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Due to quick and inexpensive deployment, decentralized wireless ad-hoc networks have many potential application domains. However, stringent resource constraints, user mobility, limited channel capacity and high error rate of packets received at the wireless network interfaces make the design and optimization of these networks a challenging task. To solve these issues involving different layers of the protocol stack, we propose a multivariable Fuzzy Logic based Cross-Layer Optimization (FLCLO) Algorithm. In this algorithm, multiple parameters from various layers are given as inputs to a fuzzy logic controller. In fuzzy multi-attribute decision making framework, several distinctive parameters with inherent uncertainties in practical wireless communication scenarios are efficiently characterized by the fuzzy linguistic information. The fuzzy logic optimization technique developed here makes decisions by simultaneously considering multiple criteria that affect network performance in terms of packets per second, number of packets lost, throughput, mean SNIR and number of collisions. The performance of the proposed crosslayer fuzzy algorithm is evaluated by conducting simulations in OMNeT++ with dynamic fuzzy logic system embedded on it. The experimental results obtained from the simulation show that FLCLO used for selecting an efficient next-hop node performs better in terms of reducing the average MAC delay, energy spent on packet transmissions, and packet error rate. A comparative analysis of FLCLO with the media access algorithm used by the original IEEE 802.11 protocol shows that FLCLO is 52-68% more energy-efficient, has 45% lower MAC delay and reduces the packet error rate by 71%. In addition, it guarantees a lower collision rate and reduced packet loss ratio when compared with the conventional IEEE 802.11 ad-hoc routing model in the same set up. Finally, the simulation results demonstrate that our algorithm offers considerable performance enhancement in terms of mitigated packet loss ratio, mean delay, and energy consumption in contrast to the previous related works existing in literature. [ABSTRACT FROM AUTHOR]

In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning techniques are capable of handling uncertain and imprecise networking information while driving toward the optimal solution set in the problem search space. The approach proposed by this paper presents the application of the fuzzy logic inference combined with the evolutionary genetic algorithm to optimize the performance parameters in wireless networks. In particular, we consider optimal bit rate allocation and transmission power consumption through the joint design of fuzzy-genetic modeling framework. The sample training data generated through simulations of IEEE 802.11 wireless access network are analyzed for optimization by supplying it to the expert hybrid model comprising of the conjunctive design of both the computational intelligent techniques. Specifically, we contemplate the binary encoding scheme, single-point crossover, reversing mutation, and two fitness functions for executing the binary genetic operations of crossover and mutation. It is generally observed that the proposed hybrid model with polynomial fitness function yields better performance with scalable network datasets than the logarithmic fitness function in terms of higher objective value. Moreover, the results obtained through simulation experiments exhibit significant throughput gains and power efficiency for the deployed fitness functions with the evolving size of training dataset. Compared with the existing methods, our hybrid learning model demonstrates performance enhancement with higher expected fitness measure, improved throughput and power efficiency. [ABSTRACT FROM AUTHOR]

Summary: Modern wireless communication networks frequently have lower application throughput due to higher number of collisions and subsequent retransmission of data packets. Moreover, these networks are characterized by restricted computational capacity due to limited node‐battery power. These challenges can be assessed for deploying fast, reliable network design with resource‐restrained operation by means of concurrent optimization of multiple performance parameters across different layers of the conventional protocol stack. This optimization can be efficiently accomplished via cross‐layer design with the aid of network coding technique and optimal allocation of limited resources to wireless links. In this paper, we evaluate and analyze intersession coding across several source–destination pairs in random access ad hoc networks with inherent power scarcity and variable capacity links. The proposed work addresses the problem of joint optimal coding, rate control, power control, contention, and flow control schemes for multi‐hop heterogeneous networks with correlated sources. For this, we employ cross‐layer design for multiple unicast sessions in the system with network coding and bandwidth constraints. This model is elucidated for global optimal solution using CVX software through disciplined convex programming technique to find the improved throughput and power allocation. Simulation results show that the proposed model effectively incorporates throughput and link power management while satisfying flow conservation, bit error rate, data compression, power outage, and capacity constraints of the challenged wireless networks. Finally, we compare our model with three previous algorithms to demonstrate its efficacy and superiority in terms of various performance metrics such as transmission success probability, throughput, power efficiency, and delay. [ABSTRACT FROM AUTHOR]

Effective data compression and sustainable management of limited bandwidth are the fundamental challenges in the design of current ad-hoc wireless networks. Dynamic Huffman coding protocol with optimal prefix-free codewords assigned to network parameters through cross-layer methodology can considerably address these issues for performance enhancement in these networks. In this paper, the entropy based Huffman binary and ternary coding schemes are implemented with cross-layer design in mobile ad-hoc networks. This cross-layer approach for achieving higher adaptivity incorporated four layers of the traditional networking stack. The simulation results demonstrated the performance tradeoff between throughput and coding metrics for the proposed cross-layer architecture. Moreover, our proposed model exhibits substantially higher compression ratio and coding efficiency than the other existing methods through the integration of this lossless coding protocol in the developed cross-layer framework. [ABSTRACT FROM AUTHOR]

Sustainable operation of energy‐restrained wireless network services requires multiple objectives to be satisfied synchronously. Among these objectives, reduced spectrum outage, energy conservation, and minimal packet transmission failures considerably affect the energy harvesting operation of these networks. These three objectives are associated with disparate protocol layers incorporating the transport, medium access control, and physical layers of traditional networking architecture. The authors investigate energy harvesting wireless communications by formulating the multi‐objective optimisation problem comprising these global networking criteria, which are simultaneously optimised with the heuristic design procedure. For this, they employ a Pareto‐based evolutionary genetic algorithm technique built in the wireless network design and operation to find the optimal set of all non‐dominated solutions traversing the entire design search space. Besides, iterative implementation of the presented genetic optimisation model with distinct feasible integrations of crossover and mutation operations is performed to evaluate the proficiency of the proposed scheme for evaluating the Pareto‐optimal frontier set. The influence of different combinations of these operations is examined and adaptively applied with appropriate genetic parameters tuning for efficient meta‐heuristic search through the candidate solution space. Simulation results demonstrate that the proposed hybrid genetic mechanism outperforms the existing methods in terms of throughput, energy efficiency, and loss rate. [ABSTRACT FROM AUTHOR]

Plant tolerance to ecological stresses is the key issue faced by the plant scientists in the agricultural sector. The impact of rapidly changing environment conditions coupled with the scarcity of natural resources has driven an increased adoption in crop adaptive strategies for their controlled growth and development under uncertain ecological conditions. In this paper, we evaluate the plant stress response to environmental factors influencing the growth and productivity of crop species. For this, we employ the multivariate fuzzy logic mechanism for qualitative assessment of different parameters affecting the response of plant stress to multiple climate attributes. These distinct attributes including the soil quality, relative humidity, growth temperature and water availability are simultaneously fed as input variables to the fuzzy inference system. These parameters controlling and determining the plant growth and performance exhibit underlying ambiguities which can be effectively modeled with fuzzy control methodology. For experimental verification of the proposed technique against the conventional method, we employ various error metrics including MAE, RMSE, mean Gamma deviance and mean Poisson deviance. Given the sample record of specific plant species information dataset for the proposed fuzzy framework, simulation results show that the mean error rate of the proposed model evaluated on these metrics is around 25.5%, 29.04%, 15.2% and 11.4%. Finally, the effectiveness of the proposed model is demonstrated by utilizing the Shannon's entropy and second-order Bossaer's fairness indices. It is observed that the predicted output has reduced entropy by a factor of 1.054 and improved Bossaer's fairness by a relative proportion of 1.53. [ABSTRACT FROM AUTHOR]

In this article, we propose and formulate a single‐objective nonlinearly constrained programming problem with delay and capacity constraints. Specifically, we consider the utility resource allocation with the goal of maximizing the throughput amongst the multiple heterogeneous sessions in the network. Several numerical experiments for multiple topologies exhibit the performance and global convergence process of the proposed iterative algorithm. These network topologies are considered with distinct source‐destination communication sessions to emphasize the traffic flow analysis and optimal rate allocation vectors. For this, we employ recursive quadratic programming method and Lagrangian multiplier approach to solve the proposed optimization problem. The presented numerical results are obtained using Octave simulation tool. The results obtained from implementation of numerical simulation demonstrate the robustness and convergence performance of the proposed optimization scheme to optimal solution within finite number of iterations. Finally, we employ various global measures of fairness including the entropy‐based index, G's fairness index, linear product‐based fairness index, and Bossaer's fairness index to assess the performance of the proposed resource allocation scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel dynamic and robust queue management algorithm based on fuzzy logic. The presented mathematical framework employs fuzzy decision-making with multiple attributes incorporated in the fuzzy logic controller. The fuzzy inputs in our system are the total queuing time, mean lifetime, and mean queue length associated with the deployed single-server queuing discipline. Besides, service time, queue capacity, and inter-arrival time represent the three fuzzy outputs. A series of extensive simulation experiments are conducted to evaluate and compare the performance of the proposed Fuzzy Logic based Queuing (FLQ) algorithm with the well-known conventional FIFO queuing mechanism. The simulation results validate the superior performance of the wireless network implemented with FLQ algorithm in terms of lower delay, lower queue size, and much higher service rate than the traditional queuing policy. In addition, it eminently reduces unsolicited packet drops and queuing delay encountered by the packets in the network. [ABSTRACT FROM AUTHOR]

In the evolving era of digitization, the success of the modern e-commerce industry is measured by several key performance metrics. These performance indicators determine its efficiency and overall service quality in online business marketing. In this paper, we investigate the feasibility performance of e-commerce based on assessment of multiple qualitative parameters influencing the behaviour of online consumers. The attributes considered for evaluating the proficiency of e-commerce platforms and improving their service quality in the highly competitive global market include setup and operational cost, scalability, execution time, and resources accessibility. These multiple independent parameters with different units and range of values exhibit implicit imprecision and uncertainties, which can be effectively handled by multivariate fuzzy logic modeling. For a given dataset application of the presented fuzzy multi-criteria decision making framework, simulation results are used to compute the absolute error, root mean square error and normalized error in the estimation of fuzzy output variable of feasibility, thereby validating the proposed model. Furthermore, numerical results and analyses depict that our model offers substantial performance enhancement in terms of achieving higher accuracy than other estimation methodologies existing in literature with equivalent dataset size. [ABSTRACT FROM AUTHOR]

Summary: In ad‐hoc wireless networks, to achieve good performance, multiple parameters need to be optimized jointly. However, existing literature lacks a design framework that investigates the synchronic impact of several parameters on overall system performance. Among several design parameters, energy conservation, end‐to‐end delay minimization, and improved throughput are considered most important for efficient operation of these networks. In this paper, we propose a novel scheme for multiple‐objective cross‐layer optimization capable of optimizing all these performance objectives simultaneously for reliable, energy‐efficient, and timely transmission of continuous media information across the network. The three global criteria considered for optimization are incorporated in a single programming problem via linear scalarization. Besides, we employ standard convex optimization method and Lagrangian technique to solve the proposed problem to seek optimality. Extensive simulation results are generated accounting for several topologies with multiple concurrent flows in the network. These results are used to validate the analytical results and demonstrate the efficiency of the proposed optimization model. Efficiency of the model is verified by finding the set of Pareto‐optimal solutions plotted in three‐dimensional objective space. These solution points constituting the Pareto front are used as the best possible balance points among maximum throughput, maximum residual energy, and least network delay. Finally, to emphasize the effectiveness and supremacy of our proposed multiple‐objective cross‐layer design scheme, we compare it with the conventional multiple‐objective genetic algorithm. Simulation results demonstrate that our method provides significant performance gain over the genetic algorithm approach in terms of the above specified three objectives. In this work, a novel multiple‐objective and multi‐constrained optimization problem in ad‐hoc networks is proposed where three performance objectives namely residual energy, network delay, and throughput are considered for joint optimization. We study the performance trade‐off among the three objectives with the aim to find a set of solutions that conjointly form the Pareto front, ideally with a good spread on a three‐dimensional graph. Through numerical simulations, we measure the performance of the proposed model for various network topologies. [ABSTRACT FROM AUTHOR]