A Dynamic Energy-Aware Algorithm for Self-Optimizing 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.

The dynamic routing algorithm for renewable wireless sensor networks with wireless power transfer

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.

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.

Popularity of Mobile Wireless Sensor Network (MWSN) has increased tremendously due to the ability to connect the physical world with the virtual world. In MWSN, Sensor nodes move freely in a target area without the need of any special infrastructure. MWSN has attracted many researchers as it has introduced unique challenges in aspects like resource management, coverage, routing protocols, security in many applications such as target tracking in military applications, detection of catastrophic events, environment monitoring, health applications etc. Due to mobility, routing process in MWSN has become more complicated as connections in the network can change dynamically. This paper proposes a Mobility-Aware Multi-Objective Hybrid Routing Algorithm (MMOHRA) for Wireless Sensor Network (WSN). MMOHRA selects the best route and forwards the data packets from source to sink using multiple metrics such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. Performance of MMOHRA is compared with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-objective Routing Algorithm (DyMORA) using metrics such as Average Energy Consumption, Packet Delivery Ratio, and Jitter. MMOHRA improves the Average Energy Consumption (AEC) by a factor of 14.1% as compared with SHRP and 8.13 % as compared to DyMORA. MMOHRA outperformed SHRP and DyMORA in terms of packet Delivery Ratio (PDR) by 10.17 % and 9.83 % respectively.

A Wireless Sensor Network (WSN) is an emerging technology for a wide range of potential applications, including the environmental monitoring, medical, and target tracking systems. A WSN comprises a set of sensor nodes deployed in an area of interest. Sensor nodes are normally located in a dense and ad hoc manner, communicating each other in a multi-hop fashion in order to collect, process, and relay data. Most of WSNs are built on wireless, battery-powered sensor nodes. In such networks, it is hard to recharge or replace the energy-depleted nodes due to the desolate or harsh environment of the target area. Therefore, the efficient use of limited energy is a primary concern in designing protocols and usage strategies for WSNs. In this work an emergency scenario is considered and a WSN is used to monitoring the emergency area. To limit the power consumption, the WSN is activated only when the emergency occurs. Moreover an integrated protocol for routing and localization is developed to reduce communication, which is the most power-consuming operation in WSNs.

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.

With development of Internet of Things, wireless sensor networks (WSN) become larger and larger and if the present clustering and routing methods are widely used in large scale WSN, “energy hole” problem will deteriorate. To alleviate “energy hole” problem of WSN, a dynamic broadcast radius-based clustering and routing algorithm (DBRCR) is proposed. The algorithm calculates node broadcast radius every round, and based on the broadcast radius the neighbor table is established. Then according to the degrees of nodes, construct clusters iteratively. Finally, finding intra-cluster communication path based on data transmission success rate and routing for the inter-cluster communication based on nodes' residual energy and transmission energy consumption to minimize the data packet-loss rate and prolong the network lifetime.

Wireless sensor networks, by providing an unprecedented way of interacting with the physical environment, have become a hot topic for research over the last few years. As with any new technology, results from real experimentations using these networks are still scarce, as real deployments are either costly, or still unfeasible in the current state of technology. There is therefore an increasing need for simulation tools allowing the testing of different architectures, communication protocols or information processing algorithms in sensor networks. In this paper, we investigate a simulation framework for the testing of data processing in wireless sensor network applications. In a first stage, data is generated using partial differential equations, allowing the modeling of a large panel of physical phenomena. In a second stage, sensing unit operating system and network constraints are simulated using an instance of a versatile simulator to account for the platform characteristics. Insights provided by the proposed simulation frame are illustrated by a set of experiments on a heat source detection task.

Radio transmission and reception consumes a lot of energy in a wireless sensor network (WSN), which are made of low-cost, low-power, small in size, and multifunctional sensor nodes. Thus, one of the important issues in wireless sensor network is the inherent limited battery power within the sensor nodes. Therefore, battery power is crucial parameter in the algorithm design in maximizing the lifespan of sensor nodes. It is also preferable to distribute the energy dissipated throughout the wireless sensor network in order to maximize overall network performance. Much research has been done in recent years in the area of low power routing protocol, but, there are still many design options open for improvement, and for further research targeted to the specific applications, need to be done. In this paper, we propose a new approach of an energy-efficient homogeneous clustering algorithm for wireless sensor networks in which the lifespan of the network is increased by ensuring a homogeneous distribution of nodes in the clusters. In this clustering algorithm, energy efficiency is distributed and network performance is improved by selecting cluster heads on the basis of (i) the residual energy of existing cluster heads, (ii) holdback value, and (iii) nearest hop distance of the node. In the proposed clustering algorithm, the cluster members are uniformly distributed and the life of the network is further extended.

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.

In this paper, a new clustering protocol employing an iterated local search (ILS) to solve cluster head selection problem is proposed. ILS uses a perturbation operator to change an initial random solution to produce a new point in the vicinity of the solution. Using a combination operator, this new point is mated with the random solution producing a new solution. A move from the current solution to the new solution is considered acceptable only for higher fitness value. If a move is rejected after a predetermined search length, the change rate of the current solution is increased in order to explore a wider search space for quality solutions. In each round, this search process continues until good solution that ensures balanced energy consumption is obtained for the network. Furthermore, we propose a sleep scheduling scheme inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed mechanism of inactive node and cluster head selection protocols are performed sequentially at every round and they form part of the main algorithm proposed, namely the Dynamic Local Search-Based Algorithm for Clustering Hierarchy (DLSACH). The ultimate goal of the DLSACH protocol is to extends the network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows an improved performance compared to state-of-the-art protocols such as LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployment.

Wireless Sensor Networks (WSNs) applications have attracted attention in Internet of Things (IoT) as a novel networking paradigm consisting of billions of small sensor nodes. These sensors collect environmental information and communicate with each other to provide solutions for real time IoT applications' requirements. Since the majority of applications require location-based services, it is necessary to improve the accuracy of localization algorithms. DV-Hop is one of the most attractive range-free localization algorithms in wireless sensor networks and several works have been undertaken to improve its accuracy, however, since sensor nodes have limited power resources, the energy consumption of nodes should be also considered. In this paper, we propose a method based on DV-Hop to improve both accuracy and power consumption. Each unknown node calculates the Hopsize of each anchor node according to the limited information it has from the network topology; therefore there is no need to broadcast the Hopsize from anchor nodes, and in this way energy can be saved. In the next step, we use Shuffled Frog Leaping Algorithm (SFLA) as an evolutionary algorithm to improve the accuracy of estimated Hopsizes and a hybrid Genetic-PSO algorithm is applied to the third step of DV-Hop to achieve more accurate values for unknown nodes' positions. Simulation results show that our proposed method decreases the localization error significantly by jointly considering the energy consumption of sensors and is overall 44% more accurate than DV-Hop.

Wireless Sensor Networks (WSNs) is one of the most important types of networks in the world where sensors collect information within a specific area and send the data to a central point called the Base Station (BS). Many researchers have attempted to develop or improve the performance of WSNs by trying to mitigate the limitations and challenges facing WSNs. The most important challenge is to reduce energy consumption in a limited battery power supply of sensor nodes. Energy is consumed by sensor nodes in WSNs to perform three significant functions namely data sensing, transmitting and relaying. Various energy-saving routing protocols have been proposed to solve this problem to maintain the lifetime of the network for longer periods. In this paper, we propose an optimal routing protocol using multi-objective cultural algorithm for wireless sensor networks (ORPMCA). The simulation results showed that the ORPMCA protocol extends the lifetime of the WSN compared to other protocols by 15%.