Improving the Battery Performance of Ad-Hoc Routing Protocols

Transcription

1 Improving the Battery Performance of Ad-Hoc Routing Protocols Q. Qi and C. Chakrabarti Department of Electrical Engineering Arizona State University Tempe, AZ {qi, Abstract In ad-hoc networks formed by battery powered nodes, the network lifetime can be significantly enhanced by incorporateing the battery properties in the routing protocol. In this paper, we propose such a routing mechanism BCRM, that enhances the network lifetime by letting some of the nodes recover part of their lost charge. This is done by putting the selected set of nodes to sleep. We integrate BCRM into wellknown on-demand protocols such as DSR, MBCR and MMBCR, and evaluate their performance. Simulation results show that BCRM based protocols can improve network lifetime significantly with slight degradation in throughput. I. INTRODUCTION The mobile nodes in an ad-hoc network, shut down when the battery is fully discharged, causing the network performance to be severely affected. Maximizing the on-time of the battery is one of the most important metrics in the design of these systems. Ad-hoc networks today use minimization of energy consumption as the primary metric for routing. However, the assumption that minimizing energy leads to maximizing node lifetime is not necessarily true since the node lifetime depends on the traffic as well as its residual battery capacity. In the recent past, there have been a few ad-hoc network routing protocols designed with network lifetime in mind. In [1], several protocols (MBCR, MMBCR and CMMBCR) have been designed that use battery capacity metric to evenly distribute power consumption in the network while minimizing overall transmission power. A power-aware maintenance protocol based on CMMBCR is presented in [2]. To conserve energy, [3] and [4] show different ways to put nodes into sleep without disrupting existing network. The protocols in [5], [6], [7], use route selection criteria that are based on the energy drain rate and predicted route lifetime. The lifetimeaware multicast (LMT) protocol in [8] maximizes the network lifetime by minimize the variance of the remaining energy of all nodes in a network. In this paper, we propose a new battery aware routing mechanism (BCRM) that exploits the capability of a battery powered node to recover part of its lost charge. BCRM puts selected nodes of the network to sleep for time t seconds every time its charge drops by c % and it would like to recover r% of the recoverable charge. We have implemented BCRM in DSR and distributive versions of MBCR and MMBCR. Through simulation, we demonstrate that m route j m,n route j p,q p Fig. 1. Upper Network a Lower Network Example of an Ad-hoc Network BCRM can improve existing protocols by prolonging the nodes life time significantly. BCRM causes only a mild degradation in the network throughput. The network lifetime and throughput of BCRM based systems can be adjusted by choosing of c, t and r judiciously. The rest of the paper is organized as follows. Section II briefly describes the analytical battery model and communication node energy model. Section III elaborates on the proposed protocols, namely, BCRM, DSRWR, DMBCRWR and DMMBCRWR. Finally, section IV presents the simulation results and performance comparisons. A. Motivation II. PRELIMINARIES In wireless communications, the path loss between two nodes is inversely proportional to the n th power of their distance (usually n is 2 or 4). Energy aware routing protocols, such as the Minimum Total Transmission Power Routing (MTPR) [1] make use of this metric. However the MTPR routing protocol does not provide the best battery performance for the network. Consider the small ad-hoc network shown in Figure 1. The routes with MTPR are j m,n and j p,q for source-destination pair (m, n) and (p, q). Note that both routes share node a. Assuming no link breakage and connections between (m, n) and (p, q) persisting for large payloads, node a will use more energy than any other node due to heavier traffic. Consequently, node a will shut down earlier due to charge depletion causing the network to be partitioned. However if node a is allowed to q n

2 rest for some time during which it recovers part of its lost charge, the network lifetime can be enhanced. In fact, proper insertion of rest time for selected nodes in an ad-hoc network can delay network partitioning without significantly degrading overall network performance, as will be demonstrated in the rest of the paper. B. Battery Model The high-level Li-ion battery model in [9] provides an accurate relationship between the current load, discharge time and the charge consumed due to the discharge. If the load profile over time T can be broken into N piece-wise constant currents I k, k N, from time t k to t k+1 of duration k = t k+1 t k, then the charge consumed C is given by Tdis (s) (b) Fig. 2. Battery Discharge Properties. (a) Discharge time T dis and (b) Normalized recoverable charge (in percentage) RC as a function of c. (a) RC (%) N N C = I k k +2 I k ( k=1 k=1 m=1 e β2 m 2 (T t k k ) e β2 m 2 (T t k ) β 2 m 2 ) (1) where β is a measure of the battery non-linearity. A battery with large β is more linear, and approaches an ideal power supply. The Li-ion battery used in Compaq Itsy computer is characterized by β 2 =.637 min 1, and is used in the simulations in this paper. An analysis of equation (1) shows that the first term corresponds to the amount of charge that is irreversibly spent, and the second term corresponds to the amount of charge that can potentially be recovered if the battery is allowed to rest. We refer to the second term as the recoverable charge, RC. The longer the sleep period, the larger is the amount of recovered charge. If the sleep period is infinite, then all of the recoverable charge can actually be recovered. We next present the effect of recovery on the battery performance. The following definitions are in order. 1) α: the initial state of charge (SOC) of a node s battery. 2) T dis : the time between two sleep periods. 3) RC : RC normalized by α. 4) c : the charge consumption C during T dis normalized by α, referred to as normalized charge consumption. 5) t: sleep period or charge recovery time. 6) r: the percentage of charge recovered with respect to RC in time t, referred to as recovery ratio. Figure 2 describes the discharge characteristics of a battery with an initial α = ma-min for a load of 2 ma. Figure 2 illustrates the relationship between c and T dis, and c and RC, where both c and RC are expressed in %. In curve (a), we see that as c increases linearly, the discharge time T dis increases exponentially. In curve (b), we see that as c increases linearly, the recoverable charge RC approaches a fixed upper limit, which is 36.2% of α. Both the non-linear trends in curves (a) and (b) are due to the continuous recovery effect experienced by the battery during active discharge. For large T dis, the rate of change of RC decreases as time progresses. Therefore, the difference of absolute recoverable charge RC is large for c. t (sec) Fig r (%) Recovery Time for Different Recovery Conditions Figure 3 demonstrates the relationship between t, c and r for the same setup (α = ma-min, and I = 2 ma). From this figure, we see that the battery recovery has the following properties. Property 1: For a fixed c, the recovery time t increases exponentially with r. The relationship between r and t for different values of c can be characterized by equations (obtained by curve fitting), such as the ones listed below. t =.9 e (12.1 r) , for c = % t = 12.3 e (5. r) +.74, for c = % Property 2: For a fixed recovery ratio r, the recovery time t varies mildly for different normalized charge consumption c. The relationship between c and t for different r can be characterized by equations (obtained by curve fitting). t = e ( 3.14 c ) , for r = % t = e ( 4.89 c ) + 929, for r = 8% C. Power Consumption Model The low power protocols described in earlier section treat each individual node as an ideal device which only consumes energy when transmitting and receiving packets. In reality,

3 Power Idle with Comm Idle Device Fig. 4. Active Power Consumption Model a wireless node has three sources of energy consumption machine-specific, device-specific and communication-specific [], as shown in Figure 4. Machine-specific energy consumption involves components besides the network interface device, such as CPU, hard disk, etc. The wireless network interface consumes device-specific energy when a node s wireless function is activated. The communication-specific energy is consumed by a node during active communication. According to measured values of a 2 Mbps Lucent Wavelan card [11], a node consumes 14 ma current in sleep mode, 178 ma in idle mode, 24 ma in receiving mode and 28 ma in transmit mode. It takes ms transition time from sleep to active mode and vice versa, and the energy consumed during the transition time is negligible. For low traffic environment, communication-specific energy is certainly not dominant. In this paper, we focus on charge drain due to device-specific operations. III. BATTERY AWARE ROUTING ALGORITHMS In this section, we first review an on-demand protocol DSR [12] in subsection III-A. We then introduce distributive versions of MBCR (DMBCR) and MMBCR (DMMBCR) built using DSR s routing discovery and maintenance mechanisms in subsection III-B and III-C. Finally, we present the battery aware protocol (BCRM) in subsection III-D and incorporate it into DSR, DMBCR and DMMBCR. A. DSR Protocol This is an on-demand protocol, where a source node uses scouting packets to discover routes to a destination node. The source node has the complete hop-by-hop information for established routes. This is stored in a routing cache table. The header of a data packet also contains the hop-by-hop information from a source node to a destination node. 1) Routing Discovery: During the route discovery process, a source node s floods the network with route request (RREQ) packets. A unique sequence number is generated for each request. Every time a RREQ gets forwarded, it records the forwarding node address and builds up the path traversed across the network. Each RREQ can only be forwarded a maximum number of times before being discarded. A route reply (RREP) is generated and routed back to source s, when either the RREQ packet reaches destination d or any intermediate node n that has a valid route to d. In a bidirectional link environment, a RREP can route itself back to s by traversing the links in RREQ backward. In case of unidirectional environment, a RREP piggybacks on the reverse RREQ packet initiated by the replying node to source node s. 2) Route Maintenance: When any link on an active route is broken, the source node s is notified through a route error (RERR) packet by the broken link s upstream node. As the RERR traverses towards s, forwarding nodes remove the broken route from their routing cache table. In case node s requires to send a packet to d again, node s has to initiate route discovery. To avoid excessive control overhead, DSR aggressively uses route caching through utilizing salvaging, gratuitous route repair, and promiscuous listening [12]. B. DMBCR Protocol The Minimum Battery Cost Routing (MBCR) is designed to maximize the total remaining battery capacity from source node s to destination node d [1]. If the remaining battery capacity of node i is denoted as c i, then MBCR uses the battery cost function, shown in equation (2), to measure the willingness of a node to forward or transmit packets. f i (c i ) = 1 c i (2) Let R j be the cost of route j between a source destination pair (s, d), with D j nodes and let R i be the route with the largest total remaining battery capacity among the candidate routes A. The R j and R i are calculated by R j = D j 1 i= f i (c i ) (3) R i = max{r j j A} (4) In order to implement MBCR in an ad-hoc environment, additional routing discovery and maintenance mechanisms are required. 1) Route Discovery: The proposed distributive MBCR protocol (DMBCR) implements the DSR routing mechanism in MBCR. Basically, a source node s floods the network with RREQ packets during route discovery. The intermediate nodes along forwarding links of a RREQ packet calculate and update routing cost R j using equation (3). Unlike DSR, in DMBCR, when destination d receives a RREQ packet, it does not immediately send a RREP packet back to source s. Instead, node d collects several RREP packets within a short period τ and uses equation (4) to choose the replying route i. When an intermediate node n receives a RREQ packet, it extracts the request sequence number, the destination address, routing cost R j, and the hop-by-hop forwarding links from the packet header, and saves them in a request cache. Node n updates the RREQ packet using equation (3). Node n then starts a timer τ j for routing request j. For any subsequent RREQ packets, node n performs the following operations: If both the request sequence number and the destination address match, node n checks for routing loop in the packet s forwarding links. In case node n already appears in the forward links, the request packet is discarded. Otherwise, node n checks for routing cost.

4 If the routing cost, R j, of the subsequent RREQ packet is larger than the cached R j, node n updates and forwards the new RREQ. Once timer τ j expires, node n stops forwarding associated RREQ packets and purges corresponding routing request from its cache. The τ j variable determines the number of entries in set A that will be entertained to determine route j. Thus large τ j usually results in better route choice but longer route request period and longer delay between route request and route reply. The range of a route request period is well defined in [12] and τ j is set to be % of a source node s route request period in our simulation. 2) Route Maintenance: DMBCR requires route maintenance either when existing routes are broken due to link failures, or when battery charge information of existing routes becomes stale. In case of link failure, the broken link s immediate upstream node n simply sends a RERR packet back to the corresponding source nodes. Once node s receives the RERR packet, it initiates a new RREQ to destination d, and the old route is purged from its routing table. Nodes on stale routes may experience excessive traffic, which leads to earlier battery depletion, and network partitioning. So periodical route request is used by source nodes to refresh battery information and discover better routes. Too many requests create excessive flooding and reduces overall battery capacity of the network. Conversely, too few requests result in stale routes not promptly purged. In our simulation, the route refresh interval is set to 6 seconds based on results in [5]. Note that periodic route rediscovery only applies to active routes; inactive routes are simply purged using DSR s maintenance mechanism. C. DMMBCR Protocol The MBCR protocol in [1] makes decisions based on the total battery capacity of a route. However, a route having maximum total remain battery capacity may include a node with little remaining battery capacity. The Min-Max Battery Cost Routing (MMBCR) accounts for such cases by defining the cost of route j (R j ) to be a function of the lowest remaining battery capacity of all nodes in route j. R j = max i j f i(c i ) (5) R i, the route between source destination pair (s, d), is then obtained using equation (4). Note that R i now represents the route with largest remaining battery capacity among all minimum battery capacity routes in set A. Next we describe the distributive version of MMBCR or DMMBCR. 1) Route Discovery: The route discovery process for DMMBCR is very similar to DMBCR. A source node s floods the network with RREQ packets when it has no known cache route for an destination d. Initially, source node s sets the route cost R j to zero. When an intermediate node n receives a RREQ packet, it compares its own f n (c n ) with the R j value in the RREQ header. Node n then updates R j using equation (5). Once destination node d receives the first RREQ packet originating from source node s, node d starts a timer with expiration period τ j. Destination d chooses the corresponding route after timer expires using equation (4), and sends a RREP packet traversing back to source node s. For duplicated RREQ packets, the intermediate node n only forwards the ones that satisfy the inequality condition. R j < f n (c n ) (6) In DMMBCR, an intermediate node n also uses the same method as in DMBCR to prevent routing loops. Node n forwards and updates qualified duplicating RREQ packets for a period of τ j, starting from receiving the first RREQ packet. The size of set A is constrained by time τ j, which is set to % of a source node s route request period as in DMBCR. 2) Route Maintenance: The routing maintenance mechanism of DMMBCR is the same as in DMBCR. DMMBCR has less nodes involved in a route discovery process than DMMBCR due to the inequality condition (6). Therefore, DMMBCR has smaller control overhead than DMBCR in both route discovery and route maintenance. D. Battery Charge Recovery Mechanism Battery Charge Recovery Mechanism (BCRM) helps nodes recover part of the lost charge by going into sleep mode. BCRM can be incorporated into any on-demand protocol. To implement BCRM, a node has to keep track of its charge consumption. When the charge in a node drops by c, it goes into sleep mode for a period t seconds in order to recovery r% of RC. 1) Recovery Time and Recoverable Charge Trade-off: The recovery ratio r and the normalized charge consumption c together decide a node s recovery period t (see Figure 3). The amount of absolute charge recovered (Q rc ) after a recovery period can be easily calculated from either r and RC or α and RC for a given c. If r and c are chosen to be very small, the recovery time t will be small. In such cases, nodes will go in and come out of sleep mode frequently, resulting in unstable routes and reducing over all network stability. If r is small and c is large, Q rc of a node will be disproportionately small compared to the amount of charge consumed (C) during T dis. In addition, the route repair overhead will cause additional charge consumption in the active nodes. Consequently, critical nodes are likely to experience few recovery opportunities before running out of charge. If r is large and c is small, even though a node recovers a higher percentage of RC, Q rc will still remain small due to small C. Meanwhile, large r means longer recovery time t. The active nodes might go into the sleep state before sleeping nodes wake up. This results in less active nodes in the network, which significantly hampers network connectivity. In case both r and c are large, C and Q rc will both be large. As curve (b) in Figure 2 suggests, the rate of increase of RC is significantly less for high C (than for low C). This means there is little difference in the value of Q rc for r above

5 6%. Critical nodes can maybe recover only once before they run out of charge. 2) BCRM Route Repair: When a node goes into sleep, it disrupts the active routes of which it is a part. A route repair mechanism is thus necessary to minimize the adverse effect. The procedure is listed below. Before a node goes into sleep, it informs all immediate neighbors about its recovery period t using special one hop sleep alarm (SA) packet. Once a neighboring node m receives a SA packet, m repairs any route with link j m,n in its active route cache. Node m repairs routes by performing salvaging operation. It also informs affected source nodes about the changes using sleep route repair (SRR) packets. Once a source node receives a SRR packet, it chooses to use salvaged route or re-initiate routing request. 3) Integrating BCRM into Existing Protocols: We have integrated BCRM into DSR, DMBCR and DMMBCR. The modified protocols are referred to as DSRWR, DMBCRWR and DMMBCRWR. The changes to original protocols are described below. Integrating BCRM into DSR is straight forward. We choose local repair option when a source node receives SRR packet, because DSR already performs network wide flooding during routing request. Local repair reduces control overhead and results in less charge consumption for the entire network. Integrating BCRM into DMBCR and DMMBCR is slightly more involved. Instead of periodically initiating route requests to avoid stale routes, source nodes use SRR packet generated by BCRM to aperiodically update battery capacity information in both DMBCR and DMMBCR. The frequency of active routes updates is controlled by c and the SOC of the weakest node. IV. SIMULATION RESULTS All routing protocols are analyzed using GloMoSim 2. simulator [13]. The simulation network consists of 64 nodes confined in a 8 8m 2 area. Mobility is not considered. The transmission range of each node is 2 meters, and the transmission power for each node is fixed. We assume that a node consumes ma in transmit mode, 2 ma in receiving/idle mode and ma in sleep mode. The battery parameters of each node are α = ma-min, and β 2 =.637 min 1. The channel capacity is 2 Mb/s is the MAC layer protocol and an abstract radio model is used to represent the physical layer. Nodes are randomly placed in each trial. There are 8 random connections established at any given time. Each connection uses constant bit rate (CBR) traffic, at 4 packets/second with a packet size of 24 bytes. The average life time of a CBR connection is 6 seconds. For every setup configuration, 2 trials are run, and all results are averaged values. The total simulation time is 6 seconds. We use the network lifetime and throughput to evaluate the protocols. The network lifetime is usually defined by the time taken for the first node in the network to die, or the time taken for all nodes in the network to die, or the time taken for K Throughput (bits/s) Network Lifetime (sec) x (a) Network Lifetime 2 2 r (%) (b) Network Throughput 7 6 r (%) Fig. 5. DMBCRWR performance results (a) Network lifetime (b) Network throughput for different values of normalized charge consumption c and recovery ratio r nodes to die. We adopt the time taken for 2 nodes to die as the network lifetime. The throughput is calculated by taking the average number of packets received by each node. We have set up the simulation environment so that majority of nodes will deplete at the end of the simulation time (6 seconds). In order to understand the effectiveness of BCRM, we first investigate the performance of DMBCRWR under parameter constraints r, c and t in subsection IV-A. We then to compare protocols with and without BCRM in subsection IV-B. A. Performance of BCRM 1) Impact on network lifetime: We see that the network lifetime increases as r increases for fixed c. For instance, when c = % and r = %, the network lifetime is 3376 seconds, while when c = % and r = %, the network lifetime is 5878 seconds. This trend is in accordance with Property 1 (see section II- B). Since larger r means longer recovery time, more nodes are in the sleep state and the number of active nodes participating in the network decreases. This ensures that all nodes get plenty of rest and last longer, thereby extending the network lifetime. The results are illustrated in Figure 5(a). We only show the results for r < 6%, since for r > 6%, there are less than 2 dead nodes at the end of the simulation. 2) Impact on network throughput: Next we study the effect of c and r on the network throughput. For small c and r, the throughput is small due to frequent route repair overhead. On the other hand, for large c and small r, too little time is spent in the sleep mode, and very little charge is recovered. For small c and large r, too many nodes stay in sleep mode for exceeding amount of time and the amount of absolute charge recovered is small. All aforementioned combination 8 9

6 of c and r result in reduced network connectivity, which decreases throughput. Figure 5(b) shows that for fixed c, as r increases from % to 9%, the network life time first increases and then decreases. For example, for c = %, the throughput is bits/s for r = %, peaks at bits/s for r = %, and drops to bits/s for r = 9%. The reason for this trend is that for small r, Q rc is small, compared to the charge consumed for the route repair. This reduces average node charge capacity and degrades the network lifetime performance. As r increases, the sleep period t increases and Q rc becomes more than the amount of charge consumed in route repair. Therefore, the network lifetime increases. But for large r, the sleep period t becomes very long and the number of nodes in the sleep mode increases. A consequence of this is that the remaining active nodes are busier, consume c % of their total charge faster and also enter the sleep mode. This reduces total number of active nodes in the network and thus the network connectivity. B. Cross Protocol Comparison We compare the performance of protocols with and without BCRM. For the protocols with BCRM, the normalized charge consumption c is set to % and the charge recovery ratio r is set to %. The results are shown in Figure 6. It is apparent that protocols integrated with BCRM perform better than the original ones by providing charge recovery for nodes with higher traffic. For instance, in DSR the 2th node dies 1574 seconds earlier than DSRWR; the 2th node in DMBCR dies 1225 seconds earlier than in DMBCRWR; the 2th node in DMMBCR dies 1162 seconds earlier than in DMMBCRWR. Thus for protocols that already have good battery-efficient routing mechanisms, such as DMBCR and DMMBCR, the improvement through incorporating BCRM is smaller. Protocols with BCRM has slightly less average throughput than protocols without BCRM. Average throughput for DSWR is 5.26% less than DSR; average throughput for DMBCRWR is 5.15% less than DMBCR; and average throughput for DMMBCRWR is 3.74% less than DMMBCR. The reason is that BCRM incurs extra routing repair overhead and reduces the average number of active nodes in the early part of the simulation. However, protocols with BCRM delay the network partition and have better throughput in the later part of the simulation. Thus on average, the difference in the throughput with and without BCRM is small. V. CONCLUSION In this paper, we have presented a battery aware routing mechanism(bcrm) for ad-hoc networks. BCRM is based on putting selected nodes of the network to sleep so that they can recover part of the spent charge. We have studied the effect of normalized charge consumption, sleep time, recovery ratio and throughput on the network lifetime. We have incorporated BCRM into several on-demand routing protocols such as DSR, MBCR and MMBCR. Simulation results show that BCRM Time (sec) Number of Dead Nodes DMMBCRWR DMBCRWR DSRWR DMMBCR DMBCR DSR Fig. 6. Cross Protocol Network Lifetime Comparison for c = % and r = % based protocols can improve network lifetime significantly at the expense of slight degradation in throughput. Acknowledgement: This work was supported in part by an NSF-ITR EEC REFERENCES [1] 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 , June 21. [2] D.-K. Kim, J. Park, C.-K. Toh, and Y.-H. Choi, Power-aware route maintenance protocol for mobile ad hoc networks, in Proc. IEEE th International Conference on Telecommunications (ICT 3), vol. 1, 23 Feb.-1 March 23, pp [3] C. Srisathapornphat and C.-C. Shen, Coordinated power conservation for ad hoc networks, in Proc. 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