# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 2 Related Work  
In SSF, the mobile devices switch to another AP or Base Station (BS) when the RSSI level of the currently associated network is lower than a threshold (e.g., \(-90\) dBm). During this threshold violation, mobile devices select the adjacent AP/BS with a stronger RSSI [25]. Network selection based only on RSSI has some shortcomings [13]--discontinuity of service, unnecessary handovers, and noticeable delays during the handover--which are more evident in dense networks. Regarding multi-criteria approaches, [PERSON] et al. [16] presented a generic handover management approach that operates in three phases. First, it identifies the appropriate place for the handoverinitiation based on the estimated coverage area. Second, it uses FL to eliminate inappropriate networks. Third, it selects an optimal network using the TOPSIS algorithm by considering several parameters such as delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. [PERSON] et al. [19] proposed a solution to ensure QoS during HM which operates as follows. First, it uses network-related parameters such as SINR in the HM initiation phase; second, it uses AHP-TOPSIS to select the network with the best predicted QoE based on user-related parameters, such as movement direction and the Mean Opinion Score (MOS), as well as device-related parameters such as the battery level. [PERSON] et al. [13] introduced an approach that combines network (i.e., available bandwidth, latency, delay jitter, packet loss ratio, and BER) and user parameters (i.e., service price and user preferences). This approach uses FAHP, entropy, and TOPSIS to calculate a utility value for all candidate networks using the parameters mentioned above. A network with a utility value higher than the current network is selected. [PERSON] et al. [17] proposed a network selection approach considering several parameters such as RSSI, velocity, bandwidth, load on evolved node B (eNB), power transmission, dwell time, and cell radius. The handover procedure is initiated when the RSSI of the current network drops below a predefined threshold, and the dwell time is higher than the handover time. This approach selects the new best AP/BS by an objective function computed by the Fuzzy Analytical Hierarchy Process. [PERSON] et al. [14] introduced an approach that, through simple mathematical formulation, gives dynamic and realistic weights to parameters, such as bandwidth, distance, velocity, power consumption, and network usage charges. This approach uses the dynamic weights to determine the best AP/BS to connect the mobile device. [PERSON] et al. [12] present an NN-based approach for HM, which includes three elements: the Vertical Handover Manager handles the handoff requests carried out by mobile devices; the Attributes Collector recovers network, device, and user criteria to feed an NN; and the Artificial Network Selector uses this NN to choose the best available wireless network. [PERSON] et al. [15] proposed an approach that uses a Feed-Forward NN (FNN) with two levels to select the best AP/BS. This approach gathers parameters from the network parameter and QoE from eNB and trains its FNN by using them and considering the experience gained from past handover decisions. Table 1 lists some shortcomings of the multi-criteria approaches. First, none of the methods mentioned above use network, device, applications, and user criteria jointly. Skipping parameters may affect the network selection, which can lead to unstable connection, unnecessary device resource consumption, and unneeded handovers. Second, approaches based on NN and FNN have a high computational complexity which can increase the time of network selection, thus decreasing throughput during the handover. Third, MADM approaches suffer when determining the most suitable weights for the criteria, which can affect network selection.