Distributed Diffusion Based Spectrum Sensing for Cognitive Radio Sensor Networks Considering Link Failure

Cognitive Radio (CR) networks have become an important component of the modern communication infrastructure due to their capability of improving spectrum usage efficiency by exploiting channels opportunistically. In CR networks, the network topology changes very frequently because of the temporarily available channels and dynamic transmitting parameters (e.g. transmission power and transmitting frequency), which may even result in network disconnectivity from time to time. Hence an interesting and open question is that: are there bounds on end-to-end delay between a source-destination pair with Euclidean distance d apart in such networks? These bounds are required for time-critical applications. This paper first investigates the nature of topology-incurred end-to-end delay in large multi-channel CR networks and then identifies the conditions under which the asymptotic topology-incurred delay scales linearly with the Euclidean distance (d); that is, the conditions under which the end-to-end delay is bounded. The results in this paper are validated through extensive simulations and can advance our understanding of CR network performance.

This article is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes, which interfere the global consensus, are challenging issues. Motivated by this observation, we propose a novel randomized gossip based distributed Kalman filtering algorithm. Information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of sensors. In addition, the computational burden can be distributed for a sensor, which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step; thus, the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorithm. Under a sufficient condition, we show that the proposed algorithm is quite appealing as it achieves better mean square error performance theoretically than the noncooperative decentralized Kalman filtering algorithm. Examples and simulations are provided to illustrate the theoretical results.

In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.

Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energy-aware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Adaptive Service Provisioning for Wireless Sensor Networks Chien-Liang Fok, Gruia-Catalin Roman and Chenyang Lu Washington University in St. Louis {liang, roman, lu}@cse.wustl.edu Abstract—Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energyaware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process, demonstrating the system’s efficacy.Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energyaware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process, demonstrating the system’s efficacy.

In this chapter, we describe the most important network protocols supporting modern applications in mobile cellular networks, wireless sensor networks (WSN) and mobile ad hoc networks (MANETs). We first focus on the handover procedure in mobile cellular networks and the network failures due this procedure. The current solutions to enable seamless handover in the existing networks technologies are presented. We then present, in the context of WSN, a framework for model-based design and performance analysis of WSN software applications. WSN are often used for applications with strict non-functional requirements. They, thus, require solid modelling approaches for non-functional attributes of WSN software applications. We finally discuss approaches and cross-layer protocols addressing the problems of security, wireless communications, and network topology changes in MANETs before presenting a framework which aims to study both the benefits of increased awareness between software layers on the same node (inter-layer awareness) and between the same layer over different nodes (intra-layer awareness), and the combination of new/existing protocols designed with a cross-layer criterion.

Robust PMU Placement in Branch Switching Power Networks for Minimum Variance State Estimation

Multicast is essential for wireless sensor network (WSN) applications. Existing multicast protocols in WSNs are often designed in a P2P pattern, assuming small number of destination nodes and frequent changes in network topologies. In order to truly adopt multicast in WSNs, we propose a base-station model-based multicast, SenCast, to meet the general requirements of applications. SenCast is scalable and energy-efficient for large group communications in WSNs. Theoretical analysis shows that SenCast is able to approximate the Minimum Nonleaf Nodes (MNN) problem to a ratio of ln ∣R∣ (R is the set of all destinations), the best known lowest bound. We evaluate our design through comprehensive simulations and prototype implementations on Mica2 motes. Experimental results demonstrate that SenCast outperforms previous multicast protocols including the most recent work uCast.

Wireless Sensor Networks (WSNs) have become a cornerstone of modern technology, especially in applications like the Internet of Things (IoT), where they provide scalable and effective solutions for communication and monitoring applications. However, the finite energy of sensor nodes introduces a significant challenge, as it directly influences the network's lifespan and reliability. Routing protocols and clustering solutions have emerged as ideal ways to address these energy concerns. By forming sensors into clusters and enhancing data transmission routes, these protocols reduce energy consumption and improve network efficiency. Modern approaches incorporate techniques such as metaheuristics, fuzzy logic, and machine learning to address issues like load balancing, node mobility, and network topology changes. Despite considerable progress, gaps remain in scalability, Quality of Service (QoS) integration, and the adaptability of clustering protocols for dynamic environments. This research reviews the state of the art in optimizing energy efficiency in Wireless Sensor Networks. It addresses routing approaches and clustering solutions and highlights performance and quality key metrics and future trends.

Human-centric autonomous systems, based at the convergence of wireless sensor networks (WSN) and unmanned aerial vehicles (UAV), mark a transformative epoch in the amalgamation of technology. The widespread adoption of UAVs is attributable to their multifaceted applications and services. Within this realm, the utilization of UAVs to augment WSNs, defined as UAV-aided wireless sensor networks (UAWSN), has emerged as a captivating focus of research. The paramount rationale lies in UAWSN's potential to significantly enhance coverage and mitigate energy consumption when compared to traditional WSNs. However, the substantial mobility, variable altitude, and dynamic trajectory of UAVs may induce unforeseen changes in network topology, giving rise to connectivity and coverage challenges that can adversely impact the routing performance of networks. In the context of WSNs, the imperative to minimize energy consumption during data transmission by sensors is a central concern. A promising solution involves leveraging UAVs to collect data from sensors distributed across the network. This paper formulates an optimization problem aimed at maximizing the residual energy of sensors post data transmission, while accounting for the limitations imposed by the UAV's travel distance. The proposed solution involves the derivation of an optimized solution through the adaptation of a Voronoi diagram to explore a set of UAV hovering locations, with a preference for Voronoi vertices for data acquisition from adjacent sensors. Capitalizing on the benefits afforded by Voronoi diagrams, the authors delineate a UAV route by iteratively adjusting each UAV's hovering location based on the energy status of the sensors.

Wireless sensor networks (WSNs) exhibit high levels of network dynamics and consist of devices with limited energy. This results in the need to coordinate applications not only at the functional level, as is traditionally done, but also in terms of resource utilization. In this paper, we present a middleware that does this using adaptive service provisioning. Novel service binding strategies automatically adapt application behavior when opportunities for energy savings surface, and switch providers when the network topology changes. The former is accomplished by providing limited information about the energy consumption associated with using various services, systematically exploiting opportunities for sharing service invocations, and exploiting the broadcast nature of wireless communication in WSNs. The middleware has been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can enable energy-aware service binding decisions that result in increased energy efficiency and significantly increase service availability, while imposing minimal additional burden on the application, service, and device developers. Two applications, medical patient monitoring and structural health monitoring, demonstrate the middleware’s efficacy.KeywordsWireless Sensor NetworkService DiscoveryStructural Health MonitoringService SelectionService AvailabilityThese 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.

We investigate sensor control and scheduling strategies to most effectively use the limited resources of an ad hoc network or closed-loop sensor network. In this context, we examine the following three problems. Where to focus sensing? Certain types of sensors, such as cameras or radars, are unable to simultaneously collect high fidelity data from all environmental locations, and thus require some sort of sensing strategy. Considering a meteorological radar network, we show that the main benefits of optimizing sensing over expected future states of the environment are when there are multiple small phenomena in the environment. Considering multiple users, we show that the problem of call admission control (i.e., deciding which sensing requests to satisfy) in the context of a virtualized private sensor network can be solved in polynomial time when sensor requests are divisible or fixed in time. When sensor requests are indivisible but may be shifted in time, we show that the call admission control problem is NP-complete. How to make sensing robust to delayed and dropped packets? In a closed-loop sensor network, data collected by the sensors determines each sensor's future data collection strategy. Network delays, however, constrain the quantity of data received by the time a control decision must be made, and consequently affect the quality of the computed sensor control. We investigate the value of separate handling of sensor control and data traffic, during times of congestion, in a closed-loop sensor network. Grounding our analysis in a meteorological radar network, we show that prioritizing sensor control traffic decreases the round-trip control-loop delay, and thus increases the quantity and quality of the collected data and improves application performance. How to make routing robust to network changes? In wireless sensor and mobile ad-hoc networks, variable link characteristics and node mobility give rise to changing network conditions. We propose a routing algorithm that selects a type of routing subgraph (a braid) that is robust to changes in the network topology. We analytically characterize the reliability of a class of braids and their optimality properties, and give counter-examples to other conjectured optimality properties in a well-structured (grid) network. Comparing with dynamic source routing, we show that braid routing can significantly decrease control overhead while only minimally degrading the number of packets delivered, with gains dependent on node density.

For Wireless Multimedia Sensor Networks (WMSN), how to increase the throughput in constrained environments has become the main challenge. Multipath routing is a promising solution because it can improve the channel utilization rate, reduce transmission delay and balance the transmission load. To pursue high efficiency for a multipath routing scheme, we need to take the characteristics of wireless environments and sensor networks into careful considerations. Based on such considerations, this paper introduces a new routing scheme which divides the whole network topology into many districts and simultaneously forwards data through these districts without interfering with each other - to achieve interference-free transmissions. Our scheme will adjust the load of each district according to the energy status of their nodes; it does not establish fixed paths in advance and will hence maintain high performance even when the topology changes rapidly. Simulation results show that, when compared with related routing schemes, our scheme can achieve higher performance at real-time transmission and meanwhile distribute transmission loads more evenly to most of the nodes in the network.

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.

Wireless Sensor Networks (WSNs) are a class of technology represented with a set of sensor nodes. The main objective of these nodes is to collect data and monitor an environment's events. The basic tasks a node can handle are sensing data (humidity, temperature, fire, etc.), transmitting over short distances, and performing limited data processing. Common WSNs are used in many application fields. The limited resources of the WSNs (low processing capacity, memory and energy reserves) makes them underexploited compared to the WSNs' spectrum utilization. Therefore their applications can be significantly increased by enhancing their performance. To address this challenge, cognitive radio sensor networks (CRSNs) examine how wireless sensor nodes with cognitive radio capabilities can address these performance challenges and improve the spectrum utilization. A new kind of WSN (CRSN) with better performance is created throgh the introduction of cognitive radio to traditional sensor networks. This new class of WSNS is more adapted to some specific applications which require more resources capabilities. In this talk, I will offer a perspective on the emerging field of cognitive radio sensor networks and its current applications.