Evaluation of Power Aware Routing Protocols Mohammad Mahmud Wireless Networks Professor: Dr. Lijun Qian

Power Aware Routing In a typical network, the route of a packet will be determined by calculating which path is either fastest, or has the least amount of hops. This may mean that some nodes in the network get far more usage than others. If nodes have a limited power supply, such as portable computers, extreme usage could quickly drain the battery. A temporary mobile network, such as an ad hoc network, would benefit from Power Aware Routing. Examples: Soldiers in the battlefield or rescue workers after a disaster.

Power Aware Routing In this network, it is clear that node 6 will receive the most usage, causing its batteries to become quickly depleted. This will cause node 6 to crash, disrupting the network. Other nodes will also quickly crash as they try to absorb the traffic of node 6.

Power Aware routing Power efficient schemes:  Physical layer Data transmission at the minimum power level to maintain links and to avoid interference  Data Link Layer Effective retransmission schemes for error free transmission. When and at what power level a mobile host should attempt retransmission  Network Layer routing protocol can balance the traffic load inside the network and thus increase the battery lifetime.

Power Aware Routing SPAN:  Topology based routing protocol based on network backbone  Connected by masters who take care of routing  Periodic Hello messages to discover their two­hop neighborhood  eligible to be a coordinator if it discovers that two neighbors cannot communicate directly or via other coordinators  A backoff interval follows  Nodes with greater effectiveness at connecting new pairs of neighbors, and higher energy reserves announce themselves as coordinators more quickly than less effective ones

SPAN( cont..) Proactive, as periodically exchanges Hello messages Random back off, as all with similar energy level may want to decide coordinators Periodically checks if it should withdraw from coordinating As network density decreases, becomes more efficient

Minimum Total Transmission Power Routing (MTPR) Minimizes total energy consumed in forwarding a packet form source to destination. Exploits exponential path loss by forwarding traffic using a sequence of low power transmission rather than a single direct transmission. Disadvantages  Selects most power efficient path. So nodes along this route may die early because of excessive power usage.

Minimum Battery Cost Routing (MBCR) The remaining battery capacity of each host is a more accurate metric to describe the life time of each host Battery cost function of a host Battery cost Rj for route I To find the maximum remaining battery capacity, we select route I that has the minimum battery cost.

Minimum Battery Cost Routing (MBCR) If all nodes have similar battery capacity, this metric will select a shorter-hop route Only consider the summation of values of battery cost; therefore can overuse any single node

Min-Max Battery Cost Routing (MMBCR) The power of each host is being used more fairly in this scheme than previous scheme. No guarantee of minimum total transmission power path under all circumstances Consume more power to transmit mean reduce the lifetime of all nodes

Conditional Max-Min Battery Capacity Routing (CMMBCR) Using previous scheme, maximize the life time of each node and use the battery fairly can’t be achieve simultaneously When battery capacity of every node is greater than a threshold, it performs minimum energy routing. If not, it switches to MMBCR.

PSR( Power Source Routing) Modification of DSR ( Dynamic Source Routing) Minimize sum of the energy cost of the links along the routing path Link cost is proportional to the inverse of the remaining battery capacity (residual energy) of the transmitting node

Dynamic Source Routing (DSR) Route discovery is done by flooding the network Nodes listen to control messages flowing through the network Caching techniques improve performance considerably Cost of a path is the number of hops along that path N1 N2 N3 N4 N5 N6 N7 N8 N5-N8 N2-N5-N8

Power-aware Source Routing (PSR) A cost is associated with every node on the path This cost is inversely proportional to the normalized residual energy of the node The cost function is graded, i.e., nodes with very low battery capacity dominate the total cost of the path

PSR ( Cont..) Similar to DSR, but with some differences: An intermediate node passes on the first RREQ and all subsequent lower-cost RREQ’s until a local timer expires Destination starts a timer after receiving the first RREQ and replies back only after that timer expires N1 N2 N3 N4 N5 N6 N7 N8 N5-N8 N2-N5-N8 N3-N4-N7-N8 N4-N7-N8 N7-N8 N8 Energy Level DSR PSR

LPR ( Lifetime Prediction Routing) Maximize the minimum link cost along routing path Maximize the minimum link cost along routing path Link cost is remaining lifetime of transmitting node Link cost is remaining lifetime of transmitting node Node lifetime is equal to the remaining battery capacity divided by the energy depletion rate Node lifetime is equal to the remaining battery capacity divided by the energy depletion rate

LPR Similar to to PSR except that Each node predicts its lifetime when it receives a RREQ Intermediate nodes attach their predicted lifetime to the RREQ packet if it is lower than the current lifetime in the header of the packet Er,i(t): remaining energy at the ith packet is being sent or relayed through the current node Rk(t): rate of energy depletion of current node N: length of the history used for calculating the simple moving average

Simulation Environment  Setting up simulation model in ns-2 or OPNET Generating Network traffic model  Implement routing scheme  Measure Data Some of the commonly used parameters of performance analysis are: Time needed to expire the first node in a networking Time needed to expire some specific percentage of nodes in a network Time needed to expire specific number of critical nodes in a network Number of dead nodes at a specific time.

Power Aware Routing Suggestions: For the stating point, I will choose some sort of on-demand protocol like Lifetime Prediction Routing (LPR) discussed in [4] as it is more energy efficient than the table driven approaches. However, I propose three new metrics to incorporate in LPR.  QoS: On demand protocols are inherently slower as it takes some initial time to find out routing path. To mitigate this problem use QoS  Essential Nodes: If the network environment is densely populated, we can use some sort of algorithm to find out if a certain node is critical or non critical in routing applications.  Mobility History: If nodes can keep track their mobility history, it can be used as a factor in deciding the routing paths. Assume that less intermediate mobile nodes in a network will tend to be more static and thus will be given priority over highly moving intermediate nodes when it comes to routing.

Power Aware Routing Reference 1. C.K. Toh, "Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad hoc Networks”, Communications Magazine, IEEE, Volume: 39 Issue: 6, June 2001 Page(s): 138 – Benjie Chen, Kyle Jamieson, Hari Balakrishnan, and Robert Morris. Span: An energy efficient coordination algorithm for topology maintenance in ad hoc wireless networks. ACM Wireless Networks Journal, 8(5): , September M.Maleki, K.Dantu, and M.Pedram, "Lifetime Prediction Routing for Mobile Adhoc Networks" Wireless Communications and Networking, WCNC IEEE, Volume: 2, March 2003 Page(s):

Power Aware Routing Questions?