Reliability Analysis of Social Network Data Transmission in Wireless Sensor Network Topology

With the limited resources, it is difficult for wireless sensor network to achieve a long time, continuous, high-speed acquisition of multimedia information and a real-time, reliable transmission of high-volume sampling data. This article proposed the novel data acquisition strategy based on compressed sensing theory, which can perfectly achieve the long time, real-time, reliable transmission of high-volume multimedia data in wireless sensor network. Reasonable experiments were designed to verify the effectiveness of the algorithms, and the experiment results show that: the proposed multimedia signal acquisition strategy is reasonable, practicable, and more suitable for the wireless multimedia sensor networks.

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.

To cope with the internal and external interference induced by other ambient signals, physical obstacles and the dynamic change of topology in wireless sensor networks, we propose an efficient and reliable MAC protocol combining TDMA and CSMA with channel hopping. The principle operation of the proposed protocol relies on the Big Slot Scheduling Algorithm that uses the tree routing and the notion of a sharable slot. A sharable slot of an appropriate size is allocated to each tree level and shared by all the nodes at the same level for data transmission. To adapt the dynamic change of topology, a node performs a slot scheduling in a distributed manner depending on its level. Data transmission is performed progressively, starting with the highest tree level. To mitigate external interference, the proposed protocol uses a blind channel hopping mechanism in which in channel switching, each node simply follows the selected channel sequence based on its ID every data transmission period. A sender follows the channel hopping sequence of a receiver or its parent for data transmission. We implemented the proposed protocol in Contiki OS and evaluated it in a 25-node testbed under various interference sources.

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.

Reliable transmission of data over the lossy wireless medium can be achieved by using an acknowledgement (ack) scheme. However, acks can be quite energy-consuming and should be used sparingly in energy-constrained networks. The simplest of acknowledgement schemes is the stop-and-wait ARQ (swack). Recent work proposes an implicit acknowledgement scheme (iack) which seems to be energy-saving, as there are no separate ackowledgements sent. IEEE 802.11e proposes a hop-by-hop block ack (eack). We modify this scheme to suit WSNs and propose that the choice of the ack scheme that would keep the network in- tact for the longest time depends on the tree overlay and the data and ack packet lengths. We theoretically compare the maximum energy spent for sending and re- ceiving acknowledgements for these three types of ack schemes. We then give a guideline to choose the most energy-saving of these three types of acknowledge- ments for convergecast in a wireless sensor network (WSN) applications with a routing tree overlay and continuous event generation rate.KeywordsWireless Sensor NetworkMedium Access ControlData PacketMedium Access Control LayerPacket LengthThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Information attacks and security in wireless sensor networks of industrial SCADA systems

The focus of this paper is to use Cellular Automata for simulating a series of topology control algorithms in Wireless Sensor Network using various environments. In this paper, we introduced the use of cellular automata in Wireless sensor networks to control Topology. In the network the sensor nodes are redundantly deployed in the same field. Due to this redundant deployment the many nodes in the network have remained in their active state simultaneously. This causes reduction in the global energy of the network and step-up in the lifetime of the mesh. Thus, the principal aim of the topology control algorithms is to cut the initial topology of wireless sensor network by avoiding noise and hold out the life of the mesh.KeywordsCellular AutomataLarge scale wireless sensor networkLifetimedistributed computing

Classification of sensory data is a major research problem in wireless sensor networks and it can be widely used in reducing the data transmission in wireless sensor networks effectively and also in process monitoring. In order to examine the huge size of data set in stream model generated by sensor network, it will be analyzed different sensor's output signal, topology of sensors network, number of sensor parameters and number of acquisition data. In our wind energy monitoring, sensor node monitors six attributes: speed, direction, temperature, pressure, humidity, and battery voltage. Every attribute value is set as four measures: average, instantaneous, minimum, and maximum. This paper presents several data mining techniques applied on the wireless sensor network's data considered: Naïve Bayes, k-nearest neighbor, decision trees, IF-THEN rules, and neural networks. Before classification, the data was clustered in order to be labeled. A similarity based algorithm, k-means, was selected in the clustering process for its simplicity and efficiency. A conclusion that decision trees are a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation.

A Wireless Sensor Network (WSN) is a collection of sensor nodes, spatially deployed in an ad hoc fashion, which performs distributed sensing tasks in a collaborative manner, without relying on any underlying infrastructure support. WSNs have usually been treated as passive networks, where sensors measure some physical phenomena periodically, or detect some events, and then send the data thus generated to the sink. Another kind of application is emerging, both in the context of a stand-alone WSN and of distributed WSNs, for the Internet of Things (IoT) where, instead of serving only as passive, information gathering mechanisms, sensors can actively process and respond to user queries to report the sensed data on demand. Irrespective of the query types, query processing tasks in WSNs pose several technical challenges, such as (i) selecting the appropriate sensors, within a stand- alone WSN or the appropriate WSNs among the distributed WSNs in the IoT, which satisfy the query specifications; (ii) providing the query response within the user-specified delay bound; (iii) ensuring higher query response accuracy; and (iv) determining the query execution plan dynamically to satisfy the QoS requirements on either delay or accuracy, or a combined specification of both delay and accuracy. This thesis aims to address these issues by developing a hierarchical data model for representing the sensor data of a stand-alone WSN at sensor, fusion node and sink levels. In the case of an aggregate query, the model can be used to select the appropriate sensors which satisfy the query specifications. In the case of an approximate query, the model can provide the approximate query response directly from the model. In both instances, the data model enables retrieving the query response with higher accuracy and less data transmission. To execute a query in delay sensitive applications, a delay aware spanning tree is designed, which provides a routing path for query dissemination to the target sensors as well as collecting a response from them. The tree is constructed based on the query load of the sensors so that the sensors with a higher query load can be reached quickly thus causing less delay in retrieving the query response. Moreover, to support the query execution according to the QoS requirements on delay and accuracy, the hierarchical data model is applied on the delay aware spanning tree to generate a QoS aware sensor model. The model organises sensor data hierarchically, with different levels of accuracy, on different hop distances of the tree. It creates the provision to collect the query response from any hop distance of the tree to satisfy the query-specified delay and accuracy bound. Then, the formulations are extended for the distributed WSNs in the context of the IoT. In doing so, a distributed WSNs information model is maintained in the web server which gathers the data models and the spatial properties of the WSNs from their associated QoS aware sensor models. The information model exploits the overlapping information among the participating WSNs and facilitates selecting the appropriate WSNs it is necessary to visit according to the query specifications. Moreover, based on the QoS requirements of the query, the information model determines the cost effective query execution plan, with the help of the QoS aware sensor models of the respective WSNs. Extensive analytical and simulation analyses confirm the efficacy of the proposed new strategies compared with notable work in this area.

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.

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.

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.

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.