Background of Ad hoc Wireless Networks Student Presentations Wireless Communication Technology and Research Ad hoc Routing and Mobile IP and Mobility Wireless Sensor and Mesh Networks Mobile and Ad hoc Networks Energy Awareness in Adhoc Networks

Outline  Introduction  Metrics for power awareness  Routing Protocols  Power Source Routing (PSR)  Local Energy Aware Routing (LEAR)  Low Energy Adaptive Clustering Hierarchy (LEACH)  References

Introduction – Power Concerns  The lifetime of a network is defined as the time it takes for a fixed percentage of the nodes in a network to die out.  Portability of wireless nodes being critical its almost mandatory to keep the battery sizes to a bare necessary  Since battery capacity is fixed, a wireless mobile node is extremely energy constrained  Hence all network related transactions should be power aware to be able to make efficient use of the overall energy resources of the network

Traditional routing metrics  Aims to minimize hop counts and propagation delay  Fails to take into account the power usage of nodes  Results in poor lifetime of networks

Energy Models  Capture the effect of the limited energy reserves of mobile devices (i.e. batteries)  Models the power levels of the device during operation so that total energy consumption can be calculated  A number of different models are used in the literature  Transmission Power Model  Transmission & Reception Model  Power State Model

Transmission Power Model  Assumes energy consumption is directly related to wireless output power  Output power in typical cards range from 1mW to 200mW  Output power is related to the square of range  Cutting transmission range in half cuts output power requirement by ¼  This means by choosing shorter hops, the total output power can be reduced (¼ + ¼ < 1)  Used by many papers (particularly theory papers)  Minimum energy routing  Minimum energy broadcast  Topology control  Flawed model ignores MANY sources of energy consumption  Fixed transmission consumption overhead  Consumption by receiver  Consumption by idle nodes

Transmission & Reception Model  Energy consumption depends on the number of packets sent & received  Energy consumption of a packet is calculated using several constants  Not very commonly used  Increased accuracy but still missing a significant contribution to energy consumption (idle power)

Power State Model  Uses different power levels depending on state of wireless card  Transmit (1.33 Watts)  Receive (0.97 Watts)  Idle (0.84 Watts)  Sleep (0.07 Watts)  Based on measurements of a real wireless card on lab equipment  Captures the majority of card power consumption effects (most accurate model in general use)  Measured values only apply for the exact model of card  Does not take into account transient consumption from mode switches  Does not take into account power consumption of host (i.e. from packet processing)  Usually assumes fixed output power (not normally used with transmission power control)

Sending Power Example Sending Receiving

Receiving Power Example

Different Transmit Options RF Transmit Power (mW)

Sleep Mode  High consumption while active  High transmit power constant  High idle & receive power  Sleep mode allows much of the electronics to be turned off  Radio cannot send or receive packets  Can be activated again by host in a small amount of time  SIGNIFICANTLY lower power levels (0.07 Watts)  Only protocols that make extensive use of sleep mode can save a large fraction (>50%) of the card power consumption  Sleep mode limit = <90% savings  Transmit power control limit = <<35% savings

Power Aware Metrics Intuition: Conserve power and share cost of routing packets to ensure increase in life of node and network Metrics: 1. Minimize energy consumed / packet 2. Maximize time to Network Partition 3. Minimize variance in node power levels 4. Minimize cost / packet 5. Minimize maximum node cost

1. Minimize energy consumed / packet Definitions:  T(a,b) = energy consumed in transmitting and receiving one packet over one hop from a to b - e j = Σ k-1 i=1 T(n i, n i+1 ) = total energy spent for packet j over k hops Goal: - Minimize e j for all packets j Note: - In lightly loaded networks this automatically finds shortest hop path - In heavily loaded networks due to contention it might not be shortest - The use of “energy/packet” alone as a cost may result in wide variations of node power consumption

2. Maximize time to network partition Definition: - Cut Set: set of nodes that divide the network into two partitions As soon as one node in the set dies the delay experienced increases Goal: - To balance load of the nodes in the Cut Set to maximize network life Problems: - The problem is similar to scheduling tasks to multiple servers so that the response time is minimized, which is known to be NP-complete

3. Minimize variance in all node power levels Goal: - To keep all nodes up and running together for as long as possible Concept: - Build a route that takes into account the amount of data waiting to be transmitted in all the intermediate nodes Merit: - Achieve some kind of load balancing to ensure similar rates of dissipation of energy throughout the network

4. Minimize cost / packet Definition: We associate a cost f i (x i ) with each node i. Thus total cost of sending packet j: c j = Σ k-1 i=1 f i (x i ) Where, - x i is the total energy dissipated by node i till now (thus far) - f i (x i ) is the cost of node i. - Intuitively, f i (x i ) indicates a node’s reluctance to forward packets (i.e. It is highly reluctant if the cost is high, when the expended energy so far is high). Goal: - Minimize c j

4. Minimize cost / packet (contd.) Advantage: - The remaining batter power level is incorporated into the routing decision - This also balances load by avoiding usage of weak nodes in presence of stronger ones - The routing starts like shortest path routing, then the routes start to get longer as the metric kicks-in to route around nodes that are reluctant to route (because they expended their energy during the shortest path mode).

5. Minimize maximum node cost Definition: - C i (t) = cost of routing a packet through node i at time t - Ĉ(t) = maximum of the C i (t)’s Goal: - Minimize Ĉ(t), for all t > 0 Merits: - Delays node failure - Reduces variance in node power levels

Power-Aware Source Routing (PSR)  This is a Reactive (On demand) protocol based on DSR  This Cost function takes into account both transmission power and remaining battery power  RREQ broadcast is initiated by source  Intermediate nodes can reply to RREQ from cache as in DSR  If there is no cache entry, receiving a new RREQ an intermediate node does the following:  Starts a timer  Keeps the path cost in the header as Min-cost  Adds its own cost to the path cost in the header and broadcast  On receiving duplicate RREQ an intermediate node re-broadcasts it only if the following is true:  The timer for that RREQ has not expired  The new path cost in the header is less than Min-cost  Destination also waits for a specific time after the first RREQ arrives  It then replies to the best seen path in that period and ignores others that come later  The path cost is added to the reply and is cached by all nodes that hear the reply

PSR Route Maintenance  Node mobility: Connections between some nodes on the path are lost due to their movement. In this case a new RREQ is issued and the corresponding entry in the cache is purged.  Energy Depletion: Energy of some intermediate node maybe depleting very quickly. This can be addressed in two ways:  Semi-global approach:  Here the source monitors the remaining battery level of the path by periodically polling the intermediate nodes  Local approach:  Each intermediate node is allowed to send back a route error at time t if its battery level is too low

PSR vs DSR – Simulation on NS(2)  Test bed of 20 nodes confined in 1000 x 1000 m^2 area  Range of each node is 250 m  100 reliable and random ftp connections  Average duration of connection is 20 sec  Total simulation time sec  Speed of movement is 10 m/s  Random mobility with pause time of 4 sec

PSR vs DSR – network lifetime

Local Energy-Aware Routing (LEAR)  Aims to balance energy consumption with shortest routing delays  Takes into account a node’s willingness to participate in the routing path which is based on its remaining battery power  Destination does not wait to reply –> non-blocking  Efficient use of route cache

The basic LEAR Algorithm Source uses a sequence number for new request If it gets no reply back it increases the sequence number and re-broadcasts

LEAR – Simulation on GloMoSim  Test bed of 40 nodes confined in 1000 x 1000 m^2 area  Range of each node is 250 m  5 Constant Bit Rate source and destination pair chosen  1024 byte packets sent every sec for a specified duration  Total simulation time 500 sec  Random waypoint mobility  Speed of movement is 5 m/s  Pause time is varied from 50 to 400 sec  Simulation results shown next are average of 100 runs  Initial Threshold value set to 90% of node’s initial power  The value of adjustment ‘d’ is taken as 0.1 or 0.4

LEAR – Standard Deviation of energy distribution Energy Consumption measured at radio layer 35% improved energy balance with high mobility (50 sec pause time) 10% improvement with moderate mobility (400 sec pause time) The ‘d’ value does not affect much

LEAR – Ratio of accepted ROUTE_REQ Ratio = total route_reqs accepted / total route_reqs received Even DSR does not have 100% ratio due to TTL ‘d’ = 0.1 drops requests more frequently due to lower adjustment

Low Energy Adaptive Clustering Hierarchy (LEACH) In this we consider a micro-sensor network where: 1. The base station is fixed and located far from sensors 2. All nodes are homogeneous and energy constrained Key features of LEACH: 1. Localized coordination and control for cluster setup and operation 2. Randomized rotation of the cluster heads and the corresponding clusters.

Data Transmission  Radios of non-heads are off when its not transmitting, to preserve energy.  When all data has been received from all the nodes the head performs signal processing to compress the data into a single signal  This is then send directly to the base station by a high energy transmission.

Direct Transmission –vs- LEACH

References - I [1]Power-Aware Routing in Mobile Ad Hoc Networks – Suresh Singh, Mike Woo, C.S. Raghavendra [1]Power-aware Source Routing Protocol for Mobile Ad Hoc Networks – Morteza Maleki, Karthik Dantu, and Massoud Pedram [2]Non-Blocking Localized Routing Algorithm for Balanced Energy Consumption in Mobile Ad Hoc Networks – Kyungtae Woo, Chansu Yu, Hee Yong Youn, Ben Lee [3]Hierarchical Power-aware Routing in Sensor Networks – Qun Li, Javed Aslam, Daniela Rus [4]Minimum Energy Mobile Wireless Networks – Volkan Rodoplu, Teresa H. Meng [5]A Location-aided Power-aware Routing Protocol in Mobile Ad Hoc Networks – Yuan Xue, Baochun Li

References - II [6] Geographical and Energy Aware Routing: a recursive data dissemination protocol for wireless sensor networks – Yan Yu, Ramesh Govindan, Deborah Estrin [7] Energy-Efficient Communication Protocol for Wireless Microsensor Networks - Wendi Rabiner Heinzelman, Anantha Chandrakasan, Hari Balakrishnan [8]Adaptive Protocols for Information Dissemination in Wireless Sensor Networks - Wendi Rabiner Heinzelman, Joanna Kulik, Hari Balakrishnan

Assignment #7  What is meant by NP-Complete?  What is the difference between NP-HARD and NP- Complete Problems  Go through the research papers of energy aware routing protocols given in References.

Q&A ??