Data Gathereing In Wireless Sensor Network Computer Science Essay

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Wireless sensor network used in many application to gather data from autonomous sensors and then send it to a base station called usually sink node, These sensor cooperate with each other to transmit the data to the sink using multi hops communication. Sensor nodes usually placed in hard to access environment, so after a specific time it is expected that these nodes will run out of energy sources (batteries) resulted to limitation in the network lifetime. The reduction in the sensors energy is non-uniform; this means that the nodes close to the sink will consume their energy before other nodes because these nodes usually responsible for delivering the data traffic to the sink. When these nodes consumed their energy, the network stops to operate although plenty of energy remains in the other nodes distant from the sink.

This thesis examines the use of mobile element as a solution to the energy depletion problem. The mobile element start an exploring tour from the static sink, collecting the data from the sensors and come back to deliver the data to the sink. This tour increase the network lifetime by obviating multi hops communications.

2. Introduction

Due to the development of computer networks to coverage distant location and hard environment, wireless sensor networks have a great attention in many researches. WSN consists of four major components: sensing unit, processing unit, transceiver unit and power unit. Another part may include depending on the application such as: mobilizer and location finding system [1]. WSNs are reliable in many applications such as military application, medical application and space exploration. WSNs are usually applied in hard to access environment, where human cannot reach it or human should not exist there in order not to influence in this environment. These restrictions make energy conservation as an essential challenge, because the sensor nodes consume their energy resources while communicating with each other also it is difficult to recharge the energy resources according to the nature of the environment.

2.1 wireless sensor network applications

In this section we give some examples of wireless sensor network applications. Detecting and tracking of moving objects [2] single object detection and tracking is done using multi hops communication between sensors. Once the object enters the network area, the sensors cooperate with each other's in order to locate and track the object movement. Environmental monitoring is another example of WSN applications [3] structural health monitoring (SHM) is a type of environmental monitoring application, in this model sensors collaborate with each other using multi hops communication in order to recognize problems in the large buildings and constructions. Another example of WSN application is habitat monitoring [4] in gulf of marine, three wireless sensor networks with different power management and different routing services including 200 nodes were applied to explore the behaviour of petrels. These networks deployed underground nesting and burrows to collect data about temperature, humidity, occupancy and pressure. After that his data sent to the sink using multi hops communication to correlate nesting patterns with microclimates.

2.2 data gathering in wireless sensor network

WSN aims to collect the data from the sensor nodes distributed in an area and send it to a base station where the end user can interact with it. Since the sensor nodes were deployed in a network far from each other, and then it must increase the transmission power so that it can collaborate with each other. Increasing the transmission power resulted in increased depletion in the energy resources. On the other hand if more sensors added to the network to cooperate with the original sensors in order to decrease the transmission power, another problem will appear where the cost of the network deployment will increase enormously. Anyway multi hops communication between sensors result in non-uniform energy consumption, where the nodes located near the sink is expected to run out of energy before other nodes so when these nodes fail the network become dis-function even their a quantity of energy in the farther nodes. Clustering used to distribute the energy consumption in the network. in this scenario the network is divided into specific number of clusters, each cluster has a powerful node called cluster head. The cluster head responsible for collecting data from its clusters node and send it to the sink node using multi hops communication [5]. This technique increased the network lifetime but the problem where the nodes around the sink run out of energy before other nodes still available.

2.3 using mobile element in data gathering

Mobile element is used in WSN to collect data from the sensor nodes and send it to the sink in behalf of the sensors itself before buffer overflow. Sensor node may vary in the data generation rate, so the mobile element must take in attention to visit the nodes with high generation rate more frequently [6]. Using mobile element will increase the network lifetime by obviating the multi hops communication. There is a multi type of mobility such as controlled, predefined and random mobility each one has special properties and specific field to apply on it. In this thesis we concentrate on the controlled mobility.

2.4 Literature review

Using the mobile sink in wireless sensor networks environment may increase the network lifetime also it maintains a balance in energy consumption among the network, so many researchers have developed a new scheme and algorithm that used the mobile sink in the wireless sensor network to gather the data; in this section we will review some of these scheme. A novel reliable & efficient data harvesting mechanism in wireless sensor networks, 2012 Bo Tang, Jin Wang, Xuehua Geng, Yuhui Zheng and Jeong-Uk Kim [7] proposed a new efficient data collection mechanism called economical and manageable sub-sink mechanism (E&MSM). This mechanism consists of two phases. At the first phase, the communication path established. After that in the second phase data harvesting method proceed to collect the data from the network.

Efficient Rendezvous algorithms for mobility-enabled wireless sensor networks, 2012, Guoliang Xing, Minming Li, Tian Wang, Weijia Jia, and Jun Huang [8] introduced a new data collection approach based on rendezvous points. in this model, a specific number of nodes serve as rendezvous points seeks to collect and buffer data generated from sources nodes in order to transfer it to the base station when it arrived. This approach balanced between data collection delay and energy consumption.

Energy-efficient heterogeneous data collection in mobile wireless sensor networks, 2010, Longfei Shangguan, Luo Mai, Junzhao Du, Wen He, and Hui Liu [9] proposed a new algorithm called (RP-ME) aims to find the best location for the rendezvous points also the best trajectory for the mobile element in order to prolong the network lifetime.

Data gathering scheme for wireless sensor networks using a single mobile element, 2010, Bassam Alqaralleh and K. Almi'ani [10] introduced a new heuristic based solution to find the optimal path for the mobile element in order to collects the data from sensor nodes and deliver it to the sink node, ensuring that the time taken by the mobile element travelling in its path is less than or equal the time constraints.

Efficient data collection in wireless sensor networks with path-constrained mobile sinks. 2010, Shuai Gao, Hongke Zhang, and Sajal K. Das [11] introduced a new data collection model called maximum amount shortest path (MASP). This algorithm maximizes the data collected and reduced the energy consumption using optimized mapping between sub-sinks and sensor nodes.

Rendezvous planning in mobility-assisted wireless sensor networks, 2011 Guoliang Xing, Tian Wang, Zhihui Xie and Weijia Jia [12] developed two rendezvous points algorithms. first one called (RP-CP) which aims to find the optimal rendezvous points when the mobile element moves along the data routing tree. Second one called (RP-UG) which try to find the optimal rendezvous points that achieve a desirable balance between energy saving and travel distance for the mobile element. Simulation results show that this technique reduce the energy consumption significantly in order to extend the lifetime of the network.

Network lifetime and throughput maximization in wireless sensor networks with a path-constrained mobile sink, 2010. Shuai Gao, Hongke Zhang, Tianfei Song and Ying Wang [13] generated a new data collection model named maximum amount maximum life time (MAMAL). In this model an optimized mapping is done between sub-sinks and sensor nodes in order to maximize the data collected and also to elongate the network lifetime.

Using sink mobility to increase wireless sensor networks lifetime, 2008. Mirela Marta and Mihaela Cardei [14] used mobile sinks that changed their locations when the sensors around it deplete their energies. The mobile sink makes balanced energy consumption among the network in order to increase the lifetime for the network.

Data gathering strategies in wireless sensor networks using a mobile sink, 2010. ZHONG Zhi, LUO Dayong, LIU Shaoqiang, FAN Xiaoping and QU Zhihua [15] introduced two data gathering strategies that can be used to collect data in wireless sensor networks using mobile sink. In the two ways the mobile sink moves in the network depending on improved gauss markov mobility model (IGMM) and full coverage mobility model (FCM) respectively.

Multi-path planning for mobile element to prolong the lifetime of wireless sensor networks, 2009. Dakai Zhu, Yifeng Guo and Ali S¸aman Tosun [16] proposed a new data collection algorithm based on multi path planning (MPP). They used two multi path planning heuristic schemes called fixed-K and adaptive-K. These two schemes used to define the multi path for the mobile sink movement. The multi path movement will achieve balanced energy consumption in the network.

3. Thesis objectives

This thesis produces a new algorithmic solutions for gathering sensors data in wireless sensor networks using mobile element. The main objective of this thesis is to:

Find the weakness and shortage of the previous methods which use the mobile element for data gathering.

Build a new algorithmic approach to resolve this weakness and to be more affective and reliable in solving data gathering problems.

4. Theoretical Framework

WSN usually placed in hard to access environment, so it suffers from some challenges such as maintaining the connectivity between sensors also maximization network lifetime. Using redundant sensor nodes or nodes with special capabilities in communication range is a special solution to maintain the network connectivity. On the other hands in order to increase the network lifetime it can use efficient approach for energy conversation or simply replace the energy resources [17]. In this thesis we use the mobile device as a solution to maximize network lifetime by balancing the energy consumption in the network also to maintain the connectivity between sensor nodes.

In this section we introduced an overview of using mobile element in WSN also we discuss the different types of mobility.

4.1 different types of mobility in WSN

Mobile element can be moved in the network using one of the three methods (i) controlled (ii) predefined (iii) random.

4.1.1 Controlled mobility

In this case the mobile element moves in the network and explores the environment gathering the data from the sensor nodes, while the movement path still controlled and can be changed at any time. Ming Ma [18] proposed a data gathering approach using mobile collector called M-collector. this collector make a tour stating from the sink, traversing the sensor nodes to collect the data and come back to the sink to upload the collected data. Any change in the network topology will result in changing the mobile collector path.

4.1.2 Predefined mobility

In this type the path for the mobile element is determined and cannot be changed during network lifetime. Predefined mobility nature is suitable for structural health monitoring (SHM) and infrastructure surveillance application, Flamini [19] proposed a warning system based on wireless sensor network to monitor railway infrastructure.

4.1.3 Random mobility

In this type the mobile element moves in the network without any predefined path also without any controlling. This type of mobility is suitable to study the behaviour of animals in the environment. Pei Zhang [20] has benefited from the nature of the random mobility to study the behaviour of zebras. GBS sensor were tied to the zebras to collect data like zebras position and send to the base station using multi hops communication, so that the end user can interact with it.

4.2 Controlled mobility approach categories

Depending on the sink mobility, Ekici [17] classify the approaches that used controlled mobility into three main categories as listed below: 1- MBS (mobile base station) based solution. 2- MDC (mobile data collector) based solution. 3. Rendezvous based solution.

4.2.1 MBS (mobile base station) based solution

An MBS aims to balance the energy in the network by changing sink location during network lifetime. In [21] Gandham partitioning the time into rounds. At the end of each round MBS change its location in the network to maintain the energy balanced through the overall network.

4.2.2 MDC (mobile based station) based solution

An MDC is a mobile sink that moves in the network and collects the buffered data from the sensors using single hop. In [22] R.Shah used mobile data collector called data mules. In this model the mules moves through the network to collect the buffered data from the sensors that located in its communication range and send it to wired access point.

In this thesis we used this solution to solve the data gathering problem in wireless sensor networks, MDC will increase the network lifetime also it handle the connectivity between sensor nodes and it reduce the delay for delivering the data to the sink node. We aims to determine the path for the mobile element so it visits all sensor nodes before the buffers become overflow.

4.2.3 Rendezvous based solution

Rendezvous based solution is a hybrid approach where the sensors send the data to a rendezvous points (RPs) using multi hops forwarding. Data remain buffered there until it uploaded to the mobile device. So this technique combines between using mobile data collector with multi hops forwarding. In [23] G. Xing proposed a new algorithmic technique based on rendezvous points, where some nodes serve as rendezvous points to collect and buffered data from source nodes until it uploaded to the mobile element when it arrived.

5. Method (Approach) of the Research

We will examine the problem of determining the mobile elements tours in a network consisting of a number of sensor nodes (data producing element) with associated transit constraint. Given a number of sensor nodes, their transit constraints, their locations and also the location of a sink node. We intend to design a set of tours for the mobile elements, such that: all nodes must be visited by only one mobile element, transit constraints must not be violated, all tours must include the sink node and the time of all the tours must be minimized.

Our research will be composed of different simulation results. There are many different platform used for wireless sensor network simulation such as J-Sim and OMNET++. These simulators used to build the wireless sensor network environment and also to handle the communication between the sensor nodes and the mobile element.

6. Contribution of this research

Wireless sensor networks have many features such as low power consumption, low cost services, mobility services and it's suitable to be used with short transmission range.

This thesis presents a new approach to achieve the goals and counter with the challenges of using mobile element in wireless sensor networks. We will examine two major scenarios:

Using multiple mobile elements to gather sensors data using multiple tours.

Using single mobile element with multi-hop communication, in this scenario the mobile element visits only some of sensor nodes while the reaming nodes must send their data to the mobile element using-multi hop communication.

Using single mobile element with multi-hop communication

The Tour-Reducing (TR) algorithm works recursively by removing nodes from the single tour obtained for the graph G, until the tour satisfy the deadline constraint. This algorithm starts by constructing the single tour (T) that visits all nodes in G. Then, since T is expected to violate the deadline constraint, the algorithm works by calculating how much the tour will benefits from removing each node. Since our objective is increasing the network lifetime, the removed node must significantly reduce tour and must not be far away from the tour( to reduce the energy used for multihop communication), therefore the benefit function is defined as follows:

B(ni)=x/y

Where x is distance reduced from T after removing node ni and y is the distance between node ni and the new construct tour. Once the benefit function for each node is calculated, the node with highest benefit is removed from the tour and re-attached to it using multi-hop forwarding tree. This step is repeated until the obtained tour satisfies the transit constraint. The tour is obtained by employing any TSP solver.

Tour-Reducing (TR) algorithm

Input: G (movement graph topology for the mobile element), L (time deadline constraint for the tour), S (special node called sink node).

Output: Final_tour (the final tour for the mobile element that starts and ends in the sink), Routing_trees R (to show the connection for the nodes which did not covered in the tour).

1. For each node in the graph calculate B(ni)=xi/yi where x is distance reduced from T after removing node ni and y is the distance between node ni and the new construct tour.

2. Remove the node (ni) that have the biggest value for B(n) function.

3. Define the nearest node to the node (ni) and add it to the routing trees to used it in multi-hop communication. R ← mj

4. After removing this node, calculate the tour length for the mobile element (T).

5. If (T<L) then return to step1

Else

Final_tour ← T, Routing_trees ← R.

Figure (1) below show a wireless sensor network diagram after deploying the tour reducing algorithm (TR). final tour for the mobile element will cover nodes A,B,C,D,E and F, other nodes H,I,J,K,L and M will send their data to the nearest nodes using multi-hop communication.

Figure (1) single mobile element with multi-hop communication

Gathering WSN data using multiple mobile element

This approach aims to define set of tours for multiple mobile elements such that: all nodes in the network must be visited only by one mobile element, the sink node must be included in all tours and time constraint must not be violated for any tour.

Partitioning network (PN) algorithm based on segmenting the network graph G from the node which is the nearest to all other nodes in the graph (the node that has the least Euclidian distances to all other nodes), to make new sub graphs.

now each sub graph represents a tour for mobile element. Each tour must satisfy the time constraints, if any tour does not satisfy the time constraints this algorithm works again recursively and partition the sub graph until the time constraints satisfied for all tours.

Partitioning network (PN) algorithm

Input: G (network graph topology), L (time deadline constraint for the tours), S (special node called sink node).

Output: Final_tours (the final tours for the mobile elements that start and end in the sink).

1. For each node (ni) in the graph calculate [∑ (Euclidian distance (ni,n1tok)]

2. Divide the graph from the node that has the least sum for Euclidian distances to make new sub graph.

3. Covered the node that has the least Euclidian distance in one of the sub graphs.

4. For each sub graph (each sub graph represent tour for mobile element) calculate the tour length for the mobile element (T).

5. If (T<L) then return to step1

Else

Final_tours ← T.

Figure (2) below show a wireless sensor network diagram, suppose that node (c) is the node that has the least sum for Euclidian distance, so the network is divided into two sub graphs from the node (c) to represent the tours for the mobile elements. For each tour if (T<L) then the sub graph must be divided recursively until the time constraint satisfied.

(A) (B)

Figure (2) multiple tours using multi mobile element

7. Time plan

Date

Task

Aug to Sep 2012

Write the literature review

Oct to Nov 2012

Analysis the problem

Dec 2012 to Jan 2013

Implementation and finalize the problem

Feb to March 2013

Simulation and results

Apr to May 2013

Thesis writing up

8. Implication for the research

Increasing the density of the sensor nodes in the working environment will maintain the connectivity between these sensors. But this will lead to an increasing in the deployment cost. Mobile device is the best solution to compensate the shortfall that occurred from reducing the number of sensors nodes, where the mobile element delivers the data to the base station on behalf of the sensors itself, so with the mobile element there is no need to increase the number of the sensor nodes or increase the transmission power for them.

Many researchers have benefited from the mobile device to maintain the connectivity between the sensor nodes and also to prolong the network lifetime. Below we surveyed some of these researches and we explained it briefly.

Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines, 2004. Arun A Somasundara, Aditya Ramamoorthy and Mani B Srivastava [6] used controlled mobile device in order to collect the data from sensor nodes and deliver it to the sink node. Taking into consideration that sensors may vary in data generation rate, so the mobile device must schedule its tours so that it must visit all sensor nodes before buffers overflow.

EDGM: Energy efficient data gathering with data mules in wireless sensor networks, 2012. Nour Brinis, Pascale Minet and Leila Azouz Saidane [24] proposed a new data gathering algorithm using data mules, where the data mules used to carry the data to the sink node also it responsible to maintain the connectivity between sensor nodes.

Optimized shortest path method for efficient data collection in wireless sensor networks, 2012. G.P.Sandhiya and M.Yuvaraju [25] proposed a new data gathering method called optimized shortest path method (OSPM). In this method, an optimal mapping between sub sinks and sensor nodes is applied to maximize the data collected by the sink node.

Data harvesting with mobile elements in wireless sensor networks, 2006. Yaoyao Gu, Doruk Bozdag, Robert W. Brewer and Eylem Ekici [26] introduced a new data gathering algorithm based on controlled mobility called partitioning based scheduling algorithm (PBS). This algorithm aims to schedule the mobile element movement, so that all sensor nodes in the network must be visited before any buffer becomes overflow.

Energy-efficient data gathering with tour length-constrained mobile elements in wireless sensor networks, 2010. Khaled Almi'ani, Anastasios Viglas and Lavy Libman [27] proposed a new data gathering algorithm based on finding the optimal tours for the mobile element in order to collect the data from the network without violating the time constraints. Simulation results show that this algorithm increase the network lifetime significantly.

Moving Schemes for Mobile Sinks in Wireless Sensor Networks, 2007. Yanzhong Bi, Jianwei Niu and Limin Sun [28] introduced two autonomous schemes for the mobile sink movement. These tow schemes named one step moving scheme and multi-step moving scheme. in each scheme the mobile element makes the movement decisions without knowing sensors energies and network topology.

Dynamic model for efficient data collection in wireless sensor networks with mobile sink, 2012. Deepak Puthal, Bibhudatta Sahoo and Suraj Sharma [29] proposed a new data collection scheme named mobile sink wireless sensor network (MSWSN). This model based on the mobile sink which collects the data from the network instead of the stationary sink that suffers from the energy consumption problem. This model proved to be effective in maximization the network lifetime.

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