8,675 research outputs found
Underwater spray and wait routing technique for mobile ad-hoc networks
1648-1655The underwater mobile ad-hoc networks comprise sensor nodes that are source nodes for gathering underwater-related data. Relay nodes are the mobile nodes for collecting data from sensor nodes and achieving intermittent connectivity among source and destination nodes. Developing an efficient routing protocol for underwater communication is a challenging issue due to limitations of the underwater environment. Underwater mobile ad-hoc networks are intermittent networks where end-to-end path does not exist from source to destination. To overcome these problems a delay and disruption tolerant network (DTN) is a good solution. In the current paper, we consider the Spray and Wait (SaW) routing technique. In SaW, source and relay nodes represents the moving nodes, and they try to send data to destination nodes. Based on this, we propose the replica based underwater SaW (USaW) routing for underwater mobile ad-hoc networks. In USaW, source nodes are fixed to the bottom of the surface. Underwater sensor nodes replicate sensor data and provide maximum copies of data to the relay nodes that they encounter. In generally, relay nodes have high capability of transmitting data as compared to sensor nodes in an underwater environment. We analyze the performance of USaW with respect to delivery ratio, network throughput, energy consumption, end-to-end delay, and packet drop rate comparing with existing SaW and prophet routing protocols
An Underwater Sensor Network with DBMS Concept
In this paper is a concept of a technique of sending and receiving message below water. There are several ways of employing such communication but the most common is using hydrophones. Under water communication is difficult due to factors like multi-path propagation, time variations of the channel, small available bandwidth and strong signal attenuation, especially over long ranges. In underwater communication there are low data rates compared to terrestrial communication, since underwater communication uses acoustic waves instead of electromagnetic waves. we present a novel platform for underwater sensor networks to be used for long-term monitoring of coral reefs and fisheries. The sensor network consists of static and mobile underwater sensor nodes. The nodes communicate point-to-point using a novel high-speed optical communication system integrated into the TinyOS stack, and they broadcast using an acoustic protocol integrated in the TinyOS stack. The nodes have a variety of sensing capabilities, including cameras, water temperature, and pres- sure. The mobile nodes can locate and hover above the static nodes for data mining and they can perform network maintenance functions such as deployment, relocation, and recovery. In this paper we describe the hardware and soft- ware architecture of this underwater sensor network. We then describe the optical and acoustic networking protocols and present experimental networking and data collected in a pool, in rivers, and in the ocean. Finally, we describe our experiments with mobility for data mining in this network. Keywords: Mobile sensor networks, underwater networks, data minin
Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks
Accurate localization for mobile nodes has been an important and fundamental
problem in underwater acoustic sensor networks (UASNs). The detection
information returned from a mobile node is meaningful only if its location is
known. In this paper, we propose two localization algorithms based on color
filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively
accomplishing accurate localization of underwater mobile nodes with minimum
energy expenditure. They both adopt the overlapping signal region of task
anchors which can communicate with the mobile node directly as the current
sampling area. PCFL employs the projected distances between each of the task
projections and the mobile node, while ACFL adopts the direct distance between
each of the task anchors and the mobile node. Also the proportion factor of
distance is proposed to weight the RGB values. By comparing the nearness
degrees of the RGB sequences between the samples and the mobile node, samples
can be filtered out. And the normalized nearness degrees are considered as the
weighted standards to calculate coordinates of the mobile nodes. The simulation
results show that the proposed methods have excellent localization performance
and can timely localize the mobile node. The average localization error of PCFL
can decline by about 30.4% than the AFLA method.Comment: 18 pages, 11 figures, 2 table
Dynamic mobile anchor path planning for underwater wireless sensor networks
In an underwater wireless sensor network (UWSN), the location of the sensor nodes plays a significant role in the localization process. The location information is obtained by using the known positions of anchor nodes. For underwater environments, instead of using various static anchor nodes, mobile anchor nodes are more efficient and cost-effective to cover the monitoring area. Nevertheless, the utilization of these mobile anchors requires adequate path planning strategy. Mzost of the path planning algorithms do not consider irregular deployment, caused by the effects of water currents. Consequently, this leads towards the inefficient energy consumption by mobile anchors due to unnecessary transmission of beacon messages at unnecessary areas. Therefore, an efficient dynamic mobile path planning (EDMPP) algorithm to tackle the irregular deployment and non-collinear virtual beacon point placement, targeting the underwater environment settings is presented in this paper. In addition, EDMPP controls the redundant beacon message deployment and overlapping, for beacon message distribution in mobile assistant localization. The simulation results show that the performance of the EDMPP is improved by increasing the localization accuracy and decreasing the energy consumption with optimum path length
Confidence-based Underwater Localization Scheme for Large-Scale Mobile Sensor Networks
The absence of Global Positioning System in underwater environment predominates in the challenges of underwater vehicles navigation or sensor nodes tracking. Localization of single or few underwater vehicles has been fostered in recent years. However, online simultaneous tracking of large-scale mobile sensor network is still a very challenging research area due to the high cost and the very limited number of vehicles that can be simultaneously localized using Ultra-Short Base Line (USBL) system. We propose a confidence-based localization algorithm for large-scale underwater mobile sensor networks that employs high precision localized sensor nodes in neighboring sensor nodes localization. Numerical simulation shows that a swarm of 100 sensor nodes can be tracked using a single USBL system, range measurement sensors and communication modems
Self-Organized Ad Hoc Mobile (SOAM) Underwater Sensor Networks.
Política de acceso abierto tomada de: https://beta.sherpa.ac.uk/id/publication/3570The need of underwater wireless sensor networks (UWSNs) having mobile sensor nodes has been there for a long time in form of underwater warfare or explorations by autonomous underwater vehicles (AUVs) or remote unmanned vehicles (ROVs). There are very few protocols for ad hoc mobile UWSNs (AMUWSNs). Designing a protocol for AMUWSN is quite challenging because of continuous random movement of the sensor nodes. In addition to random movement, the challenges to design a routing protocol for AMUWSN are more demanding than terrestrial ad hoc networks due to acoustic communications, which has large propagation delay in water. In this article, we present a self-organized ad hoc mobile (SOAM) routing protocol for AMUWSN. The sensor nodes may need to communicate with each other to the gateway (GW). The protocol, which we also refer to as SOAM, is a reactive, self-configuring, and self-organizing cluster-based routing protocol that uses received signal strength (RSS) for distance estimation. A beacon (BCN) packet will be sent by the GW, which will traverse through all the cluster heads (CHs) to form forwarding paths between the GW and the CHs. The ordinary sensor nodes (OSNs) will select the CHs every time they intend to forward a packet based on the BCN and they will receive from CHs. The formation of the forwarding path between the GW and the CHs and the selection CHs by OSN is explained in Section IV
Latency-Optimized and Energy-Efficient MAC Protocol for Underwater Acoustic Sensor Networks: A Cross-Layer Approach
Considering the energy constraint for fixed sensor nodes and the unacceptable long propagation delay, especially for latency sensitive applications of underwater acoustic sensor networks, we propose a MAC protocol that is latency-optimized and energy-efficient scheme and combines the physical layer and the MAC layer to shorten transmission delay. On physical layer, we apply convolution coding and interleaver for transmitted information. Moreover, dynamic code rate is exploited at the receiver side to accelerate data reception rate. On MAC layer, unfixed frame length scheme is applied to reduce transmission delay, and to ensure the data successful transmission rate at the same time. Furthermore, we propose a network topology: an underwater acoustic sensor network with mobile agent. Through fully utilizing the supper capabilities on computation and mobility of autonomous underwater vehicles, the energy consumption for fixed sensor nodes can be extremely reduced, so that the lifetime of networks is extended
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