A Systematic Design Method for Large-Scale Wireless Ad Hoc Network Protocol Based on Optimization Decomposition Theory

Classical Transmission Control Protocol (TCP) designs have never considered the identity of the competing transport protocol as useful information to TCP sources in congestion control mechanisms. When competing against a TCP flow on a bottleneck link, a User Datagram Protocol (UDP) flow can unfairly occupy the entire link bandwidth and suffocate all TCP flows on the link. If it were possible for a TCP source to know the type of transport protocol that deprives it of link access, perhaps it would be better for the TCP source to react in a way which prevents total starvation. In this paper, we use coefficient of variation and power spectral density of throughput traces to identify the presence of UDP transport protocols that compete against TCP flows on bottleneck links. Our results show clear traits that differentiate the presence of competing UDP flows from TCP flows independent of round-trip times variations. Signatures that we identified include an increase in coefficient of variation whenever a competing UDP flow joins the bottleneck link for the first time, noisy spectral density representation of a TCP flow when competing against a UDP flow in the bottleneck link, and a dominant frequency with outstanding power in the presence of TCP competition only. In addition, the results show that signatures for congestion caused by competing UDP flows are different from signatures due to congestion caused by competing TCP flows regardless of their round-trip times. The results in this paper present the first steps towards development of more ’intelligent’ congestion control algorithms with added capability of knowing the identity of aggressor protocols against TCP, and subsequently using this additional information for rate control.

Cooperative communications is a promising technology that tends to change the conventional access and transmission schemes in wireless networks. By enabling additional collaboration from nodes that otherwise will not directly participate in the transmission, it enables spatial diversity and dramatically improves the performance of the network. In this paper we propose a cross-layer cooperative protocol based on a MAC protocol called CoopMAC (Liu et al., 2005; Korakis et al., 2007; and Liu et al., 2006) for ad-hoc wireless networks in order to leverage cooperation in both MAC and PHY layer. Exploiting physical layer combining at the receiver, this simple yet efficient scheme illustrates a new paradigm for realistic cross-layer cooperative protocol design for next generation wireless ad-hoc networks. We have evaluated the performance of the proposed protocol by extensive simulations in a large scale wireless ad-hoc network. Simulation results show that the new protocol significantly improves the network performance in terms of throughput and delay.

Computationally efficient and time‐memory saving compact algorithms become a keystone for solving global optimization problems, particularly the real world problems; which involve devices with limited memory or restricted use of battery power. Compact optimization algorithms represent a probabilistic view of the population to simulate the population behaviour as they broadly explores the decision space at the beginning of the optimization process and keep focus on to search the most promising solution, therefore narrows the search space, moreover few number of parameters need be stored in the memory thus require less space and time to compute efficiently. Role of population‐based algorithms remain inevitable as compact algorithms make use of the efficient search ability of these population based algorithms for optimization but only through a probabilistic representation of the population space in order to optimize the real world problems. Artificial bee colony (ABC) algorithm has shown to be competitive over other population‐based algorithms for solving optimization problems, however its solution search equation contributes to its insufficiency due to poor exploitation phase coupled with low convergence rate. This paper, presents a compact Artificial bee colony (cABC) algorithm with an improved solution search equation, which will be able to search an optimal solution to improve its exploitation capabilities, moreover in order to increase the global convergence of the proposed algorithm, an improved approach for population sampling is introduced through a compact distribution which helps in maintaining a good balance between exploration and exploitation search abilities of the proposed compact algorithm with least memory requirements, thus became suitable for limited hardware access devices. The proposed algorithm is evaluated extensively on a standard set of benchmark functions proposed at IEEE CEC'13 for large‐scale global optimization (LSGO) problems. Numerical results prove that the proposed compact algorithm outperforms other standard optimization algorithms.

Proper planning of charging infrastructure can significantly facilitate the popularization of electric vehicles and alleviate users’ mileage anxiety. Charging station siting and sizing are two key challenges in the planning with each of them being a complex optimization problem. In this paper, a multi-objective optimization approach is proposed to solve them together. First, considering that accurate charging demand estimation is crucial for planning, a traffic road network is established for this purpose. A Monte Carlo method is used to estimate the spatiotemporal distribution of charging demand in a region based on the probabilistic characteristics of user trips. Since uncoordinated charging not only increases the load but also leads to unstable operation of the local power system, a heuristic algorithm is proposed to coordinate charging scheduling. Then, based on the scheduled demand, this paper proposes a framework for the siting and sizing of charging stations to optimize the benefits for both operators and users by minimizing the construction, operation and maintenance costs, and the user’s detour time. As the given problem is a complex multi-objective combinatorial optimization problem, it is easy to fall into local optimum if traditional evolutionary algorithms are employed. Therefore, a multi-objective dynamic binary particle swarm optimization method is designed to solve this problem effectively. Finally, experimental simulations show that the proposed method outperforms the other comparative algorithms in terms of solution quality. A case study is presented to demonstrate the applicability and effectiveness of the proposed method in optimizing the location and capacity of charging stations.

Systematic design methods are widely diffused in academia, representing a standard in many engineering courses. Nevertheless, some flaws related to the conceptual design phase have been ascribed to these methods, especially concerning a non-comprehensive support to innovation. However, literature acknowledges several creativity-enhancing tools that can be conveniently combined with systematic design methods. In particular, many scholars refer to TRIZ, i.e. the well-known Russian problem-solving theory. Moreover, recent literature contributions propose some alternatives to the classical Functional Decomposition and Morphology (FDM), claiming to overcome some of the related flaws. One of them is the Problem Solution Network (PSN) approach, i.e. a systematic conceptual design method strongly based on a problem-solution co-evolutionary logic. In this context, our work aims at combining the potentialities of TRIZ with the benefits claimed for the PSN, by proposing a comprehensive integration procedure. Accordingly, this paper reports a detailed description of the proposal, where TRIZ tools are exploited to support problem solving within the PSN approach. Furthermore, an application is also reported where an industrial case study is presented to argue about possible potentialities and lacks of the proposed approach.

Transmission Control Protocol (TCP) is well-known for its poor performance over wireless networks. The main reason is the unstable condition of wireless networks. Previous attempts to improve TCP performance have been centered on techniques such as queue management, congestion window adjustment, delayed acknowledgement, and etc., because lower layers are hidden in the layered Open Systems Interconnection (OSI) model. With cross layer techniques, more precise data can be retrieved from lower layers for network condition estimation. In this paper, we analyze and model the factors that affect the TCP throughput in the lower layers. Consequently, to solve the TCP optimization problem, we formulate TCP performance as a Markov Decision Process (MDP). An extensive set of simulation experiments has been performed on NS-2 in order to illustrate that TCP performance can be enhanced by cooperatively adjusting lower layer factors.

Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers from issues of missing the optimal solution or collapsing to local optimum when facing complicated problems. To conquer these drawbacks, we consider adding a novel multi-chaotic local operator to the bio-shock feedback mechanism of SMA to compensate for the lack of exploration of the local solution space with the help of the perturbation nature of the chaotic operator. Based on this, we propose an improved algorithm, namely MCSMA, by investigating how to improve the probabilistic selection of chaotic operators based on the maximum Lyapunov exponent (MLE), an inherent property of chaotic maps. We implement the comparison between MCSMA with other state-of-the-art methods on IEEE Congress on Evolution Computation (CEC) i.e., CEC2017 benchmark test suits and CEC2011 practical problems to demonstrate its potency and perform dendritic neuron model training to test the robustness of MCSMA on classification problems. Finally, the parameters’ sensitivities of MCSMA, the utilization of the solution space, and the effectiveness of the MLE are adequately discussed.

The major function of signal transduction pathways in cells is to sense signals from the environment and process the information through signaling molecules in order to regulate the activity of transcription factors. On the molecular level, the information transmitted by a small number of signal molecules is amplified in the internal signaling pathway through enzyme catalysis, molecular modification and via the activation or inhibition of interactions. However, the dynamic system behavior of a signaling pathway can be complex and, despite knowledge of the pathway components and interactions, it is still a challenge to interpret the pathways behavior. Therefore, a systematic method is proposed in this study to quantify the signal transduction ability. Based on the non-linear signal transduction system, signal transduction ability can be investigated by solving a Hamilton-Jacobi inequality (HJI)-constrained optimization problem. To avoid difficulties associated with solving a complex HJI-constrained optimization problem for signal transduction ability, the Takagi-Sugeno fuzzy model is introduced to approximate the non-linear signal transduction system by interpolating several local linear systems so that the HJI-constrained optimization problem can be replaced by a linear matrix inequality (LMI)-constrained optimization problem. The LMI problem can then be efficiently solved for measuring signal transduction ability. Finally, the signal transduction ability of two important signal transduction pathways was measured by the proposed method and confirmed using experimental data, which is useful for biotechnological and therapeutic application and drug design.

Performance evaluation of wireless TCP with rerouting in mobile networks

We investigate the interactions between the transmission control protocol (TCP) and radio link protocols (RLP) of various reliability, when used in a spectrally efficient microcellular system similar to the EDGE cellular system. The study is done through detailed downlink simulations of a large cellular system with numerous simultaneous users. Either fixed-load or WWW-like packet traffic is generated for each link, and RENO TCP is run. The results show that the choice of RLP retransmission parameters impacts the throughput loss and application packet delay significantly, and that the range of 3-10 RLP block retransmissions (60-200ms persistency) offers the smallest performance degradation caused by TCP/RLP interactions. We conclude that variable RLP persistency based on the knowledge of radio conditions for individual users, is highly desirable.

Marine Predators Algorithm (MPA) is a recent efficient metaheuristic algorithm that is enlightened by the biological behavior of ocean predators and prey. This algorithm simulates the Levy and Brownian movements of prevalent foraging strategy and has been applied to many complex optimization problems. However, the algorithm has defects such as a low diversity of the solutions, ease into the local optimal solutions, and decreasing convergence speed in dealing with complex problems. A modified version of this algorithm called ODMPA is proposed based on the tent map, the outpost mechanism, and the differential evolution mutation with simulated annealing (DE-SA) mechanism. The tent map and DE-SA mechanism are added to enhance the exploration capability of MPA by increasing the diversity of the search agents, and the outpost mechanism is mainly used to improve the convergence speed of MPA. To validate the outstanding performance of the ODMPA, a series of global optimization problems are selected as the test sets, including the standard IEEE CEC2014 benchmark functions, which are the authoritative test set, three well-known engineering problems, and photovoltaic model parameters tasks. Compared with some famous algorithms, the results reveal that ODMPA has achieved better performance than its counterparts in CEC2014 benchmark functions. And in solving real-world optimization problems, ODMPA could get higher accuracy than other metaheuristic algorithms. These practical results demonstrate that the mechanisms introduced positively affect the original MPA, and the proposed ODMPA can be a widely effective tool in tackling many optimization problems.

Huge networks simulation remains to be a challenge, although various network simulators are in place. In this paper, we demonstrate a Scalable Network Simulator Architecture (SNSA) that brings together the four major areas of networking research: network modeling, simulation, measurement and protocol design. SNSA is a tool for computing scalable design of experiments through components such as a discrete-event simulation engine, default and extensible model designs, and a state of the art XML interface. Developers will be able to design and implement their own protocol designs, network topologies and modeling scenarios. Finally, we provide sample simulation model for Transmission Control Protocol (TCP).

The arithmetic optimization algorithm (AOA) is a recently proposed swarm intelligence optimizer with a simple structure and few control parameters. However, the original AOA relies on a single update mechanism, which often leads to premature convergence and limited adaptability in complex optimization problems. To address these limitations, this paper proposes a multi-strategy improved arithmetic optimization algorithm (IAOA). The proposed algorithm constructs a heterogeneous strategy pool composed of six search strategies, including arithmetic update, differential evolution operators, competitive elite learning, interpolation-based acceleration, and curriculum education learning. Furthermore, an adaptive strategy regulation mechanism based on fitness improvement contribution is introduced to dynamically adjust the selection probability of each strategy. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark suites demonstrate that IAOA achieves a superior optimization accuracy, convergence speed, and stability compared with several classical algorithms, recent metaheuristics, and AOA variants. Statistical tests including the Wilcoxon rank-sum test and Friedman mean rank test confirm the significance of the performance improvements. In addition, the algorithm is successfully applied to a three-dimensional path planning problem for amphibious unmanned aerial vehicles, demonstrating its effectiveness in solving complex engineering optimization problems.

Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy

Named data networking (NDN) as a promising future network architecture has been widely considered as a very appropriate network protocol for multihop wireless network (MWN). In named-data MWN, congestion control, forwarding strategy, and power control are three coupled critical issues. There are already extensive works about cross-layer design for MWN with IP and they have demonstrated the advantage of cross-layer design. Enlightened by cross-layer designs for MWN with IP, in this paper, the joint congestion control, forwarding strategy, and power control problem for named-data MWN are studied, and it is modeled as a named-data network utility maximization (NDNUM) problem. In our NDNUM, we maximize data receiving rate related network utility minus power consumption, while consider NDN’s communication characteristics that are different from IP. Then a distributed iterative algorithm based on subgradient method is proposed in order to solve NDNUM. To the best of our knowledge, our algorithm is the first cross-layer design for congestion control and power control for named-data MWN. We show that network utility maximization not only can be used to facilitate cross-layer design for networks with end-to-end protocols but also can be used to design cross-layer mechanisms for information-centric network. From simulation, our algorithm outperforms existing congestion control mechanism in network throughput, resource utilization efficiency, and stabilization PIT size.