Energy balance and robustness adjustable topology control 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.

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.

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a while since its last discharging. Accordingly, the battery can work for a longer time when it takes some rest between consecutive discharging processes than when it works all the time. However, to the best of our knowledge, this impact has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm for wireless sensor networks. In this algorithm, each node in the network periodically decides to be in the dominating set or not. Nodes that have taken sleep in the preceding round are encouraged to involve in the dominating set in the current round while nodes that have worked in the preceding round are encouraged to sleep in the current round for battery recovery. Detailed design description is presented. The complexity of the proposed algorithm is deduced to be O(D2), where D represents node degree. Simulation results show that our algorithm can significantly improve the network lifetime performance as compared with existing work.

In a wireless sensor network,congestion problem not only causes packet loss,but also leads to delay increase and energy consumption.Most of the existing congestion control algorithms for wireless sensor networks seldom concern the combined problem of data compression and weighted fairness.In order to address this problem,a greedy piecewise constant e-approximation algorithm,GPCA,and a novel decentralized congestion control algorithm WFCC are proposed in this paper.GPCA approximates a subsequence using a constant and guarantees that the error between the real sequence and the approximation sequence is less than or equal to e.In addition,we prove the optimality of constant e-approximation in theory.WFCC not only mitigates congestion,but also guarantees the weighted fairness among all sensor nodes.Importantly,we give a lower bound of weighted fairness metric 1-(10c/9)2 where c is a constant and 0c0.2.We evaluate GPCA using Intel Lab Data,and the results show that GPCA has great compression performance.In addition,we evaluate the algorithm WFCC in a 22-node wireless sensor network by TOSSIM.The results of simulation demonstrate that WFCC achieves high throughput and weighted fairness(above 95% on average).

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.

To prolong network lifetime and reduce average energy consumption when nodes are unable to measure the distance to neighbor nodes or have not distance measuring hardwire, distributed power control algorithms for wireless sensor networks are proposed. SPCA and PPCA are used to calculate the transmission power according to local information (residual energy). In the SPCA, the interval of residual energy is uniformly divided into α sections. Each section corresponds to one transmission power from maximum power to minimum power. In the PPCA, the transmission power is polynomial function of residual energy. When network starts, all nodes transmit data to sink node with DLOR routing and calculated transmission power. Simulation results show that by selecting the appropriate parameters, SPCA and PPCA can prolong network lifetime and reduce average energy consumption. Under certain conditions, it is fit for wireless sensor networks in special situation.

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.

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.

Wireless sensor networks (WSNs) have gained worldwide attention in recent years. Since WSNs can be conveniently deployed to monitor a given field of interest, they have been considered as a great long-term economic potential for military, environmental, and scientific applications and so forth. One of the most active areas of research in WSNs is the coverage which is one of the most essential functions to guarantee quality of service (QoS) in WSNs. However, less attention is paid on the heterogeneity of the node and the energy balance of the whole network during the redeployment process. In this work, the energy balanced problems in mobile heterogeneous WSNs redeployment have been analyzed. The virtual force algorithm with extended virtual force model is used to improve the QoS of the deployment. Furthermore energy model is added to enhance or limit the movement of the nodes so that the energy of nodes in the whole WSNs can be balanced and the lifetime of the networks can be prolonged. The simulation results verify the effectiveness of this proposed algorithm.

Congestion in Wireless Sensor Networks has negative impact on the Quality of Service. Congestion effects the performance metrics, namely throughput and per-packet energy consumption, network lifetime and packet delivery ratio. Reducing congestion allows better utilization of the network resources and thus enhances the Quality of Service metrics of the network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks reduces congestion by considering one hop neighbor routing in the network. This paper proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for congestion control in wireless sensor networks based on two hop neighbor information. On detection of congestion, the algorithm forwards data packets around the congestion areas by spreading the excessive packets through multiple paths. The path with light load or under loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the algorithm is to build path to the destination using two independent potential fields depth and queue length. Queue length field solves the traffic-aware problem. Depth field creates a backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential field to make dynamic decision for data forwarding. Network Simulator used for simulating the algorithm is NS2. The proposed algorithm performs better.

As the study on and promotion of wireless sensor networks (WSN) are deepening, people have increasingly higher requirements for the transmission speed and transmission performance of wireless sensor networks. Network congestion is an important factor of the transmission performance of a network, so congestion control becomes a hot research topic in the studies on the improvement of WSN services. Based on the study on WSN congestion control mechanism, this paper proposes an adaptive congestion control algorithm and simulates the algorithm. The simulation result shows that this algorithm can ensure stable network performance.

Routing algorithms for large-scale sensor networks should be capable of finding energy efficient paths to prolong the lifetime of the networks in a decentralized manner. With this respect, Ant System has several proper characteristics for routing algorithm in large-scale wireless sensor networks. First, its distributed mechanism enables routing algorithm to find a solution with only local information and be robust for uncertainties in wireless sensor networks. Second, the framework of the Ant System is proper to solve dynamic problems such as routing problem. Transition probability in Ant System can be used to estimate how good a given routing path is. Capturing these features, this work proposes two Ant Systems based routing algorithms, which are AS-RWSNs (Ant System for Routing in Wireless Sensor Networks) and SAAS-RWSN (Structure-Aware AS-RWSN). The AS-RWSN applies the original Ant System to routing algorithm for wireless sensor network and SAAS-RSN upgrades AS-RWSN with considering properties of network structure such as degree of node. In SAAS-RSN, sensors with high node degree have high data traffic since they have more routing paths. Consequently, SAAS-RSN achieves an energy balance over sensor network through this routing scheme. We demonstrate the effectiveness of the proposed algorithms by comparing three existing routing algorithms.

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 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.

An object tracking sensor network (OTSN) is made of $$m$$ static wireless sensors scattered throughout a geographical area for tracking $$n$$ mobile targets. Assuming that sensors have non-rechargeable batteries, one of the most critical aspects of OTSN is energy consumption. In this paper, we propose linear programming models which handle two missions : monitoring and reporting data to a base station, and two distinct problems : minimize energy consumption and maximize network lifetime. We suppose that trajectories of targets are known and targets should be monitored by sensors. To reach our goals, we schedule the active and sleep states of the sensors and route the data to a base station while keeping track of the targets. To solve our problems, we process a temporal discretization according to the intersection points between the trajectories and the sensing ranges of the sensors. The obtained sets of sensors for each time window help us to create linear programming models. These basic problems offer perspectives in performance evaluation of energy-conservation protocols and distributed algorithms in wireless sensor networks.