Abstract Future Internet of Things (IoT) applications will require that billions of wireless devices transmit data to the cloud frequently. However, the wireless medium access is pointed as a problem for the next generations of wireless networks; hence, the number of data transmissions in Wireless Sensor Networks (WSNs) can quickly become a bottleneck, disrupting the exponential growth in the number of interconnected devices, sensors, and amount of produced data. Therefore, keeping a low number of data transmissions is critical to incorporate new sensor nodes and measure a great variety of parameters in future generations of WSNs. Thanks to the high accuracy and low complexity of state-of-the-art forecasting algorithms, Dual Prediction Schemes (DPSs) are potential candidates to optimize the data transmissions in WSNs at the finest level because they facilitate for sensor nodes to avoid unnecessary transmissions without affecting the quality of their measurements. In this work, we present a sensor network model that uses statistical theorems to describe the expected impact of DPSs and data aggregation in WSNs. We aim to provide a foundation for future works by characterizing the theoretical gains of processing data in sensors and conditioning its transmission to the predictions’ accuracy. Our simulation results show that the number of transmissions can be reduced by almost 98 in the sensor nodes with the highest workload. We also detail the impact of predicting and aggregating transmissions according to the parameters that can be observed in common scenarios, such as sensor nodes’ transmission ranges, the correlation between measurements of different sensors, and the period between two consecutive measurements in a sensor.
@cite_0: Unbalance energy consumption is an inherent problem in wireless sensor networks characterized by multi-hop routing and many-to-one traffic pattern. Uneven energy dissipation can waste a lot of energy and cost. In this paper, a new deployment strategy of WSN that gathers several means is proposed to minimize cost. The regular hexagonal cell architecture is employed to build network that satisfies the constraints of coverage and connectivity. Based on the analysis of energy consumption of sensors and sink and cost of network, an energy allocation theorem and an integer programming model are presented to minimize the cost per unit area. The key issue is to determine the number of layers of network when other parameters are fixed. Furthermore, a scheme of multi-sink network is proposed for large monitored area. In order to balance the energy consumption of sensors on the identical layer, a uniform load routing algorithm is presented. The numerical analysis and simulation results show that the waste of energy and cost of WSN can be effectively reduced with the strategy.
@cite_35: Distribution of redundant sensor nodes has significant influence on the lifetime of wireless sensor networks. Firstly, layered network architecture based on regular hexagonal cells (RHCs) is introduced. Then, taking cells as the minimum working units instead of sensor nodes, optimal distribution of redundant nodes is investigated so that network lifetime can be prolonged as much as possible. Based on the analysis of network lifetime, redundant nodes multiple matrix R and probability density function f(k) are obtained as the optimal distribution conditions, which are used to distribute redundant nodes. Finally, simulation analysis has been conducted on the networks with the same architecture but the different distributions (optimal, uniform and Poisson) of redundant nodes. The numerical results show that network with optimal distribution of redundant nodes has longer lifetime than that of the other two distributions.
@cite_19: Wireless sensor networks (WSNs) consist of a large number of small, battery-powered wireless sensor nodes and thereby constrained with energy resource. The network as a whole must minimize the energy usage in order to enable untethered and unattended operation for an extended period of time. One fundamental way of reducing such energy usage and enhancing lifetime is judicial placement of sensor nodes within the network area. The present work proposes a lifetime-enhancing node-deployment strategy. Primarily more nodes are deployed towards the sink exploiting the fact that more energy is drained out from the nodes close to the sink. Further, locations which are approximately equi-distant from the sink are assigned different classes of priorities according to their responsibility of forwarding data of nodes located in the neighbouring cells. The principle of this strategy is justified by Lemma and corresponding proof. Exhaustive simulation is conducted to observe the impact of node distribution on network lifetime. The result is compared with one of the existing deployment strategies, which shows our scheme outperforms the existing one in terms of network lifetime.
More recently, focused on calculating the most energy efficient deployment strategy for WSN using an integral programming model @cite_0 . They used a regular hexagonal cell architecture @cite_35 @cite_19 to model the location of sensor nodes in the plane, which, in fact, is similar to a ring model with @math . Using this model, they formulated the energy consumption of sensor nodes and GW based on the energy used to transmit, receive and process data in sensor nodes. Finally, they also evaluated the impact of data aggregation in the energy consumption of the sensor nodes.