# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 3 Related Work
OLSR tags every message with a sequence number to differentiate the fresh and previous information. It is an efficient method to deliver the data through an optimal path, but blind flooding can cause operation costs. In [28], safety-based applications broadcast a basic safety message (BSM), which contains information about vehicles such as location and speed, to detect and avoid collisions. To improve privacy at the application layer, genetic algorithm (GA) and public key infrastructure (PKI) are used. GA helps to find the best neighbor which actually has cached the requested data. For the privacy of requesting nodes, PKI is used, to hide the hardware and software information of the device from third parties. Ns-3 simulator is used for server and vehicular applications. GA needs limited parameter setting and initializes itself from possible solutions instead of a single solution. The main drawbacks of GA are the lack of fast convergence towards the optimal solution and the long processing time required for optimality. Study [14] focuses on enhancing the bandwidth efficiency of I2V communication considering software-define vehicular networks (SDVN). Where, RSUs are connected with the controller, to take the scheduling decisions based on received requests from vehicles. RSUs and vehicles function as a data node, connected via a backbone network with SDN controllers. When a vehicle enters a particular coverage area, it sends location, velocity, and direction information with requested and cached data information to the corresponding RSU through beacon messages. The information is collected by the controller of the corresponding RSU through a wired connection. Then, the decision is made and notified to RSU by the controller. If two vehicles have the same cached data packets, i.e., P1 and P2, then XOR operation (P = P1 XOR P2) will be performed to decode the packet. In conventional approaches, two time slots are required to broadcast the two packets separately, but with the help of coding, it is completed in a single time slot to enhance the efficiency of broadcast bandwidth. Binary particle swarm optimization-based coding scheduling (BPSO-CS) algorithm is used, in which the particle determines a set of packets for RSU to serve the vehicles to minimize the broadcast time. The fitness function is used to evaluate the particles and then updating is done to speed up the searching convergence. SUMO is used for simulation to trace the vehicles according to the Manhattan mobility model. The drawback of PSO is it does not create new birds from parents such as GA, as the particle moves towards the destination using the current best position/velocity, and the slow convergence time is another drawback of PSO. Edge caching reduces the latency and balances the network load [29]. The problem in caching is which file, at what time, and at which node should be cached. Caching based on popularity is not enough to satisfy users in high mobility, although an accurate path/route prediction also helps to solve this problem. In [30], a new caching strategy is proposed based on the GA, to optimize the best and shortest path and the Markov model, to accurately predict the next route on the basis of past/ historical data. User clustering is done by the same behavior that helps to adjust the upcoming cell. Pre-caching the popular content may not be effective in case of dramatically different content requests; moreover, search is limited to the global best. In the case of a main road or single path, route accuracy will be high. In study [31], a comparison of simulated annealing (SA), ACO, and GA is performed based on the traveling salesman problem. Performance is evaluated in terms of the shortest distance and execution time. The result shows that SA has the shortest execution time (<1 s) and performs average in the shortest distance, while ACO performs better for the shortest distance, but it takes a long execution time.