HiCoDG: A Hierarchical Data-Gathering Scheme Using Cooperative Multiple Mobile Elements

Abstract

In this paper, we study mobile element (ME)-based data-gathering schemes in wireless sensor networks. Due to the physical speed limits of mobile elements, the existing data-gathering schemes that use mobile elements can suffer from high data-gathering latency. In order to address this problem, this paper proposes a new hierarchical and cooperative data-gathering (HiCoDG) scheme that enables multiple mobile elements to cooperate with each other to collect and relay data. In HiCoDG, two types of mobile elements are used: the mobile collector (MC) and the mobile relay (MR). MCs collect data from sensors and forward them to the MR, which will deliver them to the sink. In this work, we also formulated an integer linear programming (ILP) optimization problem to find the optimal trajectories for MCs and the MR, such that the traveling distance of MEs is minimized. Two variants of HiCoDG, intermediate station (IS)-based and cooperative movement scheduling (CMS)-based, are proposed to facilitate cooperative data forwarding from MCs to the MR. An analytical model for estimating the average data-gathering latency in HiCoDG was also designed. Simulations were performed to compare the performance of the IS and CMS variants, as well as a multiple traveling salesman problem (mTSP)-based approach. The simulation results show that HiCoDG outperforms mTSP in terms of latency. The results also show that CMS can achieve the lowest latency with low energy consumption.

Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach

“…In [17] the authors have investigated the usage of multiple mobile nodes for data-gathering over a hierarchical network architecture, where mobile nodes are scheduled to meet each other at the synchronization points during certain time intervals. The proposed movement-scheduling scheme is vastly dependent on the mobility of the mobile nodes; therefore, it may be severely affected by the highly dynamic underwater environment.…”

“…Thus, removing the wasteful waiting time at the tour-point leads to a reduction in both the horizontal and vertical delays. In addition, to avoid a delay in the AUV synchronization, the proposed scheme does not require a time-out interval and speed control in contrast to the fixed-RP [16] and movement-scheduling [17], respectively.…”

“…The proposed scheme is evaluated in terms of the data transfer delays on the links of node-to-AUV (vertical), AUV-AUV (horizontal), and node-to-sink (end-to-end), AUV synchronization frequency, AUV tour-time, and the packet loss ratio comparing the following schemes: Fixed-RP scheme [16]: With a time-out interval, an AUV waits for its neighboring AUV at a tour-point that is a fixed RP.Global Relay AUV (GR-AUV) scheme [17]: An additional AUV not belonging to any specific region is employed only for collecting data from the local AUVs. The additional AUV visits each region and acts as a global relay.…”

“…Global Relay AUV (GR-AUV) scheme [17]: An additional AUV not belonging to any specific region is employed only for collecting data from the local AUVs. The additional AUV visits each region and acts as a global relay.…”

Mobile Agent Initiated Energy Efficient Data Aggregation in WSNs: MAEDA

“…In [20] authors propose a hybrid data collection scheme called Hybrid Agent Tree Scheduling (HATS) for making flexible task execution in WSN. HATS leverages the broadcast nature of wireless transmission, the flexibility of mobile agents, and the efficiency of tree-based data aggregation to provide a solution which is as adaptive as traditional agent systems and captures the efficiency of traditional tree-based aggregation systems.…”