Wireless Sensor Networks (WSNs) play a vital role in modern technology since they have recently emerged into enormous essential applications of the Internet of Things (IoT). However, WSNs encounter a shortage in the lifetime due to limitations in the power supply. Accordingly, many solutions are reported in literature to deal with energy saving problem in the WSNs. In this paper, a novel method is presented to minimize energy consumption using adaptive modulation that is jointly integrated with clustering technique. This method is considered as a promising solution for dense and sparse cluster-based WSNs to improve energy efficiency. In the suggested solution, the adaptive modulation is implemented in the communication link between cluster members (CMs) and cluster head (CH). Besides, distancebased adaptive modulation step function is proposed in which the optimum modulation order is selected to achieve the minimum energy consumption between CMs and CH. The proposed method is evaluated extensively in order to investigate the impact of adaptive modulation in cluster-based WSNs using M-ary Quadrature Amplitude Modulation (MQAM) system. The performance evaluation addresses both energy consumption and throughput by using two metrics: cluster density, and cluster size. Regarding simulation results, by varying cluster density and cluster size, the adaptive modulation shows significant saving in energy consumption where it constitutes a lower bound for energy consumption. Also, it shows a great impact on throughput where it constitutes an upper bound for the throughput. Moreover, the adaptive modulation shows a considerable leverage on energy saving for small number of clusters, conversely, the energy saving decreases as the number of clusters increase. Finally, it is concluded that these findings can provide a remarkable guidance for designing an energy efficient WSNs.

In this paper, using linear programming, we formulate the problem of maximizing the α-lifetime of wireless sensor networks with solar energy sources. The α-lifetime of a sensor network is defined as the duration in which α percentage of sensor data can be collected by the base station. Our formulation takes account of varying solar energy recovery rate at different sensors and jointly optimizes the transmission power of the sensors and data routing for maximizing α-lifetime. We study the break point, which marks the level of solar energy supply above which the sensor network can operate perpetually. We also study the changes in α-lifetime with the solar energy supply rate, distribution of solar energy, and the values of α. Our study provides useful guidance in practical deployment of sensor networks with renewable energy sources.

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Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard-to-reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV-assisted cluster-based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV-WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.