Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links

Transcription

1 1 in Low-uy-ycle Wireless Sensor Neworks wih Unreliable Links Shuo uo, Suden Member, IEEE, Liang He, Member, IEEE, Yu u, Member, IEEE, o Jiang, Suden Member, IEEE, and Tian He, Member, IEEE bsrac looding service has been invesigaed exensively in wireless neworks o efficienly disseminae nework-wide commands, configuraions and code binaries. However, lile work has been done on low-duy-cycle wireless sensor neworks in which nodes say asleep mos of he ime and wake up asynchronously. In his ype of nework, a broadcasing packe is rarely received by muliple nodes simulaneously, a unique consraining feaure ha makes exising soluions unsuiable. In his paper, we inroduce, a novel design ailored for low-duy-cycle neworks wih unreliable wireless links and predeermined working schedules. Saring wih an energy-opimal ree srucure, probabilisic forwarding decisions are made a each sender based on he delay disribuion of nex-hop receivers. Only opporunisically early packes are forwarded via links ouside he ree o reduce he flooding delay and redundancy in ransmission. We furher propose a forwarder selecion mehod o alleviae he hidden erminal problem and a link-qualiy-based backoff mehod o resolve simulaneous forwarding operaions. We show by exensive simulaions and es-bed implemenaions ha Opporunisic looding is close o he opimal performance achievable by oracle flooding designs. ompared wih, our design achieves significanly shorer flooding delay while consuming only 2% 6% of he ransmission energy. Index Terms Wireless Sensor Neworks, Low-uy-ycle Neworks, looding. 1 INTROUTION Wireless Sensor Neworks have been used for many applicaions such as miliary surveillance [12], infrasrucure proecion [47] and scienific exploraion [36]. Miniaurized ino a cubic cenimeer package and deployed wihou wired power supply, sensor nodes have very limied amoun of energy. On he oher hand, here is a growing need for susainable deploymen of sensor sysems [4], [47], [52], [53] o reduce operaional cos and ensure service coninuiy. To bridge he gap beween limied energy supplies and applicaion lifeimes, a sensor nework has o be operaed on very low duy cycles, i.e., a sensor node is acive for only a shor period of ime beween wo long dorman periods. In order o deliver a packe, a sender may have o wai for a cerain period of ime (ermed sleep laency [8]) unil is receiver becomes acive. Sleep laency degrades he performance (e.g., delay and energy consumpion) of various kinds of daa forwarding designs in low-duy-cycle neworks. While pioneering projecs have been proposed for low-duycycle unicass [8], [25], [5], research is surprisingly inadequae for low-duy-cycle flooding, an imporan funcion for disseminaing nework-wide commands, alers and configuraions [12], ime synchronizaion [26], and code binaries [14]. Inended for fas nework-wide daa disseminaion, a flooding design should be no only reliable o reach every node and keep hem updaed and consisen, bu also ime efficien wih less energy cos. There are wo feaures ha make flooding in low-duy-cycle orresponding auhor. Shuo uo is now wih risa Neworks. L. He and Y. u are wih Singapore Universiy of Technology and esign, Singapore. o Jiang is wih Universiy of Massachuses mhers. Tian He is wih Universiy of Minnesoa-Twin iy, US. n early version of his work is published a M Mobiom 9. neworks challenging. irs, a packe is unlikely o be received by muliple nodes simulaneously as in always-awake neworks. To broadcas a packe, a sender has o ransmi he same packe muliple imes if is receivers do no wake up a he same ime. Thus flooding in such neworks is realized essenially by muliple unicass. Second, unlike wired neworks, wireless communicaion is nooriously unreliable [49]. ransmission is repeaed if he previous ransmissions are no successful due o wireless loss. The combinaion of low-duy-cycle operaion and unreliable links necessiaes he design of a differen flooding mechanism han hose found in wired neworks and alwaysawake wireless neworks. One sraighforward soluion could be building a rouing ree for flooding. Ye his ype of soluions have been shown [17], [3] o be fragile, because he failure of a paren node prevens all is subree nodes from receiving messages, even if he nework is sill conneced. urhermore, exising ree-based soluions could be made energy efficien only a he cos of long delays, as hey only forward packes via a single roue. This work inroduces : a flooding mehod specially designed for low-duy-cycle wireless sensor neworks. Is main objecive is o reduce redundancy in ransmission while achieving fas disseminaion. Our soluion inheris he reliable naure of radiional flooding, allowing packes o ravel along muliple pahs. The key novely of his work lies in he forwarding decision making. node forwards a packe wih a higher probabiliy if he packe arrives opporunisically earlier, such ha flooding packes are always delivered along fas pahs. Specifically, our conribuions are as follows: To he bes of our knowledge, his is he firs nework design for flooding in sensor neworks wih exremely low duy cycles and unreliable channels. This work proposes delay-driven opporunisic forwarding.

2 2 We propose a recursive and disribued mehod o compue he probabiliy mass funcion (pmf ) of forwarding delays a each node along an energy-opimal ree. The compued pmf is hen used as he guideline in forwarding decision making o reduce he flooding delay opporunisically. To alleviae he hidden erminal problems wihou he hefy RTS/TS overhead, we propose a forwarder selecion mehod ha allows forwarding nodes wih good link qualiy o overhear each oher. We also propose a link-qualiy-based backoff mehod o resolve simulaneous ransmissions a- mong forwarding nodes. The res is organized as follows: Secion 2 describes he moivaion behind he work. Secion 3 defines he nework model and assumpions. Secion 4 inroduces our main design, and Secion 5 discusses pracical issues followed by heir evaluaion in Secions 6 and 7. Secion 8 discusses he relaed work and Secion 9 concludes he paper. 2 MOTIVTION To bridge he gap beween lifeime requiremens of sensor applicaions [4], [52] and he limied availabiliy of energy hrough fixed-budge baeries or energy harvesing [24], i is criical o have an energy-efficien communicaion archiecure. This secion idenifies he need for low-duy-cycle communicaion designs in general and he flooding design in paricular. 2.1 The Need for Low-uy-ycle Operaion Typically, he energy used in communicaion can be opimized hrough (i) physical-layer ransmission rae scaling [43]; (ii) link-layer opimizaion for conneciviy, reliabiliy, and sabiliy [33]; (iii) nework-layer enhancemen for forwarders and roues [3], [46]; and (iv) applicaion-layer improvemens for boh conen-agnosic and conen-cenric daa aggregaion and inference [11], [28]. lhough hese soluions are highly diverse, hey all assume a nework in which nodes are ready o receive packes and focus mainly on he ransmission side. In conras, wireless neworks wih inermien receivers have capured lile aenion, despie he fac ha communicaion energy is consumed mosly by being ready for poenial incoming packes, a problem commonly referred o as idle lisening. or example, he widely adoped 242 radio [41] draws 19.7m while receiving or idle lisening, which is larger han he 17.4m for ransmiing. More imporanly, packe ransmission ime is usually very brief (e.g., 1.3 ms o ransmi a TinyOS packe using a 242 radio), while he duraion of idle lisening can be orders of magniude longer. or example, mos environmenal applicaions, such as rea uck Island [4] and Redwood ores [42], sample he environmen a relaively low raes (on he order of minues beween samples). Wih a comparable curren draw and a 3 4 orders of magniude longer duraion waiing for recepion, idle lisening is a major energy drain ha accouns for he communicaion energy cos. To reduce he energy penaly in idle lisening, a node has o run a a low-duycycle sae and urn off is radio mos of ime. 2.2 The Need for a New looding esign The radiional flooding mehod and many of is advanced versions [14], [21], [23], [29], [38] have shown heir good performance in erms of delivery raio, delay and energy cos in many always-awake neworks. These soluions, however, suffer severe performance degradaion if direcly applied o low-duycycle neworks. In hose designs, a node sars broadcasing a packe as soon as i receives i from is previous-hop node. In a low-duy-cycle nework where wo neighbors seldom wake up a he same ime, a broadcasing packe canno be received by many nodes simulaneously. The delivery raio becomes even worse when unreliable links and collisions are aken ino accoun. We conduced a series of simulaions by decreasing he duy-cycle from 1% o 1% in a randomly generaed nework wih 2 nodes. The working schedules are randomly generaed, and we coun he percenage of nodes ha can receive a broadcasing packe using a pure flooding scheme, i.e., a node broadcass upon firs receiving a packe. ig.1 shows how he performance degrades as he duy-cycle decreases. Even under ideal condiions (i.e., no collisions and perfec links), only 5% of he packes are successfully delivered in a 2% duy cycle nework, a clear indicaion ha radiional mehods are no suiable for low-duy-cycle neworks, if used direcly. elivery Raio 1% 8% 6% 4% 2% Perfec Links Unreliable Links Unreliable Links + ollision % 1% 5% 2% 1% 5% 2% 1% uy ycle ig. 1. Tradiional looding in Low-uy-ycle Neworks One could argue ha radiional flooding mehods can be adaped o low-duy-cycle neworks by permiing (i) muliple ransmissions of he same packe based on he neighbor schedules, and (ii) RQ-based mechanism o deal wih unreliable links, bu his sill has problems. irs, i suffers from a high energy cos due o collisions. When a node wakes up, many of is neighbors aemp o sar ransmissions simulaneously. Wih unreliable links [33], [49], i is difficul o resolve collisions, since he nodes canno sense each oher s ransmissions. Second, even wihou collisions, he number of redundan ransmissions is large especially when he nework densiy is high. ue o hese limiaions in radiional flooding mehods, i is necessary o have a ailored design for low-duy-cycle neworks, which moivaed our work in he presen paper. 3 PRELIMINRIES This secion defines he nework model and assumpions relaed o our opporunisic flooding design. 3.1 Nework Model onsider a sensor nework where each node has wo possible saes: an acive sae and a dorman sae. n acive node is able o sense an even, ransmi a packe or receive a packe. dorman node urns off all is funcion modules excep a

3 3 <1, 2> <1, 2> <1, 2> 1 1 delay = delay = acive ig. 2. Low-uy-ycle Nework Model dorman imer o wake iself up. ll nodes have heir own working schedules. These schedules are shared wih neighboring nodes and are normally asynchronous in order o reduce informaion redundancy among emporally correlaed sensing daa [1], [45]. dorman node wakes up when (i) i is scheduled o swich o he receiving sae, or (ii) i has packes o send o an acive neighbor. In shor, a node can ransmi a packe a any ime, bu can only receive a packe when i is acive. or sensing purposes, he working schedules of sensor nodes are normally periodic [12], [4], [42]. Wihou loss of generaliy, suppose T is he working period of he whole nework (e.g, T can be any common muliple of he periods of all nodes). T can be furher divided ino a number of ime unis of lengh τ where τ is appropriae for a round-rip ime. Then each node picks one or more ime unis as is acive sae, in compliance wih is duy cycle. enoe he acive and dorman saes by 1 and, respecively. The ih node s working schedule can hen be represened as < w i, τ >, where w i is a sring of 1 s and s denoing he schedule and τ is he lengh of each ime slo. In a low-duy-cycle nework, we can compress he represenaion of w i by keeping only he offse values of acive saes. ig.2 shows an example of our nework model. In his example, T is 8 ime unis and is divided ino 4 ime slos, each of which is 2 ime unis long (i.e., τ = 2). Nodes and wih schedule < 1, 2 > are acive during he firs 2 ime unis and dorman during he nex 6 ime unis; node wih schedule < 1, 2 > is acive during he second 2 ime unis and dorman for he res of he period. ll nodes periodically change heir saes based on heir predeermined working schedules. Suppose node has a packe o send o a ime. Since a node can only receive a packe when i is acive, node has o wai unil ime 2 o sar he ransmission. Similarly, o ransmi he packe o, node has o wai unil ime 8. If boh links are perfec, he oal delay of his packe from o is 4 ime slos, and hus he delay is 4τ = 8 ime unis. 3.2 ssumpions Suppose he source nodes have flooding packes o be sen hroughou he whole nework. We make several assumpions as follows: 1) s he raffic inensiy is ypically ligh in low-duy-cycle neworks, we simply our consideraion by focusing on he scenario where only one flooding will be in process a any ime [6]. 2) node ses up is working schedule < w i, τ > and shares i wih all is neighbors when joining he nework, a process normally referred as low-duy-cycle rendezvous [5]. fer rendezvous, a node knows he schedules of all is one-hop neighbors. node changes is schedule only afer he new schedule is shared o all is neighbors. 3) We assume he exisence of unreliable links and collision. We define he link qualiy of a wireless link as he probabiliy ha he ransmission hrough his link is successful. Link qualiy can be measured using probe-based mehods in [3], [46] or hrough low-cos piggybacking on regular daa raffic. We assume a relaively sable environmen in which link qualiies can be updaed infrequenly (e.g., every en minues). We will show in Secion 6 ha our design also works in he presence of mild flucuaions in link qualiies. If wo or more ongoing ransmissions are wihin he communicaion range, a collision occurs and none of hem will succeed. Noe ha collisions can be someimes resolved by he capure effec [2]. However, even wih he capure effec, simulaneous ransmissions sill consume exra energy since only one useful packe ou of muliple ransmissions is delivered. We ry o avoid simulaneous ransmissions for he sake of energy efficiency and do no consider capure effec in our design. 4) The nework is locally synchronized so ha each node knows when o send packes o is neighbors. Local synchronizaion can be achieved by using a M-layer ime samping echnique, as described in TSP [26], which achieves an accuracy of 2.24µs wih he cos of exchanging a few byes of packes among neighboring nodes every 15 minues. Since τ is ypically beween 2, µs o 2, µs, he accuracy of 2.24µs is sufficien. 5) n hop coun is used o denoe he minimum number of hops from a node o he source. Hop coun can be easily obained by leing each node broadcas is own hop coun as soon as i is calculaed or updaed. Iniially, he source node has a hop coun of. I broadcass his informaion and all is neighbors have a hop coun of 1. Similarly, he neighbors of hop-1 nodes ha have no ye been assigned a hop coun have a hop coun of 2, and so on. To ensure he logical nework opology is loop free, we simplify our consideraion by focussing on he scenario ha a node only forwards a flooding packe o nodes wih larger hop couns. However, he proposed opporunisic flooding is applicable o any logical nework opology. We also compare he opporunisic flooding wih he energy-opimal and delayopimal schemes in Secion 6, and he resuls show ha he proposed opporunisic flooding achieves a well-balanced and near-opimal flooding performance in erms of boh he flooding delay and energy cos. 3.3 Performance Merics In his paper, we define he following wo performance merics o evaluae he performance of a flooding design. 1) looding delay: defined as he ime elapsed from a message being sen ou by he source unil i reaches 99% of he nodes in he nework. ue o he imperfecion of he links, he flooding delay exhibis inheren randomness.

4 4 1 S Source.9.9 E.5.5 (a) Original looding Srucure ig. 3. -ased looding Srucure.9 E 1 S Source.9 (b) Energy-Opimal Srucure Here we propose o use he average flooding delay as a measure of nework performance. 2) Energy consumpion: measured by he oal number of ransmissions iniiaed by a flooding packe from he source. The receiver-side energy is deermined by heir predefined working schedules, which are no changed by flooding designs. Therefore, we use only he sender-side energy as he performance meric when comparing differen flooding designs wih he same duy-cycled schedules. 4 MIN ESIN We presen he design of he in his secion. 4.1 esign Overview ased on he nework model, a flooding packe can only be forwarded from nodes wih smaller hop couns o hose wih larger ones. s a resul, he flooding srucure of he nework is a direced acyclic graph () as shown in ig.3(a). The weighs of he direced edges are he corresponding link qualiies. ree srucure can be obained from he by reaining for each node only he incoming edge wih he highes weigh, as shown in ig.3(b). s we will show laer, his ree is he energy opimal for flooding among all rees compaible wih he. In oher words, if a flooding packe is forwarded according o his ree, (i.e., a node only receives packes from is paren), he expeced oal number of ransmissions is minimized. We observe, however, ha flooding via he energy-opimal ree may resul in a long flooding delay, since he paren of a node in he energy-opimal ree may no receive a specific packe as early as is oher parens in he, due o he opporunisic naure of wireless communicaion. ased on his observaion, he key idea of opporunisic flooding is o uilize links ouside an energy-opimal ree if ransmissions via hese links have a high chance of making he receiving node receive he packe saisically earlier han is paren on he energy-opimal ree. s a summary, he proposed opporunisic flooding is applied on op of he energy-opimal ree and can furher improve he flooding performance in erms of he flooding delay. The flooding srucure of is dynamically changing. node forwards a flooding packe o a nex-hop node if and only if his ransmission is expeced o deliver a new packe o ha node, insead of an old one. The packe o be forwarded opporunisically should be saisically earlier han he packe ha would oherwise be delivered via he energyopimal ree. In order o forward opporunisically early packes while avoiding lae ones, opporunisic flooding consiss of hree major seps, as illusraed in ig.4: (a) The pmf compuaion S Source E (c) ecision Making Resul orward ase rrive early o no orward ase p p rrive lae (b) ecision Making Process.5 E.9 Selecion of looding Senders.9 E.5 Link-Qualiy-ased ackoff (d) ecision onflic Resoluion ig. 4. esign Overview of 1) The pmf ompuaion: ue o unreliable links, he delay of a flooding packe arriving a each node from is paren in he energy-opimal ree is a random variable. s shown in ig.4(a), he probabiliy mass funcion (pmf ) of his delay for each node is firs derived o guide he decision making process. rom he pmf, each node compues is p- quanile delay as he saisically significan hreshold and shares i wih all is previous-hop nodes. 2) ecision Making Process: s shown in ig.4(b), a packe is forwarded opporunisically via he links ouside he energy-opimal ree only if his forwarding can significanly reduce he delay (Noe ha he p-quanile delay is used o conrol he saisical significance). Specifically, a node makes is forwarding decision locally based on hree inpus: (i) he receiving ime of he flooding packe, (ii) he link qualiy beween iself and he nex-hop node, and (iii) he p-quanile. ig.4(c) shows one example of he final srucure of decision making. This srucure is dynamically changed for differen flooding packes. 3) ecision onflic Resoluion: Wih he disribued decision process, muliple nodes may decide o forward he same packe o a common neighbor, which is called decision conflic. onflic resoluion echniques are designed o avoid collisions and save energy furher (ig. 4(d)). Wih dynamic decisions per packe, permis a packe o ravel along an opporunisically-faser roue insead of a fixed one via he energy-opimal ree. lso, lae packes are no forwarded o reduce redundancy and save energy. The design deails are given in he following subsecions. 4.2 looding Energy os and elay The flooding process can be evaluaed from wo aspecs: he energy consumpion and he delay bou Energy Opimaliy In a low-duy-cycle nework, he probabiliy ha a node has wo neighbors wih idenical working schedules is very low. or example, in a nework wih a 5% duy-cycle, he working period

5 5 is divided ino 2 ime unis and each node randomly chooses one of hem as an acive sae. The probabiliy ha wo nodes will choose he same acive ime slo is only 5%. s a resul, flooding in low-duy-cycle neworks is essenially realized by a number of unicass. Normally a node needs o forward a packe o is neighbors (wih a larger hop coun) one-by-one due o heir differen working schedules. s discussed in 4.1, an energy-opimal ree is consruced based on he by reaining for each node only he incoming edge wih he bes link qualiy. When he flooding process is sricly realized by unicass, he energy-opimaliy of his ree among all rees compaible wih he can be easily proved by conradicion: in an energy-opimal ree, if here exiss a node whose incoming link does no have he bes link qualiy among all incoming links in he, hen given ha he nework has a low duy cycle and flooding is realized by a number of unicass, he new ree obained by replacing his link wih he bes one is more energy efficien, which conradics wih he assumpion on is energy opimaliy. Noe ha if muliple nodes wake up simulaneously, he energy-opimal ree obained will deviae from he acual flooding srucure wih he highes energy efficiency. In his case, idenifying such an opimal flooding srucure is equivalen o finding a Minimum onneced ominaing Se given he nodes deploymen, which is proven o be NP-hard [4]. However, since he muliple-receiver scenario is rare in low-duy-cycle neworks, he aforemenioned flooding ree sill achieves a good approximaion of energy opimaliy bou elay Opimaliy looding delay is anoher criical meric o evaluae he flooding performance. In he ideal case where all he links are perfec, a delay-opimal ree in he can be idenified based on solely he scheduled acive slos of sensor nodes. However, wih he unreliable links in pracice, he delay-opimal flooding srucure canno be a ree anymore. igure 5 (which is par of he in ig. 3) shows a simple demonsraion on he non-exisence of he delay-opimal ree. Le us consider he case ha for a flooding process sars a ime, E and receive he packe a ime, and will wake up a ime insances + 4, + 8,. Eiher link or E will be adoped if he delay-opimal ree exiss. When he former link is adoped, he expeced delay for o receive he packe is (1 ) + = , and if he laer is adoped, he expeced delay is (1 ) + = However, if boh of he links are adoped, he expeced delay would be + 4 (1 (1 )(1 )) +8 (1 )(1 ) (1 (1 )(1 )) + = , which is smaller han boh he above cases. This means o achieve he minimal delay, more links should be adoped in he flooding process, and hus more ransmissions are needed, which E ig. 5. elay-opimal flooding srucure canno be a ree. on he oher hand increases he energy consumpion of sensor nodes. Thus wih unreliable links, he energy-opimal ree may resul in long flooding delay, while he delay-opimal flooding srucure will inroduce more energy cos. The proposed opporunisic flooding improves he flooding process based on he energyopimal ree, and esablishes a balance beween he flooding delay and he energy cos. 4.3 The elay pmf of he Energy-Opimal Tree This secion compues he packe delay disribuion (pmf ) when packes are forwarded according o he energy-opimal ree The ompuaion of pmf iven an energy-opimal ree, he flooding packe delay of each node is a random variable due o unreliable links. In order o guide he decision making process of neighboring nodes, i is imporan o calculae he disribuion of he delay. We call a node wih hop coun l a level-l node. Suppose he ih acive ime uni of a level-l node is l (i), he packe delay pmf is denoed by a se of uples {( l (i), p l (i))}, where p l (i) is he probabiliy of receiving he packe a ime l (i). The pmf compuaion process sars from he level- node (he source) and propagaes hrough he nework level by level. Iniially, he source is always awake and he probabiliy ha i receives he packe wih delay is 1%, i.e., he pmf of he source is {(,1%)}. Then a level-1 node calculaes is pmf based on he pmf of he level- paren according o he energyopimal ree. Similarly, a level-(l + 1) node calculaes is pmf based on ha of is level-l paren. iven he pmf of his level-l node ( l (i) and p l (i) for any i), and is level-(l+1) child wih acive ime unis l+1 (j) for any j, we calculae he probabiliy ha i receives he flooding packe a is jh acive ime slo as p l+1 (j) = p l (i)q(1 q) n ij, (1) i: l (i)< l+1 (j) where q is he link qualiy saisfying q (, 1], n ij is he number of he level-(l + 1) node s acive ime unis beween l (i) and l+1 (j). p l (i)q(1 q) n ij is he probabiliy ha he packe which arrives a he level-l node a is ih acive ime uni is firs delivered o he level-(l + 1) node a is jh ime uni. learly, a node s pmf can be derived from is paren s pmf recursively, wih iniial pmf (,1%) a he source. ig.6 shows an example of he pmf compuaion process, where node compues is pmf firs based on he link qualiy.9 and is own working schedule. The probabiliy ha node receives he packe for he firs ime a ime 1 is.9. ime 2, he probabiliy becomes (1.9).9 =.9. Then node compues is pmf based on s pmf. or node a ime 15, he probabiliy is he muliplicaion of he link qualiy and he

6 6 Source 1 S.9.9 E S 1. ig. 6. The pmf ompuaion probabiliy ha node receives he packe a ime 1, which is.9 = 2. or node, a ime 25, he probabiliy is he sum of he probabiliy ha (i) node receives he packe a ime 1 and succeeds a he second ransmission, and (ii) node receives he packe a ime 2 and succeeds in he firs ransmission, which is.9 (1 ) +.9 =.22. Similarly, all he nodes wihin he nework compue heir pmf s once he pmf s of heir parens become available omplexiy nalysis each node, he number of possible delay values equals he number of enries o be calculaed in he pmf compuaion. Theoreically, he delay pmf may have infiniely many enries. However, we can accuraely approximae he pmf by including firs M enries, so ha he cumulaive probabiliy of he res enries is less han a small value (i.e., 1%). or example, in ig.6, node s pmf conains only wo enries: (1,.9) and (2,.9). In his case, M = 2. Eq. (1) akes quadraic ime O(M 2 ). However, linear ime is achievable wih he following recursive formulaion p l+1 (j) = = i: l (i)< l+1 (j) i: l (i)< l+1 (j 1) + p l (i)q(1 q) n ij i: l+1 (j 1) l (i)< l+1 (j) = p l+1 (j 1)(1 q) + p l (i)q(1 q) n i,j 1 (1 q) p l (i)q i: l+1 (j 1) l (i)< l+1 (j) p l (i)q. or example, he probabiliy for o receive he packe a ime 35 (ig. 6) is calculaed wih (2) as.22 (1 ) +.9 = Each node needs only is paren s pmf o complee he compuaion, which amouns o jus a few packes. The calculaion is repeaed when he link qualiies are updaed. We discuss how frequenly he link qualiy needs o be updaed in Secion ecision Making Process s discussed in Secion 4.1, only opporunisically early packes are forwarded o reduce flooding delay. Upon receiving a flooding packe, a node judges if is ransmission o a nex-hop node could make he node receive he packe for he firs ime wih a high probabiliy. If so, such a ransmission helps reduce he flooding delay and is considered Needed. Oherwise i only consumes more energy and is considered Redundan. (2) node finds is p-quanile delay based on is pmf, which is referred as p, and shares i wih is parens. y definiion, if a flooding packe arrives a his node laer han p, he probabiliy ha his packe has been already received by his node from is paren is greaer han p. Suppose a level-l node receives a packe a is ih acive ime uni wih delay l (i) and inends o make a forwarding decision oward one of is level-(l + 1) neighbors wih acive unis l+1 (j)s ( is no he paren of on he energy-opimal ree). compues he expeced packe delay (EP) a if forwards he packe o. Specifically, if ransmis o, he EP from o can be compued using he following equaion EP = l+1 (j)q(1 q) nij, (3) j: l+1 (j)> l (i) where q is he link qualiy, n ij is he number of s acive ime unis beween l (i) and l+1 (j). Eq. (3) is essenially he sum of a geomeric series, which can be calculaed wih a closed form. To reduce he amoun of floaing-poin compuaion, an alernaive way is o make use of he expeced number of ransmissions. or a link wih link qualiy q, 1 q ransmissions are expeced for a successful packe delivery. Thus finds in s working schedule he 1 q h acive ime slo afer l(i) (he ime ha his packe arrives a ) and akes i as s EP. fer he EP is compued, compares his value wih s delay hreshold p o decide if his ransmission (from o ) is opporunisically needed. If EP p, he probabiliy ha has already received his flooding packe via he energy-opimal ree is no greaer han p. So, his packe is considered Needed. If EP> p, he chance ha has already received his packe is more han p, and his packe is considered Redundan. Thus will no forwarded i o. s pmf Received by.5 p = 18 EP = s ry o 2 nd ry o ig. 7. n example of ecision Making Time ig. 7 shows an example of he decision process wih p =. enoing he pmf of node a is k h acive slo as p l (k), he p-quanile delay p of is calculaed by firs idenifying is earlies acive slo ill which he packe will be received by wih probabiliy no smaller han p j i = min{j : p l (k) p}, (4) k=1 hen p is assigned as he ime of s ih acive slo. In he example shown in ig. 7, because he firs wo acive slos of achieves a receiving probabiliy of =, and he second acive slo is a ime 18, hus p = 18. uring he iniializaion, shares he p value wih. Suppose a packe arrives a node a ime 15. Since he link qualiy is.5, is expeced o ransmi wice o forward he packe o

7 7 successfully. The firs ry is a ime 18 (which is he firs acive ime uni of afer ime 15) and he second ry is a ime 22. Therefore, he EP from o is 22. Since EP = 22 > p = 18, his packe is considered Redundan and is no forwarded o. Noe ha p is a conrol parameer for balancing he delay and energy cos. Wih a larger value of p, more packes are likely o be considered as Needed and hence forwarded opporunisically. This increases he chance of fas delivery, provided ha collisions are handled appropriaely. I also increases he number of ransmissions, leading o higher energy cos. On he oher hand, as p becomes smaller, fewer packes are forwarded opporunisically via energy-subopimal links, which improves he energy efficiency bu increases he delay. learly, he value of p srikes a balance beween delay and energy, which we will evaluae in Secion ecision onflic Resoluion This subsecion deals wih he conflics when wo or more nodes ransmi o he same node simulaneously The Selecion of looding Senders In wireless communicaion, a cerain percenage of collisions are caused by he Hidden Terminal Problem, which is more likely o occur in a low-duy-cycle nework, because all ransmissions o a receiver are limied o he small ime window when i is acive. If he hidden erminal problem occurs, boh senders will keep sending bu neiher of hem will succeed. One possible soluion is o use TM-based approaches o schedule he ransmissions of differen senders a differen ime slos o avoid collisions. However, due o he unpredicable wireless loss along he muli-hop links from he source o each node, he ime each sender receives he flooding packe is highly dynamic, making TM based approaches less efficien. noher possible soluion is o use he RTS/TS conrol packes as hey are in SM/. However, adding conrol packes ino every ransmission is very cosly, especially when he hidden erminal problem occurs infrequenly under low raffic loads. The key idea of our soluion is o selec a reduced sender se for each node, so ha all sending nodes can hear each oher o avoid he hidden erminal problem. We use a link qualiy hreshold l h o deermine wheher a link is good or no. ll links beween he seleced senders should have a link qualiy beer han l h. The selecion process goes as follows: irs, a node only receives flooding packes from nodes wih smaller hop couns. These nodes are he candidaes for he flooding senders, and are sored in descending order of heir link qualiies. The candidae wih he bes link qualiy is always included in he sender se, and he res are seleced inducively. onsider he bes candidae ha has no ye been esed. If he link qualiies beween his candidae and all he currenly seleced senders are beer han l h, his candidae is added o he sender se; oherwise, his link is disabled. ll he candidaes are esed one-by-one in descending order of heir link qualiies. enoe H as he oal number of sender candidaes. In he wors case, a ime of O(H) is needed o check wheher each candidae should be added o he sender se. or each checking, Source (a) Source H Original ework (c) receives he packe early H E E Source H (b) looding Sender Selecion Source ig. 8. ifferen looding Srucures H (d) receives he packe lae we need o examine he link qualiies beween all he nodes ha have been already added o he sender se and he candidae node under consideraion, which implies anoher O(H) ime in he wors case. Thus he compuaion complexiy o consruc he sender se is O(H 2 ). The impacs of l h on boh he flooding delay and energy cos will be evaluaed in Secion Link-Qualiy-ased ackoff Once a sender se is formed, we need o resolve he conflics wihin he se. We propose a link-qualiy-based backoff scheme ha no only resolves collisions bu also reduces redundan ransmissions o save energy. Ideally, a node wih beer link qualiy should have a higher prioriy o grab he channel and sar a ransmission earlier. Suppose he backoff ime bound is T backoff and he maximum size of he sender se is W. T backoff is divided ino W slos for differen backoff duraions. sender compues is backoff duraion backoff as backoff = ( W (1 q) ) T backoff W E E + X, (5) where q is he link qualiy and X is a random period of ime generaed from [ T backoff W, + T backoff W ] if 1 W (1 q) W 1 and from [, + T backoff W ] if W (1 q) =. This ensures backoff is non-negaive and wihin he backoff bound. Inroducing such randomness ino his equaion reduces he chance of collision when wo or more nodes have he same link qualiy. When muliple nodes wihin he communicaion range have he same packe o send o he same node, hey back off firs before ransmission and he one wih he bes link qualiy sars firs. If his ransmission is deeced by oher nodes, hey will abor heir own ransmissions and mark hem as Redundan. To ensure he ransmission from he bes-link node is deecable by oher nodes, he bes-link node can keep occupying he channel afer is sending of packe is finished unil he curren ime slo is passed. This will no increase he energy consumpion of he bes-link sender significanly because he energy consumpion raes when i is in acive and ransmiing saes are comparable o each oher, e.g., he curren draw of a MicaZ moe a acive and ransmiing saes are 8 m and m, respecively [41].

8 8 ig. 8 shows an example of. The original of he nework is shown in (a). fer sender selecion, all nodes in he same sender se should have good enough links beween hem, and he resul is shown in (b), wih some links deleed from (a). or he flooding of a specific packe, (c) and (d) presen he wo cases on he forwarding decision of node. If he receives he packe early enough such ha is forwarding o E and is Needed, will forward he packe o E and, as shown in (c). On he oher hand, if receives he packe oo lae, i will no forward he packe o neiher E or, because hese forwarding is Redundan, as shown in (d). verage looding elay / Time Unis Leas looding elay chievable Nework Size (a) looding elay vs. Nework Size ig. 9. Nework Size. 1 4 elay Opimal Scheme Energy Opimal Scheme Number of Transmissions Leas os chievable Nework Size (b) Energy os vs. Nework Size elay Opimal Scheme Energy Opimal Scheme 5 PRTIL ISSUES In his secion we discuss pracical issues ha could affec he performance of. 5.1 On Node ailures In real world deploymens, a sensor node can fail due o many facors such as physical damage or energy depleion. robus flooding design should be insensiive o node failures and minor opological changes. In, flooding packes are forwarded hrough a dynamically changing srucure wih redundan links where he corresponding senders make he same decisions o send. The failure of an opporunisic flooding sender only resuls in a larger delay due o lower chances for he receivers o ge early packes. Even if is paren in he energy opimal ree fails, a node sill has a high chance o receive an opporunisically early packe from oher senders, hus avoiding cascading failures as in ree-based designs. We evaluae he impac of node failures in Secion On Link Qualiy hange Link qualiy plays an imporan role in as i is a required inpu in almos every sep of he design. I is hus preferable ha he qualiies of all he links do no change once hey are measured. In pracice, however, link qualiy is affeced by many environmenal facors and changes over ime even during he inerval beween wo measuremens. Thus i is imporan o discuss if is sill suiable for neworks wih ou-of-dae link qualiy informaion. ue o he periodic measuremens, he link qualiy may deviae slighly from he laes measured value. This deviaion will possibly lead o wo consequences: he loss of opimaliy of he energy-opimal ree (which furher affecs he accuracy of p ), and an EP deviaing from is accurae value. However, he impac of boh on is limied, because p and EP only affec he decision making process. iven ha he link qualiy has only a limied deviaion, he changes of hese wo values are small. Thus, only a limied number of nodes will make wrong decisions, and hus eiher reducing he chance of receiving early packes or increasing he chance of sending redundan packes. We evaluae he impac of link qualiy change in Secion uy ycle (%) (a) looding elay ig. 11. omparison wih opimal schemes uy ycle (%) (b) Energy os 6 EVLUTION This secion evaluaes he performance of. Specifically, we compare he flooding delay and he energy cos in wih hose of radiional flooding. We also show ha he performance of is very close o he opimal ha is achievable by any flooding design. In Secion 7, we provide furher evaluaion hrough a physical esbed experimen. 6.1 Simulaion Seup The neworks used for he simulaion are randomly generaed wih he size varying from 2 nodes o 1 nodes. The links beween hese nodes are wireless pah loss channels wih shadowing effecs, and he link qualiies are calculaed as in [54]. Unless explicily specified oherwise, he parameers are l h = for sender selecion and p =.9 for decision making. The flooding delay is based on a 99% delivery raio insead of 1% o eliminae he effec of exremely low-degree nodes in a randomly generaed nework. ll he nodes pick heir acive ime unis randomly. (Noe ha random schedules are for evaluaion purpose only. works regardless of how working schedules are se up.) Since he performance is no affeced by he acual lengh of a ime uni, we measure he flooding delay by he oal number of ime unis in all simulaions. Simulaion resuls are he averaged ou of 1 nework opologies, each wih 1 flooding packes. 6.2 aseline I: Opimal Performance ounds We compare wih he bes performance achievable by any possible design. The opimal energy coss (he one wih he leas number of ransmissions) is achieved when flooding packes are forwarded via he energy opimal ree. The opimal flooding delay (he one wih he leas flooding delay) is achieved by pure flooding wih an oracle collision-free media access conrol. Noe ha he opimal energy and delay are achieved by wo differen mehods, neiher of which can achieve boh he opimal delay and he opimal energy simulaneously.

9 9 verage looding elay / Time Unis Leas looding elay chievable Number of Transmissions Leas os chievable Raio of Opporunisical elivery 55% 5% 45% 5% 1% 15% 2% uy ycle 1 5% 1% 15% 2% uy ycle (a) looding elay vs. uy ycle (b) Energy os vs. uy ycle ig. 1. looding Performance in Neworks wih ifferen uy ycles 4% 1% 2% 5% 1% 15% 2% uy ycle (c) Opporunisic elivery Raio 6.3 aseline II: To our knowledge, here is no disribued flooding algorihm specially designed for low-duy-cycle neworks. esides he opimal performance bounds described above, we also compare he performance of wih a variaion of radiional flooding. Recall from Secion 2 ha he performance of radiional flooding deerioraes dramaically when he duy cycle of a nework is significanly reduced. To make he comparison fair, we modify he radiional flooding mehod o improve is efficiency for low-duy-cycle neworks and refer o i as. irs, i uses he same link-qualiy-based backoff mehod as Opporunisic looding o avoid collisions among muliple senders. Second, a node sops sending o a cerain neighbor afer hearing he ransmission of anoher node. This grealy reduces he number of redundan ransmissions. Third, he hidden erminal problem is alleviaed by using a p-persisen backoff scheme afer a fixed number of rials. These hree echniques enable o resolve a greaer percenage of collisions, reduce energy cos, and recover from he hidden erminal problem quickly. 6.4 Performance omparison This secion compares wih opimal performance bounds and. ue o he aggregaed link dynamics over muliple hops, he flooding delays of hese mehods have a noiceable variance. or he sake of clariy, simulaion figures only plo he average performance over muliple runs. In he es-bed evaluaion, we experience less dynamics (i.e., less flucuaion on link qualiy over muliple hops), which allows us o plo boh average and variance wihou reducing he figure clariy ifferen Nework Sizes We also evaluae he performance of in differen nework sizes as shown in ig.9. or differen nework sizes from 2 o 1, he side lengh of he area changes from 2m o 4m o keep a similar densiy. In ig.9, he average flooding delay and energy cos increase as he nework size increases, as expeced. gain, Opporunisic looding ouperforms and saves abou 4% of flooding delay and 5% of energy cos. I is very close o he opimal performance, wih around 1% more delay and energy cos ifferen uy ycles We firs evaluae he performance in neworks wih differen duy cycles, where 8 nodes are randomly deployed in a 3m 3m field. ig.1(a) and (b) plo he flooding delay and energy cos of, Improved Tradiional looding and opimal soluions. In ig.1(a), he average flooding delay of opporunisic flooding is only around 8% of ha of and is very close o he opimal soluion. In ig.1(b), coss less han 5% of while providing a shorer flooding delay. ompared wih he opimal-energy soluion, he number of redundan ransmissions is around 4, which means ha in he nework consising of 8 nodes, a node receives on average only.5 redundan packes for every broadcasing packe. ig.1(c) shows he percenage of nodes whose firs flooding packes are opporunisically early packes (i.e., no from is paren in he energy opimal ree), from which we can see ha around 5% of packes are delivered opporunisically, significanly reducing he delay compared o Improved Tradiional looding. We observe ha his raio increases as he duy cycle increases. This is because he probabiliy ha a node has more han one acive neighbor is higher in a nework wih a higher duy cycle, and an opporunisically early packe is more likely o be received by more nodes omparison wih Opimal Schemes Nex we compare he performance of he wih he energy-opimal and delay-opimal schemes (ig. 11). We can see ha he performance of is quie close o he respecive opimal scheme, e.g., abou 1.7 and 1.2 of he minimal achievable flooding delay and energy cos, respecively, when he duy cycle is 5%. lso noe ha boh flooding delays under he energy-opimal scheme and energy coss under he delay-opimal scheme are significanly larger han ha achieved by. Thus we can see ha achieves a well-balanced and nearopimal flooding performance in erms of boh he flooding delay and energy cos. 6.5 Invesigaion on Sysem Parameers In, we use wo parameers (i.e., l h and p) o conrol he radeoff beween delay and energy. This secion addresses how o choose appropriae values for hese parameers under differen user requiremens.

10 1 verage looding elay / Time Unis 15 1 Leas looding elay chievable Number of Transmissions Leas os chievable Raio of Opporunisical elivery 6% 5% 4% 3% 2% 1% l h l h l h (a) looding elay vs. l h (b) Energy os vs. l h (c) Opporunisic elivery Raio ig. 12. looding Performance in Neworks wih ifferen l h verage looding elay / Time Unis 15 1 Leas looding elay chievable Number of Transmissions Leas os chievable Raio of Opporunisical elivery 7% 6% 5% 4% 3% 2% 1% p (a) looding elay vs. p p (b) Energy os vs. p p (c) Opporunisic elivery Raio ig. 13. looding Performance in Neworks wih ifferen p verage looding elay /Time Unis % 1% 2% 3% 4% 5% Link Qualiy hange (a) looding elay vs. Link Qualiy ig. 14. Evaluaion of Pracical Issues Number of Transmissions % 1% 2% 3% 4% 5% Link Qualiy hange (b) Energy os vs. Link Qualiy Percenage of looding overage 1% 95% 9% 85% 8% 5% 1% 15% 2% Percenage of ailure Nodes (c) overage Raio vs. Node ailures The Sender Se Link Qualiy Threshold l h We sudy he impac of link qualiy hreshold l h used o build a sender se. gain we use he 8-node nework which is randomly generaed on a 3m 3m field wih a 5% duycycle. p is sill fixed o.9 while l h is changed from.5 o 1.. (To guaranee delivery, a node s bes incoming link is always seleced as flooding sender, even if i is no greaer han l h.) ig.12 plos he performance comparisons under differen l h. s l h increases, he requiremen for flooding sender selecion becomes higher and fewer nodes are included in he sender se, leading o less opporunisic forwarding. This is validaed by ig.12 where we observe an increasing flooding delay, decreasing energy cos and decreasing opporunisical delivery raio as l h becomes larger. There is a significan change when l h goes from.9 o 1 compared o he slow change when l h goes from.5 o.9. This is because l h = 1 requires ha all senders have a 1% link qualiy, which is oo sric o allow enough opporunisic packes and provide beer performance. enerally, l h can be se o any value from.5 o.9, a radeoff beween he flooding delay and he energy cos. If a shorer flooding delay is preferred, l h can be se o.5 and.6. If energy is he mos imporan issue, a greaer l h in he range of o.9 is he bes choice The Quanile Probabiliy p We sudy he impac of p, he hreshold o decide wheher a packe is an early packe. gain, 8-node randomly generaed neworks are used. This ime, l h is fixed o while p is changed from.5 o.9. ig.13 (a), (b) and (c) plo he average flooding delay, he energy cos and he opporunisic delivery raio, respecively. s p increases, more nodes make he decision o sar ransmissions so ha a shorer delay and larger number of ransmissions can be expeced. Similarly, he choice of p is a rade-off beween delay and cos. If a shorer delay is more imporan, a larger p of or.9 is needed. (Recall ha in ig.1, he flooding delay when p =.9 is very close o he opimal delay, which means p =.9 can almos saisfy all delay requiremens.) On he oher hand, if a lower energy cos is more imporan, p can be as low as.5 or.6. ased on all he comparisons, we conclude ha Opporunisic looding approaches he opimal bounds. I ouperforms Improved Tradiional looding and saves flooding delay significanly while consuming only 2% o 6% ransmission energy in almos all nework seings. I could also fi differen design requiremens by choosing differen values for is parameers. 6.6 Evaluaion of Pracical Issues We injec noise ino link qualiies o simulae he scenario in which a flooding packe experiences differen link qualiies from he ones ha are measured and shared in he laes updae. We wan o see how much he performance is affeced when he link qualiy informaion known by each node is ou of dae. ig.14(a) and (b) show he flooding performance wih differen link qualiy

11 11 variaion ranges. s link qualiies deviae from measuremens, he flooding delay increases. This is because here are more nodes making wrong forwarding decisions and fewer nodes can receive early packes. The energy cos slighly increases. We nex explain why he energy cos has only a limied change in conras wih he flooding delay. Recall ha a node compares EP wih p o make forwarding decisions, and EP is affeced by (i) he ime ha a flooding packe reaches his node and (ii) he link qualiy o he nex-hop node. Since link qualiies are periodically updaed, boh p and he link qualiy o he nex-hop node remain he same in his node s compuaion. s a resul, only he ime ha he packe reaches his node affecs he decisions made by his node, and his is furher affeced by he link dynamics from he source o his node. Wih a random variaion on link qualiies, his node has roughly equal probabiliy o receive he packe early or lae, making he oal number of decisions on early packes and he oal energy cos almos unchanged. ompare ig.14(a) and (b) we find ha wih inaccurae link qualiy informaion, more packes are sen as redundan ones as hey consume energy and increase he chance of collisions wihou helping reduce he flooding delay a all. However, wih a reasonable changing range of 3%, sill ouperforms Improved Tradiional looding in erms of boh flooding delay and energy cos. ig.14(c) shows he flooding coverage for differen percenage of node failures. To beer undersand he number of nodes ha fail o receive he broadcasing packe caused by he flooding design, he percenage of coverage is calculaed as he number of nodes ha receive he flooding packes divided by he oal number of nodes subracing he number of failure nodes. s seen in he figure, as he number of failure nodes increases, he flooding coverage decreases as expeced. However, for a failure rae of no more han 1%, he flooding coverage is more han 96%, which indicaes ha is very robus o node failures and minor opology changes. 6.7 Overhead nalysis We evaluae he overhead of in his subsecion. In he simulaion, link qualiies are measured by exchanging 1 hello messages among neighbors. Since pmf s are compued based only on he pmf of he paren, only one message needs o be sen o each child. ig.15 plos he conrol overhead for differen daa packe size/conrol packe size raios, in which, he x-axis is he number of flooding packes sen per link qualiy updae period and he y-axis is he raio of he oal bis sen for conrol packes over daa packes. We see from he figure ha he conrol overhead is reduced as he flooding packes sen per link qualiy updae increases, as expeced. When he daa packe size is as small as he conrol packe, he overhead of is large especially when less han 5 flooding packes are sen during one link qualiy updae period. When he daa packe is greaer han 5 imes of he conrol packe, he conrol overhead becomes negligible when more han 5 flooding packes are sen per link qualiy updae period. In pracice, a sensor nework is deployed o have a number of asks, and he cos of measuring link qualiies is shared by all of hem. or example, he hello messages are periodically sen o mainain he nework conneciviy for almos all asks and he link qualiies can be measured and updaed a he same ime wih no exra cos. Even when flooding is he only operaion of he nework, we show in ig.15 ha sill saves energy when a reasonable amoun of flooding bis is sen per link qualiy updae period. 7 IMPLEMENTTION N EVLUTION In addiion o large-scale simulaions, we implemened Opporunisic looding and on he TinyOS/Moe plaform in nes wih 3 MicaZ moes o furher validae in pracice. 7.1 Experimen Seup We randomly deployed 3 MicaZ nodes on an in-door esbed, whose ransmission power is uned down so ha he nodes form a 4-hop nework. fer deploymen, all nodes are in he iniializaion phase wih a 1% duy cycle. Each moe randomly generaes a specified working schedule conrollable by a sandalone base saion node. Then, saring from he source, each node broadcass is exisence and is working schedule. Upon receiving a broadcas message from a neighbor wih a smaller hop coun, a node updaes is hop coun and sars o announce is working schedule o neighboring nodes. When his process ends, all nodes have heir hop couns ready and a neighbor able buil wih working schedules from all neighboring nodes. ollowed by neighbor discovery, a node begins o measure he pairwise link qualiy beween iself and each neighboring node in is neighbor able by couning he recepion raio of 2 packes. This informaion is exchanged wih neighboring nodes. onsequenly, he neighbor able of each node would conain boh incoming and ougoing link qualiies for all neighboring nodes. The link qualiy hreshold l h in he sender selecion is.6. Wih such informaion, he pmf is compued and he p quanile wih p =.9 is shared wih neighbors. fer he iniializaion phase, all nodes swich o low-duy-cycle mode. They urn on or urn off heir radios based on heir working schedules. Specifically, in his experimen we se he uni ime as 5ms. 7.2 Performance omparison In his secion, we compare he empirical flooding delay and energy consumpion for boh designs. or each specified duy cycle, he source sends 1 packes using eiher Opporunisic looding or. In order o minimize he impac of link qualiy flucuaion on he performance comparison, opporunisic flooding packes and improved radiional flooding packes are sen alernaively elay Performance We invesigaed he impac of duy-cycle on delay as shown in ig.16. duy-cycles 2% and above, boh schemes experience a comparable delay in flooding a packe o every node in he nework. he duy cycle of 1%, he delay in opporunisic flooding is abou 25% shorer. Noice ha ig.16 does no show he similar significan delay reducion ha we observed in he

12 12 onrol Packes/aa Packes Packe Size Raio = 1 Packe Size Raio = 5 Packe Size Raio = 1 verage looding elay / Seconds Number of Transmissions looding Packes Sen/Link Qualiy Updae ig. 15. onrol Overhead uy ycle ig. 16. looding elay uy ycle ig. 17. Energy os simulaion. This is because our experimens are subjec o he physical limiaions of he esbed. irs, we have o form a four-hop nework wih only 3 MicaZ nodes. onsequenly, he number of flooding senders for each node is small, which reduces he chance of sending opporunisically early packes. Second, a pure-flooding algorihm is considered delay-opimal when a nework is no congesed. ue o he low conneciviy in four-hop nework of 3 nodes, he congesion level is no as significan as ha observed in he simulaion Energy Performance ig.17 compares average energy consumpion of he wo flooding designs. ue o he small nework size and limied number of opporunisic links, he percenage of energy saved is no as significan as ha in simulaion, bu is sill noiceable. or example, a a duy cycle of 3%, he average flooding delay for and are 763ms and 7564ms, respecively. The energy coss are 68 and 13, meaning ha saves abou 34% in energy while providing a similar flooding delay. 7.3 Why is eer This secion presens insighs on why significanly improves performance over Improved Tradiional looding Observaion on elay isribuion To invesigae how a flooding packe propagaes over a nework, we recorded he receiving ime samps of individual packes and ploed he umulaive isribuion uncion of delay wih boh flooding mehods in ig.18(a). The experimen is done wih a duy cycle of 1%. s seen in he figure, 8% of he nodes receive he flooding packe quickly wihin 1 seconds. However, i akes significanly more ime o deliver he flooding packe o he oher 2%. This indicaes ha he oal flooding delay is severely affeced by only a few nodes. This figure also shows ha has a comparable delay o ha of for reaching individual nodes during he flooding process. lhough i reaches 8% of he nodes more slowly, i reaches 1% of he nodes more quickly Observaion on Energy isribuion In addiion o flooding delay, we also recorded he energy consumpion a each individual node when he nework operaed a a 1% duy cycle and compared he disribuion of singlenode energy consumpion in ig.18(b). s seen in he figure, opporunisic flooding ouperforms a any given percenile. or example, abou 7% of he nodes in ransmi he flooding packes only 3 imes, in conras o 5 for. lso, in order o reach he las a few nodes, especially he las 1%, he number of ransmissions increase significanly due o poor link qualiy and conneciviy. gain, his implies ha he flooding delay is dominaed by he las few nodes Observaion on Opporunisic Raio We sudied how helps reduce he flooding delay. ig.18(c) plos he percenage of opporunisic flooding packes received a each node. The nodes are sored according o heir hop couns, and hree verical lines show he separaion of nodes wih differen hop couns. We observe ha as hop coun increases, he chance of receiving opporunisic early packes increases significanly. or example, while no hop-one nodes receive any opporunisic early packes, abou one-hird of he nodes a hop-wo receive opporunisic early packes. hop hree, almos every node receives opporunisic early packes, and he average percenage of such packes is around 8% of he oal flooding packes received. This observaion indicaes ha design is very effecive in reducing flooding delay, especially when he nework scale becomes large. 8 RELTE WORK s essenial operaions for wireless neworks, mulicasing [13], [32] and flooding [14], [21], [23], [29], [38] have been exensively sudied in he lieraure. RM [37] presens a reliable mulicas service a he M layer using he busy one mechanism. Mobicas [13] and RU [32] provide reliable daa delivery from a sink o he sensors in specified delivery regions. P [29] proposes a M layer soluion for flooding and invesigaes radeoffs among reliabiliy, laency and energy consumpion. imed a amelioraing message implosion, Smar ossip [21] adapively deermines he forwarding probabiliy for received flooding messages a individual sensor nodes based on previous knowledge and nework opology. y exploiing nework densiy and mainaining reliable bridge links among dense clusers of nodes, RP [38] demonsraes is energy efficiency and high reliabiliy for flooding. or services such as nework reprogramming, proocols such as eluge [14] and Trickle [23] also propose echniques for efficienly propagaing code o nodes in he nework. Recenly, Zhu e al. explores link correlaion in flooding service [51]. ll hese works assume here are usually muliple neighbors available a he same ime o receive he broadcas message sen by a sender, which does no hold in low-duy-cycle neworks. On he oher hand, daa forwarding in low-duy-cycle neworks have acquired a lo of aenions in recen years [2], [7]. Zigee provides a specificaion for a suie of high level

13 13 1% 1% 1% 8% 6% 4% 2% % Time Elapsed / Seconds (a) elay ig. 18. Sysem Insighs of 8% 6% 4% 2% % Single Node Number of Transmissions (b) Energy Raio of Opporunisical elivery 8% 6% 4% 2% % Node id (c) Opporunisic elivery Raio communicaion proocols for low-power devices. However, i does no provide a soluion o muli-hop broadcasing. TMbased approaches [18], [27] have proposed scheduling mehods for eiher collision-free sender-side ransmissions or energyefficien sleep schedules. While hese works successfully save he energy cos caused by collisions and idle lisening, hey do no consider redundan ransmissions and unreliable wireless links. Scheduling also becomes less efficien wih he exisence of wireless loss. number of works have proposed rouing soluions for duy-cycled neworks [1], [8], [19], [31], [34], [35], [48]. However, he poin-o-poin rouing model makes hem no suiable o be applied in broadcasing/flooding. or broadcasing service in duy-cycled sensor nework, RS [44] proposes a soluion and shows is efficiency in erms of delay and energy cos. [39] proposes an asynchronous M proocol by disribuing he coverage informaion in broadcasing process. Jiao [15], [16] proposes scheduling algorihms for muli-hop broadcasing wihou considering unreliable links. Lai [22] proposes a broadcasing soluion ha saves energy by leing a node decide how long o wai for more receivers o wake up, bu requires relaively high duy cycle. None of hese soluions invesigaes he low-duy-cycle operaion and unreliable links a he same ime. ifferen from all hese previous works, provides a promising soluion for muli-hop broadcasing while considering he problems caused by boh low-duy-cycle operaion and unreliable wireless links simulaneously. 9 ONLUSION In his paper, we presen : a delay-driven flooding mehod ha is paricularly designed for low-duycycle wireless sensor neworks. Each node makes probabilisic forwarding decisions based on he delay disribuion of nex-hop nodes. Only opporunisically early packes are forwarded via he links ouside he energy-opimal ree o reduce he flooding delay and he level of redundancy. To resolve decision conflic, we build a reduced flooding sender se o alleviae he hidden erminal problem. Wihin he same sender se, we use a linkqualiy-based backoff mehod o resolve and prioriize simulaneous forwarding operaions. Exensive simulaions and es-bed experimens show ha approaches he opimal performance and achieves a shorer average flooding delay wih less han half of he energy cos of Improved Tradiional looding in various nework seings. In he fuure, we shall exend his work ino he scenario where working schedules can be flexibly changed o provide beer flooding performance. KNOWLEEMENT This work was suppored in par by SUT SR IST 21 2, SUT-ZJU/RES/3/211, US Naional Science oundaion (NS) grans NS , NS , NS-91797, and he Singapore Naional Research oundaion under is IM uures unding Iniiaive and adminisered by he Ineracive & igial Media Programme Office, Media evelopmen uhoriy. REERENES [1] M. rzozowski, H. Salomon, and P. Langendoerfer. ompleely isribued Low uy ycle ommunicaion for Long-Living Sensor Neworks. In SE(2) 9, 29. [2] Z. ao, Y. He, and Y. Liu. L2: Lazy forwarding in low duy cycle wireless sensor neworks. In INOOM, 212 Proceedings IEEE, pages , march 212. [3]. ouo,. guayo, J. icke, and R. Morris. 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M Trans. on Sensor Neworks, 4(1), 28. [11] T. He,. M. lum, J.. Sankovic, and T.. bdelzaher. I: dapive pplicaion Independen aa ggregaion in Wireless Sensor Neworks. M Trans. on Embedded ompuing Sysem, Special issue on ynamically dapable Embedded Sysems, 24. [12] T. He, S. Krishnamurhy, L. Luo, T. Yan, L. u, R. Soleru,. Zhou, Q. ao, P. Vicaire, J.. Sankovic, T.. bdelzaher, J. Hui, and. Krogh. VigilNe: n Inegraed Sensor Nework Sysem for Energy-Efficien Surveillance. M Trans. on Sensor Neworks, ebruary 26. [13] Q. Huang,. Lu, and.-. Roman. Spaioemporal Mulicas in Sensor Neworks. In SenSys 3, 23. [14] J. W. Hui and. uller. The ynamic ehavior of a aa isseminaion Proocol for Nework Programming a Scale. In SenSys 4, 24. [15] X. Jiao, W. Lou, J. Ma, J. ao, X. Wang, and X. Zhou. uy-ycle-ware Minimum Laency roadcas Scheduling in Muli-Hop Wireless Neworks. In IS 1, 21. [16] X. Jiao, W. Lou, X. Wang, J. Ma, J. ao, and X. Zhou. 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Krishnamachari, and. oel. elay Efficien Sleep Scheduling in Wireless Sensor Neworks. In INOOM 5, 25. [26]. S. M. Maroi,. Kusy and. Ledeczi. The looding Time Synchronizaion Proocol. In SenSys 4, 24. [27] J. Ma, W. Lou, Y. Wu, X.-Y. Li, and. hen. Energy Efficien TM Sleep Scheduling in Wireless Sensor Neworks. In INOOM 9, 29. [28] S. Madden, M. ranklin, J. Hellersein, and W. Hong. T: Tiny ggregaion Service for d-hoc Sensor Neworks. In Operaing Sysems esign and Implemenaion, 22. [29] M. J. Miller,. Sengul, and I. upa. Exploring he Energy-Laency TradeOff for roadcass in Energy-Saving Sensor Neworks. In IS 5, 25. [3] S. Nah, P.. ibbons, S. Seshan, and Z. R. nderson. Synopsis iffusion for Robus ggregaion in Sensor Neworks. In SenSys 4, 24. [31] W. Pak, K.-T. ho, and S. ahk. Energy efficien rouing proocol for wireless sensor neworks wih ulra low duy cycle. In PIMR, 29. [32] S.-J. Park, R. Vedanham, R. Sivakumar, and I.. kyildiz. 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Taming he Underlying hallenges of Reliable Mulihop Rouing in Sensor Neworks. In SenSys 3, 23. [47] N. Xu, S. Rangwala, K. K. hinalapudi,. anesan,. road, R. ovindan, and. Esrin. Wireless Sensor Nework for Srucural Monioring. In SenSys 4, 24. [48] Y. Yu,. Krishnamachari, and V. K. Prasanna. Energy-Laency Tradeoffs for aa ahering in Wireless Sensor Neworks. In INOOM 4, 24. [49] J. Zhao and R. ovindan. Undersanding Packe elivery Performance in ense Wireless Sensor Neworks. In SenSys 3, 23. [5] J. Zhu, S. hen,. ensaou, and K.-L. Hung. Tradeoff eween Lifeime and Rae llocaion in Wireless Sensor Neworks: ross Layer pproach. In INOOM 7, 27. [51] T. Zhu, Z. Zhong, T. He, and Z.-L. Zhang. Exploring Link orrelaion for Efficien looding in Wireless Sensor Neworks. In NSI 1, 21. [52] Y. Zhu and L. Ni. Probabilisic pproach o Provisioning uaraneed QoS for isribued Even eecion. In INOOM 8, 28. [53] Y. u, L. He, T. Zhu and T. He. chieving Energy Synchronized ommunicaion in Energy-Harvesing Wireless Sensor Neworks. M Transacion on Embedded ompuing Sysem, o appear. [54] M. Zuniga and. Krishnamachari. nalyzing he Transiional Region in Low Power Wireless Links. In IEEE SEON 4, 24. Shuo uo received her.s. in Elecronic Engineering a Tsinghua Universiy in 26 and is currenly a Ph.. candidae in he eparmen of Elecrical and ompuer Engineering a he Universiy of Minnesoa, Twin iies. Her research includes Wireless Sensor Neworks, Vehicular dhoc Neworks, and Real-ime and Embedded Sysems. She received a bes paper award a IEEE MSS 28 and has publicaion in many premier sensor nework journals and conferences. Liang He is currenly a posdoc research fellow a Singapore Universiy of Technology and esign. He received his.eng. degree in 26 and Ph. degree in 211 from Tianjin Universiy, hina, and Nankai Universiy, hina, respecively. uring Oc. 29 o Oc. 211, he worked a Panlab a Universiy of Vicoria as a visiing research suden. He has been a recipien of he he bes paper awards of IEEE WSP 211 and IEEE LOEOM 211. Yu (Jason) u is currenly an assisan professor a Singapore Universiy of Technology and esign. He received his Ph.. under Prof. Tian He from Universiy of Minnesoa, Twin iies, in 21. He is he auhor and co-auhor of over 21 papers in premier journals and conferences. His research includes Neworked Embedded Sysems, Wireless Sensor Neworks, yber-physical Sysems, Wireless Neworking, Real-ime and Embedded Sysems, isribued Sysems, Vehicular d-hoc Neworks and Sream ompuing Sysems. Yu u is a member of M, IEEE and SIM. o Jiang o Jiang received his.s. in Elecronic Engineering from Tsinghua Universiy in 26, and his M.S. in Elecrical and ompuer Engineering from Universiy of Massachuses mhers in 28. He is currenly a Ph.. Suden in he eparmen of ompuer Science a Universiy of Massachuses mhers. His research includes nework modeling and analysis, wireless neworks, and nework science.

15 Tian He is currenly an associae professor in he eparmen of ompuer Science and Engineering a he Universiy of Minnesoa-Twin iy. He received he Ph.. degree under Professor John. Sankovic from he Universiy of Virginia, Virginia in 24. r. He is he auhor and co-auhor of over 1 papers in premier sensor nework journals and conferences wih over 1, ciaions (H- Index 4). His publicaions have been seleced as graduae-level course maerials by over 5 universiies in he Unied Saes and oher counries. r. He has received a number of research awards in he area of neworking, including five bes paper awards. r. He is also he recipien of he NS REER ward 29 and McKnigh Land-ran Professorship. r. He served a few program chair posiions in inernaional conferences and on many program commiees, and also currenly serves as an ediorial board member for six inernaional journals including M Transacions on Sensor Neworks. His research includes wireless sensor neworks, cyberphysical sysems, inelligen ransporaion sysems, real-ime embedded sysems and disribued sysems, suppored by Naional Science oundaion, IM, Microsof and oher agencies. 15