randomly over tme n order to balance the energy dsspaton of nodes. However, LEACH s not applcable to networks deployed n large regons, snce t uses sngle-hop routng where each node can transmt data drectly to the cluster head. Another cluster-based routng protocol that can be used for large WSs s Energy Clusterng Protocol (ECP) as proposed n [18]. Unlke LEACH, ECP uses mult-hop routng. It also utlzes nodes on the cluster edge to send data to the neghborng cluster. Other cluster-based routng protocols were also proposed n [19,20]. Generally, n cluster-based routng protocols the dea of dynamc clusterng brngs extra overhead, whch may dmnsh the gan n energy consumpton. Other protocols had been presented n the lterature are based on constructng a tree between network nodes and use tree routng for data transmsson. Tree-based Routng (TR) s one of these protocols that are supported by IEEE [14]. TR protocol sutes small memory, low power and low complexty networks wth lghtweght nodes and t ams to elmnate the overhead of path searchng and updatng, therefore, t reduces extensve messages that are exchanged between network nodes. Although ths protocol works well n terms of energy savng, t suffers from two drawbacks. Frst, message transmsson depends on source depth; the deeper the node, the longer the path. Second, t suffers from node/lnk falure that causes nodes solaton. To overcome these drawbacks, many protocols have been proposed to enhance TR performance. Authors n [15] [21-23] attempted to enhance TR by fndng a shorter path to be used n data transmsson by explotng neghbor lnks rather than usng only parent-chld lnks. However, these protocols do not consder nodes power n ther solutons. Due to the dynamcty of clusterng process and the complexty of optmzng the number of clusters that may reduce the gan n power consumpton n clusterbased routng, the proposed FEAR protocol wll be based on tree routng. etwork energy wll be consdered durng dfferent stages of the proposed FEAR protocol. FEAR also enhances TR protocols by avodng ther shortcomngs n terms of solvng the node(s)/lnk(s) falure problem and decreasng the number of requred hops to transmt data messages to the snk. FEAR wll be compared wth both TR [14] and Plus-Tree Routng (PTR) protocol [15]. These two protocols were chosen for comparson as they construct the tree topology wth the mnmum cost. In addton, PTR protocol provdes solutons to recover from node(s)/lnk(s) falure. 3. FEAR Protocol The proposed Fuzzy Energy Aware tree-based Routng (FEAR) protocol s a cross-layer protocol. FEAR protocol conssts of several phases. Frstly, the protocol constructs a logcal tree between network nodes. Durng the IJCS

3 I. M. ALMOMAI ET AL. 405 constructon each node wll get a logcal ID and construct ts neghbors table. Secondly, data packets are transmtted usng neghbor lnks n addton to the parent-chld lnks. Durng ths stage both ntermedate nodes energy and depth wll be consdered. Fnally, the tree, or part of t, may be reconstructed n the event of ether node(s) or lnk(s) falure or due to a new node entrance. Durng both tree constructon and tree reconstructon stages, FEAR protocol uses a rankng system based on fuzzy nference so that nodes rank ther neghbors. By usng fuzzy rankng nference, nodes energy and depth s kept balanced among all sensor nodes. The fuzzy rankng system structure wll be dscussed n detals n the next secton Control Messages Dfferent control messages are defned by ths protocol. These messages are lsted n Table 1. Ths table shows the types of these messages, ther structures, when they are sent and the actons to be taken when they are receved etwork Model The network model of FEAR protocol s descrbed as follows: The sensors are scattered n the network feld wthout solaton. All sensor nodes have the same capabltes, the same transmsson range, and lmted power resources. Symmetrc model s assumed. That means f sensor A s wthn sensor B s transmsson range then sensor B s wthn sensor A s transmsson range. All sensors sense data and transmt t to the snk for processng. The snk node assumed to have unconstrant resources. All sensor nodes are located n fxed places wthout moblty. Table 1. FEAR protocol control messages. Message Type When to be Sent Actons By Recevers Message Structure Ready Is sent when: 1. The node gets an ID, so t s used to broadcast the ID and tell other nodes that t s ready to accept chldren. 2. The node receves a ew ode or Request Parent messages. 1. Store the nformaton n the neghbors table. 2. If recever node does not have ID, t wll fnd neghbors rank and send Engagement message. 1. ode ID 2. ode power 3. Rank average Unready It s used only to broadcast the ID. Store ID n the neghbors table. 1. ode ID 2. ode power Engagement Is sent when recevng a Ready message and used to request for ID and request for Parent. May send Engagement-Acceptance message. Engagement-Acceptance Is sent as a reply to an Engagement message. 1. Refresh neghbors table. 2. Calculate the ID. 3. Calculate the fuzzy rankng average for ts neghbors. 4. Send Ready message. 1. ode ID 2. ode power 3. Offered ID ew ode Is sent when new node wants to jon the network. Send Ready message or Unready message. Request Parent Is sent when a node cannot reach ts parent. Send Ready message or Unready message. Inform Is sent to tell the neghbors that the node wll go down. Reconstruct the tree accordng to ther relaton wth the dead node. ode ID Change ID Is sent when any node change ts ID due to some falure to tell other nodes to modfy the ID n ther tables. 1. Modfy the ID n neghbors table. 2. If the recever s one of sender s chldren t wll update ts own ID and send Change ID message. 1. ode ID 2. ode Power IJCS

4 406 I. M. ALMOMAI ET AL FEAR Protocol Stages Dfferent stages of FEAR protocol wll be dscussed n detals through the next subsectons Stage 1: Tree Constructon A snk-rooted tree should be constructed between network nodes before sendng a message to the snk. A new addressng scheme s used n ths stage to assgn a logcal ID for each node. Each node uses the assgned ID to calculate ts depth and ts neghbors depth. When a node receves an Engagement-Acceptance message, t wll calculate ts own ID as follows: ode ID = Parent ID Offered ID Each Offered ID s represented by m dgts whch are requred to represent C max nodes, where C max s the maxmum number of chldren a node can have. For example, f C max < 10 then we need only one dgt to represent the offered ID (0 9), but for large networks f C max < 100 then we need 2 dgts (00 99), and so on. Usng ths addressng scheme, each node wll be able to know the depth for a partcular ID. To construct the tree we assume that all nodes, ntally, do not have IDs and they have full energy. Parents can have at most C max chldren, and at the end of ths stage each node s assgned an ID from whch t can calculate ts depth. Frst, the snk node wll set ts rank average to 1 whch s the maxmum rank then the tree constructon s started by broadcastng a Ready message to ts neghbors, ths message contans the snk s ID (snk ID = ntal ID), snk s energy, and the average rank. Each node recevng ths message wll store the snk s nformaton n ts neghbors table and after a short perod of tme t wll send an Engagement message to the snk. The Engagement message has two purposes: request for a parent, and request for an ID. For each Engagement message, the snk node wll reply by sendng an Engagement-Acceptance message only f the number of ts chldren s less than C max. When the node gets the Offered ID, t calculates ts ID and the fuzzy rank average for ts neghbors then t broadcasts a Ready message allowng ts neghbors to send Engagement messages. ote that the Engagement messages are sent after a short perod durng whch they can receve Ready messages from other nodes and fnd the fuzzy rank for each one. Ths watng wll force the node to be assocated wth the best possble node among others (the node that has the best fuzzy rank). Thus, a balance s kept between nodes. Ths process contnues untl all nodes get IDs and no more Ready messages are sent. Table 2 llustrates neghbor table structure and Table 3 llustrates the steps of ths phase. Fgure 1 shows Value eghbor ID eghbor Power eghbor Dstance eghbor RAVG eghbor FR Is Chld Is Parent Table 2. eghbors table structure. Descrpton Ths value s contaned n the Ready message sent by the neghbor. Ths value s contaned n the Ready message sent by the neghbor. Ths value s calculated by the node when t receves the Ready message from the neghbor. The calculaton s done accordng to the strength of the receved sgnals at the physcal layer. Ths value s contaned n the Ready message sent by the neghbor. It represents the status of the neghbor s neghbors (the characterstcs of the nodes around the neghbor). Ths value s calculated by the node when t receves the Ready message from the neghbor. It s calculated usng the Fuzzy Rankng (FR) system (dscussed later n ths paper). It represents the characterstcs of the neghbor tself. Ths flag s set to 1 f the node sends an Engagement-Acceptance message to the neghbor; therefore the node becomes ts parent. Ths flag s set to 1 f the node receves an Engagement-Acceptance message from the neghbor; therefore the node becomes ts chld. Table 3. Tree constructon. Each node wll do the followng. Step 1. Wat for Ready message when a one s receved then go to step 2. Step 2. Store message nformaton n the neghbors table and check the ID f t s null then go to step 3. Step 3. Wat for a predefned perod of tme and store any Ready messages nformaton receved durng ths perod, then go to step 4. Step 4. Calculate the fuzzy rank for each stored neghbor and then go to step 5. Step 5. Send Engagement message to the neghbor wth the best rank. If Engagement-Acceptance message s receved, then go to step 6, else go to step 7. Step 6. Calculate ts ID and ts rank average then broadcast a Ready message. Then go to step 8. Step 7. Exclude the best neghbor and go back to step 5. Step 8. Ext. an example on logcal tree constructon. Fgure 1(a) represents the logcal tree vew wth the new nodes IDs and Fgure 1(b) represents the physcal dstrbuton of sensor nodes. We assume that node 6 s the snk node and t wll ntate the tree constructon. The ntal ID n ths scenaro s 0 and C max (number of chldren) = 2, thus m (number of dgts) = 1. Both nodes 5 and 10 are engaged wth the snk whch sends them 1 and 2 as Offered IJCS

5 I. M. ALMOMAI ET AL. 407 Table 4. ew node engagement. ew nodes wll do the followng. Step 1. Broadcast ew ode message and go to step 2. Step 2. Wat for Ready message when one s receved, then store ts nformaton and go to step 3. If any Unready message s receved whle watng then store ts nformaton n the neghbors table. Step 3. Wat for predefned perod and store any Ready or Unready message nformaton receved durng ths perod then go to step 4. Step 4. Calculate the fuzzy rank for each Ready message s nformaton that s stored n the neghbor table and go to step 5. Step 5. Send Engagement to the neghbor wth the best rank. If Engagement-Acceptance s receved then go to step 6 else go to step 7. (a) Step 6. Calculate the ID and the rank average then broadcast a Ready message. Then go to step 8. Step 7. Exclude the best neghbor and go back to step 5. Step 8. Ext. (b) Fgure 1. Logcal tree constructon. IDs. Then they wll calculate ther ID by concatenatng the Offered ID wth snk ID. ext, both nodes 5 and 10 send Ready message to ther neghbors. Ths process contnues untl all nodes successfully engaged wth some parent Stage 2: ew ode Engagement When a new node wants to jon a network, t should broadcast a ew ode message. Then all neghbors wll reply by ether Ready message f the number of chldren < C max, or Unready message f the number of chldren = C max. The new node wll store all nformaton n ts neghbors table and calculate the fuzzy rank number for each Ready message. Then t wll choose the parent that has the maxmum rank, and broadcast a Ready message. The steps are summarzed n Table Stage 3: Message Transmsson As stated earler the constructed tree should be snk-rooted and all other nodes wll send data to t. The message should be forwarded over the best path. To choose the next hop, the sender wll consder both neghbors depth and power. The neghbor that has the mnmum depth and a power larger than a specfc threshold wll be chosen. If all small-depth neghbors have crtcal energy then the sender wll send the data to the parent. In ths way, the load s balanced among nodes nstead of overloadng the parent node as n TR [14] or the less depth neghbor node as n [15,21-23]. The fuzzy rankng s not used n ths stage snce t s not feasble to calculate the rank when data packets need to be sent. The fuzzy rankng s only appled to control packets n order to ensure a balanced topology among nodes Stage 4: Tree Reconstructon If a node s energy reaches a specfc threshold, t should nform ts parent, chldren, and neghbors that t wll go down, so they can take an acton and prepare themselves to reconstruct the tree. Each node has a relaton wth the dead node should take an acton regardng to the relaton connectng them. There are three cases; the frst one when the dead node s a parent. In ths case the chldren have to fnd a new parent. Each chld broadcasts a Request Parent message and only neghbors wth chldren less than C max wll reply by a Ready message, other nodes send Unready messages. The chld then calculates the fuzzy rank number for each Ready message and then chooses the node that has the maxmum rank as a parent. IJCS

6 408 I. M. ALMOMAI ET AL. Then t wll broadcast a Change ID message to ts neghbors to update the ID n ther neghbors tables. If any neghbor s a chld for ths node, t wll subsequently change ts ID and broadcast a Change ID message. Ths process contnues untl all IDs are modfed. The second case s when the dead node s a chld. In ths case the parent should remove ths node from ts neghbors table and decrement the number of chldren. Fnally, the last case s when the dead node s a neghbor, then neghbors wll remove t from ther neghbors tables. In some cases nodes go down before nformng other nodes that ther power s about to end. In ths case any neghbor node (could be chld or parent) dscover the absence of ths node, should broadcast the dead node ID n an Inform message and then each node wll take an acton as dscussed above. The steps are llustrated n Table 5 and Fgure FEAR Analyss In ths secton we wll analyze FEAR protocol n terms of the number of generated sent and receved control messages and the consumed power accordng to these messages durng the tree constructon and compare them wth PTR protocol [15]. PTR was chosen for comparsons Table 5. Tree reconstructon. Each node wll do the followng. Step 1. When any Inform message s receved then go to step 2. Step 2. Remove the dead node from the neghbors table and check the relaton wth t. If t s a parent then go to step 3 f t s a chld then go to step 4. Step 3. Broadcast Request Parent message and go to step 5. Step 4. Decrement the number of chldren and go to step 11. Step 5. Wat for Ready message when a one s receved then store ts nformaton and go to step 6. If any Unready message s receved whle watng, then store ts nformaton n the neghbors table. Step 6. Wat for a predefned perod and store any Ready or Unready message nformaton receved durng ths perod then go to step 7. Step 7. Calculate the fuzzy rank for each Ready message s nformaton that s stored n neghbors table and go to step 8. Step 8. Send Engagement to the neghbor wth the best rank. If Engagement-Acceptance s receved then go to step 9 else go to step 10. Step 9. Calculate the new ID and broadcast Change ID message. Then go to step 11. Step 10. Exclude the best neghbor and go back to step 8. Step 11. Ext. Fgure 2. Change ID flowchart. as t constructs the tree topology wth the mnmum cost, the same as TR. In addton, PTR provdes solutons to recover from node(s)/lnk(s) smlar to FEAR protocol umber of Sent and Receved Messages In ths subsecton we wll compare the number of sent and receved messages n both FEAR and PTR protocols. For both protocols the worst case has been calculated snce the best case s hard to be forecasted. We derved the number of sent messages n PTR as 3 2 en. Each node sends one Assocaton 1 message, for nodes, there wll be Assocatons. In the worst case each node wll send a Reply message to each Assocaton from each neghbor, f we represent each neghbor set by e(n), then that requres the summaton of all neghbor sets, 1 e n. If we assume that at the end of the tree constructon each node wll successfully get an ID then ths requres sendng messages that contans the IDs. Fnally, when the tree s constructed, each node wll broadcast ts ID n a hello_neghbor message to construct the neghbors table. The total s hello_neghbor messages. On the other hand, each node wll reply by sendng a reply_ hello_neghbor message to all neghbors and ths requres addtonal total of en 1 e n messages. Therefore, a control messages are sent n the PTR protocol. The number of sent messages n FEAR s 2 IJCS

7 I. M. ALMOMAI ET AL. 409 en as we prove usng Theorem 1. 1 Theorem 1: In FEAR protocol, the number of sent control messages s at most 2 en 1 Proof: Three control messages are exchanged between nodes durng tree constructon; accordng to Ready messages each node wll send one Ready to broadcast ts ID, so for nodes there wll be messages assumng that all nodes got IDs. For Engagement Messages the worst case s to send an engagement to each neghbor, so for nodes f each node has a set of neghbors represented by e(n), the total s the summaton of all negh- bor sets whch s equal to 1 e n. Fnally, assumng that each node wll be successfully engaged to one parent and gets an ID there should be Engagement- Accep- tance messages. Therefore, a total of 2 en 1 control messages are sent n the FEAR protocol. We derved the number of receved messages n PTR as 4 en messages. Each node receves an 1 Assocaton from all neghbors n ts neghbors set, the total s the summaton of all neghbor sets e n. 1 On the other hand, each node n the worst case expects to receve a Reply to ts Assocaton from all of ts neghbors and that requres another 1 e n messages. Fnally, f each node successfully assocates to one parent, then t wll receve an ID and that requres ID messages to be receved. For constructng the neghbors table each node wll collect hello_neghbor messages and reply_hello_neghbor messages from ts neghbors and that needs to receve 1 message type. Therefore, a total of messages for each the number of receved control messages n the PTR protocol. The number of receved messages n FEAR protocol s 2 en messages as we prove usng 1 Theorem 2. Theorem 2: In FEAR protocol, the number of receved control messages s at most 2 en e n 1 e n 4 1 s Proof: Three control messages are used n tree con- structon. Frstly, each node broadcast ts ID to ts neghbors n a Ready message, f we represent each neghbor set by e(n) the total s the summaton of all Sets, ( en 1 the worst case occurs when each node wll receve engagements from all neghbors n ts set. So the total s e n. Fnally, each node wll be engaged to only one parent and wll receve only one Engagement-Acceptance message, so the total s messages. Therefore, a total of sages n the FEAR protocol. ). Secondly, for Engagement messages the summaton of all neghbor sets,.e., 2 en 1 1 s the number of receved control mes Consumed Power Sensor power s affected by local processng and communcaton operatons. Snce communcaton operatons consume more power than data processng, sensors wll lose most of ts power durng sendng and recevng of messages [2]. Accordng to [24] a node requres E Tx (k, d) to send k bts message to destnaton at dstance d, and E Rx (k) to receve k bts message. E Tx (k, d) and E Rx (k) are defned as: ETx k, d ETx elec ketx amp k, d (1) 2 E * k * k* d elec amp ERx k ERxelec k E * k where E elec = 50 nj/bt and ε amp = 100 pj/bt/m 2. Usng (1) and (2), the maxmum power that wll be consumed durng the tree constructon can be calculated accordng to Theorem 1 and Theorem 2. The power that s consumed by sendng and recevng control messages durng the FEAR tree constructon phase can be calculated usng (3). FEAR Consumed Power ETx k, d* 2 en 1 ERX k* 2 en 1 (3) elec 4. Fuzzy Rankng System In the FEAR protocol, we have bult a fuzzy nference system that wll be used n tree constructon, tree reconstructon, and new node engagement phases. The purpose of ths system s to assgn a rank for each neghborng node. Ths rankng helps the node to be assocated wth the best possble neghbor, so the tree s balanced ac- (2) IJCS

8 410 I. M. ALMOMAI ET AL. cordng to both nodes energy and depth. The general structure for the fuzzy rankng system s shown n Fgure 3. Ths rankng system conssts of three stages. The output from each stage s one of the nputs to the next stage. The fuzzy nference that s used n all stages s based on Mamdan fuzzy nference [25] eghbors Classfcaton In order to rank neghbors, they are classfed nto four types. Each neghbor node should belong to only one type n a partcular pont of tme. Table 6 llustrates these types. Ths classfcaton s used to know how good or bad the neghbors are. eghbors belong to the frst type wll take hgher rank than other types, so the larger the number of neghbors belong to ths type, the better the performance of our protocol snce many good neghbors can be utlzed as ntermedate nodes nstead of parent node Frst Stage of Fuzzy Rankng System Ths stage has two-nput, one-output fuzzy nference system. It fnds the cost of transmttng the data to a partcular neghbor n terms of neghbor s depth and dstance from the sender. The output from ths stage s entered to the next stage. Fgure 4 shows the structure of ths stage. The nputs are mapped to fuzzy membershp functon llustrated n Fgure 4 (a). The frst nput s the dstance whch can be Very_ear, ear, Far, and Very Far. Usually the transmsson range for sensor devces s about 250 meters, so any neghborng node should be placed wthn ths range. Ths nput can be calculated usng sgnal strength [9]. The second nput to ths stage s the depth whch can be calculated from the neghbor ID that s stored n the neghbors table. The depth s mapped nto Small, Medum, and Large membershp functons and ranges from 1 to maxmum_ tree_depth. The maxmum_tree_depth s calculated accordng to (4). Maxmum _ Tree _ Depth log C max (4) where: s the number of network nodes, and C max s the maxmum number of chldren. Both dstance and depth affect the transmsson cost; larger depth mples larger number of ntermedate nodes. If two neghbors have the same depth then t s more feasble to forward the message to the nearest one snce short dstance requres less power to send the sgnal. Transmsson_cost s mapped nto Low, Medum, and Hgh membershp functons as llustrated n Fgure 4(b) Second Stage of Fuzzy Rankng System The second stage nputs are Transmsson_cost wth Low, Medum, and Hgh membershp functons and the neghbor resdual_energy that could be Low, Medum, and Hgh as llustrated n Fgure 5(a). The resdual_ energy for each neghbor s stored n the neghbor s table. The output from ths fuzzy stage s the neghbor_rank. Dstance Frst Fuzzy Inference Transmsson Cost Second Fuzzy Inference eghbor Rank Thrd Fuzzy Inference ew Rank Depth Resdual Power eghbor Status Fgure 3. Fuzzy rankng system. Table 6. eghbors types. Type Good depth and Good energy Good depth and Bad energy Bad depth and Good energy Bad depth and Bad energy Descrpton The node belongs to ths type has a resdual energy greater than a specfc threshold, and a depth not larger than sender s parent depth. The node belongs to ths type has a small amount of resdual energy, and a depth not larger than sender s parent depth. The node belongs to ths type has a resdual energy greater than specfc threshold, but ts depth s larger than sender s parent depth. The node belongs to ths type has a small amount of resdual energy, and ts depth s larger than sender parent depth. IJCS

9 I. M. ALMOMAI ET AL. 411 Fgure 4. Frst stage fuzzy. (a) The nputs membershp functons; (b) The output membershp functon. Fgure 5. Second stage fuzzy. (a) The nputs membershp functons; (b) The output membershp functon. IJCS

10 412 I. M. ALMOMAI ET AL. Ths rank s mapped nto Good, Moderate, and Bad. See Fgure 5(b) Thrd Stage of Fuzzy Rankng System Ths fnal stage s used to fnd the fnal neghbors rank accordng to ther characterstcs and status. It takes two nputs; the neghbor_rank that resulted from the prevous stage and the neghbor_status. The neghbor_rank gves an ndcaton about the neghbor characterstc n terms of resdual_power, depth and dstance. These factors wll be used to rank the neghbor; the larger the rank, the better the neghbor chance to be chosen as a parent. Fgure 6 shows the structure for ths fuzzy stage. The neghbor_ status s mapped nto Good, Moderate, and Bad fuzzy membershp functons. Ths nput gves an ndcaton about the characterstcs of the nodes around the neghbor (neghbors of a neghbor) and s calculated by the neghbors themselves as the followng: When a node receves a ew ode, Request Parent, or Engagement-Acceptance message, t wll calculate the rank for each neghbor usng fuzzy rankng system. Calculate the average of neghbor ranks. Snce power and depth are balanced among neghbors, average wll be a good measure to reflect the status of the neghbors. Send a Ready message that contans the average value. ow each node receves the Ready message wll use the average value as the second nput to the thrd stage nference. The hgher the average value, the better the neghbor s status and the better the chance to be chosen as a parent. We assume that the Ready message that s sent by the snk wll have the hghest possble rank average snce t s the best choce for any neghbor to be assocated wth. 5. Smulaton Results and Comparson Ths secton dscusses the results obtaned usng FEAR and compare t wth TR [14] and PTR [15] protocols. We use Java programmng language to buld our smulator. Dfferent evaluaton metrcs are consdered n our smulaton that wll be dscussed n the followng subsectons. Table 7 shows the smulaton parameters that were used etwork Overhead The network overhead s evaluated n terms of number of messages that are sent and receved durng tree constructon. We use dfferent network szes 25, 50, 100 and 500. For each sze, the same nodes characterstcs (ntal power, nodes dstrbuton, and dstance from snk) are used n the three protocols. We take the average of 10 smulaton runs. Fgures 7 and Fgure 8 show the behavor of the three protocols accordng to the number of sent and receved messages, respectvely. It can be notced that TR curve does not appear as t s almost dentcal to FEAR curve, therefore, the results are also llus- Fgure 6. Thrd stage fuzzy. (a) The nputs membershp functons; (b) The output membershp functon. IJCS

11 I. M. ALMOMAI ET AL. 413 Table 7. Smulaton parameters. Area correspondng to each network sze. Smulaton parameter Value etwork Sze 25, 50, 100, T erran Area (m , , ) 1250, Moblty one Rado Range 250 m Sent Messages Receved Messages Table 8. etwork overhead. etwork Sze FEAR TR PTR Fgure 7. umber of sent messages wth dfferent network szes. luaton. The consumed power s compared wth PTR protocol snce t constructs both tre e and neghbors tables as FEAR protocol, whereas TR constructs only the tree. We use the same smulaton characterstcs that are used n the overhead evaluaton. We take the average of 10 runs for each network sze and compute the consumed power accordng to (3) (we gnored the dstance n the comparson snce we use the same nodes dstrbuton for both protocols). The results are shown n Table 9 and Fgure 9. As llustrated, FEAR protocol s better than PTR protocol snce t reduces the control messages that should be exchanged between nodes. It decreases the consumed power by up to 55.08% comparng wth PTR protocol Transmsson Delay Fgure 8. The overhead of receved messages wth dfferent network szes. trated n Table 8 for more precse comparson. As llustrated earler, the proposed FEAR protocol has the same behavor as TR protocol snce t uses the mnmum possble messages to construct the tree, t also utlzes these messages to construct the neghbor tables wthout the need to send addtonal Hello and Hello Reply messages as used n PTR protocol to ad n node(s)/lnk(s) falure recovery. Accordng to the results, FEAR protocol saves up to 85.67% n the number of sent messages and up to 64.6% n the number of receved messages. Overall, FEAR protocol saves up to 70.5% n the number of control messages. Transmsson delay s evaluated accordng to the number of ntermedate nodes between the sender and the snk. Transmsson delay s affected by sender s depth; the deeper the node, the larger the number of ntermedate nodes. The same constructed tree s used for the three consdered protocols and the average of dfferent sce- Table 9. etwork consumed power accordng to network overhead. Consumed Power (mj) etwork FEAR/PTR FEAR PTR Sze (%) etwork Power Consumpton As stated n the analyss secton (Secton 3.4) most of the network s power s consumed by the communcaton operatons. Therefore, we consder the number of sent and receved messages n the energy consumpton eva- Fgure 9. The percentage of the consumed power wth dfferent network szes. IJCS

12 414 I. M. ALMOMAI ET AL. Fgure 10. Hop count accordng to dfferent nodes depth. Table 10. Falure scenaros. Success: no solaton, Fal: solaton. Scenaro TR PTR FEAR Dead node s a leaf node Success Success Success Dead node s a parent and all of ts chldren are n snk range or each chld has at least one descendent n snk range Fal Success Success Dead node s a parent but all or some of ts chldren and de- Fal Fal Success scendents are not n snk range Dead node s a parent but all or some of ts chldren do not have neghbor(s). (Become solated after parent death). Fal Fal Fal naros s computed. Fgure 10 llustrates the results. As s hown n the fgure, FEAR protocol has the mnmum delay comparng to the TR an d PTR pro tocols Possblty to Recover from Falure Ths subsecton analyzes dfferent falure scenaros. Fo r each scenaro, the possblty to reconstruct the tree and to recover from the falure s compared wth the TR and PTR protocols. The same network characterstcs are u sed for the three protocols. We assess whether the sce- s successful or not by performng many smulaton naro runs for each scenaro on dfferent network szes. As llustrated n Table 10, TR protocol does not provde falure recovery mechansms whereas both FEAR and P TR can recover from falure. The results show that FEAR solutons are more effcent n reconstructng the tree than the one provded by PTR. 6. Conclusons and Future Work In ths paper we propose a tree-based routng protocol for Wreless Sensor etworks (WSs) that consders both shortest path and energy balance between nodes. The proposed protocol s called Fuzzy-based Energy Aware Routng Protocol (FEAR) as t uses a fuzzy nference system to rank the nodes. Ths s used to ensure that each node s assocated wth the best neghbor n the tree constructon. The protocol provdes an energy effcent soluton for both data routng and network falure, thus prolongs the network s lfe tme. FEAR has several stages: snk-rooted tree constructon, messages transmsson, and node/lnk falure problem recovery. The protocol s mplemented and compared to other tree-based protocols. The smulaton results show that the new protocol saves up to 70.5% n the number of control messages and up to 55.08% n the consumed power comparng wth other related work. As a future work we wll consder securty ssues and possble threats to develop a secure FEAR protocol. 7. References [1] R. Bradar, V. Patl, S. Sawant and R. Mudholkar, Classfcaton and Comparson of Routng Protocols n Wreless Sensor etworks, Specal Issue on Ubqutous Computng Securty Systems, UbCC Journal, Vol. 4, 2009, pp [2] A. Jamalpour and J. Zheng, Wreless Sensor etworks: A etworkng Perspectve, Wley-IEEE Press, Hoboken, [3] M. McGrath and T. Dshongh, Wreless Sensor etworks for Healthcare Applcatons, Artech House, London, [4] P. Pandan, Wreless Sensor etwork for Wearable Physologcal Montorng, Journal of etworks, Vol. 3, o. 5, 2008, pp do: /jnw [5] H. Alemdar and C. Ersoy, Wreless Sensor etworks for Healthcare: A Survey, Computer etworks, Vol. 54, o. 15, 2010, pp do: /j.comnet [6] S. Damond and M. Cerut, Applcaton of Wreless Sensor etwork to Mltary Informaton Integraton, Proceedngs of the 5th IEEE Internatonal Conference on Industral Informatcs, Vol. 1, 2007, pp do: /idi [7] M. Wnkler, K. Tuchs, K. Hughes and G. Barclay, Theoretcal and Practcal Aspects of Mltary Wreless Sensor etworks, Journal of Telecommuncatons and Informaton Technology, Vol. 2, 2008, pp [8] X. Shen, Z. Wang, and Y. Sun, Wreless Sensor etworks for Industral Applcatons, 5th World Congress on Intellgent Control and Automaton, Vol. 4, 2004, pp do: /wcica [9] J. Al-Karak and A. Kamal, Routng Technques n Wreless Sensor etworks: A Survey, IEEE Wreless Communcatons, Vol. 11, o. 6, 2004, pp do: /mwc [10] L. Vllalba, A. Orozco, A. Cabrera and C. Abbas, Rout- Sensors, Vol. ng Protocols n Wreless Sensor etworks, 9, o. 11, 2009, pp do: /s IJCS

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