L 3 (Live & Let Live)- Increasing Longevity in Sensor Networks EE 228A Professor Walrand Contributors: Tanya Roosta Anshuman Sharma

Introduction Problem Definition Existing Approaches Our Approach Future Work Conclusion Q&A Outline

Introduction Problem Definition Existing Approaches Our Approach Future Work Conclusion Q&A Outline

Introduction What are sensor networks Networks comprised of hundreds to thousand nodes, where each node is a sensor Examples of use include guidance and control, data collection and aggregation Sensor nodes are designed to be – Low cost – Non obtrusive – Dynamically reprogrammable

Introduction Problem Definition Existing Approaches Our Approach Future Work Conclusion Q&A Outline

Problem Definition Sensors must be lightweight and compact Limited Power Supply Replenishing power is not an option Important to minimize power consumption of each node to maximize battery life and lifetime of entire network Existing network protocols stress on QoS (high throughput and low delay) and high bandwidth efficiency

Problem Definition (cont…) Energy Consumption Energy consumption occurs in three domains: sensing, data processing and communication. In a wireless sensor network, communication is the major consumer of energy Example For ground to ground transmission, it costs 3J to transmit 1 Kb over a distance of 100m. However, a general-purpose processor with 100 MIPS processing capability executes 300 million instructions for the same amount of energy

Problem Definition (cont…) Design Challenges Three main classes – Hardware – Wireless Networking – Application

Problem Definition (cont…) Routing in Wireless Networks: Revisited Direct Communication Protocol: Each sensor sends its data directly to the base station Multi-hop routing protocol (MTE) Nodes route data destined to the base station through intermediate nodes At first look it seems that a multi-hop approach would be able conserve more power

Problem Definition (cont…) Multi-hop Routing Protocols Table-driven (proactive) – Destination-Sequenced Distance-Vector Routing – Cluster Gateway Switch Routing – Wireless Routing Protocol Source-initiated (Reactive) – Ad Hoc On-Demand Distance Vector Routing – Dynamic Source Routing – Temporally-Based Routing – Signal Stability Routing

Introduction Problem Definition Existing Approaches Our Approach Future Work Conclusion Q&A Outline

Existing Approaches Power-Aware Routing: Metrics Minimize energy consumed/packet: Minimizes the total energy consumed over n nodes Maximize Time to Network Partition: A load balancing problem so that the response time is minimized Minimize Cost/Packet: Assigns a cost function to each node and minimizes the total cost of routing a packet from that node

Existing Approaches (cont…) Routing in Clustered Multi-hop Networks Aggregate nodes into clusters controlled by a cluster-head Clustering on the basis of either lowest-ID distributed clustering algorithm or highest- connectivity algorithm Within a cluster, a cluster-head controlled token protocol used to allocate channel. Cluster Routing Protocol

Total system energy dissipated for the 100-node random network

Existing Approaches (cont…) Adaptive Energy-Conserving Routing BECA – Turn of radio power – Involvement of application layer information – Can increase latency and packet loss AFECA – All the nodes do not need be involved – Exploiting node density – Can interchange nodes for routing purposes

Existing Approaches (cont…) Adaptive Energy-Conserving Routing (cont…) BECA – Nodes are in three possible states: sleeping, listening, active. – Start in sleeping state. Radio is off. – After a certain time, transition to listening state – If a node has data to transmit it transitions to active state

Existing Approaches (cont…) Adaptive Energy-Conserving Routing (cont…) AFECA – Used in densely-populated networks – Each node estimates its neighborhood – Each node increases its sleeping time proportional to the number of nodes in its neighborhood

BECA versus AODV for different values of sleeping time The latency for unmodified AODV is fixed The latency grows roughly linearly The growth is slightly lower at higher traffic rates

Percentage of energy saved is (E r - E s ) / E r Less saving for higher traffic rates since more nodes in active mode High values of sleeping time give no energy improvement

PE is the loss rate PE=P/E where P is the size of data delivered and E is the total energy consumed by all nodes We can use PE to determine an optimal value for the sleeping time

Assumption: Unlimited amount of energy in the nodes As expected AFECA and BECA do worse in terms of latency and packet loss than unmodified AODV

AFECA has a better energy consumption than BECA as expected

AFECA aggressive power savings result in the consistently highest efficiency

BECA protocol is about 20% longer and AFECA is about 55% longer than unmodified AODV when the energy in the nodes is limited. Assumption: The nodes have limited amount of power

Outline Introduction Problem Definition Existing Approaches Our Approach Future Work Conclusion Q&A

Our Approach Insight Computation is much cheaper than communication Use of distributed approach to reduce – Total number of transmissions – Energy dissipated in the network Application-level/ higher layer feedback is important Establish trade-offs (complexity vs. performance improvement, etc)

Our Approach (cont…) Radio Model (First Order) E Tx (k,d)=E Tx-elec (k) + E Tx-amp (k,d) =E elec *k +  amp *k*d 2 E Rx (k)=E Rx-elec (k) =E elec *k E Tx-elec = E Rx-elec = E elec (Energy dissipated to run Rx/Tx)  amp (Energy dissipated for amplifying to get good gain) Source: Energy-Efficient Communication Protocol for Wireless Microsensor Networks: MIT

Our Approach (cont…) Additions to Radio Model Does not consider energy consumption while radios are idle Inclusion of idle time based on experiments with WaveLAN radios Most of the time the radio is idle, hence idle time dominates energy consumption Add term   idle (idle energy expended per unit time)

Our Approach (cont…) Important to determine critical transmission range Let there be – n total nodes – k cliques that we intend to form Use modified Prim algorithm to form cliques of at least 3 nodes Why the magic number 3?

Our Approach (cont…) Pick k nodes at random (for each of the k cliques) k nodes are temporary cluster-heads Start with some minimum radius of discovery -  Goal is to discover a minimum of 3 nodes for each clique Increments of , if cannot find any node in the periphery After the first node is discovered it tries to look for another node, incrementing by  each time

Our Approach (cont…) All three nodes then adjust their transmission power to reach other This results in a Hamiltonian Cycle If more than 3 nodes are possible without increasing power then OK to have > 3 nodes in clique After forming cliques, use TDMA to allocate time-slots for nodes to be cluster-head. The nodes also use TDMA to schedule updates to cluster-head (intra-clique communication).

Our Approach (cont…) The other nodes are put to sleep (turn-off radios) when not communicating, similar to PAMAS A cluster head is responsible for discovering other cliques and sharing information within the clique. Possibility of adding multiple hierarchies depending upon the trade-off between complexity and advantages

Our Approach (cont…) Considerations GPS is available but might not be viable Next generation design of Low power ICs can make adjusting duty cycle easy Exploring node density as a measure of reducing computation and communication overhead CDMA codes allow efficient use of the channel bandwidth

Outline Introduction Problem Definition Existing Approaches Our Approach Future Work Conclusion Q&A

Future Work Evaluating model through simulations Tuning density to trade operational quality against lifetime Using multiple sensor modalities to obtain robust measurements Exploiting fixed environmental characteristics Using a more comprehensive radio model that takes into account time to wake up from sleep cycles Exploring of various benchmarks for “lifetime” of a network

Outline Introduction Problem Definition Existing Approaches Our Approach Future WorkConclusion Q&A

Conclusion Our model is based on work that has already been done We exploit characteristics of proven approaches Simulations would provide a measure of advantages incurred by using our approach

Outline Introduction Problem Definition Existing Approaches Our Approach Future Work ConclusionQ&A

Q&A