Parallel Implementation of Feasible Direction Algorithm for Large-Scale Sensor Network Location Problems

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.

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data.

In a wireless sensor network, a large number of small-size cheap sensor nodes are scattered, and have limited resources such as battery power, data processing capability and memory capacity. They should show fault-tolerance, energy efficiency, and topological adaptation to the change of external environment, therefore, an effective routing algorithm is essential. In general, if a network is large in scale, multi-hop transmission is more energy efficient than direct transmission. In case a large number of nodes are deployed densely within a fixed 1-hop communication range, however, many nodes participate in data transmission, multi-hop transmission consumes much more energy in such a case. Thus, this study proposes a routing algorithm that solves above problems at the same time in a dense and large scale sensor network. The result of experiment showed that routing algorithm proposed in this study has reasonable fault-tolerance, energy efficiency, and offers its adaptability to topological changes in a dense and large scale sensor network.

Large-scale distribution, communication and processing of sensed information require the operation of a highly-dynamic, dense, and widely-distributed wireless sensor network. Existing centralized and decentralized solutions require the use of information that may not be readily available to the nodes in a timely fashion. Instead of seeking such techniques, in this paper we propose an evolutionary price-based routing and bandwidth allocation algorithm for the management of information in large-scale distributed wireless sensor networks that will be (1) adaptable and responsive to demands and seek delivery of information to desired nodes despite the fact that the source and destination are not globally known, (2) efficient in allocating bandwidth, and (3) decentralized.

Source localization in a wireless sensor network (WSN) is sensitive to the sensors' positions. In practice, due to mobility, the receivers' positions may be known inaccurately, leading to non-negligible degradation in source localization estimation performance. The goal of this paper is to develop a semidefinite programming (SDP) method using time-difference-of arrival (TDOA) and frequency-difference-of-arrival (FDOA) by taking the sensor position uncertainties into account. Specifically, we transform the commonly used maximum likelihood estimator (MLE) problem into a convex optimization problem to obtain an initial estimation. To reduce the coupling between position and velocity estimator, we also propose an iterative method to obtain the velocity and position, by using weighted least squares (WLS) method and SDP method, respectively. Simulations show that the method can approach the Cramér-Rao lower bound (CRLB) under both mild and high noise levels.

Data acquisition from multi-hop large-scale outdoor wireless sensor network (WSN) deployments for environmental monitoring is full of challenges. This is because of the severe resource constraints on tiny battery-operated motes (e.g., bandwidth, memory, power, and computing capacity), the data acquisition volume from large-scale WSNs, and the highly dynamic wireless link conditions in outdoor harsh communication environments. We present a novel compressed sensing approach, which can recover the sensing data at the sink with high fidelity when a very few data packets need to be collected, leading to a significant reduction of the network transmissions and thus an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is both efficient and simple to implement on the resource-constrained motes without motes’ storing of any part of the random projection matrix, as opposed to other existing compressed sensing-based schemes. We further propose a systematic method via machine learning to find a suitable representation basis, for any given WSN deployment and data field, which is both sparse and incoherent with the random projection matrix in compressed sensing for data collection. We validate our approach and evaluate its performance using a real-world outdoor multi-hop WSN testbed deployment in situ . The results demonstrate that our approach significantly outperforms existing compressed sensing approaches by reducing data recovery errors by an order of magnitude for the entire WSN observation field while drastically reducing wireless communication costs at the same time.

In large-scale Wireless Sensor Networks (WSNs) the amount of data gathered require energy efficient data dissemination and data retrieval techniques. Data Centric Sensor (DCS) networks is a better approach in which the sensed data are sent to a sensor node whose name is associated with sensed data. Due to unattended nature of Wireless Sensor Networks, these sensor nodes are susceptible to different types of attacks. In this paper we propose a Secure Data Centric Sensor (SDCS) Networks that includes security and privacy support to DCS networks. In addition, we propose a multi-query optimization technique that aggregates similar queries and reduces the number of messages. Simulation and experimental results show that our work provides a secure data centric sensor network based on cryptographic keys and reduces the message overhead and incurs a minimum communication cost compared to previous works.

Various communication services have been provided. They include environmental monitoring and/or control, ad-hoc communication between mobile nodes, and inter-vehicle communication in intelligent transport systems. As a means of facilitating the above advanced communication services, autonomous decentralized networks, such as wireless sensor networks (Akyildiz et al., 2002; Rajagopalan & Varshney, 2006), mobile ad-hoc networks (Perkins & Royer, 1999; Johnson et al., 2003; Clausen & Jaquet, 2003; Ogier et al., 2003), and wireless mesh networks (Yamamoto et al., 2009), have been intensively researched with great interests. Especially, a wireless sensor network, which is a key network to construct ubiquitous information environments, has great potential as a means of realizing a wide range of applications, such as natural environmental monitoring, environmental control in residential spaces or plants, object tracking, and precision agriculture (Akyildiz et al., 2002). Recently, there is growing expectation for a new network service by a wireless sensor network consisting of a lot of static sensor nodes arranged in a service area and a few mobile robots as a result of the strong desire for the development of advanced systems that can flexibly function in dynamically changing environments (Matsumoto et al., 2009). In this chapter, a large scale and dense wireless sensor network made up of many static sensor nodes with global positioning system, which is a representative network to actualize the above-mentioned sensor applications, is assumed. In a large scale and dense wireless sensor network, generally, hundreds or thousands of static sensor nodes limited resources, which are compact and inexpensive, are placed in a service area, and sensing data of each node is gathered to a sink node by inter-node wireless multi-hop communication. Each sensor node consists of a sensing function to measure the status (temperature, humidity, motion, etc.) of an observation point or object, a limited function of information processing, and a simplified wireless communication function, and it generally operates on a resource with a limited power-supply capacity such as a battery. Therefore, a data gathering scheme and/or a routing protocol capable of meeting the following requirements is mainly needed to prolong the lifetime of a large scale and dense wireless sensor network composed of hundreds or thousands of static sensor nodes limited resources. 1. Efficiency of data gathering 2. Balance of communication load among sensor nodes 3. Adaptability to network topology changes

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled.

Fluid models for large-scale wireless sensor networks

Sensor node localization is a crucial aspect of many location-related applications that utilize wireless sensor networks. Among the many studies in the literature, multidimensional scaling-based localization techniques have been proven to be efficient, obtaining high accuracy with lower information requirements. However, when applied to large-scale wireless sensor networks with coverage holes, which are common in many scenarios, such as underground mines, the transmission path can become deviated, degrading the localization performance of this type of connectivity-based technique. Furthermore, in such complex wireless environments, non-line-of-sight reference objects, the presence of obstacles and signal fluctuations change the communication range and make it difficult to obtain an accurate position. In this article, we present a anchor-free localization scheme for large-scale wireless sensor networks called the ranging and multidimensional scaling–based localization scheme. We use ranging and non-line-of-sight error mitigation techniques to estimate accurate distances between each node pair and attempt to find inflection nodes using a novel flooding protocol to correct transmission paths that have become deviated by a coverage hole. Moreover, we replace the singular value decomposition with an iterative maximum gradient descent method to reduce the computational complexity. The results of the simulations and experiments show that our scheme performs well on wireless sensor networks with different coverage holes and is robust to varying network densities.

Aiming at the defects of large-scale large scale wireless sensor network data processing network traffic and high task latency, a data collection scheme of mobile node based on discrete elastic collision optimization algorithm and adaptive block compression sensing is proposed. Firstly, by analyzing the relationship between the network partitioning and the node deployment, an adaptive block compressed sensing data collection strategy is proposed to realize sensor node based on adaptive network block compressed sensing data collection. Designing mobile node data acquisition path planning strategy and multiple mobile nodes The collaborative computer system adopts the fitness value constraint transformation processing technology and the parallel discrete elastic collision optimization algorithm to achieve the purpose of balancing network node energy consumption and reducing data processing task delay. Finally, the simulation results show that the data collection scheme can effectively realize high-efficiency processing of large-scale sensor network data, and reduce network traffic and network task delay, and better balance network node energy consumption.

QoSNET: An integrated QoS network for routing protocols in large scale wireless sensor networks

Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

In this paper, we consider the multiple sinks placement problem in energy constrained large-scale Wireless Sensor Networks (WSN). First, some fundamental design parameters in WSNs are investigated such as nodes deployment, the network architecture, sink velocity and transmission range. Each of these parameters is analysed and discussed according to its influence on the energy consumption in a WSN. Second, a simple and efficient approach for the placement of multiple sinks within large-scale WSNs is proposed. The objective is to determine optimal sinks’ positions that maximize the network lifetime by reducing energy consumption related to data transmissions from sensor nodes to different sinks. Balanced graph partitioning techniques are used to split the entire WSN into connected sub-networks. Smaller sub-networks are created, having similar characteristics and where energy consumption can be optimized independently but in the same way. Therefore, different approaches and mechanisms that enhance the network lifetime in small-size WSN can be deployed inside each sub-network. Performance results show that the proposed technique significantly enhances the network lifetime.KeywordsLarge-Scale Wireless Sensor Networksmultiple sinksdesign parametersenergy efficiencygraph partitioning