A Hybrid MAC Protocol with Channel-dependentOptimized Scheduling for Clustered

Underwater Acoustic Sensor Networks

Jithin Jagannath, Anu Saji, Hovannes Kulhandjian, Yifan Sun, Emrecan Demirors, and Tommaso MelodiaDepartment of Electrical Engineering

State University of New York at Buffalo, Buffalo, New York 14260Email: { jithinja, anusaji, hkk2, ysun24, emrecand, tmelodia}@buffalo.edu

ABSTRACTWe propose a novel optimal time slot allocation scheme forclustered underwater acoustic sensor networks that leveragesphysical (PHY) layer information to minimize the energyconsumption due to unnecessary retransmissions thereby im-proving network lifetime and throughput. To reduce theoverhead and the computational complexity, we employ atwo-phase approach where: (i) each member node takes aselfish decision on the number of time slots it needs dur-ing the next intra-cluster cycle by solving a Markov deci-sion process (MDP), and (ii) the cluster head optimizes thescheduling decision based on the channel quality and an ur-gency factor. To conserve energy, we use a hybrid mediumaccess scheme, i.e., time division multiple access (TDMA)for the intra-cluster communication phase and carrier sensemultiple access with collision avoidance (CSMA/CA) for thecluster head-sink communication phase. The proposed MACprotocol is implemented and tested on a real underwateracoustic testbed using SM-75 acoustic modems by TeledyneBenthos. Simulations illustrate an improvement in networklifetime. Additionally, simulations demonstrate that theproposed scheduling scheme with urgency factor achievesa throughput increase of 28% and improves the reliabilityby up to 25% as compared to the scheduling scheme thatneither use MDP nor optimization. Furthermore, testbedexperiments show an improvement in throughput by up to10% along with an improvement in reliability.

1. INTRODUCTIONProlonging the network lifetime is crucial for long-term

monitoring missions such as oceanographic data collection,pollution monitoring, offshore exploration, among others.Thus, in this work we propose a medium access control(MAC) protocol for underwater acoustic sensor networks(UW-ASNs) with the objective to maximize the networklifetime and improve the throughput, while maintaining areliable service.

UW-ASNs present severe challenges to the development ofefficient MAC protocols. A number of MAC protocols have

Acknowledgment: This work was partially supported bythe National Science Foundation under grants CNS-1055945and CNS-1126357.

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.WUWNet’13, November 11 - 13, 2013, Kaohsiung, Taiwan.Copyright 2013 ACM 978-1-4503-2584-4/13/11 ...$15.00http://dx.doi.org/10.1145/2532378.2532382 .

been proposed that aim at combating multipath, long prop-agation delay, and overall energy consumption in UW-ASNs[1, 2, 3, 4]. The energy of the underwater acoustic (UW-A)modem’s battery is limited and replacing and/or rechargingthe battery is in many cases not practical. Therefore, it iscrucial to design a MAC protocol that minimizes the overallenergy consumption to extend the network lifetime. An im-portant factor to take into consideration is the large transmitpower of the UW-A modems, which is in the range of 1.78W-20 W [5], compared to 88.2 mW [6] in terrestrial RF sensornetworks. This work aims at minimizing the energy con-sumption due to unnecessary retransmissions and overhead,which is the main cause of energy depletion in UW-ASNs.The underwater environment is highly noisy due to shippingactivities, wind, marine organisms, which results in low sig-nal to noise ratio (SNR), i.e., high bit error rate (BER) inthe order of 10−4 to 10−2 [1, 7], leading to unreliable com-munication. The severely impaired UW-A channel resultsin numerous retransmissions due to packet collisions. Toovercome this challenge, we take link quality into considera-tion in our protocol design, with an intention to reduce thepacket drop rate and hence, reduce packet retransmissions.We leverage PHY layer information by considering the linkquality (i.e., SNR) and residual energy level of the modem’sbattery with an objective to maximize the network lifetime.

We adopt a clustered topology to further optimize the net-work energy consumption, owing to its inherent ability tosupport data gathering [8]. We consider a TDMA schemefor communication within a cluster. The time-slotted struc-ture helps to save energy, by letting the nodes not involvedin transmission to go to sleep mode. A contention basedCSMA/CA scheme is used for communication between thecluster head and the sink. All of the aforementioned func-tionalities of our MAC protocol are designed with the objec-tive to extend the network lifetime of UW-ASNs. Thus, inthis paper, we present a novel optimized-scheduling scheme,which can be incorporated in any dynamically scheduledMAC protocols for UW-ASNs, with the aim to maximize theenergy efficiency and improve the throughput. We have im-plemented and tested the proposed MAC protocol on a realunderwater acoustic testbed using SM-75 acoustic modemsby Teledyne Benthos [5]. Simulations and testbed experi-ments show that significant improvement in throughput andreliability can be achieved, while minimizing the overall en-ergy consumption due to unnecessary retransmissions.

The rest of this paper is organized as follows. In Section 2,we review the related work. We discuss the system archi-tecture in Section 3, while in Section 4, we describe intra-cluster and cluster head-sink communication phase. Thetime slot allocation using MDP and optimization problemformulation are discussed in detail in Section 5. We presentthe simulation and testbed evaluation results in Sections 6and 7 respectively, followed by conclusions in Section 8.

2. RELATED WORKSeveral MAC protocols have been proposed to control the

medium access of sensor nodes in UW-A channel. In [9],Peleato and Stojanovic adapted the MACA [10] protocolfor UW-ASNs, by adjusting the time required for RTS/CTShandshake between sender-receiver pairs by calculating thedistance between them. To transmit a data packet, a nodeinitially sends an RTS to the intended receiver. Upon recep-tion of the RTS, the receiver transmits a CTS to the senderand waits for the data packet from the sender. The waittime is calculated based on the round trip time measuredduring the RTS/CTS exchange. The sender and receiveruse the wait time and short warning messages to preventdata packet collisions that may arise from neighboring nodetransmissions.

In [11], a hybrid MAC for underwater wireless sensor net-works (UWSNs) was proposed, which includes scheduled andnon-scheduled periods in a time slotted frame. In the sched-uled portion of the frame, the sensor nodes communicate ina collision-free manner based on a predefined schedule, whilein the non-scheduled portion of the frame, the protocol oper-ates in a contention-based manner by letting nodes adapt tochanging traffic conditions. The number of scheduled slotsis defined by the network operator or clustering protocol andremains constant once the network operation begins. Thenon-scheduled slots are temporarily assigned to the nodesbased on the distributed state. In [11], the authors assumethat the nodes are synchronized.

In [12], an energy efficient MAC protocol called UWAN-MAC was proposed, which aims to minimize the energy con-sumption by determining listen cycles and sleep period toturn off its transceiver circuit to save energy. The protocolproposed in [13], (called T-Lohi), is a contention based MACprotocol where sensor nodes contend by sending a short toneto reserve the channel for data transmission. T-Lohi con-serves energy by allowing the sensor nodes to sleep untilthey hear the wake up tone. The wake up tone is also veryshort and thus reduces energy consumption due to smalloverhead.

There are several other works proposed for channel-dependent scheduling of sensor nodes in terrestrial wirelesssensor networks (WSN). Among these, [14] is a TDMA-based MAC protocol, which uses link state dependentscheduling (LSDS) and gathers samples of the channel qual-ity, i.e., label slots as bad if transmission/reception fails orgood if transmission/reception is successful. All patterns ofgood and bad states are used as a training set to establish aprediction set that is the shortest repetition of the trainingset. Using the LSDS approach, for each independent slot, asensor node only transmits/receives packets when a slot isgood and delays transmission/reception when a slot is bad.

An energy efficient transmission strategy for WSNs thatoperate in a strict energy-constrained environment is dis-cussed in [15]. The binary-decision based transmission in[15] makes a decision on whether to transmit or not basedon the current channel conditions. Another component dis-cussed in [15] is a channel-aware backoff adjustment, whichfavors sensor nodes with better channel quality, when decid-ing and prioritizing the transmission.

Unlike [11], [12] and [13], our proposed protocol lever-ages the channel quality information from the PHY layerfor scheduling, which helps to reduce retransmissions andcontribute to energy saving. The protocol proposed in [14]requires neighbor discovery and schedule exchange betweenall sensor nodes in the network, which can increase the over-head involved in communication. In [11], the authors assumethat each node can overhear its neighbors and share stateinformation to acquire time slots. However, in our approacha member node does not need to share the state information

with its neighbors. Instead, it makes a selfish decision on thenumber of time slots to request from the cluster head andthe cluster head optimally allocates time slots to each nodeusing the channel state information. By considering a two-step approach in the intra-cluster scheduling we minimizethe overhead involved in communication. Thus, consideringchannel quality and adopting the two-step approach in theintra-cluster scheduling helps to elevate the energy-savingand throughput by minimizing the overhead and redundantretransmissions.

Unlike [11], we do not assume that the nodes are synchro-nized. Instead, the cluster head synchronizes the membernodes within a cluster at the start of every cycle, by in-serting a time stamp in the header of a broadcasted slotannouncement (SA) packet. This way we avoid the use ofseparate time synchronization packets. Our protocol differsfrom [12] and [13], in that we consider the residual energylevel and buffer occupancy of each node to reduce the energyconsumption while maintaining reliability.

The main contribution of this paper is the design andtestbed implementation of a novel channel-dependent opti-mized scheduling scheme within a cluster. The objectiveof the proposed scheduling scheme is to conserve energy byavoiding unnecessary retransmissions and at the same timeimproving the throughput, while maintaining reliability, byconsidering the channel quality of each link and minimizingbuffer overflows.

3. SYSTEM ARCHITECTUREWe consider an UW-ASN, in which a set of nodes are

deployed on the ocean bed and a sink node is located onthe surface. We consider a static clustered topology, whereclusters are already formed and we assume there is no inter-cluster interference, which can be obtained by using differ-ent carriers or orthogonal spreading codes. The membernodes communicate with the cluster head using lower trans-mit power levels as they are within the transmission rangefrom the cluster head. Cluster heads monitor and coordinatethe transmission of their member nodes. Since the clusterhead handles several functionalities, its energy consumptionis higher than the rest of the member nodes, and thus it ismore likely to fail due to its non-uniform energy consump-tion. Accordingly, it is necessary to rotate the role of clusterheads based on their residual energy level. Cluster formationand cluster head rotation are not the focus of this work.

The clustered topology, shown in Fig. 1, depicts a sinknode S and two clusters with their cluster heads denotedby CH1 and CH2, respectively. The member nodes areone-hop away from the cluster head and within the commu-nication range of their respective cluster heads. The clusterheads communicate directly with the sink node. We pro-pose a hybrid scheme for the intra-cluster and cluster head-sink communication, in which TDMA scheduling is used forintra-cluster communication and a CSMA/CA scheme forcommunication between the cluster head and the sink node.There are two phases, namely, (i) intra-cluster phase and (ii)cluster head-sink communication phase, which are discussedin Section 4. The intra-cluster communication phase is sub-divided into several cycles. Each cycle is further dividedinto time slots, where each member node will transmit datapackets to their cluster head in its allocated time slots. Thetiming diagram, shown in Fig. 2, demonstrates the exchangeof control and data packets in one intra-cluster cycle.

The time slot scheduling within the cluster is based onthe channel quality, node priority, buffer occupancy and theresidual energy level of each node. The cluster head hasto collect information about buffer occupancy and residualenergy level from its member nodes in order to efficientlyallocate time slots for each member node. This may leadto large overhead, due to the exchange of this information

Figure 1: Cluster topology.

within the cluster. Accordingly, we let each member nodeask for the required time slots, which are determined basedon a selfish decision, by taking into account their own bufferoccupancy and residual energy levels. Therefore, in this casethe cluster head needs to know the time slots requested byeach member node only. The time slots are chosen froman optimal slot policy matrix at the start of the next cycle,which is formulated and obtained using an MDP, details ofwhich are discussed in Section 5. Once the member nodeshave informed the cluster head as to the number of timeslots they need for the next cycle, the cluster head will allo-cate the time slots in an optimal way based on the receivedslot requests from all member nodes and their channel con-ditions (SNRs). The intra-cluster scheduling functionalitiesare explained in Section 5.

4. PROTOCOL DESCRIPTION

4.1 Intra-cluster Communication PhaseIn the intra-cluster communication phase, a member node

transmits the collected data to the cluster head during itsallotted time slot, while the other member nodes sleep tosave energy. During this phase, the member nodes and thecluster head use their lowest transmit power level for com-munication within the cluster. The member nodes appendthe number of time slots they need for the next cycle as atrailer to the data packet sent in the current cycle. Thecluster head learns about the time slot requirements of eachmember node and allocates the available time slots opti-mally using the proposed optimization algorithm discussedin Section 5. After computing the number of time slots forthe member nodes, the allotted time slots are included inthe SA packet along with the member node’s IDs.

From the timestamp, position and allotted time slots inthe SA packet each member node will compute the starttime for transmission of its data packets. Several cyclesof intra-cluster communication phase can take place beforetransitioning to the cluster head-sink communication phase.The cluster head stores the data packets received from itsmember nodes over several cycles in the intra-cluster phaseto be sent as a long train of packets to the sink node.

Figure 2 shows the communication between the clusterhead CH and the member nodes A and B for one intra-cluster cycle followed by the cluster head-sink communica-tion phase. The member nodes (A and B) join their respec-tive clusters by transmitting a small packet, JOIN requestmessage, to the CH. The JOIN request message contains thenumber of time slots requested from each node. The clusterformation phase occurs prior to the intra-cluster phase.

The cluster head CH performs channel dependent opti-mized intra-cluster scheduling to allocate the time slots tonodes A and B, and broadcasts the slot information to themember nodes through an SA packet. Upon reception ofthe SA packet, the member nodes are informed on when totransmit. The guard-band, GB, accounts for additional de-

Figure 2: Timing diagram.

lays e.g., multipath. In our example, node B learns from theSA packet that it has the opportunity to transmit during aparticular time slot. While node B is transmitting, nodeA sleeps to save energy and wakes up only when needed.Similarly, node B goes into the sleep mode when A is trans-mitting. After a successful reception of the data packets,cluster head CH broadcasts a cumulative acknowledgment(CA) packet to acknowledge the reception of data packets.We avoid the use of individual acknowledgement of eachdata packet to minimize the control overhead by acknowl-edging the member nodes within a cluster with a single CApacket, as discussed in [3]. The received data packets arethen gathered at the CH. Packets not received correctly willbe retransmitted in the next cycle. We name this sequenceof data exchanges between the cluster head and membernodes as the intra-cluster communication phase. Since thetransmission within each cluster is time-slotted, the membernodes need to be synchronized. We assume that the clus-ter head knows the location of its member nodes, and hence,can compute the propagation delay with respect to its mem-ber nodes, which is used for synchronization. In Section 5,we explain the channel dependent optimized intra-clusterscheduling.

4.2 Cluster Head-Sink Communication PhaseDuring the cluster head-sink communication phase, the

cluster head transmits the packet train to the sink node us-ing CSMA/CA scheme. In this phase, the cluster heads usea higher transmit power level in order to reach the sink. Ac-cordingly, in Fig. 2, the cluster head CH transmits an RTSto the sink S and waits for a CTS. The CTS packet containsinformation about the period during which the CH intendsto keep S node occupied. Hence, when the other clusterheads overhear CTS, they will learn that S is busy and willdefer their transmissions. Upon reception of the CTS, theCH will transmit it’s packet train to S, which will be ac-knowledged with an ACK packet. The RTS/CTS-packettrain and the ACK exchange between the cluster head andsink node is named as the cluster head-sink communicationphase.

5. INTRA-CLUSTER SCHEDULINGIn the intra-cluster scheduling, a TDMA scheme is

adopted to minimize the energy consumption by avoid-ing contention and packet collisions. Additionally, membernodes utilize their state information, such as buffer occu-pancy and residual energy level of its battery, to computethe number of time slots they will need in the next cycle.One way to address the time slot allocation problem is totransmit the member node’s state information to the clusterhead and let it compute a global optimal solution. However,by doing so, the packet overhead and optimization prob-lem complexity will increase exponentially, resulting in highcomputational delay and memory requirements. Hence, welet each member node use the MDP, discussed later in thissection, to make a selfish decision on the number of time

slots they need during the next cycle. To further improvethe reliability of our scheme, the cluster head takes the ulti-mate time slot allocation decision, which helps to minimizethe number of corrupted packets due to poor channel con-ditions. The intra-cluster scheduling involves the followingtwo steps:

(i) MDP based time slots request decision at themember node: The member nodes request from the clusterhead the number of time slots it needs during the next cy-cle. The buffer occupancy and residual energy states changeevery cycle as shown in Figs. 3 and 4.

The time slots are requested such that the energy con-sumption is minimized, while at the same time maintainingreliability. Time slots are requested at the start of everycycle in the intra-cluster phase. A stochastic optimizer for-mulates the problem as an infinite horizon MDP, which con-siders the buffer occupancy and residual energy level of themember node as the input states. The MDP selects an opti-mal action (number of time slots required) with the objectiveof minimizing the energy consumption at each member node,while maintaining reliability by preventing buffer overflow.Every member node performs this operation to obtain therequired number of time slots to request from the clusterhead.

(ii) Channel dependent time slot allocation at thecluster head: Based on the obtained requests and the qual-ity of link to the member node, the cluster head will opti-mally allocate time slots to its member nodes. We namethe process of optimal allocation of time slots based on thereceived time slot requests and channel quality as channel-dependent optimized scheduling.

5.1 Markov Decision Process FormulationSystem state space: An optimal policy is a function

mapping node state information (residual energy level andbuffer occupancy) to the number of time slots required totransmit. Consider packet transmissions from the membernode to cluster head over the UW-A channel. Assume thateach cycle, in the intra-cluster phase, is divided into equallysized time slots. We assume that all packets transmitted aresuccessfully received at the cluster head. At each cycle, thejoint state of the node is defined by the buffer occupancyand residual energy level.

Figure 3: Buffer state of MDP.

The transmission buffer is modeled as a first-in-first-outqueue and can hold a maximum of B packets. We define thebuffer state to be the buffer occupancy, and denote b ∈ B ={0, 1, 2, · · ·B} as the buffer state space. The buffer stateevolves recursively as follows

bn+1 =min(B, bn+kn−max(0, an)) , (1)

where bn is the buffer state in the nth cycle, the initial bufferstate is denoted by b0, and bn+1 is the buffer state in the(n+ 1)thcycle, which depends on the action an taken in thenth cycle. We define an action as the number of time slotsrequested by the member node, and represent a ∈ A ={amin, · · · , 0, · · · , amax} as the action state space, whereamin = −x is the minimum value of action, which impliesthat the member node prefers to sleep for x cycles to saveenergy, and amax = B denotes the maximum value of anaction, which is equal to the buffer capacity B.

We assume that during one time slot only one packet canbe transmitted. Therefore, the number of time slots re-quested is equivalent to the number of packets ejected fromthe buffer. At each cycle, the sensor node injects kn packets,each of size K bits. The arrival process {kn : n = 0, 1, · · · } ismodelled as a Poisson distribution, pk (k) ∼ Poi (λ), whereλ is the mean of the number of packet arrivals. Basedon the buffer recursion in (1), and the arrival distribution,the sequence of buffer states can be modeled as a con-trolled Markov chain with transition probability given bypb (b′|b, a), which is the probability that a node in state btransitions to state b′ by taking an action a. We introducea buffer cost β (b), which is expressed in (2) as the expectedsum of holding cost and overflow cost to penalize the mem-ber node for queuing delays and buffer overflows, therebyavoiding packet losses, due to the buffer overflow,

β (b) =∑k

pk (k) {(b−max (0, a))︸ ︷︷ ︸holding cost

+

+ η max (0, (b−max (0, a) + k −B))︸ ︷︷ ︸overflow cost

}.(2)

The holding cost is associated with the number of packetsthat were not transmitted at the beginning of a cycle andremain in the buffer to be transmitted at the next cycle. Theoverflow cost represents the number of packets that weredropped from the buffer, due to packets leaving the bufferslower than the arrival rate. Overflow cost imposes a penaltyof η on each dropped packet. The selection criterion for η isdiscussed at the end of this section.

Each node initially starts with a full battery capacity ofE, we denote e ∈ E = {0, 1, 2, · · ·E} as the residual energystate space. At each cycle of the intra-cluster phase, thetotal energy consumed is given by

σ (a) =

{Plow (tmax − ta) + Ptxta + Etr, ∀a > 0

Plowtmax, ∀a ≤ 0

(3)where Plow and Ptx are the power consumed in the low powerstate of the sensor node and the power consumed to transmitone packet, respectively, in units of (Watt), Etr is the energyconsumed in transitioning between the low power and activestates in units of (Joule), tmax and ta are the duration ofone intra-cluster cycle, and the duration of a number of timeslots, respectively in seconds. The energy state recursion isgiven by

en+1 = max{0, en − Plow (tmax −max (0, ta)) +

+ Ptx max (0, ta ) + Etr min (1,max (0, a ))},(4)

where en and en+1 are the current and next residual energystates, respectively with initial residual energy state denotedby e0. Energy states can be modeled as a Markov chain withtransition probability denoted by pe (e′|e, a), which is theprobability that the member node in residual energy state etransitions to e′ by taking an action a. To penalize the nodethat runs out of energy we define a failing-penalty as

τ (e) = 1, for e = 0, (5)

in which a member node that runs out of energy gets penal-ized by τ (e). We also give a sleep reward for a node thatprefers to sleep for an a number of cycles expressed by

µ (a) = min (0, a) , ∀ a < 0, (6)

in which the member node is rewarded by µ (a).We define the joint state of the node as a vector comprised

of the buffer state b and the residual energy state e, as s4=

(b, e) ∈ S. The action is defined as the number of time slotsrequired to transmit in the next cycle, where a ∈ A. The

Figure 4: Energy state MDP.

sequence of states {sn : n = 0, 1, 2, · · · } can be modeled asa controlled Markov chain with transition probabilities thatcan be determined from the conditionally independent bufferstate and energy state as follows

ps(s′|s, a

)= pb

(b′|b, a

)pe(e′|e, a

), (7)

where ps (s′|s, a) denotes the probability that a node in jointstate s will transition to joint state s′ by taking an actiona. The objective of the MDP is to find an optimal time slotpolicy that minimizes the overall cost. Let π : S → A denotethe time slot policy mapping states to actions such thata = π (s) and P (b, e) is the optimal slot policy matrix withbuffer state b denoted by the column index and energy statee denoted by the row index, which represents the possiblenumber of time slots a node will need at a given buffer andenergy state. The cost function, based on four factors: (i)power cost σ (a), (ii) buffer cost β (b), (iii) failing cost τ (e),and (iv) sleep reward µ (a), is expressed as

C (s, a) = σ (a) + λ1β (b) + λ2τ (e) + λ3µ (a) , (8)

where λ1, λ2 and λ3 are the weights over these factors. Theoptimal slot policy is obtained by solving the MDP usingthe value-iteration algorithm, discussed in (9) and (10). Theoptimal state value function quantifies how good it is to bein the joint state s = (b, e). The optimal state value functionand optimal slot policy are expressed as

V ∗π (s) = mina∈A

{C (s, a) + γ

∑s′∈S

ps(s′|s, a

)V ∗π(s′)}

, (9)

π∗(s) = arg mina∈A

{C (s, a) + γ

∑s′∈S

ps(s′|s, a

)V ∗π(s′)}, (10)

where γ ∈ (0, 1] is a discount factor. The penalty factor η isdefined as

η =γ

1− γ . (11)

The penalty of dropping a packet must be greater or equalthe cost of admitting it into the buffer, as discussed in [16].Performing MDP real-time with dynamically changing net-work conditions will increase the energy consumption anddelay, due to the high computational and/or memory com-plexity. Thus, MDP is performed offline and the obtainedoptimal slot policy matrix P (b, e), for all possible buffer bn

and energy en states, respectively, are stored in a table in-side each node. A member node selects the appropriate timeslots required for the next cycle from the optimal slot pol-icy matrix P (b, e) according to its current buffer and energystate.

5.2 Optimal time slot allocation problemAfter the cluster head obtains the number of time slots

requested by the member nodes, it allocates the availabletime slots with an objective to maximize the overall through-put while maintaining a reliable transmission. To maximizethe overall throughput it considers the channel quality interms of the SNR. On the other hand, to maintain a reliabletransmission it minimizes the packet loss due to the bufferoverflows. The allocated time slots should not exceed themaximum available time slots in one cycle.

The time slot allocation problem can be formulated as anoptimization problem as follows

P1 : maximizeti

N∑i=1

(1− PERi) ti (12)

subject to :

N∑i=1

ti ≤ Tmax (13)

0 ≤ ti ≤ ai, ∀ i ∈ [1, N ], (14)

where (1− PERi) is the probability of successful packetreception from the ith node, where PERi is the packet errorrate of the received packets from the ith node, ti is number oftime slots allocated to the ith node, Tmax is the maximumnumber of time slots available per cycle and ai is the numberof time slots requested by the ith node.

For different applications (e.g. tsunami detection, envi-ronmental monitoring or undersea explorations) each nodemight have different priority levels for handling the data tobe transmitted. We denote the nodes with higher prior-ity data (eg. tsunami detection messages) as primary nodeswhile less urgent data transmissions (eg. oceanographic datacollection) as secondary.

Accordingly, we reformulate P1 to take that into consider-ation by introducing an additional node priority constraintas follows

P2 : maximizeti,tj

N∑i=1

(1− PERi) ti +

M∑j=1

(1− PERj) tj (15)

subject to :

N∑i=1

ti +

M∑j=1

tj ≤ Tmax (16)

0 ≤ ti ≤ ai, ∀ i ∈ [1, N ], (17)

0 ≤ tj ≤ aj , ∀ j ∈ [1,M ], (18)

N∑j=1

tj = min

(N∑j=1

aj , Tmax

), (19)

where aj is the number of time slots requested by the pri-mary (higher priority) node j and tj is the number of timeslots allotted to the primary node j. In this case, the indexi represents the secondary nodes.

In the optimization problem P2, with the additional pri-ority constraint given by (19), the cluster head takes intoconsideration the priority of each member node while allo-cating time slots. The nodes with higher priority are servedfirst and the remaining time slots are distributed among thelower priority member nodes.

By considering the PERi during scheduling we take intoaccount SNRi, the SNR of the received packet of node i.The number of time slots allocated for the primary and sec-ondary users is assigned such that it does not exceed themaximum number of time slots available per cycle, Tmax,which is considered by (16). Constraints (17) and (18) makesure that it does not allocate more than what is asked.

One drawback in P1 and P2 formulations is that they con-sider only the nodes with higher SNR (lower PER) valueswithout considering their urgency. Thus, the nodes with arelatively bad channel condition may never have a chance totransmit, which will eventually cause their buffers to over-flow, resulting in packet loss. Accordingly, we modify theoptimization problem by introducing an urgency factor θi,which takes into account the urgency of the member nodes.

Table 1: Simulation Parameters: Network lifetimeSimulation parameters ValuesNumber of member nodes per cluster 9Maximum buffer size of each node 40 packetsNumber of intra-cluster cycles 6500SNR range 1 dB - 15 dBMean packet per node per cycle 1Transmission energy per packet 1.78 WLow power listening energy 16.8 mWWake up energy 0.2 W

The modified optimization problem is formulated as

P3 : maximizeti

N∑i=1

[(1− θi) (1− PERi) + θi] ti (20)

subject to :

N∑i=1

ti ≤ Tmax (21)

0 ≤ ti ≤ ai, ∀ i ∈ [1, N ], (22)

where θi = aiamax+ε

is the urgency factor. In the case ofhigh traffic, a member node may request up to amax timeslots, the size of its buffer, which may cause the weight onthe SNRi term to vanish in the absence of ε. Hence, εis introduced and is set to a very small value ≈ 0.0001.A high value of θi will give smaller weight to (1− PERi)and as a result, the urgency of the node will be taken intoaccount, thereby preventing buffer overflow. Accordingly,the optimization problem P3 allocates time slots based onthe received signal quality (SNR) and urgency factor withthe objective to maximize the throughput and at the sametime prevent buffer overflows.

6. SIMULATION RESULTSAn intra-cluster scenario comprising of 9 member nodes is

simulated in a custom-designed network simulator to analyzethe network lifetime. The parameters used for simulation arepresented in Table 1. We compare our proposed scheme em-ploying MDP with optimized time slot allocation; (i) withan urgency factor, (ii) without an urgency factor and (iii) ascheme that neither uses MDP nor an optimization at thecluster head. In (iii), each member node informs the clusterhead their current buffer occupancy. The cluster head col-lects the buffer occupancy information from all the membernodes in its cluster and allocates time slots as ti = biTmax∑N

j=1 bj,

where bi and bj correspond to the buffer occupancy of theith and jth nodes, respectively. Which implies that membernodes with higher buffer occupancy will receive proportion-ally higher share of the total time slots available during anintra-cluster cycle.

Figure 5 illustrates the network lifetime by measuring thenumber of failed member nodes in the cluster as the numberof intra-cluster cycles increases. In Fig. 5, we can observethat the scheme with optimization and without urgency fac-tor has the highest network lifetime, as the number of intra-cluster cycles elapse before the first member node fails ismuch higher compared to the other two schemes. This isdue to the fact that time slots are allotted to member nodeswith a better channel quality, thus reducing the number ofpackets lost. As a result, it reduces the number of retrans-missions, which leads to a reduction in the energy consump-tion. The scheme that neither uses MDP nor optimizationperforms worse than the one with optimization and urgencyfactor. This is because it takes into account the urgency ofthe slot requirement along with channel quality of the mem-ber node. As a result, time slots may be allocated to mem-bers with slightly poorer channel quality, but with higherurgency factor.

5600 5700 5800 5900 6000 6100 6200 6300 6400 65000

1

2

3

4

5

6

7

8

9

Number of intra cluster cycles

Nu

mb

er o

f D

ead

No

des

Optimization without Urgency FactorOptimization with Urgency FactorWithout Optimization and MDP

Figure 5: Network lifetime.

Table 2: Simulation Parameters: For varying trafficSimulation parameters ValuesNumber of member nodes per cluster 9Maximum buffer size of each node 40 packetsNumber of intra-cluster cycles 500SNR range 1 dB - 20 dB

1 dB - 15 dBRange of mean packet per node per cycle 1 - 5Transmission energy per packet 1.78 WLow power listening energy 16.8 mWWake up energy 0.2 W

The next set of simulations represent the network behaviorin varying traffic conditions. The set of simulation param-eters are shown in Table 2. All simulations are carried outwith two different channel conditions; relatively good chan-nel condition with SNR range varying uniformly between1 dB to 20 dB, and relatively poor channel condition withSNR varying uniformly between 1dB to 15dB. The traffic isrepresented as the average number of packets produced byeach member node per cycle and is varied from 1 to 5.

Figures 7 and 6 represent the throughput for relativelypoor channel conditions and good channel conditions, re-spectively. It is evident that the schemes with optimizationhave higher throughput as compared to scheme that neitheruses optimization nor MDP. At higher traffic level, as thebuffer is almost always likely to be full, all nodes may berequesting a large number of time slots. Thus, the schemewith optimization and urgency factor not only reduces thebuffer overflow, but also favors the nodes with better chan-nel condition, due to the factor ε, as discussed in Section 5.Thus, we can see from Fig. 7, which describes a relativelypoor channel conditions, the scheme with optimization andurgency factor has the highest throughput at higher networktraffic.

We define reliability as the total number of packets suc-cessfully received at the cluster head divided by the totalnumber of packets generated. Figures 8 and 9 show the re-liability of the network for the three different schemes. Wecan see that the schemes with optimization perform betterin terms of reliability compared to the scheme without op-timization and MDP, because the packet loss rate of theoptimized schemes are lower, as they favor nodes with bet-ter channel quality. The optimized scheme with urgencyfactor reduces the buffer overflow as the traffic increases,as shown in Figs. 10 and 11, which describe the numberof packets dropped due to the buffer overflow. Since thenumber of packets dropped at the buffer is lowest for theoptimized scheme with urgency factor it has the highest re-liability among the three schemes.

1 1.5 2 2.5 3 3.5 4 4.5 5100

200

300

400

500

600

700

800

Average packet produced by member node per cycle

Th

rou

gh

pu

t (b

its/

s)

Optimization without Urgency factorOptimization with Urgency factorWithout Optimization and MDP

Figure 6: Throughput (SNRrange: 1dB-20dB).

1 1.5 2 2.5 3 3.5 4 4.5 5100

200

300

400

500

600

700

800

Average packet produced by member node per cycle

Th

rou

gh

pu

t (b

its/

s)

Optimization without Urgency factorOptimization with Urgency factorWithout Optimization and MDP

Figure 7: Throughput (SNRrange: 1dB-15dB).

1 1.5 2 2.5 3 3.5 4 4.5 50.7

0.75

0.8

0.85

0.9

0.95

1

Average packet generated by member node per cycle

Rel

iab

ility

Optimization without Urgency factorOptimization with Urgency factorWithout Optimization and MDP

Figure 8: Reliability (SNR range:1dB-20dB).

1 1.5 2 2.5 3 3.5 4 4.5 50.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Average packet generated by member node per cycle

Rel

iab

ility

Optimization without Urgency factorOptimization with Urgency factorWithout Optimization and MDP

Figure 9: Reliability (SNR range:1dB-15dB).

1 1.5 2 2.5 3 3.5 4 4.5 50

1000

2000

3000

4000

5000

6000

7000

Average packet generated by member node per cycle

Pac

kets

Dro

pp

ed

Optimization without Urgency factorOptimization with Urgency factorWithout Optimization and MDP

Figure 10: Packets dropped (SNRrange: 1dB-20dB).

1 1.5 2 2.5 3 3.5 4 4.5 50

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Average packet generated by member node per cycle

Pac

kets

Dro

pp

ed

Optimization without Urgency factorOptimization with Urgency factorWithout Optimization and MDP

Figure 11: Packets dropped (SNRrange: 1dB-15dB).

Figure 12: Experimental setup.

7. TESTBED EVALUATIONExperiments were conducted in a water tank of dimen-

sions 8 ft× 2.5 ft × 2 ft as shown in Fig. 12 to evaluate theproposed intra-cluster scheduling scheme. The testbed con-sists of four SM-75 SMART acoustic modems by TeledyneBenthos [5]. A bit rate of 800bits/s with multiple frequencyshift keying (MFSK) modulation scheme was used to con-duct the experiments. The modems were connected throughan RS-232 serial port and controlled by a PC with modemmanagement protocol (MMP), as shown in Fig. 13. MMP isa binary interface for machine-based command and controlof the modem. Using the MMP, the PC can control the lowerlayers of the modem’s protocol stack. The MMP serves as anapplication programming interface (API) that interfaces thePC to the modem. MMP layer abstracts all the main PHYand logical link control layer (LLC) functionalities of the mo-dem to the PC [17]. It also offers access to status monitoringsignals that carries information about SNR, round-trip time(RTT) among others. The logic in control of the proposedscheduling is implemented in C++ language. The node, sec-ond from the left corner of tank shown in Fig. 12, is assignedas the cluster head CH, while the other three nodes are themember nodes. The intra-cluster scheduling scheme with ur-gency factor was experimented and the obtained results are

Figure 13: Communication architecture.

Table 3: Experimental ParametersExperimental parameters ValuesNumber of member nodes per cluster 3Maximum buffer size per node 40 packetsNumber of intra-cluster cycles 20Range of mean packet per node per cycle 1 - 5

compared with the scheduling scheme, which neither usesthe MDP nor the proposed optimization scheme. The ex-periments were conducted to analyze; (i) the throughputand (ii) the reliability, under varying traffic conditions.

Initially, all member nodes were allocated one time sloteach. A total of 20 cycles of the intra-cluster communica-tion phase were conducted. In every cycle, the sequence ofdata and control packets exchange took place as describedin Fig. 2. The parameters used in the experiment are shownin Table 3. The modems were set to transmit at their lowesttransmit power level (1.78 W).

We compare the proposed scheduling scheme employingMDP with optimized time slot allocation; (i) with an ur-gency factor, and (ii) the scheme that uses neither MDPnor an optimization at the cluster head. In (ii), each mem-

1 1.5 2 2.5 3 3.5 4 4.5 5100

200

300

400

500

600

700

Average packet generated by member node per cycle

Th

rou

gh

pu

t (b

its/

sec)

Optimization with Urgency factor

Without Optimization and MDP

Figure 14: Throughput versus traffic load (testbedevaluation).

1 1.5 2 2.5 3 3.5 4 4.5 50.7

0.75

0.8

0.85

0.9

0.95

1

Average packet generated by member node per cycle

Rel

iab

ility

Optimization with Urgency factorWithout Optimization and MDP

Figure 15: Reliability versus traffic load (testbedevaluation).

ber node informs the cluster head about their current bufferoccupancy. The cluster head collects the buffer occupancyinformation from all the member nodes in it’s cluster andallocates time slots proportionally among them as describedin Section 6.

Figure 14 illustrates the throughput in [bits/s], which isdefined as the number of data packets successfully deliveredat the cluster head from the member nodes per unit time.It is evident, from Fig. 14, the scheme with optimizationand urgency factor improves the throughput by up to 10%,because it takes into account the urgency of the time slot re-quirement along with channel quality of the member nodes.At higher traffic level, as the buffer level will almost alwaysbe full, all member nodes may be requesting large numberof time slots. Thus, the scheme with optimization and ur-gency factor not only reduces the buffer overflow, but alsofavors the member nodes with better channel condition, dueto the ε factor as discussed in Section 5. Thus, we can seefrom Fig. 14 that the scheme with optimization and urgencyfactor has the highest throughput at higher traffic load.

We also compute the reliability following the discussions inSection 6. Figure 15 shows the reliability of the network forthe two different schemes. It is evident that the scheme withoptimization and urgency factor performs better in termsof reliability, compared to the scheme without optimizationand MDP, because the loss rate of the optimized scheme islower, as it favors the member nodes with better channelquality.

8. CONCLUSIONSIn this work, we have proposed a channel-dependent op-

timized intra-cluster scheduling, which takes into consider-ation the residual energy level, buffer occupancy, urgencyfactor of the member nodes and the SNR of each link. Fromthe simulation results we can see that the optimized schemewithout urgency factor provides better network lifetime withan increase in packet drop rate at high traffic. The perfor-mance of optimized scheme with urgency factor lies in be-tween the other two schemes in terms of network lifetime,however less packets are dropped from the buffer. Optimizedscheme with urgency factor performs well at higher traffic

loads. The schemes with optimization shows significant im-provement in throughput, reliability and network lifetime incontrast to the scheme without optimization and MDP. Sim-ulations demonstrate that the proposed scheduling schemewith urgency factor achieves a 28% improvement in through-put and improves reliability by up to 25% as compared to thescheduling scheme which does not use MDP and optimiza-tion. The testbed evaluations substantiate an improvementin throughput by up to 10% and also improves reliability.

9. REFERENCES[1] T. Melodia, H. Kulhandjian, L. Kuo, and E. Demirors.

Advances in Underwater Acoustic Networking. In MobileAd Hoc Networking: Cutting Edge Directions, Eds. S.Basagni, M. Conti, S. Giordano and I. Stojmenovic, JohnWiley and Sons, Inc., Hoboken, NJ, second edition, 2013.

[2] L. Kuo and T. Melodia. Distributed Medium AccessControl Strategies for MIMO Underwater AcousticNetworking. IEEE Trans. Wireless Communications,10(8):2523–2533, August 2011.

[3] H. Kulhandjian, T. Melodia, and D. Koutsonikolas.CDMA-based Analog Network Coding through InterferenceCancellation for Underwater Acoustic Sensor Networks. InProc. of ACM Intl. Conf. on UnderWater Networks andSystems (WUWNet), Los Angeles, CA, USA, Nov. 2012.

[4] D. Pompili, T. Melodia, and I. F. Akyildiz. A CDMA-basedMedium Access Control Protocol for Underwater AcousticSensor Networks. IEEE Transactions on WirelessCommunications, 8(4):1899–1909, April 2009.

[5] Teledyne-Benthos, Acoustic Modems. [Online]. Available:http://www.benthos.com.

[6] The Sensor Network Museum. [Online]. Available:http://www.snm.ethz.ch/snmwiki/Projects/MicaZ.

[7] I. F. Akyildiz, D. Pompili, and T. Melodia. UnderwaterAcoustic Sensor Networks: Research Challenges. Ad HocNetworks (Elsevier), 3(3):257–279, May 2005.

[8] M. Khan, B. Bhargava, S. Agarwal, and L. Lilien.Self-configuring node clusters, data aggregation, andsecurity in microsensor networks. Technical report, PurdueUniversity, West Lafayette, Indiana, USA, March 2003.

[9] B. Peleato and M. Stojanovic. A mac protocol for ad-hocunderwater acoustic sensor networks. In Proc. of ACM Intl.Workshop on UnderWater Networks (WUWNet), pages113–115, Los Angeles, CA, USA, 2006.

[10] P. Karn. MACA – a new channel access method for packetradio. In ARRL/CRRL Amateur Radio 9th ComputerNetworking Conference, pages 134–140, September 1990.

[11] K. B. Kredo and P. Mohapatra. A hybrid medium accesscontrol protocol for underwater wireless networks. In Proc.of ACM Intl. Workshop on UnderWater Networks(WUWNet), pages 33–40, Montreal, Quebec, Canada, 2007.

[12] M. K. Park and V. Rodoplu. UWAN-MAC: AnEnergy-Efficient MAC Protocol for Underwater AcousticWireless Sensor Networks. IEEE Journal of OceanicEngineering, 32(3):710–720, 2007.

[13] A. A. Syed, W. Ye, and J. Heidemann. T-Lohi: A new classof MAC protocols for underwater acoustic sensor networks.In Proceedings of the IEEE Intl. Conf. on ComputerCommunications (INFOCOM), pages 231–235, Pheonix,AZ, USA, April 2009.

[14] V. Phua, A. Datta, and R.C.Oliver. A TDMA-Based MACProtocol for Industrial Wireless Sensor NetworkApplications using Link State Dependent Scheduling. InProc. of IEEE Global Telecom. Conf. (GLOBECOM), SanFrancisco, CA, USA, November 2006.

[15] C. V. Phan, Y. Park, H. H. Choi, J. Cho, and J. G. Kim.An energy-efficient transmission strategy for wireless sensornetworks. IEEE Transactions on Consumer Electronics,56(2):597–605, May 2010.

[16] N. Mastronarde and M. van der Schaar. Fast reinforcementlearning for energy-efficient wireless communication. IEEETrans. Sig. Proc., 59(12):6262–6266, December 2011.

[17] H. Kulhandjian, L. Kuo, T. Melodia, D. A. Pados, andD. Green. Towards Experimental Evaluation ofSoftware-Defined Underwater Networked Systems. In Proc.of IEEE UComms, pages 1–9, Sestri Levante, Italy,September 2012.