     |
|
Mobile Wireless Sensor Network Connectivity Repair with K-Redundancy
In this paper, we are proposing a new method to provide connectivity in a wireless sensor network which consists of mobile nodes (without loss of generality, we assume static nodes can also be present and treated as non-moving mobile nodes). In this system, there are two groups of mobile nodes, the ones whose motion we can control and those we cannot. In the rest of the paper, we will call controllable nodes as robots and uncontrollable nodes as mobile nodes. Our goal is to improve connectivity of the system with the help of mobile robots. Our approach is based on in-network computing, in which the robots do not know the intentions of the mobile nodes, but mobile nodes direct them to locations to provide better connectivity. Our main contribution is the introduction of a new metric, k-redundancy, to determine communication characteristics of a dynamic wireless sensor network. This metric provides a tool to identify low-connectivity parts of a communication graph and means to reinforce the network structure before disconnection happens. We define k-redundancy of a node as the minimum number of node removals required to disconnect any two neighbors of that node. This provides a measure to represent the importance of a node in connecting its neighbors. K-redundancy is also important for the throughput of the network because as the redundancy of nodes increase, the routes between neighboring nodes increases. We introduce two approaches, reactive repair and proactive repair methods, to maintain connectivity and discuss how to use k-redundancy information for improvement in repair performance. Using simulations with a realistic network simulator (NS-2), we show that by using k-redundancy, we can reduce the disconnections in a dynamic mobile network. We also provide the real hardware experiments with several mobile robots and motes to show the applicability of our algorithm to real systems.
Nodes have different redundancy values which is a representation of their role in the connectivity. For example, n8 is 0-redundant because n9 and n7 can communicate only with the help of n8, so removing it from the graph results in disconnection. n3 is 1-redundant because in case it fails, all of its neighbors can communicate with the help of n1, removing also n1 partitions the graph. n6 is 2-redundant because after removing it, at least two more nodes need to be removed to partition its neighbors (n3 and n5 can be removed to isolate n4) Videos We present the videos of i) bridge forming, ii) node failure handling, iii) movement in large environment, iv) movement in a large environment with obstacles, and v) real robot and mote experiments. In the simulations, red cylinders represent mobile nodes and blue spheres represent robots responsible for maintaining connectivity.
Bridge Forming:
Node Failure
10 nodes and 10 robots:
10 nodes and 10 robots with obstacles:
Real experiments: (One of the AmigoBots failed during videotaping, so it has been replaced by another robot)
|
|||||||||||||||||||||||||||
Connectivity is a fundamental requirement for wireless sensor networks for effective use of such systems. For example, in an urban rescue scenario, we would like to have continuous communication with each team to get real-time updates and send them directives. However, as the teams move in and out of communication range, we may lose connection with them. Another example is in habitat monitoring. If wireless sensors are attached to wild animals, an external agent may use the information from sensors to make intelligent decisions to move the animals to desired locations. However, once again, the individual movements of animals may cause the network be disconnected and fail to send the information to external agent. These examples can easily be extended to broader areas. Hence, providing improved connectivity is an important requirement or wireless sensor networks. In fact, the importance of connectivity has been observed by several researchers such as where local communication improved the system performance in multi-robot applications.
