This paper proposes a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters, and extends this algorithm to generate a hierarchy of clusterheads and observes that the energy savings increase with the number of levels in the hierarchy.
Abstract:
A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.
TL;DR: It is proved that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks.
TL;DR: A survey of state-of-the-art routing techniques in WSNs is presented and the design trade-offs between energy and communication overhead savings in every routing paradigm are studied.
TL;DR: A taxonomy and general classification of published clustering schemes for WSNs is presented, highlighting their objectives, features, complexity, etc and comparing of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.
TL;DR: This work proposes SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (the authors refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable.
TL;DR: A protocol is presented, HEED (hybrid energy-efficient distributed clustering), that periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree, which outperforms weight-based clustering protocols in terms of several cluster characteristics.
TL;DR: The Low-Energy Adaptive Clustering Hierarchy (LEACH) as mentioned in this paper is a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network.
TL;DR: LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network, is proposed.
TL;DR: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits persecond under a noninterference protocol.
TL;DR: S-MAC as discussed by the authors is a medium access control protocol designed for wireless sensor networks, which uses three novel techniques to reduce energy consumption and support self-configuration, including virtual clusters to auto-sync on sleep schedules.
TL;DR: S-MAC uses three novel techniques to reduce energy consumption and support self-configuration, and applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network.
Q1. What are the contributions in "An energy efficient hierarchical clustering algorithm for wireless sensor networks" ?
The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. In this paper, the authors propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. Results in stochastic geometry are used to derive solutions for the values of parameters of their algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.
Q2. How many hops away from the clusterhead?
since all the sensors within a cluster are at most k hops away from the cluster-head, the clusterhead can transmit the aggregated information to the processing center after every t units of time.
Q3. How much memory would be needed to store the optimal probability values?
if one chooses to store the numerically computed values of optimal probability in the sensor memory, only a small amount of memory would be needed.
Q4. What is the idea of the derivation of the optimal probability of becoming a clusterhead?
The basic idea of the derivation of the optimal parameter values is to define a function for the energy used in the network to communicate information to the information-processing center and then find the values of parameters that would minimize it.0-7803-7753-2/03/$17.00 (C) 2003 IEEE IEEE INFOCOM 20031) Computation of the optimal probability of becoming a clusterhead:
Q5. how many sensors will not join a cluster?
(11)This means that the expected number of sensors that will not join any cluster is αn if the authors set = − λ α 1 1 )7/ln(917.01 pr k . (12)To ensure minimum energy consumption, the authors will use a very small value for α , which implies that the probability of all sensors being within k hops from at least one volunteer clusterhead is very high.
Q6. What is the cost of communication in a network of sensors?
In networks of sensors with higher communication radius, the distance between a sensor and the processing center in terms of number of hops is smaller than the distance in networks of sensors with lower communication radius and hence there is lesser scope of energy savings.
Q7. What is the value of the Poisson process?
by properties of the Poisson process, level-i CHs, hi ,...,2,1= are governed byhomogeneous Poisson processes of intensities, ∏= =ij ji p 1 1 λλ .
Q8. how many sensors will join a cluster?
For 001.0=α and values of p and k computed according to (10) and (12), for a network of 1000 sensors, on an average 1 sensor will not join any volunteer clusterheads and will become a forced clusterhead.