Extending Lifetime of Wireless Sensor Network Using Cellular Automata

The multi-source and single-sink (MSSS) topology, in wireless sensor networks, is defined as the network topology, where all of nodes can gather, receive and transmit data to the sink. We consider the problem of finding the joint optimal scheme with consideration of physical, medium access control (MAC), and network layers to maximize the network lifetime (NL) for the MSSS topology in the energy-constrained wireless sensor networks (WSNs). The optimization problem, when the link access is an interference-free time division multiple access (TDMA) scheme, can be formulated as a mixed integer-convex programming and relaxed to the convex programming. According to the transcendental equations in programming model, we take two rather different approaches to maximize the NL in such network. First, based on the result of the SSSS network, we decompose the MSSS problem into the various SSSS issues. Another approach is to utilize the Karush-Kuhn-Tucker (KKT) optimization conditions to analytically solve this convex problem. Analytical expression of the optimal NL is a function of the initial energy and quantities of data held by the sources, taking the influence of data rates, link access and routing method into account. Although some dual gaps exist, the analytical result agrees well with numerical result of optimization solver, and can be applied to algorithms in WSNs aiming at maximizing NL.

The hierarchical topology of wireless sensor networks can effectively reduce the consumption in communication. Clustering algorithm is the foundation to realize herarchical structure, so it has been extensive researched. On the basis of Leach algorithm, a distance density based clustering algorithm (DDBC) is proposed, considering synthetically the distribution density of around nodes and the remaining energy factors of the node to dynamically banlance energy usage of nodes when selecting cluster heads. We analyzed the performance of DDBC through compared with the existing other clustering algorithms in simulation experiment. Results show that the proposed method can generare stable quantity cluster heads and banlance the energy load effectively.

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.

Wireless sensor networks (WSNs) are an essential technology for the Internet of Things (IoT) and Machine-to-Machine (M2M), and node locations on them are indispensable for IoT and M2M applications. Therefore, we proposed a Cloud-based Self-Organizing localization (Cloud-SOL), which is composed of a WSN and a cloud computing environment with which the WSN connects. The Cloud-SOL aggregates the WSN's neighbor node list to the cloud computing environment and can estimate precise node locations using the WSN's aggregated topology information. However, it requires multi-hop topology with a high hop count for the WSNs. In this paper, we propose a virtual network topology for Cloud-SOL and show its implementation. The virtual network topology dynamically and ideally configures the WNS topology in cloud computing environment to the Clod-SOL without dependence on actual WSN topology.

Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of , humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City.

In this paper, the reliability of data transmission in social networks is thoroughly studied and analyzed using wireless sensor network topology technology. This paper, based on the introduction of sensor network reliability analysis-related technology, combined with the characteristics, and needs of the sensor network itself, focuses on the study of the reliability analysis of the sensor network under the state of perturbation scheme. Based on the idea of making full use of data changes to respond to the sensor state, this paper takes the actual monitoring data of the wireless sensor network as the research object, selects the temporal correlation and spatial correlation of the measured environmental data as the reliability index by extracting the features of the wireless sensor network data, and proposes the Evidential reasoning rule- (ER-) based wireless sensor network data reliability assessment model based on Evidential reasoning rule (ER) is proposed. The data are mined, analyzed, and quantified from the perspective of content popularity, and the interest indicators of nodes on data under content popularity are analyzed to derive stable interest quantification values. Combined with the network properties, i.e., node autoassembly community, we analyze the data dissemination characteristics of social networks in wireless sensor network topology environment and derive the upper and lower bounds of data transmission capacity under node interest-driven and its variation on network performance. Social relationships among nodes affected by social attributes are considered; in turn, the data forwarding behavior of nodes is modeled using data transmission probability and data reception probability; finally, the data forwarding process is analyzed and a closed expression for the average end-to-end transmission capacity is derived in turn.

The topology of wireless sensor networks (WSNs) can be controlled through changing the transmission power of each node. We propose an adaptive energy efficient topology (AEET) algorithm where each node adaptively adjusts its transmission radius by local decisions about its residual power and optimal transmission radius. We give detailed AEET and analyses the communication radius in an ideal linear model and two cases of the adaptive topology control. The AEET mechanism and the topology control procedure are also given. AEET guarantees a suitable connected node set through interaction. The resulting network topology increases WSNs lifetime by adaptively adjusting transmission power. Through simulating AEET, the result proves AEET scheme is able to prolong lifetime.

With 17.5% of world population, food safety and security is of primary concern in India. Despite increase in food grain production in last decade, there is heavy loss of food grains during transit and storage. This is due to lack of adequate infrastructures and technologies to monitor adverse environmental conditions that result in storage losses. In this paper, we propose a topology for wireless sensor networks called two-tailed chain topology that is suitable for efficiently monitoring of environmental factors in food grain depots. The proposed topology is designed by considering all infrastructure constraints of a food grain depot. Results show that the proposed topology is better than traditional topologies in terms of detection accuracy and coverage.

Power-Law topology has been proved to be an effective technique to improve fault tolerance. With the motivation of construct reliable topology for wireless sensor networks, a Power-Law Evolving Model (PLEW) of wireless sensor networks by stochastic placement is proposed in this article. In a dynamic evolving process of Wireless Sensor Networks, there are four types of events as follows: adding new nodes, nodes failure, adding new links, and links failure. The dynamics of nodes and links are integrated into the evolving model consequently. By using the continuum theory, the theoretical computing and simulated results show that the degree distribution of this model follows a power law. The scale-free properties revealed in this model can be applied into the scenarios required high dependability, such as national defense or survivability systems.

In this paper, we use complex network theory to analyze the evolving topology of wireless sensor networks (WSNs). Based on the BA model, we propose a novel complex network model, i.e., Neighborhood Log-on and Log-off model with energy awareness (NLL-E). NLL-E assumes locally preferential attachment, which exists in many complex networks. We further consider node addition and invalidation in topology evolvement, and add compensation mechanism for network robustness. The statistical properties and dynamics of NLL-E are analytically studied. Numerical simulations indicate that, comparing with BA, NLL-E has decreased average path length and increased network connectivity.

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.

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.

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.

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.

ABSTRACTWith reference to reducing dimensionality or predicting data missing mechanism, this paper approaches the function between distribution of missing data and topology of wireless sensor networks, and presents a recombination information missing data process method. As the missing data normally come from communication especially media accessing mechanism of carrier sense multiple access (CSMA) or time division multiple access (TDMA), the paper shows that the packets loss process is normally Poisson stochastic process. And the paper studies maximum missing data rates of some different topology networks (for example, the topology of star, hierarchical cluster tree, and mesh). In the end, the paper estimates the recombination information missing data processes method by simulations and experiments.