1,045 research outputs found
Energy Optimal Data Propagation in Wireless Sensor Networks
We propose an algorithm which produces a randomized strategy reaching optimal
data propagation in wireless sensor networks (WSN).In [6] and [8], an energy
balanced solution is sought using an approximation algorithm. Our algorithm
improves by (a) when an energy-balanced solution does not exist, it still finds
an optimal solution (whereas previous algorithms did not consider this case and
provide no useful solution) (b) instead of being an approximation algorithm, it
finds the exact solution in one pass. We also provide a rigorous proof of the
optimality of our solution.Comment: 19 page
EDOCR: ENERGY DENSITY ON-DEMAND CLUSTER ROUTING IN WIRELESS SENSOR NETWORKS
Energy management is one of the critical parameters in Wireless Sensor Networks. In this paper we attempt
for a solution to balance the energy usage for maximizing the network lifetime, increase the packet delivery
ratio and throughput. Our proposed algorithm is based on Energy Density of the clusters in Wireless
Sensor Networks. The cluster head is selected using two step method and on-demand routing approach to
calculate the balanced energy shortest path from source to sink. This unique approach maintains the
balanced energy utilization among all nodes by selecting the different cluster heads dynamically. Our
simulation results have compared with one of the plain routing scheme (EBRP) and cluster based routing
(TSCHS), which shows the significant improvements in minimizing the delay and energy utilization and
maximizing the network lifetime and throughput with respect to these works
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Lifetime centric load balancing mechanism in wireless sensor network based IoT environment
Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model
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