Mobile relay configuration in data-intensive Wireless Sensor Networks

Abstract

Abstract-Wireless Sensor Networks (WSNs) are increasingly used in data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit all the data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies. We propose using low-cost disposable mobile relays to reduce the energy consumption of data-intensive WSNs. Our approach differs from previous work in two main aspects. First, it does not require complex motion planning of mobile nodes, so it can be implemented on a number of low-cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless transmissions into a holistic optimization framework. Our framework consists of three main algorithms. The first algorithm computes an optimal routing tree assuming no nodes can move. The second algorithm improves the topology of the routing tree by greedily adding new nodes exploiting mobility of the newly added nodes. The third algorithm improves the routing tree by relocating its nodes without changing its topology. This iterative algorithm converges on the optimal position for each node given the constraint that the routing tree topology does not change. We present efficient distributed implementations for each algorithm that require only limited, localized synchronization. Because we do not necessarily compute an optimal topology, our final routing tree is not necessarily optimal. However, our simulation results show that our algorithms significantly outperform the best existing solutions.

Maximizing data gathering capacity of wireless sensor networks using mobile relays

“…In our previous work [19], we developed two algorithms that iteratively refine the configuration of mobile relays and converge to the optimal solution. Although the approach in [19] minimizes the total energy consumption of both movement and wireless communications, it does not necessarily lead to the maximum amount of data reaching the sink.…”

“…Although the approach in [19] minimizes the total energy consumption of both movement and wireless communications, it does not necessarily lead to the maximum amount of data reaching the sink. In particular, our previous work ignores the actual amount of energy stored at any node and thus may save energy at the wrong nodes.…”

“…We adopt the same energy consumption models as used in [19]. Specifically, we develop models for the energy consumed by nodes during both transmissions and movement.…”

“…Paraphrasing the models from [19], we base our mobility energy consumption models on two-wheeled sensor nodes where each wheel is controlled by independent engines such as Khepera [3], Robomote [2] and FIRA [4]. We adopt the distance proportional energy consumption model [24] where the energy ( ) consumed by moving a distance is ( ) = where the parameter depends on the speed of the node.…”

“…The MMRC problem of maximizing system data gathering capacity is much more complex than the seemingly similar problem of minimizing the total energy required to transmit a fixed amount of data in a network [19] because we must account for the energies consumed by each individual node relative to its initial energy rather than worrying only about the total energy consumed by all nodes together. To the best of our knowledge, we are the first to optimize data gathering capacity taking into account the energy consumed by both movement and communication.…”