Routing Problem In Wireless Sensor Networks Research Articles

On the Best Fitness Function for the WSN Lifetime Maximization: A Solution Based on a Modified Salp Swarm Algorithm for Centralized Clustering and Routing

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs are often limited by their energy resources, making energy efficiency a critical concern in designing WSNs. Clustering and multi-hop routing are effective methods for maximizing the lifetime of WSNs. The energy efficient clustering and routing problem in WSNs can be formulated as a multi-dimensional knapsack problem (d-kp). This formulation can generalize all clustering and routing protocols in regard to energy efficiency. In this paper, we investigate the hardness of the energy-efficient clustering and routing problem in WSNs based on the d-kp formulation and introduce a modified Salp Swarm Algorithm (SSA) as a method to solve this problem. SSA is a metaheuristic algorithm inspired by the behavior of salps in the ocean. Moreover, we prove that it is impossible to design a polynomial time fitness function to maximize a long-term metric such as FND (time of first node death), which is a commonly used metric for network lifetime. Our results have implications for the design of energy-efficient WSNs and call for the development of more efficient algorithms that can handle the complexity of this problem. furthermore, we evaluate our proposed algorithm using simulation and compare it to existing algorithms. Our results show that using SSA with a carefully designed fitness function based on the theoretical findings and a unique one-to-one routing protocol to mitigate the energy hole problem, outperforms many of the existing algorithms in terms of energy efficiency and network lifetime.

Determining the trade-offs between data delivery and energy consumption in large-scale WSNs by multi-objective evolutionary optimization

Autonomous Wireless Sensor Networks (WSNs) form the basis of several Internet of Things (IoT) applications. Efficiency in routing data is a major concern in these networks since sensor nodes are energy-constrained and mostly unaware of the optimal routes regarding a set of QoS metrics. In this paper, the energy consumption and data delivery reliability are deemed the most important factors to the adequate overall network and application operation. Nevertheless, to enhance the chances of data packets to arrive at destinations, in general, energy consumption and reliability have to compromise. Although many routing techniques exist for WSNs, it is hard to determine, in general, if they really can find good solutions considering such trade-off. In light of this, we present a multi-objective integer problem (MOIP) to the routing problem in WSNs to optimize both energy consumption and delivery reliability. Moreover, to solve the proposed MOIP, we introduce an efficient evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The solution method is capable of constructing Pareto set approximations in a low computational time that can be used to better evaluate the solutions provided by routing protocols prior to deployment. The results of experiments with medium-sized and large-scale scenarios based on real case studies indicated the clear compromise between energy efficiency and delivery ratio. Moreover, by the results achieved by NSGA-II, the scalability does not affect the performance of the routing in optimizing the two objectives.

Extending the lifetime of wireless sensor networks through mitigating the hot spot problem

Wireless sensor networks (WSNs) are used to retrieve information in hostile environments that are not always accessible to human beings. Once they are deployed, the sensors are considered autonomous. Their lifetime is equivalent to the lifetime of their battery. The energy factor is the center of all the concerns of the sensors: economic routing protocols, adapted wireless technology, etc. Energy costs must be minimized because energy is a key constraint in sensor networks. This paper is part of the study of the routing problem in WSNs. Indeed, the problem consists of the nodes located around the base station (BS), which transmit data to other nodes, their energy is depleted more quickly. This causes energy holes and areas called hot spot. To address this problem, we used the unequal clustering mechanism in order to solve the network hot spot problem. Clusters closer to the BS have smaller sizes than those farther from the BS to overcome the energy over-consumption around the BS. The evaluation of our solution will be carried out using the platform Omnet++/Castalia. The simulation results obtained allow a glimpse of the energy gained by the proposed protocol compared to the protocol Eeuc. Energy efficiency is achieved thanks to the use of a network management policy which enables to balance the energy levels of different nodes, consequently, extends the lifetime of the network.

Solving the load balanced clustering and routing problems in WSNs with an fpt-approximation algorithm and a grid structure

Clustering is an efficient technique in designing routing algorithms for Wireless Sensor Networks (WSNs), which prolongs the network lifetime and leads to scalability. However, in clustered WSNs, the cluster heads consume more energy than the other nodes and thus die sooner. Therefore, the load of sensor nodes must be balanced among the cluster heads in order to prolong the network lifetime. This problem is called load-balanced clustering problem, which is an NP-hard problem. In this paper, we propose an approximation algorithm to solve this problem with an approximation ratio of 1.1. This algorithm runs in fixed-parameter tractable time. We use a virtual grid infrastructure in the network, which makes the algorithm practical for large-scale WSNs. We also propose a routing algorithm based on this structure. The routing algorithm reduces and balances the energy consumption in the network by finding proper paths between each cluster head and the sink. The simulation results show that the proposed algorithm is practical for large-scale WSNs in addition to having a better performance compared with other similar algorithms.

Energy-efficient Routing Model Based on Vector Field Theory for Large-scale Wireless Sensor Networks

Routing design is a key issue for large-scale wireless sensor networks (WSNs). Energy consumption associated with allocated resources should be considered. This paper proposes the integration of an energy-efficient model, which is based on vector field theory, in large-scale WSNs. Source nodes in WSNs have the characteristics of source points in a vector field, whereas sink nodes could be characterized as gathering points. Our scheme demonstrates that we can solve a set of partial differential equations in electrostatic theory to determine the routes that result in energy efficiency. Thus, the routing problem in WSN for energy efficiency becomes a typical PDE solution. Our simulation results show significant improvement in energy consumption. Compared with the traditional shortest path approach, the proposed model shows considerable improvement in the lifetime of the network.

Routing in Wireless Sensor Networks based on Generalized Data Stack Programming Model

Generalized Data Stack Programming (GDSP) model describes that any affected activity or varying environment is intelligently self-recorded inside the system in form of stack-based layering types where stack is re-defined to be one of six classes. The multi-stacking network is presented thinking of that any system connects to other system to work properly. This addresses a novel way to investigate and analyze any system. Wireless Sensor Networks (WSNs) monitor the environment and take action accordingly. However, WSN suffers from some weakness due to nodes failure or interference which affects the network topology and the routing table at each node. In this paper, the GDSP model is applied on the routing problems in WSNs. A history matrix at the user side is proposed to retrieve and backward the events affected the network.

A distributed routing algorithm for sensor networks derived from macroscopic models

While Greedy Forwarding (GF) has been suggested as a promising scheme for routing in wireless sensor networks (WSNs), inappropriate node deployment may cause a packet to be trapped at a local minimum, in which case GF will fail to deliver the packet. Though many algorithms have been proposed to allow packets to escape the local minimum trap, they commonly result in excess energy consumption of hole boundary nodes, which could lead to more serious hole problems. This paper approaches this problem from a macroscopic perspective, in which the routing paths for load balancing can be formulated as a set of partial differential equations (PDEs). We present a distributed algorithm, the distributed Gauss–Seidel iteration (DGSI), that embeds the finite-difference and Gauss–Seidel iteration methods into WSNs to solve the PDEs, and analyze performance for parallelism and errors. Furthermore, we discuss the limitations of DGSI and present a possible remedy for instances in which DGSI may not converge due to significant variations in node density. Since the proposed approach transforms a routing problem into a linear equation solving problem, we believe this paper will open up a potential research direction toward the application of many existing equation solving strategies for routing problems in WSNs.