Notes

ADHOC and SENSOR NETWORK
While both ad hoc wireless networks and sensor networks consist of wireless nodes communicating with each other, there are certain challenges posed by sensor networks. The number of nodes in a sensor network can be several orders of magnitude larger than the number of nodes in an ad hoc network. Sensor nodes are more prone to failure and energy drain, and their battery sources are usually not replaceable or rechargeable. Sensor nodes may not have unique global identifiers, so unique addressing is not always feasible in sensor networks.
Sensor networks are data-centric, that is, the queries in sensor networks are addressed to nodes which have data satisfying some conditions. For instance, a query may be addressed to all nodes "in the south-east quadrant," or to all nodes "which have recorded a temperature greater than 30 °C." On the other hand, ad hoc networks are address-centric, with queries addressed to particular nodes specified by their unique address. Hence, sensor networks require a different mechanism for routing and answering queries. Most routing protocols used in ad hoc networks cannot be directly ported to sensor networks because of
limitations in memory, power, and processing capabilities in the sensor nodes and the non-scalable nature of the protocols.
An important feature of sensor networks is data fusion/aggregation, whereby the sensor nodes aggregate the local information before relaying. The main goals of data fusion are to reduce bandwidth consumption, media access delay, and power consumption for communication.

ARCHITECTURE
The design of sensor networks is influenced by factors such as scalability, fault tolerance, and power consumption [1]. The two basic kinds of sensor network architecture are layered and clustered
A layered architecture has a single powerful base station (BS), and the layers of sensor nodes around it correspond to the nodes that have the same hop-count to the BS. This is depicted in Figure 12.2.
Figure 12.2. Layered architecture.
Layered architectures have been used with in-building wireless backbones, and in military sensor-based infrastructure, such as the multi-hop infrastructure network architecture (MINA) [2]. In the in-building scenario, the BS acts an an access point to a wired network, and small nodes form a wireless backbone to provide wireless connectivity. The users of the network have hand-held devices such as PDAs which communicate via the small nodes to the BS. Similarly, in a military operation, the BS is a data-gathering and processing entity with a communication link to a larger network. A set of wireless sensor nodes is accessed by the hand-held devices of the soldiers. The advantage of a layered architecture is that each node is involved only in short-distance, low-power transmissions to nodes of the neighboring layers.

Clustered Architecture
A clustered architecture organizes the sensor nodes into clusters, each governed by a cluster-head. The nodes in each cluster are involved in message exchanges with their respective cluster-heads, and these heads send messages to a BS, which is usually an access point connected to a wired network. Figure
12.3 represents a clustered architecture where any message can reach the BS in at most two hops. Clustering can be extended to greater depths hierarchically.
Figure 12.3. Clustered architecture.

Clustered architecture is specially useful for sensor networks because of its inherent suitability for data fusion. The data gathered by all members of the cluster can be fused at the cluster-head, and only the resulting information needs to be communicated to the BS. Sensor networks should be self-organizing, hence the cluster formation and election of cluster-heads must be an autonomous, distributed process.


Data Dissemination
Data dissemination is the process by which queries or data are routed in the sensor network. The data collected by sensor nodes has to be communicated to the BS or to any other node interested in the data. The node that generates data is called a source and the information to be reported is called an event. A node which is interested in an event and seeks information about it is called
a sink. Traffic models have been developed for sensor networks such as the data collection and data dissemination (diffusion) models

Data diffusion, on the other hand, consists of a two-step process of interest propagation and data propagation. An interest is a descriptor for a particular kind of data or event that a node is interested in, such as temperature, intrusion, or presence of bio-agents. For every event that a sink is interested in, it broadcasts its interest to its neighbors and periodically refreshes its interest. The interest is propagated across the network, and every node maintains an interest cache of all events to be reported. This is similar to a multicast tree formation, rooted at the sink. When an event is detected, it is reported to the interested nodes after referring to the interest cache. Intermediate nodes maintain a data cache and can aggregate the data or modify the rate of reporting data. The paths used for data propagation are modified by preferring the shortest paths and deselecting the weaker or longer paths. The basic idea of diffusion is made efficient and intelligent by different algorithms for interest and data routing.
Data Dissemation Models
Flodding : In flooding when a node receives a packet it broadcasts it to its neighbours assuming that it itself is not the destination or max hop is not reached . This ensures data is send all over the network
Gossiping : Gossiping is a modified version of flooding, where the nodes do not broadcast a packet, but send it to a randomly selected neighbor. This avoids the problem of implosion, but it takes a long time for a message to propagate throughout the network .
Rumour Routing : Rumour routing is an agent-based path creation algorithm [6]. Agents, or "ants," are long-lived entities created at random by nodes. These are basically packets which are circulated in the network to establish shortest paths to events that they encounter. They can also perform path optimizations at nodes that they visit.


Data Gathering :
The objective of the data-gathering problem is to transmit the sensed data from each sensor node to aBS. One round is defined as the BS collecting data from all the sensor nodes once. The goal of algorithms which implement data gathering
is to maximize the number of rounds of communication before the nodes die
and the network becomes inoperable. This means minimum energy should be consumed and the transmission should occur with minimum delay, which are conflicting requirements. Hence, the energy × delay metric is used to compare
algorithms, since this metric measures speedy and energy

12.4.1 Direct Transmission
All sensor nodes transmit their data directly to the BS. This is extremely expensive in terms of energy consumed, since the BS may be very far away from some nodes. Also, nodes must take turns while transmitting to the BS to avoid collision, so the media access delay is also large. Hence, this scheme performs poorly with respect to the energy × delay metric.
12.4.2 Power-Efficient Gathering for Sensor Information Systems
Power-efficient gathering for sensor information systems (PEGASIS) [15] is a data-gathering protocol based on the assumption that all sensor nodes know the location of every other node, that is, the topology information is available to all nodes. Also, any node has the required transmission range to reach the BS in one hop, when it is selected as a leader. The goals of PEGASIS are as follows:
• Minimize the distance over which each node transmits
• Minimize the broadcasting overhead
• Minimize the number of messages that need to be sent to the BS
• Distribute the energy consumption equally across all nodes

MAC PROTOCOLS
MAC protocols in sensor networks must create a network infrastructure to establish communication links among the thousands of randomly scattered sensors. It must also ensure fair and efficient sharing of communication resources among the nodes, so that the overall lifetime of the network can be maximised
There are three basic kinds of MAC protocols used in sensor networks: fixed- allocation, demand-based, and contention-based. Fixed-
allocation MAC protocols share the common medium through a predetermined assignment. They are appropriate for sensor networks that continuously monitor and generate deterministic data traffic, since all nodes which have been allotted the channel can make use of their slot in each round. Fixed-allocation protocols provide a bounded delay for each node. However, in the case of bursty traffic, where the channel requirements of each node may vary over time, a fixed allocation may lead to inefficient usage of the channel. Demand-
based MAC protocols are used in such cases, where the channel is allocated according to the demand of the node. Though they require the additional overhead of a reservation process, variable rate traffic can be efficiently transmitted using demand-based MAC protocols. Finally, the contention-
based MAC protocols involve random-access-based contention for the channel when packets need to be transmitted. They are again suitable for bursty traffic, but there is a possibility of collisions and no delay guarantees can be provided. Hence, they are not suitable for delay-sensitive or real-time traffic. Some of the popular sensor network MACprotocols have been briefly described in the next section.

Traditional CSMA-based schemes are more suitable for point-to-point stochastically distributed traffic flows. On the other hand, sensor networks have variable but periodic and correlated traffic. A CSMA-based MAC protocol for sensor networks has been described in [18]. The sensing periods of CSMA are constant for energy efficiency, while the back-off is random to avoid repeated collisions. Binary exponential back-off is used to maintain fairness in the network. An adaptive transmission rate control (ARC) is also used, which balances originating and route-through traffic in nodes. This ensures that nodes closer to the BS are not favored over farther nodes. ARC uses linear increase and multiplicative decrease of originating traffic in a node. The penalty for dropping route-through traffic is higher, since energy has already been invested in making the packets reach until that node. ARC performs phase changes, that is, it staggers the transmission times of different streams so that periodic streams are less likely to collide repeatedly. Hence, CSMA based MAC protocols are contention-based and are designed mainly to increase energy efficiency and maintain fairness.

12.6.1 Indoor Localization
Indoor localization techniques [19] use a fixed infrastructure to estimate the location of sensor nodes. Fixed beacon nodes are strategically placed in the field of observation, typically indoors, such as within a building. The randomly distributed sensors receive beacon signals from the beacon nodes and measure the signal strength, angle of arrival, and time difference between the arrival of different beacon signals. Using the measurements from multiple beacons, the nodes estimate their location. Some approaches use simple triangulation methods, while others require a priori database creation of signal
measurements. The nodes estimate distances by looking up the database instead of performing computations. However, storage of the database may not be possible in each node, so only the BS may carry the database.
12.6.2 Sensor Network Localization
In situations where there is no fixed infrastructure available and prior measurements are not possible, some of the sensor nodes themselves act as beacons. They have their location information, using GPS, and these send periodic beacons to other nodes. In the case of communication using RF signals, the received signal strength indicator (RSSI) can be used to estimate the distance, but this is very sensitive to obstacles and environmental conditions. Alternatively, the time difference between beacon arrivals from different nodes can be used to estimate location, if RF or ultrasound signals are used for communication. This offers a lower range of estimation than RSSI, but is of greater accuracy.