Optimal Network Selection in Heterogeneous Wireless Environment for Multimedia Services

Ahuja, Kiran; Singh, Brahmjit; Khanna, Rajesh

doi:10.1007/s11277-015-2402-6

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…At the receiving antenna, RSS [7] indicates the strength of the signal received calculated. In addition, the RSS is identified using the transmission power, distance calculated among the transmitter as well as receiver by taking into consideration of the radio environment.…”

Section: Vii) Received Signal Strengthmentioning

confidence: 99%

Hybrid GOA and GA algorithm based Deep Belief Network for Network Controlled Vertical Handoff

Kulkarni¹

2022

JNACS

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The inventive character of diverse heterogeneous wireless networks presents improved service. The different WAN comprise wireless Metropolitan Access Network (WMANs), as well as cellular networks. For the selection of dynamic networks, several models are formulated however these models are determined on obtaining the enhanced performance. A new technique is formulated to enhance the energy effectuality of disparate heterogeneous wireless networks to the addressed aforesaid problem. Moreover, the adopted Hybrid Grasshopper Optimization Algorithm (GOA) and Genetic Algorithm (GA) is exploited in this paper. Moreover, the fitness model is taken into consideration with certain metrics which comprises Average bit rate (ABR), Delay, jitter, Bit Error Rate (BER), packet loss, energy utilization, hotspot probability, as well as Received signal strength (RSS). The adopted Hybrid GOA-GA Optimization approach is exploited to train DBN to choose optimal weights. Moreover, the selection of network metrics is subjected as input to adopted Hybrid GOA) and GA based Deep Belief Network (DBN), whereas the optimal decision is made to hold vertical handoff. The adopted Hybrid GOA-GAbased DBN presented enhanced performance with minimum probability of call drop, delay, energy utilization, and maximum throughput.

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Section: Vii) Received Signal Strengthmentioning

confidence: 99%

Hybrid GOA and GA algorithm based Deep Belief Network for Network Controlled Vertical Handoff

Kulkarni¹

2022

JNACS

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“…The RSS 40 refers to the strength of the signal received computed at the receiving antenna. Here, the RSS is formulated as…”

Section: G Received Signal Strengthmentioning

confidence: 99%

“…h. Available bit rate ABR 40 is a service utilized in asynchronous transfer network (ATM) networks wherein the source and destination do not need to be synchronized. In this method, ABR does not certify delay or data loss.…”

Section: G Received Signal Strengthmentioning

confidence: 99%

“…The RSS 40 refers to the strength of the signal received computed at the receiving antenna. Here, the RSS is formulated as

{RSS}_{v} = - italic174 + ((), S_{v} / N_{v}) - italic10 * \log_{10} ((), y) + italic10 * \log_{10} ((), F_{v} * {Nused}_{v} / {Nfft}_{v}) + {NF}_{v},

where y indicates distance interval, ( S v / N v ) represents the signal to noise ratio, F signifies frequency, count of subcarrier used is represented as Nused , size of FFT is represented as Nff , and noise figure is indicated by NF .…”

Section: Proposed Vho Mechanism For Disparate and Hwnmentioning

confidence: 99%

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Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

Patil

2021

Int J Communication

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The innovative characteristics of different heterogeneous wireless networks provide better service. The disparate wireless access networks consist of wireless metropolitan access network (WMAN) and cellular networks. Various techniques are devised for dynamic network selection, but these techniques concentrated on attaining improved performance. To address this issue, a novel method is devised to improve the energy efficiency of disparate heterogeneous wireless networks. Here, the proposed fractional squirrel-dolphin echolocation (FrSqDE) algorithm is devised by integrating squirrel search optimization (SSA) and fractional dolphin echolocation (fractional DE) algorithm. In addition, the fitness function is considered with certain factors that include delay, bit error rate (BER), jitter, packet loss, energy consumption, probability of hot spot, average bit rate (ABR), and received signal strength (RSS). The proposed FrSqDE optimization algorithm is used for training deep belief networks (DBNs) to select optimal weights. Here, the network selection factors are given as input to the proposed FrSqDE-based DBN, wherein optimal decision is made to handle vertical handoff. The proposed FrSqDE-based DBN offered improved performance with a minimal call drop probability of 0.050, minimal delay of 0.013 s, minimal energy consumption of 0.393 J, and maximal throughput of 117.972 kbps.

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“…where, in Equation , sampling distance is denoted by d s ( d s = 1 m) and k an integer with k ε [D/d s, 0,]. In the work of Ahuka et al they considered scenario; it is assumed that users are traveling at a constant speed in a straight line and initially move from the cell served by the base station. Both the base stations are assumed to be located and operating from the centre to the respective cells with equation transmitting power.…”

Section: Proposed Dynamic Network Selection Functionmentioning

confidence: 99%

An optimized multi‐attribute vertical handoff approach for heterogeneous wireless networks

Pradeep¹,

Sampath²

2019

Concurrency and Computation

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Wireless networking is becoming an increasingly important and popular way of providing global information access to users on mobility. Considering the accessibility of different advances, it is hard to choose the best system that guarantees client fulfilment. The best network selection in heterogeneous condition is a primary research issue. In Dynamic Network Selection Function (DNSF) method, which employs physical layer metrics as criteria for network selection, computation of functions-based relative dynamic weight optimization is proposed. This method is implemented through simulation in a typical heterogeneous wireless environment of Universal Mobile Telecommunications Service (UMTS) and Wireless Local Area Network (WLAN). The number of unnecessary handover occasions with the Handoff failure rate of the client between accessible systems is utilized as the execution measurements, and constantly best-associated system is chosen in view of the cost function. It is inferred that DNSF based approach yields ideal system determination in heterogeneous condition. KEYWORDS heterogeneous wireless networks, vertical handoff, WLAN and UMTS 1 Concurrency Computat Pract Exper. 2019;31:e5296.wileyonlinelibrary.com/journal/cpe

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Optimal Network Selection in Heterogeneous Wireless Environment for Multimedia Services

Cited by 12 publications

References 27 publications

Hybrid GOA and GA algorithm based Deep Belief Network for Network Controlled Vertical Handoff

Hybrid GOA and GA algorithm based Deep Belief Network for Network Controlled Vertical Handoff

Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

An optimized multi‐attribute vertical handoff approach for heterogeneous wireless networks

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