MMAC: A Mobility-Adaptive, Collision-Free MAC Protocol forWireless Sensor Networks

Muneeb Ali, Tashfeen Suleman, and Zartash Afzal UzmiComputer Science Department, LUMS

{muneeb,tashfeens,zartash}@lums.edu.pk

AbstractMobility in wireless sensor networks poses unique

challenges to the medium access control (MAC) pro-tocol design. Previous MAC protocols for sensor net-works assume static sensor nodes and focus on energy-efficiency. In this paper, we present a mobility-adaptive, collision-free medium access control protocol(MMAC) for mobile sensor networks. MMAC catersfor both weak mobility (e.g., topology changes, nodejoins, and node failures) and strong mobility (e.g.,concurrent node joins and failures, and physical mo-bility of nodes). MMAC is a scheduling-based protocoland thus it guarantees collision avoidance. MMAC al-lows nodes the transmission rights at particular time-slots based on the traffic information and mobility pat-tern of the nodes. Simulation results indicate thatthe performance of MMAC is equivalent to that ofTRAMA [1] in static sensor network environments.In sensor networks with mobile nodes or high networkdynamics, MMAC outperforms existing MAC proto-cols, like TRAMA and S-MAC [2], in terms of energy-efficiency, delay, and packet delivery.

1 IntroductionWireless sensor networks have emerged as one of

the first real applications of ubiquitous computing.Sensor networks play a key role in bridging the gapbetween the physical and the computational world byproviding reliable, scalable, fault tolerant, and accu-rate monitoring of physical phenomena. Sensor net-work environments, inherently different from the In-ternet, pose some unique challenges to systems re-searchers. Energy efficiency has been considered as thesingle most important design challenge in sensor net-works [3]. Hence, the recent work on medium accesscontrol (MAC) protocol for sensor networks focused onenergy efficiency instead of, traditional wireless MACdesign goals such as fairness, delay, and bandwidthutilization [4].

In previous work on MAC protocols for wirelesssensor networks, it is generally assumed that the

sensor nodes are static. Researchers have, how-ever, envisioned sensor networks with mobile sensornodes [5]. In this paper, we show that the currentMAC protocols for wireless sensor networks are notsuited for mobile sensor network environments, andpresent a mobility-adaptive, collision-free medium ac-cess control (MMAC) protocol for sensor networks.MMAC follows the design principles of TRAMA [1] -a scheduling-based MAC protocol for static multi-hopwireless sensor networks.

In mobile environments the fixed frame time of cur-rent MAC protocols causes performance degradationin a number of ways: a) the mobile nodes, upon joininga new neighborhood, need to wait for a long time be-fore they can send data, b) in contention-based MACprotocols, there is a considerable increase in packetcollisions, c) in schedule-based MAC protocols, thetwo-hop neighborhood information at each node re-mains inconsistent for a long period which could ef-fect the correctness of the protocol. A dynamic frametime, that is inversely proportional to level of mobility,is required to cope with these problems.

MMAC introduces a mobility-adaptive frame timethat enables the protocol to dynamically adapt tochanges in mobility patterns, making it suitable forsensor environments with both high and low mobility.MMAC assumes that the sensor nodes are aware oftheir location. This location information is used topredict the mobility pattern of the nodes according tothe AR-1 [11, 12] model. We present a novel mobility-adaptive distributed algorithm that dynamically ad-justs the MAC frame time according to mobility. Ex-perimental results indicate that the performance ofMMAC is equivalent to that of TRAMA [1] in staticsensor network environments. In sensor networks withmobile nodes or high network dynamics, MMAC out-performs existing MAC protocols, like TRAMA and S-MAC, in terms of energy-efficiency, delay, and packetdelivery.

We discuss related work in section 2. Section 3presents the MMAC protocol and section 4 presents

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S-MAC

T-MAC

sleep

sleep

active

active

sleep

sleep

active

active

constant active time

traffic-adaptive dynamic active time

Figure 1: Constant active time (S-MAC) vs Traffic-adaptive dynamic active time (T-MAC)

simulation and testing results. We summarize conclu-sions in section 5.

2 Related WorkTraditional MAC protocols for wireless net-

works [20, 19], were designed to maximize bandwidthutilization, promote fair usage of channel by all nodes,and to reduce latency. In sensor networks, the typ-ically low data rate relaxes the need for maximumbandwidth utilization. These sensors generally col-laborate with each other to perform a common task,reducing the importance of fair channel usage by eachnode. Further, the sensor network applications aretypically not sub-second delay sensitive. Hence, therecent work on MAC protocol design in sensor net-works [1, 6, 7] focused on energy efficiency and co-ordination instead of fairness, delay, and bandwidthutilization.

The most widely used MAC protocol for sensor net-works is S-MAC [2]. S-MAC introduced a low-duty-cycle operation in multi-hop wireless sensor networks,where the nodes spend most of their time in sleepmode to reduce energy consumption (Figure 1). Pa-pers on T-MAC [6] and TRAMA [1] showed that S-MAC, with fixed sleep and awake periods, does notperform well with variable traffic loads. T-MAC andTRAMA introduced traffic-adaptive dynamic sleepand awake periods for sensor nodes. Traffic-adaptivemechanisms were also later introduced in S-MAC [7].The frame time in S-MAC, TRAMA and T-MAC isfixed whereas we introduce mobility-adaptive dynamicframe times in MMAC (Figure 2).

3 MMAC ProtocolWe only discuss the issues relevant to mobility and

the reader is encouraged to see [1] for a detailed discus-sion on basic protocol functionality, traffic-adaptivity,schedule maintenance, neighbor discovery, and proto-col correctness.

Scheduled Access Random Access

Fixed Frame Time (TRAMA)

Scheduled Access Random Access

Dynamic Frame Time (MMAC)

Scheduled Access Random Access

Figure 2: Fixed frame time (TRAMA) vs Mobility-adaptive dynamic frame time (MMAC)

3.1 Mobility in Sensor NetworksSensor networks have high network dynamics;

nodes may fail due to hardware failure or batteryconsumption, other new nodes may join the network.The network topology is effected by such node joinsor failures. We define these regular network topologychanges as weak mobility. Sensor networks with staticnodes can also exhibit weak mobility. More than onenodes may concurrently fail or join the network. Suchconcurrent node joins and failures are, generally, moredifficult to handle, by the MAC protocol, than indi-vidual ones. Further, the sensor nodes may physicallymove from their location, either because of motion inthe medium (e.g. water, air) or by means of specialmotion hardware in the mobile sensor nodes. We de-fine concurrent node joins/failures and physical mo-bility of nodes as strong mobility.3.2 Design Tradeoffs

In deciding between schedule-based or contention-based MAC protocol design, we preferred theschedule-based design as different nodes, in schedule-based MAC protocols, are scheduled to communicatein different non-interfering sub-channel slots,theseprotocols are largely collision free. Further, as thereceiving nodes need to listen in their own slot alone,a node can turn the radio off for all other slots butthe one scheduled to it. This naturally support a low-duty-cycle operation and avoids over-hearing of pack-ets by neighbor nodes.3.3 Problem Definition

Consider a multi-hop wireless sensor network withhomogenous sensor nodes. Let,

Ni(α) → {i-hop neighbors of a node α}PPi(α, β) → probability that α ∈ Ni(β)

The network topology could change due to: a)node joins, b) node failures, c) concurrent nodejoins/failures, d) physical mobility of individual nodes.Let,

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α ↓ Ni(β) → in-mobility transaction, whereα /∈ Ni(β) before transaction, andα ∈ Ni(β) after transaction

α ↑ Ni(β) → out-mobility transaction, whereα ∈ Ni(β) before transaction, andα /∈ Ni(β) after transaction

In static network model (SNM), the only factor ef-fecting PPi(α, β), when initially α ∈ Ni(β), is nodefailure. In addition to node failure PPi(α, β), wheninitially α ∈ Ni(β), is also effected by α ↑ Ni(β) inmobile network model (MNM).

In SNM, node join can occur if: a) new static nodesare deployed, b) nodes wake up after a long time, c)nodes recover from failure and were considered deadbefore. In MNM, node join can occur for the addedreason of α ↓ Ni(β). Let,

Fi → a complete frame i, under considerationwhere, τ = frame time

↓i (α) → {nodes expected to join N2(α) in Fi}↑i (α) → {nodes expected to part N2(α) in Fi}

In MNM, we assume the nodes to be static duringFi. The mobility behavior of N2(α) in Fi is predictedduring Fi−1. If a node β is expected to leave N2(α)during Fi then β /∈ N2(α) from the START of Fi.Similarly, if a node β is expected to join N2(α) duringFi then β /∈ N2(α) from the START of Fi. In otherwords, {↓i (α)

⋃ ↑i (α)} /∈ N2(α) from the START ofFi.3.4 Mobility Estimation

MMAC uses location information to predict themobility behavior of sensor nodes. Localization is awell studied problem in wireless sensor networks [8,9, 10]. Most sensor network applications require thatnodes are aware of their physical location, this loca-tion information is also used by MMAC. Let,

Γ(α, Fi) → current mean (x,y) of α in Fi

where, x = x co-ordinateand, y = y co-ordinate

Γ(α, Fi−1) → stored mean (x,y) of α in Fi−1

Γ(α, Fi+1) → expected mean (x,y) of α in Fi+1

We use the AR-1 model [11, 12] for mobility esti-mation. The mobile node’s state, at time t, is definedby a column vector.

st = [xt, xt, �xt, yt, yt, �yt]′, (1)

where st is the mobility state, (xt, yt) specify po-sition, xt and yt specify velocity, notation ′ specifies

the matrix transpose operator, and �xt and �yt specifythe acceleration in the x and y directions. The AR-1model [11] gives,

st+1 = Ast + ωt, (2)

where A is a 6×6 transformation matrix, the vectorωt is a 6 × 1 discrete-time zero mean, white Gaussianprocess with autocorrelation function Rω(k) = δkQ,where δ0 = 1 and δk = 0 when k �= 0. The matrix Qis the covariance matrix of ωt. The values for matrixA and the covariance matrix Q is estimated based ontraining data using the Yule-Walker equations [14].See [11, 12, 13] for details.

The mobility state information st, at any given timet could be used to predict the mobility state at anytime t+ i. The optimal predicted state st+i of the mo-bile node in the minimum mean-square error (MMSE)sense is given by,

st+i = Aist, (3)

3.5 Mobility-Adaptive AlgorithmBasic idea: If a large number of nodes are expected

to enter or leave the two-hop neighborhood of a nodeβ; reduce the frame time and vice versa.

1. ∀α ∈ N, where N = set of allnodes, calculate optimal predicted statesst+0, st+1, . . . , st+j , . . . , st+max, where max =frame time, and t = starting time of Fi+1

2. ∀α ∈ N2(β), calculate

Γ(α, Fi+1) =average(st+0, st+1, . . . , st+j , . . . , st+max)

3. Using Γ(β, Fi+1) and ∀α,Γ(α, Fi+1), populate thesets ↓i+1 (β) and ↑i+1 (β)

4. If α ∈ {↓i+1 (β)⋃ ↑i+1 (β)} remove α from N2(β)

5. If | ↓i+1 (β)⋃ ↑i+1 (β)| ≥ λmax,

τnew = τ − ( η100 × τ)

where τ = frame time, λmax is a threshold value,and η is a variable.

6. If | ↓i+1 (β)⋃ ↑i+1 (β)| ≤ λmin,

τnew = τ + ( η100 × τ)

where τ = frame time, λmin is a threshold value,and η is a variable.

7. Adjust the number of scheduled access and ran-dom access slots according to τnew.

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3.6 Protocol IssuesWe identify the following issues with the generic

mobility adaptive algorithm described above:

1. Mobility Information: Individual nodes can pre-dict their future mobility state as described inSection III-D, but in the mobility adaptive algo-rithm each node requires future mobility state in-formation of all the current and potential two-hopneighbor nodes.

2. Synchronization: Using the mobility adaptive al-gorithm, individual nodes could independentlycalculate frame times different from each other;leading to synchronization problems in theschedule-based MMAC protocol.

To address these issues we introduce cluster headsin MMAC. Time is divided into rounds with exactlyone node as cluster head for a given round, r. Theresponsibility of being a cluster head is rotated amongsensor nodes to conserve energy. We use a variation ofthe cluster head selection and rotation mechanism ofLEACH [15] to select cluster heads in MMAC. Eachnode α determines a random number between 0 and1. If the number is less than a threshold λhead, thenode becomes a cluster-head for the current round.The threshold is set as [16],

λhead = P

1 − P (r mod1P

)× Ecurrent

Emax∀α ∈ G

λhead = 0 ∀α /∈ G

where P is the cluster-head probability, r is thenumber of current rounds, G is the set of nodes thathave not been cluster-heads in the last 1

P rounds,Ecurrent is the current energy of the node and Emax

is the initial energy of the node. We define round ras r = k × τ where, τ = frame time, and k is an in-teger variable > 1. The number of cluster heads isset as 5% of the total sensor nodes, which is a reason-able number [15]. Each node α becomes member of acluster with exactly one node as cluster-head as in theLEACH protocol [15]. The protocol issues addressedin the Sections III-G and III-H.3.7 Mobility Information

We modify the signal header and the data header ofMAC packets to include the predicted mobility stateinformation. At the start of frame Fi each node αindependently calculates the expected mean (x, y) ofα in frame Fi+1 as,

Γ(α, Fi+1) =average(st+0, st+1, . . . , st+j , . . . , st+max)

and then sends Γ(α, Fi+1) in the header of everysignal and data packet generated by α. The headnode always keeps the radio to listen mode and collectsΓ(α, Fi+1) for each node that transmitted a data orsignal packet during Fi. The last frame slot is reservedfor a BROADCAST from the head. This BROAD-CAST from the head sends all stored Γ(α, Fi+1) tothe member nodes. This ensures that each node αhas ’best-effort’ knowledge of the predicted mobilitystates of it’s current and potential two-hop neighbors.We define this knowledge as best-effort because clearlythe head would not have information about a node βthat would actually move into the the two-hop neigh-borhood of α but has yet to transmit anything. Thehead node would get mobility information of such anode β as soon as it transmits a packet.3.8 Synchronization

To address the synchronization problem we changethe last step of the generic mobility adaptive algo-rithm. Each node α independently calculates τnew

but instead of adjusting the number of scheduled ac-cess and random access slots, α includes τnew in thedata and signal header along with Γ(α, Fi+1). Thehead node of cluster c collects τnew from the head-ers of transmitting nodes α ∈ cluster c. The headcalculates τmean = average(all received τnew) in eachframe. We introduce a global synchronization period(GSP), consisting of p empty slots, that occurs at theend of every round r, where r = k× τ . At the start ofGSP, the latest values of τmean are collected from allcluster heads and their mean value τGSP is dissemi-nated in the entire network. All participating nodes ofthe network adjust the scheduled access and randomaccess slots according to τGSP , new cluster heads areelected and the next round begins.

The frame time could ONLY change during a GSP.τGSP is the new frame for the next round with respec-tive scheduled access and random access slots. A GSPoccurs after k frames (i.e. after one round) and therecould be changes in the mobility rate during this time.MMAC dynamically adapts to these changes by al-tering the division between scheduled access and ran-dom access slots after each frame. Each cluster headsends the calculated τmean in each frame to all mem-ber nodes during the BROADCAST message duringthe last reserved frame slot. If the value of τmean isless than that of the previous one stored at the nodes,they increase the number of random access slots anddecrease the scheduled access slots keeping the totalframe time constant and vice versa. Therefore,

• After a GSP, all frame times, schedule accesstimes, and random access times would be the

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Scheduled Access Random Access

Scheduled Access Random Access

Edge Node Problem

Figure 3: A node α receiving random access slot num-bers from more than one head node

same and they would reflect the mobility of allnodes in the network e.g. if recently most of thenodes exhibited greater mobility the frame timewould be reduced.

• After each frame before the next GSP, the frametimes in the network would remain the same butthe random access period of each cluster-memberswould increase or decrease reflecting the mobilitypatterns of cluster nodes.

• Frame times would be the same ∀α ∈ network.

• If all two-hop members of a node α ∈ a clusterc, then their random access time and scheduledaccess time would be the same.

We define an edge node e as a node who has two-hopneighbors belonging to more than one virtual cluster.In the two-hop neighborhood of e the frame size of two-hop nodes α would be the same but the random accesstime could be different (Figure 3). Such a node eshould use the shortest data transmission time and theshortest random access time out of the different accesstimes in-use i.e. according to figure 3 e should NOTtransmit anything between the overlapping region.

4 Simulation ResultsWe did a comparative study of MMAC with

TRAMA [1], SMAC [2], and CSMA. TRAMA embod-ies schedule-based MAC protocols for wireless sensornetworks, whereas SMAC represents contention-basedMAC protocols. As CSMA has no energy saving fea-tures at all it is included in the comparison protocolset as a worst case protocol. The study was carriedout by doing extensive simulations in NS2.

The underlying physical model, in all our exper-iments, is based on TR1000 [17]. For SMAC, theSYNC-INTERVAL is 10sec and the duty cycle is var-ied as either 10% or 50%. For TRAMA and MMAC,SCHEDULE-INTERVAL is 100 transmission slots. Ran-dom access period is 72 transmission slots and is re-peated every 10000 transmission slots. MMAC dy-namically changes the number of random access pe-riod slots and the respective repeat rate. Nodes have

4 6 8 10 12 14 1610

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Packet Generation Interval (seconds)

Avg

. Del

ay (

seco

nds)

TRAMASMACCSMAMMAC

Figure 4: Average packet delay (variable traffic)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 110

−3

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Speed (meters / second)A

vg. D

elay

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cond

s)

TRAMASMACCSMAMMAC

Figure 5: Average packet delay (increasing mobility)

transmission range of 100 meters and they are ran-domly deployed on a 500m × 500m plane. Traffic isgenerated, at a variable rate, on the sensor nodes. Allsinks are corner-sinks. In order to route a packet tothe sink, at each hop the node simply forwards thepacket to the node closer to the sink. The simulationis allowed to run for 500 seconds and the results areaveraged over several hundred simulation runs.

Figure 4 gives average packet delay for the network.The average mobility of the nodes is set at 0.5 me-ters per second. Nodes generate traffic at variablerates. Average delay values of contention-based pro-tocols CSMA and SMAC, are much less then that ofschedule-based protocols. This is because of the la-tency introduced by random scheduling in TRAMAand MMAC.

Figure 5 shows the change in average packet delayas we increase the average mobility of the participatingnodes in the network. As, MMAC adapts it’s frametime, number of data-transfer frames, and number ofrandom-access frames, the average delay remains, al-most, constant with increase in mobility rate. How-ever, CSMA, SMAC, and TRAMA exhibit degradingaverage delay with increase in mobility rate.

Figure 6 shows the average percentage of variable-traffic packets successfully delivered to sink nodes. As,

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Packet Generation Interval (seconds)

Per

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age

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d

TRAMASMACCSMAMMAC

Figure 6: Percentage of packets received (variable traf-fic)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1−0.1

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Figure 7: Percentage of packets received (increasingmobility)

MMAC and TRAMA are collision-free MAC proto-cols they outperform SMAC and CSMA in this ex-periment. When we increase the mobility rate (Fig-ure 7), the number of successfully delivered packets forCSMA, SMAC, and TRAMA decrease significantly,whereas MMAC exhibits a minimal decrease.

Energy-efficiency is the single most important per-formance metric for wireless sensor networks [3]. Weaverage the energy consumption values for SMAC forall the active and sleep intervals and compare them

0 1 2 3 4 5 6 7 8 9 10 110

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Figure 8: Average energy consumed per node (variabletraffic)

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)

TRAMASMACCSMAMMAC

Figure 9: Average energy consumed per node (increas-ing mobility)

with those of CSMA, TRAMA and MMAC. Results(figure 8) show that, as expected, CSMA is the leastenergy-efficient protocol. TRAMA nodes consume lessenergy than SMAC because TRAMA adapts better tovariable traffic. MMAC performs slightly better thanTRAMA in the first part of the energy consumptionexperiment.

Figure 9 shows that apart from CSMA, all protocolsare energy efficient when the mobility of nodes is mini-mal or almost zero. As the nodes become more mobilethere are more packet collisions and respective packetretransmissions in CSMA and SMAC. Data packets inTRAMA, sent to a node β moving out of the two-hopneighborhood of node α, are lost and cause retransmis-sions. MMAC however, adapts to the mobility patternof the nodes; resulting in, on average, less energy con-sumption by nodes when compared to TRAMA.

5 ConclusionIn future ubiquitous environments the individual

tiny wireless sensors may be mobile in nature. Weshowed that the current MAC protocols for sensor net-works are not suited for mobile environments and pre-sented a new scheduled-based MAC protocol (MMAC)that adapts the frame time, transmission slots, andrandom-access slots according to mobility. Our simu-lation results indicate that MMAC performs parallelto current MAC protocols when there is little or nomobility in the environment. However, in sensor net-works with mobile nodes or high network dynamics,MMAC outperforms existing MAC protocols in termsof energy-efficiency, delay, and packet delivery.

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