The optimization of sensor relocation in wireless mobile sensor networks

Wang, Chu‐Fu; Lee, Chun‐Chia

doi:10.1016/j.comcom.2009.12.001

Cited by 61 publications

(66 citation statements)

References 16 publications

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“…2(d) is the result after the algorithm terminates with a solution. [5]) Power consumption coefficient for data processing E elec = 50 nJ/bit (by referring to [14]) Power consumption coefficient for signal amplification ϵ amp = 100pJ/bit/m 2 (by referring to [14]) Power consumption coefficient for sensing E sens = 0.018J (by referring to [7]) Power consumption coefficient for idle time E listen = 0.043J/s (by referring to [7]) Power consumption coefficient for sleep time E sleep = 0.000054J/s (by referring to [7]) Power consumption exponent n = 2 (by referring to [14]) Area of sensing disk of each sensor range = 20m 2 (by referring to [15]) Degree of coverage in the target field k = 1,2,and 3 Size of data for sensed information D = 128bit (by referring to [16]) Sensing frequency 0.1Hz (by referring to [16]) Maximum radio transmission distance 300m (by referring to [10])…”

Section: Wakeup Methodsmentioning

confidence: 99%

Extending k-Coverage Lifetime of Wireless Sensor Networks Using Mobile Sensor Nodes

Katsuma

Murata

Shibata

et al. 2009

2009 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications

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In the wireless sensor network (WSN) for periodically sensing and gathering environmental information uniformly in a vast sensing field, reducing the system operating cost taking into account sensor node installation and WSN lifetime is essential. In this paper, we propose a method to prolong the lifetime of such a data gathering WSN, by randomly scattering "surplus" sensor nodes over the target field. In our method, we assume that each sensor node has three operation modes: sensing, relaying, and sleeping. Each sensing node senses environmental data and sends/relays the data to the sink node via multi-hop wireless communication. Each relaying node just forwards the data received from its uplink node to its downlink nodes. Each sleeping node does nothing and keeps its battery. We propose an algorithm that dynamically changes mode of each sensor node so that the WSN lifetime becomes as long as possible by switching the least number of nodes for achieving k-coverage of the field to sensing mode. Through computer simulations, we confirmed that our method can prolong the WSN lifetime almost proportionally to the number of deployed sensor nodes.

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Section: Wakeup Methodsmentioning

confidence: 99%

Extending k-Coverage Lifetime of Wireless Sensor Networks Using Mobile Sensor Nodes

Katsuma

Murata

Shibata

et al. 2009

2009 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications

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“…However, when nodes are mobile, redeployment is possible. In fact, it has been shown [17], [18] that the integration of mobile entities into WSNs improves coverage, and hence, utility of the sensor network deployment. This enables more versatile sensing applications as well [1].…”

Section: Advantages Of Adding Mobilitymentioning

confidence: 99%

“…In sparse networks, or when a node drops off the network, mobile nodes can position themselves to maintain network connectivity [16], [17]. In this case, they behave as network access points.…”

Section: Mwsn Architecturesmentioning

confidence: 99%

A Survey on Localization for Mobile Wireless Sensor Networks

Amundson

Koutsoukos

2009

Lecture Notes in Computer Science

253

142

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Abstract. Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

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“…Additionally, there may be a robot that is closer to the desired destination due to the redeployment of Beacons, but since the launch selection is based on random timeouts, the closest robot is not guaranteed to deploy. Various strategies exist that would ensure the closest robot is selected [24]. SID is a fixed deployment scheme without adjustable parameters.…”

Section: Single Incremental Deployment (Sid)mentioning

confidence: 99%

Energy-Time Efficiency in Aerial Swarm Deployment

Stirling

Floreano

2013

Springer Tracts in Advanced Robotics

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A major challenge in swarm robotics is efficiently deploying robots into unknown environments, minimising energy and time costs. This is especially important with small aerial robots which have extremely limited flight autonomy. This paper compares three deployment strategies characterised by nominal computation, memory, communication and sensing requirements, and hence are suitable for flying robots. Energy consumption is decreased by reducing unnecessary flight following two premises: 1) exploiting environmental information gathered by the robots; 2) avoiding diminishing returns and reducing interference between robots. Using a 3-D dynamics simulator we examine energy and time metrics, and also scalability effects. Results indicate that a novel strategy that controls the density of flying robots is most promising in reducing swarm energy costs while maintaining rapid search times. Furthermore, we highlight the energy-time tradeoff and the importance of measuring both metrics, and also the significance of electronics power in calculating total energy consumption, even if it is small relative to locomotion power.

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The optimization of sensor relocation in wireless mobile sensor networks

Cited by 61 publications

References 16 publications

Extending k-Coverage Lifetime of Wireless Sensor Networks Using Mobile Sensor Nodes

Extending k-Coverage Lifetime of Wireless Sensor Networks Using Mobile Sensor Nodes

A Survey on Localization for Mobile Wireless Sensor Networks

Energy-Time Efficiency in Aerial Swarm Deployment

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