## 7 Edge Intelligence and Networking for IoV
### Edge Network Intelligence for IoV
#### 7.3.3 Mobility Support for IIoV  
Mobility is an integral part of IoV. Thus, mobility management is an important network function, which still presents a challenge. Mobility management should support continuous user communication while on the move without interruption. Different mobility types and scenarios should be considered when developing mobility support solutions for IIoV [107]. In an ICN/NDN-based IoV environment, each vehicle might operate simultaneously as an information producer and consumer. Vehicular consumers send their requests directly to the next NDN router mentioning the content name, but they do not know the location of the content in which they are interested. The NDN router is then responsible for forwarding the consumer's interest to the node where the content is stored. Experimental-based NDN solutions for VANET supporting various communications categories (V2V, V2I, and V2R) have been produced and have demonstrated their potential to improve the QoS of VANETs [108]. Furthermore, the authors of [109] examined the promising potential of NDN for connected vehicle applications. However, the support of fast mobility and a large number of vehicle-aware solutions and frameworks needs to be improved. Most ICN architecture designs claim to inherently support consumer mobility; however, the challenge is when the producer node also moves. Recent studies, such as [110], argue that the existing schemes to address consumer mobility have shortcomings as they waste bandwidth and mobile consumers may miss real-time content during handover. The paper proposes a softwarized and MEC-enabled protocol architecture supporting consumer mobility in ICN and claims improvement in the communication overhead by 99.93%. Producer mobility is more complicated since the NDN naming scheme does not include location information. A number of studies, such as those of [111; 112; 113; 114; 115], address producer mobility. Four producer mobility approaches have been presented in the literature: location resolution, triangular, locator/identifier separation, and routing-based. They can be divided into two categories: anchor-based and anchorless schemes. Most approaches utilize anchor nodes to hide the producer node movement. These use an anchor node or rendezvous server to track the current locations of contents. The anchor node holds the binding information for content and its location. It always interacts with the mobile producer to maintain updated binding information. This is a similar approach to that of home agent mobility in IP networks. However, such an approach causes suboptimal routing problems and signaling overhead. In the anchor-less scheme [112], mobile producers send special announcements to the previously contacted NDN access router (NAR) whenever it changes the point of attachment. Recently, intelligent mechanisms have been proposed to address producer mobility in the ICN context. In [116] a proxy-based mobility support approach called PMNDN is proposed. The proxy entity is responsible for maintaining the reachability of the user. The authors of [113] present producer mobility solutions using NDN in an IoT context. Seamless service-driven mobility support, as a service that caters to both host-driven and network driven mobility, is considered in [117]. In [118], anchor-less producer mobility management in named data networking for real-time multimedia is proposed. Similarly, in [119], the authors proposed supporting producer mobility via named data networking in an integrated space-terrestrial scheme. The optimal producer mobility support solution (OPMSS) [111] is proposed to minimize unnecessary interest packet losses and delay, while providing an optimization path for data when the mobile producer relocates. In [115], the authors propose a lightweight machine-learning-based strategy to predict the position of the producer in advance and to determine whether or not handover occurs. It uses an echo state network (ESN), which is a type of fast converging recurrent neural network that uses simple linear regression.