Latency and Energy Efficient MAC (LEEMAC) Protocol for Event Critical Applications in WSNs

Transcription

1 Latency and Energy Efficient MAC (LEEMAC) Protocol for Event Critical Applications in WSNs Syed Waqar Hussain, Tashfeen Khan, Dr. S.M.H. Zaidi NUST Institute of Information & Technology ABSTRACT Wireless sensor networks are appealing to researchers due to their wide range of application potential in areas such as target detection and tracking, environmental monitoring, industrial process monitoring, and tactical systems. However, lower sensing ranges result in dense networks, which bring the necessity to achieve an efficient medium access protocol subject to power constraints. Various MAC protocols with different objectives were proposed for wireless sensor networks. The paper focused mainly on latency issue for event critical applications. In this paper, we propose LEEMAC, a Latency and Energy Efficient MAC Protocol which is an improvement of DMAC protocol in a sense that it gives us better latency and a slight improvement in energy efficiency than by DMAC which was designed and optimized for data gathering trees where data is collected from all the source nodes to the sink node. The paper first outlines the sensor network properties that are crucial for the design of MAC layer protocols. Then, we discussed briefly DMAC protocol with respect to the enhancements brought in LEEMAC. Then data modeling results are shown to compare the latency and energy for DMAC and LEEMAC protocols. Finally, we point out open research issues on MAC layer design. KEYWORDS Sensor networks, MAC protocol, energy-efficiency, event-critical applications 1. INTRODUCTION Wireless Sensor Networks come up with different issues altogether when compared with other wireless technologies. They have low-cost sensor nodes which are composed of a single chip with embedded memory, processor, and transceiver. Low power capacities lead to limited coverage and communication range for sensor nodes compared to other mobile devices. Hence, for example in target tracking and border surveillance applications, sensor networks must include a large number of nodes, to cover the target area successfully. Unlike other wireless networks, it is generally hard (or impractical) to charge/replace the exhausted battery, which gives way to the primary objective of maximizing node/network lifetime, leaving the other performance metrics as secondary objectives. Since the communication of sensor nodes will be more energy-consuming than their computation, it is a primary concern that the communication is minimized while achieving the desired network operation. The attributes which are generally the primary concerns in traditional wireless voice and data networks include fairness, throughput, latency, and bandwidth utilization. Another important attribute is the scalability to the change in network size, node density and topology. But in Sensor Networks these are considered as secondary because the major concern in WSNs is the energy efficiency. In many applications where it is critical to detect an event as quickly as possible are called event critical applications or real-time applications. These applications require more attention to the latency issue. This issue is resolved drastically by previously proposed DMAC[7] protocol. It suggested the nodes to be in data gathering tree with staggered wakeup schedule such that there would be no data forwarding interruption problem which resulted in a reduced latency than the traditional MAC layer protocols. Typically in WSNs, nodes coordinate locally to perform data processing and deliver messages to a common sink. However, the medium access decision within a dense network composed of nodes with low duty-cycles is a hard problem that must be solved in an energy-efficient manner. Having these in mind, next section emphasizes the peculiar features of sensor networks including reasons of potential energy wastes at medium access communication. Then, next section gives brief definitions for the key MAC protocols proposed for sensor networks listing their advantages and disadvantages. Finally, we discuss the DMAC protocol with its efficiencies and more importantly the deficiency which we tried to come up with the scheme provided in our LEEMAC protocol and compared both through data modeling in terms of energy utilization and the latency involved in transferring the message from source nodes to the sink node. In the end, /06/$ IEEE 370

2 our work concludes the review on MAC protocols with a comparison of investigated protocols and provides a future direction to researchers for open issues that have not been studied thoroughly. 1.2 MAC layer related sensor networks properties In all wireless networks, nodes must share a single medium for communication. Network performance largely depends upon how efficiently and fairly the nodes can share this medium. Note that the packet transmission is directly handled by the MAC layer. Compared to a wired medium, a significant portion of the node s energy is spent on radio transmissions and on listening to the medium for anticipated packet reception. On the other hand, wireless networks always have restricted power sources; thus, careful design of the MAC scheme is necessary for the optimal performance and extended lifetime of the network. The important design features for medium access control (MAC) protocols in a WSN are: Energy: The nodes in WSNs possess unique characteristics, especially the energy constraints, compact hardware, low transmission ranges, event or task-based network behavior, and high redundancy. It is often not feasible to replace or recharge batteries for sensor nodes. Energy efficiency is a critical issue in order to prolong network lifetime. In particular MAC protocols must minimize the radio energy costs in sensor nodes. Latency: Many sensor applications require delayguaranteed service. Latency requirements depend on the application. In surveillance applications, an event detected needs to be reported to a sink in real time so that appropriate action can be taken promptly. Throughput: Throughput requirement varies with different applications too. Some applications need to sample the environment with fine temporal resolution. In such applications, the more data the sink receives the better. In other applications, such as fire detection, it may suffice for a single report to arrive at the sink. Fairness: Having collective or atomic network functionality can reduce the value of MAC fairness while in many applications, particularly when bandwidth is scarce, it is important to ensure that the sink receives information from all sources in a fair manner. Thus, pernode fairness is an important issue in wireless ad-hoc Networks. Among these important requirements for MAC, energy efficiency is typically the primary goal in WSNs. Others such as latency, throughput and bandwidth utilization may be secondary in WSNs. Other important attributes are scalability and adaptability to changes. Since we will be, in this paper, considering real time applications for WSNs, we need to be focused more on latency issue so that the event detected by a sensor can be transmitted with lowest possible latency and an appropriate action can be taken quickly. Along with the latency issue, though, we have also taken care of energy constraint. 1.3 Energy Problems for the MAC layer MAC schemes for sensor networks can be fundamentally categorized into contention-based or scheduling-based schemes. The inherent advantages of contention-based schemes in the context of WSNs include: No synchronization requirements No central scheduler required More robust to network dynamics No clustering necessary More suitable to event-driven WSNs However, in terms of energy savings, contention-based schemes are not very attractive. Several sources of energy wastage in contention-based schemes during communication can be identified: Collisions: Usually data gathered by a node are exchanged with others using the radio. Two nodes may transfer data to each other at the same time or several nodes transfer data to the same node at the same time. When a transmitted packet is corrupted, it must be discarded and, thus, the follow-on retransmission increase energy consumption. Collision increases latency as well. However if it can be guaranteed for the particular sensor network application at hand that the load is always sufficiently low, collisions are no problem. Overhearing: When a node picks up packets destined to other nodes, overhearing occurs. In an ad-hoc fashion, a transmission from one node to another is potentially overheard by all the neighbors of the transmitting node; thus, all of these nodes consume power even though the packet transmission was not directed to them. For higher node densities overhearing avoidance can save significant amounts of energy. On the other hand, overhearing is sometimes desirable, for example, when collecting neighborhood information or estimating the current traffic load for management purposes. Control Packet Overhead: Sending and receiving control packets such as routing updates consumes energy and effectively reduces the network bandwidth for data packets. It is induced by MAC-related control frames like, for example, RTS and CTS packets or requests in demand 371

3 assignment protocols, and furthermore by per-packet overhead like packet headers and trailers. Idle listening: Nodes must listen to the channel often in order to receive possible traffic that is not sent. This is especially true in many sensor network applications because, if nothing is sensed, nodes are in idle mode for most of the time. Actual measurements have shown that idle listening consumes 50 to 100% of the energy required for receiving in such networks. Most of the MAC protocols developed for wireless sensor networks attack one or more of these problems to reduce energy consumptions. Scheduling-based schemes attempt to determine network connectivity first and assign collision-free links to each node. The task of assignment of channels (like TDMA slots) to links between neighbors so that packets do not collide is difficult, mainly because of the wide range of deployment, lower transmission ranges, and less control packet transmissions permitted due to energy constraints. 2. RELATED WORK Several works on MAC layer protocol have been proposed for WSNs. All these previous works (in particular [2], [5], [6], [7], [8], [13]) have identified idle listening as a major source of energy wastage. As traffic load in many sensor network applications is very light most of the time, it is often desirable to turn off the radio when a node does not participate in any data delivery. The scheme proposed in [5] puts idle nodes in power saving mode and switches nodes to full active mode when a communication event happens. However, even when there is traffic, idle listening still may consume most of the energy. Consider a sensor node with 1 report per second at 100 bytes per packet data transmission takes only 8ms for a 100Kbps radio, 992 ms are wasted in idle listening between reports. This section discusses few MAC protocols and their schemes with their merits and demerits in the context of WSNs initially and then with respect to the latency critical applications for WSNs. In order to save energy, S-MAC[5] provides a tunable periodic active/sleep cycle for sensor nodes. During sleep periods, nodes turn off radio to conserve energy. During active periods, nodes turn on radio to Tx/Rx messages. Although a low duty cycle MAC is energy efficient, it still has three shortcomings. First, it increases the packet delivery latency. An intermediate node may have to wait until the receiver wakes up before it can forward a packet. This is called sleep latency in SMAC. The sleep latency increases proportionally with respect to number of hops, with the constant of proportionality being the duration of a single cycle (active period plus sleep period). Secondly, a fixed duty cycle does not adapt to the traffic variation in sensor network. A fixed duty cycle for the highest traffic load results in significant energy wastage when traffic is low while a duty cycle for low traffic load results in low message delivery and long queuing delay. Thirdly, the sleeping patterns are coordinated in order to minimize the latency while increasing collision possibilities i.e. a fixed synchronous duty cycle may increase the possibility of collision. If neighboring nodes turn to active state at the same time, all may contend for the channel, making a collision very likely. There are several works on reducing sleep delay and adjusting duty cycle to the traffic load. Those mechanisms are either implicit (e.g. [2], [6]), in which nodes remain active when they overhear ongoing transmissions in the neighborhood; or they are explicit (e.g. [7]), in which there are direct duty cycle adjustment messages. In the adaptive listening scheme proposed in SMAC is used to minimize latency. When a sensing event occurs, it is desirable that the sensing data can be passed through the network without much delay or no sleep delay. The technique is that, a node who overhears its neighbor s transmission wakes up for a short period of time at the end of the transmission, so that if it is the next hop of its neighbor, it can receive the message without waiting for its scheduled active time minimizing latency. WiseMAC[15] offers a method to dynamically determine the length of the preamble to reduce the power consumption incurred by the predetermined fixed-length preamble. In addition, the hidden terminal problem comes along with WiseMAC model as in the Spatial TDMA and CSMA with Preamble Sampling algorithm. Its dynamic preamble length adjustment results in better performance under variable traffic conditions. In addition, clock drifts are handled in the protocol definition which mitigates the external time synchronization requirement. Traffic-Adaptive MAC Protocol (TRAMA) [12] is introduced for energy-efficient and collision-free access in WSNs. It is a TDMA-based algorithm and proposed to increase the utilization of classical TDMA in an energy efficient manner. It is similar to Node Activation Multiple Access (NAMA), where for each time slot a distributed election algorithm is used to select one transmitter within two-hop neighborhood. This kind of election eliminates the hidden terminal problem and hence, ensures all nodes in the one-hop neighborhood of the transmitter will receive data without any collision. It uses traffic-based information to decide on schedules for individual nodes and thus is adaptive to network traffic. It is claimed that, because of this adaptability, it can deliver adequate performance and energy efficiency in both network types. However, NAMA is not energy efficient, and incurs overhearing. Major concern is the duty cycle which is at least 12.5 %, which is a considerably high value. 372

4 Sift[16] is compared with MAC protocol and it is shown that Sift decreases latency considerably when there are many nodes trying to send a report. Since Sift is a method for contention slot assignment algorithm, it is proposed to co-exist with other MAC protocols like SMAC. The motivation behind Sift is that when an event is sensed, the first R of N potential reports is the most crucial part of messaging and has to be relayed with low latency. In TMAC[6], to solve the problem of idle listening in WSNs, each node dynamically adapts a listen/sleep duty cycle in a novel way, through fine-grained timeouts, and keeps listening and potentially transmitting as long as it is in an active period. An active period ends when no activation event has occurred for a certain time. The activation time events include reception of any data, the sensing of communication on the radio, etc. On the other hand it does not solve the problem of letting all the nodes know about ongoing transmission in a multihop path but only adds one more node to know than SMAC by using FRTS control packet. The authors in [10] proposed a slot-based power management mechanism. If the number of buffered packets for an intended receiver exceeds a threshold, the sender signals the receiver to remain on for the next slot. The receiver sends back an acknowledgement, indicating its willingness to remain awake in the next slot. The sender can then send packets in the following slot. Even this scheme does not cater the issue of sleep latency that would be involved in multihop path. In all these previously proposed mechanisms, nodes on the path to the sink that are more than one or two hops away from the receiver cannot be notified of the ongoing traffic, and therefore packet forwarding will stop after a few hops. Packets then will have to be queued until the next active period. This data forwarding interruption problem causes significant sleep latency for packet delivery. In SMAC, it occurs after only 2 hops while TMAC can forward packets to 3 hops in multi-hop path without having data forwarding interruption problem. Thus, the hearing/interference range is not a useful tunable parameter because it results in an undesirable energylatency tradeoff. DMAC employs a staggered active/sleep schedule to solve this problem and enable continuous data forwarding on the multihop path. The most significant traffic pattern in WSN is data gathering from sensor nodes to sink. For a sensor network application with multiple sources and one sink, the data delivery paths from sources to sink are in a tree structure, a data gathering tree. Flows in data gathering tree are in unidirectional from sensor nodes to sink. In DMAC, the activity schedule of nodes is staggered on the multihop path to wake up sequentially like a chain reaction. Depending on its depth d in the data gathering tree, a node skews its wake-up scheme dμ ahead from the schedule of the sink. Figure 1 shows a data gathering tree and the staggered wake-up scheme. The reason why we choose this structure and the scheme for our LEEMAC protocol is simply because it gives us the four basic advantages: 1. Reduced sleep delay 2. Increase duty cycle promptly 3. Contention is reduced 4. Nodes, not in the path, do not participate saving energy DMAC is proposed to deliver data along the data gathering tree, aiming at both energy efficiency and low latency. To make it adaptive to the traffic load, DMAC employed a slot-by-slot renewal mechanism. Each node piggybacks a more data flag in the MAC header to indicate the request for an additional active period with little overhead. In DMAC, data prediction is used to enable active slot request when multiple children of a node have packets to send in a same sending slot, while More-to-Send packet is used when nodes on the same level of the data gathering tree with different parents compete for channel access. DMAC achieves very good latency compared to other sleep/listen period assignment methods. 3. PROBLEM IN DMAC In DMAC, even if a node decides to hold an additional active period, it does not remain active for the next slot but schedules a 3μ sleep then goes to the receiving state. The reason is that it knows the following nodes on the multihop path will forward the packet in the next 3 slots. To accommodate the possibility of shorter range between 373

5 Figure 2. Single-Slot Renewal Mechanism two neighbor nodes, in DMAC a node will only send one packet every 5μ (1μ Tx, 1μ Rx, 3μ sleep delay) in order to avoid any possible contention among nodes on the same level in the tree and avoid collision as much as possible in renewing additional slots. So whenever a node has to send multiple packets, it has to renew slots each time having a little sleep delay of 3μs in between every slot. Therefore sleep delay not solved very much. Actually, rather, we should be having one full active slot for transmitting all the data in one go without having any sleep delay in between. By this mean the data can be received at the sink with the lowest latency. We focused on this sleep delay to reduce latency further than did by DMAC in our proposed LEEMAC. The slot-by-slot renewal mechanism employed in DMAC also costs us an overhead of more data flag bit in every MAC header. The receiver has to check that bit every time and respond with his own more data flag bit set in its ACK packet to the sender. So by incorporating this overhead in the protocol we are sacrificing some energy here at the cost of getting traffic load adaptation. We reduce energy in LEEMAC also by reducing node s switching back and forth in active and sleep periods to renew additional active slots. As measurements have shown that the cost for switching radio between active and sleep is not free. 4. LEEMAC PROTOCOL DESIGN The wide variety of requirements and objectives for different applications in sensor networks impose various design criteria and lead to different solutions. An approach cannot usually optimize its performance in all aspects. Instead, based on the relative importance of its requirements, an application usually trades less important criteria for optimizing the performance with respect to the most important attribute. For instance, for mission-critical applications, the end-to-end latency is certainly the most important attribute and needs to be kept below a certain threshold, even at the expense of additional energy consumption. In many WSN applications, either there is a burst of data traffic or the nodes are sitting idle waiting for an event to occur. Therefore we followed the data gathering tree structure proposed in DMAC and its staggered wake-up schedule scheme to cater latency for event-critical applications. As this is the most appropriate technique for reducing latency in sending data packets from sensor node to the sink. But we made some changes in its slot-by-slot renewal scheme that is used for requesting additional slots in case of accommodating the burst of data which is produced after in result of any activity sensed by a sensor node. Since the sensor node that starts transmission after detecting an activity and processing the data knows exactly how much to transmit information. So we can 374

6 have a length field only once in the MAC header of first packet of the burst in place of more data bit showing number of slots that should be renewed. Using this singleslot renewal mechanism not only gives us the quick and better traffic load adaptation but also improves the latency by reducing the overhead of having more data bit checked at every slot. 4.1 Single-slot renewal mechanism Figure 3. Passing Length for Scheduling Next Slot Node A looks at its filled buffer length sends it to renew those much contiguous slots at receiver initially only once. Then this request is forwarded through the staggered wake-up schedule to all nodes on the multihop path in the data gathering tree. Using single-slot renewal mechanism, there will be sleep delay of only 3μ initially to send length field to renew one single slot of size length of the source node s buffer. So after that there will be a continuous full single slot available without any sleep delay to accommodate all the data to transfer smoothly. It not only gives us improved latency but it is also slightly energy efficient as measurements have shown that the cost for switching radio between active and sleep is not free. 4.2 Data Prediction problem in DMAC There is an overhead entailed by the data prediction scheme. After the reception of the last packets from its children, a node will remain idle for a receiving slot which wastes energy in idle listening. But using length field initially in the MAC header, node will know for how long it should have a receiving slot. 5. LEEMAC DATA MODELLING RESULTS We initially did data modeling for our proposed LEEMAC to see where we lie among DMAC and the traditional SMAC protocols for WSNs. Since in sensor networks either there is no activity i.e. no information is needed to send or there comes the bursts of data which is processed at each node and begin to send the information to the sink node through the cooperation of other intermediate nodes. So whenever we are dealing with event critical applications, we need to process that bursts of data quickly and send it to the sink with the lowest latency. Keeping this in mind, the DMAC s data gathering tree scheme is useful for having reduced latency as compared to previously proposed MAC schemes in which duty cycle is not optimized much. While in DMAC s data gathering scheme, each node is scheduled according to its depth in the tree to provide smooth flow of data from the source node to the sink. Although TMAC has somehow solved this and made the duty cycle adaptive to traffic but since this is an implicit adaptive duty cycle technique therefore there exists the data forwarding interruption problem (DFI) because the overhearing range is limited by the radio sensitivity. In this the nodes that are out of overhearing range, are not aware of ongoing data transmission. So if this node is in sleep mode, it has to wait for its active period to start for receiving data. This data forwarding interruption problem also occurs in explicit adaptive duty cycle mechanism like in previously proposed adaptive SMAC[8]. Transferring information which is processed on bursts of data is believed to be bulky. So for event critical applications, we have to send information to the sink in the least possible time i.e. as quickly as possible so that the action has to be taken on it with in no time. Like, for instance, in any fire tracking application, it is needed that the fire information is sent rapidly to the sink node so that a quick action can be taken on it to prevent the fire to spread. But in SMAC only one hop next to receiver is aware of data transmission by overhearing of ACK or CTS packet from the receiver. While with TMAC only one more hop is aware by overhearing the ACK packet of FRTS from receiver to its next hop in the path to the sink. So all other nodes in the path are not aware of any data transmission and may be in their sleep mode causing data forwarding interruption problem. To solve this DMAC came with the staggered wake up schedule in which each node has its wake up schedule depending on its location in the data gathering tree and to make its duty cycle adaptive to the traffic load, it uses slot-by-slot renewal mechanism. But it also has 3μ sleep delay in renewing each additional slot. The reason for having this 3μ sleep delay is because a node has to wait for the following nodes on the multihop path to forward the data packet in the next 3 slots in order to avoid collision as much as possible. Suppose only one packet one hop can be sent in one slot and each sending and receiving slot length is defined as: μ = BP + CW + DATA + SP + ACK Where BP and SP are two inter-frame spaces with BP>SP in order to avoid the collision. CW is a fixed contention window since the length of a sending slot is only enough 375

7 Figure 4. Latency and Energy Comparisons for one packet transmission. So whenever there is a burst of data to transfer, additional active slots can be hold in DMAC with a 3μ delay between them. A node using slotby-slot renewal mechanism requests an additional active slot by piggybacking a more data flag bit in the MAC header with little overhead. Since a typical slot lengths are on the order of 10ms in length. It means 10ms for receiving slot, 10ms for transmitting, and 30ms for sleep delay. Thus only one packet can be transferred in every 50ms time. Hence DMAC adopts the traffic flow with a delay of 30ms for each packet which is the source of increasing the latency. We focused on to improve the latency caused by this 3μ sleep delay in our proposed scheme LEEMAC. In this the source node checks its buffer size in which data is queued after processed. Let us suppose if it is 50kbytes of data which a source node has to transfer to the sink then in case of DMAC it would take 25sec as, 1 pkt = 100bytes = 50ms(20+30), 10 pkts = 1 kbytes = 500ms, and for 50 kbytes = 500 pkts = 25000ms or 25sec To compare it with SMAC, let us suppose the length of duty cycle be 200ms i.e. 100ms for active and 100ms for sleep period. If a node found any activity, then active period is utilized fully otherwise it would be the source of energy wastage. In 100ms period, active slot of 50ms is for receiving and 50ms is for transmitting. Therefore, 5 packets can be transmitted in one fully utilized active period i.e. whole duty cycle can accommodate 5 packets in every 200ms. By looking at the graph in the figure above, it is shown that SMAC performs better than DMAC for latency while considering only that if there is no sleep delay involved i.e. each node immediately starts transmission to the next hop after finishes receiving from the previous hop so that it does not need to wait for the node to be in its active state. While we also know that SMAC can forward a packet to 2 hops by means of overhearing through which a node next to receiver overhears and schedules itself to be the next receiver. Therefore practically, the latency in SMAC sees a jump in every 3 hops. Moreover it is also not energy efficient in case of inactivity which is a major concern for WSN applications. While LEEMAC employs single-slot renewal mechanism in which initially length field, showing filled buffer length, in the MAC header is transmitted along the path on the tree. Then to transmit 50kbytes of data it will take only 5sec as, 1 pkt = 100bytes = 20ms(10+10), 10 pkts = 1 kbytes = 200ms, and for 50 kbytes = 500 pkts = 10000ms or 10sec Therefore to adjust the duty cycle or active period, the length (len=10sec) parameter is passed in the MAC header to all the nodes in the multi hop path. Each node in the path then schedules its next active period with 3μ sleep delay initially only once, but adjusts the length of that next active period according to the len parameter passed all along the multihop path. The sender then starts its next receiving slot of length 5sec and then transmitting slot of len 5sec to transmit all 50kbytes to the next hop. Hence LEEMAC performs better than both SMAC and DMAC because it not only has all the advantages of 376

8 staggered wakeup schedule for data gathering tree proposed in DMAC but also it has the single-slot renewal mechanism which helps in transferring data smoothly and adaptive to the traffic load. Thus the LEEMAC scheme is more adaptive to the traffic flow and can be very helpful for event-critical applications in WSNs. 6. OPEN ISSUES AND CONCLUSIONS Table I represents a comparison of MAC protocols investigated. MAC development for wireless sensor networks has really just begun. Since there are a wide variety of network applications (from multimedia distribution to the transmission of daily weather reports), many different network topologies, and many performance metrics from which to choose, the wireless sensor network MAC is of much interest. Many types of traffic occur in bursts in so-called event-driven applications. stack, in which a cross layer approach is needed instead of the traditional layer-by-layer protocol design. Although there are various MAC layer protocols proposed for sensor networks, there is no protocol accepted as a standard. One of the reasons behind this is the MAC protocol choice will, in general, be application-dependent, which means that there will not be one standard MAC for sensor networks. Another reason is the lack of standardization at lower layers (physical layer) and the (physical) sensor hardware. Integration of the layers is also a promising research area which has to be studied more extensively. This paper has proposed LEEMAC, an energy-efficient and low latency MAC protocol for tree-based data gathering in wireless sensor networks. Single-slot renewal mechanism gives us an adaptive energy-efficient and Improved-Latency MAC for wireless sensor networks. LEEMAC solves the problems found in DMAC. In our future work, we are in the phase of implementing this MAC in NS-2 and then aim to implement it on a Motebased sensor network platform and evaluate its performance through real experiments. ACKNOWLEDGEMENTS We also thank Mr. Gang Lu from university of Southern California for giving worthy references and his comments on our work and for providing his code for our enhancements. In addition to minimizing energy expenditures while maximizing QoS (however it is defined for the network in question), there are a few other areas that deserve investigation. One is the study of how different MACs perform when they are placed in the same channel, as often happens in unlicensed wireless bands. Is it possible to establish some global rules for MAC operation that can aid coexistence between services competing for the same channel? Is it possible to predict, without a specialpurpose, event-driven simulator, the performance of two (or more) coexisting services. Also it seems that the channel sensing needed for effective cognitive radio operation would be incompatible with the energy-expenditure requirements of WSNs, but perhaps this problem awaits only sufficiently clever approach for a solution. Because of the interdependence of energy consumption, delay, and throughput, all these issues and metrics are tightly coupled. Thus, the design of a WSN necessarily consists of the resolution of numerous trade-offs, which also reflects in the network protocol REFERENCES [1] Chandra, A., V. Gummalla, and J. Limb, Wireless Medium-Access Control Protocols, IEEE Communication Surveys, Vol. 3, No. 2, 2000, pp [2] Raghavendra, C., and S. Singh, PAMAS-Power Aware Multi-Access Protocol with Signaling for Adhoc Networks, Computer Communication Review, Vol. 28, No. 3, 1998, pp [3] A. Woo, and D. Culler, A Transmission Control Scheme for Media Access in Sensor Networks, Mobicom, [4] Woo Chool Park et al. Trade-off Energy and Delay between MAC Protocols for Sensor Networks, Advanced Communication Technology, Volume 1, 2004, pp [5] W. Ye, J. Heidemann, and D. Estrin, An Energy- Efficient MAC Protocol for Wireless Sensor 377

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