QoE-based mobility-aware collaborative video streaming on the edge of 5G

Today's Internet traffic is dominated by video streaming applications transmitted through wireless/cellular interfaces of mobile devices. Although ultrahigh-definition videos are now easily transmitted through mobile devices, video quality level that users perceive is generally lower than expected due to distance-based high latency between sources and end-users. Mobile edge computing (MEC) paradigm is expected to address this issue and provide users with higher perceived quality of experience (QoE) for latency-critical applications, deploying MEC servers at edges. However, due to capacity concerns on MEC servers, a more comprehensive approach is needed to meet users' expectations applying all possible operations over the resources such as caching, prefetching, and task offloading policies depending on the data repetition or memory/CPU utilization. To address these issues, this article proposes a novel collaborative QoE-based mobility-aware video streaming scheme deployed at MEC servers. Throughout the article, we demonstrate how the proposed scheme can be implemented so as to preserve the desired QoE level per user during entire video sessions. Performance of the proposed scheme has been investigated by extensive simulations. In comparison to existing schemes, the results illustrate that high efficiency is achieved through collaboration among MEC servers, utilizing explicit window size adaptation, collaborative prefetching, and handover among the edges

A honeybees-inspired heuristic algorithm for numerical optimisation

© 2019, The Author(s). Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks

A multi-agent based approach for change management in manufacturing enterprises

© 2013, Springer Science+Business Media New York. Change management becomes an unavoidable necessity for manufacturing enterprises. Since change in business processes carries significant impact on the performance of manufacturing companies, a change management model is definitely required to remain competitive. Moreover, utilizing agent based systems will provide computational provision and integrity to manage and measure the capabilities to follow the change in a progressive approach by employing the cooperation and collaboration properties of various agents helping for retrieval of the required information in a rapid way. Therefore, in this paper, a multi-agent based change management model is proposed to handle the changes in manufacturing enterprises. The model is validated through a case study done to measure the performance of change management capabilities in a manufacturing company. A sensitivity analysis on the results of this case study is also conducted to reveal the system reactivity to various parameters

ORGANİZE SANAYİ ATIKSULARININ ZEHİRLİLİĞİ

In this work, wastewater samples were taken from the sewerage system of Konya I st. Organized Industrial Zone and 72 hours toxicity tests were carried out. Toxicity levels of wastewater samples were determined employing fish biyoanalysis in terms of LC50 which is the concentration that kills 50 % of the test organisms in 72 hours and toxicity dilution factor (TDF). In addition phytotoxicity levels of waste waters were determined by employing terrestrial plant Lepidium Sativum (L. Sativum). As aquatic tests organism fresh water fish Lepistes Reticulates (L. Reticulates) was employed. As a result of this  work  toxicity  dilution  factors  of  waste  waters  were  found  within  the  acceptable  range  for discharging into sewerage system according to industrial wastewater discharge rules of Turkish Water Pollution Control Regulations. Result of phytotoxicity test was not determined as EC50 (concentration that effected 50 % of the test organisms in 72 hours), but for all of the sampling points determined as phytotoxic according to germination percent values.  Values of Lepistes Reticulates and Lepidium Sativum as test organisms were also evaluated in terms of ease of application and reliability of the results.Bu çalışmada Konya Birinci Organize Sanayi Bölgesinden alınan atıksu örneklerine 72 saat süren toksisite testleri uygulanmıştır. Atıksu örneklerinin zehirlilik seviyeleri balık biyodeneyi yapılarak test organizmalarının 72 saatlik zaman periyodunda % 50’si için öldürücü olan atıksu konsantrasyonu (LC50) ve zehirlilik seyrelme faktörü (ZSF) ile belirlenmiştir. Ayrıca L. Sativum (Lepidium Sativum) kullanılarak atıksu örneklerinin fitotoksik seviyeleri de belirlenmiştir. Akuatik testlerde test organizması olarak tatlı su balığı L. Reticulates (Lepistes Reticulates) kullanılmıştır. Sonuç olarak Su Kirliliği Kontrol Yönetmeliği, endüstriyel atıksu deşarj standartlarına göre zehirlilik seyreltme faktörü uygun aralıkta tespit edilmiştir. Fitotoksisite testlerine göre test organizmalarının 72 saatlik zaman peryodunda %50’sinin etkilendiği konsantrasyon seviyesi (EC50) belirlenememiş fakat çimlenme yüzde değerlerine göre her iki nokta da fitotoksik olarak belirlenmiştir. Ayrıca test organizması olarak Lepistes Reticulates (L. Reticulates) ve Lepidium Sativum (L. Sativum)’un kullanıldığı toksisite testleri uygulanabilirlik ve hassaslık yönünden değerlendirilmiştir.

Adaptive binary artificial bee colony algorithm

Metaheuristics and swarm intelligence algorithms are bio-inspired algorithms, which have long standing track record of success in problem solving. Due to the nature and the complexity of the problems, problem solving approaches may not achieve the same success level in every type of problems. Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm and has originally been developed to solve numerical optimisation problems. It has a sound track record in numerical problems, but has not yet been tested sufficiently for combinatorial and binary problems. This paper proposes an adaptive hybrid approach to devise ABC algorithms with multiple and complementary binary operators for higher efficiency in solving binary problems.} Three prominent operator selection schemes have been comparatively investigated for the best configuration in this regard. The proposed approach has been applied to uncapacitated facility location problems, a renown NP-Hard combinatorial problem type modelled with 0-1 programming, and successfully solved the well-known benchmarks outperforming state-of-art algorithms