This thesis focuses on routing and node activity scheduling (i.e. sleeping node scheduling) problems in wireless sensor networks. The routing and node schedul- ing solutions to be developed, however, depend on the wireless sensor network application. There are various sensor network application scenarios, and depend- ing on the scenario, the requirements for a routing and scheduling solution are different.
The following are our assumptions about the features of sensor networks and application scenarios we consider in this thesis.
• The sensor nodes are homogeneous and energy constrained. • Sensor nodes and sink are stationary and located randomly.
• Every node knows the geographic location of itself by means of a GPS device
or using some other localization techniques [7, 25, 27, 28].
• Every node senses periodically its nearby environment and has data to send
CHAPTER 2. SYSTEM MODEL AND PROBLEM STATEMENT 18
• The nodes have a maximum transmission range denoted by R. Sensor
nodes are thus normally not in direct communication range of each other. Therefore applying centralized approaches will have a high communication cost for gathering network information at a node.
• Data fusion or aggregation is used to reduce the data volume. We assume a
perfect aggregation or correlation of data which means combining n packets, each packet being of size k, results in only one packet of size k.
• We also assume that the sensing period (the duration of a round) is much
larger than the time required for transmitting all the information from all nodes to the sink.
• The nodes are capable of controlling their power. This means the nodes
can adjust their power levels to transmit to different distances.
• The nodes can be put into sleep mode if it does not harm network func-
tionality.
In the application scenario we consider for this thesis, sensor nodes periodically sense the environment and generate data in each round of communication. Given a routing plan, each sensor node receives the data from its children, aggregates or fuses them into one single packet, and sends the packet to the next node on its way to the sink. Instances of such an application can be event (fire, intrusion) detection systems or average data (temperature, humidity) extraction systems.
Note all nodes need to be active. Some nodes can be put into sleep provided that the remaining active nodes can cover the region. How many nodes and which nodes will be active affect the coverage and energy consumption performance of the network. One problem we focus in this thesis is determining the optimum number of nodes (which may not be the minimum number of nodes) that need to be active without harming network functionality. Then, over the active nodes a routing plan has be used to carry the data to the sink node.
The problem is to find an energy efficient routing plan which maximizes the network lifetime. The routing plan determines for each round the roles of each
CHAPTER 2. SYSTEM MODEL AND PROBLEM STATEMENT 19
node and incoming and outgoing neighbors for data forwarding and aggregation for each alive node. In other words, firstly the nodes which should be alive must be found on each round, and finally a tree spanning the alive nodes must be found for each round as the routing plan. The routing scheme should also include mechanisms to handle node failures and support new node arrivals.
Chapter 3
Related Work
In this chapter, we will discuss the related work done on wireless sensor network routing and node scheduling considering energy efficiency as the most important goal. There are many routing protocols and node scheduling algorithms proposed in the literature that try to use the energy efficiently and improve the sensor network lifetime as much as possible. We will also briefly discuss some other approaches, reducing data traffic volume, mobility and efficient deployment and topology construction which can be used to improve network lifetime. We will start our discussion with those other approaches to reduce the unnecessary energy consumption and prolong network lifetime.
3.1
Minimization of Transmitted Data Volume
One of the most effective techniques to reduce the power consumption in a sensor network is to minimize the transmitted data volume, since the most power con- suming component of a sensor node is its wireless communication unit: the less we use that component, the more we save energy. There are different methods to achieve this goal in the literature.
The most common and easily applicable method is data aggregation. The idea
CHAPTER 3. RELATED WORK 21
behind this approach is that since usually the collected data from sensors is too much for an end-user to process, the collected data can be aggregated – eg. with functions like max, min, count, avg – and presented to end-user as a single value. Instead of doing the aggregation after all the data is collected to the base station, if we can do it in the network while the data is gathered we can save a large amount of energy. One disadvantage of this method is that it cannot be used for applications where each individual sensed data need to be collected at the base station.
Another way of reducing the packet size is the data fusion technique. By using the data fusion technique the unreliable data measurements can be com- bined to produce a more accurate and high quality signal by reducing the noise and enhancing the common signal [26]. For instance, the sound signals can be combined by using beamforming algorithms into one single packet that contains all the relevant information from the individual signals. One important disad- vantage of this method is being highly application dependent which means that its applicability is related to the type of sensed signal.
In [59] different in-network aggregation algorithms are presented. The paper also gives a comparison of the algorithms with respect to trade-offs between en- ergy efficiency, data accuracy and freshness. We encourage the interested users to read that work.
Another interesting way of minimizing the transmitted data volume is predic-
tion based methods [19]. If the application is tolerant to small errors, a precision
clause can be added to the query which indicates the permitted error. The main idea behind this technique is to predict the value of the data sensed in children. If it can be correctly predicted within the given precision there is no need to transfer the newly sensed data to the parent. Since the child and parent nodes uses the same prediction function, the child can know what its parent predicts and send the data only when the prediction does not guarantee the precision value given in the query. In this way the energy saving is maximum since the communication only occurs when the source will send an unexpected value.
CHAPTER 3. RELATED WORK 22