06-10-2016, 02:16 PM
A Distributed mechanism for Mobile Data Gathering using mobile elements with Concurrent Data Uploading in Wireless Sensor Networks
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We look at mobile data acquisition in wireless sensor networks (WSN) from a mobile collector with multiple antennas. By considering the elastic nature of wireless link capacity, and the power control for each sensor, data uploading, which is forced to by flow saving, energy consumption, connection capacity, compatibility among sensors and the total residence time of the bound mobile collector at all anchor points. We propose a polling-based mobile gathering approach and formulate it into an optimization problem, named bounded relay hop mobile data gathering (BRH-MDG). Centralized and Decentralized algorithm for selecting polling points that buffer locally aggregated data and upload the data to the mobile collector when it arrives. One of the main features of the this framework is that it simultaneously enables data upload of shorten sensors to the mobile collector to focus data collection latency and significantly reduce the use of energy. Each sensor will be referred directly to the drain single-hop relay [7]. It requires large transmission power and can be feasible in large geographical areas. Sensors, which act as relay for other sensor nodes are known as multi-hop routing in wireless sensor networks. Data packets are forwarded to data sink through multi-hop relay between sensors. power consumption is, while the data packets in multihop routing. To achieve this, the uniform energy consumption, the single will hop data collection problem used (SHDGP). The Mobile data acquisition used algorithm to find the minimal set of points in the sensor network. We also enter the part algorithm for find the optimum residence the mobile collector at different anchor points. Finally we have Provide numerical results show the convergence of the proposed algorithm and its advantages over the algorithm without Simultaneous data uploading and power control in regard to the data collecting latency and energy consumption.
EXISTING SYSTEM
In existing system, data gathering cost minimization framework for mobile data gathering in wireless sensor networks by considering dynamic wireless link capacity and power control jointly. Our new framework not only allows concurrent data uploading from sensors to the mobile collector, but also determines transmission power under elastic link capacities. We study the problem under constraints of flow conservation, energy consumption, elastic link capacity, transmission compatibility, and sojourn time. We employ the subgradient iteration algorithm to solve the minimization problem. We first relax the problem with Lagrangian dualization, then decompose the original problem into several subproblems, and present distributed algorithms to derive data rate, link flow and routing, power control, and transmission compatibility. For the mobile collector, subalgorithm to determine sojourn time at different stopping locations is used.
DISADVANTAGES
• The cost function used may not completely reflect the overall pricing structure in the network.
PROPOSED SYSTEM
We proposed a WSNs with Mobile Elements (MEs) for Mobile Data Gathering with Concurrent Data Uploading in Wireless Sensor Networks. We will specifically focus on data collection, i.e., the process which makes the communication feasible between the sensor nodes and the sink. Indeed, there are many other issues which can be addressed by exploiting mobility in WSNs.
Advantages
1. Sparse WSN – Since the nodes are mobile we can manage with fewer nodes. So sparse WSN becomes a feasible option.
2. Less Cost – Since fewer nodes can be deployed, the network cost reduces. We can make use of mobile elements already present like moving vehicles, public transport etc. to transport data,
3. More Reliable – Data loss is reduced as the mobile elements visit the nodes to collect data directly through single- hop transmission as opposed to multi-hop transmissions. Interferences and collisions are avoided.
4. Funneling effect avoided – In traditional networks, the nodes near the sink are overloaded as they have to relay the data to the sink node. By making use of mobile collectors we can ensure that energy consumption is more uniform, since the mobile element collects the information by visiting the node.
5. Improved Coverage – Since the data collector is mobile it has improved coverage and it also has the additional advantage of connecting disconnected networks.
LIST OF MODULES
1 Re-locatable nodes
2. Mobile Data Collectors (MDCs)
2.1. Mobile Sinks
2.2. Mobile Relays
3 Mobile peers
MODULES DESCRIPTION
3.1 Re-locatable nodes
These nodes change their location to forward data to the destination. They do not carry any information in contrast to mobile nodes. They change the topology of the network. Predefined Intelligent Lightweight tOpology managemenT (PILOT) nodes are used during link failure to re-establish connectivity in network. They act as bridges when nodes are unstable. Algorithms for placement of re-locatable nodes in order to improve the network connectivity.
3.2. Mobile Data Collectors (MDCs)
They visit each node and gather data. They can be mobile sinks or mobile relays.
3.2.1. Mobile Sinks
These mobile nodes are the destination and have high energy. They move around and collect data. The collected data can be made available to users using wireless internet connection. The path between the sink and node is not fixed and changes with time. Path between node and sink is multi–hop.
3.2.2. Mobile Relays
They are not the source or destination of the data, but they are intermediate nodes used for data passing. The data-MULE system was proposed . This data-MULE system consists of a three-tier architecture, where the
second tier is represented by relays, called Mobile Ubiquitous LAN Extensions (MULEs). The MULEs pass through the network collect the data from the sensor nodes and when they to near the sink they pass the collected data to the sink. The MULEs move in straight line and to reduce delay the data can be relayed by other nodes to reach the sink. If obstacles are present then the MULE will not be able to move along a straight line. When there are only a few MULEs and when all sensors are not connected, data MULEs may not cover all sensors by navigating along a straight line. A message ferrying scheme provide the relaying of messages in sparse and mobile networks. Message ferries move through the field and get data from nodes. Message ferries can be thought of as a moving communication infrastructure which allows data communication in wireless networks that are sparse. Mobile Data Collectors called M-Collectors were proposed. Here M-Collector will move through the network and gather information. This is called as M-Collector tour and it is based on the optimal path as found out by a variant of the spanning tree algorithm called spanning tree covering algorithm. Since delay will be high if we use a single mobile collector, we can use many M-Collector to collect data in a parallel manner. But these techniques cannot be used to transfer emergency data. This problem can been addressed by the usage of long range wireless communication between the sink and MDC to transmit the emergency data as soon as it is sensed, instead of the MDC sending it to sink at the end of the tour. We can also use a technique where we design the topology as a minimum hop transmission. Certain nodes with high energy are designated as polling points. The sensors move the data from the sensor node to the polling point and the polling point sends control signals to base station. The base station will dispatch the mobile collector to get the data from the polling points. In this scheme the cost of having multiple data collectors is avoided by making use of a single data collector.
3.3 Mobile peers
Mobile peers are ordinary mobile sensor nodes in WSN-MEs. They can be the source or they can be
used to relay the messages in the network. Mobile peers are depicted in Figure 4. Their interactions are symmetrical
because the sink itself might also be mobile. When a peer node is in communication range, it can transfer its own data and it can gather data from the other peer nodes while moving in the sensing area. Mobile peers have been successfully employed in the context of wildlife monitoring applications, such as tracking of zebras in [9][10][11]. Sensor nodes are attached to animals and act as peers, so they generate data and also carry and forward all data coming from other nodes which they have been in contact with earlier. When mobile peers get near a base station, they push all the gathered data. Redundant data are discarded by peers in order to save storage. The problem with using animals to collect the data is that the movement is unpredictable. This can be avoided by using a predictable robot or vehicle as suggested in the [1].Mobile peers can also be used for data collection in urban sensing scenarios [2]. Sample applications include personal monitoring (e.g., physical exercise tracking), civil defense (e.g., hazards and hotspot reporting to police officers) and collaborative applications (e.g., information sharing for tourism purposes). Here, sensors are not used primarily for monitoring the environment, but are rather utilized to characterize people in aspects of interactions and context (or state) information. An example is represented by handheld mobiscopes where handhelds devices –such as cell phones or PDAs – gather data from the surrounding environment and report them to servers, which give services to users.