In delay tolerant WSNs mobile ferries can be used for collecting data from sensor nodes, especially in large-scale networks. Unlike data collection via multi-hop forwarding among the nodes, ferries travel across the sensing field and collect data from sensors. The advantage of using a ferry-based approach is that, it eliminates the need for multi-hop forwarding of data, and as a result energy consumption at the nodes is significantly reduced. However, this increases data delivery latency and as such might not be suitable for all applications. In this paper an efficient data collection algorithm using a ferry node is proposed while considering the overall ferry roundtrip travel time and the overall consumed energy in the network. To minimize the overall roundtrip travel time, we divided the sensing field area into virtual grids based on the assumed sensing range and assigned a checkpoint in each one. A Genetic Algorithm with weight metrics to solve the Travel Sales Man Problem (TSP) and decide on an optimum path for the ferry to collect data is then used. We utilized our previously published node ranking clustering algorithm (NRCA) in each virtual grid and in choosing the location for placing the ferry’s checkpoints. In NRCA the decision of selecting cluster heads is based on their residual energy and their distance from their associated checkpoint which acts as a temporary sink. We simulated the proposed algorithm in MATLAB and showed its performance in terms of the network lifetime, total energy consumption and the total travel time. Moreover, we showed through simulation that nonlinear trajectory achieves a better optimization in term of network lifetime, overall energy consumed and the roundtrip travel time of the ferry compared to linear predetermined trajectory. In additional to that, we compared the performance of your algorithm to other recent algorithms in terms of the network lifetime using same and different initial energy values.

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Depending on the application, mobile ferries can be used for collecting data in a WSN especially those at a large-scale with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry based approach eliminates or minimizes the need for multi-hop forwarding of data, and as a result energy consumption at the nodes will be significantly reduced especially nodes that are near the base station as they are used by other nodes to forward data to the base station. However, this increases data delivery latency and as such might not be suitable for all applications. In this paper an efficient data collection scheme using a ferry node is proposed with emphasis of the effect of ferry's path. In this scheme the decision of selecting cluster heads is based on their residual energy and their distance from the ferry path. We simulated the proposed scheme in MATLAB using different scenarios to show their performance in terms of the network lifetime and total energy consumption in the network. We found that the centered and the diagonal fitted paths performed better than the diagonal path in terms of the network lifetime and energy consumed. We also found that increasing the check points increases the lifetime of the network

Purpose – This paper aims to propose a new node energy-efficient algorithm with energy threshold to replace cluster heads. The proposed algorithm uses node ranking to elect cluster heads based on energy levels and positions of the nodes in reference to the base station (BS) used as a sink for gathered information. Because the BS calculates the number of rounds a cluster head can remain for as a cluster head in advance, this reduces the amount of energy wasted on replacing cluster heads each round which is the case in most existing algorithms, thus prolonging the network lifetime. In addition, a hybrid redundant nodes duty cycle is used for nodes to take turn in covering the monitored area is shown to improve the performance further. Design/methodology/approach – Authors designed and implemented the proposed algorithm in MATLAB. The performance of the proposed algorithm was compared to other well-known algorithms using different evaluation metrics. The performance of the proposed algorithm was enhanced over existing ones by incorporating different mechanisms such as the use of an energy-based threshold value to replace CHs and the use of a hybrid duty-cycle on nodes. Findings – Through simulation, the authors showed how the proposed algorithm outperformed PEGASIS by 15 per cent and LEACH by almost 70 per cent for the network life-time criterion. They found that using a fixed pre-defined energy threshold to replace CHs improved the network lifetime by almost 15 per cent. They also found that the network lifetime can be further improved by almost 7 per cent when incorporating a variable energy threshold instead of a fixed value. In addition to that, using hybrid-redundant nodes duty-cycle has improved the network lifetime by an additional 8 per cent. Originality/value – The authors proposed an energy-efficient clustering algorithm for WSNs using node ranking in electing CHs and energy threshold to replace CHs instead of being replaced every round.