Cluster-Based Wireless Sensor Networks Using Ant Colony Optimization

Water delivery scheduling was investigated in this study using fuzzy state, action, reward, state, action (SARSA) learning (FSL) and ant colony optimization (ACO) methods to find the advantages of a new robust model (FSL) over a conventional model (ACO) in both normal and emergency conditions. The mathematical models of these methods were developed. Three water shortages of 10%, 20%, and 30% were considered in the East Aghili canal, Iran, for the simulation process. Water depth and delivery indicators were used for evaluating the performance of the developed models. The results revealed that the FSL and ACO methods offered almost the same performance for the normal operation condition with high and acceptable indicators. However, the FSL method outperformed the ACO method in terms of performance in three considered emergency operations. It can be concluded that the FSL, as a new method, can schedule water delivery efficiently, adequately, equitably, and dependably. Furthermore, the FSL method is likely to lead to less maximum absolute error (MAE) and integral of absolute magnitude of Error (IAE) in comparison to the ACO method and is therefore recommended.

Ant Colony Optimization is the optimization method used for the analysis of the cluster. This method performs clustering data well. This method has limitations on computation time. This paper proposed a method of pattern reduction enhanced ant colony optimization to resolve the problem. Functions reduction pattern used to bypass loop system. This function ensures pheromone value if it meets certain iteration, so the next ant agent definitely choose the path that has been determined. The test is done through testing parameters and test accuracy. accuracy test compared with ant colony optimization method(ACO) with the results of the proposed method has the advantage of computing time. The test results show that the proposed method has 46 second faster than ACO method (73 second) for iris dataset, 141 second faster than ACO method (272 second) for wine dataset, and 150 second faster than ACO method (204 second) for synthetic dataset.

Ant colony optimization (ACO) methods, which imitate a mechanism of pheromone secretion when ants carry food to their nest, are one of efficient heuristic search methods for combinational optimization problems such as traveling salesman problems (TSPs) and so on. In this paper, we analyze the queen ant strategy AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> that is one of ACO methods more in detail by applying it to six kinds of city configurations included in the TSPLIB. Furthermore, in order to improve searching ability of the AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> , we propose a new method named "stimulative queen ant strategy AS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> ". As experimental results, we have clarified that the AS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> shows better performance than the conventional AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> in the viewpoint of both "discovery rate of optimal solution" and "average number of iterations"

A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures.

Cutter head is one of the most important components for the full-face rock tunnel boring machine (TBM), in which the layout design of the disc cutters is a key issue. Considering the complex engineering technical requirements, this paper studied an ant colony optimization (ACO) algorithm to solve the disc cutters plane layout problem. The searching space of the design variables (disc cutters’ position) were dispersed by setting several different search steps, then an ACO algorithm was adopted to search the best searching step of each design variable dynamically during the whole optimization process. Finally, the disc cutters layout design instance was solved by the ACO method to demonstrate its feasibility and effectiveness. The computational results showed that that ACO method can provide various disc cutters layout schemes for engineers choosing from.KeywordsFull-face tunnel boring machineDisc cutterslayoutAnt Colony Optimization

Wireless Sensor Networks (WSN) are becoming a viable tool for many monitoring applications. These applications may be of critical nature where the transportation of the information of events from the region of interest to some base station is crucial, where the data loss can not be tolerated. In the cluster-based two-tier WSN, where cluster-head nodes gather data from sensors in their clusters and then transmit to base station, when cluster head nodes start to die, the coverage of those clusters is lost and it leaves the region unmonitored. Even if the cluster heads are rotated and reassigned after some time, until the next rotation that cluster in question will be out of cluster head and will lose coverage. A lot of information is lost which is sensed and sent by the sensor nodes of the cluster to the dead cluster head. We propose here to select backup cluster heads (BCHs), for those cluster heads which are close to deplete their energy. The cluster head, when about to die, sends an SOS with the gathered information until then, to the respective BCH which takes over the responsibility and continues to work as a new cluster head. To evaluate the effect and results of BCH we used LEACH-C protocol and compared the data loss ratio with and without a BCH.

Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.

Clustering is a useful mechanism in wireless sensor networks that helps to cope with scalability problems and, if combined with in-network data aggregation, may increase the energy efficiency of the network. At the same time, by assigning a special role to the cluster head nodes, clustering makes the network more vulnerable to attacks. In particular, disabling a cluster head by physical destruction or jamming may render the entire cluster inoperable temporarily until the problem is detected and a new cluster head is elected. Hence, the cluster head nodes may be attractive targets of attacks, and one would like to make it difficult for an adversary to identify them. The adversary can try to identify the cluster head nodes in various ways, including the observation of the cluster head election process itself and the analysis of the traffic patterns after the termination of the cluster head election. In this paper, we focus on the former problem, which we call the private cluster head election problem. This problem has been neglected so far, and as a consequence, existing cluster head election protocols leak too much information making the identification of the elected cluster head nodes easy even for a passive external observer. We propose the first private cluster head election protocol for wireless sensor networks that is designed to hide the identity of the elected cluster head nodes from an adversary that can observe the execution of the protocol.

Routing is a critical issue in the Delay Tolerant Network as finding the most optimized path, with minimum loss of data and in the shortest span of time is a challenge. Various researchers have proposed different strategies for detection of the effective and efficient path.In this paper we apply a meta-heuristic method of ant colony optimization (ACO) to propose a routing mechanism for Delay Tolerant Networks to find the optimized path with maximum throughput. Modifications have been carried out in ACO to incorporate characteristics of another meta-heuristic technique known as particle swarm optimization for building up effective routing scheme. Experiments on simulated networks using agent-based modeling tool named NetLogo shows that our technique performed better as compared to the original ACO. Here the throughput has been specially taken as the performance factor of the network.

Internet of Things (IoT) is an important technique in the modern wireless telecommunications field. It is based on a collection of sensor nodes connected through wireless sensor networks (WSNs). The lifetime of this network is affected by the battery power of the connected sensor nodes. Network clustering techniques are used to improve energy consumption and extend the lifetime of the WSN. These techniques divide the sensor nodes into clusters and every cluster has a unique cluster head (CH) node. Recently, clustering-based metaheuristic techniques are used to solve this problem and find the optimal CH nodes under certain considerations such as less energy consumption and high reliability. This paper proposes a new clustering scheme for heterogeneous WSN using Coyote Optimization based on a Fuzzy Logic (COFL) algorithm. It uses the coyote optimization algorithm (COA) in conjunction with fuzzy logic (FL) system to reinforce and balance the clustering process for increasing the wireless network lifetime and reducing energy consumption. FL based clustering is adapted to determine a tentative set of CHs. The output of the FL is added as a solution within the initial solutions of the COA. Furthermore, a new fitness function has been adapted to minimize the total intra-cluster distance between each CH node and its cluster members and minimize the inter-cluster distance between the CHs nodes and the base station. An extensive simulation with three different scenarios is performed. The performance of the proposed COFL algorithm is compared with the well-known algorithms; namely low-energy adaptive clustering hierarchy protocol (LEACH) and stable election protocol (SEP) as traditional protocols and also coyote optimization algorithm (COA), grey wolf optimization (GWO), and particle swarm optimization (PSO). The COFL algorithm outperforms other algorithms in terms of alive node analysis, energy consumption, throughput, and central tendency measurements for alive nodes and normalized energy.

Energy sources that are often used to generate electricity are non-renewable energy sources which, if used continuously, will run out, such as petroleum, natural gas and coal. So, renewable energy sources are needed which are in abundant supply and do not run out quickly, one of which is water energy. Picohydro power plants (PLTPH) are an alternative small-scale power plant that can be applied in rural areas where there is a river flow that has a continuous water discharge and a relatively low water fall to drive turbines that can produce electrical power. To be able to produce electrical power with such potential, a pico-hydro power plant is needed. To optimize the performance of a picohydro power plant, a controller called PID (Proportional Integral Derivative) is needed. Then this PID is combined with the ACO (Ant Colony Optimization) method. ACO is a method for optimizing PID control parameters in a system adapted from the ability of an ant colony to find the shortest path to a food source from its nest

Due to the emerging of modern evolution of the Fifth Generation (5G) wireless technology and the growth of data traffic to meet various communication requirements of future applications. Routing and path planning in 5G wireless networks is one of the challenges brought about by the variation of network features like traffic load and topology which may differ in stochastic and time varying manner. However, as the mobile users' growth, there is a need to efficient routing strategy for shortest path to forward user requests to the servers to reach services. To meet this aim, an adaptive Meta-heuristic Ant Colony Optimization (ACO) algorithm has proposed in this paper. ACO based on swarm intelligence techniques that has considered inspired algorithm by imitating the sociological attitude related to ants crowd. ACO like the other evolutionary algorithms based on population; it initiates with random solutions of the population. This work was implemented using NS3 simulation tool with different scenarios and parameters. The computational results have shown that the ACO method is better to find the optimal solution in 5G networks than the traditional inelastic way. The simulation outcome indicates the efficiency of the proposed method where the packet delivery ratio increased while the drop packets ratio and the transmission delay reduced.

Using a single fixed sink in wireless sensor networks (WSNs) creates the hotspot problem. Recently, the mobile sink technique is considered as a good solution to the hotspot problem. Mobile sinks travel physically in the network to collect data directly from special nodes, such as cluster heads (CHs). Find the optimal path for the mobile sink is of utmost importance in order to achieve energy efficiency. The ant colony optimization (ACO) algorithm has been founded to be a good solution to meet this objective. Whereas the traditional ACO algorithm and its versions do not restrict the spread of ants which consumes a lot of energy and makes the convergence speed slow. In this paper, we propose an Intelligent ACO-based Routing Protocol with multiple mobile sinks support for wireless sensor networks (named IARP). This proposed protocol divides the WSN into clusters where each one is managed by a CH. The CH selection is conceived in such a way to use the minimum control overhead and also avoid the greedy re-clustering process. It considers not only pheromone but also remaining energy and hop count heuristic parameters. As well, the scope of ants search is restricted to save a lot of energy. Additionally, we have conceived a fault-tolerant algorithm to resume the normal functionality of the network after the failure of critical nodes i.e. CHs. The proposed protocol is tested by using simulations. The results show that the proposed IARP can significantly improve the performance of WSNs compared to other benchmark protocols.

The main source of energy consumption in the wireless sensor network is due to transmission and receiving of data packets. However the cluster head (CH) near to the base station (BS) in cluster based wireless sensor network depletes energy faster and die quickly due to transmission of heavier data traffic of other CHs to the BS. In this paper we propose a particle swarm optimization (PSO) based approach which construct the routing path and distribute the routing load over the CHs near to the BS and maximize the lifetime of networks. We also consider the fault tolerance issue due to the sudden failure of CHs during data routing phase. The simulation results show the effectiveness of the proposed algorithm over the existing algorithms.

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.