## 8 Cross-Layer Optimization  
Cross-layer approaches typically entail additional interfaces between layers, parameters jointly affected by multiple layers, as well as protocol design coupled with specifications of other layers [128]. It is evident from Figure 1 and discussion throughout preceding sections that this type of inter-relationship beyond conventional layer boundaries is vital for spectrum-based adaptations. This is hardly unexpected as [128] points out the deviation from reference layered architecture is an inevitable practice for the design of intelligent wireless networks. It should be noted that this concept is by no means exclusive to spectrum-related optimization, and this section thus reviews several adaptive approaches that exploit general cross-layer synergy. A cross-layer measure incorporating PHY, MAC and network layer to maximize network lifetime is described in [129]. It first forms a convex optimization problem to deduce the sets of optimal data rates and transmission powers. Then an iterative process is carried out to find the optimum medium access schedule that satisfies the feasibility of rate and power identified previously. One potential drawback of the algorithm is the computational complexity, since at each iteration, links are adapted and the convex problem is resolved to ensure most advantageous transmission parameters. The protocol proposed in [130] couples non-conterminous layers in order to exploit the interdependency between local events and end-to-end congestion. An intelligent congestion detection (ICD) mechanism is introduced so that the interval time between two consecutive receipts of packets are measured at MAC. The ratio of average packet service time over average packet inter-arrival time is calculated and kept as the indicator of congestion degree. Such an index is propagated from receiver to the senders. The transport layer upon receptions of the congestion degree then adapts its transmission rate accordingly. The similar cross-layer collaboration is presented in [131]. In this work, scheduling at MAC is coupled with congestion control at transport layer in a way that they become mutually dependent, achieving joint optimization of the pair of significant factors of network performance. The schemes mentioned earlier share a design ideology that retains the existing network structure whilst optimization is achieved by adapted cross-layer interactions as depicted in Figure 1. Thus, they can be perceived as an evolution rather than an elimination of layered architectures [128], necessitated by the demand of more adaptive communication paradigms in the era of _IoT_. There are more radical approaches in exploiting cross-layer adaptation opportunities. Unlike the previously described \"moderate\" way of optimization, the layered architecture is made implicit or even redesigned in these works [132]. One of the examples is the single unified cross-layer module (XLM) proposed in [133], which aims to replace the entire conventional protocol hierarchy for wireless sensor networks. The novel concept of \"initiative\" is the cornerstone of the module. The function of \"initiative determination\" is invoked by the receipts of ready-to-send packets broadcast from nodes willing to transmit, generating a binary variable indicating whether or not to participate in transmissions. Criteria used for this purpose involve information that traditionally resides at multiple network layers. For instance, the received signal-to-noise ratio (SNR) of RTS packets (PHY) must meet certain thresholds;the packet relay rate (network layer) and generation rate (application layer) also need to satisfy certain constraints. Accordingly, such a mechanism merges congestion control, hop-by-hop reliability and so on into the singular XLM entity and is hence implicitly cross-layer. The research topic of content-centric networking (CCN) may inspire some even more revolutionary measures [134].