Tree‐based channel assignment schemes for multi‐channel wireless sensor networks

Many sensor node platforms used for establishing wireless sensor networks (WSNs) can support multiple radio channels for wireless communication. Therefore, rather than using a single radio channel for whole network, multiple channels can be utilized in a sensor network simultaneously to decrease overall network interference, which may help increase the aggregate network throughput and decrease packet collisions and delays. This method, however, requires appropriate schemes to be used for assigning channels to nodes for multi‐channel communication in the network. Because data generated by sensor nodes are usually delivered to the sink node using routing trees, a tree‐based channel assignment scheme is a natural approach for assigning channels in a WSN. We present two fast tree‐based channel assignment schemes (called bottom up channel assignment and neighbor count‐based channel assignment) for multi‐channel WSNs. We also propose a new interference metric that is used by our algorithms in making decisions. We validated and evaluated our proposed schemes via extensive simulation experiments. Our simulation results show that our algorithms can decrease interference in a network, thereby increasing performance, and that our algorithms are good alternatives for static channel assignment in WSNs. Copyright © 2015 John Wiley & Sons, Ltd.     

Graph-Theoretic Complexity Reduction for Markovian Wireless Channel Models

Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this paper, we present a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget. We also demonstrate the implication of unused states in complexity reduction of higher order Markov model. Our trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models.     

Wireless sensor networks based on modular arithmetic

The authors propose the method for data coding in wireless sensor networks (WSN) based on the transformation of the residue number system and multipath routing. It enables to utilize efficiently the bandwidth of communication channels as well as reduce message delivery time. It is explored the ant algorithm to search an optimal route of data transmission in the wireless sensor network. There is introduced a limitation for the communication radius of the wireless unit that shorts a number of the search decision modes and improves the accuracy of the wireless network simulation. The correspondent software is designed, it allowes exploring the dynamics of finding the optimal transmission path in WSN in case of different algorithm settings, and exploring the impact of the elite ants on the accuracy of the found path.     

无线传感器网络WSN探究

集传感器技术、无线传输技术、网络通信技术、分布式信息处理技术与编程技术于一体而形成的无线传感器网络（WSN）,是一种全新的信息获取和处理系统,具有十分广阔的应用前景。介绍了无线传感器网络WSN的发展历程和现状,论述了无线传感器网络WSN系统的构成,阐述了WSN系统的主要技术指标和设计特点。同时,结合实际应用,对无线传感器网络系统的架构、网络通信模型结构以及当前的主要应用领域作了深入地探讨和研究,最后指出了当前WSN关键技术亟需解决的问题。     

Comprehensive event based estimation of sensor node distribution strategies using classical flooding routing protocol in wireless sensor networks

We derive a new investigation for the wireless sensor networks (WSNs) when the underlying sensor node distribution strategies have strong influence on event specific communication performance. In this paper, we inclusively evaluated eight sensor network distributions namely: normal, gamma, exponential, beta, generalized inverse Gaussian, poison, Cauchy and Weibull. We designed and illustrated our proposed model with these node distributions for data dissemination. Moreover, performance evaluation matrices like sense count, receive count and receive redundant count are also evaluated. Additionally, we emphasized over the routing protocol behavior for different distribution strategies in the deployed WSN framework. Finally, simulation analysis has been carried out to prove the validity of our proposal. However, routing protocol for WSNs seems intractable to the sensor node distribution strategies when varied from one to another in the scenario.     

Secured routing in wireless sensor networks using fault‐free and trusted nodes

Wireless sensor network (WSN) should be designed such that it is able to identify the faulty nodes, rectify the faults, identify compromised nodes from various security threats, and transmit the sensed data securely to the sink node under faulty conditions. In this paper, we propose an idea of integrating fault tolerance and secured routing mechanism in WSN named as fault tolerant secured routing: an integrated approach (FASRI) that establishes secured routes from source to sink node even under faulty node conditions. Faulty nodes are identified using battery power and interference models. Trustworthy nodes (non‐compromised) among fault‐free nodes are identified by using agent‐based trust model. Finally, the data are securely routed through fault‐free non‐compromised nodes to sink. Performance evaluation through simulation is carried out for packet delivery ratio, hit rate, computation overhead, communication overhead, compromised node detection ratio, end‐to‐end delay, memory overhead, and agent overhead. We compared simulation results of FASRI with three schemes, namely multi‐version multi‐path (MVMP), intrusion/fault tolerant routing protocol (IFRP) in WSN, and active node‐based fault tolerance using battery power and interference model (AFTBI) for various measures and found that there is a performance improvement in FASRI compared with MVMP, IFRP, and AFTBI. Copyright © 2014 John Wiley & Sons, Ltd.     

Energy efficiency of dense wireless sensor networks: to cooperate or not to cooperate

Decentralized detection in a network of wireless sensor nodes involves the fusion of information about a phenomenon of interest (PoI) from geographically dispersed nodes. In this paper, we investigate the problem of binary decentralized detection in a dense and randomly deployed wireless sensor network (WSN), whereby the communication channels between the nodes and the fusion center are bandwidth-constrained. We consider a scenario in which sensor observations, conditioned on the alternate hypothesis, are independent but not identically distributed across the sensor nodes. We compare two different fusion architectures, namely, the parallel fusion architecture (PFA) and the cooperative fusion architecture (CFA), for such bandwidth-constrained WSNs, where each sensor node is restricted to send a I-bit information to the fusion center. For each architecture, we derive expression for the probability of decision error at the fusion center. We propose a consensus flooding protocol for CFA and analyze its average energy consumption. We analyze the effects of PoI intensity, realistic link models, consensus flooding protocol, and network connectivity on the system reliability and average energy consumption for both fusion architectures. We demonstrate that a trade-off exists among spatial diversity gain, average energy consumption, delivery ratio of the consensus flooding protocol, network connectivity, node density, and Poll intensity in CFA. We then provide insight into the design of cooperative WSNs     

基于深度强化学习的协作通信中继选择

协作通信是无线传感器网络(Wireless Sensor Network,WSN)实现数据可靠传输的关键技术,而协作通信技术的关键在于中继方案的选择。为此,提出了一种基于深度强化学习的协作通信中继选择算法(Deep Q-Learning Based Relay Selection Scheme,DQ-RSS)。首先,将WSN中具有中继选择的协作通信过程建模为马尔科夫决策过程,并采用Q学习在未知网络模型的情况下获取最佳中继选择策略;其次,针对高维状态空间下Q学习收敛时间长的问题,采用DQN(Deep-Q-Net)算法来加速Q学习的收敛。对比仿真实验结果表明,DQ-RSS在中断概率、系统容量和能耗方面均优于现有的中继选择方案,且能够有效节省收敛时间。     

Connectivity Analysis for Wireless Sensor Networks with Antenna Array Integrated Central Node

In a wireless sensor network (WSN), after gathering information, tiny sensor nodes need to transmit data to a sink. It is important to guarantee that each node can communicate with a sink. Due to the multi-hop communication of WSNs, an essential condition for reliable transmission is completely connectivity of a network. Adaptive or smart antenna (SA) techniques in WSNs have been a topic of active research in recent years. These techniques have been shown to be effective with respect to decreasing energy consuming via specified regions which are formed by the SA beams. In this paper, we propose a probabilistic technique to determine the network connectivity probability of the SA integrated WSN. We employ the geometric shape model to evaluate the network connectivity probability of the WSN using the SA beam specifications. The sensor node density to satisfy the desired network connectivity is determined in terms of the beam-width of the antenna array and node transmission range. The analytical results agree with the simulation results by less than 4.7 % error in the average.     

Energy-Efficient Map Interpolation for Sensor Fields Using Kriging

We propose a spatial autocorrelation aware, energy efficient, and error bounded framework for interpolating maps from sensor fields. Specifically, we propose an iterative reporting framework that utilizes spatial interpolation models to reduce communication costs and enforce error control. The framework employs a simple and low overhead in-network coordination among sensors for selecting reporting sensors so that the coordination overhead does not eclipse the communication savings. Due to the probabilistic nature of the first round reporting, the framework is less sensitive to sensor failures and guarantees an error bound for all functional sensors for each epoch. We then propose a graceful integration of temporal data suppression models with our framework. This allows an adaptive utilization of spatial or temporal autocorrelation based on whichever is stronger in different regions of the sensor field. We conducted extensive experiments using data from a real-world sensor network deployment and a large Asian temperature dataset to show that the proposed framework significantly reduces messaging costs and is more resilient to sensor failures. We also implemented our proposed algorithms on a sensor network of MICAz motes. The results show that our algorithms save significant energy and the out of bound errors due to packet loss are below 5%.     

Smartphone with Augmented Gateway Functionality as Opportunistic WSN Gateway Device

Sensors and actuators are being increasingly deployed for monitoring and controlling different phenomena and processes in a stand-alone manner or attached to sensor nodes with communication capabilities as part of larger wireless sensor networks (WSN). In addition to the protocols for communication within the WSN, sensor nodes may also provide the gateway functionality towards other networks and/or support communication with other external devices. These devices either represent additional gateways for exposing data and metadata to external networks, or serve as connection points to WSN for instance for the on-site calibration and maintenance. Smartphones as advanced mobile terminals appear particularly suitable for such role. This paper investigates the role a smartphone augmented with WSN gateway functionality can play in WSN with respect to regular dedicated sensor and gateway nodes. As a practical example we show the implementation of a gateway augmented smartphone using a Samsung i8910 phone and a VESNA sensor node connected via Bluetooth. The role of a gateway augmented smartphone is to interface between WSN, which is using a proprietary networking protocol, and the mobile network. The data obtained from WSN can be enriched using smartphone’s embedded sensors before being sent to the remote server. We demonstrate this on an example of geo-tagging the collected data from WSN with the smartphone’s Global Positioning System-based location data.     

Tracking in Wireless Sensor Networks Using Particle Filtering: Physical Layer Considerations

In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach ldquochannel-aware particle filtering.rdquo Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramer-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters.     

A Distributed MAC Protocol Using Virtual Control Channels for CRSNs

A cognitive radio sensor network (CRSN) is a promising fusion network that combines conventional wireless sensor network (WSN) with cognitive radio (CR) functionalities. With abilities to sense for available channels and to adapt to the radio environment, a CRSN is able to be more opportunistic in accessing spectrums than a WSN, but it also has problems due to hardware constraints and energy limitations. In many studies on distributed CR MAC protocols, establishing dedicated control channels (DCCs) has been considered as an important problem because it is essentially used for coordination and negotiation of spectrum assignments. However, directly applying DCCs into CRSNs is impractical, due to its high-cost complexity in hardware and deployment. To solve it, in this paper we propose a distributed MAC protocol using virtual control channels for CRSNs, in which CR sensor nodes called secondary users are grouped into clusters, and commonly shared available channels are utilized as virtual DCCs. In addition, a channel selection scheme is developed, based on an approximated partially observable Markov decision process. Our numerical simulation results indicate that the proposed MAC protocol can not only improve the throughput rate but also reduce the energy consumption under no DCCs.     

Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.     

Coverage and Lifetime Optimization of Wireless Sensor Networks with Gaussian Distribution

A wireless sensor network (WSN) has to maintain a desirable sensing coverage and periodically report sensed data to the administrative center (i.e., base station) and the reporting period may range from months to years. Coverage and lifetime are two paramount problems in a WSN due to constraint of associated battery power. All previous theoretical analysis on the coverage and lifetime is primarily focused on the random uniform distribution of sensors or some specific network scenarios (e.g., a controllable WSN). In this paper, we provide an analytical framework for the coverage and lifetime of a WSN that follows a two-dimensional Gaussian distribution. We also study the coverage and lifetime when the dimensions of Gaussian dispersion (i.e., x, y) admit different Gaussian parameters (i.e., standard deviation, $sigma_x neqsigma_y$). We identify intrinsic properties of coverage/lifetime in terms of Gaussian distribution parameters, which is a fundamental issue in designing a WSN. Following the results obtained, we further determine the sensor deployment strategies for a WSN that could satisfy a predefined coverage and lifetime. Two deployment algorithms are developed based on using our analytical models and are shown to effectively increase the WSN lifetime.     

Energy conscious deterministic self-healing new generation wireless sensor network: smart WSN using the <Emphasis Type="Italic">Aatral</Emphasis> framework

It is expected that in the next year, over a billion wireless sensor network (WSN) nodes will be deployed throughout the world, constituting a wide variety of sensor applications. In such a domain, management of the randomly distributed sensor networks is complicated by issues such as knowledge of energy consumption and coverage, extended lifetimes and demands for improved quality of service parameters. Several researchers have addressed these issues through their own innovations and discoveries of different schemes, methods, techniques or mathematical models and architectures or applications, using a variety of node designs. This in turn, has lead to multiple different choices of hardware and software options. However, this has not simplified the process of setting up application testbeds considering energy consumption. There is no readily available solution for setting up a WSN with user selected profiles and parameters. Multiple communication protocols, routing protocols, signal calibration and propagation methods, data aggregation schemes, clustering formations with multiple variations have been proposed for different research objectives. This paper proposes a method for consolidating all the initiatives and integrating these in a service panel framework that helps manage the desired WSN with options to set up an individual WSN profile and supporting the energy engineering processes involved in the WSN.     

Application modeling for performance evaluation on event-triggered wireless sensor networks

This paper presents an approach for event-triggered wireless sensor network (WSN) application modeling, aiming to evaluate the performance of WSN configurations with regards to metrics that are meaningful to specific application domains and respective end-users. It combines application, environment-generated workload and computing/communication infrastructure within a high-level modeling simulation framework, and includes modeling primitives to represent different kind of events based on different probabilities distributions. Such primitives help end-users to characterize their application workload to capture realistic scenarios. This characterization allows the performance evaluation of specific WSN configurations, including dynamic management techniques as load balancing. Extensive experimental work shows that the proposed approach is effective in verifying whether a given WSN configuration can fulfill non-functional application requirements, such as identifying the application behavior that can lead a WSN to a break point after which it cannot further maintain these requirements. Furthermore, through these experiments, we discuss the impact of different distribution probabilities to model temporal and spatial aspects of the workload on WSNs performance, considering the adoption of dynamic and decentralized load balancing approaches.     

Modeling Communication Networks With Hybrid Systems

This paper introduces a general hybrid systems framework to model the flow of traffic in communication networks. The proposed models use averaging to continuously approximate discrete variables such as congestion window and queue size. Because averaging occurs over short time intervals, discrete events such as the occurrences of a drop and the consequent reaction by congestion control can still be captured. This modeling framework, thus, fills a gap between purely packet-level and fluid-based models, faithfully capturing the dynamics of transient phenomena and yet providing significant flexibility in modeling various congestion control mechanisms, different queueing policies, multicast transmission, etc. The modeling framework is validated by comparing simulations of the hybrid models against packet-level simulations. It is shown that the probability density functions produced by the ns-2 network simulator match closely those obtained with hybrid models. Moreover, a complexity analysis supports the observation that in networks with large per-flow bandwidths, simulations using hybrid models require significantly less computational resources than ns-2 simulations. Tools developed to automate the generation and simulation of hybrid systems models are also presented. Their use is showcased in a study, which simulates TCP flows with different roundtrip times over the Abilene backbone.     

Large-Scale Network Parameter Configuration Using an On-Line Simulation Framework

As the Internet infrastructure grows to support a variety of services, its legacy protocols are being overloaded with new functions such as traffic engineering. Today, operators engineer such capabilities through clever, but manual parameter tuning. In this paper, we propose a back-end support tool for large-scale parameter configuration that is based on efficient parameter state space search techniques and on-line simulation. The framework is useful when the network protocol performance is sensitive to its parameter settings, and its performance can be reasonably modeled in simulation. In particular, our system imports the network topology, relevant protocol models and latest monitored traffic patterns into a simulation that runs on-line in a network operations center (NOC). Each simulation evaluates the network performance for a particular setting of protocol parameters. We propose an efficient large-dimensional parameter state space search technique called “recursive random search (RRS).” Each sample point chosen by RRS results in a single simulation. An important feature of this framework is its flexibility: it allows arbitrary choices in terms of the simulation engines used (e.g., ns-2, SSFnet), network protocols to be simulated (e.g., OSPF, BGP), and in the specification of the optimization objectives. We demonstrate the flexibility and relevance of this framework in three scenarios: joint tuning of the RED buffer management parameters at multiple bottlenecks, traffic engineering using OSPF link weight tuning, and outbound load-balancing of traffic at peering/transit points using BGP LOCAL_PREF parameter.      

基于P2P的无线传感器网络应用架构研究

通过基础网络互联多个传感器网络，为用户提供大规模、大范围、多样化的信息服务成为未来无线传感器网络的应用模式之一。据此，提出了一种基于P2P（Peer-to—Peer）技术的无线传感器网络应用架构。采用P2P技术，解决了大数据量的通信瓶颈，传感器网络亦可自由加入、变更或退出，方便部署，网络可扩展性好．同时屏蔽底层网络差异及多种接入方式．为用户提供多个访问入口。