Embed Size (px)
Citation preview
Telecommun Syst (2013) 52:2489–2502DOI 10.1007/s11235-011-9568-3
Saving energy and improving communications using cooperativegroup-based Wireless Sensor Networks
Miguel Garcia · Sandra Sendra · Jaime Lloret ·Alejandro Canovas
Published online: 4 August 2011© Springer Science+Business Media, LLC 2011
Abstract Wireless Sensor Networks (WSNs) can be usedin many real applications (environmental monitoring, habi-tat monitoring, health, etc.). The energy consumption ofeach sensor should be as lower as possible, and methodsfor grouping nodes can improve the network performance.In this work, we show how organizing sensors in coopera-tive groups can reduce the global energy consumption of theWSN. We will also show that a cooperative group-based net-work reduces the number of the messages transmitted insidethe WSNs, which implieasa reduction of energy consumedby the whole network, and, consequently, an increase of thenetwork lifetime. The simulations will show how the num-ber of groups improves the network performance.
Keywords Wireless Sensor Networks · Cooperativegroup-based network · Saving energy · Efficientcommunications
1 Introduction
A WSN can be defined as a network of small embedded de-vices strategically located in a physical environment that are
M. Garcia · S. Sendra · J. Lloret (�) · A. CanovasResearch Institute for Integrated Management of Coastal Areas,Universitat Politècnica de Valéncia, Camino de Vera s/n, 46015Valencia, Spaine-mail: [email protected]
M. Garciae-mail: [email protected]
S. Sendrae-mail: [email protected]
A. Canovase-mail: [email protected]
able to gather data from it. This type of structure usuallyconsists of sensor nodes that take measurements from theenvironment and send the information to the base stationthat usually has greater processing capacity and storage [1].The base station is considered the gateway between the sen-sor network and the data network. WSNs offer many goodfeatures for the users such as easy to deploy, mobility, flexi-bility, cost reduction and scalability [2]. The improvementsin the wireless technology and the lower cost of the sensorshave helped the WSN market growth.
There are many applications where the WSN could beused. Some examples are: a monitoring system for fire de-tection [3], for habitat monitoring [4], a fish farm monitoringand control [5], etc.
However, when we want to cover large extensions andareas with difficult access, there are certain technical limita-tions that require an exhaustive network design, and study,to choose the appropriate communication algorithm and agood network topology. One of the main concerns of theresearchers and developers is the whole network power con-sumption because devices are powered with batteries, andlow maintenance is desired.
In order to reduce the power consumption, the hardwaredesign is very important. Some of the techniques employedare related to knowing when the device must be in one mode(active mode, sleep mode or idle mode) or in another [6],thereby achieving to reduce unnecessary power consump-tion. But it also ensures minimum levels of quality of infor-mation flowing through the network. There are many sys-tems, architectures, and protocols that can be used for WSN[7], but in our previous works, we have demonstrated thatwireless sensors group-based topologies and networks im-prove the performance and the efficiency of the whole net-work [8, 9]. Group-based topologies allow the sensor to op-erate more flexibly, efficiently and less energy consuming
2490 M. Garcia et al.
than regular network topologies [10]. Moreover, in one ofour previous works we propose a group-based protocol forlarge wireless ad hoc and sensor networks [11].
In this paper, we demonstrate that cooperative group-based networks have very good features to be used in WSNs.Cooperative networks improve WSNs by saving energy andproviding efficient communications to the WSNs.
The rest of this paper is organized as follows. Section 2shows others architectures for saving energy in WSNs. Thefeatures of cooperative group-based networks and the dif-ferences between the regular networks are explained inSect. 3. Section 4 describes our cooperative group-based ar-chitecture. A mathematical analysis to prove that coopera-tive group-based networks save more energy is provided inSect. 5. Section 6 describes our proposal using the graphtheory, and shows the number of messages needed for co-operative group-based networks. A simulation test using anetwork simulator is presented in Sect. 7. This performancetest let us improve the analytical results shown in previoussections. Finally, Sect. 8 shows the conclusion and futurework.
2 Related work
No one of the papers found in the related literature analyzethe power consumption and the communications using thegroup-based strategy (except the in a paper from the sameauthors [12], where we introduce these ideas). On one hand,some works show that organizing the sensors into groupsprovide greater benefits than doing otherwise. On the otherhand, some works are focused on network architectures todecrease the energy consumption, but without forgetting thecommunications efficiency.
The paper with reference [13] shows that grouping nodesin WSN gives better performance to the group and to theglobal system, because the system avoids unnecessary mes-sage forwarding and additional overhead. In this paper, wecan see the efficiency of MANET routing protocols whenthe nodes are organized in groups. To do this, the authorssimulate several protocols such as DSR, AODV and OLSRand study the advantages of grouping the individual nodesin each protocol for fixed and mobile networks (mobilenodes with a random behavior). Grouping nodes increasethe productivity and the network performance providing lowoverhead and low traffic. Therefore, good scalability can beachieved in group-based networks.
J. Lloret et al. propose a new group-based protocol forWSN in [14]. First, they compare some wireless technolo-gies (IEEE 802.11a/b/g, Bluetooth and Zigbee) for their usein WSNs. Then, they estimate the number of sensor nodesthat would be needed to cover a large area and propose ananalytical model comparing the energy consumed by each
device over time. Finally, they compare their new group-based protocol with other protocols such as DSDV, AODVand DSR. The simulations show that the proposed proto-col sends less number of packets to the network and conse-quently there is less energy consumption. Following theseworks, we see that the use of group-based networks providebenefits such as traffic, delay, etc. in networks.
There are also several published works related with theanalysis of energy consumption and energy saving mecha-nisms in WLANs. These types of analysis may help us knowsome power saving issues when they are used in WSNs.
The paper in reference [15] describes several techniquesto reduce the dynamic energy consumption in WLAN IEEE802.11n standard systems in order to increase the batterylife in mobile systems. They propose to reduce the devicesconsumption from their initial design by building smallerdevices because they consume less energy.
V. Raghunathan et al. describe in [16] some considera-tions about the architecture and protocols to be consideredin order to make an energy-efficient design of a sensor nodein a WSN. The paper shows an analysis of the energy con-sumption characteristics of a node, using multihop architec-tures. Moreover, they identify the key factors that can affectthe life of the global system. They show the sensor energyconsumption, the power-aware computing, the energy-awaresoftware, the power management of wireless communica-tion, the energy-aware packet forwarding and traffic distri-bution, among others. This work is important to know whatthe most important factors are, in order to take them intoaccount when a system is developed.
In paper [17], the authors discuss the required keytechnologies for low-energy distributed microsensors andpresent a power aware Application Programming Interface(API) to calibrate the energy efficiency of various parts ofthe application. They took care of the power aware compu-tation/communication component technology, low-energysignaling and networking, system partitioning consideringcomputation and communication trade-offs, and a poweraware software infrastructure. This work also shows someanalytical models of the device behavior and the results ofapplying different parameters, such as the number of sen-sors, type of protocol used to communicate the sensors, thesystem power consumption by controlling when they shouldenter a sleep mode and applying dynamic voltage scaling.These models will help us to develop our models.
Reference [18] shows the relationship between the powerusage and the number of neighbors in a WSN. They statethat many of the topologies proposed for wired networkscannot be used for wireless networks because wireless net-works depend on the physical neighborhood and the trans-mission power while in a wired network depends on thephysical connections among nodes. A. Salhieh et al. ana-lyze various 2D and 3D structures with different numbers
Saving energy and improving communications using cooperative group-based Wireless Sensor Networks 2491
of nodes. They measured the network performance for dif-ferent network topologies and the node’s power dissipationin order to determine what the best topology is for a WSN.But, they assume that they control the placement of thesesensors and the sensor locations are fixed respect to eachother. Moreover, the authors do not consider the effects ofcommunication with a base station. They conclude that thebest power efficiency in 2D topologies is achieved with fourneighbors, and although 3D topology is better, it may not befeasible for some applications.
The energy consumption must not deteriorate the com-munications quality. We have found some works that at-tempt to improve the efficiency of the communications inWSNs, but trying to reduce energy consumption. Many ofthem are related with the development of protocols at theMAC layer. Here, we present some works.
In [19], S. Jayashree et al. present a MAC protocol thattakes into account the state of the battery and improves itsefficiency. The main objectives of this protocol are to re-duce energy consumption, to extend the battery life and tohave higher performance. They present the BAMAC modelbattery that is based on Markov chain process. Each nodecontains a table with the information of the battery chargeof each neighbor that is from a jump. The informationin the table organizes battery charges in descending order.RTS, CTS, Data and ACK packets carry information of theremaining battery charge of the node that originated thepacket. A node that listens one of these packets, collectsand stores the information in its table. The simulation re-sults, of this work, show that the battery life is prolongedin about 70% and reduces the nominal battery consumptionfor packet transmission by 21% compared with IEEE 802.11MAC protocols DWOP.
In [20], the authors present sensor-MAC (S-MAC), a newMAC protocol designed for WSN. First, the authors iden-tify the main causes of energy consumption: (1) when atransmitted packet is corrupted and it has to be discardedbecause retransmissions increase the energy consumption,(2) the overhearing (a node picks up packets that are des-tined to other nodes), (3) the control packets due to the repet-itive sending and receiving, and (4) being listening becausemeasurements have shown that this functional mode con-sumes around 50–100% of the energy required for receiv-ing. The main goal of this MAC protocol is to reduce theenergy consumption by taking care of these main causes,while supporting good scalability and collision avoidance.The authors show that their protocol achieves a reduction ofthe 30% in the device consumption and the protocol has theability to make trade-offs between energy.
C. Ching and C. Schindelhauer, presented in [21] a pro-posal that takes into account the power consumption of boththe motion and the radio communications because they arethe primary energy consumers. In order to achieve their
goal, they used a scenario with a series of robots and sev-eral antennas that communicate among themselves. Eachrobot is assigned to different tasks (searching, exploring,sensing, foraging, etc.). The authors reduce the energy con-sumption in mobile receivers within a WSN considering ahybrid wireless network formed by two scenarios: a sin-gle autonomous mobile node communicating with multi-ple static relays through single hop, and secondly, a sin-gle mobile node communicating with a static base sta-tion via a mobile relay. The mobile node interacts withthe relays within its vicinity by continuously transmittinghigh-bandwidth data. The authors introduce Radio-Energy-Aware (REA) path computation strategy and propose a novelenergy-aware that finds the best paths. The energy consump-tion for both mobility and communications is minimized formobile receivers and for the devices that keep communica-tions in all mobile nodes. They compare their method REAwith Motion-Energy-Aware (MEA) method. The simulationresults show that their proposed strategy improves the en-ergy efficiency of mobile nodes.
The paper with reference [22], authored by Q. Gao et al.,show the relationship between optimal radio range and traf-fic. The authors adjust the communication systems proper-ties, according to some optimum strategies, in order to savethe average power. They carry out different approaches andmathematical developments in order to adjust the maximumperformance of a sensor node model. The formulae used aremade taking into account factors such as: the network size,and the distance between nodes and between the nodes andthe base station. The authors proved that a good design andnode’s positioning choice inside the WSN could lead to dou-ble the network lifetime because of the significant reductionof the energy consumption.
In [23], the authors analyze the broadcast routing accord-ing to the energy cost. In this case, the energy cost is definedas the sum of each individual node energy cost that transmitsbroadcast messages. So, in order to solve this problem, theauthors try to minimize the number of broadcast messages.They use three centralized heuristic methods to minimizeit. According to their simulations, we can see that there aresome algorithms better than others. But the main problemof these solutions is that all of them are based on central-ized solutions and nowadays the architectures are evolvingto decentralized solutions.
Tiago Camilo et al. present in [24] a new routing proto-col specially designed to maximize the life time of the sen-sor nodes in WSNs. This protocol is based on Ant ColonyOptimization (ACO) metaheuristic method. In this protocol,the communication with the next network node is selectedaccording to a probability (which is function of the node en-ergy and the number of nodes of the path). In order to checkthe truthfulness of their proposal, they present some simula-tions that compare their protocol with other ant-routing pro-
2492 M. Garcia et al.
tocols. The main drawback of this type of protocols is themanagement of the ants (the backbone of routing protocol).
In [25], the authors show that the cluster-based sensornetworks have a better energetic behavior than regular sen-sor networks. They present a routing protocol for managingthe sensor network with the main objective of extending thelife of the sensors in a particular cluster. Their proposal usesa gateway node which acts as a cluster-based centralized net-work manager that sets routes for sensor data, monitors la-tency throughout the cluster, and arbitrates medium accessamong sensors. Finally they present some simulations thatshow the benefits of their algorithm.
Another type of sensor lifetime extension mechanism isthe one based on deployment strategies. In [26], the authorsexplore these strategies. They propose a general frameworkfor the analysis of the network lifetime for several networkdeployment strategies, and the authors consider the extracosts associated with each deployment strategy to determinethe best overall strategy for a given scenario. This is an im-portant work because it shows several issues that influencethe node’s lifetime, and demonstrates and disagrees someintuitions.
Finally, another work where the authors present anenergy-efficient deployment algorithm based on Voronoidiagrams is [27]. Three different deployment methods areproposed by the authors. The first one is a deployment algo-rithm for mobile nodes where each node is equally importantand a peer-based structure is obtained. The second one usesthe clustering idea to increase the amount of local control.The third one uses the algorithm based on Voronoi diagramsto provide an estimation of the lifetime of each node in a dis-tributed fashion. They simulate their algorithm and check itsbenefits, but the main problem of this research is that it isonly based on one-hope architecture and nowadays almostall networks are multi-hop networks.
None of the previous works have used cooperative group-based sensor network strategies in order to save energy orimprove the communications in the WSN. For this reason,we presented a main idea in [12]. In this paper, we show the
analytical model and its simulations which describe the im-provement of cooperative group-based systems versus regu-lar architectures.
3 Differences between regular WSNs, group-basedWSNs and cooperative group-based WSNs
In this section we review the main differences between reg-ular WSNs and cooperative group-based WSNs.
Regular WSNs are networks where all nodes have thesame function from the point of view of the network level.Each node measures a parameter, then, it is processed and,finally, the information is sent to its neighbors. Its neighborswill send the sensed information to their neighbors in orderto reach the base station or the sink.
Passive WSNs are those ones whose sensors sense the en-vironment and send the information to a sink without takingany other action. In an active WSN, when an event occurs,it is notified to the sink and/or to the manage center (MC).Then, the MC sends the necessary information to all nodes(or to some selected nodes) of the network in order to takethe appropriate action (send the information to other nodes,sense more variables, etc.).
Currently passive WSN are not useful because in manyenvironments an intelligent WSN is required. Sometimes,when an event occurs inside the WSN certain tasks shouldbe carried out to perform a specific reply. For example, ifwe build a WSN to detect fire, the sensors should be able tosense different variables in order to verify the fire and evento monitor it (temperature, CO2, humidity, wind direction,etc.). In this case, sensors may collect these variables and,after some data processing, send the necessary informationto a higher processing capacity node in order to activate theappropriate fire fighting mechanisms.
Figure 1 shows the differences between passive and ac-tive WSNs. Red arrows indicate the messages from sensornodes to the MC and the blue arrows indicate the communi-
Fig. 1 Passive WSN vs. activeWSN
Saving energy and improving communications using cooperative group-based Wireless Sensor Networks 2493
Fig. 2 Regular WSN vs.cooperative group-based WSN
cation from the MC to the sensor nodes in order to take theappropriate actions. Blue arrows only exist in active WSNs.
Cooperative group-based sensor networks do not workin the same way. First, they are group-based networks, sothe network is logically divided into several small networks(groups). A group is defined as a small number of interde-pendent nodes with complementary operations that interactin order to share resources or computation time, or to ac-quire content or data and produce joint results. In a wire-less group-based architecture, a group consists of a set ofnodes that are close to each other (in terms of geographi-cal location, coverage area or round trip time) and neigh-boring groups could be connected if a node of a group isclose to a node of another group. In [12], we can see thatthe group-based WSNs provide many benefits. Moreover,cluster-based networks could be considered as a subset ofthe group-based networks, because every cluster could bea group [11, 28]. But, a group-based network is capableof having any type of topology inside the group, not onlyclusters. Furthermore, in a group-based network, each groupcould use a different type of routing protocol. A cluster-based network is made of a CH, Gateways, and ClusterMembers (CM). In a cluster, CH nodes fully control thecluster, while in group-based networks, no node controls thegroup.
Cooperative group-based sensor networks imply cooper-ation between nodes from the same group and cooperationbetween nodes from different groups. Thus, the informa-tion may be shared only between the most appropriate nodes(from the same groups or from different groups). An eventproduced by a sensor will imply the exchange of informa-tion between different nodes in order to take a final decisionand produce an action in the same place or in other locationof the network. Moreover, in a cooperative WSN only thoseparameters that can affect the final decision, i.e. the informa-tion coming from the neighboring groups of the group thatgenerated the alert, are considered. Following the exampleof the WSN deployment for fire detection, when a sensor
detects a fire, the alert message is sent to all nodes in itsgroup, and, after processing the information, and taking intoaccount other parameters such as wind direction, the alert issent to the affected neighboring groups. Affected neighbor-ing groups will perform the appropriate actions that othergroups will not have to do.
From the point of view of the network messages and en-ergy consumed, in a regular WSN, when a node registers analert, it transmits the alert to all its neighbors, these neigh-bors transmits the alert to their neighbors and so on, the alertis spread to the entire WSN without control. Moreover, anode could receive several times the alert message from dif-ferent neighbors (see Fig. 2a). This situation leads to exces-sive energy consumption. A collaborative group-based WSNis built based on defined areas or as a function of the nodes’features. Moreover, each group is formed by nodes that in-teract to share resources or to acquire data to produce jointresults [29]. In this case, when a sensor detects a new event,this sensor sends the information to all the members of thegroup and, depending on the case, the neighboring groupscould share this information in order to reach all sensors ofthe WSN or just some groups. Only the closest sensors to theedge of the group will transmit the information to the sen-sors of other groups (see Fig. 2b). This fact avoids raisingconsiderably the global energy consumption of the WSN,which is very important to enlarge the lifetime of the WSN.
Although we are mainly talking about collaborativegroup-based WSN with fixed sensors, they could be mobilesensors. In our study we will consider group-based WSNswhere mobile sensors move only inside the boundaries ofthe groups. Failures could happen in each group. In thiscase, the mobility will affect in the same manner as in anon-group-based WSN.
In [11] we presented an example of cooperative group-based sensor network. The WSN formation is performed inthe same manner of a group-based network but introduc-ing cooperation issues. In addition, each group selects thebest connection between sensor nodes taking into account
2494 M. Garcia et al.
Fig. 3 Comparison ofgroup-based WSN andcooperative group-based WSN
Table 1 Notation and definition
Parameter Definition Parameter Definition
ϕ11 Power required to turn on the transmitter r Bit rate (bits per second)
ϕ12 Power required to turn on the receiver R Average area radius of a WSN
ϕ1 ϕ11 + ϕ12 Ri Average area radius of the groupi
ϕ2 Power required to transmit s # of sensors in the WSN
d Distance between 2 communicating sensors Si # of sensors in the groupi
dopt Optimum distance between 2 sensors Jm # of cooperative groups
n Path loss exponent (typical values: 2 or 4) J # of groups, m ∈ [1, J ]
the proximity and the nodes’ capacity [30]. In order to havean efficient group-based wireless sensor network, the groupshave to communicate with their neighboring groups. Whena node detects an event, it warns the alert, jointly with theparameters measured, to the nodes of its groups and, routingthe information, to its neighboring groups (not to all groups)based on the location of each group or any other parameter.The location of the sensors could be entered manually or us-ing GPS [31], and a position-based routing algorithm [32]could be used to send the message to the appropriate situa-tion. Neighboring groups could reply to the group that firstlysent the alert if any of the parameters that caused the alert ischanged, in order to take the appropriate actions. Coopera-tion with other groups could change the direction of the alertpropagation and the level of the alert.
Figure 3a shows a group-based topology example. In agroup-based WSN, all groups will be aware of an event pro-duced inside the Group 1. The network efficiency would beyet higher than in a regular topology [10]. But, in coopera-tive group-based networks this efficiency is greater, becausethe decision is taken based on the information shared andonly the neighboring groups will be aware of it. The othergroups could be in sleep mode. Sleep groups will be sav-ing energy and they would not transmit unnecessary infor-mation. Figure 3b shows that the neighboring cooperativegroups of group 1 are group 2 and 5. In this case they are
the physical neighbors. An example is explained in moredetailed in [29].
4 Energy analysis
This section analyzes the energy needed to transmit pack-ets in a cooperative group-based WSNs architecture and wecompare it with a regular WSN architecture.
The notation used in our analysis is shown in Table 1.ϕ11, ϕ12 and ϕ2 are constant radio parameters, typical val-ues are ϕ11 = 50 nJ/bit, ϕ12 = 50 nJ/bit, ϕ2 = 10 pJ/bit/m2
(when n = 2) or 0.0013 pJ/bit/m4 (when n = 4).We follow the model presented in [33]. The energy con-
sumed by a sensor to transmit and receive a data packet be-tween two nodes is given by (1).
P = (ϕ11 + ϕ12) · r + ϕ2 · (dn) · r (1)
where d is the distance between both nodes.Let be D the distance between the sending node of a
group and the closest node from other group. Thus, P(D) ≥P opt(D), being P opt(D) the minimum power to transmit adata packet from the node to the other group. P opt(D) is
Saving energy and improving communications using cooperative group-based Wireless Sensor Networks 2495
equal to (2) if and only if D is multiple integer of dopt =n
√ϕ1
ϕ2·(n−1), as we can see in [34].
P opt(D) =(
ϕ1 · n
n − 1· D
dopt− ϕ12
)· r (2)
P opt(D) is the lower bound of energy consumption in theflat scheme without data aggregation, which indicates anideal case where the per-hop distance for transmission isdopt meters. According to the energy model in [33] and theprevious assumptions, the expected energy consumption persecond of a group is given by (3).
P = (Si − 1) ·(
ϕ1 + ϕ2 · 2 · Rni
n + 2
)· r
+ (ϕ11 + ϕ2 · Dn) · r (3)
We assume that there are J groups in the network and eachregular node only needs to transmit its data packet to thecentral node or to the border node of its group. The averageradius of each m group can regard it as R/
√Jm, and the ex-
pected energy consumption per second in all regular nodeswill be given by (4).
Prn = (s − Jm) ·[ϕ11 + ϕ2 · 2
n + 2·(
R√J
)n]· r (4)
The energy consumption for all border nodes is given by (5).
Pbor = (s − Jm) · ϕ12 · r + Jm ·[ϕ11 + ϕ2 ·
(R√J
)n]· r
(5)
In order to evaluate the energy consumed in a network with-out cooperative groups, we consider (4) and (5) for a singlegroup (J = 1), and only one sink node. Then, we obtain (6)and (7) respectively.
Prn = s ·[ϕ11 + ϕ2 · 2
n + 2· (R)n
]· r, (6)
Pbor = [s · ϕ12 + ϕ11 + ϕ2 · Rn] · r (7)
The global energy consumption in both cases cooperativenetwork and a network without cooperative groups is givenby (8).
PT = Prn + Pbor (8)
Another important issue that we have noticed is the impor-tance of the number of group nodes (high J ) than the num-ber of cooperative groups (Jm). If the number of coopera-tive groups is greater, the consumption will be lower, butthis consumption decreases more quickly if we create moregroups.
Fig. 4 Energy of the cooperative group-based WSN
4.1 Analytical comparison
In order to observe the energy-saving improvements pro-vided by a cooperative group-based WSN compared to reg-ular WSN, we have taken into account (8) for both cases(regular WSN and cooperative group-based WSN). Follow-ing the typical values of the constants that appear at the be-ginning of this section (ϕ11 = 50 nJ/bit, ϕ12 = 50 nJ/bit,ϕ2 = 10 pJ/bit/m2), we have used n = 2 because it is themost appropriate value for outdoor communications. Fur-thermore, we have chosen m = 2, our network has 100nodes (s = 100) and the network radius equals to 200 me-ters (R = 200). We have programed the above equations andvarying analytically the number of groups and bitrate we ob-tain the Fig. 4. Figure 4 shows the global energy consump-tion for different values of Jm. When Jm = 1, we talk aboutjust one group (the whole WSN), so we can see that the en-ergy consumption is higher than in any other value of Jm
(higher values of Jm imply more groups). By increasing Jm
we see that consumption decreases, but each time the differ-ence of the slope is lower.
5 Communication analysis
In a cooperative group-based WSN all nodes are alike fromof the point of view of network level and may be mobile.There are not base stations, sinks or management nodes. Forthis reason, all nodes can make decisions of their connec-tions according to an algorithm. In our case the algorithm isdefined in [29]. Moreover, all communications between thesensor nodes are done by wireless links.
As usual, we can model a WSN using the graph theory,where a WSN is defined as G = (S,E), where S is the setof sensor nodes (|S| = s) and e = (i, j) ∈ E represents awireless link between the nodes i and j only if they are intheir communication range.
2496 M. Garcia et al.
Any two nodes that are connected by an edge are neigh-bors to each other. Nodes in G can send (and receive) mes-sages to (from) their neighbors. Every node v ∈ S has aunique identity ID(v). Each node initially knows its ownidentity and the identities of its neighbors in one jump in-side G.
The size of a group is the number of nodes belonging toit. According to the definition of our WSN, there are threetypes of nodes: central, intermediate and border nodes [10].For example, the central node of the group k is defined asC(k). The number of border nodes in the group is B , whereB is smaller than n.
Other parameters that are used for defining communica-tions in sensor networks is the distance between two nodes,d(i, j), it is the Euclidean distance between the nodes i
and j . As it is a group-based network, each group will bedefined as G(k), in this case this is the group k.
From the graph theory textbook [35] we will denote �k(i)
the k-neighborhood of a node i, e.g., �k(i) = {v ∈ S|0 <
d(i, j) ≤ k} and we will denote δk(i) = |�k(i)|. Besides, wewill denote e(i/G) = maxv∈G(i)(d(i, j)) the eccentricity ofa node i inside its group. Thus the diameter of a group willbe D(G(i)) = maxv∈G(i)(e(v/G)).
Once the definition of a group-based WSN has beendone, we analyze the number of messages sent inside thenetwork when an event occurs and this information has tobe sent to other nodes. According to [36], in the worst sit-uation, the number of messages in a regular network when
there is an alert is O(s), while in a cooperative group-basednetwork the number of messages is O((m+1)B2), where m
is the average number of cooperative groups in the network.If B � s we can affirm that the number of messages in a
group-based network will be less than in regular networks,for this reason O((m + 1)B2) � O(s).
5.1 Analytical comparison
In order to validate this analysis, we performed a simulationtest. There were s nodes distributed in a square area of lengthl units. The nodes are randomly placed. The average densityof nodes per unit length is s/ l2. In this model, the nodeshave a link with another node if and only if they are withina distance d units of each other. Consequently, a node willhave an average of (πd2s/ l2) − 1 neighbors. This leads toan edge probability of (πd2s/ l2 − 1)/(s − 1) which is theprobability that two nodes chosen at random in the networkare connected by a link. In particular, for a large value of s,the edge probability is approximately equal to πd2/l2.
Table 2 shows the average number of border nodes, theaverage number of groups, the likelihood of border nodes,the number of messages that will be in the case of l = 25units of length and d = 1 unit and the percentage of im-provement respect to a regular architecture (without groups).The observed data in this table have been obtained usingthe previous boot parameters and applying the formulas pre-sented in this section. We have used a mathematical program
Table 2 Results of the communication analysis
s Average Average Likelihood Number of Percentage
number of number of of border of messages of
border nodes of groups nodes improvement
B = 1, 250 0.256 − 0.0001028 250 –
m = 0 500 1.512 − 0.00303 500 –
1000 4.024 − 0.00403 1000 –
B = 2, 250 0.256 125 0.0001028 8 96.8%
m = 1 500 1.512 250 0.00303 8 98.4%
1000 4.024 500 0.00403 8 99.2%
B = 4, 250 0.256 62.5 0.0001028 64 74.4%
m = 3 500 1.512 125 0.00303 64 87.2%
1000 4.024 250 0.00403 64 93.6%
B = 8, 250 0.256 31.25 0.0001028 256 No improvement
m = 3 500 1.512 62.5 0.00303 256 48.8%
1000 4.024 125 0.00403 256 74.4%
B = 16, 250 0.256 15.625 0.0001028 1536 No Improvement
m = 5 500 1.512 31.25 0.00303 1536 No improvement
1000 4.024 62.5 0.00403 1536 No improvement
Saving energy and improving communications using cooperative group-based Wireless Sensor Networks 2497
Fig. 5 Maximum number of broadcasting messages for differentgroup size and varying the average number of cooperative groups
where the formulas have been programed and then we haveintroduced the different starting values for obtaining theseresults (see Table 2). We have estimated all these parame-ters when B has 1, 2, 4, 8 and 16 nodes, with an averagenumber of cooperative groups (m) ranging from 0 to 5 (wehave selected 0, 1, 3 and 5), and when the number of nodesin the whole WSN has 100, 200 and 500.
According to the results shown in Table 2, when we havea cooperative group-based WSN with B = 4 and m = 3 orB = 8 and m = 3, the average number of nodes is adequate,but when there are many border nodes the efficiency of thecollaborative group-based WSN is low (in red in Table 2,e.g. B = 16 and m = 5). This happens because when thegroups are large, B ↑↑, so the WSN becomes a regular WSNand moreover the architecture needs more control messagesto manage the collaborative group-based architecture. Thereis a tradeoff between the number of network nodes and theappropriate number of groups, so a group-based network isbetter than the regular network.
Figure 5 shows the maximum number of broadcastingmessages needed when we have several sizes of groups andseveral average cooperative groups. In this case the numberof nodes s was 1000 and l was 25 units. In this figure wecan observe that increasing the group size (B) and the num-ber of cooperative groups, it increases the number of mes-sages in the WSN. The simulation shows that when a highnumber of messages is expected in the WSN, it is better agroup-based WSN with a low B and with few cooperativegroups. This happens with the broadcasting messages, butif we look the management messages, when we have smallgroups there are a lot control messages to create and managethe groups. For this reason, there has to be a compromise be-tween the group size, the number of cooperative groups andthe number of broadcasting messages. Seeing the Fig. 5, thebest solution could be a group size between 4 and 6 and anaverage number of cooperative groups between 3 and 5.
Fig. 6 Injected traffic comparison
6 Cooperative group-based network test with mobility
In order to evaluate the system proposed in this paper. Wehave simulated several scenarios using the OPNET Modelernetwork simulator [37]. In next simulations we are going tosee the behavior of the cooperative group-based WSN ac-cording to the number of groups in network. The test sce-nario has 100 sensor nodes placed in 500 × 500 meters. Wehave increased the number of groups in each simulation. Wehave chosen DSR as routing protocol because in [14] wesaw that this protocol has the worst behavior. We select theworst routing protocol because we want to show the positiveaspects of our system, even using poor conditions.
Instead of a standard structure we have chosen a randomtopology. The nodes can move randomly during the simula-tion. The physical topology does not follow any known pat-tern. The obtained data neither depend on the initial topol-ogy of the nodes nor on their movement pattern because allof it has been fortuitous.
The sensor nodes have a 40 MHz processor, 512 KBRAM memory, a radio channel bandwidth of 1 Mbps andtheir working frequency is 2.4 GHz. Their maximum cover-age radius is 50 meters. This is a conservative value becauseusually the nodes in sensor networks have larger coverageradius, but we preferred to have lower transmitting powerfor the sensor devices in order to enlarge their lifetime.
The traffic load used in the simulations is MANET traf-fic generated by OPNET. We inject this traffic 100 secondsafter the beginning. The traffic follows a Poisson distribu-tion (for the arrivals) with a mean time between arrivals of30 seconds. The packet size follows an exponential distribu-tion with a mean value of 1024 bits. The injected traffic has arandom destination address, obtaining a simulation that doesnot depend on the traffic direction. In Fig. 6 we see that thetraffic injected into the simulation follows the same patternfor all scenarios. The average traffic is 100 Kbps (an ade-quate traffic for WSNs).
Figures 7 and 8 show the total traffic in the WSN. InFig. 7, the total traffic in the network is too high compared
2498 M. Garcia et al.
Fig. 7 Total traffic comparison
Fig. 8 Total traffic comparison of cooperative group-based WSNs
with the topology that uses collaborative groups. When thenetwork does not have collaborative groups, the average to-tal traffic is around 6500 Kbps, when we have 2 cooper-ative groups it decreases 95%. In Fig. 8, we only see thecooperative group-based topologies in order to show betterthe results. The total traffic decreases when the number ofgroups increases. As we can see in Fig. 8, when we have2 groups the average total traffic is around 310 Kbps, butwhen we have more groups (e.g. 6 groups), the total trafficdecreases down to 140 Kbps. This demonstrates that collab-orative group-based WSNs have lower traffic.
Figures 9 and 10 show the delay in the network. In Fig. 9,we can see that the difference between cooperative group-based WSNs and a regular WSN is very high. In a regularWSN, the delay is around 120 seconds (which is too highfor any application). This high value is obtained because themobility of nodes. Constant mobility causes that the nodesneed to compute their routing table constantly, and this pro-cess takes more time to arrive at the convergence state. Thisdelay could change according to the used routing protocol.Anyway, its difference with cooperative group-based WSNis high.
In Fig. 10 we can see the delay when we apply coopera-tive groups in the WSN. In all cases the delay is lower than
Fig. 9 Delay comparison
Fig. 10 Delay comparison of cooperative group-based WSNs
0.05 seconds. There is a peak at around 100 samples, be-cause in this moment the simulation starts. Then, when theWSN converges the delay is lower and very constant. Thereis quite difference between cooperative group-based WSNand regular WSN. When there are not collaborative groups,the nodes are not segmented, and, for this reason, we needmore resources to manage the network. When the WSN isdivided into cooperative groups, the management process isalso divided, for this reason we need less resources.
In Fig. 11, we show the average number of hops neededto arrive to a destination when we are using groups and whenwe are not. As we can see, when we have a regular WSN,the average number of hops is around 6, so we need to cross6 nodes to arrive to the destination. When we have collab-orative groups in the network, the average number of hopsis the half. We need 3 hops to arrive to the destination whenwe use collaborative group-based topologies.
In Figs. 12 and 13 we analyze the number of ACKs sentin regular and group-based WSNs. In these cases we ob-serve the same behavior as in previous figures. The regulararchitecture needs more ACKs (2400) than the cooperativegroup-based architectures (see Fig. 12). This is because reg-ular WSNs need more messages to manage the architecture.
Saving energy and improving communications using cooperative group-based Wireless Sensor Networks 2499
Fig. 11 Hops per route needed to arrive to the destination
Fig. 12 Total number of ACKs sent
In order to better see the total number of ACKs sent incooperative group-based WSNs, we show Fig. 13. In thisfigure we can see that the number of ACKs sent follow thesame pattern for all topologies independently of the num-ber of groups, although each topology inserts more or lessACKs. When we have 2 groups, where each group manages50 sensor nodes, the average total ACKs sent is around 400.This number drops to half when the number of groups isequal to 4. But, Fig. 13 shows that although we increase thenumber of groups, the total ACKs sent will not be less than acertain value. In this case, for a topology with 6 or 12 groups,the total number of ACK sent approximately equals 75.
Figures 14 and 15 show the retransmission attempts forall cases aforementioned. In Fig. 14 we see that the regu-lar WSN needs approximately 1.5 retransmission packets toguarantee the correct running of the system. But this retrans-mission is not needed when our system is based on collabo-rative groups, because the required management is done bycooperative group-based WSN.
When we increase the number of groups we need lessnumber of retransmissions. When we have two groups, theaverage number of retransmissions is less than 0.1 packets,so it is negligible (see Fig. 15). We can notice that when the
Fig. 13 Total number of ACKs sent in cooperative group-based WSNs
Fig. 14 Retransmission attempts
Fig. 15 Retransmission attempts in cooperative group-based WSNs
number of groups is less than 4, the retransmission packetscould be zero. Observing these simulations (Figs. 14 and15) we can affirm that when we use cooperative groups inour WSNs, we needs less retransmissions, even they couldbe zero, depending of the system.
Finally, in Fig. 16, we present the load processed by a col-laborative group. In this figure we only focus on the group-based WSNs, because, as we have seen in the previous fig-ures, the regular WSN has worse performance. In this figure
2500 M. Garcia et al.
Fig. 16 Load processed by a group
we see that when we have 2 groups in our network, the loadis around 150 Kbps, this load decreases down to 60 Kbpswhen we have 4 groups, 25 Kbps for 6 groups, and lessthan 10 kbps for 12 groups. This happens because when wehave more collaborative groups, the number of nodes man-aged per group is lower. When we have a lot of collabora-tive groups in the WSN, we need more control informationto manage it correctly. For this reason, when we select thenumber of groups, we should think several issues: to takeinto account the efficiency at network level, and take care ofthe management information needed to create and manageeach collaborative group.
7 Conclusion and future work
In this paper we have analyzed cooperative group-basedWSNs. In this type of WSNs when a sensor detects a newevent, the alert is sent to its group and it is distributed toan appropriate neighboring groups based on the informationshared between sensors. Cooperation between groups couldbe used to change the direction of the alert propagation andthe level of the alert in order to take the appropriate actions.
Using several analytical analyses we have proved that thecooperative group-based WSNs save energy and improvethe efficiency of the WSN communications. Moreover, wehave seen that there are some WSN topologies that havebetter results than others. For this reason, our future workis based on this issue. We will analyze the best collabora-tive group-based WSN topology. Moreover, in future workswe will study the energy issues related with mobile sensorsand the communication procedures of joining and leavingthe groups.
References
1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E.(2002). Wireless sensor networks: a survey. Journal of ComputerNetworks, 38(4), 393–422.
2. Garcia, M., Bri, D., Sendra, S., & Lloret, J. (2010). Practical de-ployments of wireless sensor networks: a survey. Journal on Ad-vances in Networks and Services, 3(1&2), 1–16.
3. Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A wirelesssensor network deployment for rural and forest fire detection andverification. Sensors, 9(11), 8722–8747.
4. Mainwaring, A., Polastre, J., Szewczyk, R., & Culler, D. (2002).Wireless sensor networks for habitat monitoring. In ACM work-shop on sensor networks and applications (WSNA’02), Atlanta,GA, USA, September.
5. Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2010, in press).Monitoring and control sensor system for fish feeding in marinefish farms. IET Communications, pp. 1–9. doi:10.1049/iet-com.2010.0654.
6. Sinha, A., & Chandrakasan, A. (2001). Dynamic power manage-ment in wireless sensor networks. IEEE Design & Test of Com-puters, 18(2), 62–74.
7. Garcia, M., Coll, H., Bri, D., & Lloret, J. (2008). Using MANETprotocols in wireless sensor and actor networks. In The secondinternational conference on sensor technologies and applications(SENSORCOMM 2008), Cap Esterel, Costa Azul, France, 25–31August.
8. Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANETprotocols performance in group-based networks. In Wireless andmobile networking: Vol. 284 (Chap. 13, pp. 161–172). Berlin, Hei-delberg, Boston: Springer.
9. Lloret, J., García, M., & Tomás, J. (2008). Improving mobile andad-hoc networks performance using group-based topologies. InWireless sensor and actor networks 2008 (WSAN 2008), Ottawa,Canada, 14–15 July. Berlin, Heidelberg, New York: Springer.
10. Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improv-ing networks using group-based topologies. Journal of ComputerCommunications, 31(14), 3438–3450.
11. Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: a group-based protocol for large wireless ad hoc andsensor networks. Journal of Computer Science and Technology,23(3), 461–480.
12. Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANETprotocols performance in group-based networks. In 10th IFIP in-ternational conference on mobile and wireless communicationsnetworks (MWCN 2008), Toulouse, France, 30 September–2 Oc-tober.
13. Garcia, M., Sendra, S., Lloret, J., & Lacuesta, R. (2010). Savingenergy with cooperative group-based wireless sensor networks.In LNCS: Vol. 6240. Cooperative design, visualization, and engi-neering: CDVE 2010 (pp. 231–238), September. Berlin: Springer.
14. Lloret, J., Sendra, S., Coll, H., & García, M. (2010). Saving energyin wireless local area sensor networks. Computer Journal, 53(10),1658–1673.
15. Meiyappan, S. S., Frederiks, G., & Hahn, S. (2006). Dynamicpower save techniques for next generation WLAN systems. InProceedings of the 38th southeastern symposium on system the-ory (SSST), Cookeville, Tennessee, USA, 5–7 March.
16. Raghunathan, V., Schurgers, C., Park, S., & Srivastava, M. (2002).Energy aware wireless microsensor networks. IEEE Signal Pro-cessing Magazine, 19(2), 40–50.
17. Min, R., Bhardwaj, M., Cho, S.-H., Shih, E., Sinha, A., Wang, A.,& Chandrakasan, A. (2001). Low power wireless sensor networks.In Proceedings of international conference on VLSI design, India,Bangalore, 3–7 January.
18. Salhieh, A., Weinmann, J., Kochha, M., & Schwiebert, L. (2001).Power efficient topologies for wireless sensor networks. In Pro-ceedings of the IEEE international conference on parallel process-ing (pp. 156–163), 3–7 September.
19. Jayashree, S., Manoj, B. S., & Murthy, C. S. R. (2004). A bat-tery aware medium access control (BAMAC) protocol for Ad-hoc
Saving energy and improving communications using cooperative group-based Wireless Sensor Networks 2501
wireless network. In Proceedings of the 15th IEEE internationalsymposium on personal, indoor and mobile radio communications(PIMRC 2004), Barcelona, Spain, 5–8 September (Vol. 2, pp. 995–999).
20. Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficientMAC protocol for wireless sensor networks. In Proceedings IEEEINFOCOM 2002, the 21st annual joint conference of the IEEEcomputer and communications societies, New York, USA, 23–27June.
21. Ching, C., & Schindelhauer, C. (2010). Utilizing detours forenergy conservation in mobile wireless networks. Journal ofTelecommunication Systems. doi:10.1007/s11235-009-9188-3.
22. Gao, Q., Blow, K., Holding, D., Marshall, I., & Peng, X. (2004).Radio range adjustment for energy efficient wireless sensor net-works. Journal of Ad Hoc Networks, 4(1), 75–82.
23. Li, D., Jia, X., & Liu, H. (2004). Energy efficient broadcast routingin static ad hoc wireless networks. IEEE Transactions on MobileComputing, 3(1), 1–8.
24. Camilo, T., Carreto, C., Silva, J., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks.In Lecture notes in computer science: Vol. 4150. Ant colony opti-mization and swarm intelligence (pp. 49–59). Berlin: Springer.
25. Younis, M., Youssef, M., & Arisha, K. (2002). Energy-aware rout-ing in cluster-based sensor networks. In Proceedings of the 10thIEEE international symposium on modeling, analysis, and simu-lation of computer and telecommunications systems (MASCOTS’02) (pp. 129–136). Washington: IEEE Computer Society.
26. Cheng, Z., Perillo, M., & Heinzelman, W. B. (2008). Generalnetwork lifetime and cost models for evaluating sensor networkdeployment strategies. IEEE Transactions on Mobile Computing,7(4), 484–497.
27. Heo, N., & Varshney, P. K. (2005). Energy-efficient deploymentof intelligent mobile sensor networks. IEEE Transactions on Sys-tems, Man and Cybernetics Part A Systems and Humans, 35(1),78–92.
28. Vlajic, N., & Xia, D. (2006). Wireless sensor networks: to clus-ter or not to cluster? In International symposium on a world ofwireless, mobile and multimedia networks, WoWMoM 2006.
29. Garcia, M., & Lloret, J. (2009). A cooperative group-based sen-sor network for environmental monitoring. In LNCS: Vol. 5738.Cooperative design, visualization, and engineering: CDVE 2009.(pp. 276–279). Berlin: Springer.
30. Garcia, M., Bri, D., Boronat, F., & Lloret, J. (2008). A new neigh-bour selection strategy for group-based wireless sensor networks.In 4th int. conf. on networking and services, ICNS 2008. 16–21March (pp. 109–114).
31. Kaplan, E. D. (1996). Understanding GPS: principles and appli-cations. Boston: Artech House.
32. Stojmenovic, I. (2002). Position based routing in ad hoc networks.IEEE Communications Magazine, 40(7), 128–134.
33. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H.(2002). An application-specific protocol architecture for wirelessmicrosensor networks. IEEE Transactions on Wireless Communi-cations, 1(4), 660–670.
34. Bhardwaj, M., Garnett, T., & Chandrakasan, A. P. (2001). Upperbounds on the lifetime of sensor networks. In: International con-ference on communications (ICC’01). June 2001 (pp. 785–790).
35. Gibbons, A. (1985). Algorithmic graph theory. Cambridge: Cam-bridge University Press.
36. Fraigniaud, P., Pelc, A., Peleg, D., & Perennes, S. (2000). Assign-ing labels in unknown anonymous networks. In Proceedings of the19th annual ACM SIGACT-SIGOPS symposium on principles ofdistributed computing, Portland, OR, USA (Vol. 1, pp. 101–111).
37. OPNET Modeler® Wireless Suite network simulator (2011).Available at http://www.opnet.com/solutions/network_rd/modeler_wireless.html.
Miguel Garcia received his M.Sc.in Telecommunications Engineer-ing in 2007 at Universitad Politèc-nica de Valencia and a postgraduate“Master en Tecnologías, Sistemas yRedes de Comunicaciones” in 2008.He is currently a Ph.D. student inthe Department of Communicationsof the Universitat Politècnica de Va-lencia. He is a Cisco Certified Net-work Associate Instructor. He isworking as a researcher in IGIC inthe Higher Polytechnic School ofGandia, Spain. Until 2011, he had
more than 35 scientific papers published in national and internationalconferences, he had several educational papers. He had more than 25papers published in international journals (most of them with JournalCitation Report). Mr. Garcia has been technical committee memberin several conferences and journals. He has been in the organizationof several conferences, for example nowadays he is involved in theconference IEEE MASS 2011. Miguel is associate editor of Interna-tional Journal Networks, Protocols & Algorithms. He is IEEE graduatestudent member. [email protected]
Sandra Sendra received her de-gree of Technical Engineering inTelecommunications in 2007. Shereceived her M.Sc. of ElectronicSystems Engineering in 2009. Cur-rently she is working as a researcherin the research line “communi-cations and remote sensing” ofthe Integrated Management CoastalResearch Institute (UniversidadPolitécnica de Valencia). She hasseveral scientific papers publishedin national and international confer-ences, several book chapters related
with Sensors and some papers in international journals with JCR. Sheis associate editor and reviewer in two international journals relatedto network communications (Networks Protocols and Algorithms andAdvances in Network and Communications). She has been involved inseveral Program committees and in the organization of internationalconferences until 2009 (CENIT 2009, ICAS 2010, CENICS 2010,ICCGI 2010, INTERNET 2010 and ACCESS 2010, among others).She has been involved in the organization of several international con-ferences like ICNS 2009, INTENSIVE 2009, ICWMC 2010, ICCGI2010, ACCESS 2010 and she will be involved in the organizationcommittee of the international conferences as AICT 2011, ICDT 2011,FISN 2011, MMEDIA 2011, SOTICS 2011, ENERGY 2011, ICWMC2011 and IEEE MASS 2011. [email protected]
2502 M. Garcia et al.
Jaime Lloret received his M.Sc.in Physics in 1997, his M.Sc. inelectronic Engineering in 2003 andhis Ph.D. in telecommunication en-gineering (Dr.Ing.) in 2006. He isa Cisco Certified Network Profes-sional Instructor. He worked as anetwork designer and as an admin-istrator in several enterprises. He iscurrently Associate Professor in thePolytechnic University of Valenciaand he is the research line coordi-nator of the “communications andremote sensing” of the Integrated
Management Coastal Research Institute. He is the director of the Uni-versity Expert Certificate “Redes y Comunicaciones de Ordenadores”and of the University Expert Certificate “Tecnologías Web y Comer-cio Electrónico”. He is currently the Cognitive Networks TechnicalCommittee (IEEE Communications Society) Vice-chair for the Eu-rope/Africa Region. He has more than 70 scientific papers publishedin national and international conferences, he has more than 34 papersabout education and he has more than 38 papers published in interna-tional journals (more than half of them with Impact Factor in JournalCitation Report). He has been the co-editor of 15 conference proceed-ings and guest editor of several international books and journals. Heis editor-in-chief of the international journal “Networks Protocols andAlgorithms”, editor-in-chief of the international Journal “Advances inNetwork and Communications”, IARIA Journals Board Chair (8 Jour-nals) and he is associate editor of several international journals. He
has been involved in more than 120 Program committees of interna-tional conferences and in several organization and steering commit-tees until 2010. He has been the chairman of SENSORCOMM 2007,UBICOMM 2008, ICNS 2009 and ICWMC 2010 and co-chairmanof ICAS 2009 and INTERNET 2010. He is the co-chairman of IEEEMASS 2011. He is IEEE Senior Member and IARIA Fellow [email protected]
Alejandro Canovas received hisM.Sc. in Telecommunications En-gineering in 2009 at PolytechnicUniversity of Valencia. He is cur-rently studying a postgraduate of“Master en Inteligencia Artificial,Reconocimiento de Formas e Imá-gen Digital” in the Department ofDSIC of the Polytechnic Univer-sity of Valencia. He is researchingin the line of “communications andremote sensing” of the IntegratedManagement Coastal Research In-stitute. He is researching in “IPTV”
and “IPTV stereoscopic”. He has several scientific papers publishedin international conferences and journals with impact factor. He hasbeen involved in the organization of ICNS 2009, ICAS 2009, Intensive2009, ICWMC 2010, INTERNET 2010, ACCESS 2010 and ICCGI2010. He is IEEE student member. [email protected]
