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Improved Medium Access Control for Wireless Sensor Networks-A Study on the S-MAC Protocol T. Chiras, M. Paterakis, P. Koutsakis 1 Dept. of Electronic and Computer Engineering Information & Computer Networks Laboratory Technical University of Crete, Chania, Greece e-mail: {theras, pateraki, polk}@telecom.tuc.gr

Abstract—An efficient Medium Access Control (MAC) protocol is critical for the performance of a Wireless Sensor Network (WSN), especially in terms of energy consumption. In this work we discuss the efficiency of S-MAC, a well-known MAC protocol for WSNs, and propose an improvement on the protocol. Via an extensive simulation study considering the cases of both simple and complicated topologies, the enhanced protocol is shown to outperform the original version of S-MAC in energy conservation. Index Terms— Energy Consumption, MAC protocol, Wireless Sensor Networks.

I.

INTRODUCTION

T

HE ever-growing advent of technology in the area of telecommunications electronics has made feasible the development of low cost multiprocessing sensors, which are able to process data and communicate in a wireless manner, within short distances, with each other. WSNs have met a huge growth and have significant future prospects of evolution, meeting applications from medical, environmental surveillance, robotics, military, smart vehicles and domestic areas. The main reasons for this growth are the high fault tolerance, fast deployment and self-organizing capabilities of WSNs, as well as their low cost (even lower than 1 $ per node) and high density of deployment, which does not affect the functionality of the application when sensor nodes fail or are destroyed. Other business applications of WSNs include climate control in buildings and interactive games.

1 This research was supported by the Operational Program for Education and Initial Vocational Training "PYTHAGORAS: Funding of research groups in Technical University of Crete", M 2.2 MIS 89192, co-funded by the EU through the Third Community Support Framework.

WSNs consist of tens to thousands of distributed autonomous nodes, which form a wireless multihop network and are placed near or inside the area of interest. Each node contains the sensor or sensors unit, a digital-to-analog converter, a processor, a low consumption transceiver and a power supplier. They might also contain a Global Positioning System (GPS) system, a power generator or a motion system [1]. The deployment phase in most cases is random, which means that protocols for WSNs must provide self-organizing capabilities. All nodes are trying to coordinate with their closest neighbors in order to achieve a common task. Innetwork processing is used to reduce the size of traffic. The combination of the above characteristics with high fault tolerance makes WSNs a powerful tool for the wide range of applications mentioned above. Regarding communication protocols, many solutions have been proposed from the scope of ad-hoc networks, but they cannot meet the needs of WSNs because sensor nodes: • Are prone to failures, • have constrained processing capabilities and energy, • their number is often significantly larger than the corresponding number of nodes in ad-hoc networks, • they have a high level of denseness and • their topology changes often. Moreover, sensor nodes use broadcast communication with each other while in ad-hoc networks point-to-point communication is preferred. Also sensor nodes may not have a global ID because of their large number and in order to reduce overhead. An efficient MAC protocol for WSNs should minimize collisions and give priority to the reduction of the nodes’ energy consumption, thus prolonging network lifetime. MAC protocols should also provide reduced latencies, high throughput and bandwidth utilization. The provision of energy-awareness resides next to the reduction of energy consumption by the nodes. To accomplish that, a MAC protocol must reduce collisions, overhearing,

2 control packet overhead and idle listening. The last factor is especially significant, as nodes often need to hear the channel for possible reception of data, like in the 802.11 family of protocols. The research group of the IEEE 802.11b showed that the consumed energy ratios are 1:2:2.5 for idle listening: reception: transmission, respectively [2]. As WSNs need to support applications for long periods of time, sensor nodes must “sleep” for as long as they can. The trade-off between minimized energy consumption and deterioration in delays, throughput and efficiency is clear and different from the IEEE 802.11 family of protocols, where bandwidth utilization is the primary target. A power-save mode, such as the one used in IEEE 802.11, is necessary but in WSNs a more aggressive policy is needed to ensure maximum energy conservation. In this work we discuss the efficiency of S-MAC [3], a wellknown MAC protocol especially designed for WSNs, and we propose an improvement on the protocol. Via an extensive simulation study, the enhanced protocol is shown, in both the cases of simple and complicated topologies, to outperform SMAC in terms of energy conservation. II. MAC PROTOCOLS FOR WSNS MAC protocols for WSNs provide primarily energy conservation, and secondly QoS and fair bandwidth allocation. Classic demand-based MAC schemes are inappropriate for WSNs because of their large overhead and the significant start-up time of the links. A common solution is the use of power-save modes and time-outs instead of acknowledgments, however these solutions increase delays and reduce channel throughput. The most well known MAC protocols for WSNs can be divided in two main categories: • Contention or Demand-based • TDMA/FDMA-based A major representative protocol of the first category is DCF (Distributed Coordinated Function) [4] of the IEEE 802.11. It is based on the MACAW [5] project and is well-suited especially for ad-hoc networks because of its simplicity and robustness. However, it does not succeed in the area of energy conservation. Another representative protocol is PAMAS [6] which adopts out-of channel signaling to avoid overhearing (increasing that way the cost of the transceiver). TDMA and FDMA-based MAC protocols have the inherent advantage of the low duty cycle of the transceiver and the absence of collisions between neighboring nodes. Still, TDMA forces nodes to form clusters, thus introducing complexity as interclustering communication is not an easy task.

III. S-MAC S-MAC [3] includes approaches to reduce energy consumption from all the sources that we have identified to

cause energy waste, i.e., idle listening, collision, overhearing and control overhead. The basic assumptions of S-MAC about wireless sensor networks and their applications are that sensor networks will consist of large numbers of nodes, to take advantage of shortrange, multi-hop communications to conserve energy [7]; most communications will occur between nodes as peers, rather than from a node to a single base-station; due to their large number, nodes will be scattered in an ad hoc fashion, rather than carefully positioned (nodes must therefore perform self-configuration); in-network processing is critical to sensor network lifetime [8], [9], since techniques such as data aggregation can reduce traffic, while collaborative signal processing can both reduce traffic and improve sensing quality (in-network processing implies that data will be processed as whole messages at a time in a store-and forward fashion, since packet or fragment-level interleaving from multiple sources only increases the overall transmission delays); applications will have long idle periods and can tolerate latency on the order of network messaging time. The Piconet [10] protocol is similar to S-MAC, but it does not support any synchronization between nodes. The power-save (PS) mode of IEEE 802.11 DCF has more similarities to SMAC than any other protocol, but still S-MAC uses much looser synchronization and is especially designed for multihop operation while DCF’s PS mode is designed mainly for single-hop operation. A. Periodic Listen and Sleep In many sensor network applications nodes are idle for a long time if no sensing event happens. Given the fact that the data rate during this period is very low, it is not necessary to keep nodes listening all the time. S-MAC reduces listening time by letting nodes go into periodic sleep mode. Each node goes to sleep for some time, and then wakes up and listens to see if any other node wants to talk to it. During sleep, the node turns

Fig. 1.

Frame = TListen + TSleep.

off its radio, and sets a timer to awake itself later. A complete cycle of listen and sleep is called a frame (Figure 1). The listen interval is normally fixed according to physicallayer and MAC-layer parameters, e.g., the radio bandwidth and the contention window size. The sleep interval can be varied according to different application requirements, which actually changes the duty cycle. All nodes are free to choose their own listen/sleep schedules. However, to reduce control overhead, neighbouring nodes synchronize together. Nodes exchange their schedules by periodically broadcasting a SYNC packet to their immediate neighbours. This ensures that all neighbouring nodes can talk to each other even if they

3 have different schedules. The period for each node to send a SYNC packet is called the synchronization period. The disadvantages of the scheme are that delays are increased due to the periodic sleeping of each node, and can accumulate on each hop. B. Collision Avoidance If multiple neighbours want to talk to a node at the same time, they will try to send when the node starts listening, in which case they need to contend for the medium. The S-MAC protocol follows similar procedures to the 802.11 family of protocols for collision avoidance, including both virtual and physical carrier sensing and request-to-sent/clear-to-send (RTS/CTS) exchange. The RTS/CTS mechanism is adopted to address the hidden terminal problem [11]. After the successful exchange of RTS and CTS, the two nodes will use their normal sleep time for data packet transmission. They do not follow their sleep schedules until they finish the transmission. With the low-duty-cycle operation (low ratio of listen/(listen+sleep)) and the use of the contention mechanism during each listening interval, S-MAC addresses effectively the energy waste due to idle listening and collisions. C. Adaptive Listening WSNs are usually event driven networks; they take measurements in a periodic fashion. When there are major changes in the measurements, the Adaptive Listening mechanism offers the chance to enter a more active mode. The adaptive listening mechanism used in S-MAC is a mechanism that virtually increases the nodes’ duty cycle, by putting them in a more active mode when traffic load is increased; this way throughput is increased and delays are reduced. The basic idea of the mechanism is to let the node who improperly hears its neighbouring transmissions (ACKs or RTS), to wake up for a small period of time just after the end of the transmission. If it is the next receiver, then his immediate neighbour can pass the data to him without having to wait to wake up according to his schedule, that way reducing delay. If the node does not receive anything for a certain time, he switches off his transceiver following again his normal schedule. It needs to be emphasized that the adaptive listening mechanism is used in order to accomplish the primary target of this work, which is to reduce energy consumption, not delays. The Inactive Sleep periods between Listen periods, as shown in Figures 1 and 2, cause significant delays.

Figure 2. Listen period format

IV. IMPROVING S-MAC: RESULTS AND DISCUSSION A. Topologies Two wireless sensor network topologies were studied in this work. The first was the linear topology which was studied in S-MAC (shown in Figure 3) and the second is a new, more complex topology consisting of 33 nodes (shown in Figure 4), This complex topology is used in our effort to investigate a topology scenario closer to real-world topologies [1, 12-14] than the linear one. The “special” node in this topology is actually just another simple node, but it has to carry out heavier tasks than the others because of its position in the topology. The two source nodes are the only data origins, while the sink node used in the linear topology still remains the final destination.

Figure 3. Linear Topology

Figure 4. Complicated Topology

B. System Parameters Our simulator is written in C++. Each simulation consisted of 50 runs (Monte Carlo simulation). We considered that 20 messages (100 bytes each) had to be transmitted from the source to the sink (i.e., 40 messages in the complex topology). The heaviest traffic load occurred when all messages were generated at the same time at source node (message

4 interarrival time = 0 sec) and the lightest traffic load considered was that of each packet being generated every 10 seconds (message interarrival time = 10 sec). Each run was completed when the last packet arrived at the sink. C. The proposed Protocol Modification In order to reduce energy consumption, we have introduced the following simple idea in S-MAC: S-MAC allows nodes to start transmitting after the completion of the CTS period. Therefore, a node which has received a CTS early in the CTS period has to suffer an undesired and unnecessary delay in its transmission. Hence, we let the nodes initialize their

original version of S-MAC. As the traffic load decreases, our proposed modification still provides better results than SMAC in all the studied cases of traffic loads, but the decrease in energy consumption diminishes, as data need more time to pass through the network, that way increasing the duration of the simulation and the total energy consumption by the nodes. We have reached the same conclusion when our modification of S-MAC was implemented on the complex topology: the best results, in terms of energy conservation, are produced for high traffic loads, when nodes almost always have data to send when they wake up from their sleep. Again, in this case, our mechanism is used more often, providing results up to 4.5% better than S-MAC in energy conservation. The results for both topologies were derived for a 10% duty cycle with adaptive listening, and are shown in Figure 5. The reason for acquiring better results for the linear topology than for the complex one is the existence of the “bottleneck” at the “special node” which causes a large bulk of data to be awaiting transmission from the special node to the sink while all previous nodes have no other data to send and follow their normal schedule. D. More results for the Complex Topology It is a well-known fact that in ad-hoc topologies not all nodes have the same energy consumption. This is clear in Figure 6, where the “special node” is shown to have twice as much energy consumption as the source nodes in high traffic loads, and even larger energy consumption when the traffic load decreases; the reason for the latter result is that for lower traffic loads the total simulation time increases and much more time is needed for data to “travel” through the network.

transmission just after the successful reception of a clear-tosend (CTS) packet, whereas the original version of S-MAC would allow the node to initiate its transmission only after the completion of the CTS period, as shown in Figure 2. Via an extensive simulation study, we have found that, using the above policy in the linear topology, results in a maximum decrease in total energy consumption of 5% in high traffic loads compared to the respective energy consumption in the

Figure 7 presents the mean message delays under the very low traffic load of 1 message generated at the sink nodes every 10 seconds. For the schemes with and without adaptive listening and for a 10% duty cycle we observe that the delay on each hop increases in a linear fashion up to the 10th hop and continues to increase linearly after the 10th hop, but with a larger slope. The reason for this is that the right segment of the complex topology has to carry the data from both the left

Fig. 2. Linear Topology

Fig. 6. Difference in nodes’ energy consumption, with the proposed modification and the use of Adaptive Listening.

Fig. 7. Average message delays with and without adaptive listening, with the proposed modification and the use of Adaptive Listening.

5 parallel segments of the topology which lead to the “special node”. Our results show that the delay increase is much more significant in the absence of the adaptive listening mechanism used in S-MAC. Figure 7 also shows that, for up to a significant number of hops, the adaptive listening mechanism produces delay results only slightly worse than the case where nodes never sleep, which of course is an ideal scenario in terms of incurred delays but a worst-case scenario in terms of energy consumption. Figure 8 presents the system’s data throughput versus the data messages’ interarrival time. It is evident from the results in Figure 8 that when the data generated from the source nodes of the two linear topologies pass through the last part of the complex topology, throughput decreases dramatically due to the respective delay increase. Also, as shown in the Figure, when the message interarrival time is as low as 10 seconds all schemes’ results approach the results of the “10% duty cycle without adaptive listening” case. This result indicates that, in this case, there is no point in

[4] [5] [6] [7] [8]

[9]

[10] [11]

[12] [13] [14]

Fig. 8. Data throughput under different traffic loads, with the proposed modification and the use of Adaptive Listening.

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