Sensor nodes heterogeneity if not properly utilized could lead to uneven energy consumption and load imbalanced across the network, which degrades the performance of the network. Routing algorithms should try to achieve energy-efficiency and load-balancing among the heterogeneous nodes to prolong network lifetime. One of the solutions is by using duty-cycling in cluster-based routing such as in Sleep-awake Energy Efficient Distributed (SEED) clustering algorithm to minimize redundant transmission to achieve energy efficiency. However, this scheme suffers from idle listening problem, which lead to energy wastage across the network. Moreover, SEED cannot cope with an environment with sensor nodes with heterogeneous traffic rate. To cope with energy and traffic heterogeneity issues among sensor nodes, a traffic and energy aware routing protocol (TEAR) is proposed. TEAR avoids selecting node with low energy and high traffic rate for cluster head role to achieve load balancing. However, TEAR does not avoid redundant transmission from the sensor nodes that are in close distances. In this paper, we proposed a hybrid method called energy and traffic aware sleep-awake (ETASA) mechanism to improve energy efficiency and enhanced load balancing in heterogeneous wireless sensor network scenario. Unlike prior methods, in ETASA, the paired nodes alternate into sleep and awake mode based on node's energy and traffic rate. Moreover, we revised the conventional TDMA scheduling in SEED by allocating one slot for group of pairs in a cluster. This is done to address idle listening problem to minimize energy consumption. The proposed method improves the cluster head selection technique that selects high energy, low traffic and nodes with high number of pairs to improve balanced energy consumption. The proposed approach is evaluated and compared against the state-of-the-art baseline protocols. The result shows that the proposed ETASA has 16% and 15% lifetime improvements against TEAR and SEED.
Abstract:In this paper, we propose an extended multi-objective version of single objective optimization algorithm called sperm swarm optimization algorithm. The proposed multi-objective optimization algorithm based on sperm fertilization procedure (MOSFP) operates based on Pareto dominance and a crowding factor, that crowd and filter out the list of the best sperms (global best values). We divide the sperm swarm into three equal parts, after that, different types of turbulence (mutation) operators are applied on these parts, such as uniform mutation, non-uniform mutation, and without any mutation. Our algorithm is compared against three well-known algorithms in the field of optimization. These algorithms are NSGA-II, SPEA2, and OMOPSO. These algorithms are compared using a very popular benchmark function suites called Zitzler-Deb-Thiele (ZDT) and Walking-Fish-Group (WFG). We also adopt three quality metrics to compare the convergence, accuracy, and diversity of these algorithms, including, inverted generational distance (IGD), spread (SP), and epsilon (∈). The experimental results show that the performance of the proposed MOSFP is highly competitive, which outperformed OMOPSO in solving problems such as ZDT3, WFG5, and WFG8. In addition, the proposed MOSFP outperformed both of NSGA-II or SPEA2 algorithms in solving all the problems.
Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.
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