## 1 Introduction
The main concern is that the period of data gathering is shorter, because the speedy data collector has a higher energy consuming rate and has to replenish its energy after a round of trip. This paper proposes an efficient data gathering scheme called ADG at mobile wireless sensor networks, which belongs to the category of mobile ad hoc network (MANETs). All nodes are mobile and ad hoc. The movements and sparse deployment of nodes usually lead to intermitted connected links and create some form of opportunistic communications. Also, the data gathering is assumed to be delay-tolerant as there is some delay for the mobile data collector to visit the in-network nodes, pick up data from them and then go back to the base station for data uploading. The idea behind ADG is that with the advancement of mechanical and electronic technologies, a speedy and track-free mobile node could be used as a data collector for the purpose of data gathering, and through the collection of network meta-data, the rough moving pattern of nodes could be adopted to guide the data collector. The collector could then be programmed to move to the optimized locations to collect the sensing data through one hop transmissions, which avoids lots of transmissions and greatly improves the overall performance. Also, as the nodes are mobile, ADG also learns the period when nodes are relatively stationary, so that the mobile collector (\(MC\)) is able to pick up the data from them during the limited data gathering period. The main characteristics of the proposed scheme lies in the following aspects:  
* It is the first step on the research of data gathering using speedy mobile elements in mobile sensor networks within a limited data gathering period, while most of the existing research focuses on data gathering on stationary sensor network. \(MC\) is assumed to move faster in ADG, yet with much smaller time length within a data gathering round. So the contact opportunities with other ordinary mobile nodes are fewer, shorter, and more opportunistic in nature, which makes the data gathering a challenging problem;
* The scheme does not need predefined paths or tracks for the data gathering, while other schemes [4,8,9,19] do need compute a track or path for \(MC\). The dynamic nature of mobile networks makes it impossible for the \(MC\) to move along a precomputed path for the data gathering. Instead, ADG calculates a set of proxy nodes that act as an intermediate storage to receive sensed data from ordinary nodes, which would make it efficient for the \(MC\) to collect the sensed data;
* It maps the data gathering into the a variant of the Knapsack problem [20], the target of which is to maximize the expected amount of gathered data under the constraints of a \(compatible\) schedule and a limited data gathering period. ADG would schedule the time slots and orders to gather as much data as possible. Our work is orthogonal to the compressive sensing techniques, e.g., sub-nyquist sampling [21], which could be integrated into our scheme to reconstruct images or signals accurately from far smaller data size than the desired resolution of the image/signal. Extensive experimental results show that the proposed algorithm outperforms other epidemic and probabilistic data gathering algorithms on the overhead of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period. The rest of paper is structured as follows: Section 2 surveys some existing research related to this paper; Section 3 gives some assumptions about the network model; Section 4 describes the detailed mechanism of the proposed scheme ADG, including the meta-data extraction, proxy node selection, time slot allocation, and data gathering from proxy nodes. Finally, Section 5 describes the experimental setup and performance evaluation, and Section 6 concludes the paper.