Wireless sensor networks consist of many restrictive sensor nodes with limited abilities, including limited power, low bandwidth and battery, small storage space, and limited computational capacity. Sensor nodes produce massive amounts of data that are then collected and transferred to the sink via single or multihop pathways. Since the nodes’ abilities are limited, ineffective data transmission across the nodes makes the network unstable due to the rising data transmission delay and the high consumption of energy. Furthermore, sink location and sensor-to-sink routing significantly impact network performance. Although there are suggested solutions for this challenge, they suffer from low-lifetime networks, high energy consumption, and data transmission delay. Based on these constrained capacities, clustering is a promising technique for reducing the energy use of wireless sensor networks, thus improving their performance. This paper models the problem of multiple sink deployment and sensor-to-sink routing using the clustering technique to extend the lifetime of wireless sensor networks. The proposed model determines the sink placements and the most effective way to transmit data from sensor nodes to the sink. First, we propose an improved ant clustering algorithm to group nodes, and we select the cluster head based on the chance of picking factor. Second, we assign nodes to sinks that are designated as data collectors. Third, we provide optimal paths for nodes to relay the data to the sink by maximizing the network’s lifetime and improving data flow. The results of simulation on a real network dataset demonstrate that our proposal outperforms the existing state-of-the-art approaches in terms of energy consumption, network lifetime, data transmission delay, and scalability.
The Social Internet of Things (SIoT) means that every node can use a set of nodes that are considered as friends to search for a specific service. However, this is a slow process because each node is required to manage a high number of friends. Thus, the SIoT issue consists of how to select the right friends that improve the network navigability. The enhancement of the network navigability boosts the search for a service to be rapid but not guaranteed. Furthermore, sending requests from the shortest paths involves the rapid search, but the network lifetime can be reduced due to the number of requests that can be transmitted and processed by the nodes that have low power energy. This paper proposes a new approach that improves the network navigability, speeds up the search process, and increases the network lifetime. This approach aims at creating groups dynamically by nodes where each group has a master node, second, using a consensus algorithm between master nodes to agree with a specific capability, finally adopting a friendship selection method to create a social network. Thus, the friends will be sorted periodically for the objective of creating simultaneously a balance between the energy consumption and the rapid search process. Simulation results on the Brightkite location-based online social network dataset demonstrate that our proposal outperforms baseline methods in terms of some parameters of network navigability, path length to reach the providers, and network lifetime.
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