RAGOBOT: A New Platform for Wireless Mobile Sensor Networks

The authors propose a graph intersection-based benchmarking algorithm to determine the sequence of longest-living stable data gathering trees for wireless mobile sensor networks whose topology changes dynamically with time due to the random movement of the sensor nodes. Referred to as the Maximum Stability-based Data Gathering (Max.Stable-DG) algorithm, the algorithm assumes the availability of complete knowledge of future topology changes and is based on the following greedy principle coupled with the idea of graph intersections: Whenever a new data gathering tree is required at time instant t corresponding to a round of data aggregation, choose the longest-living data gathering tree from time t. The above strategy is repeated for subsequent rounds over the lifetime of the sensor network to obtain the sequence of longest-living stable data gathering trees spanning all the live sensor nodes in the network such that the number of tree discoveries is the global minimum. In addition to theoretically proving the correctness of the Max.Stable-DG algorithm (that it yields the lower bound for the number of discoveries for any network-wide communication topology like spanning trees), the authors also conduct exhaustive simulations to evaluate the performance of the Max.Stable-DG trees and compare to that of the minimum-distance spanning tree-based data gathering trees with respect to metrics such as tree lifetime, delay per round, node lifetime and network lifetime, under both sufficient-energy and energy-constrained scenarios.

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Mobile Manipulator Networks: Platform Development and Applications

Volume 4: ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications and the 19th Reliability, Stress Analysis, and Failure Prevention Conference ◽

10.1115/detc2007-34412 ◽

2007 ◽

Author(s):

Austin M. Jensen ◽

YangQuan Chen

Keyword(s):

Sensor Networks ◽

Mobile Sensor Networks ◽

Robotic Manipulators ◽

Mobile Manipulator ◽

Mobile Manipulation ◽

Mobile Sensor ◽

Platform Development ◽

Wireless Mobile

This paper presents a new platform with a team of lab-scale networked mobile robotic manipulators (SumoMote) which merges a mobile manipulator with wireless mobile sensor networks. Many existing platforms built for mobile manipulation are big and expensive. Our SumoMote is built small and inexpensive for applications where quantity is more important than size. The hardware and software of the SumoMote will be described. Then two application scenarios will be presented to illustrate SumoMote’s capability in mobile sensor networks and how the added manipulator can help.

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Cognitive routing optimization protocol based on multiple channels in wireless sensor networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720914511 ◽

2020 ◽

Vol 16 (4) ◽

pp. 155014772091451 ◽

Cited By ~ 1

Author(s):

Shuguang Deng ◽

Buwen Cao ◽

Xiang Xiao ◽

Hua Qin ◽

Bing Yang

Keyword(s):

Sensor Networks ◽

Network Lifetime ◽

Large Scale ◽

Mobile Sensor Networks ◽

Network Throughput ◽

Sensor Nodes ◽

Multiple Channels ◽

Mobile Sensor ◽

Substantial Impact ◽

Wireless Mobile

With the development of modern communication, available spectrum resources are becoming increasingly scarce, which reduce network throughput. Moreover, the mobility of nodes results in the changes of network topological structure. Hence, a considerable amount of control information is consumed, which causes a corresponding increase in network power consumption and exerts a substantial impact on network lifetime. To solve the real-time transmission problem in large-scale wireless mobile sensor networks, opportunistic spectrum access is applied to adjust the transmission power of sensor nodes and the transmission velocity of data. A cognitive routing and optimization protocol based on multiple channels with a cross-layer design is proposed to study joint optimal cognitive routing with maximizing network throughput and network lifetime. Experimental results show that the cognitive routing and optimization protocol based on multiple channels achieves low computational complexity, which maximizes network throughput and network lifetime. This protocol can be also effectively applied to large-scale wireless mobile sensor networks.

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