Abstract
With the rapid development of Internet of Things (IoT), more and more static and mobile sensors are being deployed for sensing and tracking environmental phenomena, such as fire, oil spills and air pollution. As these sensors are usually battery-powered, energy-efficient algorithms are required to extend the sensors’ lifetime. Moreover, forwarding sensed data towards a static sink causes quick battery depletion of the sinks’ nearby sensors. Therefore, in this paper, we propose a distributed energy-efficient algorithm, called the Hilbert-order Collection Strategy (HCS), which uses a mobile sink (e.g., drone) to collect data from a mobile wireless sensor network (mWSN) and detect environmental phenomena. The mWSN consists of mobile sensors that sense environmental data. These mobile sensors self-organize themselves into groups. The sensors of each group elect a group head (GH), which collects data from the mobile sensors in its group. Periodically, a mobile sink passes by the locations of the GHs (data collection path) to collect their data. The collected data are aggregated to discover a global phenomenon. To shorten the data collection path, which results in reducing the energy cost, the mobile sink establishes the path based on the order of Hilbert values of the GHs’ locations. Furthermore, the paper proposes two optimization techniques for data collection to further reduce the energy cost of mWSN and reduce the data loss.
Highlights
The Internet of Things (IoT) is emerging as a new computing paradigm that connects uniquely identifiable objects to an internet-like network
In Hilbert-order Collection Strategy (HCS), mobile sensors self-organize themselves into groups and the sensors of each group elect a group head (GH)
To further reduce the energy cost of the mobile wireless sensor network (mWSN), we propose two data collection optimization techniques to the proposed HCS, namely the Phenomena-aware Collection Technique (PCT) and Lazy techniques to the proposed HCS, namely the Phenomena-aware Collection Technique (PCT) and Update Technique (LUT)
Summary
The Internet of Things (IoT) is emerging as a new computing paradigm that connects uniquely identifiable objects (sensing devices) to an internet-like network. This paper proposes a distributed energy-efficient data collection strategy, called the Hilbert-order. Collection Strategy (HCS), to collect environmental data using a mobile wireless sensor network (mWSN) and detect possible phenomena. Note that the time window w is the time taken by GHs to collect data from the mobile sensors within their groups, process the data and wait for the mobile sink to pass by to collect the data This suggests that a mobile sink should carefully plan its path to visit all GHs in the shortest time possible. A distributed energy-efficient algorithm, called Hilbert-order data collection strategy (HCS), to collect environmental data and detect possible phenomena. Two data collection optimization techniques, namely Phenomena-aware Collection Technique (PCT) and Lazy Update Technique (LUT), to reduce the data loss and overall energy cost of the network.
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