One of the key challenges facing wireless sensor networks (WSNs) is extending network lifetime due to sensor nodes having limited power supplies. Extending WSN lifetime is complicated because nodes often experience differential power consumption. For example, nodes closer to the sink in a given routing topology transmit more data and thus consume power more rapidly than nodes farther from the sink. Inspired by the huddling behavior of emperor penguins where the penguins take turns on the cold extremities of a penguin “huddle”, we propose mobile node rotation, a new method for using low-cost mobile sensor nodes to address differential power consumption and extend WSN lifetime. Specifically, we propose to rotate the nodes through the high power consumption locations. We propose efficient algorithms for single and multiple rounds of rotations. Our extensive simulations show that mobile node rotation can extend WSN topology lifetime by more than eight times on average which is significantly better than existing alternatives.

Wireless Sensor Networks (WSNs) are increasingly used in data-intensive applications such as microclimate 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.

Recently, the availability of numerous low-cost robotic units (e.g., Packbot, Robomote, and Khepera) has made it possible to massively deploy mobile sensors in a network and use them in a disposable manner. It has been shown that the controlled mobility offered by sensors can be exploited to improve the energy efficiency of a network. In this paper, we study a new problem called max-data mobile relay configuration (MMRC) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. Different from previous controlled mobility approaches, we account for several characteristics of existing practical mobile sensing platforms including limited mobility and the high energy consumption of locomotion. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies. Our extensive simulations based on realistic energy models of existing mobile sensing platforms show that our approach can increase the data gathering capacity by a factor of at least 2 in most scenarios. Moreover, our distributed algorithms converge quickly and incur low messaging overhead.

Recently, wireless sensor networks (WSNs) have become increasingly available for 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 the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. In this paper, we propose to use low-cost disposable mobile relays to reduce the energy consumption of data-intensive WSNs. Different from previous work, our approach does not require complex motion planning of mobile nodes, and hence can be implemented on a number of low-cost mobile sensor platforms. Moreover, we integrate the energy consumption due to both mobility and wireless transmissions into a holistic optimization framework. The optimal relay configuration is shown to depend on both the positions of nodes and the amount of data to be sent. We develop two algorithms that iteratively refine the configuration of mobile relays and converge to the optimal solution. These algorithms have efficient distributed implementations that do not require explicit synchronization. Our simulation results based on realistic energy models obtained from existing mobile and static sensor platforms show that our algorithms significantly outperform the best existing solutions.