首页|Heterogeneous load balancing improvement on an energy-aware distributed unequal clustering protocol using BBO algorithms
Heterogeneous load balancing improvement on an energy-aware distributed unequal clustering protocol using BBO algorithms
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NETL
NSTL
Springer Nature
With the advancement of ICT technology today, we can equip every single object with tiny and inexpensive radio modules; hence, they can interact and cooperate to perform complex tasks that have not been possible previously. During recent years several practical IoT-based applications have been proposed by deploying this improvement. With the expansion of IoT-based systems, many challenges began to emerge in this area. Energy optimization has been considered, as one of the most important problems in this domain, and data transmission has been identified as the primary accused. To tackle this problem, clustering has been suggested as a promising solution for reducing transmission distance and consequently energy consumption. The non-deterministic polynomial-time hard problem nature of clustering; alongside a variety of considerable parameters, limitations, and their contradiction, have made this problem more complex. As a result, various approaches have been proposed during recent years, each of which considered some parameters and real-world constraints. Here we present an efficient improvement on the existing energy-aware distributed unequal clustering protocol (EADUC). Our solution deploys a well-known swarm intelligence algorithm (SI), named Biogeography-based optimization (BBO) in a distributed manner to achieve heterogeneous load balancing. Our proposed work reflected a variety of real-world limitations such as energy, time, communication radius, and buffer size that have not been considered in many previous works simultaneously. Our simulations show approximately a 26% drop in the total number of dead nodes and a 1.59 % drop in energy consumption in comparison to the existing EADUC algorithm.
Internet of thingsEnergy consumption optimizationLoad-balancingSwarm intelligenceDistributed clusteringBiogeography-based optimization
NETL
NSTL
Springer Nature
