Wireless Sensor Networks

Wireless sensor networks provide bridges between the virtual world of information technology and the real physical world. They represent a fundamental paradigm shift from traditional inter-human personal communications to autonomous inter-device communications. They promise unprecedented new abilities to observe and understand large-scale, real-world phenomena at a fine spatio-temporal resolution. As a result, wireless sensor networks also have the potential to engender new breakthrough scientific advances.

Networked wireless sensor devices

As shown in Figure 1.2, there are several key components that make up a typical wireless sensor network (WSN) device:

1. Low-power embedded processor: The computational tasks on a WSN device include the processing of both locally sensed information as well as information communicated by other sensors. At present, primarily due to economic

Figure 1.2 Schematic of a basic wireless sensor network device

constraints, the embedded processors are often significantly constrained in terms of computational power (e.g., many of the devices used currently in research and development have only an eight-bit 16-MHz processor). Due to the constraints of such processors, devices typically run specialized component-based embedded operating systems, such as TinyOS. However, it should be kept in mind that a sensor network may be heterogeneous and include at least some nodes with significantly greater computational power.
Moreover, given Moore’s law, future WSN devices may possess extremely powerful embedded processors. They will also incorporate advanced low-power design techniques, such as efficient sleep modes and dynamic voltage scaling to provide significant energy savings.

2. Memory/storage: Storage in the form of random access and read-only memory includes both program memory (from which instructions are executed by the processor), and data memory (for storing raw and processed sensor measurements and other local information). The quantities of memory and storage on board a WSN device are often limited primarily by economic considerations, and are also likely to improve over time.

3. Radio transceiver: WSN devices include a low-rate, short-range wireless radio (10–100 kbps, <100 m). While currently quite limited in capability too, these radios are likely to improve in sophistication over time – including improvements in cost, spectral efficiency, tunability, and immunity to noise, fading, and interference. Radio communication is often the most power-intensive operation in a WSN device, and hence the radio must incorporate energy-efficient sleep and wake-up modes.

4. Sensors: Due to bandwidth and power constraints, WSN devices primarily support only low-data-rate sensing. Many applications call for multi-modal sensing, so each device may have several sensors on board. The specific sensors used are highly dependent on the application; for example, they may include temperature sensors, light sensors, humidity sensors, pressure sensors, accelerometers, magnetometers, chemical sensors, acoustic sensors, or even low-resolution imagers.

5. Geopositioning system: In many WSN applications, it is important for all sensor measurements to be location stamped. The simplest way to obtain positioning is to pre-configure sensor locations at deployment, but this may only be feasible in limited deployments. Particularly for outdoor operations, when the network is deployed in an ad hoc manner, such information is most easily obtained via satellite-based GPS. However, even in such applications, only a fraction of the nodes may be equipped with GPS capability, due to environmental and economic constraints. In this case, other nodes must obtain their locations indirectly through network localization algorithms.

6. Power source: For flexible deployment the WSN device is likely to be battery powered (e.g. using LiMH AA batteries). While some of the nodes may be wired to a continuous power source in some applications, and energy harvesting techniques may provide a degree of energy renewal in some cases, the finite battery energy is likely to be the most critical resource bottleneck in most WSN applications.

Depending on the application, WSN devices can be networked together in a number of ways. In basic data-gathering applications, for instance, there is a node referred to as the sink to which all data from source sensor nodes are directed. The simplest logical topology for communication of gathered data is a single-hop star topology, where all nodes send their data directly to the sink. In networks with lower transmit power settings or where nodes are deployed over a large area, a multi-hop tree structure may be used for data-gathering. In this case, some nodes may act both as sources themselves, as well as routers for other sources.

One interesting characteristic of wireless sensor networks is that they often allow for the possibility of intelligent in-network processing. Intermediate nodes along the path do not act merely as packet forwarders, but may also examine and process the content of the packets going through them. This is often done for the purpose of data compression or for signal processing to improve the quality of the collected information.

Network Topology

The communication network can be configured into several different topologies, as seen in Figure 2.1. We describe these topologies below.

Single-hop star
The simplest WSN topology is the single-hop star shown in Figure 2.1(a). Every node in this topology communicates its measurements directly to the gateway. Wherever feasible, this approach can significantly simplify design, as the networking concerns are reduced to a minimum. However, the limitation of this topology is its poor scalability and robustness properties. For instance, in larger areas, nodes that are distant from the gateway will have poor-quality wireless links.

Multi-hop mesh and grid
For larger areas and networks, multi-hop routing is necessary. Depending on how they are placed, the nodes could form an arbitrary mesh graph as in Figure 2.1(b) or they could form a more structured communication graph such as the 2D grid structure shown in Figure 2.1(c).

Two-tier hierarchical cluster
Perhaps the most compelling architecture for WSN is a deployment architecture where multiple nodes within each local region report to different cluster-heads. There are a number of ways in which such a hierarchical architecture

Figure 2.1 Different deployment topologies: (a) a star-connected single-hop topology, (b) flat multi-hop mesh, (c) structured grid, and (d) two-tier hierarchical cluster topology

may be implemented. This approach becomes particularly attractive in heterogeneous settings when the cluster-head nodes are more powerful in terms of computation/communication. The advantage of the hierarchical cluster-based approach is that it naturally decomposes a large network into separate zones within which data processing and aggregation can be performed locally. Within each cluster there could be either single-hop or multi-hop communication. Once data reach a cluster-head they would then be routed through the second-tier network formed by cluster-heads to another cluster-head or a gateway. The second-tier network may utilize a higher bandwidth radio or it could even be a wired network if the second-tier nodes can all be connected to the wired infrastructure. Having a wired network for the second tier is relatively easy in building-like environments, but not for random deployments in remote locations. In random deployments there may be no designated cluster-heads; these may have to be determined by some process of self-election.

Connectivity using power control:

Regardless of whether randomized or structured deployment is performed, once the nodes are in place there is an additional tunable parameter that can be used to adjust the connectivity properties of the deployed network. This parameter is the radio transmission power setting for all nodes in the network.

Power control is quite a complex and challenging cross-layer issue. Increasing radio transmission power has a number of interrelated consequences – some of these are positive, others negative:

• It can extend the communication range, increasing the number of communicating neighboring nodes and improving connectivity in the form of availability of end-to-end paths.
• For existing neighbors, it can improve link quality (in the absence of other interfering traffic).
• It can induce additional interference that reduces capacity and introduces congestion.
• It can cause an increase in the energy expended.

Most of the literature on power-based topology control has been developed for general ad hoc wireless networks, but these results are very much central to the configuration of WSN. We shall discuss some key results and proposed techniques here. Some of these distributed algorithms aim to develop topologies that minimize total power consumption over routing paths, while others aim to minimize transmission power settings of each node (or to minimize the maximum transmission power setting) while ensuring connectivity. These goals are not necessarily complementary; for instance, providing minimum energy paths may require some nodes in the network to have high transmission powers, potentially limiting network lifetime due to partitions caused by rapid battery depletion of these nodes.

Wireless characteristics

Wireless communication is both a blessing and a curse for sensor networks. On the one hand, it is key to their flexible and low-cost deployment. On the other hand, it imposes considerable challenges because wireless communication is expensive and wireless link conditions are often harsh and vary considerably in both space and time due to multi-path propagation effects.

Wireless communications have been studied in depth for several decades and entire books are devoted to the subject. The goal of this chapter is by no means to survey all that is known about wireless communications. Rather, we will focus on three sets of simple models that are useful in understanding and analyzing higher-layer networking protocols for WSN:

1. Link quality model: a realistic model showing how packet reception rate varies statistically with distance. This incorporates both an RF propagation model and a radio reception model.

2. Energy model: a realistic model for energy costs of radio transmissions, receptions, and idle listening.

3. Interference model: a realistic model that incorporates the capture effect whereby packets from high-power transmitters can be successfully received even in the presence of simultaneous traffic.

Figure 5.1 A realistic packet reception rate contour

Figure 5.2 Realistic packet reception rate statistics with respect to inter-node distance