Understanding the power characteristics of the mobile radio used in wireless devices is important for the efficient design of communication protocols.. This section reports work presente
Trang 1CHAPTER 19
Power Optimization in Routing Protocols for Wireless and Mobile Networks
STEPHANIE LINDSEY and KRISHNA M SIVALINGAM
School of Electrical Engineering and Computer Science, Washington State University
CAULIGI S RAGHAVENDRA
Department of Electrical Engineering, University of Southern California
Wireless data networks are increasingly becoming an important part of the next-genera-tion network infrastructure This is made possible by the availability of inexpensive wire-less network devices such as Bluetooth [1] and wirewire-less LANs [20] The objective of these networks is to provide users with “anytime, anywhere” data access The end-user devices range from small handheld PDAs to larger laptops The computing and storage capabili-ties of these devices cover a wide spectrum
One of the chief limitations of these wireless networks is the limited battery power of the network nodes Therefore, power management is one of the challenging problems in wireless communication, and recent research has addressed this problem Examples in-clude a collection of papers available in [26] and a recent conference tutorial [21], both devoted to energy-efficient design of wireless networks A summary of research done on energy-efficient network protocols is available in [11]
Wireless networks are typically classified as: (i) infrastructure networks, in which all end node communication is through a more powerful entity called the base station, which
is connected to a wired network infrastructure; and (ii) ad hoc networks, in which end nodes establish a network among themselves and communicate with each other in a multi-hop manner Newer types of networks such as the personal area networks (PANs) [9] and wireless sensor networks [16, 6] are becoming prevalent These networks tend to be char-acterized as infrastructure, ad hoc, or hybrid
This chapter specifically considers ad hoc networks and packet routing in these networks Routing is a significant consumer of battery power since a packet is routed through many in-termediate nodes before reaching its destination Energy costs related to communication can
be high in mobile nodes but this chapter only considers the costs related to routing The de-sign of energy-efficient routing protocols has attracted the attention of researchers in the past few years [4, 7, 22, 25] This chapter presents a summary of some of this research
ac-407
.
Handbook of Wireless Networks and Mobile Computing, Edited by Ivan Stojmenovic´
Copyright © 2002 John Wiley & Sons, Inc ISBNs: 0-471-41902-8 (Paper); 0-471-22456-1 (Electronic)
Trang 2tivity The objective is to outline the key concepts of the several proposed solutions in order
to stimulate the design and implementation of more solutions to the problem
This section provides a brief background on the different types of wireless networks and the basics of energy consumption issues
19.2.1 Wireless Network Types
Wireless networks may be classified into these two different general categories:
1 Infrastructure-based networks Wireless networks often extend, rather than replace,
wired networks, and are referred to as infrastructure networks A hierarchy of wide area and local area wired networks is used as the backbone network The wired backbone connects to special switching nodes called base stations They are respon-sible for coordinating access to one or more transmission channel(s) for mobiles lo-cated within their coverage area The end-user nodes communicate via the base sta-tion using their respective wireless interfaces Wireless LANs and WANs are a good example of this type of network
2 Ad hoc networks Ad hoc networks consist of radio-equipped nodes such as laptops
and personal digital assistants (PDAs), which communicate with each other without
a central authority Ad hoc networks are characterized by dynamic, random, multihop topologies with typically no infrastructure support The end users are assumed to be mobile, resulting in constant changes in network topology Thus, mobility has a sig-nificant effect on protocol design and system performance All nodes cooperate to maintain connectivity and packets are routed through the network in a multihop man-ner
Mobile ad hoc networks have attracted considerable attention, as evidenced by the IETF working group MANET (mobile ad hoc networks) This has produced various Inter-net drafts, RFCs, and other publications [13, 14] Also, a recent conference tutorial pre-sents a good introduction to ad hoc networks [23] Ad hoc networks have largely been studied for military applications, but they are expected to be used commercially in the near future
Newer wireless network types, such as sensor networks and personal area networks, are beginning to emerge Sensor networks consist of inexpensive sensor nodes that are ployed for data collection from the field [2, 5, 12] A personal area network (PAN) is de-fined as a wireless network consisting of devices within 10 meters of an individual Stan-dardization efforts for PANs are in progress [9]
19.2.2 Sources of Power Consumption
The sources of power consumption, with regard to network operations, can be classified into two types: communication-related and computation-related
Trang 3Communication involves usage of the transceiver at the source, intermediate (in the case of ad hoc networks), and destination nodes The transmitter is used for sending con-trol, route request, and response messages, as well as data packets originating at or routed through the transmitting node The receiver is used to receive data and control packets, some of which are destined for the receiving node and some of which are forwarded Understanding the power characteristics of the mobile radio used in wireless devices is important for the efficient design of communication protocols A typical mobile radio may exist in three modes: transmit, receive, and standby Maximum power is consumed in the transmit mode, and the least in the standby mode Thus, the goal of protocol develop-ment for environdevelop-ments with limited power resources is to optimize the transceiver usage for a given communication task Computation costs, involving packet processing and the CPU, are not considered in this chapter
19.2.3 Routing Protocols
Routing protocols for mobile ad hoc networks can be categorized as on-demand and proactive With on-demand protocols, the route selection process is initiated by the sender only when it has a packet to transmit With proactive protocols, mobiles periodi-cally exchange routing control packets (like OSPF or RIP in the Internet) and update their routing tables The former approach results in fewer control packets and is more adaptive to topology changes, but leads to longer route setup delay before a packet may
be sent The AODV protocol (ad hoc on-demand distance vector) [15] is a good exam-ple The latter approach requires more control packets but does not incur the additional route setup delay However, it is possible that the precomputed route is incorrect, lead-ing to potential lost packets A survey of routlead-ing protocols for ad hoc networks is avail-able in [19]
Since routing is an important and significant energy-consuming activity in ad hoc net-works, research attention has been devoted to designing energy-efficient routing proto-cols The rest of this chapter describes the various research efforts done in the area of power-aware routing protocols
Section 19.3 describes work done on analysis of the energy consumption of the AODV and DSR routing protocols considered in the IETF MANET working group [7, 14] Sec-tion 19.4 presents work described in [22, 25] on power-aware link metrics that enable se-lection of appropriate routes Section 19.5 presents research reported in [4] that studies routing techniques based on balancing nodes’ battery reserves to maximize network life-time Section 19.6 describes research done in design of energy efficient broadcast and uni-cast trees reported in [24] Section 19.7 discusses work reported in [17] on the use of topology control to maximize the lifetime of the network
This section reports work presented in [7] that evaluates the energy consumption behavior
of two ad hoc network routing protocols: AODV (ad hoc on-demand distance vector) and DSR (dynamic source routing) [10, 15]
19.3 ENERGY ANALYSIS OF AODV AND DSR ROUTING PROTOCOLS 409
Trang 4AODV and DSR have been well studied for their routing capabilities, but their energy characteristics had not been studied until now Both protocols are deemed on-demand pro-tocols since they discover and maintain routes only when needed All network nodes par-ticipate equally in the routing process These two protocols differ in that AODV is destina-tion-oriented, based on the Bellman–Ford algorithm, and uses distance vector routing information DSR is a topology-oriented source routing protocol that uses aggressive caching of network-wide topology information More details on how these protocols work can be found in the respective references listed earlier
Energy Cost Equations
Feeney [7] presents the energy calculations for various routing operations In general, there is a fixed channel-acquisition cost and an incremental cost proportional to the size of the packet:
cost = m · size + b
where m denotes the packet size multiplicative factor and b the fixed channel acquisition
cost The fixed cost relates to acquiring the channel, for example, as part of the medium access control procedure The variable cost depends on the packet size, distance, receiver sensitivity, and so on The total cost is the sum of all the costs incurred by the source and destination nodes
Traffic is classified as broadcast traffic and point-to-point For broadcast traffic, the sender listens briefly to the channel and sends data if the channel is clear If the channel is not clear, the sender waits and retries later Fixed channel-access costs and incremental payload costs combined in the previous equation result in a new cost equation:
cost = msend· size + bsend+ n冱僆S (mrecv· size + brecv)
where msendis the unit cost for sending a byte, mrecvis the cost for receiving a byte, and S
denotes the set of nodes that are in radio range of sender’s transmitter
For point-to-point traffic, the fixed cost includes channel access and the MAC negotia-tion The incremental costs associated with the payload are the same as in broadcast traf-fic Nodes which discard traffic also consume energy whose amount is dependent on the MAC implementation Small control messages are assumed to have the same fixed cost for the sake of simplicity The costs at the source are:
cost = bsendctl+ brecvctl+ msend· size + bsend+ brecvctl
and the costs for the destination are:
cost = brecvctl+ bsendctl+ mrecv· size + brecv+ bsendctl
The first two costs above are for the RTS/CTS message pair, the next two are for sending (receiving) the packet, and the final are for the ACK message Since messages
Trang 5may be lost due to collision, the equations also factor in the total number of transmis-sion attempts
The nondestination nodes in the range of the sender overhear the RTS messages and data, whereas the nodes in the range of the destination overhear the CTS and ACK mes-sages The analysis considers nondestination nodes operating in promiscuous mode and otherwise The cost for nodes not operating in promiscuous mode is:
cost = n冱僆S bdiscardctl+ n冱僆D bdiscardctl+ n冱僆S (mdiscard· size + bdiscard) + n冱僆D bdiscardctl (19.1)
where bdiscardctldenotes the cost for discarding a control packet; bdiscarddenotes the cost for discarding a data packet, including the cost associated with entering a reduced energy
state during data transmission; S denotes the set of nodes in the sender’s transmit range; and D denotes the set of nodes in destination’s transmit range Feeney [7] also presents
cost equations for promiscuous nodes, but those are not repeated here
In the worst case, nodes receive packets and then ignore them if they were not destined for them A more efficient strategy is for nondestination nodes to enter a reduced energy consumption state while the media carries uninteresting traffic The Lucent WaveLAN IEEE 802.11 PC card uses the following strategy: based on the information size in the control message, nondestination nodes in the range of the sender and receiver enter a re-duced energy consumption mode when data is being transmitted
Some concerns of protocol designers were addressed in [7] First, receiving a message incurs a high cost If a broadcast message is received by approximately four neighbors, then the total cost of receiving the message is more than the cost of sending it Second, the fixed cost of sending or receiving a packet is large compared to the incremental cost For small packets, the fixed cost is greater than the incremental cost of sending or receiving Source router headers are quite inexpensive in terms of energy consumption Third, dis-carding a packet usually consumes much less energy than receiving it Finally, although the cost of broadcast traffic is higher for receiving, point-to-point traffic has higher send/receive costs but allows nondestination nodes to discard traffic If discarding costs are high, then the advantages of point-to-point traffic are collision avoidance and data ac-knowledgment However, there are some substantial energy savings if discarding costs are low
Simulation Results
A modified version of the CMU Monarch Project’s mobility-enhanced ns-2 simulator was used along with the model to analyze the energy consumption of the routing protocols [7] For the simulations, transmit and receive characteristics were based on specifications for the Lucent WaveLAN 2.4 GHz DSSS IEEE 802.11 PC card The transmission range is
400 meters, and 50 mobile nodes were used for a 2400 m × 480 m network for 900 sec-onds of simulation time The node density used was 10.9 nodes per 400 m radius Each node waits a certain interval of time and then moves to a random destination at a constant velocity in the range of 0 m/s to 32 m/s, then the node waits again The networks were ei-ther stationary or mobile with varying degrees of mobility Twenty source–destination pairs were chosen and four 64-byte IP packets were sent to the destination each second
19.3 ENERGY ANALYSIS OF AODV AND DSR ROUTING PROTOCOLS 411
Trang 6DSR-np, a variant of DSR that does not include eavesdropping, was also studied in the analysis
In summary, the results shows that although DSR is usually the most efficient in terms
of bandwidth utilization, it is less energy efficient than AODV and DSR-np due to eaves-dropping The details follow
Figure 19.1 shows the total estimated energy consumption with respect to traffic sent, received, dropped due to collisions, discarded, or received in promiscuous mode Broad-cast traffic is used in all three protocols for on-demand route discovery DSR and DSR-np use this less often and more efficiently than AODV For DSR and DSR-np, most routing traffic is sent point-to-point The proportion of broadcast traffic is large enough to con-tribute to the energy costs The amount of traffic received is so much larger than the amount of traffic sent that it accounts for 40–70% of the energy consumption
Figure 19.2 shows the routing overhead energy consumption, which includes routing packets, source routing headers, and all traffic received in promiscuous mode (for DSR) DSR does not require the use of promiscuous mode In DSR-np, only the forwarding nodes extract topology information from source routing headers Therefore, nodes must initiate the route discovery process more frequently, resulting in higher energy costs for broadcast and point-to-point traffic However, since overheard traffic can be discarded, energy savings outweigh the additional costs incurred DSR-np reduces the cost of the route discovery process because rebroadcast messages are jittered in time to reduce the
0
100000
200000
300000
400000
500000
600000
700000
0
max mobility
zero mobility
pause time(s)
DSR/DSR-np/AODV discard
recv(promisc) drop
recv send
Figure 19.1 Energy comparison of all traffic (From [7], reprinted with permission from Laura Feeney.)
Trang 7risk of collisions An expanding ring search, in which a sequence of hop-count-limited route discoveries limits the route request messages dispersed, is also used
The results also show that operating in ad hoc mode of the network interface incurs a significant cost Allowing the use of the low-power sleep mode will be important to the practical development of ad hoc networks It will also be necessary for energy-aware tocol design in the future Variable transmit power could be used in an ad hoc routing pro-tocol that could also be used as a QoS metric for network-wide resource management and load balancing
Typical metrics used to evaluate ad hoc routing protocols are shortest hop, shortest delay, and locality stability [25] However, these metrics may have a negative effect in wireless networks because they result in the overuse of energy resources of a small set of mobiles, decreasing mobile and network life
The research in power-aware routing protocols has considered two types of traffic: uni-cast and broaduni-cast Uniuni-cast traffic is defined as traffic in which packets are destined for a single receiver Broadcast traffic is intended for all network nodes
19.4 POWER-AWARE ROUTING METRICS 413
0
100000
200000
300000
400000
500000
600000
700000
0
max mobility
zero mobility
pause time(s)
DSR/DSR-np/AODV discard
recv(promisc) drop
recv send
Figure 19.2 Routing overhead comparison (From [7], reprinted with permission from Laura Feeney.)
Trang 819.4.1 Global Information-Based Algorithms
In [25], routing of unicast traffic is addressed with respect to battery power consumption The authors’ research focuses on designing protocols to reduce energy consumption, in-crease the life of each mobile, and inin-crease network life To achieve this, five different metrics were defined: (i) energy consumed per packet; (ii) time to network partition, where the network is partitioned because of node death; (iii) variance in power levels across mobiles; (iv) cost per packet; and (v) maximum mobile cost
In order to conserve energy, the goal is to minimize all the metrics except for the sec-ond, which should be maximized As a result, a shortest-hop routing protocol may no longer be applicable; rather, a shortest-cost routing protocol with respect to the five
ener-gy efficiency metrics would be pertinent For example, a cost function may be adapted to accurately reflect a battery’s remaining lifetime The premise behind this approach is that although packets may be routed through longer paths, the paths contain mobiles that have greater amounts of energy reserves Also, energy can be conserved by routing traffic through lightly loaded mobiles because the energy expended in contention and retransmis-sion is minimized
The properties of power-aware metrics and the effect of the metrics on end-to-end de-lay are studied in [25] using simulation A comparison of shortest-hop routing and the power-aware, shortest-cost routing schemes was conducted The performance measures were delay, average cost per packet, and average maximum node cost Results show that usage of power-aware metrics result in no extra delay over the traditional shortest-hop metric This is true because congested paths are often avoided However, there was signif-icant improvement in average cost per packet and average maximum mobile cost, in which the cost is in terms of the energy efficient metrics defined above The improvements were substantial for large networks and heavily loaded networks Therefore, a more energy-efficient routing scheme may be obtained by adjusting routing parameters
19.4.2 Local Information-Based Algorithms
Most of the routing protocols may be considered global algorithms that incorporate global topology and other information Stojmenovic and Lin [22] consider the concept of local-ized routing algorithms in which routing decisions are made based on the location of a source node’s neighbors and the destination Their paper assumes that the nodes have global positioning system (GPS) receivers to provide location information to nodes, which allows the nodes to use the least transmission power needed for reception The research considers networks that may be static, quasistatic, or mobile
Stojmenovic and Lin define a new power cost metric based on the combination of a node’s lifetime and distance-based power metrics Power, cost, and power cost, GPS-based localized routing algorithms are also proposed The goal of the power-aware algorithm is
to minimize the total power needed to route a message from source to destination The goal of the cost-aware algorithm is to extend a node’s worst-case lifetime The goal of the combined power cost algorithm is to minimize the total power needed and to avoid nodes with short battery lifetimes Stojmenovic and Lin also show that the algorithms are loop-free—an important characteristic
Trang 9Stojmenovic and Lin generalize the model of Rodoplu and Meng [18] and assume that
the power needed for transmission and reception of a signal is u(d)= ad␣+ bd + c in order
to include models that attenuate signal power of various exponents The coefficient a
de-pends upon the physical environment, unit of length considered, unit size of a signal, and
so on The distance between two nodes is denoted as d The factor ␣represents signal at-tenuation and is adjusted depending on the model used Typically, ␣= 2 and ␣= 4 are used
for free-space and urban environments The factor c represents energy consumption for
activities such as computer processing and encoding/decoding
General Concepts of Localized Algorithms
A localized algorithm defines each node as being capable of making forwarding decisions based on its own location, the locations of its neighboring nodes and the destination, and a constant amount of additional information
It is assumed that every node stores the geographic location information of all other nodes in the network in its routing table This includes the time when the location of the node is established The location update is done as follows The sender attaches its latest location to an outgoing message Intermediate nodes may use their most recent location information, replace the location information in the header, and also update their own Path adjustments can be made as the message travels closer to the destination The routing table is only used to provide approximate location information of the destination node and accurate information about the location of neighboring nodes
If nodes have information about the position and activity of all other nodes in the net-work, then Dijkstra’s single source, shortest weighted path algorithm can be applied as the
optimal power saving algorithm For this algorithm, each edge has a weight of u(d) = ad␣ + bd + c, as described earlier This paper [22] describes a corresponding localized routing algorithm A source node or intermediate node, S, selects one of its neighbors, A, to for-ward a packet tofor-wards its destination node so that the power required to transmit from S to
A is minimized If we assume a triangle with vertices A, B, and D, where r = |AB|, d = |BD|,
and s = |AD|, then the power needed for transmission from B to A is u(r) = ar␣+ br + c It
is assumed that the power consumption for the rest of the routing algorithm is optimal
This means the power needed for transmission from A to D is approximately v(s) = bs + sc[a(␣– 1)/c]1/␣+ sa[a(␣– 1)/c](1–␣)/␣ When ␣is equal to 2, v(s) = 2s(ac)1/2+ bs.
Power-Aware Algorithms
In the localized power-efficient routing algorithm, each node B selects one of its neigh-bors A that will minimize p(B, A) = u(r) + v(s) If the destination node, D, is a neighbor of
B, then the packet is sent directly to D if it reduces energy D can be treated as any other
neighbor, and the algorithm proceeds until the destination is reached, if possible If
loop-ing is detected, then the algorithm stops The algorithm attempts to minimize p(B, A) =
u(r) + tv(s), where t is a network parameter In the experiments reported in this paper [22],
t is set to one.
Another metric measuring a node’s lifetime is studied in [25] The cost of each node is
represented as f (A) = 1/g(A), where g(A) stands for the remaining lifetime This paper
de-scribes a localized version of this algorithm, and constant power for each transmission is
assumed The cost, c(a), of a route from B to D using a neighboring node A is the sum of
19.4 POWER-AWARE ROUTING METRICS 415
Trang 10the cost f (A) = 1/g(A) and the estimated cost of the route from A to D Node B has knowl-edge of the cost f (A) of each of its neighbors It is assumed that the cost of the remaining nodes on the path between A and D is proportional to the number of hops between A and
D The number of hops is proportional to the distance between A and D and is inversely
proportional to radius R Thus, the cost can be represented as ts/R, where different values
of t have been investigated The cost definitions, c(A) = f (A)ts/R and c(A) = f (A) + ts/R are
suggested for investigation, since it is not clear which will give the best results
Then, power and cost factors are merged into a single routing algorithm Merging based on the product or sum of the two metrics is proposed In the first case, the power
cost of sending a message from B to a neighbor A is represented as power cost(B, A) =
f (A)u(r), where r is equal to the distance between A and B The power cost algorithm can
find the optimal power cost by applying the single-source, shortest weighted path
Dijk-stra’s algorithm In the second case, it may be represented as power cost(A, B) = ␣u(r) +
f (A), with suitable values for ␣and 
The power-cost-efficient routing algorithm can be described as follows Let A be the neighbor of B that minimizes pc(B, A) = power cost(B, A) + v(s)f ⬘(A), where s = 0 for D, if
D is a neighbor of B This algorithm is referred to as power cost 0 when power cost(B, A)
= f (A)u(r) Power-cost 1 refers to power cost(B, A) = f⬘(S)u(r) + u(r⬘)f (A) The packet is delivered to neighbor A The packet does not have to be delivered to D when D is B’s
neighbor The algorithm keeps running until the destination node is reached, if possible The second term can be modified to compensate for different network conditions A
vari-ation, power cost 2, minimizes pc(B, A) = f (A)[u(r) + v(s)], and power cost P switches
se-lection criteria from power cost to the power metric when destination D is a neighbor of current node A Stojmenovic and Lin [22] provide proofs to show that these three routing
algorithms are loop-free
Simulation Results
Experiments are conducted using random 100-node unit graphs, as reported in [22] The
average node degree, k = 10, is controlled Disconnected graphs are ignored The
distrib-uted power efficient routing algorithm was seen to outperform the GPS-based algorithms for all network sizes The results assume greater significance for a larger network Also, the power-efficient algorithm produced paths close to the optimal ones (obtained by SP) For the evaluation of cost and power-cost-efficient routing algorithms, it is assumed that nodes have different remaining powers An iteration is defined as a routing task spec-ified by a random choice of source and destination nodes Experiments are run to deter-mine the number of iterations until the first node dies The simulations are run for 20 graphs for different network sizes and for HCB models [8]
Both cost functions and the different power-cost methods give similar simulation re-sults The performance of the proposed localized cost and power cost methods and the corresponding nonlocalized shortest path cost and power cost algorithms are found to be comparable The cost and power cost algorithms last significantly longer in terms of itera-tions than the power algorithm The average remaining power at each node after the net-work dies for the most competitive methods were analyzed It was seen that the cost
meth-ods have more remaining power only when m = 10 (smallest network) Two better power
cost methods leave about 15% more power at nodes than the cost method for larger values