On the Route Refresh Frequency for On-demand Maximum Battery Life Routing in Ad Hoc Networks Natarajan Meghanathan
[email protected]
Andras Farago
[email protected]
Department of Computer Science Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas Richardson, TX 75083 Email: {nxm023000, farago}@utdallas.edu
Abstract In peer-to-peer ad hoc networks, any node can become a source or destination. So loss of connectivity to even one node is significant. Accordingly, we define the network lifetime as the time by which the first node runs out of battery power. Conditional Min-Max Battery Cost Routing (CMMBCR) [13] is a generic power-aware routing algorithm proposed to strike a balance between the contrasting objectives of maximizing the node lifetimes and minimizing the energy consumption. CMMBCR works by defining a battery protection threshold γ to switch between a routing scheme in which the total energy consumption is minimized and a routing scheme in which the minimum residual energy of any node on the route is maximized. In an on-demand power-aware routing protocol, existing routes have to be changed / refreshed to take into account of the available battery power of the nodes and extend the time at which the nodes run out of their battery power. Very few works have studied the performance of an on-demand distributed version of CMMBCR. We claim that the route refresh frequency is critical to the performance of CMMBCR and it has to be chosen depending on the battery protection threshold γ and the level of node mobility. We also claim that when the network topology changes dynamically (moderate to high mobility), there is no need to do route discovery to refresh an existing route. Mobility itself will guarantee that an existing route will be short-lived and the residual battery levels of the nodes can be learnt when a route discovery is done due to route failures. Our simulation results are based on implementing CMMBCR on the top of DSR in ns-2 [3]. We study the impact of the route refresh frequency on the network lifetime and end-to-end delay per packet under different values of γ, node mobility and offered traffic load. The results presented in this paper can aid in the on-demand performance study of existing power aware routing algorithms in mobile ad hoc networks.
Key Words: Power-aware Routing, Mobile Ad Hoc Networks, On-demand Routing, CMMBCR 1 Introduction In the presence of a dynamically changing topology such as that of the mobile ad hoc networks (MANETs), the strategy of discovering routes only when required (on-demand routing) has been preferred over the strategy of discovering and maintaining routes irrespective of their requirement (proactive routing) [1][2]. Route discovery in on-demand routing is often source-initiated and accomplished using a broadcast query and reply cycle. To reduce the impact of the route discovery on the available bandwidth and the end-to-end delay per packet, the learnt route is used until it breaks. In other words, ondemand routing protocols are often designed to stay with a route as long as it exists or a better route is learnt at minimal additional control overhead. Unfortunately, all nodes are not equally used and there is a tendency to overuse certain centrally located nodes [7]. It has been also shown in [4] that energy
2 consumption and bandwidth utilization are substantially different metrics and resource-utilization in ad hoc networks cannot be fully take care by bandwidth-efficient routing protocols. In energy-constrained environments, inadvertent over usage of the energy reserves of a small set of nodes in favor of others can have a significant impact on the node and network lifetimes. As routes in ad hoc networks are often multi-hop, intermediate forwarding nodes are forced to expend their battery power, while receiving and transmitting the packets for which they are neither the source nor the destination. In this paper, we target peer-to-peer ad hoc networks such as the personal communication networks. In these networks, wireless devices are associated with individuals and hence any node can become a source or destination. Loss of connectivity to even one node is significant. Accordingly, we define the network lifetime as the time by which the first node runs out of battery power. Power-aware routing in wireless ad hoc networks is often associated with twin objectives: maximize the node lifetimes and minimize the network-wide overall energy consumption. To maximize the node lifetime, routes have to be equally distributed across all nodes. This cannot be achieved easily when the overall energy consumed for each connection also has to be minimized. In [13], a hybrid power aware routing algorithm called Conditional Min-Max Battery Cost Routing (CMMBCR) has been proposed to strike a balance between these two contrasting objectives. CMMBCR works by defining a threshold γ to switch between a routing scheme in which the total energy consumption is minimized and a routing scheme in which the minimum residual energy of any node on the route is maximized. For a given source-destination pair, if there exist at least one route in which the residual battery power of each node in route is at least γ, then the route with the minimum energy consumption is chosen. On the other hand, if no such route exists, the route that maximizes the minimum residual energy is used. Very few works have reported the performance of CMMBCR for on-demand routing in MANETs. In [10], CMMBCR was implemented on the top of an on-demand routing protocol called DSR (Dynamic Source Routing). DSR [9] is a power-unaware protocol, but uses the minimum hop path learnt as part of route discovery and as a result also guarantees a minimum energy route when the transmission energy per hop is fixed. To maximize the network lifetime by avoiding nodes with limited residual battery power, routes are refreshed regularly for every 10 seconds of the simulation by doing a network-wide route discovery. This route refresh frequency of 10 seconds has been used irrespective of the battery protection threshold γ, node mobility and offered traffic. We claim that a constant value for the route refresh frequency for all the above conditions can have an adverse impact on the bandwidth utilization and also on the energy reserves at the nodes. For example, if the protection threshold γ is low enough that a node is considered to be part of a minimum energy route as long as its residual battery power is at least 10% of its initial value, then there is no need to do the frequent route discovery and exhaust the available bandwidth and energy reserves network-wide. In this paper, we claim that the route refresh frequency has to be adjusted depending on the level of node mobility and the battery protection threshold γ used in CMMBCR. To the best of our knowledge, we could not find a paper that proposes this idea. We study the impact of the route refresh frequency on the network lifetime and end-to-end delay per packet under different values of γ, node mobility and offered traffic load. We also claim that when the network topology changes dynamically (moderate to high mobility), there is no need to do route discovery to refresh an existing route. Mobility itself will guarantee that an existing route will be short-lived and the residual battery levels of the nodes can be learnt when a route discovery is done due to route failures. Our simulation results are based on implementing CMMBCR on the top of DSR in ns-2. The rest of the paper is organized as follows: In Section 2, we briefly review some of the power-aware on-demand routing schemes proposed for ad hoc networks. In Section 3, we briefly review the CMMBCR routing scheme. In Section 4, we describe our simulation environment and present the results. In Section 5, we draw our conclusions from the results obtained.
2 On-Demand Power-Aware Routing Protocols for Wireless Ad Hoc Networks PCR: In [5], a power control strategy is proposed for reactive ad hoc routing protocols. The idea is to find
3 an energy efficient route by selecting the appropriate hop-by-hop transmitting and receiving powers when route request packets are broadcasted throughout the network. Such a strategy has been claimed to equally distribute the routing load throughout the network and increase the overall network lifetime. The drawbacks of this strategy are it is insensitive to the available battery power of the node and introduces a back-off delay at each intermediate node before deciding to propagate the received RREQ packet. The latter can introduce significant route establishment latency unless the network density is sufficiently high. PSR: In [12], the authors propose a power-aware source routing protocol in which the link cost is defined as a function of the available battery power of the upstream node of the link. The path with the minimum sum of its constituent link costs is used for sending data packets. In addition, an intermediate node is allowed to send a route error packet (invalidation of the current route) to the source when the difference between its current battery power and that at the time of route discovery is above a threshold. We feel both these strategies are still not adequate to completely protect overused nodes. Selecting the path based on the sum of link weights is a risky thing to do as there could be a combination of two link weights, one high and one very low and the sum of these would be less than that of another pair of links of moderate link weights. Also, the strategy of invalidating a route after being used for a threshold time period may not be enough to avoid a heavily used node from being used again. PAOD: PAOD [11] is another power-aware on-demand routing protocol proposed for MANETs. This protocol assumes that at the time of route request, the source node could anticipate the number of data packets to be transmitted and include this information in the request packets. An Intermediate node receiving the request packet will forward it only if the residual energy at the node after forwarding all the anticipated traffic is above a threshold. Thus, the intermediate node sort of makes energy reservation for the traffic from the requesting source node. The threshold residual energy used to decide the inclusion of a node in the route is time-variant and is determined based on the average and the standard deviation of the residual energy available at all the nodes in the network. Note that these statistical data could be obtained by including the residual battery powers of the intermediate forwarding nodes of the route request and reply packets. The drawback of PAOD is its heavy reliance on traffic anticipation and energy reservation. In real-time applications and emergency situations, it is often not possible to predict the anticipated traffic accurately. Also, the energy reservation has to be carefully handled because due to node mobility, the route could break and all the intermediate nodes of the route have to be appropriately notified so that the reserved energy could be recycled. A passive timeout mechanism has been proposed in [11] to handle this issue. DEAR: DEAR [6] is an on-demand energy-efficient routing algorithm in which the rebroadcast time of a route request packet received at a node is decided based on the residual battery power estimated for the network and the current residual battery power at the node. The intermediate nodes compute the average residual battery power of the entire network based on the battery power information included in the route request packets. If the residual battery power at the node is smaller than the average residual battery power of the network, then the retransmission time will be longer and vice-versa. Initially the variation in the residual battery power will be small and most of the nodes would be forwarding the route request packets quickly. As a result, more candidate routes are learnt and the minimum hop route is likely to be selected. As time proceeds, the variation in the residual battery power in the network increases and the protocol selects paths that guarantee fairness in energy consumption.
3 Conditional Min-Max Battery Cost Routing (CMMBCR) CMMBCR is a generic power aware routing algorithm proposed for wireless ad hoc networks. The algorithm operates based on a battery protection threshold parameter γ that decides the type of routes chosen. For a given source-destination (s-d) pair, if there exist at least one route in which all the intermediate forwarding nodes have their residual battery power above the protection threshold γ, then the
4 route with the minimum total energy consumption is selected. Such a route will be selected independent of the available battery power of the nodes and is based only on the energy consumed for transmission and reception at each hop in the route. If the energy consumed per hop is fixed (i.e., no power control is employed), then a minimum hop route will be the route with minimum total energy consumption. On the other hand, if all the s-d routes have intermediate nodes with residual battery power below γ, then the route that maximizes the minimum residual battery power of any node on the route is selected. The value of γ can range from 0 to the maximum battery power of a node. When γ = 0, CMMBCR always selects the route that gives the minimum total energy consumption and is called as the minimum total transmission power routing (MTPR). When γ = the maximum battery power of a node, then CMMBCR always selects routes in which the minimum residual energy of any node on the route (here after called the bottleneck node) is maximum and is called min-max battery cost routing (MMBCR). If all the nodes have identical values of their initial battery power (which is also the maximum battery power possible), then the value of γ can be expressed in percentage (0 to 100%). The battery capacity of a route j at time t, R tj , is defined as the residual battery power of the bottleneck node at t. In other words, R tj = Mint ( Bit ) , where Bit is the residual (available) battery power of node i at i∈R j
time t. Let Q be the set of all available routes between a given source-destination (s-d) pair at time t. Let A
⊆ Q be the set of s-d routes that satisfy the following condition: ∀j ∈ A, R tj ≥ γ, where γ is the battery power protection threshold. Then, the following route selection principle is used: - If A ∩ Q ≠ Φ, then use MTPR and select the route in A that would minimize the sum of the energy consumed per hop. -
Otherwise, select a route k with the maximum battery capacity, i.e., Rkt = Max( R tj ) . j∈Q
4 Simulations We implemented CMMBCR on the top of DSR in ns-2. The route discovery procedure of DSR is slightly modified by allowing the intermediate forwarding nodes to record their available battery power in the route request and reply packets. The source node selects the best route by collecting the route replies transmitted by the destination. Routes are maintained using the normal DSR maintenance procedures. Periodically, the source node initiates a new route discovery process to know the current values of the available battery power of the nodes and change the existing route if required (according to the CMMBCR route selection principles). We avoid the route cache optimization techniques used by DSR because if the intermediate nodes reply for a route request packet, it is not possible to know the available battery power of all the nodes in the network. DSR has been shown to exhibit a decent performance in the absence of promiscuous listening [4][8]; so there is no overhearing by the nodes in our simulations. The time between two successive route discoveries initiated for refreshing the existing route is called the route refresh interval (Trefresh). At the first instance, periodic route refreshing may appear to be adding considerable overhead on the network. We observed that if Trefresh is carefully chosen, the network lifetime can be increased substantially with minimum additional control overhead on the network. Our goal in this paper is to stress the importance of varying Trefresh depending on the battery protection threshold γ, the mobility conditions and the offered traffic load. The simulation parameters are summarized in Table 1. In Figure 1, we show the origination time of the source-destination (s-d) sessions. The origination time between successive sessions are selected to be approximately equally spaced in the range [0 … 150 seconds]. The corresponding s-d pairs for a session are selected arbitrarily. A node can be a source for at most one session and a destination for at most one session. Once started, we assume the sessions can run indefinitely until the first node failure occurs in the network. The distribution of the origination time of
5 the s-d sessions also has an impact on the performance of CMMBCR and will be discussed in Sections 4.3 and 4.4. Each data point in figures 2 and 3 is averaged over 5 runs of the routing schemes for the same traffic model, but different randomly generated mobility scenarios. We measure the network lifetime and end-toend delay per packet. The network lifetime is defined as the time by which the first node runs out of battery power. The end-to-end delay per packet is the sum of the delays a packet experiences at every hop in the path from the source to the destination. This includes the processing delay, queuing delay and propagation delay.
Simulator
Physical Layer MAC Layer Routing Protocol
Mobility Model
Traffic Model
Energy Consumption Model
NS2 version 2.26 [3] Network Size 1500 m x 300 m Nodes 50 Initial Battery Power per Node 100 Joules Signal Propagation Model Two-ray ground reflection model Transmission Range 250 m IEEE 802.11 Link Bandwidth 2 Mbps Interface Queue FIFO-based, Size 50 DSR CMMBCR Battery Protection Threshold, γ 10%, 50%, 100% Random-way point model Minimum Node Speed 0 m/s Maximum Node Speed (vmax) 1 and 15 m/s Pause Time 0 Second Constant Bit Rate (CBR), UDP Number of Source-Destination (s-d) Pairs 15 and 30 Data Packet Size 512 bytes Packet Sending Rate per s-d Pair 4 Packets/sec Transmitting Power 1.327 W Receiving Power 0.967 W Idle Power 0W Promiscuous Listening No
Table 1: Simulation Parameters for On-demand CMMBCR
4.1 Energy Consumption Model The power consumption at a node in an ad hoc wireless network can be divided into three categories: (1) power utilized for transmitting a message, (2) power utilized while receiving a message and (3) power utilized in idle state. In [10], it has been shown that in the presence of overhearing at the intermediate nodes, no real optimization in the energy consumption or the network lifetime can be achieved. Hence, in this paper, we do not consider the power lost in the idle state (there is no overhearing in our simulations) and focus only on the issues concerned with minimizing power consumption during communication i.e., the power consumed for transmitting and receiving a message and power consumed due to route discoveries. We do not perform any power control. Irrespective of the length of a hop (physical distance), we use the same fixed transmission power per hop. We model the energy due to broadcast traffic and point-to-point traffic as linear functions of the packet transmission time, network density, transmission and reception power per hop. A similar linear modeling for power consumption has been used in [4].
6 The initial battery power at all the nodes is the same (100 Joules). Thus, the CMMBCR threshold parameter γ has been expressed in % with respect to the initial battery power. For example if γ = 50%, then CMMBCR resorts to MTPR if there exists at least one route in which all the intermediate nodes have their available battery power at least 50 Joules. Otherwise, the route with the maximum battery power for the bottle neck node is chosen.
Figure 1.1: 15 sessions
Figure 1.2: 30 Sessions
Figure 1: Distribution of the Origination Time of the s-d Sessions 4.2 Performance in the absence of mobility In the absence of node mobility, the number of alternate routes available for a given source-destination session is limited. As a result, there cannot be much of route diversity. This impact of the lack of route diversity on the improvement in the network lifetime can be seen when γ is too low (γ=10%). When γ=10%, route selection is almost similar to that of MTPR and insensitive to the available battery power of the nodes. Even if the residual battery power of a node goes down below the threshold, there is still a high probability that the same node is selected for the next route. The advantage of using a lower value of γ is that the total energy consumption is relatively lower when compared to that incurred at higher values of γ. If more than one node failures can be tolerated, then it is recommended to use a lower value of γ. We observed that the average node lifetime (average of the failure times of all nodes) is relatively high for lower values of γ. But, in this paper, we assume that all the nodes are equally important and we want to extend the time of the first node failure as much as possible. When γ = 100%, route selection is always sensitive to the available battery power of the nodes. Even though there cannot be much route diversity due to lack of mobility, routes chosen for a sourcedestination session are always designed to go around nodes that have relatively lower residual battery power. Note that in our simulations we assume all the nodes have the same initial battery power. Shielding a source/destination node from the route forwarding load of other source-destination sessions is very important to extend the lifetime of the first node failure. The drawback of this strategy is that the number of hops in the route is relatively high when compared to that incurred for γ = 50% and 10%. As a result, the total energy consumed per packet and the end-to-end delay per packet is relatively higher. The advantage is that the gain in the network lifetime can be as high as 20-70% compared to that obtained when γ = 50% and 10%. When γ = 50%, CMMBCR gives a performance that matches to that of MMBCR at smaller route refresh intervals and to that of MTPR at larger route refresh intervals. When a route is selected for a newly originating source-destination session, it is unlikely to have a bottleneck node with only 50% of the initial battery power. But, such a route cannot be used for a long time and the route has to be refreshed
7
Figure 2.1: No Mobility
Figure 2.2: No Mobility
Figure 2.3: vmax = 1 m/s
Figure 2.4: vmax = 1 m/s
Figure 2.5: vmax = 15 m/s
Figure 2.6: vmax = 15 m/s
Figure 2: Performance with 15 s-d Pairs
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Figure 3.1: No Mobility
Figure 3.2: No Mobility
Figure 3.3: vmax = 1 m/s
Figure 3.4: vmax = 1 m/s
Figure 3.5: vmax = 15 m/s
Figure 3.6: vmax = 15 m/s
Figure 3: Performance with 30 s-d Pairs
9 frequently to divert traffic from the heavily used nodes. As the frequency of route refreshing is decreased (i.e., larger route refresh intervals), the routing scheme loses its sensitivity to the available battery power of the nodes and starts behaving like MTPR. The delay incurred per packet is close to that incurred for γ=100% at smaller refresh intervals, while it is closer to that incurred for γ=10% at larger refresh intervals. This shows the tradeoff between network lifetime and delay in power-aware routing. At smaller route refresh intervals, the delay for all the three values of γ is significantly high. This is due to the congestion caused by the route query and reply packets in addition to that of the traffic forwarding load. Depending on the application requirements, the route refresh interval has to be appropriately selected.
4.3 Performance in the presence of mobility In the presence of node mobility, the number of alternate routes increases, thus causing a better route distribution. In most of the cases, especially at high traffic, this translates into an improvement in the network lifetime. Even though a significant amount of route discovery latency is incurred due to route failures, power-aware routing and mobility increases the chances of route diversity and stops the delay per packet from becoming prohibitively high. We observed that when γ = 100%, the stability of the routes chosen is also high when compared to that chosen for γ = 10% and γ = 50%. This reduces the route discovery latency significantly and yields a delay per packet that is only slightly larger (5-20%) than that incurred for γ = 50% and 10%. When compared to the delay obtained in the absence of mobility, the delay per packet obtained when vmax is at most 1m/s is less. This is due to better route distribution achieved without significant route discovery latency.
4.3.1
Performance when vmax = 1 m/s
The topology is neither completely static nor truly dynamic. When γ = 10%, the routing scheme is still insensitive to declining battery power level at the nodes. Frequent route refreshing only causes congestion and increases the delay per packet. When γ is increased to 50% and 100%, routes have to be refreshed at a moderate frequency (around 40 – 60 seconds for 15 sources, 20 – 40 seconds for 30 sources). If we refresh beyond these critical time intervals, then it might not be possible to shield heavily used nodes and the performance degrades to that observed for the static case. At smaller refresh intervals, there appears to be no appropriate improvement in the network lifetime; so it is better to stay with the critical refresh intervals and obtain a lower delay.
4.3.2
Performance when vmax = 15 m/s
The topology is completely dynamic. The route distribution is taken care of by node mobility itself and there is no need for route refreshing. In fact, there is no appreciable change in the delay after refresh intervals of 20 – 40 seconds. This indicates the lifetime of routes are around at most 20 – 40 seconds and beyond these there is no major route refreshing that is possible. Even at smaller refresh intervals, there is no major improvement in the network lifetime since topology changes are completely dynamic and the refreshed route does not last for a long time. It would be better not to attempt refreshing because this could save the energy lost in broadcasting and also decrease the level of congestion.
5
Conclusions
The results presented in this paper can aid in the on-demand performance study of existing power aware routing protocols in mobile ad hoc networks. From the discussions in Section 4, it is evident that the route refreshing frequency plays an important role in the performance of CMMBCR. The refresh interval cannot be arbitrarily selected. Based on the observations from the simulations, it is clear that for low values of the battery protection threshold γ, there is no point in attempting for a route refresh as there is
10 not much scope to extend the time of the first node failure significantly. At the same time significant reduction in delay can be obtained by not opting for frequent route refreshing. When route selection is always sensitive to the available battery power (γ = 100%), then for the no mobility case, it is not required to go for route refreshing if the origination times of the sessions are not much overlapping with each other as in our simulations. Each time a route is chosen, priority is given to nodes that have not been heavily used so far. The drawback of this scheme is that it incurs a slightly higher delay per packet due to the increase in the transmission and channel acquisition delay (due to higher hop count). At higher mobility, the dynamic nature of the topology reduces the lifetime of a route. Since the routing scheme is always sensitive to available battery power, routes chosen bypass the heavily used nodes. When the topology changes slowly, we have to be careful in selecting a refresh interval. This is because, the route selected at the time of session origination may always exist, but new better routes (in terms of the available battery power at the bottleneck node) may become available after sometime and we have to make use of them. When γ = 50%, in the case of no mobility and vmax = 1m/s, the routing scheme may not be sensitive to the available battery power of the nodes at the time of session origination. But after sometime, it is necessary to divert the traffic towards less used nodes, otherwise the network lifetime degrades to that observed for γ = 10%. There exists a real tradeoff between delay and network lifetime when we choose γ = 50%. If we want to extend the time of fist node failure, then we have to go for smaller refresh intervals, but incur a larger delay. If we want a lower delay close to that observed for γ = 10%, then we should opt for larger route refresh intervals and sacrifice in the network lifetime.
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