Adaptive Service Provisioning for Wireless Sensor Networks

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.

This chapter aims to provide discussions of active period selection technique for the cluster-tree type wireless sensor networks (WSNs) employing traffic adaptive IEEE 802.15.4 beacon enabled mode under dynamic network changes, and proposes an autonomous distributed superframe duration (SD) selection scheme to reduce beacon collisions by using simple control mechanism. The dynamic network changes, including network topology changes and frame structure changes, cause severe beacon collisions in the cluster-tree type WSNs and greatly degrade their system performance. To overcome this problem, the proposed scheme autonomously selects an active SD by using beacon reception power monitoring with distributed fashion and also introduces a beacon status notice from sensor nodes (SNs) to their parent cluster heads (CHs) in order to prevent unnecessary SD selection at CHs. To enhance the system performance, this chapter also applies the traffic adaptive distributed backoff mechanism, previously proposed in our recent work, to the autonomous distributed SD selection scheme and investigates an effect of the distributed backoff to the system performance. The results evaluated by computer simulation show that the proposed scheme can improve the transmission performance while keeping the better power consumption performance in cluster-tree type WSNs under cluster mobility environments.

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

Background Autism spectrum disorder (ASD) is characterized by aberrant functional brain connectivity and deficits in network dynamics. Transcranial direct current stimulation (tDCS) has emerged as a promising intervention with potential therapeutic effects; however, its effects on both static and dynamic functional brain network organization remained insufficiently understood. Methods A total of 42 children with ASD aged 4–6 years were enrolled and randomly assigned to either active tDCS or sham stimulation groups. Resting-state electroencephalography (EEG) data were acquired before and after the intervention. Low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) networks were constructed, followed by graph-theoretical analyses to assess clustering coefficient, characteristic path length, global efficiency, and local efficiency. Furthermore, state entropy was employed to evaluate dynamic network transitions between integrated and segregated states. Results Active tDCS was associated with increased LOFC strength in the delta, alpha, and beta bands, and more widespread increases in HOFC across all examined frequency bands. Changes in network topology were primarily observed in HOFC, with reductions in characteristic path length and increases in global and local efficiency, particularly in the delta and theta bands. Dynamic network analysis indicated that tDCS modulated state entropy at specific time scales in both LOFC and HOFC networks. These findings suggest shifts in functional coordination and temporal variability among the recorded regions. Behavioral measures exhibited a trend toward improvement in the active group; however, these changes were not the focus of the present analysis, and their relationship to neural modulation remains to be clarified in future work. Conclusions tDCS modulated functional interaction patterns and dynamic state characteristics among the recorded brain regions in children with ASD. These results provide preliminary neurophysiological evidence regarding the influence of tDCS on both static and dynamic network organization and highlight potential network-based markers to guide future individualized neuromodulation research. Further studies with larger samples and longitudinal follow-ups are needed to clarify the functional and clinical significance of these network-level changes.

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.

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.

Distributed computation in dynamic networks via random walks

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.

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

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 Ad Hoc network routing algorithm is one of the core technologies of wireless Ad Hoc networks. Ad Hoc network is dynamic topology on characteristic, mainly reflected on the network node number and node movement, which lead to network topology changes, in order to study the Ad Hoc network size and topology changes influence on packet delivery, time delay and routing control, through experiment it is concluded that, the dynamic source routing protocol is an ideal choice.

In this paper, we propose a system leveraging the peculiarities of the wireless medium, such as the broadcast nature of wireless communication and the unpredictability of indoor signal propagation to achieve effective protection against attacks based on the injection of fake data in wireless sensor networks (WSNs). Using a real-world WSN deployment and a realistic implementation of an attacker, we analyze this protection scheme and demonstrate that neither position change, transmission power manipulation, nor complete knowledge of wireless parameters can help an attacker to successfully attack the network. As a result, this work demonstrates how the chaotic nature of radio communication, which is often considered a disadvantage in regard to security objectives, can be exploited to enhance protection and support implementation of lightweight security mechanisms.

Towards energy balancing optimization in wireless sensor networks: A novel quantum inspired genetic algorithm based sinks deployment approach

Wireless sensor networks (WSNs) typically use license-free industrial, scientific, and medical frequency bands, and by increasing the demand for different usages of WSNs, it is anticipated that this band gets saturated. One solution to deal with this spectrum scarcity in WSNs is utilizing the cognitive radio concept, called cognitive radio sensor networks (CRSNs). One of the most critical challenges in CRSNs is spectrum sensing, which can be implemented by centralized, cluster based or distributed methods. In CRSNs, distributed methods have better spectrum sensing performance due to their fast adaptation to the network changes. Also they have lower power consumption level, which is critical in CRSNs. In this paper, we propose a novel distributed diffusion-based spectrum sensing method for CRSNs that improves the robustness of the spectrum sensing method against link failure and network topology changes. It also increases the algorithm’s convergence rate while its accuracy is acceptable. We prove the proposed method convergence and calculate its mean square error, considering link failure assumption. Simulation results confirm that the proposed method improves the convergence rate compared with conventional distributed methods, and using the proposed method increases the convergence rate and needs less communications to make a decision about the absence or presence of the primary user.