Energy-Efficient Communication Protocol for Wireless Microsensor Networks by Mikhail Nesterenko Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan

Outline I/O automaton definition examples of I/O automata execution operations on I/O automata –composition –hiding fairness properties and proof methods –invariants –trace properties –compositional reasoning –hierarchical proofs complexity randomization

Radio and CommunicationModel nodes have sensors and limited energy supply nodes sense data and send it to centralized location – base station; base station has large energy supply transmitter and receiver dissipate (use) the same amount of energy (50nJ/bit) –plus transmitter (amplifier) is using additional amount proportional to square of distance to transmit radio channel is symmetric constant bit rate data generation (every node is sensing and always ready to transmit)

Energy Analysis of Routing Protocols two models –direct communication to base station drains the power of transmitters since only base station receives no sensor’s energy is spent on the receivers hence if base station is close to the nodes or energy to receive message is large, this strategy may be optimal –“minimum energy” multihop routing – in addition to sensing nodes act as routers to other nodes’ data conventional protocol optimize transmitter’s energy minimum transmitter’s energy – MTE) but not receiver’s see Figs. 3, 4, 5

More Problems with MTE MTE quickly drains the energy of nodes closest to base station (and they die) which, in turn, drains even more energy from nodes that are slightly off the base station – cascading effect simulation –MTE – nodes closest to base station die first –direct – nodes furthest from bases station dies first see Fig. 6

Clustering conventional clustering assumes that there is a cluster head (local base station) that collects the sensor data and transmits to base station –cluster head is close to the sensors so their energy is conserved –cluster head routes all messages from the cluster and quickly dies

Low-Energy Adaptive Clustering Hierarchy (LEACH) to conserve energy a cluster head LEACH includes randomized rotation of a cluster head cluster head “compresses” (aggregates) the data before sending to base station node randomly (proportional to the amount of remaining energy remaining) elects itself cluster head (independently of other nodes in the network) –informs neighbors about it –gives schedule as two when the neighbors send their data to cluster head –aggregates the sent data and sends it to base station the number of clusters is determined in advance to conserve overall energy (see Figs 7, 8, 9, 10) –there exists an optimum number of cluster heads (~5%) –with this optimum LEACH reduces energy dissipation up to 7 times –less cluster heads, less detailed picture

System Lifetime in LEACH besides decreasing energy dissipation, LEACH increases system lifetime –under LEACH it takes approx 8 times longer for the first node to die and 3 times longer for the last node to die (compared to any other protocol) – see Table 1 –the pattern of nodes deaths is uniform which increases usability of the system as nodes dies; see Fig 12, compare to Fig 6

LEACH Details functioning is in rounds, each round has two phases –setup phase (short) – clusters are organized –steady phase (long) – data is transferred to the base station phases –advertisement – each node probabilistically elects to be a cluster head and sends a “cluster-head-advertisement” message with the same energy, a non-head node picks the head on the basis of received signal strength –cluster setup – the nodes inform cluster heads about their choice –schedule creation – cluster head creates TDMA schedule and broadcasts it to the cluster –data transmission – each node sends data to cluster head, cluster head aggregates and forwards to base station to reduce interference between clusters CDMA spreading codes are used (different frequencies?) LEACH can be extended to hierarchy