Abstract
Mobile Sink (MS) based routing strategies have been widely investigated to prolong the lifetime of Wireless Sensor Networks (WSNs). In this paper, we propose two schemes for data gathering in WSNs: (i) MS moves on random paths in the network (RMS) and (ii) the trajectory of MS is defined (DMS). In both the schemes, the network field is logically divided into small squares. The center point of each partitioned area is the sojourn location of the MS. We present three linear programming based models: (i) to maximize network lifetime, (ii) to minimize path loss, and (iii) to minimize end to end delay. Moreover, a geometric model is proposed to avoid redundancy while collecting information from the network nodes. Simulation results show that our proposed schemes perform better than the selected existing schemes in terms of the selected performance metrics.
Highlights
Wireless Sensor Networks (WSNs) consist of wireless sensors/nodes, which are equipped with a processor, a radio transceiver, a GPS, memory, and a battery [1]
We proposed two schemes random paths in the network (RMS) and DMS, which collect data on priority and periodic bases, According to the bounds provided in (10b), (10c), and (10d), Figure 9 shows the bounded region formed by intersecting lines L1, L2, L3, and L4
In our proposed RMS and DMS schemes, the field is logically divided into small squares to find Mobile Sink (MS) locations and to calculate its maximum distance from the nodes
Summary
WSNs consist of wireless sensors/nodes, which are equipped with a processor, a radio transceiver, a GPS, memory, and a battery [1]. The distant nodes consume more energy than nodes near the sink and die soon. Intermediate nodes receive data of faraway nodes and relay it to the sink This process minimizes the energy consumption of distant nodes. In clustering and multihop schemes, nodes close to the sink die quickly due to forwarding data of distant nodes. Sink mobility in the network minimizes energy consumption among the neighboring nodes It minimizes energy consumption in unnecessary processes [5], like cluster formation and CHs selection. MS is considered as a small vehicle which moves in the field and collects data from nodes, either directly or via multihop In this way, the communication distance is minimized that leads towards minimized energy consumption and maximized throughput [6].
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