Data Propagation with Guaranteed Delivery for Mobile Networks

Abstract

Abstract. In this paper, we consider wireless sensor networks where nodes have random and changeable mobility patterns. We study the problem where a particular node, called the base station, collects the data generated by the sensors/nodes. The nodes deliver the data to the base station at the time when they are close enough to the base station to ensure a direct transmission. While the nodes are too far to transmit to the base station, they store the data in a limited capacity internal FIFO queue. In the case where the queue is full, the new generated data are inserted in the queue and the oldest data are lost. In order to ensure, with a high probability, that the base station receives the generated data, the nodes disseminate the generated data in the network. The dissemination process consists in transmitting the data to others mobile nodes which are close enough to ensure a direct transmission. The nodes must control the dissemination process. Indeed, if the nodes send systematically the data to the neighbouring nodes then, the FIFO queues are going to be quickly saturated and the data lost (the dissemination process duplicate the generated data). On the other hand if the nodes do not disseminate the data, the data queued first are prone to be systematically lost if the capacity of the queue is too limited. We propose a protocol based on the estimate of the delivery probabilities of the data. Each node estimates the delivery probabilities of all the queued data. These probabilities depend on the position of the data in the queue and, on the dissemination process. The lower is the delivery probability the more the nodes disseminate the data to increase the delivery guarantee to the base station. In that way, all the messages get a high probability to be delivered to the base station (higher that some predefined threshold). Experimental validations of the protocol show that the protocol performs well and outperforms an existing protocol.

Energy-Efficient Deadline-Aware Data-Gathering Scheme Using Multiple Mobile Data Collectors

“…For example, Keung et al [14] and Aslanyan et al [15] considered multiple MDCs for data collection from source nodes. They assumed that MDCs have random mobility patterns.…”

Incentives for Delay-Constrained Data Query and Feedback in Mobile Opportunistic Crowdsensing

“…The delay-constrained category contact probability is intrinsically the cumulative distribution function of delivery delay [ 34 ], which is ideal to support QoS data delivery, but impractical to maintain in continuous time under an arbitrary delay distribution. Thus, we adopt discrete time slots to construct approximate delay distributions, where a slot is Δ minutes.…”

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

“…In addition, Aslanyan et al [ 21 ] considered the use of multiple MEs that move with a random mobility pattern in the area of interest to collect the data, and deliver the collected data to the sink when they are close enough to the sink to ensure a direct transmission. Due to the random mobility, the data-gathering latency may not be estimated.…”

“…In order to avoid the energy-hole problem, mobile data-gathering schemes have been studied [ 3 – 21 ]. In such schemes, mobile elements (MEs) (e.g., autonomous robots) are used to collect the data from sensors and bring the data to the sink.…”