A Novel DSR-based Energy-efficient Routing Algorithm for Mobile Ad-hoc Networks J.-E. Garcia, A. Kallel, K. Kyamakya, K. Jobmann
J.-C. Cano, P. Manzoni
Institute of Communications Engineering (IANT) University of Hannover Appelstrs. 9, 30167 Hannover, GERMANY Email: garcia,kallel,
[email protected]
Department of Computer Engineering (DISCA) Polytechnic University of Valencia Camino de Vera, s/n, Valencia, SPAIN Email: jucano,
[email protected]
Abstract— Mobile Ad-Hoc Networks (MANETs) are wireless networks consisting of a collection of untethered nodes with no fixed infrastructure. An important design criterion for routing protocols in ad hoc networks is power consumption reduction. We describe an energy-efficient mechanism that can be used by a generic MANET routing protocol to prevent nodes from a sharp drop of battery power. We apply the mechanism to the Dynamic Source Routing (DSR) and propose a novel DSR-based energyefficient routing algorithm referred to as the Energy-Dependent DSR (EEDSR). We compare the EDDSR algorithm with two of the most recent proposals in this area: the Least-Energy Aware Routing (LEAR) and the Minimum Drain-Rate (MDR) mechanism. We show that EEDSR is the best approach to reduce and balance power consumption in a wide spectrum of scenarios. Index Terms— Mobile Ad Hoc Network, Power-aware, Overhearing
I. I NTRODUCTION The advent of wireless communication and mobile devices has opened the door to research on self-organizing networks. Mobile or “Spontaneous” networks provide to the mobile users ubiquitous communication capabilities and information access. Mobile Ad-Hoc Networks (MANETs) [1] are wireless networks consisting of a collection of untethered nodes with no fixed infrastructure. Nodes in a MANET participate in forwarding data packets when the two end-points are not directly within their radio range. The MANETs present characteristics such as dynamic topologies, bandwidth-constrained, variable-capacity links, and energy-constrained operations that will affect protocol design [2]. Routing protocols design for MANETs is a very active research area and many proactive and reactive protocols have been proposed [3]. Proactive protocols find routes between all source-destination pairs regardless of the actual need for such routes. The more traditional proactive protocol can reduce the needed time to get a route by inducing a high routing load over the network. Reactive protocols, on the other hand, are based on the reduction of the routing load by initiating new routing activities only in the presence of data packets in need of a route. Each different approach utilize its own design criteria to optimize the tradeoff between efficiency and resources consumption. Although most of the nodes in MANETs rely on batteries to correctly operate, only a few routing proposal have appeared
recently whose main design criteria focuses on providing an efficient power utilization. A hybrid approach, called Conditional Max-Min Battery Capacity Routing (CMMBCR) was devised by C.K Toh [4]. CMMBCR relies on the residual battery capacity of nodes. Its mechanism considers both the total transmission energy consumption of routes and the remaining power of nodes. When all nodes in some possible routes have sufficient remaining battery capacity (i.e., above a threshold γ), a route with minimum total transmission power among these routes is chosen. However, if all routes have nodes with low battery capacity (i.e., below the threshold), a route including nodes with the lowest battery capacity must be avoided to extend the lifetime of these nodes. In [5] the authors propose a new metric, the drain rate, to be used in conjunction with the residual battery capacity to predict the lifetime of nodes according to the current traffic conditions. The authors compares the performance of MDR with respect to CMMBCR. Finally, the Local EnergyAware Routing (LEAR) [6] is a power aware route selection mechanism that distributes the decision on whether or not to cooperate in forwarding nodes among all nodes in the network. The final goal of LEAR is to equally balance the total energy consumption among all nodes in the network. This paper proposes a novel routing mechanism called EDDSR that tries to avoid the use of nodes with a weak battery supply. To achieve this goal, EDDSR uses information related to the residual energy in the route discovery procedure. EDDSR has been been implemented using DSR as the base protocol since it was proved to be one of the more efficient reactive routing protocol in bounded networks [7]. We compared the EDDSR mechanism with the MDR and the LEAR proposals using simulations. The simulations evaluated a set of specified environments over dense and sparse network scenarios with several topology and mobility conditions. We also present the performance evaluation of the different proposals when the energy model includes the energy dissipation due to overhearing. This paper is organized as follows. Section II describes a brief review of the routing schemes related with this work. Section III presents the details of the EDDSR protocol. Section IV compares the performance of EDDSR against MDR and LEAR by using the ns-2 simulator. Finally, concluding remarks are found in section V.
II. R ELATED W ORK In this section we explain the basic operation of the reactive DSR routing protocol. We then present a brief review of two protocols related to power-aware routing algorithm that are based on the DSR protocol, MDR and LEAR respectively. A. The DSR Routing Protocol In the Dynamic Source Routing (DSR) [8] each data packet to be transmitted carries the complete sequence of nodes by which the packets must pass to reach the target. This property is known as source routing, and requires the sender to know the complete route to the destination. The protocol is based on two basic processes: (a) the route discovery process and (b) the route maintenance process. The route discovery process is based on flooding and is used to dynamically discover new routes. The route maintenance process periodically detects and notifies networks topology changes. In the route discovery procedure a node wishing to establish a route broadcasts a route request (RREQ). Each node receiving the RREQ appends its own address to the packet header and rebroadcasts it. The RREQ flooding terminates when it reaches either the destination or an intermediate node with a route to the destination. In this case a route reply (RREP) containing the series of accumulated addresses is sent back to the source. Upon receiving the RREP, the source node can start transmitting the data packets towards the destination using the route recorded in the RREP. Each node running the DSR protocol is equipped with a route cache which maintains the routes that a node is aware about. DSR uses the cache intensively in order to reduce the overhead caused by the route discovery. The major objective of the route maintenance procedure is to detect a broken link and find a new route to the destination. When a node along an established route detects a link disconnection due to the neighbour’s movement, it informs the source using the route error (RERR) packet. The source then removes the broken link from its cache and attempts to find a new route to the intended destination. B. The Minimum Drain Rate mechanism The Minimum Drain Rate Mechanism (MDR) [5] introduces a new cost function which predicts the lifetime of the nodes. The cost function depends on the traffic load conditions and residual battery power. The route selection is based on the MinMax Algorithm. Each node piggybacks its current cost in the received RREQ and rebroadcasts it. Upon receiving the first RREQ the destination sets a timer. During a specified interval REPLY TIMEOUT the node collects all incoming RREQ. When the timer expires the destination selects the route using the Min-Max algorithm and includes it in the generated RREP. Note that intermediate nodes are not allowed to reply to the source using information from their route caches. A cached route may be suboptimal from the algorithm’s point of view since may not reflect actual traffic load and battery rate depletion conditions. Moreover, each source initiates periodically a new route discovery in order to obtain routes reflecting more accurately the
power condition of the nodes. This contributes to a fair distribution of the network load. However, its means that MDR mechanism is incompatible with the route cache used in DSR protocol. Since the aggressive use of the route cache is one of the main optimization that fueled up the performance of DSR, this incongruity is the main disadvantage of MDR when it is applied to the DSR protocol. The MDR mechanism is a fully source-destination-based algorithm. All decisions made during the routing procedure are taken either in the source or the destination. Intermediate nodes only are allowed to piggyback its current cost function in routing messages. C. The Local Energy-Aware Routing (LEAR) The key distinguishing feature of the LEAR [6] is its skill to distribute within all nodes in a route the decision concerning to the willingness to forward packets for a specified sourcedestination pair. Contrary to the routing criteria of MDR, in LEAR a node participates in the route discovery process only when its remaining energy is above a specified threshold. If its remaining energy is below the specified threshold, it drops the RREQ and generates a message called drop route request (DRREQ) to inform subsequent nodes about the dropped RREQ. A RREQ message will reach the destination only if it has passed through nodes with enough high energy level. The destination replies immediately to the first received RREQ. The route contained in the generated RREP refers to the shortest path among all the energy-rich routes. When the source fails to receive a RREP after a time period, that is calculated by the backoff algorithm used in the DSR specification, it starts a second attempt to acquire a route for the intended destination. A node that has previously dropped a RREQ or received a DRREQ decrements its threshold by a specified adjustment value and rebroadcasts the RREQ only if its remaining energy lies above the new computed threshold. The protocol makes extensive use of the route cache. It defines a message called route cache (RCAC) which is generated by an intermediate node when it founds a corresponding entry in its cache. This message is sent towards the destination. The destination node replies to the first incoming RREQ or RCAC and ignores all later messages. The RCAC is processed in a similar manner as the RREQ. A node which receives a RCAC forwards the packet only if its residual energy is above the threshold. Otherwise a message called drop route cache (DRCAC) is sent towards the destination. This message provides the same function as the DRREQ. Another message called cancel route cache (CRCAC) is sent backwards to the node that has started sending the RCAC, so that it removes the corresponding entry from its cache. This will enforce this node to explore other paths when it receives the next RREQ. III. T HE E NERGY D EPENDENT DSR M ECHANISM (EDDSR) We propose a power-aware optimization that can be applied to the route discovery process of DSR. Each node will determine its willingness to participate in forwarding based on their current energy level. We describe a novel DSR-based routing
algorithm whose main design objective is to prolong the lifetime of nodes with low energy reserves. Each node ni in the network has to periodically compute its residual battery power RBPi . When a node has enough residual battery it participates in the network activities behaving exactly as a DSR node. When its residual battery power has fallen below a specified threshold, the node delays rebroadcasting of a received RREQ by a time period which is inversely proportional to its predicted lifetime. i As defined in MDR, the ratio RBP DRi represents the predicted lifetime of node ni , where RBPi denotes the residual battery power and DRi the drain rate, that is an estimation of how much energy is consumed per second, on the average, by node ni . Thus, the predicted lifetime provides an assessment about when the battery energy of node ni will be exhausted. In [5] the authors detail how to evaluate RBPi and DRi for each node in the network. The EDDSR mechanism attempts to discourage nodes with small lifetime from participating in the route discovery process, thus prolonging its lifetime. In fact, it is more likely that the RREQ sent from a node with a small predicted lifetime will be dropped by the nodes closer to the destination since in the DSR protocol intermediate nodes only forward the first received RREQ. The EDDSR mechanism also modifies the route maintenance process of the DSR protocol. When the energy of a node along an active route falls below a critical threshold, it will immediately inform the source by sending a RERR packet. The source will try to find another route to the same destination by initiating another route discovery process. The critical node will be more reluctant to participate in the forwarding activities of a new route to the destination. Finally, the EDDSR algorithm makes use of the route cache in a similar manner suggested by the LEAR protocol. Thus, the RRCAC message is processed by the intermediate nodes is the same manner as the RREQ.
is repeated for the duration of the simulation. We considered a maximum speed of 10 meters per second and a PAUSE TIME of 10 seconds. A total of 12 Constant Bit Rate (CBR) sources generated UDP data packets at a sending rate of 3 packets/sec and a packet size of 512 bytes. The signal transmission power is 0,2818w, which corresponds to a radio range of 250m. The total simulation time has been set to 900 simulated seconds.
IV. S IMULATION E NVIRONMENT The simulation results presented in this paper were obtained using the ns-2 [9] simulator. This is an object-oriented, discrete, event-driven network simulator developed by the VINT project research group at the University of California at Berkley. The simulator has been extended by the Monarch Research group at Carnegie Mellon University [10] to support node mobility, a realistic physical layer that includes a radio propagation model, radio network interfaces and a Medium Access Control. We adopted the DSR protocol as the underlying routing protocol and introduced the code related to the MDR, LEAR and EDDSR algorithms. Node movement was modelled using the Random Waypoint model [8]. This model is characterized by two parameters: the maximum speed and the PAUSE TIME. Each node randomly selects a destination and a speed, where the speed value is uniformly distributed between 0 and the maximum speed. The node then moves to its selected destination at the selected speed. Once it reaches the destination, it stop for a random pause time. The pause time is uniformly distributed between 0 and PAUSE TIME. The node eventually selects a new destination and speed, and repeats the previous steps. This behavior
, where Etx , Erx , and Eo denote the amount of energy expenditure by transmission, reception, and overhearing of a packet, respectively. N represents the average number of neighboring nodes affected by a transmission from node ni . Eq.(1) implies that when the network is more dense, the packet overhearing causes more energy consumption.
A. Energy Consumption Model A generic expression to calculate the energy required to transmit packet p is: E(p) = i ∗ v ∗ tp Joules, where: i is the current consumption, v is the voltage used, and tp the time required to transmit the packet. We suppose that all mobile devices are equipped with IEEE 802.11b network interface cards (NICs). The energy consumption values were obtained by comparing commercial products with the experimental data reported in [11]. The values used for the voltage and the packet transmission ph pd time were: v = 5V and tp = ( 2∗10 6 + 11∗106 )sec, where ph and pd are the packet header and payload size in bits, respectively. We calculated the energy required to transmit and receive a packet p by using: Etx (p) = 280mA ∗ v ∗ tp and Erx (p) = 240mA ∗ v ∗ tp , respectively. Moreover, we account for energy spent by nodes overhearing packets. As shown in [11], we assume the energy consumption caused by overhearing data transmission is the same as that consumed by actually receiving the packet. For the purpose of evaluating the effect of overhearing, we modified the energy model to account not for the energy expenditure due to transmission and reception but also the battery cost to be consumed by overhearing the wireless channel. Thus, the total amount of energy, E(ni ), consumed at a node ni is determined as: E(ni )
= Etx (ni ) + Erx (ni ) + (N − 1) ∗ Eo (ni ) (1)
V. P ERFORMANCE R ESULTS We evaluate the performance of EDDSR mechanism compared against pure DSR, MDR and LEAR in a dense network scenario and a sparse network scenario. We analyzed the energy consumption behavior of the four mechanisms. We mainly concentrate on the node expiration time, i.e., the time it takes for a node to stop working due to lack of battery capacity. To evaluate how the different layers affect the total energy consumption we also classify the total energy spent depending on the packet type (Application, Routing and MAC). Finally, we also study how NIC activities contribute to the total energy expenditure. For the purpose of investigating the effect of overhearing, we repeated all simulation by considering the energy cost due to the overhearing activities.
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We now evaluate how the NIC activities (transmissions (Tx), receptions (Rx), overhearing (Over) and Idle) contribute to the total power consumption. Figure 4 shows the obtained results. We observe that, when the overhearing activities are not considered, the energy spent in Idle mode dominates the total energy consumed for all protocols. This result underlines that to reduce this huge energy expenditure some techniques, similar to those proposed in [12] must be combined with the mechanisms under study. When the overhearing activity is considered, the most of the consumed energy is due to the overhearing activity. This effect hides the merit of those mechanism that try to balance the total energy consumption.
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A. Results with a Dense Network Scenario We first evaluated the three different routing mechanism in a dense network scenario. We randomly distributed a total of 50 nodes over an area that represents an open-air square field of 670 m × 670 m. 1) Static Network: Figure 1 compares how many nodes have died over time due to the expiration of the battery capacity in a static environment. We can clearly observe different results between the two considered cases, namely when the energy due to overhearing is ignored and when it is included. In an static network basically all protocols behave similarly. We notice a slight improvement of the EDDSR at the end of the simulation. This behavior is due to the EDDSR use of the rerouting technique that helps to avoid the use of nodes with a weak battery. The relatively low performance of MDR can be attributed to the use of longer routes, thereby increasing the relaying load and consequently the energy consumption at a larger number of nodes.
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Fig. 1. Node Expiration time: dense scenario, static network.
When overhearing is considered, the power aware algorithms fail to balance the energy consumption due to the enormous amount of energy spent in overhearing activities. 2) Dynamic Network: We now evaluate how node mobility affects performance indexes. We repeated all the simulations using a maximum node speed equal to 10 m/sec. When the energy due to overhearing is considered, all protocols behaves similarly (see Figure 2). When the energy model exclude overhearing, we observe that MDR substantially delays the time for first node’s failure. However, EDDSR obtains the highest number of survived nodes and improves MDR in terms of the average node lifetime. The LEAR protocol suffers from the flooding problem caused by the DRREQ packets sent in a broadcast manner. This negative effect is more clearly observed in the dynamic scenario where the route recovery procedure needs to be executed frequently due to disconnections. This characteristic suggests that the LEAR protocol does not offer a good scalability. Figure 3 highlights the energy consumption depending on the packet type. The bad performance of LEAR is mainly due to the great amount of energy spent by control packets.
B. Results with a Sparse Network Scenario We now present the obtained simulation results in an openair sparse network where 50 nodes have been randomly placed in a square area of 1500 m × 1500 m. 1) Static Network: In the sparse scenario the number of available routes is very limited. Moreover, where no mobil-
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Fig. 4. Energy consumption depending on network card activity: dense scenario, dynamic network.
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ity is considered the distribution of the network load is quite unbalanced. In most cases there is only one route between a source-destination pair. MDR presents the best performance, especially at the beginning of the simulation (see Figure 5). This result is explained because the MDR periodically executes the route selection process thus allowing battery depletion detection at a very early stage. Contrary to the MDR behavior, the LEAR protocol concentrates the whole traffic on a single route once it has been first discovered. Finally, the EDDSR algorithm acts similarly as the LEAR approach at the beginning of the simulation. The rerouting technique used by EDDSR will be more likely not to be able to find alternative paths because of the reduced number of routes. According to this results we argue that under this scenario the MDR mechanism achieves a better energyconsumption balancing along the network. We can also observe that all protocols achieve almost the same results at the end of the simulation. It seems that the death of some particular nodes restricts further the number of available routes making the distribution of the network load even impossible. 2) Dynamic Network: Finally, we consider nodes mobility in the sparse network. Figure 7 shows that all the energyefficient algorithms, particularly EDDSR and MDR, outperform DSR. All the energy-based mechanisms postpone the first node’s battery exhaustion. The number of survived nodes at the end of the simulation confirms also the improved performance of LEAR, MDR and EDDSR. In such scenarios, the aggressive use of the route cache in DSR, carries the drawback that it often provides invalid paths. This results is an extra expenditure of energy consumed in the exchange of control packets. Figure 6 shows that DSR consumes a high percentage of energy due to the routing activities. The LEAR mechanism also induces a high amount of energy expenditure. This effect is mainly due because of the frequent use of the route discovery process, that will make use of DRREQ packets sent in a broadcast manner. The EDDSR and MDR approaches seem to profit from the
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mobility of the nodes. Mobility allows new routes to appear after a route breakage. Thus, MDR and EDDSR can balance the nodes utilization and consequently the energy consumption. MDR does not rely on the route cache and additionally has the ability to acquire fresh routes through the periodical initiating of the route discovery procedure. These routes not only reflect the residual power of the nodes, but also the actual topology which banishes the overhead caused by the route recovery. In the EDDSR case an intermediate node is not allowed to send a RREP containing an invalid route back to the source. According to this appreciations we also observe a small routing energy expenditure for both mechanisms. VI. C ONCLUSION We described a novel power-aware route discovery algorithm called Energy Dependent routing algorithm. Its main goal is to extend the average lifetime for each node while balancing the total energy consumption among all nodes in the network. We then modified the DSR protocol to include our proposal and called it the energy dependent DSR protocol (EDDSR).
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Using the ns-2 simulator, we compared EDDSR against DSR, MDR and LEAR mechanisms. Our study proved that MDR and EDDSR clearly outperform DSR in terms of node lifetime especially in dynamic scenarios. The LEAR mechanism generates an high energy expenditure due to its route discovery process especially in dense networks. Thereafter, this protocol should be used only in sparse networks with static nodes or nodes moving with low speed. The continuous evaluation of the energy budget of a node along an active route in EDDSR prevent nodes from being overwhelmed by network traffic, thereby contributing to better load balancing and a fair energy utilization. EDDSR shows a similar behavior that MDR, however EDDSR has the additional merit of being compatible with the use of the route cache used by DSR.
R EFERENCES [1] Internet Engineering Task Force, ”Manet working group charter,” http://www.ietf.org/html.charters/manet-charter.html. [2] S. Corson and J. Macker, ”Mobile Ad hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations,” RFC 2501, January 1999. [3] E.Royer and C.-K. Toh, ”A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks,” IEEE Personal Communications Magazine, Vol. 6, No. 2, April 1999. [4] C.-K. Toh, ”Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks,” IEEE Communications Magazine, June, 2001. [5] D.Kim, J.J.Garcia-Luna-Aceves, K.Obraczka, J.Carlos Cano, P.Manzoni, ”Power Aware Routing Mechanism Based on the Energy Drain Rate Metric in Mobile Ad Hoc Networks,” 11th IEEE International Conference on Computer Communications and Networks (IC3N), 2002. [6] K.Woo, C.Yu, H.Y.Youn, and B. Lee, ”Non-Blocking, Localized Routing Algorithm for Balanced Energy Consumption in Ad Hoc Networks,” International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication System, MASCOTS, 2001. [7] J.Carlos Cano, P.Manzoni, ”A Performance Comparison of Energy Consumption for Mobile Ad Hoc Networks Routing Protocols,” IEEE/ACM MASCOTS 2000: Eighth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2000. [8] D.B. Johnson, D.A. Maltz, Y.C. Hu, and J.G. Jetcheva, ”The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks,” Internet Draft, MANET Working Group, draft-ietf-manet-dsr-04.txt, November 2000,Work in progress. [9] K. Fall and K. Varadhan, ”ns Notes and Documents,” The VINT Project. UC Berkeley, LBL, USC/ISI, and Xerox PARC, February 2000, Available at http://www.isi.edu/nsnam/ns/ns-documentation.html. [10] The CMU Monarch Project. The CMU Monarch Project’s Wireless and Mobility Enhancements to ns. http://www.monarch.cs.cmu.edu. Work in progress. [11] Laura Feeney and M. Nilsson, ”Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment,” IEEE INFOCOM 2001. [12] Suresh Singh , C. S. Raghavendra, ”PAMAS power aware multi-access protocol with signalling for ad hoc networks,” ACM SIGCOMM Computer Communication Review July 1998.
