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Due to the limited communication range of WSN, the sensor is unable to establish direct connection to the data collection station, therefore the collaborative work of nodes is highly necessary. Wireless sensor network consists of a large number of sensors depending on the applications’ demands [1-3]. Due to the limited range of communication, ensuring the direct connection between a sensor and the base station may make the nodes transmit their messages with such a high power that their resources could be quickly depleted.

Collaborative work between sensors requires an intelligent organization to transmit information from the sensing field to the base station in order to save energy resources of the network.

In this paper, we will show an intelligent adaptive solution for data routing so that no node will receive multiple copies of the same message, and there would be no need for Flooding or Gossiping.

Before we starting the routing algorithm we proposed, we would explain what a group of sensors is. In this section, we will provide recursive algorithm to divide the overloaded sensors in to separated groups. Group of Sensors: is a set of sensors that are overlapped while covering the same region in the sensing field. To facilitate the calculations, assume that the numerical value of sensors in the matrix is either 1 or 0 as below. We can see that each group of overlapped sensors is a square matrix, and the value of each element of the matrices is 1. Finding a group of sensors indirectly can be manipulated by dividing ungrouped vectors into smaller vectors so that the sensors that are directly grouped already are not needed. 6) Repeat the steps from step 1 to step 5 recursively until no new groups are found (Algorithm 1). The analysis depends on the preparation work to divide the input, the size of the ungrouped vectors, the number of recursive calls and the concluding work to combine the results of the recursive calls.

The routing algorithm is started by dividing the wireless sensors network to independent groups (explained in Subsection 3.3).

The distance between the source node and the sink node is an important parameter to control and improve the performance of packet forwarding in the overall network. We assumed each that group is taking into account the information of its neighbor groups’ distance. The selection process of the leader is running simply by choosing the nearest neighbor and checking the availability of the leader connected to this neighbor. In this section, we will evaluate the impact on network performance of the proposed routing algorithm.

We have proposed an algorithm where the source group forwards a packet to one neighbor only and there is no need of flooding or forwarding packets to all neighbors. The authors would like to acknowledge The National Natural Science Foundation of China, the National Science Technology Major Project and the China Scholarship Council for their supports. In the Method 5, RemnantSensors, the inputs are, a list of matched groups, and, ungrouped vector. After filtering all ungrouped vectors, we can continue counting the repetition of all filtered ungrouped vectors until we find new direct groups. Bank Routing Numbers List.Regular routing numbers serve to identify the bank and its associated presentment point.

The idea behind this project is to route multiple robots to service spatially distributed requests at specified time instants, while optimizing some criterion, for instance the total distance travelled, or the total time of travel.

The advantage of such a spatio-temporal construction is that a notion of directionality appears in the otherwise NP-hard routing problem, allowing us to apply the framework of assignment problems towards finding solutions, with the resulting reduction in complexity. The problem of spatio-temporal routing is musically inspired in that it requires a bunch of robots to reach a series of planar positions at specified time instants, much like a musician that uses multiple fingers to play a series of notes on an instrument at specified time instants. Consider a two-dimensional magnetic-based surface (wall) with a grid of strings in different pitches that generate sound when plucked, as illustrated in the figure above. With this set-up, we can interpret any piece of music consisting of a series of notes to be played at specified time instants, as a series of corresponding spatio-temporal requests (timed positions) on the music wall. A realistic approach to the spatio-temporal routing problem would be to introduce, say, a maximum velocity that the robots cannot exceed.

Another interesting version of the spatio-temporal routing problem would be to require that range constrained robots be connected at all times, or more formally, the underlying information exchange graph induced by the positions of the robots be connected at all times during the execution of a particular Score. Husqvarna lawn mower parts - Lawnmower parts lookup, lookup parts for chainsaws, free shipping!

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The data routing is one of the most fundamental processes exploring how to transmit data from the sensing field to the data collection station via the least possible number of intermediate nodes. The primary function of these devices is to monitor a natural phenomenon, an environmental phenomenon, or more complicated applications in either medical field or military sphere [1,3]. Thus, the collaboration of nodes ensures the communication between distant nodes and base station. Because of the insignificant computational capability and the lack of energy sources, the Flooding algorithms are not a proper solution for routing of WSN application [13-15]. Section 2 describes the grouping theory, and a combinatorial computing for intersection areas and intersection points of overlapped sensors. We will used this idea to divide the WSN into groups and then organize these groups in order to facilitate the routing process.

Simply, we just find out the square matrices with the following conditions: The maximum possible number of rows and columns should be alighted together and the value of each element is 1. Considering the power consumption, the nearest nodes to the sink could save more power by building a shorter path with a minimum number of hops. With adaptive routing, when a source group has to forward a packet towards a particular group, it can choose the leader sensor to use from a set of alternative leaders associated to the group. When a packet has been forwarded to a group, the group should know well its leaders and neighbors and therefore make the decision of packet routing to the next hop. Moreover, if more than one leader is connected to the same neighbor group, they are called twin leaders.

This way, first, the nearest neighbor is selected, and then we should find the leaders of the source node, which are connected to the selected neighbor.

For this purpose, we have developed a detailed simulator that allows us to estimate the network performance, power consumption, and the number of hops.

Say we have n nodes deployed randomly in the sensing field, and want to compute the number of possible groups that can be generated. The grouping adaptive routing saves more power therefore, ends up maximizing the lifetime of the wireless sensor network. Evaluation of routing performance based on the grouping algorithm on different coverage degrees.

This vector runs as a filter for ungrouped vector, and only those sensors which appear in the solid vector can appear in the ungrouped vector as well.

The routing problem is similar to the well known Multiple Traveling Salesman Problem (m-TSP) or the Vehicle Routing Problem (VRP), except that it is temporally constrained, i.e. We call such a series a Score, which contains positions that must be reached at specified time instants. Thus, for a given Score and max velocity, we derive results for the minimum number of robots required, as well as find the corresponding routes that the robots must follow.

For such a routing problem, we derive the minimum number of robots required, as well as provide an algorithm for finding the corresponding routes that the robots must follow. We incorporate heterogeneity in the spatio-temporal routing problem by associating one or more skills with each robots as well as each request. In accordance of the Creative Commons Attribution License all Copyrights © 2014 are reserved for SCIRP and the owner of the intellectual property Ammar Hawbani et al.

This paper addresses the problem of data routing based on the sensors grouping; it provides a deep insight on how to divide the sensors of a network into separate independent groups, and how to organize these independent groups in order to make them work collaboratively and accomplish the process of data routing within the network. Sensors are battery-powered devices having a limited lifetime, restricted sensing range, and narrow communication range [4].

In this method, intermediate nodes transmit messages so that a path with multiple links or hops to the base station is established [10-12]. The flooding algorithms broadcast the data to all overlapped nodes to the extent that cause an implosion and some nodes redundantly receive multiple copies of the same message.

With an adaptive routing, when the sensor has to forward a packet towards the base station, it can choose the path to use from a set of alternative paths. Using the idea of graph theory, we can say that each vertex is represented by a group of sensors.

Since our algorithm is based on grouping, we need to define the term of grouping distance, the distance of group is the least distance among sensors inside the group to the base station. This selection can be done upon two conditions: the first condition is the current state of the leader (busy or free) and therefore, the busy leaders are skipped. Thus, after selecting the nearest neighbor group, we must ensure that the connecting leader is free, otherwise, the packet cannot be forwarded via this leader.

There will be more than one adaptive channel when there are more than one leaders (parallel leaders) connected to nearest neighbor.

The forwarding decision of packets is deterministic and adaptive in each source group (Algorithm 3).

A pattern of groups is a deployment way for sensors groups such that all sensors in the group are connected.

For each ungrouped vector, we will find the matching direct groups by dividing the ungrouped vectors into smaller vectors. The output is a list of vectors contains only those sensors that appeared in the solid vector.

Moreover, we might even require that multiple positions are reached simultaneously, akin to a musician that has to play multiple notes of an instrument simultaneously with different fingers.

Moreover, we require that in order to service a request, a robot must have at least one skill in common with the skill set of that request. The entire shortcomings in sensor networks drive the researchers to develop viable solutions [5,6]. Gossiping algorithm comes with a better performance, avoiding implosion as the sensor and sending the message to a selected neighbor instead of informing all of its neighbors. If a sensor belongs to group A and belongs to group B, we say there is a link between group A and B, and we call this sensor by coordinator or the group leader.

The second condition is the least distance to the base station and therefore, the nearest group to base station can be selected. If the current state of all parallel leaders is busy, then there will be two ways to deal with: the first way, the packet should wait until one of the parallel leaders becomes free.

The packet routing from sensor (3) to the station is going according to the steps below (see Figure 3). Multiple robots with the ability to traverse the wall can reach these positions and pluck at the strings above them.

We characterize the feasibility aspects of such a routing problem, and provide algorithms for finding the minimum number of robots required to service the requests, and for constructing the corresponding routes of the robots. Multi-Robot Routing under Connectivity Constraints. IFAC Workshop on Estimation and Control of Networked Systems, Santa Barbara, CA, Sept. One of the main design aims of WSNs is to transfer data communication while trying to extend the lifetime of the network and avoid connectivity degradation by employing aggressive energy management techniques [7-9].

As long as the graph is connected, there will be a path from the source node connecting the base station, hence the packet must be reaching the sink node.

If there is only one leader in the source node, the packet should be delayed until the leader becomes free. However, this is not respectable in case the application’s demand is a real time stream of monitoring. The source groups (G1) have one neighbor and one leader, forwarding data towards group (G4) via sensor (7) obligatory. The first step of this algorithm is to find the sensors overlapping relations based on their distance; each sensor has a vector of sensors. The second way: if the source node contains other leaders connecting to other neighbors, the packet can be forwarded to any other neighbor. When the packets arrived to (G4), it has four leaders and three neighbors, the min group distance is to (G3), hence the next hop is (G3) via sensor (4). In case the grouping process run internally in the local node, each node send its vector to the adjacent node only. However, this might lead to an increment of the number of routing hops, hence might maximize the usage of energy in the overall network, this might lead to the death of a sensor.

After the packet has arrived to (G3), this Input: a group of sensors, and a list of Neighbor groups. If there is only one neighbor associated to the group and all leaders are busy, then the first way is obligatory. In case of all parallel leaders are free, any leader election algorithm can be applied to manage the selection of the leader.

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