Categorization of routing protocols: Function based

Query based

In these protocols the base station need to ask the network when it needs data from some region or from the whole network. This could be done by sending a request to the sensor network. This query is either broadcasted in the whole network or the desired region; nodes listen to this query and transmit the data. Otherwise sensor nodes do not transmit any data.

Periodic data communication

In these routing algorithms data is sensed periodically. Network does not make any decisions. It transmits the data to the base station. Data is processed there in order to make use of the information.

Event driven.

Data sensing is done periodically but the some of the decision making is done with in the network. So it determines at which point the data to be transmitted and which point it should not.

18 | P a g e So only when the sensed data meets criteria it transmits the data. This strategy itself can be used to reduce the number of data transmitted thus the energy spending.

Hybrid (Event driven and periodic)

This version is much similar to the event driven situation, but there is a periodic data transmission in large periods. Because in a scenario in which some nodes dysfunction. In that case, they stop sending data to the BS. But if they are meant to be ‘event driven’ the base station might understand this normal. With an additional inclusion of a condition that all nodes should transmit after a large period, the nodes which do not transmit can be identified as malfunctioning nodes by the base station.

19 | P a g e 3. Chapter 3: Hierarchical routing implementation and comparison

In this chapter we would like to introduce more routing protocols in hierarchical networks. There are number of reasons that a hierarchical routing is discussed more. In fact not only mere

description, we implement them compare the results and also further analyze them to look for the relative drawbacks and benefits.

Clustered tier network has advantages when it comes to energy conservation of the network.

Unlike normal ad-hoc networks micro sensor networks are designed for specific application. All the sensors collect similar data and the data transmission is from micro sensor nodes to a central location (base station).Since all the nodes do a similar data sensing, there is a huge correlation between the sensed data in all the nodes. So if there were a data aggregation of the nodes in the same region, more likely the data could be enormously reduced. By clustering the network with respect to the geographical proximity, this benefit could be leveraged. So every cluster in that way needs to have a cluster head [1]. In reality this cluster head does the most energy dissipation.

Because even with data infusion this cluster head will have to do the maximum transmission.

Thus it is clear that the cluster head must be a high energy node. Unfortunately we cannot make sure that the network has some high energy nodes in each region. Because sensor network deployment usually a random one.(Figure 2) In most networks it is quite difficult to make a targeted sensor deployment. So it is impossible to have targeted high energy node deployment to each region of the network. To overcome this challenge [2] comes up with a protocol called Low Energy Adaptive Clustering Hierarchy – LEACH, that dynamically changes the cluster heads of the clusters so that there will not be an issue of the evenness of the energy dissipation of the network.

3.1. Low Energy Adaptive Clustering Hierarchy (LEACH)

3.1.1. Introduction [2]

20 | P a g e As we mentioned in the previous section, LEACH is a groundbreaking communication protocol based on hierarchical clustering. It is a distributed algorithm. Each node runs the algorithm inside; instead it is run in a central location.

Operation of LEACH is broken into rounds. Each round consists of following phases. Set-up phase, when the clusters are organized, followed by a steady-state phase, when data transfers to the base station occur. In order to minimize overhead, the steady-state phase is long compared to the set-up phase.

3.1.2. Advertisement Phase

Initially, when clusters are being created, each node decides whether or not to become a cluster-head for the current round. This decision is based on the suggested percentage of cluster cluster-heads for the network (determined a prior) and the number of times the node has been a cluster-head so far.

This decision is made by the node n choosing a random number between 0 and 1. If the number is less than a threshold T (n), the node becomes a cluster-head for the current round. The threshold is set as:

Equation 1

Where P = the desired percentage of cluster heads (e.g., P = 0.05), r = the current round, and G is the set of nodes that have not been cluster-heads in the last 1/P rounds. Using this threshold, each node will be a cluster-head at some point within 1/P rounds. During round 0 (r = 0), each node has a probability P of becoming a cluster-head. The nodes that are cluster-heads in round 0 cannot be cluster-heads for the next 1/P rounds. Thus the probability that the remaining nodes are

21 | P a g e heads must be increased, since there are fewer nodes that are eligible to become cluster-heads. After 1/P -1 rounds, T=1 for any nodes that have not yet been cluster-heads, and after 1/P rounds, all nodes are once again eligible to become cluster-heads. Future versions of this work will include an energy-based threshold to account for non-uniform energy nodes. In this case, it is assumed that all nodes begin with the same amount of energy and being a cluster-head removes approximately the same amount of energy for each node. Each node that has elected itself a cluster-head for the current round broadcasts an advertisement message to the rest of the nodes.

For this “cluster-head-advertisement” phase, the cluster-heads use a CSMA MAC protocol, and all cluster-heads transmit their advertisement using the same transmit energy. The non-cluster-head nodes must keep their receivers on during this phase of set-up to hear the advertisements of all the cluster-head nodes. After this phase is complete, each non-cluster-head node decides the cluster to which it will belong for this round. This decision is based on the received signal strength of the advertisement. Assuming symmetric propagation channels, the cluster-head advertisement heard with the largest signal strength is the cluster-head to whom the minimum amount of transmitted energy is needed for communication. In the case of ties, a random cluster-head is chosen.

3.1.3. Cluster Setup Phase

After each node has decided to which cluster it belongs, it must inform the cluster-head node that it will be a member of the cluster. Each node transmits this information back to the cluster-head again using a CSMA MAC protocol. During this phase, all cluster-head nodes must keep their receivers on.

3.1.4. Schedule Creation

The cluster-head node receives all the messages for nodes that would like to be included in the cluster. Based on the number of nodes in the cluster, the cluster head node creates a TDMA schedule telling each node when it can transmit. This schedule is broadcast back to the nodes in the cluster.

22 | P a g e 3.1.5. Performance of LEACH

There are many performance parameters they use to evaluate and compare the protocols. Authors of LEACH and many others protocol designers have used the following metrics to determine the energy efficiency capabilities of a sensor network.

1. The time (the round) of the first node dies.

2. The number of nodes alive with respect to time (number of nodes)

In the following section we analysis the performance metrics of the sensor networks in detail. But here we give the performance of LEACH as given by [2]

Figure 6: Initial positions of the nodes

23 | P a g e Figure 7: No of alive nodes Vs. rounds (time ) curve

0.5J battery per node 1J battery per node

Direct method LEACH Direct method LEACH

First node dying round

101 940 223 1853

Last node dying round

235 1325 472 2602

Table 2: Comparison : LEACH and direct method

3.1.6 Discussion on LEACH performance

As you can see, LEACH tremendously improves the lifetime of the network while keeping the network distributed. Undoubtedly it is a very good protocol, because it maintains the distributiveness of the network (So that the central base station does not need to know the locations of each node).

24 | P a g e 3.2. LEACH-C (LEACH-Centralized)

While there are advantageous to using LEACH’s distributed cluster formation algorithm, where each node makes autonomous decisions that result in all the nodes being placed into clusters. this protocol renders no guarantee about the placement and or number of cluster head nodes. Since the clusters are adaptive obtaining a poor clustering set up during a given round will not greatly affect overall performance of LEACH. However using a central control algorithm to form the clusters may produce better clusters by dispersing the cluster head nodes throughout the network.

This is the basis for LEACH-C LEACH Centralized a protocol that uses a centralized clustering algorithm and the same steady state protocol as LEACH.

In addition to determining good clusters the base station needs to ensure that the energy load is evenly distributed among all the nodes To do this the base station computes the average node energy and whichever nodes have energy below this average cannot be Cluster-heads for the current round. Using the remaining nodes as possible Cluster-heads the base station runs a simulated annealing algorithm [3] to determine the best k nodes to be Cluster-heads for the next round and the associated clusters This algorithm minimizes the amount of energy the non-Cluster-head nodes will have to use to transmit their data to the Cluster non-Cluster-head by minimizing the total sum of squared distances between all the non-Cluster-head nodes and the closest Cluster-head. At each iteration the next state which consists of a set of nodes in C’ is determined from the current state the set of nodes in C by randomly and independently perturbing the x and y coordinates of the nodes c in C to get new coordinates x’ and y’. The nodes that have location closest to (x’,y’) become the new set of Cluster-head nodes c that makes up set C. Given the current state at iteration k represented by the set of Cluster-head nodes C with cost f(C) the new state represented by the set of Cluster-head nodes C’ with cost f(C) will become the current state with probability,

25 | P a g e Equation 2

where αk is the control parameter equivalent to the temperature parameter in the thermodynamic model and f() represents the cost function defined by,

Equation 3

where d(i,c) is the distance between node i and node c. The parameter αk must be chosen to be increasing with increasing αk to ensure that the algorithm converges. However, if αk increases too quickly, the system will get stuck in local minima. On the other hand, if αk increases too slowly, the system will take a very long time to converge.

3.3. TEEN (Threshold sensitive Energy Efficient sensor Network protocol) [4]

3.3.1. Functioning

In this scheme, at every cluster change time, in addition to the attributes, the cluster-head broadcasts to its members, Hard Threshold (HT): This is a threshold value for the sensed

26 | P a g e attribute. It is the absolute value of the attribute beyond which, the node sensing this value must switch on its transmitter and report to its cluster head.

Soft Threshold (ST): This is a small change in the value of the sensed attribute which triggers the node to switch on its transmitter and transmit. The nodes sense their environment continuously.

The first time a parameter from the attribute set reaches its hard threshold value, the node switches on its transmitter and sends the sensed data. The sensed value is stored in an internal variable in the node, called the sensed value (SV). The nodes will next transmit data in the current cluster period, only when both the following conditions are true:

1. The current value of the sensed attribute is greater than the hard threshold.

2. The current value of the sensed attribute differs from SV by an amount equal to or greater than the soft threshold.

Whenever a node transmits data, SV is set equal to the current value of the sensed attribute. Thus, the hard threshold tries to reduce the number of transmissions by allowing the nodes to transmit only when the sensed attribute is in the range of interest. The soft threshold further reduces the number of transmissions by eliminating all the transmissions which might have otherwise occurred when there is little or no change in the sensed attribute once the hard threshold.

3.3.2. Important Features

The main features of this scheme are as follows:

1. Time critical data reaches the user almost instantaneously. So, this scheme is eminently suited for time critical data sensing applications.

2. Message transmission consumes much more energy than data sensing. So, even though the nodes sense continuously, the energy consumption in this scheme can potentially be much less than in the proactive network, because data transmission is done less frequently.

3. The soft threshold can be varied, depending on the criticality of the sensed attribute and the target application.

27 | P a g e 4. A smaller value of the soft threshold gives a more accurate picture of the network, at the

expense of increased energy consumption. Thus, the user can control the trade-off between energy efficiency and accuracy.

5. At every cluster change time, the attributes are broadcast afresh and so, the user can change them as required.

The main drawback of this scheme is that, if the thresholds are not reached, the nodes will never communicate; the user will not get any data from the network at all and will not come to know even if all the nodes die. Thus, this scheme is not well suited for applications where the user needs to get data on a regular basis. Another possible problem with this scheme is that a practical implementation would have to ensure that there are no collisions in the cluster. TDMA scheduling of the nodes can be used to avoid this problem. This will however introduce a delay in the reporting of the time-critical data. CDMA is another possible solution to this problem.

This protocol is best suited for time critical applications such as intrusion detection, explosion detection etc.

3.4.APTEEN (Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network Protocol) [5]

In APTEEN once the CHs are decided, in each cluster period, the cluster head first broadcasts the following parameters:

Attributes(A): This is a set of physical parameters which the user is interested in obtaining data about.

Thresholds: This parameter consists of a hard threshold (HT ) and a soft threshold (ST ). HT is a particular value of an attribute beyond which a node can be triggered to transmit data. ST is a small change in the value of an attribute which can trigger a node to transmit data again.

28 | P a g e Schedule: This is a TDMA schedule similar to the one used in [APTEEN PAPER 8], assigning a slot to each node.

Count Time(TC): It is the maximum time period between two successive reports sent by a node.

It can be a multiple of the TDMA schedule length and it accounts for the proactive component. In a sensor network, close-by nodes fall in the same cluster, sense similar data and try to send their data simultaneously, causing possible collisions.

In the following section, data values exceeding the threshold value are referred as critical data.

The main features of our scheme are :

1. By sending periodic data, it gives the user a complete picture of the network. It also responds immediately. to drastic changes, thus making it responsive to time critical situations. Thus, It combines both proactive and reactive policies.

2. It offers a flexibility of allowing the user to set the time interval (TC) and the threshold values for the attributes.

3. Energy consumption can be controlled by the count time and the threshold values.

4. The hybrid network can emulate a proactive network or a reactive network, by suitably setting the count time and the threshold values.

The main drawback of this scheme is the additional complexity required to implement the threshold functions and the count time. However, this is a reasonable trade-off and provides additional flexibility and versatility.

Implementation

In order to do a fair comparison we implemented all the algorithms above in MATLAB and then used randomly generated 100 node network with some properties given.Not only we used the same network, we also employed the same network energy model as described in the following.

29 | P a g e 3.5. The simulation network

We assumed a network with 100 nodes.Nodes are deployed randomly in a 50mx50m area.The deployment is done with a random selection of x and y coordinates of each node’s location with a uniform probability.All the nodes in the network are identical in all aspects.They all are tasked with the same , have same properties related to energy. All are equipped with a battery power of 0.5J (otherwise specified), where this battery has a linear battery discharge charactersitics.Through out the network and the through out the whole duration, the environmental conditions related to energy dissipation (such has humidity and temperature) remain same.Nodes are static and do not move from the initial location (due to wind, water etc.) through out the time of the experiment.Nodes have some computational ability to run a distributed algorithms like LEACH.

Figure 8: Randomly deployed network in a 50mX50m area.

3.5.1. First order Radio Model

30 | P a g e There are different research on the energy efficient radios on the table.Also there are different assumption on how energy is spent while transmitting and receiving a chunk of data by the radio transmitter and the reciever.These depends not only the channel conditions, the radio characteristics and the Tx and Rx modes. But here we stick to a simple model which is given in [2],where the radio dissipates E_elec = 50 nJ/bit to run the transmitter or receiver circuitry and Є_amp

= 100 pJ/bit/m² for the transmit amplifier with an acceptable SNR.

No

Figure 9: First order radio model

Thus, to transmit a k-bit message a distance d using our radio model, the radio expends,

Equation 4

31 | P a g e And to receive this amount of k-bit message, it expends.

Equation 5

We make the assumption that the radio channel is symmetric such that the energy required to transmit a message from node A to node B is the same as the energy required to transmit a message from node B to node A for a given SNR. For our experiments, we also assume that all sensors are sensing the environment at a fixed rate and thus always have data to send to the end-user.

3.6.Results

Figure 10: Direct transmission (red), LEACH-C (blue)and LEACH(green) comparison no of

32 | P a g e nodes alive vs. rounds

Figure 11: no of alive nodes vs. rounds TEEN

33 | P a g e Figure 12: No of nodes alive vs. Rounds APTEEN

34 | P a g e 4. Chapter 4: Performance Metrics of WSN communication protocols

In this chapter we introduce the energy goals of the sensor networks and the parameters to measure them.

As we discussed in the first chapter, the sensor networks are energy constrained networks.

Therefore the success of a communication protocol is measured by the energy efficiency it can render to the network. So it is important to define the energy goals of the network we need to

Therefore the success of a communication protocol is measured by the energy efficiency it can render to the network. So it is important to define the energy goals of the network we need to