Abstract-We study data preservation in intermittently connected sensor networks, wherein the sensor nodes do not always have connected paths to the base station. In such networks, the generated data is first stored inside the network before being uploaded to the base station when uploading opportunity arises. How to preserve the data inside the network is therefore an important problem. The problem becomes more challenging when sensor nodes have finite and unreplenishable battery energy. In this paper, we identify, formulate and study the data preservation problem in the intermittently connected sensor networks under energy constraints at sensor nodes. The problem aims to preserve the data inside the network for maximum possible time, by distributing the data items from low energy nodes to high energy nodes. We first show that this problem is NP-hard. We then design a centralized greedy heuristic and a distributed data distribution algorithm, and compare their performances using simulations. Keywords -Data Preservation, Algorithms, Intermittently connected Sensor Networks Data gathering is one of the important functionalities of sensor networks. In many data gathering applications such as object tracking [11] and intrusion detection [19] , data is timesensitive and needs to be transmitted back to the base station in near-real-time fashion. However, there are many applications that do not need real-time data transmission and access, such as acoustic sensor networks [14] , underwater or ocean sensor networks [10, 22] , and environmental monitoring [13, 15] . They are mainly used in scientific applications by domain scientists to collect scientific data for further analysis. For example, environmental scientists deploy a sensor net to study light variations on the forest floor due to canopy closure, and only need to collect the data months later when the experiment is over [15] . Another example is EnviroMic [14] , a large-scale and long-term audio sensor network deployment to collect data for bird vocalization monitoring and recording. In such applications, there is no longer a need to maintain base stations in the sensor field to collect real-time data. Data generated inside the network is first stored in the network and then uploaded to the faraway base station via different means. These uploading opportunities could be periodic visit by human operator or data mule [6, 7] , or transmission to the base station through wireless communication such as a low rate satellite link [16] . We refer to such sensor networks intermittently connected sensor networks 1 . The main function of the intermittently connected sensor networks in these applications is to collect and store the data in the network before the next uploading opportunity arises. There are three main factors contributing to the data loss in such sensor networks: energy depletion of sensor nodes, storage depletion of sensor nodes, and sensor node hardware failure. Overcoming the obstacle of data loss and preserving data in-network until upload opportunities arise is a new challenge. We have addressed the storage depletion induced data loss in our previous research [20] . In this paper, we focus on the energy depletion induced data loss. Our goal is to preserve the complete set of data inside the sensor network for the maximum amount of time, considering that each sensor node has limited battery energy. In our model, sensory data is generated and initially stored at some sensor nodes (referred to as source nodes).
In REF , they aim to store the data inside the network for maximum of the time, by spreading the data items from lower energy nodes to higher energy nodes.
Energy-efficient data preservation in intermittently connected sensor networks
2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Computer Science