Priority modeling for public urban park development in feasible locations using GIS, Intuitionistic Fuzzy AHP, and Fuzzy TOPSIS

As feasible locations of public urban park in Bogor Municipality have been acquired in a previous study, decision makers are urgently needed to be informed on which locations should be prioritized for public urban park (PUP) development. Therefore, this study aggregates four multi-spatial criteria for PUP development priority modeling, namely distance to slum neighborhood, accessibility, slope, and land value. These four criteria in form of vector datasets were weighted using intuitionistic fuzzy analytical hierarchy process (IF-AHP) to consider the hesitancy, vagueness, and fuzziness might arise from experts’ judgement as well as from multi-spatial data processing. Resulted criteria weights from IF-AHP show that accessibility weight 0.261, land value weight 0.259, distance to slum weight 0.255, and slope weight 0.225, respectively. Criteria weights were inputted into fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) and geographic information system (GIS) to rank location priority. Results from fuzzy TOPSIS show that very high priority class which has the biggest CCi values range (0.654-0.76) provides 0.14 km2 area of feasible PUP development scattered in 10 locations. The biggest area for feasible PUP development is generated by medium priority class (CCi values 0.439-0.546) in 26 locations and approximately area of 0.38 km2.

DEFENSE STRATEGY AGAINST SELFISH NODES TAKING INTO ACCOUNT TRAFFIC DIFFERENTIATION IN VEHICULAR DELAY-TOLERANT NETWORKS

Vehicular Delay-Tolerant Networks (VDTNs) are vehicle networks where there is no end-to-end connectivity between source and destination. As a result, VDTNs rely on cooperation between the different nodes to improve its performance. However, the presence of selfish nodes that refuse to participate in the routing protocol causes a deterioration of the overall performance of these networks. In order to reduce the impact of these selfish nodes, proposed strategies, on the one hand, use the nodes transmission rate that does not take into account the message priority class of service, and on the other hand, are based on traditional buffer management systems (FIFO, Random). As a result, quality of service is not guaranteed in this type of network where different applications are derived from messages with different priorities. In this paper, we propose a strategy for detecting selfish nodes and taking action against them in relation to priority classes in order to reduce their impacts. The operation of this strategy is based, on a partitioned memory management system taking into account the priority and the lifetime of messages, on the calculation of the transmission rate of the node with respect to the priority class of the node with the highest delivery predictability, on a mechanism for calculating the nodes degree of selfishness with respect to the priority class, and on the monitoring mechanism. . The simulations carried out show that the proposed model can detect selfish nodes and improve network performance in terms of increasing the delivery rate of high-priority messages, reducing the delivery delay of high-priority messages, and reducing network overload.

Performance Analysis of Cache Based on Popularity and Class in Named Data Network

The communication network is growing with some unique characteristics, such as consumers repeatedly request the same content to the server, similarity in local demand trend, and dynamic changes to requests within a specific period. Therefore, a different network paradigm is needed to replace the IP network, namely Named Data Network (NDN). The content store, which acts as a crucial component in the NDN nodes is a limited resource. In addition, a cache mechanism is needed to optimize the router’s content store by exploiting the different content services characters in the network. This paper proposes a new caching algorithm called Cache Based on Popularity and Class (CAPIC) with dynamic mechanism, and the detail explanation about the static method also presented. The goal of Static-CAPIC was to enhance the total cache hit ratio on the network by pre-determining the cache proportion for each content class. However, this technique is not appropriate to control the cache hit ratio for priority class. Therefore, the Dynamic-CAPIC is used to provide flexibility to change the cache proportion based on the frequency of requests in real-time. The formula involves considering the consumers’ request all the time. It gives a higher cache hit ratio for the priority content class. This method outperforms Static-CAPIC, and the LCD+sharing scheme in the total network cache hit ratio parameter and channels it to the priority class.

Spectrum handoff analysis for multiple secondary users in cognitive radio networks

<p>The cognitive radio network permits secondary users to reach unused spectrum of primary users. In this paper, a mixed preemptive/non-preemptive resume priority M/G/1 queuing model is proposed for characterizing multiple handoff delay and to minimize the extended data delivery time of secondary users that has experienced multiple spectrum handoff during its transmission. The proposed queuing model supports delay-sensitive secondary user applications. The secondary users’ traffic is classified into two priority classes, the highest priority class for delay sensitive services and the lowest priority class for delay insensitive services. Furthermore, the proposed model assigns higher priority for the interrupted secondary users over uninterrupted secondary users for each class of secondary users in order to minimize the handoff delays for secondary users that experience multiple interruptions. Analytical formulas for the average extended data delivery time are derived for two different proactive spectrum handoff strategies (always stay and always change strategies) for both classes of secondary users. Simulation of proposed system model was performed to validate the analytical results and a good agreement was obtained. The performance of the suggested model is assessed and compared with other spectrum handoff models. Numerical results illustrate that the proposed system model outperforms existing models and can reduce the extended data delivery time for the secondary users.</p>

“Patiently waiting”: a multivariate logistic regression analysis to understand waiting times

Background Outpatient visits waiting time poses a significant challenge in countries facing rising health demands, particularly those that have universal access to public health care. In Italy, despite major improvements, many patients still experience extensive delays accessing specialist care. Our study describes waiting times for the 14 most critical specialist “first” visits provided by the national health care system in the Milan Health Protection Agency territory (3,48 million inhabitants) and investigates whether specific patient, prescription and hospital variables are associated to an increased risk of delay in waiting time. Methods A multivariate logistic regression analysis of the relationship between specialty, age, sex, priority class, exemption from prescription charges, hospital organization, patient region, and Hospital district was performed to investigate whether specific variables are associated to the odds ratios (OR) for having to wait more than the maximum time limit. Results Out of the 1,174,283 visits performed in 2019, 90% were provided within the maximum waiting time. Visits were provided beyond maximum time in 20% (median delay=2 days) of priority class 1 visits, 24% (median delay=7 days) of class 2, 22% (median delay=37 days) of class 3, and 4% (median delay=65 days) of class 4. All analysed variables were significantly correlated (p &lt; 0.001) to the OR for having to wait beyond the priority class specific limit. In particular: female sex (OR = 1.074), residing outside Lombardy (OR = 0.696), class 1 priority (OR = 4.939), exemption from prescription charges (OR = 1.107), research Hospital (private: OR = 5.937, public: OR = 5.156) and ophthalmology (OR = 8.822). Conclusions Our results show that most visits were provided within the time limits. However, waiting times seem to be a major issue when assessing certain specialties, hospitals, and priority classes. This data should guide health policy makers interested in tackling the waiting time issue. Key messages This study highlights the importance of monitoring outpatient waiting times. Strategies and policies to tackle the problem of waiting times should be made upon reliable data and transparent criteria in order to meet patients’ needs.

THE ISRAELI QUEUE WITH INFINITE NUMBER OF GROUPS

The so called “Israeli Queue” is a single server polling system with batch service of an unlimited size, where the next queue to be visited is the one in which the first customer in line has been waiting for the longest time. The case with finite number of queues (groups) was introduced by Boxma, Van der Wal and Yechiali [3]. In this paper we extend the model to the case with a (possibly) infinite number of queues. We analyze the M/M/1, M/M/c, and M/M/1/N—type queues, as well as a priority model with (at most) M high-priority classes and a single lower priority class. In all models we present an extensive probabilistic analysis and calculate key performance measures.