Energy optimization and coverage area optimization of wireless sensor networks (WSN) are two major challenges to accomplish reliability optimization in the field of WSN. Reliability optimization in the field of WSN is directly connected to the performance and efficiency and consistency of the network. In this paper, the authors describe how these challenges can be resolved by designing an efficient WSN with the help of meta-heuristic algorithms. They have configured an optimized route/path using ant colony optimization (ACO) algorithm and deployed static WSN nodes. After configuring an efficient network, if we can maximize the coverage area, then we can ensure that the network is a reliable network. For coverage area optimization, they used a hybrid differential evolution-quantum behaved particle swarm optimization (DE-QPSO) algorithm. The result has been compared with existing literature, and the authors found good results applying those meta-heuristic and hybrid algorithms.

In this paper, the establishment of efficient Wireless Sensor Network (WSN) networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach (SMOWA) algorithm for solving a multi-objective problem. The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficient Wireless Sensor Network to minimize energy consumption. After that, the cluster head for each cluster has been selected with the help of the duty cycle. After configuring the WSN networks, the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks. Energy minimization, as well as the amount of day-saving, has been calculated for the different WSNs which has been configured through different deployment policies. The major finding of the research paper is to improve the durability of Wireless Sensor Network (i) applying different deployment strategies: (Random, S pattern and nautilus shell pattern), and (ii) using a new Meta-heuristic algorithm (SMOWA Algorithm). In this research, the lifetime of WSN has been increased to a significant level. To choose the best result set from all the obtained results set some constraints such as "equivalent distribution", "number of repetitions", "maximum amount energy storage by a node" has been set to an allowable range.

Wireless Sensor Network (WSN) is generally used for constructing an efficient network with minimum infrastructure. With this note, the WSN can be considered to construct smart communication in the existing city to facilitate the inhabitants. Nowadays in India, so many cities are going to be transformed into smart cities. In those cities, the maximum electronic gadgets will be IoT enabled. These IoT enabled gadgets should be connected through a robust network. The WSN can be an alternative for constructing a robust network between the electronic devices and the local server (Sink Node). The WSN can also sense many external environmental factors to facilitate the user, such as traffic status, rainfall, heat or smoke, vibration, and pollution detection. One of the major important challenges for the construction of an effective Wireless Sensor Network is to use the existing infrastructure of a city. In this paper, the effective Wireless Sensor Network has been constructed using the existing infrastructure and this is the major reason to choose the existing roads to deploy the WSN nodes. One can easily use the existing lamp posts of those roads to deploy the WSN nodes. In this paper, the modified Ant Colony Optimization (ACO) technique has been used to construct efficient WSN. The ACO is the probabilistic technique which is used to solve computational problems of choosing a minimized

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