An Energy Efficiency Scheme for Wireless Sensor Networks *

Transcription

1 An Energy Efficiency Scheme for Wireless Sensor Networks * Dimitrios J. Vergados 1,2, Dimitrios D. Vergados 2 and Nikolaos Pantazis 2 1 School of Electrical and Computer Engineering National Technical University of Athens Iroon Polytexneiou 9, Zografou, Athens, Greece emal: djvergad@telecom.ntua.gr 2 Department of Information and Communication Systems Engineering University of the Aegean GR Karlovassi, Samos, Greece {vergados, npantazis}@aegean.gr Abstract. Wireless Sensor Networks (WSNs) facilitate, monitoring and controlling of physical environments from remote locations with better accuracy. Sensor networks are dense wireless networks consisting of groups of small, inexpensive nodes, which collect and disseminate critical data. Thus, sensor nodes have various energy and computational constraints, because of their inexpensive nature and ad hoc method of deployment. Considerable research has been focused at overcoming these deficiencies through low-energy consumption schemes. In this paper, we propose a Scheduling Scheme for Energy Efficiency in Wireless Sensor Networks. The basic concept of this scheme is to try to maximize the time each sensor node remains in SLEEP mode, and to minimize the time spent in IDLE mode, taking into account not only the consumed power, but also the end-to-end transmission delay. Key-Words Wireless Sensor Networks, Power Control, Low-energy Consumption 1 Introduction Wireless Sensor Networks (WSN) have been increased dramatically the recent years as they are used more and more in the daily life. Medical, Environmental and Military Sectors are some of the most important areas that the recent developments have been applied in. In order to guarantee the Wireless Sensor Networks survivability and increase network lifetime in such special purpose environments, various energy efficiency schemes have been proposed in the literature. In some cases, sensor networks are expected to be able to operate for a long period of time in standby, and transmit the gathered data when required, as soon as possible. Under theses assumptions, most of the time the network is not in operation, but the network nodes * This work has been supported by GSRT, Hellenic Ministry of Development, through the PENED 2003 Project: Design and Development Models for QoS Provisioning in Wireless Broadband Networks.

2 waste energy in the RECEIVE mode. Energy is a valuable commodity in wireless networks due to the limited battery of the portable devices. The energy problem becomes harder in adhoc wireless sensor networks due to their limitations arising from their nature. In this paper, we propose a sleep-wakeup scheme for WSNs, which ensures that every node can transmit information to the sensor gateway the soonest possible, and that the network nodes can be in the sleep mode as long as possible. This is accomplished through the synchronization of the wake up times of all the nodes in the sensor network. More specifically, the gateway gathers the available connectivity information between all the nodes in the network, and uses existing energy-efficient routing algorithms to calculate the paths from each node to the gateway. Then, the gateway constructs a TDMA frame which ensures the collision avoidance. This schedule is broadcasted back to the sensor nodes, allowing every sensor to know when it can transmit and when it should expect to receive a packet. Most of the time all the sensor nodes are in the sleep mode, and they wake up according to the TDMA frame. In case a sensor node actually has data to transmit to the gateway, it uses its next transmission opportunity to send a WakeUp (WU) message. This message is forwarded to the corresponding nodes, which wake up, and the information is sent (at a time outside the TDMA frame). After the transmission of the information, all nodes return to the sleep mode, and periodically wake up, according to the TDMA frame. The interval between the periodic TDMA wake-ups is bounded by the maximum access delay. On the other hand, the delivery time does not depend on this interval, allowing a lower duty cycle than other synchronization schemes. Moreover the TDMA nature ensures maximum signaling energy conservation. This paper is organized as follows: Section 2 discusses the power conservation schemes in WSNs. Section 3 presents the proposed TDMA Scheduling Algorithm for Energy Efficiency in WSNs, focusing on the basic concept, that is, the node wakeup vs path wakeup, the path WU message aggregation and TDMA wakeup scheduling. Following, section 4 discuses the analytic models and the performance evaluation results of the proposed algorithm. Finally, section 5 concludes the research work. 2 RELATED WORK Sensor networks are wireless networks consisting of an adequate number of small in size, inexpensive, low-power sensor nodes, which are densely deployed either inside the phenomenon or very close to it. Sensor nodes consist of sensing, processing and communicating components because their function is to collect and disseminate critical data while their position need not be predetermined [Akyildiz, I. et all, (2002]. Therefore, sensor network protocols and algorithms must possess self-organizing capabilities. Wireless ad-hoc sensor networks bear important applications in conferencing, monitoring disaster areas providing relief, home, environmental monitoring, health, file exchange, military purposes and gathering sensing information in inhospitable locations. It is very important that the network architecture and power control schemes, applied on, should guarantee the network connectivity securely, with minimum delay and collisions and at the same time minimizing the energy consumption. Numerous different approaches on different network layers may be found in the literature for energy conservation. On the one hand TCP was not originally designed to be power-aware. ATCP and TCP-Probing alter TCP s retransmission behavior by minimizing unnecessary retransmissions to achieve lower

3 power consumption and higher throughput [Tsaoussidis, V. and Badr H., (2000)], [Liu, J. and Singh, S., (2001)]. On the other hand, two basic schemes are employed at the network layer: Power-aware routing and maximum lifetime routing. In power-aware schemes, routing protocols are designed in such a way as to find a route that consumes less power. This approach, however, may shorten the lifetime of some nodes if they happen to be on the minimum power path of several flows. In contrast, maximum lifetime routing balances energy dissipation among nodes to prolong the operational lifetime of the network. Besides that, the function of several ad hoc routing protocols is based on the mechanism of creating a virtual infrastructure over a flat network to reduce the number of nodes involved in routing which results in better power utilization. A comparison study on energy consumption of different ad hoc routing protocols may be found in [Cano, J.-C. and Manzoni P., (2000]. Since retransmission of frames leads to unnecessary wastage of energy, techniques such as frame transmissions (based on channel state sensing and reducing the number of collisions), can lead to efficient battery usage [Issariyakul, T. et all, (2003)]. Turning-off the transceiver during idle period and during period when the transmission is forbidden or not likely to be successful (e.g. the NAV period in IEEE ) may lead to better energy-efficient MAC design. Some basic power conservation mechanisms in ad hoc wireless sensor networks are described below. A. Power-Aware Multi-Access with Signaling (PAMAS) PAMAS (Power-Aware Multi-Access with Signaling) is a multi-access protocol for ad hoc radio networks based on the original MACA protocol with the addition of a separate signaling channel [Singh, S., and Raghavendra, CS., (1998)]. It saves nodes battery power, by turning off the nodes which are not in active transmission or sending packets. In PAMAS protocol the receiving mobile nodes transmit a busy tone (in a separate control channel) when they start receiving frames so that other mobile nodes know when to turn off. When a mobile node does not have data to transmit, it should power itself off if a neighbor begins transmitting to some other node. A node should turn off even if it has data to transmit if at least one of its neighborpairs is communicating. A mobile node, which has been turned off when one or more of its neighbor-pairs started communicating, can determine the length of time that it should be turned off by using a probe protocol. In this protocol, the node performs a binary search to determine the time when the current transmission will end. B. DPM (Dynamic Power Management) A scheme, contributing to dynamic increase of the lifetime of the sensor network, is proposed by Sinha et al. [Sinha, A., and Chandrakasan, A., (2001)]. Once the system is designed, additional power savings can be obtained by using dynamic power management (DPM). The basic idea behind DPM is to turn off the devices when not needed and get them back when needed. The switching of a node, from one state to another, takes some finite time and resource. So we have to be very careful when using DPM to accomplish maximum life of a sensor node. We may achieve good savings in power with this turning off of the node. However, in many cases it may not be known beforehand when a particular device is needed. Stochastic analysis can be used to predict the future events. The DPM sensor model deals with switching of node state in a power efficient manner. All the components in a node can be in different states. In general, if we have a number of N components in a sensor node, each

4 node s sleep state corresponds to a particular combination of component power modes. Each sleep state is characterized by latency and power consumption. The deeper the sleep state, the lesser the power consumption, and more the latency. For example, if a processor is in idle state then memory should be in sleep state. This removes some combinations from the node states. C. S-MAC S-MAC (Sensor-MAC) is a distributed protocol, which gives the possibility to nodes to discover their neighbors and build sensor networks for communication without being obliged to have master nodes. There are no clusters or cluster heads here. The topology is flat. This solution, proposed by [Ye, W., et al. (2002)], focuses mainly on the major energy wastage sources while achieving good scalability and collision avoidance capability. The major energy wastage sources may be classified into overhearing, idle listening, collisions and control packet overhead [Hoesel, L. V., et al. (2004)]. S-MAC introduces two techniques to achieve the reduction of energy consumption. Firstly, neighboring nodes are synchronized to go to sleep periodically so that they do not waste energy when a neighboring node is transmitting to another node or by listening to an empty channel. The overhearing problem is avoided this way. Secondly, the control packet overhead of the network is kept low because synchronized neighboring nodes form virtual clusters to synchronize their wake-up and sleep periods. Actually, there is no real clustering and no inter-cluster communication problem. The main components of S-MAC are Periodic Listen and Sleep, Collision and Overhearing Avoidance and Message Passing D. Periodic Hibernation According to this protocol, the wireless transceiver (transmitter/receiver) is powered off during the periods where the sensor node can neither transmit nor receive [Issariyakul, T. et all, (2003)]. In IEEE , a sensor node hearing NAV may switch to sleep mode, powering off the transceiver during this period. Here we must take into consideration that the NAV timer always decreases (counts down) regardless of the channel status. Note that, the back-off timer decreases only when medium is idle. In IEEE , every mobile node must wake up during an announcement traffic indication message (ATIM) period during which transmitters inform their destination not to go to power save mode. If no notification is received, the mobile node can go to power save mode and wake up in the next ATIM period [Tobagi, F.A., and Klienrock, L., (1975)]. Again a transmitting mobile node can defer its transmission (or at least reduce the transmission rate) when channel quality is bad and may try to compensate the loss when the channel becomes better. E. PBS-based Approach It is possible for the mobile sensor nodes to periodically select a PBS as the coordinator for channel access [Issariyakul, T. et all, (2003)]. during periodic hibernation which can be assisted through pseudo-centralized control. The function of the PBS is to allocate the uplink and the downlink time slots to each mobile sensor node resulting in the reduction of the number of collisions and hence the reduction of energy loss. The sensor nodes which do not

5 receive the allocation can power off their transceiver and wake up only to listen to the next frame. The loss in battery-power and channel bandwidth, for the discovery of a new PBS, must be taken into account for the performance evaluation of a PBS-based approach. A PBS must be re-elected when its power drops below a certain threshold [Jin, K.T., and Cho, D.H., (2001)]. F. Awake/Doze Mode in MAC Protocol Design The Network Interface Card (NIC) is always in the awake-mode [Woesner, H. et all., (1998)]. When there are no transmissions (doze mode) the NIC may be switched off in order to save energy. As it can be seen here, the doze mode has the potential to improve the Power-Saving (PS) gain substantially. Of course, this implies loss of the capability to communicate in both directions; when a station is in this kind of PS mode would never know of any data destined to it during this time. However, there is certainly a possibility to power off the NIC if two problems are correctly addressed in the MAC protocol design. First problem: How can a station be sure to receive packets from other stations, even if it is in the sleep mode most of the time? Second problem: How does a station send data to another station which is in the sleep mode? Except the PS influence on the MAC protocol, there are some other factors that influence the rest of the protocol layers. G. Prolonging the Lifetime of Wireless Sensor Networks by Cross-Layer Interaction A cross-layered approach for networking in Wireless Sensor Networks can be found in [Hoesel, L. V., et al., (2004)]. According to this approach, a self-organizing MAC (Medium Access Control) protocol makes use of an algorithm of a sensor node intending to create a connected network based on local information only, and an integrated, efficient routing protocol. Operation here is entirely distributed and localized. Network lifetime is used as the metric to make the evaluation of the performance of the cross-layer optimized protocols. It measures the amount of time before a certain number of sensor nodes run out of battery power. It is proved that this scheme prolongs the lifetime of the network significantly in the mobile sensor scenario. The lifetime of this scenario is at least three times better than those of DSR and S-MAC. We must notice here that the lifetime in S-MAC and DSR is almost independent of message frequency. The explication of this fact is that the nodes use their receiver anyhow during the time interval they are awake. S-MAC and DSR protocols perform better in the static case than in the mobile one, in contrast to this protocol. The reason is that in the static scenario, routes need be established only once, while in the mobile scenario they have to be updated regularly. H. Energy-Aware Routing for Cluster-based Sensor Networks [Younis, M. et al., (2002)] propose a hierarchical routing algorithm based on a three-tier architecture. The architecture consists of clustered sensors which have been put to this position prior to network operation. The gateways (cluster heads), are less energy-constrained than the rest of the sensor nodes; they maintain the states of the sensors and their main duty is to set-up multi-hop routes for collecting sensors data. The gateway informs each sensor node, the location of which is known to the gateways, about slots status or availability (listen transmit slots). The command node (sink) communicates only with the gateways [Akkaya, K. and Younis, M., (2005)]. Energy conservation is achieved by setting the clustered sensor nodes into sensing only, relaying only, sensing-relaying, and inactive mode, and powering only the appropriate modules.

6 I. Geographic Adaptive Fidelity (GAF) GAF [Xu, Y., et al.] was initially designed as a power-aware location-based routing algorithm for mobile ad hoc networks, but is also applicable in sensor networks. This algorithm conserves energy by powering off sensor nodes, not needed in the network, without affecting much the level of routing fidelity. Each sensor node uses its location, which is indicated by GPS (Global Positioning System), in order to connect itself with a point in the virtual grid. Sensor nodes connected to the same point on the grid are considered equivalent in terms of the cost of packet routing. Energy is saved by keeping some sensor nodes, located in a particular grid area, in sleeping state. Hence, GAF can effectively increase the sensor network lifetime as the number of nodes augments. The sensor nodes change of states, from sleeping to active, is done in turn and in such a way that the load is balanced. 3. TDMA SCHEDULING FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS Basic Concept In this paper, we [Vergados, D. D., et al.] propose a Scheduling Scheme for Energy Efficiency in Wireless Sensor Networks. This scheme takes advantage of the power conservation mechanism of S-MAC [Ye, W., et al. (2002)], and extends it in order to minimize the end-toend delay. The main disadvantage of the S-MAC scheme is that each wireless sensor must wait until the next Wake UP time of the next hop before forwarding a message. This makes the end-to-end delay proportional to the number of intermediate forwarders times the sleep time of each node. On the contrary, in the proposed scheme all nodes in the network are synchronized to sleep at the same time, and wake at the WakeUP period. Instead of transmitting the entire message during the WakeUP period, the nodes transmit a short WakeUP packet, which is forwarded until it reaches the gateway. Nodes that receive the WakeUP packet remain in idle mode, anticipating the following packet reception, whereas nodes that do not receive a WakeUP packet go to sleep mode. Also, multiple WakeUP packets can be aggregated when two paths merge, in order to minimize the WakeUP duration and to avoid unnecessary transmissions. A further improvement to the scheme is not to let the transmitter of each node on for the entire WakeUP period, but only for the specific timeslots the node anticipates a reception. The question that arises is which slot should each node use to transmit its WU messages (originated or forwarded) and at which slots should each node listen to. Path WakeUP requires that the first nodes in the path should be assigned in timeslots earlier than the nodes that follow. On the other hand, collisions can be avoided if nodes that receive simultaneously are not one-hop neighbors (TDMA does not suffer from the exposed terminal situation). Also, possible transmissions to the same destination should be assigned in different time slots. However, the scheduling algorithm should maximize the concurrent receptions made by nodes that are not one-hop neighbors, in order to minimize the total frame length. Therefore timeslots scheduling should take into account the routing paths and the neighboring information. These limitations make distributed TDMA scheduling schemes inefficient, since they do not take into account the desired order of transmissions. The following algorithm can create a TDMA schedule appropriate for WU transmissions in sensor networks.

7 The TDMA scheduling algorithm assigns a transmission slot for every node in the sensor network, and a number of reception slots for every forwarding node, one for each corresponding transmitting node. In order to calculate the TDMA schedule, the algorithm needs the following information for every node in the network: The number of hops from the node to the gateway The one-hop neighbors of the node The next hop of every node Based on the above information, the number of time slots that each node has to receive prior to transmitting is calculated. The following piece of pseudo-code illustrates the operation of the algorithm. The nodelist is considered to be sorted in descending hops to gateway. Also node dest is the next hop of the node, and node revc represents the remaining receptions that the node has to make before transmitting. do { slot = new Slot(); collision_set = null; foreach node in nodelist { if (node recv==0 && (node not in collision_set)) { slot[node] = SEND ; slot[node dest] = RECV ; collision_set.add( node dest neighbours) collision_set.add(node->dest) node dest recv--; nodelist.remove(node); } } Frame.add(slot); } while (slot.length > 0). 4 PERFORMANCE EVALUATION The performance of power-aware MAC protocols depends on many different parameters, such as the traffic arrival rates, the channel congestion, the topology of the sensor network, and the routing algorithms. Here we will quantify the power savings achieved by the proposed protocols, in various network and traffic conditions and compare it to other power saving approaches. In general, the power consumption of a node in a sensor network can be approximated by the following equation P=P rob {SEND}P send +P rob {RECV}P recv +P rob {IDLE} P idle + P rob {SLEEP} P sleep (1) where P rob {SEND}, P rob {RECV}, P rob {IDLE}, P rob {SLEEP} are the probabilities of the transmitter of the node being in SEND, RECV, IDLE and SLEEP state, and P send, P recv, P idle, P sleep are the amounts of power consumed when the node is in each state. In our analysis we consider that P sleep is very small.

8 If a specific node in the network produces originating packets of average size L (in transmission time units) at a rate λ O, and forwards packets through at a rate λ T, then it must be in the SEND and RECV states with the following probabilities: P rob {SEND}= (λ O + λ T )L, P rob {RECV}= λ T L (2) If the node is never in the sleep mode when idle, then P rob {IDLE}= 1-λ O L-2λ T L (3) The above probabilities are calculated without taking into account possible re-transmissions and without considering control packets, like ACK s, RTS s, CTS s etc. Moreover they are accurate only when there is no congestion i.e. λ O L+2λ T L «1. In the rest of the document we investigate power efficiency in sensor networks that produce very little traffic under normal conditions, so the above assumptions are close to reality. The power consumed by the node, if it never enters SLEEP mode, is given by the following expression P ALWAIS_ON =(λ O + λ T )L P send + λ T L P recv +(1-λ O L-2λ T L) P idle (4) The power consumption of the S-MAC protocol is given by the following equation: P S-MAC =(λ O + λ T )L P send + λ T L P recv +(T i / T f )(1-λ O L-2λ T L) P idle (5) where T f is the period, and T i is the wake up time. The time a node spends in periodical wakeups is equal to n(t slot /T f ), where n is the number of timeslots the node can receive in, T f is the period as above, and T slot is the length of each TDMA time slot. The transmission and reception of the WU messages that happens prior to transmitting a data packet, is assumed to require much less energy than the data transmission and reception, and is ignored. But after the reception of a WU message, the node has to remain awake until the reception of the data packet. This waiting time, denoted L Wait, can be approximated by the product of the number of nodes from the source of the packet until the examined node, times the data packet transmission time. In case the packet arrival rate is low, compared to the sleep-wakeup interval, the WU packets arrival is equal to the data packet arrival rate λ O. All the above give the following expression for the power consumption of the TDMA algorithm: P TDMA =(λ O + λ T )L P send +λ T LP recv +n(t slot /T f )P idle +L Wt L Wait P idle (6) Power-efficient MAC protocols in wireless sensor networks have a negative effect on the system delay. The reason for this is that transmissions have to wait until the receiving node switches on its radio unit. The end-to-end delay, in a sensor network, is the sum of the transmission delay, the access delay, the queuing delay, and the propagation delay. When the packet arrival rate is relatively low, the queuing delay can be neglected, and the small distance between the wireless sensors makes the propagation delay small. In a multi-hop transmission

9 with (N-1) intermediate forwarders, the average delay can be expressed by the following formula: E{D(N)} = N(t cs +L) (7) where t cs is the access time and t tx is the transmission delay. The delay is proportional to the number of hops. The delay in the S-MAC protocol is calculated in [16]. The end-to-end delay is found to be equal to E{D(N)}= N T f T f /2 + t cs +L (8) for the S-MAC, and equal to E{D(N)}= N T f /2 + 2t cs +2L T f /2 (9) when the adaptive listening technique is used. In both cases, the end-to-end delay is proportional to the number of hops times the period T f. This happens because the transmission at each hop is delayed until the next receiving node wakes up. Adaptive listening reduces the end-to-end delay, but only by a factor of 2. The end-to-end delay in the algorithm presented in this paper, is equal to the end-to-end delay of the Always-on case plus the delay introduced by power-saving. When a new packet is generated, the node must wait until the next TDMA frame. Then it transmits the Path-WU to wake-up all the nodes. Since the packet is generated randomly, the average time until the next frame is T f /2. Thus, the delay is given by the following expression: E{D(N)} = N(t cs +L)+ T f /2 (10) In order to illustrate the advantages of the proposed scheme, we plotted the end-to-end delay that is produced by the three schemes as a function of the power conservation, and as a function of the number of forwarders. The numeric parameters we used are the following: Delay (sec) End-to-End delay as a function of the achieved Power Conservation SMAC Adaptive TDMA ,1 0,01 0,001 0,0001 P/P_idle Fig. 1. End-to-End delay as a function of the achieved Power Conservation

10 End-to-End delay as a function of the number of intermediate nodes SMAC Adaptive TDMA Delay (sec) nodes Fig. 2. End-to-End delay as a function of the number of intermediate nodes Ti = Tslot = sec. This value is essentially determined by the accuracy of synchronization between the sensor nodes. Since nodes are considered inexpensive, and re-synchronization should be as rare as possible, we selected a relatively large value. N = 10, for the first plot, variable in the second plot. L = 0.02 sec. The message length is considered to be relatively short, since the sensor messages would typically generate a simple measurement. (e.g. temperature) n = 3. The number of timeslots that every sensor would be listening to, in each interval, depends on the topology. The chosen value is small, due to WU message aggregation. P/Pidle = 0,001, for the second plot, variable in the first plot. The traffic load of a long-lived sensor network must be small. We considered the case where the traffic is so limited, that the consumed power, due to message transmission, can be ignored. Power consumption is mainly caused by idle listening. Fig.1 and Fig.2 illustrate clearly that the end-to-end delay increases with the achieved power conservation in all cases, and increases rapidly, in the S-MAC based schemes, as the number of intermediate nodes increase. The proposed scheme provides much lower end-to-end delay times for messages sent from the sensor nodes to the gateway, in scenarios where numerous sensor nodes are used for sensing rare events for a long period of time 5 Conclusions In wireless sensor networks, which are expected to operate unattended for a long period of time, power conservation is a major issue, since it determines the network lifetime. Several power conservation schemes have been proposed for prolonging the lifetime of the sensor network, which usually take advantage of the sleep mode capabilities of sensor nodes. However, the putting of nodes to periodical sleep introduces a sleep related access delay that increases with the achieved power conservation. Moreover, the unavoidable idle listening

11 limits the power consumption under low traffic load. This paper presented the Path-WakeUp strategy, which is used for minimizing the end-to-end delay caused by sleep mode, and the wakeup message aggregation that can be used for minimizing the idle listening time. Centralized TDMA scheduling can be used for calculating the appropriate listening and sending times related to wakeup messages. The proposed scheme can be used for achieving energy conservation, while minimizing the end-to-end delay for traffic sent from the sensor to the gateway. Analytic models show the advantage in power consumption and/or end-to-end delay that the proposed energy conservation scheme achieves, compared to other related strategies. The proposed scheme can be useful for sensor networks that monitor rare events, and require low delay times. References Akkaya, K., and Younis, M., A Survey on Routing Protocols for Wireless Sensor Networks, ELSEVIER, Ad- Hoc Networks, 2005, Vol. 3, pp Akyildiz, Ian F., Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci, A Survey on Sensor Networks, IEEE Communications Magazine, Aug. 2002, Vol. 40, No. 8, pp Cano, J.-C. and Manzoni, P., A Performance Comparison of Energy Consumption for Mobile Ad Hoc Network Routing Protocols, in Proc. of 8 th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, San Francisco, CA, 2000, pp Hoesel, L. V., Nieberg, T., Wu, J., and Havinga, P.J.M., Prolonging the Lifetime of Wireless Sensor Networks by Cross-Layer Interaction, IEEE Wireless Communications, Vol. 11, No. 6, Dec. 2004, pp Issariyakul, T., Hossain, E., and Kim D., Medium Access Control Protocols for Wireless Mobile Ad Hoc Networks: Issues and Approaches, Wireless Communications and Mobile Computing, 2003, Vol. 3, pp Jin, K.T., and Cho, D.H., Optimal Threshold Energy Level of Energy Efficient MAC for Energy-Efficient MAC for Energy-Limited Ad-Hoc Networks, in Proceedings of IEEE GLOBECOM 01, San Antonio, TX. Nov Liu, J. and Singh, S., ATCP: TCP for Mobile Ad Hoc Networks, IEEE Journal on Selected Areas in Communications, Wireless Communications Series, Vol. 19, No. 7, July 2001, pp Singh, S., and Raghavendra, CS., PAMAS: Power-Aware Multi-Access Protocol with Signaling for Ad-Hoc Networks, Computer Communication Review, 1998, 28 (3), pp Sinha, A., and Chandrakasan, A., Dynamic Power Management in Wireless Sensor Networks, IEEE Design and Test of Computers, 2001, Vol. 18, Issue 2, pp Tobagi, F.A., and Klienrock, L., Packet Switching in Radio Channels: Part II The hidden terminal problem in carrier sense multiple access and the busy tone solution, IEEE Transactions on Communications, 1975, Vol. 23, pp Tsaoussidis, V. and H. Badr, TCP-Probing: Towards an Error Control Schema with Energy and Throughput Performance Gains, in International Conference on Network protocols, Osaka, Japan, Nov

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