P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks

Transcription

1 J Supecomput (217) 73: DOI 1.17/s P-: a polong stable election outing algoithm fo enegy-limited heteogeneous fog-suppoted wieless senso netwoks Paola G. Vinueza Naanjo 1 Mohammad Shojafa 1 Habib Mostafaei 2 Zaha Pooanian 3 Enzo Baccaelli 1 Published online: 29 June 216 Spinge Science+Business Media New Yok 216 Abstact Enegy efficiency is one of the main issues that will dive the design of fog-suppoted wieless senso netwoks (WSNs). Indeed, the behavio of such netwoks becomes vey unstable in node s heteogeneity and/o node s failue. In WSNs, clustes ae dynamically built up by neighbo nodes, to save enegy and polong the netwok lifetime. One of the nodes plays the ole of Cluste Head (CH) that is esponsible fo tansfeing data among the neighboing sensos. Due to pevasive use of WSNs, finding an enegy-efficient policy to opt CHs in the WSNs has become inceasingly impotant. Due to this motivations, in this pape, a modified Stable Election Potocol (), named Polong- (P-) is pesented to polong the stable peiod of Fog-suppoted senso netwoks by maintaining balanced enegy consumption. P- enables unifom nodes distibution, new CH selecting policy, and polong the time inteval of the system, especially befoe the failue of the fist node. P- B Mohammad Shojafa m.shojafa@yahoo.com Paola G. Vinueza Naanjo paola.vinueza@unioma1.it Habib Mostafaei habib.mostafaei@unioma3.it Zaha Pooanian zaha.pooanian@unioma1.it Enzo Baccaelli enzo.baccaelli@unioma1.it 1 Depatment of Infomation Engineeing, Electonic and Telecommunication, Sapienza Univesity of Rome, Rome, Italy 2 Depatment of Engineeing, Roma Te Univesity, Rome, Italy 3 Depatment of Compute Science, Sapienza Univesity of Rome, Rome, Italy

2 734 P. G. V. Naanjo et al. consides two-level nodes heteogeneities: advanced and nomal nodes. In P-, the advanced and nomal nodes have the oppotunity to become CHs. The pefomance of the poposed appoach is evaluated by vaying the vaious paametes of the netwok in compaison with othe state-of-the-at cluste-based outing potocols. The simulation esults point out that, by vaying the initial enegy and node heteogeneity paametes, the netwok lifetime of P- impoved by 31, 29, 2 and 4 % in compaison with, Low-Enegy Adaptive Clusteing Hieachy with Deteministic Cluste-Head Selection (), Modified () and an efficient modified (E), espectively. Keywods Wieless senso netwok (WSN) Fog computing (FC) Data aggegation Lifetime Enegy efficiency Clusteing 1 Intoduction Wieless senso netwoks (WSNs) consist a lage numbe of small and tiny nodes which ae scatteed ove the physical envionment to monito humidity, tempeatue, pessue, vibation, etc. [1]. These devices ae highly esouce constained, equipped with small pocessos and wieless communication antennas, and battey poweed. A senso netwok is typically expected to pefom multiple tasks [2,3]. Tasks can be categoized into multiple sub-tasks. As an example, monitoing a lage aea can be categoized into monitoing multiple smalle aeas. In heteogeneous netwoks, the equiement of a task is specified by its needs fo diffeent types of sensos [3]. In such cases, sensos must be bundled togethe befoe pefoming task assignments. Due to the limited numbe of sensos and potentially lage numbe of tasks, competition will aise. Given all cuently available infomation, the goal is to pefom the best assignment of available sensos to tasks, to maximize the utility of the netwok [3,4]. A lage mass of senso nodes ae supposed to scatte in a geometic egion, with neaby nodes communicating with each othe diectly. Without the help of a lage amount of unifomly deployed seed nodes, this scheme fails in WSNs with possible holes [5]. The global geomety and topology of a WSN has a geat influence on the design of basic netwoking functionalities, fo example, point-to-point outing and data collecting mechanisms, o if we ae desious to spead some mobile sensos in an unknown egion fomed by static senso nodes, knowing the bode of the egion pemits us to guaantee that newly added sensos ae deployed only in the expected egion [5,6]. Recently, they have found many attentions in the aea of suveillance systems, which ae used fo secuity applications such as baie monitoing [7]. These types of netwoks have many esouce constaints, such as computing, tansmission and powe capabilities [8]. The main eason is that the nodes in the netwok will lose thei powe and cannot be used futhe. Theefoe, WSNs need to exploit efficient techniques to decease these limitations. Thee ae many issues in WSNs which have to be consideed, such as coveage, lifetime, enegy efficiency [9 11] and secuity. Among these concens, lifetime and enegy efficiency ae the most cucial issues and they ae elated to the nodes battey usage.

3 P-: a polong stable election outing algoithm 735 Enegy-efficient outing potocols ae used to tansfe sensed data though netwoks nodes to the Base Station (BS) in the netwok. In this case, besides outing poblem, inceasing the netwok lifetime is anothe impotant issue. Lifetime of sensos constains the amount of utility povided by the netwok. It has a close elationship with the enegy efficiency of the WSN. In such way, some advanced nodes can tansfe the netwok infomation via multi-hop communications to BS. To polong netwok lifetime, hieachical outing potocols such as Low-Enegy Adaptive Clusteing Hieachy (LEACH) [12], LEACH with Deteministic Cluste-Head Selection () [13], Stable Election Potocol () [14], and Modified () [15] can be used. In hieachical outing potocols, the concept of clusteing is used as basic concept to develop vaious potocols. In the clusteing appoach, some specific nodes ae chosen as Cluste Head (CH) to tansfe the netwok data to the sink node. Theefoe, selecting some nodes as CH nodes in the deployed netwok is consideed as one of the main tagets of these eseach woks, besides consideing the lifetime issue. As we know, sensos in the huge WSN face with unpedicted vaiety of data volume. It takes huge amount of time to pocess these infomation, theefoe, the industies look fowad to using some new tends such as Fog achitectues (possibly, Fog computing) to decease the pocessing time. The tem Fog computing was fist poposed by Ciscoin[16] that enables a new beed of applications and sevices, and thee is a fuitful inteplay between the WSN and the Fog, paticulaly when it comes to data management and analytics. The Fog Nodes (FNs) closest to the netwok edge ingest the data fom Intenet of Things (IoT) devices. Combining the eliability equiements of cloud paadigms with the equiements of WSNs and actuatos helps to design a eliable Fog computing platfom [17]. On one hand, FNs can be deployed anywhee with a netwok connection: on a factoy floo, on top of a powe pole, alongside a ailway tack, in a vehicle, o on an oil ig. Any device with computing, stoage and netwok connectivity can be an FN at the netwok edge. Examples include industial contolles, switches, outes, embedded seves (see Fig. 1). Accoding to Cisco, Fog node will be on-the-fly achitectue which helps IoT to minimize latency, and avoiding the duplicated back and foth taffic between cloud and mobile uses, not only the backbone bandwidth can be significantly saved, the enegy consumption of coe netwoks can also be geatly educed, which contibutes to the sustainable development of netwoking. We ague that the above chaacteistics make the Fog the appopiate platfom fo a numbe of citical IoT in geneal and Wieless Sensos location awaeness. Seveal eseaches have been done in these topics such as [18 23], but most of them lack management of the CHs selection to polong the aliveness in WSN. In addition, the eseach on Fog computing and elated systems still emains at a vey ealy stage. On the othe hand, the WSN speeds up awaeness and esponse to events in industies such as manufactuing, oil and gas. On a factoy floo, a tempeatue senso on a citical machine sends eading associated with imminent failue. In oil and gas exploation, sensos on oil pump slows down/aises up intake valve to contol the system to pevent the disaste happen by monitoing the pump valve automatically. Theefoe, the FNs closest to the gid sensos can look fo signs of poblems and if it happens then sends contol commands to actuatos to stop thei actions. Theefoe, we need to popose an enegy-efficient solution to polong the aliveness of the netwok. This eseach addesses the above issues.

4 736 P. G. V. Naanjo et al. Fig. 1 The oveall Fog stuctues and elation system achitectue. Inta-Fog nodes achitectue opeate P-. FN Fog node 1.1 The goal of the pape In this pape, we take enegy efficiency and lifetime into consideation to popose a new appoach fo the mentioned poblem. We devise an enegy-awae Polong Stable Election Potocol (P-) fo node clusteing in WSNs, to maximize the netwok lifetime. We conside two-level heteogeneity of nodes which ae called as nomal nodes and advanced nodes, to devise a new appoach. Fog infastuctue is, by definition, a location-awae system; it makes available the infomation about the Inte-senso distances and senso-to-senso link gains which is exploited by the poposed CH election algoithm. This is the eason why ou election algoithm well matches emeging Fogsuppoted WSNs. Ou appoach outpefoms state-of-at woks in this aea in tems of netwok lifetime and numbe of CHs. The basic idea of the P- potocol is the andom selection of the numbe of the CH nodes at each ound of the cycle, to achieve balance, educe enegy consumption and extend the lifetime of the netwok. The main contibutions of the P- can be summaized as follows: (1) P- attempts to contol the inheent andomness of the CH selection in each ound; (2) P- exploits heteogeneity enegy theshold to avoid low-enegy nodes to nominate CH in the next ounds; (3) P- takes into account fo the numbe of nodes that ae associated with each CH and optimizes the minimum distance (md) between the CHs and FN; (4) using P- at each ound leads to nominate some CHs fo the tansmission of aggegated data to the FN, so that thee is no the netwok feezing at each ound; (5) thee is no any pioity consideed fo selecting CH between nomal and advanced nodes, except that the node should be alive (its enegy must be highe than the theshold) and should not have been CH in the pevious ound. We use this policy to avoid continuous selection of each node, egadless of its type; (6) P- consides the aveage of the

5 P-: a polong stable election outing algoithm 737 enegy fo each node to incease efficiency and easonably deceases the ovehead on each FN. Remakable featues of the poposed potocol ae its aliveness, fainess, full distibution in CH selection, polongation of the lifetime, obustness of pefomance in the pesence of enegy heteogeneity, and ease of pogamming. The est of this pape is oganized as follows. In Sect. 2, we shotly eview the elated wok. In Sect. 3, we pesent ou poposed clusteing model and detail the Polonged Stable Election Potocol, called P-. In Sect. 4, we evaluate the pefomance of P- via simulations and compae the esults with most ecent elated ones. Finally, Sect. 5 concludes the pape and daws some possible futue wok. 2 Related wok Clusteing and enegy efficiency have been consideed in wide aea of WSN applications ove the couple of yeas. The most impotant issues in the aea of WSNs ae enegy efficiency, outing and coveage, because these issues have diect effect on the pefomance of netwok applications. In this section, we povide a eview on eseach woks that have been done in the liteatue on this topic. A compehensive and fine-gained suvey on clusteing outing potocols poposed in the liteatue fo WSNs is pesented in [24]. Authos of [24] point out the advantages and objectives of clusteing fo WSNs, and devise a novel taxonomy of WSN clusteing outing methods based on complete and detailed clusteing attibutes. In the sequel, we eview some of eseach liteatue that ae elated to ou poposed algoithm. LEACH is the fist hieachical outing potocol fo WSNs to impove the netwok lifetime [12]. In LEACH, authos geneate a andom numbe between and 1 and compae it to a theshold as a base method to compute pobability of nodes to be CH nodes. A Deteministic Cluste Head Selection based on LEACH, called (), is poposed in [13]. Authos use the emaining enegy level of each node to compute the pobability of nodes to become CH. An Advanced LEACH (ALEACH) appoach is poposed in [25] to extend the netwok lifetime. ALEACH otate CH positions, to distibute the enegy consumptions ove all nodes. Thee ae many eseach woks that have been done in the aea of clusteing in diffeent aspects such as node deployment, enegy consumption, natue of nodes and coveage. Hee, we eview the woks that ae elated to the stability of netwoks. The stability of netwoks means that time inteval fom beginning befoe the fist node exhausts its enegy. Stable Election Potocol () is a heteogeneity-awae potocol which extends the stability peiod (e.g., time inteval befoe the death of the fist senso) of WSN [14]. This wok is used as a basis wok to popose a simila potocol with highe pefomance in diffeent liteatue. It uses two levels of heteogeneity fo the deployed nodes, e.g., advanced nodes and nomal nodes. Advanced nodes have highe enegy level than nomal nodes and they compute the pobability of nodes to be CHs in the netwok. In this appoach, CH selection is done based on the pobability value. Ou method extends stability of netwok moe than by consideing the aveage enegy state of nodes fo CH selection in each ound.

6 738 P. G. V. Naanjo et al. A Modified Stable Election Potocol () fo heteogeneous WSN is devised in [15]. consides two types of nodes (i.e., advanced and nomal nodes) with diffeent initial enegy. Advanced nodes have moe initial enegy than the nomal ones at the beginning of the election algoithm. Theefoe, those nodes have high pobability to be CHs in this appoach, because enegy level is used as a facto to select CH in M-. Although is pomising in netwok aliveness, it feezes the system in some cycles (o ounds) which is an idealistic condition. Moeove, compaed to P-, M- is unable to keep aliveness of the netwok with an efficient enegy eduction. Efficient modified vesion of (E) is intoduced in [26] to impove the stability peiod of netwok by maintaining balanced enegy consumption. E has two main chaacteistics. Fist, advanced nodes have moe possibility than nomal nodes to become CH. Second, if two sensos have the same pobabilities to become CH, the senso with highe enegy level is selected. Hence, E suffes fom keeping the system alive in long time and is unable to decease the enegy of the netwok in a smooth way which is accomplished by P-. On the othe hand, P- can outpefom all the mentioned methods in tems of netwok paametes which ae mentioned in the wok. Fuzzy appoach is used in some eseach woks fo clusteing of the WSNs. A fuzzybased clusteing appoach is poposed in [27] to impove the netwok lifetime. In this method, a supe-ch (SCH) is elected among the CHs who can only send the infomation to the mobile BS by choosing suitable fuzzy desciptos, such as emaining battey powe, mobility of BS, and centality of the clustes. In the fuzzy engine, authos use Mamdani s ule to select CHs. Authos devised a Fuzzy Relevance-based Cluste head selection Algoithm (FRCA) to solve the poblems in existing wieless mobile ad hoc senso netwoks [28]. In this method, fuzzy elevance is used to select the CHs fo clusteing of wieless mobile ad hoc senso netwoks. Some othe fuzzy-based woks fo clusteing ae intoduced in [29 33]. The supeioity of P- ove these appoaches is that ou method can use Fog technology to tansfe data outside of netwok. Fo the lage WSNs, a clusteing potocol which is called fan-shaped clusteing (FSC) is poposed to patition a lage-scale WSN into fan-shaped clustes [34]. FZ- LEACH is a LEACH-based potocol fo clusteing lage-scale WSN which uses Fa- Zone concept as a basic appoach [35]. Fa-Zone is a goup of sensos which ae placed at locations whee thei enegies ae less than a theshold. Multi-hop dynamic clusteing LEACH (MDLEACH) is also used fo clusteing of lage netwoks [36]. The main idea fo MDLEACH elies on dynamic clusteing and multi-hop tansmission. With espect to effots on the afoementioned techniques, P- can wok with FNs to take benefit of Fog computing which is neve used in the ecent clusteing eseach woks. Recently, some liteatue focuses on applying Fog technology to enhance the communication between the FNs [37 39]. Specifically, the authos of [37] pesent a joint contolle fo the adaptive, scalable and distibuted management of the enegy and bandwidth esouces of inte FNs, to povide vehicula clients the mobile Intenet access which is equied by the emeging cloud-assisted FN applications. Moeove, [38] applies new fall detection algoithms and implements them into a distibuted fall detection system in inte-fns. On the othe hand, the wok [39] pesents a pedictable sevice demands of mobile uses using Fog computing to delive the localized sevice equests. The main focus of these woks is on Mobile-Fog-Cloud achitectue and ich

7 P-: a polong stable election outing algoithm 739 potential applications, in both mobile netwoking and Intenet-of-Things. They fail to conside the communication between the CHs and the way of CH selection. In a nutshell, these woks ae pomising but none of them focus on the inta-fns, the CH selection and data aggegation pocedues ove the Fog coveage aeas [4,41]. 3 The poposed algoithm This section is devoted to pesent the Fog-suppoted CH selection potocol (e.g., P- ). The goal is to polong the netwok lifetime and decease enegy consumption. 3.1 The consideed netwok model The netwok infastuctue of the poposed algoithm is pesented in Fig. 1. Figue 1 shows the main blocks which opeate at the Middlewae laye of the consideed Fog infastuctue (see the gay ectangle in Fig. 1). Roughly speaking, the consideed Fog Infastuctue (FI) is composed by seveal FN coveages (FNCs) (see the gay cicles in Fig. 1). Hence, FNC is composed by: (1) seveal nodes (see the coloed cicle inside the Fog node coveage in Fig. 1) with low-level enegy that equie the infomation fom the envionment; (2) some CHs (see the ed colo cicles inside FNCs of Fig. 1) with thei sensing anges (see the tiny gay colo lines inside FNCs of Fig. 1), which ae esponsible to tansmit the aggegated infomation to the FNs; (3) a netwok of wieless nodes and a fixed base station (possibly, WiFi) as a sink, which is called Fog Node (FN) (see the antenna in Fig. 1) with high enegy. Each FNC coves n nodes with set N ={N 1, N 2,...,N n } unde two-level heteogeneity sets (e.g., N = NRM AD); Nomal nodes, NRM {NRM 1, NRM 2,...,NRM NRM } (e.g., the blue colo cicles inside each FNC of Fig. 1) and Advanced nodes AD {AD 1, AD 2,...,AD AD } (e.g., the geen colo cicles inside each FNC of Fig. 1). In P-, due to enegy saving, some nodes will be selected as a CH using some policies, to tansmit the eceived data fom its neighbo nodes (e.g., which is called cluste as is shown in each section of the FNC in Fig. 1) diectly to the FN. Theefoe, P- s goal is to find the lowe numbe of CHs in the WSN. Specifically, we pesented how to obtain the optimal numbe of clustes, to incease enegy efficiency and polong the netwok. Fist of all, we assume that advanced nodes locations ae pedetemined and nomal nodes ae placed andomly. The diffeence between advanced and nomal nodes ae thei enegy scales. The cadinality of AD, e.g., AD AD, is a pecentage of N which is denoted by m. Letm be the faction of the total numbe n of nodes and mn is the numbe of advanced nodes (i.e., n mn is the numbe of nomal nodes). Let M be the netwok space, whee the space is assumed to be a ectangle. We denote R as the ound (cycle) of the poblem testing, whee R {1,..., max } is a set of max cycles. In addition, we denote α as the coefficient of the enegy which is applied to the diffeence of advanced and nomal nodes. 3.2 Advanced nodes distibution Each node tansmits data to close CH and the CH pefoms data aggegation. The distance between FN and nodes is < d s < ds, whee d s is a fixed ange consideed

8 74 P. G. V. Naanjo et al. to avoid data taffic jam of nodes in one cluste (i.e., ds = 1). The position of the advanced nodes ae defined as: p = (mn+1) M, and p > i 2, whee i decides how many advanced nodes should be distibuted in one p(m 2 ). Hence, one node may become a CH with a pobability p, whee 1/p is the epoch of the clusteed senso netwok, and totally the ate np would be the total pobability of the WSN in one ound. Note that, in P-, one node cannot become a CH seveal times in a same ound. We denote G as the set of binay values with length n fo one node: if G (N i ), N i N is 1, it means the ith node becomes CH in the th ound, othewise, it will not be CH in th ound and will be nomination to be a CH in the cuent ound ( + 1), it guaantees that one alive node is able to be CH once in one ound (the same policy applied in [5,6]). 3.3 Nodes pobabilities and enegy definitions Let m be the faction of advanced nodes and the additional enegy facto between advanced and nomal nodes denoted by α. Thee ae n(1 + αm) nodes with enegy equal to the initial enegy of a nomal node (e.g., at ound = 1), so E 1 nm E, whee E is the initial enegy consideed fo each node. The initial enegy (e.g., at ound = 1) of advanced nodes ae defined as: E 1 adv E (1 + α). Letp be the pobability of the nodes selection which influences on the nomal and advanced node pobability selections in th ound. To maintain the minimum enegy consumption in each ound within an epoch, the aveage numbe of cluste heads pe ound pe epoch must be constant and equal to np.letp nm and p adv be the weighted pobabilities fo nomal and advanced nodes, espectively. The weighted pobabilities p nm and p adv ae defined as: p nm = p (1 + αm), (1) p adv = p(1 + α)e n() (1 + αm)eadv 1, (2) whee E n () is the aveage enegy of all nodes (e.g., it includes nomal and advanced nodes) in th ound which is defined as: E n () = 1 n n E N j (), if E N j () >, (3) j=1 whee E N j () is the enegy of node N j in th ound. In the heteogeneous scenaio, the aveage numbe of cluste heads pe ound pe epoch is equal to n(1 + αm)p nm (i.e., each vitual node has the initial enegy of a nomal node). To tansmit infomation fom each CH to the FN, we must conside some node chaacteistics and the distance fom CH to FN. Indeed, each node has a adio enegy dissipation to attain an acceptable signal-to-noise-atio (SNR) tansmitting k numbe of the bit message ove a distance d (distance fom CH to FN). We define E Tx and

9 P-: a polong stable election outing algoithm 741 E Rx which ae the enegies consumed by the tansmitte and eceive cicuits in the netwok, espectively. In this model, we use the fee space ( f s ) (with d 2 the coefficient of powe loss) and the multi-path fading (mp) (with d 4 the coefficient powe loss) channel models depending on the distance fo the E TX and E RX [14]. It means, when the distance d is less than d,the f s enegy denoted by ε fs is used; othewise, mp enegy denoted by ε mp is applied fo calculating E Tx. Theefoe, the E Tx is calculated as in: { k(eelec + ε E Tx (k, d) fs d 2 ), if d d o, k(e elec + ε mp d 4 (4) ), if d > d o, whee d ε fs /ε mp, E elec E Tx + E DA, while E DA is the data aggegation enegy fo each node and E elec is the enegy consumed in electonic cicuit to tansmit/eceive the signal, and d (X CH X FN ) 2 + (Y CH Y FN ) 2. Finally, the eceive consumed enegy is defined as: E Rx (k, d) E elec k. The enegy of CH (e.g., CH selection which is descibed in the next subsection) will be updated afte the calculation d and E Tx as E Ni () = E Ni ( 1) E Tx, (5) whee E Ni () is the enegy of ith node in the th ound. 3.4 CH selection using enegy thesholds We denote by Th(Ni nm ) and Th(Ni adv ) the enegy thesholds fo the nomal and advanced node to be selected as CH in the th ound, espectively. Eqs. (6) and (8) pesent thesholds Th(Ni nm ) and Th(Ni adv ) fo the nomal and advanced nodes, espectively. p nm )E Th(Ni nm 1 Ni (), if N i G ) = 1 p nm (mod pnm, othewise, whee Th(Ni nm ) is the theshold applied to n(1 m) nomal nodes in the cuent ound ; G is the set of nodes that do not become CHs in the last 1/p nm ounds of the epoch; E Ni () is the aveage of alive nodes stating fom the fist node selection to ith node in the th ound as shown in (7): (6) E Ni () = 1 n i E N j (), if E N j () >, (7) j=1 This confims that each nomal node becomes a CH node at ate 1 p (1 + αm) pe epoch and the aveage numbe of CHs ae nomal nodes pe ound pe epoch, which is equal to n(1 αm)p nm. In the same way, fo the advanced nodes, we have

10 742 P. G. V. Naanjo et al. p adv )1/E Th(Ni adv 1 Ni (), if N i G ) = 1 p adv (mod p adv, othewise, (8) whee Th(Ni adv ) is the theshold fo the advanced nodes at the ound, and G is the set of advanced nodes that do not become CH in the last: 1/p adv ounds of the epoch. This guaantees that each advanced node will become a CH stictly once pe 1 p (1 + αm)/(1 + α) ounds, pe epoch and pe 1/E n (). This confims that P- is capable to make fully distibuted and suitable election of the CH unde heteogeneous nodes (e.g., the nodes do not have the same initial enegy levels). In ode to minimize the total enegy consumptions in the cuent netwok it is enough to elect the pope ach. 3.5 Distance computation In this subsection, we define the distance between CH and FN (denoted by d tofn ) and the minimum distance fom the nodes to CHs (denoted by md toch ), to find the minimum path to save enegy fo tansfeing the infomation to FN. In othe wods, using the Fog-based model, simila to [42], sevice stations with bound-less capacity ae defined as between gateway and common nodes based on the lagest hop count fom the gateways, wheeas in [42,43] the othe nodes ae modeled as sevice stations with cetain capacity and the nodes do not fowad loads fo othe nodes in the same hop, as ou model does. The goal is to minimize the communication cost. Then, we attempt to save the enegy of the WSN to polong WSN aliveness and keep nodes unning moe ounds (we find minimum distance of each node to the CH fo each ound). Specifically, at each ound, we select the desied atio of CHs andomly, then we stat to the pocess of finding CH set CH on the alive nodes. If we find a node that should be consideed as a CH, then we add it into the CH set and count the numbe of established CHs (i.e., CH_ct). We continue this loop to find at most the desied numbe of CHs (in each ound), then stop the loop and calculate the equied distances d tofn and md toch in each ound as in: d tofn = (X i X FN ) 2 + (Y i Y FN ) 2, (X i, Y i ) N \CH, md toch = agmin (Xi,Y i ) (Xi X CH ) 2 + (Y i Y CH ) 2, (9) (X i, Y i ) N \CH, (X CH, Y CH ) CH, (1) and d min = min (d tofn, md toch ), N i N \CH, (11) N i N \CH whee (X FN, Y FN ) and (X CH, Y CH ) ae the Catesian coodinates of the FN and CHs, espectively. Specifically, in Eq. (9), we calculate the distance of each non-ch nodes (N \CH) to FN. Then, Eq. (1) computes the distance of the neaest CH to each alive node. Finally, Eq. (11) selects the minimum between the values Eqs. (9) and

11 P-: a polong stable election outing algoithm 743 (1) fo each non-ch node on a pe-ound basis and calculate the enegy accoding to Sect Algoithm 1 pesents the steps of the poposed P-. The output of the algoithm is defined as: A {E n (), E Ni (), Alive, CH}. Th1 and Th2 ae theshold values fo selecting nomal and advanced nodes as CH, espectively. Algoithm 1 P- Algoithm Input: n, max, CH = ; Output: A {E n (), E Ni (), Alive, CH}; 1: Advanced nodes placement (see Sec. 3.2); 2: fo = 1to max do 3: CH = ; 4: calculate p nm, p adv, E n by using Eqs.(1)-(3); 5: fo i = 1ton do 6: calculate E Ni () by using Eq.(7); 7: no_ch = uni f (CH min, CH max ); 8: if CH_cnt > no_ch then 9: beak 1: end if 11: calculate Th(Ni nm ) by using Eq.(6); 12: calculate Th(Ni adv ) by using Eq.(8); 13: if (Th1 Th(Ni nm )) & (E Ni > ) & 14: fo j = 1toi do (G (){N i }==) then 15: if (distance > d s ) then 16: CH = CH {N i },andn i NRMis selected as a CH; 17: CH_cnt = CH_cnt + 1; 18: update E TX (k, d) and E Ni () by using Eq.(4); 19: end if 2: end fo 21: end if 22: if (Th2 Th(Ni adv )) & (E Ni > ) & (G (){N i }==) then 23: CH = CH {N i } and N i AD is selected as a CH; 24: CH_cnt = CH_cnt + 1; 25: update E TX (k, d) and E Ni () by using Eq.(4); 26: end if 27: end fo 28: fo i = 1ton do 29: if ({N i } N \CH) & (E Ni > ) then 3: calculate d tofn by using Eq.(9); 31: calculate md toch by using Eq.(1); 32: calculate d min by using Eq.(11); 33: end if 34: end fo 35: end fo 36: etun A 3.6 Some explicative emaks about Algoithm 1 The Algoithm 1 points out the steps of the poposed method. Specifically, we define the set CH as the list of nodes which ae selected to be CH in th ound. Line 1

12 744 P. G. V. Naanjo et al. Fig. 2 Time chat of the opeation of P- attempts to establish the advanced nodes as detailed in Sect Lines 2 stats a loop of the P- to un on each node. Lines 6 8 show that we define a paamete, called no_ch, to keep the numbes of desied CH at each ound as an unifom distibution (denoted by uni f (.,.) between [CH min, CH max ]). Lines and Lines detail the selection of nomal nodes and advanced nodes as CHs, espectively. Note that distance is a Euclidean distance value which is calculated between the coesponding nomal node: N i NRM\N i and othe nomal nodes. At the end, the last Lines update the enegy of the nodes which ae not selected as a CH at th ound using minimum distance computations. 3.7 MAC setup fo P- P- elies on a time division multiple access (TDMA)/code-division multiple access (CDMA)-based MAC potocol to educe inte-cluste and inta-cluste collisions. It consists of two time phases that is a steady-state phase and a setup phase. Duing the steady-state phase, the senso nodes sense and tansmit data to the CHs. The CHs compess the data aiving fom nodes that belong to the espective cluste, and send a fused packet diectly to the FN (Figs. 1 and 2). Afte a time, 1 which is planned a pioi, the netwok goes back into the setup phase again and entes anothe ound of CH election. As an altenate analysis, the efficiency of the MAC unde diffeent topologies and taffic pattens can be evaluated by measuing total one-hop netwok thoughput, to tansmit them as soon as possible and deceasing latency at each hop by balancing the netwok load among good fowades [45,46]. Theefoe, the FN s gateways educe enegy consumption and extends the lifetime of the cluste heads. The FN s gateways decease pobability of failue nodes and polong the time inteval befoe the death of the fist node and inceasing the lifetime in heteogeneous WSNs. The use of TDMA/CDMA techniques allows a hieachy and makes clusteing on seveal levels, to save moe enegy. About the P- implementation complexity, we note that the convegence ate of the P- is constant and elated to the numbe of the iteations (ounds). In detail, P- pusues a local stategy, in which nodes execute the algoithm inde- 1 i.e., Minimizing stat up time yields moe powe pe day, hous, minutes to pefom othe tasks, such as outing and communication [44].

13 P-: a polong stable election outing algoithm 745 pendently and base thei cluste membeship decisions on thei own state and the state of thei neighbos. In P-, since the decision to change the CH is pobabilistic, thee is a good chance that evey node is selected as a CH egadless of its enegy level. Futhemoe, P- foms one-hop inta- and inte-cluste topology, in which each node can tansmit diectly to the CH and theeafte to the FN. As a consequence, the pe-ound implementation complexity of the poposed algoithm is O(1). 4 Pefomance evaluation This section pesents the simulated pefomance of the P- unde vaious scenaios and compaes it with [14], [15] E[26], and [13] pefomances. 4.1 Simulation setup To analyze the pefomance of the P- algoithm, we un the simulation unde the MATLAB testbed. The netwok paametes used in ou expeiments ae listed in Table 1. The simulated senso netwoks include diffeent factions of advanced and nomal nodes (m ={.1,.2,.3}, i.e., m =.1 means the atio of the advanced node is 1 % of total nodes numbe and the est all nomal nodes). To evaluate P- unde diffeent envionments, we built up fou scenaios, two of them with two case senso densities: node numbes n ={1, 2}, espectively, poblem space/aeas M ={1 1, 2 2}, the numbe of iteation which is called ounds = 5, advanced node selection coefficients ae α ={1, 2, 3}, and the sensing anges of each node (d )is5.28(m) [47] in ou implementations. Two othe scenaios which ae concentate on the big-size netwok M ={1 1} with n = 1 nodes and medium-size with M ={5 5} with n = 5, espectively. Table 1 Default values of the main simulated paametes Paamete Value M {1 2, 2 2, 5 2, 1 2 }(m 2 ) n {1, 2, 5, 1} m {.1,.2,.3} k 4 (bit) p.1 E.5 (Joule) E DA 5 (njoule)/(bit) E elec 5 (njoule)/(bit) E fs 1 (pjoule)/(bit/m 2 ) E mp.13 (pjoule)/(bit/m 4 )

14 746 P. G. V. Naanjo et al. 4.2 Numeical esults In the following, we test the P- enegy pefomance unde vaious settings and compae the attained esults against the afoementioned potocols Effect of paamete settings In the fist scenaio, we aim at testing the pefomance of the P- and evaluate the esulting aveage numbe of nomal and advanced nodes, pocess the WSN with diffeent α, n, m, and M unde a synthetic (e.g., independent and identically distibuted) infomation aival and andom sequence of nomal nodes fo 5 ounds inspiing [12,13]. Figue 3a, b epots the aveage of the nomal NRM and advanced AD nodes, which ae defined as: NRM NRM = 1/ τ=1 CH nm τ, and AD AD = 1/ τ=1 CH adv τ, espectively, whee CH nm τ and CH adv τ ae the numbe of nomal and advanced nodes which ae in the set CH at the τth ound, espectively. NRMand AD ae selected as CH fo vaious m and unde mediumsize (n = 5, M = 5 2 ) and big-size (n = 1, M 2 = 1) envionments. In Fig. 3c, d, we pesent diffeent E n -vs.- fo vaious m fo coesponding medium-size (n = 5, M = 5 2 ) and big-size (n = 1, M = 1 2 ) envionments. We daw five conclusions fom these figues. Fist, by inceasing the ound the NRM (see thee continuous downwad plots in Fig. 3a, b) and AD (see thee dashed upwad plots in Fig. 3b) declines, due to death of nomal and advanced nodes, which is influenced by the numbe of CH selections. This ate is highe fo nomal nodes, because thei population is highe than the one of the advanced nodes. Second, the numbe of 25 m =.2, NRM m =.2, NRM.4 NRM m =.3, NRM m =.1, NRM m =.1, AD AD NRM 1 5 m =.3, NRM m =.1, NRM m =.1, AD.3.2 AD ( n, M) = (1, 1 2 ) big size ( n, M) = (5, 5 2 ) medium size (a) (b) m=.1 m=.2 m= m=.1 m=.2 m=.3 En 8 6 En (c) E n-vs.- in big size poblem (d) E n-vs.- in medium size poblem Fig. 3 E n and NRM/AD fo diffeent m in big-/medium-size poblems

15 P-: a polong stable election outing algoithm 747 Fig. 4 E Cons n -vs.-e Res n -vs.- unde the scenaio of Table 1 at α = m =.1 E Cons n n =1, Cons n =5, Cons n =1, Cons n =1,Res n =5,Res n =1,Res E Res n 4 3 α =1 α =2 α =3 8 6 α =1 α =2 α =3 E n 2 E n (a) (n, M) = (1, 1 2 ) (b) (n, M) =(2, 2 2 ) Fig. 5 E n of P- unde the scenaio of Table 1 at m =.1 with vaious α available advanced nodes will be highe fo inceasing m, and the deceasing ate will be smoothly lowe at lowe m values (see thee dashed upwad plots in Fig. 3a, b). Thid, the compaison of Fig. 3a, b points out that when the netwok size and the numbe of nodes in the WSN incease, the pobability of nomal and advanced nodes to be CH inceases. Fouth, in compaison between Fig. 3c and d, we elicit that, while inceases, the aveage enegy of nodes: E n E n () deceases. This is due to eduction of the alive nodes (as well as deceasing the CHs as ae shown in Fig. 3a, b). Finally, the m values have diect impact on the numbe of CHs and the netwok enegy eduction. Figue 4 epots the aveage enegy usage of nodes (i.e., consumed enegy) E Cons n and the aveage enegy of nodes (i.e., esidual enegy) E es n = E n at diffeent nodes (1, 5, 1) unde the scenaio of Table 1 diffeent ounds. Specifically, Fig. 4 points out that: (1) when the numbe of ounds inceases, E Cons n inceases and E es n deceases; (2) when the numbe of senso nodes inceases, the enegy consumption inceases (see thee uppe most black continue plots of Fig. 4) and the E es n deceases (see thee lowe dashed plots of Fig. 4). Figue 5 epots the aveage enegy of nodes E n fo diffeent α, n unde vaious netwok sizes. Specifically, we conclude the following esults: (1) while inceases, moe nodes die and E n () deceases; (2) at each ound, when α inceases, the enegy of advanced nodes (e.g., Eadv 1 ) inceases while the E n( ) inceases; (3) the compaison of Fig. 5a, b points out that, by inceasing the values of n and M,

16 748 P. G. V. Naanjo et al. the E n (.) inceases at each ound, due to using moe nodes and moe netwok spaces Pefomance compaisons In the next test scenaio, we un the poposed algoithm and evaluate the esulting numbe of alive nodes and pe-node aveage enegy E n ove 5 ounds unde the simulated paametes of Table 1. The dained esults ae epoted in Figs. 6 and 7.To cay out fai enegy compaisons, all the enegy values epoted in this subsection also account fo the enegy wasted by the coesponding signaling tasks caied out by the simulated algoithms. Figue 6 shows that ou P- potocol outpefoms othe potocols in tems of stability and netwok lifetime. Specifically, Fig. 6 points out that: (1) when m inceases, the node lifetime inceases; (2),, ae vey sensitive to heteogeneity, so nodes die at a faste ate with esult to P-, which uses the fainess in CH selection among its two-level heteogeneity. Figue 7 epots E n in all the potocols. It shows that the aveage enegy of each alive node deceases duing the time (ound), but P- could keep moe nodes alive, which is against othe potocols which die soon and the netwok aveage enegy goes to zeo in less than 2 ounds, in tun, P- could be alive in all ounds and keeps the netwok enegy especially when m is.2 and.3. Theefoe, in this potocol, senso nodes deplete thei enegy at vey low ate. Oveall, it may be concluded that P- is moe enegy efficient, has longe stability peiod and longe lifetime than the othe outing potocols. Table 2 epots the maximum (e.g., E max n i=1 E Ni (1) fo = 1), minimum (e.g., E min n i=1 E Ni ( max ) fo = max ) enegies and aveage enegy of the WSN unde P-, [14], [15] E[26], and [13] #Alive P - E #Alive P - E #Alive E P (a) (n, m) = (1,.1) (b) (n, m) =(1,.2) (c) (n, m) =(1,.3) Fig. 6 Numbe of alive nodes (system lifetime) fo P-, [14], [15] E[26], and [13] En P - E En P - E En P - E (a) (n, m) = (1,.1) (b) (n, m) = (1,.2) (c) (n, m) = (1,.3) Fig. 7 E n vs. fo P-, [14], [15] E[26], and [13]

17 P-: a polong stable election outing algoithm 749 Table 2 The maximum Emax, minimum Emin enegies of the WSN and En fo diffeent m among P-, [14], [15], E [26], and [13] Potocols Emax Emin En m =.1 m =.2 m =.3 m =.1 m =.2 m =.3 m =.1 m =.2 m =.3 P E

18 75 P. G. V. Naanjo et al. fo diffeent m values. In detail, we undestand that at m =.1, the E n impovements of P- compaed to [14], [15] E[26], and [13] ae of about 48, 46, 43 and 67 %, espectively. Futhemoe, by inceasing the numbe of advanced node to 3 times (i.e., m =.3), the savings of the poposed algoithm compaed with [14], [15] E[26], and [13] potocols with lowe numbe of advanced node (i.e., at m =.1) ae of about 9, 35, 34, and 28 %, espectively. Moeove, we conclude that, when m inceases, the minimum enegy E min of the netwok fo the P- is highe than coesponding potocols, and this confims that the P- is able to polong of the lifetime of the netwok. Table 3 epots the Fist Node Die (FND), Mean Node Die (MND), Last Node Die (LND), and the aveage of ounds taken each node among 1 nodes alive (Alive) of the afoementioned potocols unde vaious m and at M = 1 2 (m 2 ). An examination of the numeical esults epoted in Table 3 leads to thee main conclusions. Fist, FND and MND of the P- ae highe than othe methods. It is impotant to stess that P- attempts to adapt itself and polong the netwok lifetime, especially at m =.2. It means that, although P- fist node dies soone than othe potocols, but the MND ate in P- is highe than othes, this confims that, peound, P- elect suitable CHs to incease the aliveness of the netwok. Second, by inceasing m, FND and MND incease too, due to the pesence of moe advanced nodes in the WSN. Thid, the aveage numbe of alive nodes at each ound (e.g., Alive) in the P- is highe than all othe methods and this confesses P- aliveness is highe than othes. Figue 8 epots the solution of P- algoithm (e.g., A) at(n, m, M,α) = (2,.2, {1 2, 2 2 }, 1) against the ones of the afoementioned potocols. Specifically, Fig. 8a d epots thee pefomance paametes (e.g., # Alive, CHs, and aveage enegy of the netwok E n )atm = 1 2 (m 2 ) and the next thee plots ae pesented fo M = 2 2 (m 2 ). Hence, fom Fig. 8a vs. e and Fig. 8dvs. h, we conclude, while netwok size inceases, the nomal nodes dispesion inceases and this leads to consume moe enegy to send the infomation though the nodes to CHs and/o CHs to FN. Hence, this situation leads to decease the numbe of alive nodes as well as E n, and the inteesting point is that P- may use suitable CHs at each ound, polonging the netwok aliveness and even be alive, even afte 5 ounds compaed to othes which die. Fom the CH selection and packet tansfeing point of view, P- could select CHs and send packets fom CHs to FN with less fluctuations, especially within the fist 2 ounds compaing with othe potocols (see Fig. 8c vs. g). This is due to the fact that the othe potocols ae unable to adapt nodes thesholds, to select the suitable CHs, while P- adapts the thesholds (see Eq. (7)). In the last simulation, Fig. 9 epots the total tansfeed packets (e.g., # Pkts) unde diffeent potocols and at vaious m. We daw two emaks fom this figue. Fist, CHs in P- is lowe than [14], [13], and [15], due to the selection of optimal CHs. Second, P- is lowe than E [26] (bits) because, at each ound, P- attempts to select some nodes as CHs that have not be selected in the pevious ound, to be fai, distibute the enegy, and polong lifetime of the netwok.

19 P-: a polong stable election outing algoithm 751 Table 3 Compaison of P- and othes potocols with = 5; NND := node neve die; FND := fist node die; MND := mean node die; and, LND := last node die, Alive := aveage nodes takes to be alive in each ound PROTOCOLS FND MND LND Alive m =.1 m =.2 m =.1 m =.2 m =.1 m =.2 m =.1 m =.2 P NND NND NND NND NND NND E NND NND

20 752 P. G. V. Naanjo et al. #Alive P - E CHs P- E E n CHs P- E #Alive E n-vs.- in M = 1 2 Alive-vs.- in M = (a) (c) P- E (e) Alive-vs.- in M = 1 2 CHs-vs.- in M =1 2 E n (b) (d) P - E P- E CHs-vs.- in M = 2 2 E n-vs.- in M =2 2 Fig. 8 Numbe of alive nodes (i.e., # Alive), CHs numbe (i.e., CHs), and E n vesus fo P-, [14], [15]E[26], and [13]foM = 1 2 (Fig. 8a c) and M = 2 2 (Fig. 8d f) at n = 2,α = 1, and m =.2 (f) Fig. 9 Total tansfeed packets in bits thow CHs between P-, [14], [15] E [26], and [13] with diffeent m #Pkts m =.3 m =.2 m =.1.5 P- E The epoted pefomance esults suppot the conclusion that knowledge of enegy level and netwok lifetime bing impotant advantages: ou algoithm given both these values significantly outpefoms the algoithm using heteogeneous enegy levels. Indeed, P- has bette pefomance than taditional outing algoithms, such as,,, and E, especially in educing the enegy consumption and extends the lifetime of the netwok. Besides, the nodes suvival ate is significantly highe in P- unde diffeent spectum of opeating conditions.

21 P-: a polong stable election outing algoithm Conclusion and futue wok In this pape, we popose a new technique to oganize the advanced nodes and to select the CHs in WSNs. We poposed two enegy levels based on poposed Polong (P-) fo the nomal and advanced nodes. P- is completely fai fo the CH selection between nodes, it means, all nodes have the same pobability to be selected as CHs. The attained expeimental esults show that the poposed algoithm achieves much highe enegy saving than the taditional ones. In the attained pefomance evaluation, we compaed P- against,, and E. We select the most eligible nodes as the CHs, the nodes die ate is less than the othe potocols and incu good impact in case of data messages eception at the FN and save enegy of the (advanced, nomal) nodes and esults in polongation of aliveness of the WSN. In addition, P- inceases the stability peiod and packet tansmission ate as compaed with othe outing potocols. In the futue, we would like to pocess and send the infomation/equest to the Intenet thoughout the FN whee this potocol has the main contol. Besides, we would like to focus on tansfe potocol that compises the enegy and pefomance saving, and implement high-powe sensos as a gateway between the CH and FN of Fog achitectue. Refeences 1. Akyildiz IF, Su W, Sankaasubamaniam Y, Cayici E (22) Wieless senso netwoks: a suvey. Comput Netw 38(4): Pooanian Z, Baati A, Movagha A (211) Queen-bee algoithm fo enegy efficient clustes in wieless senso netwoks. WASET 73: Rowaihy H, Johnson MP, Liu O, Ba-Noy A, Bown T, Pota TL (21) Senso-mission assignment in wieless senso netwoks. ACM Tans Sens Netw (TOSN) 6(4):36 4. Shojafa M, Codeschi N, Baccaelli E (216) Enegy-efficient adaptive esouce management fo eal-time vehicula cloud sevices. IEEE Tans Cloud Comput PP(99):1 5. Wei W, Qi Y (211) Infomation potential fields navigation in wieless ad-hoc senso netwoks. Sensos 11(5): Wei W, Yang X-L, Shen P-Y, Zhou B (212) Holes detection in anisotopic sensonets: topological methods. Int J Distib Sens Net 7. Mostafaei H (215) Stochastic baie coveage in wieless senso netwoks based on distibuted leaning automata. Comput Commun 55: Dabbagh M, Hamdaoui B, Guizani M, Rayes A (215) Efficient datacente esouce utilization though cloud esouce ovecommitment. In: 215 IEEE Confeence on Compute Communications Wokshops (INFOCOM WKSHPS). IEEE, pp Shuja J, Gani A, Shamshiband S, Ahmad RW, Bilal K (216) Sustainable cloud data centes: a suvey of enabling techniques and technologies. Renew Sustain Enegy Rev 62: Mostafaei H, Chowdhuy MU, Islam R, Gholizadeh H (215) Connected P-Pecent coveage in wieless senso netwoks based on degee constaint dominating set appoach. In: Poceedings of the 18th ACM Intenational Confeence on Modeling, Analysis and Simulation of Wieless and Mobile Systems. ACM. pp Mostafaei H, Shojafa M (215) A new meta-heuistic algoithm fo maximizing lifetime of wieless senso netwoks. Wieless Pesonal Communications 82(2): Handy M, Haase M, Timmemann D (22) Low enegy adaptive clusteing hieachy with deteministic cluste-head selection. In: 4th intenational wokshop on mobile and wieless communications netwok, pp Liu Y, Gao J, Jia Y, Zhu L (28) A cluste maintenance algoithm based on leach-dchs potocol. In: Intenational Confeence on Netwoking, Achitectue, and Stoage. IEEE, pp

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