wireless sensor network lifetime constraints

1

A survey on Wireless Sensor Network Lifetime Constraints

Student name \ Musaab Mohammed Jasim . ID student \ f0114063. Computer engineering department ,wireless sensor network course.

Abstract

Deployment manner , routing and forwarding data algorithm

and using appropriate radio transmission power of the

nodes to satisfy continuous sensing with extended network

lifetime while maintaining coverage and connectivity in the

sensing region is the major challenges in wireless sensor

networks , in this paper we present some of the important

reasons of energy consumption and its influences on the

lifetime of the WSN and the details about the routing

protocol types and mechanisms and its role in maintaining

the WSN lifespan Then we will look into Energy‐efficient

routing protocol algorithms and deal with two of them.

Introduction

The convergence of the Internet, communications, and

information technologies, coupled with recent engineering

advances, is paving the way for a new generation of

inexpensive sensors and actuators, capable of achieving a

high order of spatial and temporal resolution and accuracy

and this generation of devices led to widely distribution of

the Wireless Sensor Network (WSN) in recent years . WSNs

have become a subject of interest for many different

applications not only in science and engineering, but equally

important, on a broad range of applications relating to

critical infrastructure protection and security, health care,

the environment, energy, food safety, production

processing, quality of life, and the economy. In addition to

reducing costs and increasing efficiencies for industries and

businesses . furthermore have opened up a new field of

application and research in the area of Ad‐hoc networks .

A wireless sensor network is an infrastructure comprised of

large numbers of nodes which involve sensing (measuring),

computing, and communication elements that gives an

administrator the ability to instrument, observe, react to

events and phenomena in a specified environment where

these nodes are deployed. The administrator typically is a

civil, government, commercial, or industrial entity. The

environment can be the physical world, a biological system,

or an information technology (IT) framework[1].

These nodes fall in two types (1) sensor node which gather

the sensing data and direct it to (2) sink node which be

interested for this data , the both types of nodes are limited

in their power resources because it usually run on batteries

and this imposes a hard limit on the lifespan of a WSN

especially for large‐scale WSNs when it integrate with other

networks , hence the radio communication became the most

expensive actions are performed by node and this make the

battery lifetime is a key consideration in design of WSNs .

To maximize the lifetime of WSN , we need to comprehend

some important aspects ,such as : how the power of nodes is

consumed in general form , what the influence of the nodes

deployment ways on the node's power is , and the major

role of routing protocol on manage the WSNs and minimize

the energy consumption. all these subjects will discuss in this

survey.

Generic energy consumption

The major energy consumption of a node (usually in free rectangular area) includes the energy which is consumed for:

1- message reception . 2- message transmission . 3- event sensing.

The energy for event sensing is generally linearly proportional to the time period of operation and is assumed to be

Where

is the average power consumption.

is a time interval.

The energy for message reception depends only on the number of received messages. For the energy for message transmission, it depends not only on the number of transmitted messages but also on the radio propagation model . here we assumed applying a simple propagation loss model which expressed as

where d denotes the transmission distance; L(d) (in dB units) denotes the propagation loss; L(d0) (in dB units) is the propagation loss at a reference distance d0; and β denotes

Wireless Sensor Network Lifetime Constraints

2

the path loss exponent. We also adopted the energy consumption by the electronic circuit Eelec , it is representing the energy dissipated to operate the transmitter or receiver

circuitry per bit (in Nano‐Joule), and , denoting the energy dissipation of the transmitter power amplifier for transmitting a bit to a receiver with a distance of d = 1 unit away in order to achieve an acceptable quality. By assuming that each sensing message contains k bits, we have the energy dissipation for transmitting a message to a receiver with a distance d away being

the energy dissipation for receiving a message is

Based on what is stated in the above , the increasing in the overhead on the WSN meaning minimize the lifetime of the node's battery ,thus early death of the network [2].

WSN Deployments Even if there are many energy‐efficient strategies have been devised and provided for message reception , message transmission and event sensing . However, it is inevitable that nodes must consume considerable energy in forwarding either the self‐measured data or the data coming from other nodes during the data gathering process. Since the energy consumption is location dependent , the nodes in different locations will suffer from different message forwarding burdens ,i.e., nodes closer to the sink have heavier relay load , thus uneven energy consumption. So, a good deployment leads to improve network coverage, achieve load balance, and prolong the network lifetime[2].

Deployment is application dependent process which concerned with setting up an operational sensor network in a real‐world environment to provide completely area coverage with different levels of nodes density and take into account that each sensor has a probability (P) to be active and it can be cover a circular region centered at itself with radius (r) , There are two major sorts of deployment are distinguished in WSNs :

1- Deterministic deployment : the deployment method in which the nodes of WSN are homogenous , i.e. with same sensing ability and the same energy, and can be distributed in a structured way by putting them in pre‐planned positions. There are many models of this deployment such as :

A. Linear-based deployment where the nodes deploy in linear shape such as: a hierarchical linear topology for border surveillance , highway traffic monitoring , safeguarding railway tracks, oil and natural gas pipeline protection, structural monitoring and surveillance of bridges and long hallways[3].

Figure(1) A Herarchical Linear Network

B. Grid-based deployment : It is conducted by dropping sensors row‐by row using a moving carrier. The time interval between consecutive droppings is controlled to achieve the desired distance. There are three types of grid‐based deployment corresponding to three regular shapes which can tile a plane without holes, namely, square , hexagon and equilateral triangle[4].

B-1- Square Grid Popular grid layouts are a unit square, an equilateral

triangle, a hexagon, etc. Among them, we investigate a

square grid because of its natural placement strategy over a

unit square. A grid‐based deployment is considered as a

good deployment in WSN, especially for the coverage

performance. Figure (2) shows a grid deployment of n

sensors in a circular field, where each of the n grid points

hosts a sensor[5].

Figure (2)

The approximate length of a unit square, d′, can be

calculated in the following way:

First, the approximate area of a unit square with length d′

can be computed by dividing the whole area of a given field

having radius R, with the number of cells, k. We do not know


3

the value of k, but it is approximately equal to (pn−1)2 for

the square grid. From this rela on, we derive Equa on 1 for

rsense, the sensing radius. However, since we consider an

initial adjustment for a star ng point, Equa on 1 cannot be

applied directly. According to simula on results, Equa on 2

gives more precise values than Equa on 1. Although we use

these equations to find out the rsense (i.e., the length of a

square, d′) given n and R, this formula allows the

approximate computation of any one parameter out of n,

rsense, and R given the other two parameters.

B-2- Tri-Hexagon Tiling (THT) A strategy is based on tiling. A tiling is the covering of the

entire plane with figures which do not overlap nor leave any

gaps. Tilings are also sometimes called tessellations. Among

different tilings we use a semi‐regular tiling (which has

exactly eight different tilings) where every vertex uses the

same set of regular polygons. A regular polygon has the

same side lengths and interior angles. We consider a semi‐

regular tiling that uses triangle and hexagon in the two

dimensional plane, the so‐called 3‐6‐3‐6 Tri‐Hexagon Tiling.

The name comes from going around a vertex and listing the

number of sides each regular polygon has, as illustrated in

Figure (3). Here we combine the advantages of a triangle

grid and a hexagon grid . the area of a regular triangle with

rsense achieves 3‐coverage , and thus 3‐coverage of the whole

region, simultaneously[5].

Figure(3)

However, a triangle grid uses a larger rsense than a square grid

for the same n and R. In particular, the square grid uses

about 5% of rsense less than the triangle grid. In a hexagon

grid, rsense is about 17% less than in the triangle grid. In this

aspect, the hexagon grid seems better than others, but with

respect to other performance metrics it does not behave

well. For this reason, we consider THT deployment, which

uses 13% of rsense less than the triangle grid. In a way similar

to the square grid, an approximate formulation for rsense can

be found for THT. This approximate solution can be

computed using Equa on 3.

B-3- Equilateral Triangle grid Deployment the equilateral triangle grid will require the least number of sensor nodes to cover a given sensing field in the ideal case, i.e. no deployment errors[6] . For example take the area with three sensor at three vertexes of the equilateral

triangle (its edge length is equal to ) as illustrated in Figure(4)

Figure (4)

The sensor deployment has the maximum efficient coverage

area and the efficient coverage area is


4

Efficient coverage area is given by

However, in some cases when the terrain which is supposed

to be monitored are dangerous region or difficult to reach .

the use of pre‐defined deployment will become very difficult

and not feasible. So, in such circumstances, WSNs allow

replaced by an unstructured way (Random deployment)

method. But with bigger amount of nodes than in

Deterministic deployment .

2- Random deployment The deployment methods in which the nodes of WSN are

randomly distributed over the desired area by, e.g., dropping

them out of an helicopter as in the volcano monitoring

system or in a hostile environment . Randomized sensor

deployment is quite challenging in some respects, since

there is no way to configure a priori the exact location of

each device and it is required a good self‐configuration

mechanisms to obtain the desired coverage and connectivity

Random deployment of sensor nodes in the physical

environment may take several forms. It may be a one‐time

activity where the installation and use of a sensor network

are strictly separate actions. Or, it may be a continuous

process, with more nodes being deployed at any time during

the use of the network . there two types of random

deployment :

A. Uniform random deployment It means deployment the nodes of WSN in randomly fashion

with the same density over the entire sensing area can

achieve the best network sensing coverage in the initial

phase. However, under the assumption that all nodes are

homogeneous with the same available energy, uniform

deployment is not the optimal strategy in the viewpoint of

network lifetime. Those nodes located at an area with high

energy consumption will run out of available energy and die

off quickly . in addition the nodes closer to the sink tent to

consume more energy than those farther away from the sink

, this mainly because , besides transmitting their own

packets ,they forward packets on behalf of other nodes that

are located farther away , thus they will deplete their energy

first and this problem called (Energy Hole). Therefore, in an

early phase, the network sensing coverage may lose in some

area and even the network may become disconnected[2].

There are many papers which addressed this problem and

put the solution of it , some of it proposed use another

deployment approach called (Heterogeneous deployment)

which assume using two types of sensors, low energy

sensors and high energy sensors, to construct a hierarchical

network structure. However, this scheme raises the

deployment and implementation complexity and cannot be

easily applied. the others considers the Non‐uniform random

deployment as an alternative manner to overcome the

effect of uneven energy depletion. There are many uniform

deployment models for WSN , e.g., Coronas Model [7] in

which the monitored area divide in coronas as illustrated in

Figure (5).

Figure (5) corona model for uniform deployment

A message transmitted from corona Ci is forwarded by

sensor nodes in coronas Ci−1, Ci−2, and so on until it reaches

corona C1 from where it is transmitted to the sink which

reside in the center. Corona width is chosen such that a

message is forwarded by only one sensor in each corona.

B. Non-uniform random deployment The basic concept of this random deployment approach is

that different node densities are assigned to different sub‐

regions , nodes shall be deployed with a high density on a

location with high energy consumption, whereas, a low

density for a location with low energy consumption In an

attempt to balance the communication load of each sub‐

region . and to eliminates the "Energy Hole" problem there

are various non‐uniform solutions have been proposed[8] :

The simplest one is the non‐uniform node deployment, according to which the surface density of nodes is variable, yielding more nodes on the areas near the sink. These extra nodes are sensing and relaying data as the normal ones do.


5

In the very similar solution to the first one, the difference is that the extra nodes are actually dormant, waiting for a neighbor node to exhaust its battery until they take action.

Thus , non‐uniform deployment strategy fitting to the energy consumption distribution in order to achieve good network lifetime and network sensing coverage . Figure(6) illustrates a model of non‐uniform sensor deployment , where the desired sensing area is assumed to be a rectangle with

length L and width W, i.e. the area is A = L × W. and the data sink is assumed to be beside an edge of the sensing area as base‐station, and the sensor node deploy in non‐uniform random way with sensing radius is R and available sensing area is πR2.

Figure (6)

WSN Routing Protocols

Because the different data forwarding mechanisms lead to

different amount and different distribution of energy

consumption in WSN. So, the good choice of routing

protocol for the certain types of deployment under

particular radio features plays the main role in saving the

nodes power , thus maximize of the WSN lifespan [9].

Routing protocols in WSN play a major role in managing the network , where in addition to its ordinary tasks to provide shortest path between source and destination , it have to:

a) minimizing energy consumed per packet . b) maximize time to network partition . c) minimizing variance in node power levels . d) minimizing maximum node cost .

so , it are considered the substantial responsible about the

network lifespan .

Routing in WSNs Routing is the process of enabling data transfer over a network from a source node (Sensor) to a destination node (Sink) . This is achieved in a two‐step process: (1) paths have to be established in the network along which data traffic can then be forwarded if the sender and receiver are not on the same link. Once the paths have been selected, (2) data traffic is forwarded from one endpoint of the transmission via intermediate nodes to the other endpoint. Routing algorithms are used to determine the paths the data will take and should fulfill the following properties: the routes should be chosen such that data reaches its destination in the 'best' way possible. 'Best' is defined by one or more metrics, depending on the application requirements. For example, one widely used metric is using the route with the lowest end‐to‐end delay, or the highest throughput, whilst other ones could be to use the route with the least hop distance, the best link quality, or least energy consumption. The restrictions imposed by WSNs, which were represented by (limited power supply, severely constrained memory, self‐organization, and lossy wireless communication),add further requirements to suitable routing algorithms: they have to deal efficiently with an ever changing topology, whilst imposing as little control traffic overhead as necessary on the network, as the transmission of messages is very costly in terms of energy. In order to enable the nodes in the network to actually send data to each other, the routing protocol needs to provide the route it computed between them. Some protocols periodically recompute these routes, whereas others do so only on demand, i.e. when a data packet needs to be transmitted. This behavior is used to distinguish between different types of routing protocols[10]. WSN Routing Strategies At present , array of routing protocols for WSNs are existent, which use different strategies to address one or more of the restrictions [10]and the most commonly utilized strategies are :

A. Flooding strategy The most straightforward way to diffuse information in a WSN is to flood it throughout the network . Flooding means that every node which receives new information will forward it to all its neighbors until it reaches its destination. To prevent broadcast storms, several mechanisms are available: nodes check for duplicates, i.e. messages they have already received, and packets may contain information on how many times they are allowed to be retransmitted. Whilst being easy to implement, flooding causes several drawbacks: amongst others, nodes may receive duplicated messages and a large amount of energy is wasted as there is no mechanism to include energy constraints. There are many protocols use this strategy such as: Temporally‐Ordered Routing Algorithm (TORA). B. Interest advertisement-request strategy In this routing protocol strategy the information is described by meta‐data which initially is exchanged between the nodes. Nodes which acquired new data advertise it via its


6

meta‐data classification. Neighboring nodes which have an interest in that kind of data reply with a request, on which the advertising node transmits the data to the requesting node. After receiving the new data, the requesting node advertises it to its neighbors. interest advertisement‐request strategy achieves a high energy efficiency compared to flooding strategy, as only requested information is transported in the network . However, there is no standard meta‐data format, as this is supposed to be application specific. Also, the delivery of data is not guaranteed by its advertisement mechanism, as the nodes interested in a specific class of data might be distant from the node acquiring this data. If intermediate nodes are not interested in the given class of data, the interested node will never receive it. Sensor Protocols for Information via Negotiation(SPIN) is one of the routing protocol which use this strategy . C. Spatial Location Strategy A third strategy routing found in WSN routing protocols uses the knowledge about the spatial position of sensor nodes to query the WSN in a localized way. If the deployment of sensor nodes is known, queries for data can be directed to the area of interest, thus reducing the overhead in transmissions on the entire network. Some of the protocols falling into this category, e.g., Minimum Energy Communication Network (MECN) or Geographic adaptive fidelity (GAF) were originally designed for mobile ad‐hoc networks, which be applicable for WSNs as they are energy aware.

Routing Protocols in WSNs Routing protocols are classified into three main categories,

proactive, reactive based on how and when they acquire

routes in the network. and the third type is hybrid protocol

which is attempting to incorporate the benefits of

proactivity and reactivity. So, it is only a blend of the first

two types nothing else [9] .

A. Reactive Protocols In reactive protocols, routes are acquired by nodes on demand when a packet needs to be forwarded and no path to the destination is currently known. The node triggers a route discovery process, e.g. by diffusing a route request packet through the network and then waiting for a response from the destination node. This response might take time to arrive, causing the packet delivery to be delayed. In reactive protocols, the overhead of control traffic is depending on the data traffic in the network. By acquiring routes on demand, a node has only a partial knowledge about the network, as routes are computed only for destinations to which data traffic has to be forwarded. This might be advantageous in terms of state, as reactive protocols do not require each node to store routes for the entire network. The Ad‐hoc On‐Demand Distance Vector (AODV) is an example of a reactive protocol.

B. Proactive Protocols Proactive routing protocols take a different stance: nodes regularly compute routing tables of the complete network, thus pre‐provisioning all possible paths for the entire network topology. In this way, data traffic can be sent out to its destination immediately, without the delay imposed by route acquisition in reactive protocols. However, a certain amount of control traffic is needed to keep routing tables up to date and consistent over the whole network. This control traffic is always present, independently of data traffic on the network. Amongst proactive routing protocols, Optimized Link State Routing (OLSR) is a prominent example as it is used in real world deployments .

In addition to what has been mentioned previously, WSN routing protocols can be classified according to other criteria , e.g. the WSN routing protocols fall into two types depending on the network structure :

‐ Flat‐based routing protocols where each node plays the same role and is typically assigned the same functionality. The message forwarding is achieved via multi‐hop transmission .

‐ Hierarchical‐based routing protocols (also known as clustering‐based routing protocol): this type of protocols addresses the issues of scalability and energy preservation to achieve long lifespan for WSNs by avoiding an overload of sink nodes by too many received messages, as well as reducing the amount of overall message transmissions and to achieve this goal the nodes have to self‐organize themselves into several local clusters, each of which has one node serving as the cluster‐head. A cluster‐head collects all the messages in that cluster and then forwards an aggregate message to a remote base station (BS). the Low Energy Adaptive Clustering Hierarchy protocol (LEACH) is considered one of the most common protocol in this categorize . According to the above mentioned , we will realize that the routing protocol play the major engine role in the lifespan of WSN by control the overhead signal and the motion of the data via the network which need the big part of the node's battery power and provide a balanced energy distribution over the network , and there are three major issues involved in energy aware routing protocols[9]. First, the goal is to find the path that either minimizes the absolute power consumed or balances the energy consumption of all nodes. Second, energy awareness has been either implemented at purely routing layer or routing layer with the help from other layers such as MAC or application layer. Third, some routing protocols assume that the transmission power is controllable and nodes’ location information is available (e.g., via GPS). Under these assumptions, the problem of finding a path with the least consumed power becomes a conventional optimization problem on a graph where the weighted link cost corresponds to the transmission power required for transmitting a packet between the two nodes of the link.


7

Energy- Efficient Routing Protocol Algorithms

Energy efficient routing algorithm can be categorized as follows: data centric routing algorithm, location based routing algorithm and hierarchical routing algorithm as shown in the figure(7). Data centric routing algorithm uses meta data to find the route from source to destination before any actual data transmission to eliminate redundant data transmission Location based routing algorithm requires actual location information for every sensor node. Hierarchical routing algorithm divides the network into clusters. Cluster head (CH) is elected in each cluster. CH collects data from its members, aggregates the data and sends to sink[11].

Figure (7)

Data centric protocols it are query based and they depend on the naming of the desired data, thus it eliminates much redundant transmissions. The BS sends queries to a certain area for information and waits for reply from the nodes of that particular region. Since data is requested through queries, attribute based naming is required to specify the properties of the data. Depending on the query, sensors collect a particular data from the area of interest and this particular information is only required to transmit to the BS and thus reducing the number of transmissions. e.g. SPIN was the first data centric protocol.

Location based routing protocols It needs some location information of the sensor nodes. Location information can be obtained from GPS (Global Positioning System) signals, received radio signal strength, etc. Using location information, an optimal path can be formed without using flooding techniques. e.g. Geographic and Energy‐Aware Routing(GEAR) .

Hierarchical routing protocols

It is used to perform energy efficient routing, i.e., higher energy nodes can be used to process and send the information; low energy nodes are used to perform the sensing in the area of interest. e.g. LEACH, TEEN, APTEEN. In this paper we will look to two of hierarchical routing protocol and then we will compare between them.

LEACH (Low Energy Adaptive Clustering Hierarchy) W.Heinzelman, introduced a hierarchical clustering algorithm for sensor networks, called Low Energy Adaptive Clustering Hierarchy (LEACH). LEACH arranges the nodes in the network into small clusters and chooses one of them as the cluster‐head. Node first senses its target and then sends the relevant information to its cluster‐head. Then the cluster head aggregates and compresses the information received from all the nodes and sends it to the base station. The nodes chosen as the cluster head drain out more energy as compared to the other nodes as it is required to send data to the base station which may be far located. Hence LEACH uses random rotation of the nodes required to be the cluster‐heads to evenly distribute energy consumption in the network. Based on many studies in this protocol , it was found that only 5 percent of the total number of nodes needs to act as the cluster‐heads. TDMA/CDMA MAC is used to reduce inter‐cluster and intra‐cluster collisions. This protocol is used were a constant monitoring by the sensor nodes are required as data collection is centralized (at the base station) and is performed periodically[11]. Figure(8) illustrate the structure of LEACH protocol.

Figure (8)

LEACH operations can be divided into two phases:‐ a. Setup phase b. Steady phase In the setup phase, the clusters are formed and a cluster‐head is chosen for each cluster. While in the steady phase, data is sent and sent to the central base station. The steady phase is longer than the setup phase. This is done in order to minimize the overhead cost. Setup phase :‐ During the setup phase, a predetermined fraction of nodes, p, choose themselves as cluster‐heads. This is done according to a threshold value, T(n). The threshold value depends upon the desired percentage to become a cluster‐head‐ p, the current round r, and the set of nodes that have not become the cluster‐head in the last 1/p rounds, which is denoted by G. The formulae is as follows :

T(n) = p/1‐p[r mod(1/p)] if n E G T(n) = 0 otherwise Every node wanting to be the cluster‐head chooses a value,

Energy‐ efficient routing protocol

algorithms

Data centric Location based Hierarchical


8

between 0 and 1. If this random number is less than the threshold value, T(n), then the node becomes the cluster‐head for the current round. Then each elected CH broadcasts an advertisement message to the rest of the nodes in the network to invite them to join their clusters. Based upon the strength of the advertisement signal, the non‐cluster head nodes decide to join the clusters. The non‐cluster head nodes then informs their respective cluster‐heads that they will be under their cluster by sending an acknowledgement message. After receiving the acknowledgement message, depending upon the number of nodes under their cluster and the type of information required by the system (in which the WSN is setup), the cluster‐heads creates a TDMA schedule and assigns each node a time slot in which it can transmit the sensed data. The TDMA schedule is broadcasted to all the cluster‐members. If the size of any cluster becomes too large, the cluster‐head may choose another cluster‐ head for its cluster. The cluster‐head chosen for the current round cannot again become the cluster‐head until all the other nodes in the network haven't become the cluster‐head. In the below the Flow chart of the Set‐up phase of the LEACH protocol.

Steady phase :‐ During the steady phase, the sensor nodes i.e. the non‐cluster head nodes starts sensing data and sends it to their cluster‐head according to the TDMA schedule. The cluster‐head node, after receiving data from all the member nodes, aggregates it and then sends it to the base‐station. After a certain time, which is determined a priori, the network again goes back into the setup phase and new cluster‐heads are chosen. Each cluster communicates using different CDMA codes in order to reduce interference from

nodes belonging to other clusters. In the below the Flow chart of the Steady phase of the LEACH protocol

According to the last flow charts the algorithm of LEACH's phases will be as shown below :


9

DECSA (Distance-Energy Cluster Structure Algorithm): it is hierarchical routing algorithm based on the same

concepts of the classic clustering routing algorithm LEACH

and it is considered an improved version of LEACH. The WSN

structure under this algorithm is divided in three hierarchal

level , which divides the nodes into four categories: Base

Station(BS) , Base Station Cluster head(BCH), ordinary cluster

head node (CH), and common sensor node (SN). As shown in

figure (8) below .

Figure(8)

The DECSA algorithm is a distributed competitive unequal

clustering algorithm, it considers both the distance and

residual energy information of nodes. Similar to that of

LEACH, DECSA protocol continues by round and each round

can be divided into initialization stage and stable working

stage. In order to minimize energy consumption, the stable

working stage should be greatly longer than the initialization

stage.

a. Initialization stage In the initialization stage, cluster head is elected and TDMA time slots are distributed to ordinary member nodes by the cluster head. Within a given time slot, ordinary member nodes are joined an appropriate cluster. The process of cluster head select consists of following 2 parts: elec on of ordinary cluster head node (CH) and election of Base Station Cluster head (BCH).In the part of election CH, the main difference between LEACH and DECAS in this part is DECSA employs both residual energy and distance parameter. First, each sensor node generates a random number between 0 and 1.If the random number for a par cular node is smaller than the predefined threshold T, then that sensor node becomes the first round cluster‐head, we call it false‐cluster‐head there. And then all the nodes in the cluster are respectively calculate their k(i), and compared it with their current false‐cluster‐head. If it is greater than the false‐cluster‐head’s k(i), then announced that he become the CH of this cluster. If it is smaller, then the false –cluster‐ head

become the CH. Thus, the election of cluster head considers both the nodes’ energy consumption and the communication between the network, comparing the difference of k(i), let the high residual energy, high efficiency of communication node has the bigger probability to be elected as CH, it will prolong the lifetime of the network.

Where k(i)is the threshold of elect CH , En(i) is the residual

energy of node i, d0(i) is the average distance between node

i with all other nodes in the same cluster. After the election

of cluster‐head, in the part of election base‐station‐cluster‐

head, we use threshold TBCH to select which CH will become

the BCH. We select those CH whose TBCH(i) are larger than

the predefined threshold TBCH0 as the base‐station cluster‐

head( BCH). The rest of the cluster heads as ordinary cluster

head nodes CH. We define TBCH(i) as follow:

where En(i) is the current residual energy of node i, E0 is the initial energy of node in the network, d(i) is the distance between node i with base station.

b. Stable working stage In the stable working stage, base station broadcasts the

message to the entire network. After received the messages,

according to the different value of TBCH(i) , base‐station‐

cluster‐head select the maximum TBCH cluster‐head as its

next hop ,and the rest hop can be selected in the same

manner until all of the cluster head nodes are connected

,forming a complete communication path. In order to reduce

the direct communication between the base station and the

cluster‐head which is far away from the base station and has

low residual energy. Common nodes (SN) in the cluster will

transmit data packet to their closest cluster‐head, then

cluster‐head will collect and fusion those data and transmit

them to the base‐station‐cluster‐head, rather than transmit

them to the base station directly. And then, base‐station‐

cluster‐head will communicate with the base station.

Avoiding the narrowness of the election of base‐station‐

cluster‐head, balance the consumption of energy and data

transmission, the value of threshold TBCH0 should be dynamic

changed according to the real‐time network’s state, thus

could guarantee the base‐station‐cluster‐head of the whole

network be elected is the most appropriate. The value of

TBCH0 should between the average TBCH and the maximum

TBCH in network .Of course, the difference of the TBCH0

threshold will cause different influence the performance of

the network directly. The simulation experiments show that

when TBCH0 takes 75% of the maximum TBCH, the network will

have its best performance.


10

based on what is stated in the above and if we compare

between the DECSA and LEACH protocols , we will realize

that LEACH does not consider the location of sensor nodes,

and the selection of cluster head nodes is random, causing

the uneven distribution of cluster head nodes That is, the

cluster‐heads can concentrate a specific area within the

network. So, LEACH cannot guarantee a good cluster‐head

distribution and it does not consider the residual energy of

nodes where its algorithm supposes the initial energy of all

nodes are same, and the energy consumption of becoming

cluster head node are basically the same in the first cluster

head election. Therefore, this protocol is not good for

unbalanced‐energy network and it leads to the early death

of some nodes in this network. thus, we get the overall

invalidity of the WSN , while the DECSA improves the

process of cluster head selecting and the process of cluster

forming. It reduces the adverse effect on the energy

consumption of the cluster head, resulting from the non‐

uniform distribution of nodes in network and avoided the

direct communication between the Base Sensor and cluster

head, which has low energy and far away from Base Sensor.

thus it effectively balances the energy consumption and

prolongs of the WSN lifetime .

Conclusions

In this paper , we addressed the WSN lifetime and we realized that the lifespan of WSN is affected by many reasons such as nodes deployment manner , the energy of transceiver and the amount of overheads signals which are forwarded via network , and we realized the "Energy Hole" problem and we comprehend the solutions of it . and infer that the different data forwarding mechanisms lead to different amount of and different distribution of energy consumption , thus the good choice of routing protocol for the certain types of deployment under particular radio features plays the main role in saving the nodes power , thus maximize of the WSN lifespan and this make the routing protocol is the major engine of the saving energy in the WSN . then we addressed the WSN routing protocol types and understand its mechanisms and its influences on the WSN lifetime , then study the LEACH and DECSA efficient‐energy clustering routing algorithms and we concluded the DECSA has a better performance than the original LEACH protocol.

References

1. WIRELESS SENSOR NETWORKS (Technology, Protocols, and Applications) by KAZEM SOHRABY , DANIEL MINOLI and TAIEB ZNATI .

2. Non‐uniform node deployment for lifetime extension in large scale randomly distributed wireless sensor networks by Yuh‐Ren , Kai‐jie and Sz‐Yi Yeh .

3. On the deployment of wireless sensor nodes by Xin Liu and Prasant Mohapatra .

4. Comparative study of energy consumption for wireless sensor networks based on random and grid deployment strategies by Monica and Ajay K Sharma .

5. Node Deployment in Large Wireless Sensor Networks: Coverage, Energy Consumption, and Worst‐Case Delay by Wint Yi Poe and Jens B. Schmitt.

6. Numerical Analysis of Sensor Deployment in Wireless Sensor Network by Sudeep Shakya , Nirajan Koirala and Narayan Nepal .

7. Non‐uniform sensor deployment by Jie wu , Yinying and Mihaela cardie.

8. Avoiding Energy Holes in Wireless Sensor Networks with Non‐Uniform Energy Distribution by Apostolos Demetzis , Konstantions Oikonomou .

9. Routing algorithms for balanced energy consumption in Ad hoc networks by Hee youn and Chansu Yu and Ben lee.

10. Routing in wireless sensor network : an experimental evaluation(master thesis) by Varonika Baurer .

11. AN ENERGY EFFICIENT ROUTING PROTOCOL IN WIRELESS SENSOR NETWORKS (master thesis) , by Divya Prabha

12. A Energy‐Efficient Clustering Routing Algorithm Based on Distance and Residual Energy for Wireless Sensor Networks ‐ Zhu Yonga, Qing Pei .

wireless sensor network lifetime constraints

Engineering