describe the Ant-Based Energy Conservation (ABEC) mechanism to prolong network lifetime. By imitating the pheromone trail laying and following behavior of ...
Ant-Based Energy Conservation for Ad hoc Networks Chavalit Srisathapornphat∗ and Chien-Chung Shen† Department of Computer and Information Sciences University of Delaware Email: {∗ srisatha,† cshen}@cis.udel.edu Abstract— Nodes in ad hoc networks typically rely on battery energy. The wireless interface is one of the major drain on energy reserves of the nodes. Turning off network interfaces of idle nodes while maintaining effective network forwarding capacity is an effective energy conservation technique. In this paper, we describe the Ant-Based Energy Conservation (ABEC) mechanism to prolong network lifetime. By imitating the pheromone trail laying and following behavior of biological ants, ABEC allows nodes to alter the forwarding behavior of ant packets to control pheromone trail concentration when energy consumption needs to be reduced. These nodes safely turn off network interfaces while other nodes maintain pheromone trails to preserve network coverage and forwarding performance. We present the details of ABEC and evaluate its performance when it collaborates with AODV and an ant-based routing protocol. The results show that in comparison to AODV, ABEC successfully extends network lifetime by a factor of 2 to 3, while only slightly reducing network forwarding performance.
Pheromone trails are created via special ant packets linking nodes in the network. By altering the concentration of pheromone on these trails, ABEC safely allows nodes located on trails of lower pheromone concentration to to turn off their network interfaces for a certain time period. By safely, we mean ABEC maintains network coverage and network forwarding performance at an acceptable level by keeping nodes located on high concentration pheromone trails active. The remainder of the paper is organized as follows. Section II reviews ant-based routing algorithms and energy conservation techniques for ad hoc networks. The details in ABEC are described in Section III. In Section IV, we present the simulation results of ABEC in terms of forwarding performance and energy conservation behavior, and discuss important issues and future work. The last section concludes the paper.
I. I NTRODUCTION Nodes in ad hoc networks typically rely on battery energy. The wireless interface is one of the major drain on energy reserves of the nodes. In nodes with low or moderate traffic load, network interfaces are idle most of the time. Therefore, turning off idle network interfaces is one technique to conserve energy. However, by turning off idle network interfaces, we reduce the number of nodes available to the routing protocol, resulting in reduced network forwarding capacity and possibly network partitions. In essence, effective energy conservation techniques should maintain a balance between the amount of energy a network conserves and the spare forwarding capability to sustain acceptable network performance. Hence, to maintain forwarding performance while turning off network interfaces of idle nodes, the energy conservation mechanism has to closely collaborate with the routing protocol used. In this paper, we propose the Ant-Based Energy Conservation (ABEC) protocol that adaptively turns off idle nodes to prolong network lifetime. The protocol is inspired by the foraging behavior of biological ants in discovering paths to their food sources. Ants indirectly communicate through the environment (a scheme termed stigmergy) by laying pheromones along the paths they travel. The pheromone laying behavior creates pheromone trails with various concentrations linking the food sources and the nest together. ABEC conserves energy by imitating the pheromone trail laying/following behavior of biological ants as follows. This work is supported in part by National Science Foundation under grant ANI-0240398.
II. R ELATED W ORK A. Ant-Based Routing Ant-based routing schemes were first introduced as proactive routing protocols for static, wired networks as in AntNet [1], [2]. The main concept of AntNet is to deploy small ant packets to collaboratively discover forwarding paths between pairs of nodes. Periodically, forwarding ants are generated and randomly destined to other nodes in the network. At each visited node, forwarding ants probabilistically select their next hop based on the decisions made by previous ants that visited the node and/or any heuristics available. Ants carry the history of all visited nodes with themselves. Once the destinations are reached, these ants will be discarded and the backward ants are generated and transmitted back to the sources following the history carried by forward ants. Backward ants update the probability of the routing table at each visited node based on the goodness of the paths discovered by forward ants. Subsequently, data packets are routed deterministically to the destination via next hop nodes with the highest probability. The mentioned ant-based routing algorithms cannot be directly applied to ad hoc networks where the topology is dynamic. Using periodic unicast ants to discover routes in such networks would incur a large delay and the adaptability to topology changes would be unacceptably slow. Most recently proposed ant-based ad hoc routing protocols have to rely on other mechanisms to deal with the above mentioned issues. For instance, ARA [3] utilizes the concept of on-demand routing
by broadcasting forward ants only when necessary and allows data packets to reinforce existing routes in order to reduce overhead of sending ants. Ant-AODV [4] incorporated AODV (a purely reactive ad hoc routing protocol) with the proactive feature of ant-based routing algorithms. ANSI [5] deployed two types of ants. Local proactive ants are broadcasted with limited scope to construct routes within the local vicinity of a node, and global reactive ants are only flooded to the entire network when a required route is not available. ABEC includes the mechanism of proactive ants which set up pheromone trails to decide when a node should turn off its network interface device.
ant−ID
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active nodes without invoking any explicit global connectivity maintaining process. • Minimal forwarding interruption – Nodes running ABEC will only turn off their network interfaces when there is no forwarding activity for a certain period unless they are running out of energy. This is to reduce interrupting current traffic streams. The operations of ABEC are described as follows.
B. Energy Conservation Techniques
A. Pheromone trail setup
Existing energy conservation mechanisms for ad hoc networks can be classified into two categories: active and passive. Active techniques conserve energy by performing energy conscious operations, such as transmission scheduling using a directional antenna [6], energy-aware routing [7], [8], [9], and improving TCP retransmission behavior [10]. In contrast, passive techniques conserve energy by scheduling network interface devices to the sleep mode when a node is not currently taking part in communication activity. In PAMAS [11], the MAC layer determines whether the network interface device of a node could be turned off when its neighboring nodes communicate. GAF [12] divides the entire network area into many smaller grid areas based on geographical information and arranges to have only one active node in each grid area while the rest conserve energy by turning off their network interface devices. Span [13] and CPC [14] maintain the connectivity and forwarding capability of the network by always keeping nodes that constitute a backbone infrastructure active, and conserve energy by turning off the rest. EDP [15] adaptively adjusts nodes’ participation in network forwarding activity based on their remaining energy. ABEC is a passive energy conservation mechanism, where nodes coordinate their schedules through the concentration of pheromone trails set up by proactive ants. Idle nodes located along a low concentration pheromone trail may decide to conserve energy by altering the behavior of forwarding ants to eliminate pheromone trails passing through themselves before turning off their network interface devices.
The process of pheromone trail setup in ABEC utilizes ant packet broadcasting with limited scope. Every proactive ant interval (Tant ), a node broadcasts an ant with a specified ttl. Each ant keeps track of the sequence of nodes it has visited (Vs ), and carries the sequence along when it is broadcast. A node receiving an ant updates pheromone trails passing through itself based on the amount of pheromone calculated from the incoming ant and Vs . Pheromone trails are setup in a backward manner. An ant originating from d, forwarded by an intermediate node j to a next-hop node i will cause i to create (or update) a trail from i passing through j and ending at d. Note that this backward trail updating scheme is similar to the pheromone trail creation process of real ants. In addition to the trail between i and d, node i also creates (or updates) all trails starting from itself to all other intermediate nodes k in Vs . After updating ttl and Vs of the ant, the nodes rebroadcast the ant if the ttl of the ant does not reach zero, otherwise the ant is discarded. The ant is also discarded if a loop is detected. The details are described in the following subsections.
III. A NT-BASED E NERGY C ONSERVATION As an ant-based energy conservation protocol, ABEC is designed to achieve the following goals. • Distributed decision making – Nodes running ABEC independently decide if they should turn to the sleep mode based on information obtained from both local neighbors through incoming ants and current data forwarding activity. • Connectivity maintenance – By considering the information obtained from incoming ants and data forwarding activity, ABEC safely turns network interfaces of certain nodes into the sleep mode to conserve energy, while maintaining network connectivity among the remaining
B. Data structures The following data structures are required for the operation of ABEC. 1) Structure of ants: Figure. 1 illustrates the structure of an ant packet. ant-ID is a combination of source ID and sequence number to uniquely identify an ant. dest-ID is normally a broadcast address, but a node may specify its neighbor address if it wants to send out a unicast ant. More details on unicast ants are found in subsection III-D. ttl determines the scope of ant forwarding. Each node decreases ttl value by one upon receiving an ant, with the ant being discarded once its ttl reaches zero. The next two fields, state and residual-energy, contain current ABEC operational states and residual battery energy level of a node that forwards an ant. Each node updates these two fields with its own current values before forwarding each ant. In addition to this fixed structure, each ant also carries Vs which is a sequence of node ID and pheromone amount deposited at each node along its path. Subsection IIIC describes pheromone model in more details. 2) Pheromone table: Pheromone table, Ai , is a collection of pheromone trails maintained at node i. Each trail is uniquely identified by a tuple (i, j, d), where i is an ID of a node that
maintains the table, d is an ID of the node at the other end of the trail, and j is an ID of the next-hop node of i toward d. Pheromone concentration on the trail (i, j, d) is represented by τ (i, j, d). An incoming ant originating from a node s passing through a sequence of nodes V = vn , vn−1 , ..., v1 (where vn = s and v1 = j) toward a node i will cause i to update the pheromone concentration on all the trails (i, j, d) where d ∈ V . In other words, i will have pheromone trails connecting between itself and every node that an incoming ant has visited.
To serve the energy conservation purpose of ABEC, we model the amount of pheromone deposited at each node based on the amount of power used to forward an ant in a link-bylink basis. The amount of pheromone deposited at node i when an ant is forwarded from node j to i, represented by τ (i, j), is computed using the following formula: τ (i, j) =
prx (i) − RXthres (i) ptx (j) − RXthres (i)
(1)
where ptx (j) is the transmission power node j uses to transmit the ant, prx (i) is the power node i receiving the ant, and RXthres (i) is the minimum threshold in which a packet can be correctly received by node i. Note that before transmitting an ant, node j records its identity and transmission power into the ant packet, which will be used by the receiving node i to compute the amount of pheromone associated with the link between i and j. Therefore, pheromone concentration on a trail (i, j, d) can be calculated using the following equation: n
τ (vk , vk+1 ), (vk , vk+1 ∈ V ) (2)
k=1
where V is a node sequence on the trail from d to j consisting of vn , vn−1 , ..., v1 with vn = d, v1 = j, and n = |V |. ABEC allows pheromone trails to decay over time, similar to pheromone trails laid by biological ants that serve to gradually reduce the significance of old trails. The pheromone decaying model follows the half-life decaying model of radioactive substances, in which pheromone amount at time t, τt (i, j, d), can be obtained from the equation: τt (i, j, d) ← τt0 (i, j, d) · e−(ln2∗t/T1/2 )
(3)
where τt0 is the pheromone concentration at time 0, and T1/2 is the half-life, or the average time, for the amount of pheromone to decay by 50 percent. D. Energy conservation operations By monitoring pheromone trails and node’s communication activity, ABEC modifies the ant forwarding behavior of a node, and operates through a cycle of 3 states: Active (AC), Wait-tosleep (WS), and Sleep (SP), as shown in Figure 2. ABEC starts up by setting a timer for an interval TAC and stays in the AC state. In this state, it does not modify any behavior of the node, but only participates in the pheromone trail setup process and monitors forwarding activity of the node. The interval TAC
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has to be carefully chosen to provide enough time for nodes to successfully establish pheromone trails with neighbors. After TAC expires, ABEC changes to the WS state if the node is idle from any communication activities and starts a timer for an interval TW S . In order to determine whether the node is idle, ABEC obtains cross-layer forwarding information from the network layer. The node is considered idle if there is no forwarding activity for a certain interval of Tf wd−idle , which is an adjustable parameter that depends on the application traffic pattern. During the WS state, ABEC periodically checks whether the sleeping criteria are met and the node is still idle from any communication activity. It then turns to the PS state and powers off the network interface for an interval TP S . If the sleeping criteria does not hold when the timer expires, ABEC reverts to the AC state. One of the most important operations of ABEC is to determine whether a node can safely go to sleep, i.e. the sleeping criteria are met. The two factors involved are the node’s internal factor and external (network) factors. Internal factors may include only one or a combination of the following: forwarding activity, idle time (Tf wd−idle ), and residual battery energy. If the required internal factors are met, ABEC then prepares the sleeping process by moving the pheromone trails out of the node. ABEC first announces that its new operational state is WS1 by using an out-of-schedule proactive ant with ttl=1. ABEC then stops generating proactive ants. In case there is an incoming ant, the node will not forward this ant any further and just simply discards it. Other nodes, learning that one of their neighbors wants to turn off, check the number of their neighbors in the AC state. A special unicast ant with ttl=1 will be directed to the neighbor in WS state if the other nodes see that the number of neighbors in AC state is lower than a certain threshold or they require the neighbor in WS state to be active in order to maintain pheromone trail to a specific destination. Once all pheromone trails passing through the node drop below a certain threshold (τthres ), it is safe for ABEC to change to the PS state and turn off the network interface. To accelerate the transferring to PS state, ABEC could start a timer for an interval Tant−idle , and turns to PS state if there is no incoming ant when the timer expires. 1 To prevent a large group of nodes from going into the WS state at the same time, which may cause network partition, a maximum number of nodes allowed to be in the WS state could be used to avoid this situation.
IV. S IMULATION E XPERIMENTS AND D ISCUSSIONS
TABLE I ABEC PARAMETERS USED IN THE SIMULATIONS
A. Simulation model We simulate ABEC using QualNet to study its performance and energy efficiency in comparison with AODV. Since ABEC is designed to collaborate with a routing protocol, we integrated ABEC with AODV and with an ant-based routing protocol. In the integration with AODV, we did not modify AODV to utilize any local neighbor information collected by ABEC in its routing mechanism. Our intention is to show that ABEC can be incorporated to an existing ad hoc routing protocol without any modification, and can still improve energy efficiency of the routing protocol. We call the integration of AODV and ABEC as AODV/EC. In the integration with an ant-based routing protocol, we chose to modify the hybrid protocol ANSI [5]. Since ABEC and ANSI share the same functionality of broadcasting proactive ants, we disabled the proactive ant mechanism of ANSI and modified ANSI to obtain local routing information through ABEC instead. The integration is called ANEC. In the simulations, 60 nodes are uniformly placed on a 1500 x 300 m2 free space terrain. Signals are transmitted using a 2.4 GHz frequency channel at 2 Mb/s speed. The propagation path loss is the two-ray model without fading. Node movement follows a random-waypoint model with randomly chosen mobility speed between 0 and 10 m/s. We model network traffic by generating 5 constant bit rate (CBR) flows. Each source node transmits one 512-byte CBR packet at every 400, 100, 80, and 50 ms, which is equivalent to the application data rate of 10, 40, 50, and 80 Kb/s, respectively. To calculate the amount of energy consumed, we based our energy consumption model on the WaveLan PC/Card energy consumption behavior study by Feeney and Nilsson [16]. They showed that the average currents drawn by the card at 4.74 V power supply are 280, 204, 178, and 14 mA for transmit, receive, idle, and sleep modes, respectively. These values are equivalent to the power consumption rate of 1327 mW for the transmit mode, 967 mW for the receive mode, 844 mW for the idle mode, and 66 mW for the sleep mode. We performed two sets of experiments. One set was to study how ABEC affects the network forwarding performance. The experiments ran for 300 seconds with unlimited energy supplies for all nodes. Another set was to show the ability in extending the network lifetime. Therefore, only 5 source and destination pairs were supplied with unlimited energy, while the rest 50 nodes were empowered with limited amount of energy to continuously operate for approximately 330 seconds. In each set of the experiments, we performed 10 different seed runs and presented average results. ABEC parameters used in the simulations are shown in Table I. B. Simulation results In all graphs, we presented the results from three protocols: AODV, AODV/EC, and ANEC. Two different proactive ant configurations were used for AODV/EC and ANEC as specified in the graphs as “1 hop” and “3 hops” for ttl = 1 and
Parameters Proactive ant interval (Tant ) Proactive ant ttl Minimum pheromone threshold (τthres ) Pheromone half-life (T1/2 ) AC state timer (TAC ) WS state timer (TW S ) Forwarding activity idle time (Tf wd−idle ) Ant-idle timer (Tant−idle )
Values 2, 10 seconds 1, 3 0.005 5 seconds 4 seconds 4 seconds 2 seconds 2 seconds
ttl = 3, respectively. For ttl = 1 case, Tant is set to 2 seconds and for ttl = 3 case, Tant is set to 10 seconds. Figure 3(a) shows that the delivery ratio of AODV/EC is very close to that of AODV, with the maximum difference of 2 percent lower than AODV at high load for both 1 hop and 3 hops cases. However, ANEC shows a comparable delivery ratio as AODV at low load but its delivery ratio drops to approximately 10 percent lower than AODV at 80 Kb/s. Figure 3(b) illustrates the end-to-end delay comparison. We observed a similar pattern as in Figure 3(a) where ANEC incurred larger delay than both AODV and AODV/EC at high load. The low performance of ANEC in a high traffic load situation can be explained as follows. As described in [5], the reactive route discovery mechanism adopted by ANEC may cause broadcast storms since ANEC simply performs global broadcast once a route break is detected. In addition, a forward reactive ant which serves as a route request must carry a history list (Vs ) while it is being forwarded. These mechanisms put more load on the network than in the case of AODV, especially in the low network forwarding capacity situation after ABEC has already turned off some nodes. The effect of high routing overhead is more severe in a high traffic load situation. Figure 3(c) shows that the control packet overhead of ANEC is always higher than that of AODV and AODV/EC2 . Figure 3(d) indicates both AODV/EC and ANEC achieved higher energy efficiency than AODV in terms of the number of successfully delivered packets per unit of energy consumed in the entire network. This is the result of ABEC turning some nodes into the PS state, while the remaining active nodes perform more forwarding work than being idle. When considering the effect of different proactive ant configurations, the results also show that using a large proactive ant scope or high ttl does not always benefit the network. Figure 3(a) and (b) indicates that ANEC with ttl = 3 achieved better delivery ratio and shorter delay than when ttl = 1. On the other hand, figure 3(c) and (d) shows that ANEC with ttl = 3 incurred higher control packet overhead and were less energy efficient than when ttl = 1. Therefore, an appropriate proactive ant configuration depends on many factors, such as traffic patterns and performance/efficiency trade-off. Figures 4(a) and (b) show the fraction of average remaining energy for the entire network and the fraction of alive nodes, 2 Control packet overhead includes routing control packets and all packets generated by ABEC.
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(b) Fraction of alive nodes Fig. 4. Fraction of remaining battery energy (a) and fraction of remaining alive nodes (b) comparisons between AODV, AODV with ABEC (AODV/EC), and an ant-based routing with ABEC (ANEC) at traffic load 80 Kb/s and two different proactive ant configurations: ttl = 1, Tant = 2 seconds and ttl= 3, Tant = 10 seconds. Maximum node speed is 10 m/s.
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respectively, as a function of simulation time. The graphs represent average values obtained from 10 seed runs when the traffic load is 80 Kb/s per flow, and are calculated from only the 50 nodes that are not sources or destinations. From both figures, we can see that ABEC extends network lifetime by conserving the nodes’ energy. At the simulation time of 333 seconds when the battery energy of AODV networks is already exhausted, 40 percent of energy still remains in ANEC networks and 57 percent in the AODV/EC networks. The amount of conserved energy leads to extended network lifetime. Figure 4(b) shows that all nodes in AODV networks ran out of energy at almost the same time, after approximately 330 seconds, while the last node in ANEC networks died after 650 seconds and the last node in AODV/EC with ttl = 1 died afetr 900 seconds. This indicates that ABEC extends the network life time of a network when running AODV by almost 3 times. C. Discussion and future work In a passive energy conservation protocol, where nodes conserve energy mainly by turning off network interface devices, there is always a trade-off between network performance and energy efficiency. During the development of ABEC, we discovered several interesting issues involving this trade-off.
1) Determination of a proper sleep time: In a fully distributed decision system, as in ABEC, nodes independently decide how long they should be in the sleep mode. While longer sleep time results in more energy saved for the sleeping nodes, the active nodes will then drain power for a longer period. This may cause an unbalanced energy consumption among nodes and lead to pre-mature network partitioning. To determine a proper sleep interval, information from neighboring nodes, such as remaining energy levels or estimated residual operational lifetime, and the current forwarding traffic load should also be considered. There are few protocols that apply various kinds of coordination in sleep time determination. For instance, GAF [12] lets all sleeping nodes in the same grid wake up at the same time to re-elect a new active node. The current implementation of ABEC uses a fixed sleep interval, which might not be perfect for different traffic patterns. One approach is to make this interval adaptive to network and node status or to introduce a proper coordination scheme. 2) Overhead of ABEC: As an ant-based protocol, ABEC causes high overhead by sending periodic proactive ants. However, ABEC also controls the behavior of generating and forwarding ants. ABEC could reduce the number of ant generated by adjusting proactive ant ttl. It also suppresses proactive ant generation while nodes are in the WS or PS states. On the contrary, ABEC increases the number of ants by forcing outof-schedule proactive ant transmissions when a node changes its state. Therefore, a proper protocol parameter adjustment is required to balance the trade-off between increased overhead and energy conserved. 3) Collaboration with routing protocols: Periodic proactive ant transmission of ABEC results in local routing information available to the routing protocol. If the routing protocol could benefit from this information, a reduction in routing control overhead can be expected. One example is the integration of ABEC and an ant-based routing protocol. The information collected by proactive ants can be shared by both ABEC and the routing protocol. Modifications to existing ad hoc routing protocols are also being investigated. For instance, AODV may retrieve local routing information from ABEC and if a route to its destination has already been discovered by ABEC, AODV does not have to initiate its own route discovery. In addition, integration with a source routing protocol, such as DSR, may allow active nodes who are still carrying certain traffic load, to turn off themselves “gradually” without interrupting the traffic by proactively diverting the traffic with the help of ABEC before going to the sleep mode. 4) Maintaining network connectivity in ABEC: The current implementation of ABEC relies on a node in the WS state to check the number of active neighbors before turning itself off to reduce the possibility of network partitioning. Even though this scheme can maintain local connectivity, there still be a chance that ABEC breaks the global network connectivity. A distributed technique to guarantee global connectivity is necessary. We are currently researching such a technique which could be integrated into ABEC to eliminate the possibility of network partitioning when nodes sleep.
V. C ONCLUSION In this paper, we proposed the ABEC energy conservation protocol that utilized ant-based routing technique to conserve energy of ad hoc networks. Nodes in ABEC autonomously decide whether they should turn off their network interfaces to conserve energy based on pheromone trail concentration and a technique to shift the pheromone trails out of themselves. We evaluated ABEC by integrating it with AODV and an ant-based routing protocol to study its performance when collaborating with routing protocols. The results indicated that ABEC slightly reduced network forwarding performance in terms of delivery ratio and end-to-end delay when carrying CBR traffic. However, ABEC successfully extended battery life of nodes and prolonged network lifetime up to a factor of 3. Future research directions include adapting the sleep time of nodes and developing schemes to smoothly turn off nodes without interrupting on-going traffic. R EFERENCES [1] G. D. Caro and M. Dorigo, “AntNet: A Mobile Agents Approach to Adaptive Routing,” Universite Libre de Bruxelles, Belgium, Tech. Rep. IRIDIA/97-12, 1997. [2] ——, “Two Ant Colony Algorithms For Best-Effort Routing In Datagram Networks,” in the Tenth IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS’98), Las Vegas, Nevada, October 28-31 1998. [3] M. Gunes, U. Sorges, and I. Bouazizi, “ARA – The Ant-Colony Based Routing Algorithm for MANETs,” in International Conference on Parallel Processing Workshops (ICPPW’02), Vancouver, B.C., Canada, August 18–21 2001. [4] S. Marwaha, C. K. Tham, and D. Srinavasan, “Mobile Agents based Routing Protocol for Mobile Ad hoc Networks,” in IEEE Global Telecommunications Conference (GLOBECOM’02), Taipei, Taiwan, November 17–21 2002. [5] S. Rajagopalan, C. Jaikaeo, and C.-C. Shen, “Unicast Routing for Mobile Ad hoc Networks with Swarm Intelligence,” University of Delaware, Tech. Rep. 2003-07, May 2003. [6] A. Spyropoulos and C. Raghavendra, “Energy Efficient Communications in Ad Hoc Networks Using Directional Antennas,” in IEEE INFOCOM 2002, New York, NY, June 23–27 2002. [7] S. Banerjee and A. Misra, “Minimum Energy Paths for Reliable Communication in Multi-hop Wireless Networks,” in The ACM Symposium on Mobile Adhoc Networking and Computing (MOBIHOC 2002), Lausanne, Switzerland, June 9–11 2002. [8] S. Doshi and T. X. Brown, “Minimum Energy Routing Schemes for a Wireless Ad Hoc Network,” in IEEE INFOCOM 2002, New York, NY, June 23–27 2002. [9] C.-K. Toh, “Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks,” IEEE Communications Magazine, vol. 39, no. 6, pp. 138–147, June 2001. [10] J. Liu and S. Singh, “ATCP: TCP for Mobile Ad Hoc Networks,” IEEE Journal on Selected Areas in Communications, Wireless Communications Series, vol. 10, no. 7, July 2001. [11] S. Singh and C. Raghavendra, “PAMAS: Power Aware Multi-Access Protocol with Signalling for Ad Hoc Networks,” SIGCOMM Computer Communication Review, vol. 28, no. 3, July 1998. [12] Y. Xu, J. Heidemann, and D. Estrin, “Geography-informed Energy Conservation for Ad Hoc Routing,” in MobiCom’2001, Rome, Italy, July 2001. [13] B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, “Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks,” in MobiCom’2001, Rome, Italy, July 2001. [14] C. Srisathapornphat and C.-C. Shen, “Coordinated Power Conservation for Ad hoc Networks,” in IEEE International Conference on Communications (ICC 2002), New York, NY, April 28–May 2 2002. [15] M. R. Pearlman, J. Deng, B. Liang, and Z. J. Haas, “Elective Participation in Ad Hoc Networks Based on Energy Consumption,” in IEEE GLOBECOM 2002, Taipei, Taiwan, November 17-21 2002. [16] L. M. Feeney and M. Nilsson, “Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment,” in Infocom 2001, Alaska, April 2001.
