MMOHRA: Mobility aware multi-objective hybrid routing algorithm for Wireless Sensor Networks

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

In order to improve the location accuracy of unknown nodes in APIT algorithm in mobile wireless sensor networks, an improved APIT algorithm is proposed. A new test method called gravity center combination has been proposed for the InToOut and OutToIn errors in APIT. The algorithm further builds an anchor box by combining one-hop and two-hop information of nodes to limit the range and finally determine the location of unknown nodes to reduce the number of nodes that cannot be located. By improving the coverage and node availability, the positioning accuracy is eventually improved. The complexity is lowered by the lack of requirement of additional hardware, thus the improved algorithm is relatively easy to implement. The simulation results of the verification demonstrate that the updated algorithm improves the coverage ratio and the node usage efficiency compared with the other algorithm, thus makes the mobile node location more accurate.

Recently, network localization systems that are based on inter-node ranges have received significant attention. Geographic routing has been considered an application which can utilize the location information from these localization systems. In this paper, we firstly recognize that sensor network localization algorithms generate positioning data with different error patterns compared to those networks where node positions are determined directly from GNSS measurements. Secondly, by simulating practical sensor network scenarios using data from our localization algorithm, we observe that existing geographic routing algorithms in wireless sensor networks (WSNs) adopt very simplistic methods in the treatment of position error, without due consideration of error distribution. Additionally, an insight is given into localization algorithms for WSNs with inhomogeneous error environments. Our observations represent an initial step toward a detailed understanding and design of efficient geographic routing algorithms in location aware WSNs.

The technology of Heterogeneous Wireless Sensor Networks (HWSNs) is critical to the efficient operation and deployment of a variety of real-time Internet of Things (IoT) and Mobile Ad hoc Networks (MANETs). It is crucial for reducing overall energy dissipation and ensuring consistent energy distribution throughout the network. Bio-inspired hybrid optimization algorithms are emerging as a possible option for overcoming basic difficulties in Wireless Sensor Networks (WSNs), with a focus on sensor lifespan restrictions. A significant topic that must be considered prior to network configuration is attaining energy efficiency and optimal communication. Several papers have been published on the use of bio-inspired algorithms in WSNs. Few articles, however, addressed the hybrid strategy for routing and clustering in WSNs with communication. This research focuses on hybrid bio-inspired optimization algorithms and elaborates on their taxonomy and problem domains in WSNs. Furthermore, we explored and investigated the hybridization of the Whale Optimization Algorithm (WOA) with other meta-heuristic algorithms such as the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and others. This review can assist researchers in exploring the uses of such algorithms within and outside of this study area.

Wireless sensor networks (WSNs) have been an emerging field in recent years. WSNs have major advantages in battlefields and emergency situations. However, due to the limited power of the sensor nodes, WSNs are restricted with their applications. The effective management of energy over the sensor nodes leads to good performance of the network. In this paper, we are proposing the energy-aware cluster head selection algorithm (ECHSA) for WSNs. The cluster head selection plays a major role in reducing the energy consumption of the cluster. To support our approach, the minimum energy transmission is proposed, and the simulation of the ECHSA is carried out with the NS-2 simulation. The results are compared with the sectored low energy adaptive cluster head (S-LEACH) and Theins algorithms. The results proved the efficiency of the proposed algorithm.

Most of the proposed algorithms focus on static networks of sensors with either static or mobile anchors, in which the Monte Carlo localization algorithm is a typical one for localizing nodes in a mobile wireless sensor network. But the radio range being all different or inconstant in this algorithm leads to reduce the accuracy of localization and the efficiency of the algorithm itself. In this article, we propose the novel rang-based stochastic Monte Carlo localization algorithm for wireless sensor networks specifically designed with mobility to improve the accuracy of localization by dealing with the different radio ranges of sensors, and being bound to the narrow sampling area. Our simulation experimental results show that the rang-based stochastic Monte Carlo localization algorithm has improved the accuracy and stability of the estimated locations.

Energy balance is an important performance index in the design of routing algorithm for wireless sensor networks. A swarm intelligence optimization based routing algorithm for wireless sensor networks is proposed, whose kernel idea is taking less hop numbers into consideration and choosing the nodes with less pheromone as next hop to avoid some nodespsila prematurely exhausting their energy because of too concentrated routes through the nodes. The experiments show that the algorithm proposed in this paper is better than the directed diffusion routing protocol both in end-to-end delay and global energy balance and can effectively balance the global energy consumption and prolong the network lifetime.

Recent years have witnessed a proliferation of routing algorithms for Wireless Sensor Networks (WSNs), hence complicating the choice of the proper algorithm to be used for a given application. Simulation frameworks represent a viable solution to anticipate crucial choices, however existing solutions do not encompass the impact of changes (e.g., route updates, node crashes) on the nodes behavior and vice-versa. This article proposes a novel adaptive modeling approach to master the complexity of the thorough simulation of routing algorithms for WSN. Experimental results are provided showing the effectiveness of the proposed approach at managing changes, and dealing with detailed aspects, during the simulation and comparison of several routing algorithms.

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

The wireless sensor networks (WSNs) are usually deployed among widely area for environmental monitoring, data collection and other applications. In order to reduce the energy consumption and extend the lifetime of WSNs, as well as receive the data quickly, a source-based data aggregation algorithm (SBDA) for clustering wireless sensor networks is proposed. The method aggregated the data from sensor nodes and marked different transmission priority for the data. Simulation results show that, compared to existing protocols, the proposed method achieves a longer life time, and a lower packet drop ratio.

The main cause of the wasted energy consumption in wireless sensor networks is the packet collision.The packet scheduling algorithm is therefore introduced to solve this problem.This paper presents an analytical model for predicting the performance of packet scheduling algorithms in wireless sensor networks.This model can be used to predict the performance of the packet scheduling algorithm with different node density and CBR in wireless sensor networks.The proposed prediction model can give the packet collision and packet delivery ratios very close to the experimental results.The accuracy of our model is 77% of packet collision model and 81% for packet delivery ratio model.

Wireless Sensor Network (WSN) is a collection of sensory nodes forming a provisional network without the assistance of any recognized infrastructure.Due to the minimal range of each node's transmissions, it may be essential for one sensory node to request for the aid of other sensory node in transferring a packet between the source and sink.The important issue while designing WSN is the routing protocol that makes the best use of the severely minimal resource, especially the energy consumption.We propose a Energy Conscious multipath Routing Algorithm designed to improve the latency, resiliency and efficiency through discovering multiple routes between the source and sink.A Sink Originated Route Discovery (SORD) process provides the locality information of the source to the sink.One dominant and multiple alternate routes are generated at the end of the route discovering process.Apart from the dominant route, all the other nodes are put to sleep mode in order to conserve energy and create a concussion free route.Data is transmitted between the source and sink through the dominant route and if there is a disturbance in the existing route, the next preferred alternate route is used.If the route doesn't exist between the source and sink, the process starts from the beginning.Further, we analyze how the proposed protocol overcomes the drawback of the existing protocols.This system is implemented by using NS-2.34.By altering route update guidelines of existing on-demand routing schemes the Performance gains in the order of 10-15 % could be achieved.The simulation results show that the proposed protocol has less control packet overhead, less average energy consumption and the algorithm is faster.

A large number of small sensors in wireless sensor network are battery-powered; one of the most important design criteria for this type of network is energy efficiency. Clustering provides an effective way for extending the lifetime of a sensor network. In this paper, we propose a maximum-votes and load-balance clustering algorithm (VLCA) for wireless sensor network Each sensor collects votes from their neighbors and calculates the total vote received. The more votes a sensor accumulates, the more important it is in the whole network During the clustering phase, sensors compete with each other based on the total votes each has received. The algorithm is completely distributed, locating-unaware and independent of network size and topology. Simulation results show that our VLCA can reduce the number of clusters by 20-50% and prolong the lifetime of a sensor network.

Wireless sensor networks (WSN) have been widely used in urban network system and networked monitoring system, which provide easy connectivity and high physical data rate. Considering the battery-limited property of sensor nodes, recently, energy harvesting (EH) technology is introduced into WSN, which can alleviate traditional WSN problems (energy consumption, energy equilibrium, transmission efficiency, etc.). Current EH-WSN routing algorithms generally use the continuous energy harvesting mode, therefore, how to design an efficient routing algorithm for discrete energy harvesting mode and ensure the overall energy balance and conservation is still a great challenge and needs to be solved. Especially, under the mobile environment, the impact of route stability needs to be considered, which makes the design more complicated. To address the above problems, we propose a learning automata (LA) theory based stable and energy-efficient routing algorithm for discrete EH-mobile WSN (DEH-LA-SERA, for short). Firstly, we construct a multi-factors measurement model for sensor nodes, which contains node stability model, energy ratio function, expected harvesting energy model (using Markov decision process method) and direction judgement model. On this basis, we derive the node weighted value, i.e., selecting probability, which can be used to determine whether a node can be chosen as relay node. Secondly, with the help of LA theory, we construct a feedback mechanism to adjust the optimal path. With this solution, we can ensure the overall energy balance and conservation while holding the stability of selected path. As demonstrated in simulation experiments, our algorithm, DEH-LA-SERA, achieved the best performance in route survival time, energy consumption, energy balance and acceptable performance in end-to-end delay and packets delivery ratio.

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.