Sensor Networks' Communication Protocols
Current architectures for Internet and ad hoc wireless networks may not be used for wireless sensor networks. Here are few of the reasons:
Spatio-Temporal Correlation in Wireless Sensor Networks
Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, and the nature of the physical phenomenon, sensor observations are highly correlated in the space and time domains, which constitutes the spatial and temporal correlation.
These spatial and temporal correlations along with the collaborative nature of the WSN bring significant potential advantages for the development of efficient communication protocols well-suited for the WSN paradigm. In this project, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to model the spatial and temporal correlations in sensor networks. Based on this framework, possible approaches are discussed to exploit spatial and temporal correlation for efficient medium access and reliable event transport in WSN, respectively.
Event-to-Sink Reliable Transport (ESRT)
Reliable detection of event features necessitates an event-to-sink reliability notion in contrast to existing end-to-end reliability (TCP, SRM, PSFQ). This is due to the fact that correlated data flows from several source nodes to a single sink are loss tolerant to the extent that event features are reliably detected. The use of strict end-to-end reliable transport in such a case can lead to over-utilization of scarce sensor resources.
ESRT seeks to achieve reliable event detection with minimum energy expenditure and congestion control. It has been tailored for use in sensor networks with adaptability to dynamic topology, collective identification, energy conservation and biased implementation at the sink.
There may be several sinks in a sensor network. The sinks are gateways between the sensor network and the backbone network, e.g. Internet. Note that the sink may be in a ground-based site possibly set up by a rapid response team, in an unmanned airborne vehicle or plane, or a low earth orbit satellite. Depending on the DoD mission, the sinks and sensor nodes may be mobile. The objective of the routing protocol is to deliver sensed information from the sensor nodes, i.e., the sources, to the appropriate sinks. Sensed information will be represented by descriptors, which will be fused, i.e. if local neighbors have same descriptors, the descriptors will be combined, before they are routed to the sinks.
The routing protocol must meet the following design targets:
Topology Control for Geographical Routing
Geographical routing algorithms are considered in sensor networks because of their scalability. In a geographical routing algorithm, the packets are forwarded by a node to its neighbor based on their respective positions. Thus, there is no need to store and maintain routing tables in constrained sensor nodes. Geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied.
In this project, a
novel analytical framework has been introduced, which allows
to analyze the relationship between the energy efficiency of the
routing tasks and the extension of the range of the topology
knowledge for each node. A wider topology knowledge can improve the
energy efficiency of the routing tasks but can also increase the cost of
topology information due to signaling packets that each node must
transmit and receive to acquire this information, especially in
networks with high mobility. It is demonstrated that a limited knowledge
of the topology is sufficient to take energy efficient forwarding
Data Link Protocol: MAC
Wireless Sensor Networks (WSN) are characterized by dense
deployment of sensor nodes that collectively communicate event information to the sink. However, due to the spatial
correlation between sensor observations, it is not necessary for every node to
transmit its data. Exploiting spatial correlation in the
context of collaborative nature of the WSN brings significant potential advantages
Data Link Protocol: Error Control
Traditionally, error control has been viewed as
a means to improve energy efficiency at the cost of bandwidth efficiency.
In sensor nets, they are better described as techniques to improve performance
at the cost of reduced lifetime. Additional energy consumption in transmitting
parity bits and encoding/decoding energies must be taken into account in
evaluating the coding gain in sensor nets. From this perspective, coding
may not always be energy efficient. This is one of the areas of our current
Power Management Scheme
Sensor nodes are very sensitive to power, because
their operational lifetime are related to their battery live. They
are non-functional if they run out of battery, and they are usually deployed
in a sensor field with a small battery. At some scenarios, such as
battle and toxic zones, replacing the batteries of these sensor nodes are
impossible. By managing the power usage of these sensor nodes, the
lifetime of the whole sensor network can be extended. The power management
scheme integrates with the sensor node's application, routing protocol, MAC
protocol, and physical layer.
Time Synchronization Scheme
Small intelligent devices can be deployed in homes, plantations, oceans, rivers, streets, and highways to monitor the environment. Events such as target tracking, speed estimating, and ocean current monitoring require the knowledge of time between sensor nodes that detect the events. In addition, sensor nodes may have to time-stamp data packets for security reasons. With time synchronization, voice and video data from different sensor nodes can be fused and displayed in a meaningful way at the sink. Instead of time synchronization between just the sender and receiver during an application like in the Internet, the sensor nodes in the sensor field have to maintain a similar time within a certain tolerance throughout the lifetime of the network. Combining with the criteria that sensor nodes have to be energy efficient, low-cost, and small in multi-hop environment, this requirement makes an interesting problem to solve.
As the state-of-the-arts use signal sources to self-calibrate,
there is a need to have localization methods that do no require signal sources. This is essential because signal sources may die or may be out-of-range.
In addition, methods based on beacons may not be deployed in different
environments such as underground caves, and low-end sensor units may behave
non-linearly when determining the range between sensor nodes. As
a result, sensor nodes should collaboratively work together to provide a
better estimate of the relative positions. If each node knows the
relative positions of its neighbors, the location of any event can be determined
by aggregating the relative positions of the nodes along the route.
Physical layer research is driven by power-aware modulation and hardware design rather than targeting high data rates as in most other communication systems. Binary modulation techniques have been shown to be more energy efficient under start-up dominant conditions encountered in low-power short range wireless transceivers. Adaptive transmit power and dynamic voltage scaling are a couple of energy-efficient hardware strategies for sensor nets.