Data aggregation

In our proposed protocol both sensing nodes and cluster-head nodes perform aggregation to remove redundant messages and reduce transmissions. Each node will aggregate received data within a certain period of time and send all accumulated data in one message. Data aggregation has been identified in [51] as a useful technique for routing in sensor networks. It aims to combine received data to reduce redundancy and minimize energy consumption by decreasing the number of transmissions. Data aggregation can be implemented in a number of ways to combine data from different sources. The simplest function is duplicate suppression in which a node forward only one copy out of multiple same received data. Other aggregation function could be any function with multiple inputs like max or min. Nodes use the max or min to transmit data within a certain range or threshold that is a point of concern. The main factors that can affect the performance of data aggregation methods are the position of the sources in the network, the number of sources, and the communication network topology. In looking at the tradeoffs involved when applying data aggregation we consider three measures that affect the overall protocol performance [19]:

1. Energy savings: by aggregating the messages sent from sources, transmissions to the sink will be fewer leading to more energy savings.

2. Delay: "data from nearer sources may have to be held back at intermediate node in order to be aggregated with data coming from sources that are farther away." Data from nearer sources will get to sink before data from farther resources if no aggregation is applied.

The worst delay time due to aggregation is proportional to the number of hops between the sink and the nearest node. Besides the

distance between the source and the sink, the delay time depends on the aggregation functions.

3. Robustness: data aggregation makes routing protocols more robust.

Due to data aggregation, the marginal energy cost of connecting additional dynamics in the sensed phenomena.

Data aggregation is critical when multi-paths are used to transmit data, it is likely that multiple copies of a data event arrive at the same data forwarding node. By dropping the later arrived copies at this point, transmission overhead can be reduced.

4.3 Simulation

In order to implement the new routing protocol and study its properties, we have used a sensor network simulator called "SenSor" [54]. SenSor is a realistic and scalable Python based sensor networks simulator. In SenSor each sensor node runs in its own thread and behaves like a real sensor. SenSors have a fixed API, with customizable internals. This enables us to experiment with different algorithms for managing the network topology, fault management and so on, within the same simulation. Sensors are modelled as a pool of concurrent, communicating threads. Individual sensors are being able to:

 Gather and process data from a model environment;

 locate and communicate with their (geographically or otherwise) nearest neighbours and

 Determine whether they are operating "correctly" and act accordingly to alter the network topology.

Separate interfaces gather information from the network and display it.

This partitioning allows us to experiment with different ways of processing individual node data into information. Using this simulation framework, we implement the new protocol and LEACH. For our

simulation, we gave all the nodes virtually infinite supply of energy and ran each protocol until it converged. Since the energy is unlimited we have used other metrics to measure energy consumption like the number of messages sent. For our experiments, we created a 100-node network in such a way that nodes are scattered randomly in area of 600 x 600 m². Figure 8 shows a random topology of 100-nodes where the edges represent communication neighbours. The power of the sensor radio transmitter is set to cover all nodes within a 20 meter radius. The processing delay for transmitting a message is randomly chosen between 5s and 10s. Our test assumes no network losses and there is 15s approximate delay between different nodes transmitting data. Using this network configuration we ran protocol and tracked its progress in terms of number of messages sent and delays. The result of experiments is represented in the following.

Figure 8: Random topology of 100-nodes

Experiment 1:

In this experiment we study how the introduction of the "NumHop"

metric and "ImCH" message delay time can affect the network performance. Figure 9 shows four topologies, each resulting from different experiment. The lines indicate the borders of different clusters.

In each experiment we studied how the delay time can help to form more uniform clusters besides choosing the best "NumHop" metric received during the waiting period.

(9-a) (9-b)

(9-c) (9-d)

Figure 9: Uniform cluster formation

In the above four experiments, Figure 9, we have used the same configuration described before. In Figures (9-a) and (9-b), we did not use a waiting time (waiting time was set to zero) neither the number of hops metric; the resulted clusters are shown in the Figure. Whereas, Figures

(9-c) and (9-d) are two experiments with the waiting time set to 20s and the number of nodes metric initially set to infinite value. It is clear that the waiting time together with the "NumHop" metric lead to the formation of more energy efficient clusters. In Figures (9-a) and (9-b), the number of nodes is not distributed fairly among clusters and the area of different clusters varies largely. This cluster distribution is not energy efficient because data needs to traverse large number of nodes to reach the sink or cluster-head. While the use of "NumHop" metric and waiting time will make routes shorter. Furthermore, the number of "ImCH" advertisement message will be reduced due to the fact that node will only forward the advertisement received within the waiting time with the best "NumHop"

metric. This behaviour will stop unnecessary flood early at the border of neighbouring clusters leading to less network messages. In Figure 10, the number of network setup messages is studied against the waiting time.

When the waiting time is zero the total number of sent messages will be similar to that in LEACH. The Figure also shows that as the waiting time is getting larger, the number of messages will decrease until the time becomes large enough to receive advertisements from all cluster-heads.

This shows that waiting time should be shortened enough to receive possible advertisements and keep delay smaller and much as possible on the other hand. In Figure 11, we study network convergence time versus the waiting time. It is obvious that the time needed to establish routes is directly proportional to the waiting time. As the waiting time increase, time needed to achieve network convergence will increase. In time critical applications it is very important that convergence time is reduced which push toward reducing the waiting time to a minimal value to capture the advantages of waiting time with minimal delay to achieve high performance.

Num ber of netw ork m essages

Convergence tim e versus w aiting tim e

20 common view of what fault tolerance is and how it can help in the design of reliable routing protocols. In the context of fault tolerance, there are two main attributes of a system: reliability (deals with the continuity of service) and availability (deals with readiness of usage). In sensor networks, fault detection and isolation is of high value as they may help to increase the life time of the network [55]. Newman et. al. has identified three factors to determine the performance of fault management techniques: the fault detection time, the false detection rate, and the fault correction time. It was found that the fault detection time is inversely proportional to the error rate.

Most of the proposed protocols in the field are fault tolerance poor in the face of frequent nodes failures. In our new protocol we provide robust network fault tolerance through path routing strategy. The multi-paths are learned by nodes through the setup phase from messages sent for multiple purposes. For example, every node learn the nearest neighbour to the sink when it receive the discovery "INICH" messages, no special messages are sent to find out paths, also, the path to the

cluster-head node is learnt from the advertisement "ImCH" message sent by the cluster-head. Each node knows how to reach the cluster-head and how to communicate to the sink directly by remembering only the first node in the path. Each node joins the cluster for a certain period of time, called TnC, before it expires and re-registers with the cluster again or registers with new cluster. This is useful for nodes to recover from faulty paths (built during previous cluster round) and form more efficient paths.

The cluster has also a life called T_Cl; cluster-head ends the cluster by handing the cluster-head role to the backup node. The cluster life is usually longer than the lifetime of sensing nodes in a cluster to achieve efficiency. However, tradeoffs must be kept between the two life times because it affect the fault detection and isolation time. In this approach, the worst time, T_R, to recover from a node failure is the time a node leave a cluster or need to renew registration with the same cluster-head it currently belong. This is written as:

TR = TCl - TnC (2)

Nodes keep track of neighbouring nodes that they use to communicate by sending shorter range signals. When a node detects a failure in the neighbouring node connecting it to the cluster-head, it starts sending data directly to the sink. This scenario usually happens when the cluster-head ends the cluster session giving way for the backup node to start new cluster. However, if both paths fail at the same time the node will broadcast its data to neighbours. Neighbours will then pass the message to the cluster-head they belong to. The cluster-head in this case may receive multiple copies of the message and it can eliminate redundancy through aggregation techniques. If the node is on the border of more than one cluster and it fails to reach the sink or the neighbouring node connecting it to the cluster-head, the sink may receive multiple copies of the message from different cluster-head nodes. To avoid any packet loss,

before nodes expire, all the stored aggregated data is sent before the aggregation timer expires.

To measure fault tolerance capabilities of this protocol we enforce some nodes to sleep and drop all packets it receives to affect the communication paths. These nodes will be blocked after paths are setup and continue to function correctly in the new cluster formation round. In this experiment we study the fault tolerance capabilities of our routing protocol by measuring the Data Delivery Ration (DDR) against the number of nodes. DDR is a service level parameter that indicates the network effectiveness in transmitting offered data in one direction of virtual connection [52]. DDR parameter is the ratio of successful packets received to attempted packets transmitted. Attempted packets transmitted are referred to as DataOffered. Successfully delivered packets are referred to as DataDelivered. Then the ration can be written as:

DDR = DataDelivered DataOffered

(3)

Data Delivery Ratio Tracing

0.4 0.5 0.6 0.7 0.8 0.9 1

20 40 60 80 100

Number of Nodes

Data Delivery Ratio

Figure 12: Fault tolerance capability measuring

Throughout the experiments about three to ten percent of the nodes were blocked to represent faulty nodes. Figure 12 shows the result of five experiments with the same configuration as described before; the Figure shows that the protocol maintained a high delivery ratio using two paths.

Conclusions and Future Work

This thesis has presented a robust and energy efficient routing protocol for wireless sensor nodes. By utilizing multipath the protocol is able to support flexible communications without sacrificing efficiency or energy.

This protocol has been implemented and validated through SenSor simulator. This chapter summarises this work and its conclusions, and highlights areas that deserve further investigation.

We have defined sensor networks and reviewed the basics of routing in Chapter 1. Previous research into routing in sensor networks has highlighted the features that make routing protocols in traditional networks invalid for sensor networks. Chapter 2 addresses these differences and asserts routing protocols design factors in sensor networks. Given these factors a new routing protocols is required. In Chapter 3 we attempt to identify existing protocols and give an overview of current protocols under six different classes. Chapter 4 describes new robust multipath hierarchal routing protocol that use the number of hops metric to allow for the sensing nodes to select the nearest cluster-head.

We have added a waiting time that allow nodes to receive cluster-head advertisements during that time and choose the advertisement with the lowest number of hops metric after the waiting time ends. We argue that the number of hops metric with the waiting time could lead to the formation of more uniform clusters.

To evaluate the protocol, simulations were conducted with 100-nodes random topology. In experiment one; we studied the effect of introducing the "NumHop" metric with the waiting time. In this experiment we have measured the waiting time against the number of messages and we have

seen that the waiting time helped to reduce significantly the number of setup messages by aggregating advertisement messages and then selecting the best one based on the "NumHop" metric. In the second experiment we use the Data Delivery Ratio to measure the level of robustness. DDR parameter is the ratio of successful packets received to attempted packets transmitted. The results of the second experiment revealed that the protocol kept a high ratio during five runs which is strong evidence that the protocol used different paths to transmit messages successfully when a path failure occurs.

The new protocol described is a prototype and future work will need to be undertaken before it can be deployed in a real environment. The protocol behaviour needs to be compared with other protocols and the energy consumption need to be measured as well. In the future we are planning to develop new algorithm to select cluster-head nodes more efficiently particularly depending on the location information. Several issues regarding fault tolerance and robustness deserve further investigation.

Although this dissertation describes how the proposed protocol deals with different failures, however, more detailed examples and more complex simulations need to be studied. It would be desirable to define fault tolerance framework to help in the design of reliable protocols.

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