Cross-layer collaboration handoff mechanism based on multi-attribute decision in mobile computation offloading

Ecological and human health risk assessment of tungsten and other heavy metal(loid)s in farmland around a typical tungsten mining area in southern Jiangxi, China

“…In this study, we employed a novel method for evaluating potential ecological risks, referred to as the Nemerow integrated risk index (NIRI) (Men et al 2020). This approach was derived from the combination of the Nemerow integrated pollution index (NIPI) and the potential ecological risk index (RI) methods, which are commonly utilised for quantifying soil HMs contamination and ecological risk (Yang et al 2011, Wang et al 2017, Li et al 2019. However, each of these methods has significant limitations.…”

A User Prioritisation Algorithm for Horizontal Handover in Dense WLANs

“…Many works classify HO into the following five strategies: (1) RSSbased strategies [4]; (2) Decision function-based algorithm strategies, which include the sum of the weighted function of some parameters such as monetary cost, trust, preference, compatibility, load and capacity [5][6][7][8][9][10]; (3) QoS and User-centric algorithm strategies, which take into account different features like bandwidth, cellular cost, coverage area, SINR, and QoS requirements [3,[11][12][13][14][15][16]; (4) multiple attribute decision (MAD) strategies in which a selection is made from a limited number of candidate networks, depending on different criteria such as Multiple Attributes and Multiple Objectives [4,17,18]; and (5) Fuzzy logic and neural network-based algorithm strategies, which are characterised by the capability to monitor and analyse different parameters such as RSS, load and bandwidth in the Fig. 1 HO in WLANS case of both real-time and non-real-time applications [2,[19][20][21][22].…”

“…• Works presented in [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] do not meet the requirements of STA applications in terms of both QoS and QoE, due to the lack of intelligence of current networks, especially considering the massive increase in mobile devices and the evolution of applications. Note that [15] and [16] address only the QoS requirements in terms of data bit rates of wireless users.…”

“…3.2. • Works presented in [3][4][5][6][7][8][9][10][11][12][13][14] and [4,[17][18][19][20][21][22] are proposed with the assumption that the wireless network is small in size or implemented in a simple scenario with a very limited number of users. However, wireless networks now and going forward are expected to become even denser due to the increasing number of mobile devices such as laptops, smartphones, tablets, Internet of Things (IoT) devices as well as network providers.…”

“…As we will detail in the next section, the process involves the calculation of these parameters for each Low and High priority user which are currently connected to the APs managed by the SDWNbased controller. The controller makes the final decision by combining the statistics related to these parameters [3,[11][12][13][14] QoS and User-centric-based Users' requirements in terms of QoS and QoE not considered High density not considered Presence of users that expect service delivery with high priority not considered [15,16] QoS and User-centric-based Users' requirements in terms of QoE not considered Presence of users that expect service delivery with high priority not considered [4,17,18] MAD-based Users' requirements in terms of QoS and QoE not considered High density not considered Presence of users that expect service delivery with high priority not considered [2] Fuzzy logic and neural network-based Presence of users that expect service delivery with high priority not considered [19][20][21][22] Fuzzy logic and neural network-based Users' requirements in terms of QoS and QoE not considered High density not considered Presence of users that expect service delivery with high priority not considered with the priority of the users to provide them with services accordingly.…”