**3. Cross-layer design**

4 Wireless Sensor Networks / Book 1

192 Wireless Sensor Networks – Technology and Protocols Cross-Layer Design for Smart Routing in

The communication technologies chosen are presented as a single scenario but typically depend on the range of communication required, node density and required bandwidth

In terms of node interaction, cluster-heads perform supervisory control of sensors and optimize resource allocations for sensors that they govern. Sensors correspondingly inform

Ideally, mesh nodes, including cluster-heads, are organized in a hexagonal topology for maximum connectivity [1]. These mesh nodes are placed at the center of their clusters around which sensors are typically positioned randomly. However, while we would ideally like to maintain a hexagonal topology of mesh nodes, this may not always be practical because the organization of mesh nodes depends highly on the sensory locations of interest. For example, if a sensor cluster is deployed to monitor stresses on bridge infrastructure, or the military is interested in monitoring certain high security areas, it will likely not be possible to deploy a mesh node at an ideal location. It is important to note that, in networks for which a hexagonal mesh topology is not possible, network planners must be aware of potential single points of failure. In these cases, load balancing or redundancy should be explored to ensure that mesh

In this analysis, neither sensor nodes nor mesh nodes are wired to power sources. This allows us to explore a general architecture and expands the number of environments in which, and applications for which, the system can be deployed. In reality, certain mesh nodes may be connected to power sources if the locations in which the mesh nodes are deployed have power sources readily available. Another option is energy harvesting to replenish energy reserves

In wireless sensor networks (WSNs) that are based on multiple technologies, software radio is required to convert operating parameters between otherwise incompatible communication protocols. The conversion must consider the varying dependencies of these technologies on a number of characteristics that affect communication and performance. For example, in our network architecture, the cluster-head must convert transmission parameters between sensor

WSNs that are based on software radio enable for the deployment of large-scale and distributed systems that are designed with technologies that are most suitable to their applications. Various technologies may be selected based on throughput requirements, cost of deployment and energy efficiency. Software radio enables these systems to dynamically tune

operating parameters around current networking conditions to improve capacity.

requirements of the applications of interest.

nodes are not overburdened.

**2.1. Software radio**

• Operating bandwidth,

• Modulation scheme.

• Transmission frequency, and

• Transmit power,

over time, which we will cover later in this chapter.

and mesh communication technologies. These parameters include:

their cluster-heads of the state of their resources periodically.

Layering systems are the norm in the design of communication protocol stacks. However, wireless systems are not always suited to the common layered protocol stack architecture. For example, in a layered architecture using the Transmission Control Protocol (TCP), a failed packet is considered a sign of congestion, as opposed to simply a lost or corrupted packet which is the case in wireless systems. For sensor networks, and smart routing specifically, given the need to conserve sensor energy and maximize application performance, cooperation between several layers in the protocol stack is crucial. This can only be achieved in a cross-layer architecture. Cross-layer design ensures that the route that best meets both performance and energy requirements can be determined.

**Figure 3.** Various Cross-Layer Design Protocols [11]

Figure 3 presents a number of general ways in which a typical layered architecture can be modified by cross-layer design:


In next-generation wireless sensor networks (WSNs), a number of these protocols may be used. For example, upward information flow (Figure 3a) may be used to provide the application layer with available routes from the network layer, channel availability from the link layer and remaining energy information from the physical (PHY) layer. Furthermore, downward information flow (Figure 3b) or back-and-forth information flow (Figure 3c) may be used between the application layer and the PHY layer. For example, the application layer may inform the PHY layer of transmission parameters such as transmit power and operating frequency to use during transmission.

The design of a cross-layer optimization algorithm for WSNs that consider both performance and energy factors requires efficient communication between protocol stack layers such as the PHY, link, network and application layers. Direct signaling between application layers reduces latency in the communication between multiple layers and is crucial in the design of cross-layer optimization algorithms [9]. The direct signaling scheme for our protocol stack model is illustrated in Figure 4.

**Figure 4.** Protocol Stack Model to Enable Smart Routing

The goal of direct signaling is to exchange information between important protocol layers for smart routing. This ensures that the required information to perform cross-layer optimization is retrieved, and allocation decisions are sent, with minimal delay. For example, the cluster-head's PHY layer will inform the application layer of the sensor and mesh node state information which includes energy rating information, surrounding interference and more. State information and coordination protocols to provide feedback to the cluster-head are covered in Section 9. The link layer and network layer will also inform the application layer of the channel conditions and available path information, respectively. Furthermore, upon executing the cross-layer optimization policy, the application layer will inform the PHY layer of the necessary resource allocations and the link layer of the next-hop information. Direct signaling enables these interactions with minimal delay for optimized and timely responses in our distributed network.

For any two non-adjacent layers, *lx* and *ly*, the propagation latency *TDS lx*,*ly* for the direct signaling method is calculated as,

$$T\_{l\_x, l\_y}^{DS} = \frac{T\_{l\_x, l\_y}^L}{(n - 1)}\tag{1}$$

where *T<sup>L</sup> lx*,*ly* is the propagation latency between layers *lx* and *ly* in a traditional layered protocol stack with (*n* − 1) layers between them. Hence, the direct signaling method provides a speed-up factor of (*n* − 1) [9].
