Optimal deployment of wireless mesh sensor networks based on Delaunay triangulations

Wireless sensor networks (WSNs) are based on the random deployment of a large number of tiny sensor nodes and actuators into or very close to the phenomenon to be observed. These networks face with several essential challenges; for example, power consumption and secure routing are critical problems in the wireless sensor networks. To reduce power consumption, this paper proposes an efficient architecture with optimal power management for WSNs. This architecture merges wireless sensor network into wireless mesh networks. The other aspect considered in this paper is the secure routing in the new scheme of wireless mesh sensor network. This paper presents a secure routing algorithm for the proposed wireless mesh sensor network.

The combination of Wireless Sensor Networks (WSNs) and edge computing not only enhances their capabilities, but also motivates a series of new applications. As a typical application, 3D Underwater Wireless Sensor Networks (UWSNs) have become a hot research issue. However, the coverage of underwater sensor networks problem must be solved, for it has a great significance for the network’s capacity for information acquisition and environment perception, as well as its survivability. In this paper, we firstly study the minimal number of sensor nodes needed to build a diverse k-coverage sensor network. We then propose a k-Equivalent Radius enhanced Virtual Force Algorithm (called k-ERVFA) to achieve an uneven regional coverage optimization for different k-coverage requirements. Theoretical analysis and simulation experiments are carried out to demonstrate the effectiveness of our proposed algorithm. The detailed performance comparisons show that k-ERVFA acquires a better coverage rate in high k-coverage sub-regions, thus achieving a desirable diverse k-coverage deployment. Finally, we perform sensitivity analysis of the simulation parameters and extend k-ERVFA to special cases such as sensor-sparse regions and time-variant situations.

In Wireless sensor networks (WSNs), group mobility is important in many practical application scenarios, and it is inconvenient for traditional static WSNs to collect information. In this paper, we propose a WSN model based on IPv6 over Low power Wireless Personal Area Networks (6LoWPANs) in mobile sensing application scenarios. Wireless mesh sensor network (WMSN) infrastructure and Network Mobility (NEMO) protocol are deployed to ensure the continuity of the communications. The routing optimization mechanism in the nested network is then discussed. By taking mobile network partition and IP address configuration in the nested network, the corresponding signaling flow is discussed. Simulation results indicate that our mechanism is able to minimize the transmission costing, handoff delay, throughput and energy consumption of sensor nodes. Energy of each mobile router (MR) saves around 0.2 J per 3 seconds.

Mesh communications is emerging as a popular networking solution. Mesh networks have a decentralized and multihop design. These characteristics arouse interest for the research of the following features: cooperation, task distribution, scalability and communication with limited infrastructure support. This chapter studies relevant solutions in Wireless Sensor Networks (WSNs) with a mesh design that is used with mobile devices. The use of mobile devices on WSNs has recently grown due to: hardware evolution, large number of embedded sensors and daily high utilization of handheld devices. Consequently, novel requisites in the design and implementation of WSNs urge to be satisfied: autonomy of sensors battery and, efficient data exchange amongst sensors and the Internet. A real mesh testbed with two Layer 2 mesh solutions (Open802.11s and B.A.T.M.A.N) was implemented with different topologies. Some relevant results for a mesh network are discussed in terms of its scalability, performance and volatility.

This paper considers wireless sensor (hyper) networks by single-valued neutrosophic (hyper)graphs. It tries to extend the notion of single-valued neutrosophic graphs to single-valued neutrosophic hypergraphs and it is derived single-valued neutrosophic graphs from single-valued neutrosophic hypergraphs via positive equivalence relation. We use single-valued neutrosophic hypergraphs and positive equivalence relation to create the sensor clusters and access to cluster heads. Finally, the concept of (extended) derivable single-valued neutrosophic graph is considered as the energy clustering of wireless sensor networks and is applied this concept as a tool in wireless sensor (hyper) networks.

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

With the popularization of computers and various mobile intelligent terminals, intelligent teaching systems based on learners are becoming more and more popular among learners. The above phenomenon has greatly affected and changed the current teaching quality diagnosis methods and models. However, the author found through investigation that the current intelligent teaching quality diagnosis still has different degrees of deficiencies in the design and implementation. In response to the above problems, this paper proposes a teaching quality intelligent diagnosis model based on the combination of wireless sensor networks and fuzzy comprehensive evaluation algorithms. First of all, this article is based on the wireless sensor network to link various levels of intelligent teaching systems, and constructs the information transmission structure of the teaching intelligent diagnosis system. Secondly, this article uses fuzzy comprehensive evaluation and convolutional neural network algorithms to evaluate and excavate intelligent teaching information. Finally, the model successfully passed the simulation test and simulation application, which can provide intelligent diagnosis of teaching quality for modern intelligent teaching system.

Clustering has been proposed by researches to group a number of nodes to form a cluster that managed by cluster head. The main goal of clustering protocols is to minimize the energy consumption and enhance the network lifetime of wireless sensor networks. We propose DEBC protocol, which is distributed energy balanced clustering for wireless sensor networks. DEBC forms clusters, selects cluster heads, selects cluster senders and determines appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. DEBC compared to well known protocol with two metrics network lifetime and energy dissipation. The simulation results show that DEBC minimizes energy consumption of sensor nodes and enhance the network lifetime.

By taking the data-receiving energy consumption into account, we propose an extended analytical model to derive the optimal parameters ? the number of clusters for wireless sensor networks. Our analyses show that the previous existing model underestimates the optimal number of clusters within a sensor network. The simulation results verify our analyses and demonstrate the modified model is more accurate in deriving the optimal number of clusters to maximize the network lifetime of the wireless sensor networks. Using our extended model, we also develop the energy-threshold-driven based clustering reconfiguration schemes with the optimal number of clusters to further improve the wireless sensor-network lifetime. The simulation experiments indicate that our clustering reconfiguration schemes outperform the existing ones in terms of sensor-network lifetime while imposing low overhead due to clustering reconfigurations.

Wireless sensor networks is a key technology for new ways of interaction between computers and the physical environment. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. Since sensor nodes are densely deployed, redundant data may occur. While cluster-based data gathering is efficient at energy and bandwidth, it's difficult to cluster network efficiently. In this work, a new distributed clustering algorithm for ubiquitous sensor network is presented. Clustering is based on the distance between nodes and the number in a cluster for wireless sensor networks. Simulation results show that the proposed algorithm balances the energy dissipation over the whole network thus increase the amount of data delivery to the sink

Energy efficiency is a critical factor for operation of a wireless sensor network. In general, wireless sensor networks perform clustering and routing using localized neighbor information and simple rules only. In this paper, we propose an energy efficient clustering and routing method for wireless sensor networks by exploiting energy state information of neighbor sensor nodes for selection of cluster heads. We compared the performance of the proposed method with that of a well known clustering and routing protocol. Our computational experiments demonstrate that the lifetime and distribution of energy consumptions of the proposed method are better than those of the compared method.

The combination of wireless sensor networks and real-time localisation systems can enable many new exciting applications. Ultra-wideband is the natural choice for such location aware wireless sensor networks. DecaWave’s ScenSor IC implements the IEEE 802.15.4a standard to meet the needs of WSN and RTLS manufacturers. This chapter discusses the design of the ScenSor chip.

The Internet of Things (IoT) is a new evolution in technological advancement taking place in the world today. This paradigm allows physical world objects in our surroundings to be connected to the Internet. This idea comes to life by utilizing two architecture; the Sensing Entity in the environment that collects data and connects itself to the cloud and the Cloud Service that hosts the data from the environment. The combination of wireless sensor networks and cloud computing is becoming a popular strategy for the IoT era. The cold chain requires controlled environment for sensitive products in order for them to be fit for use. The monitoring process is the only assurance which tells if a certain process has been carried out successfully. Taking advantage of IoT and its benefits to monitor cold chain logistics will result in better management and product handling. This paper looks at a system comprising of Arduino wireless sensor network and Xively sensor cloud which can be an ideal system to monitor temperature and humidity of cold chain logistics.

We present the design, development, and deployment of an inexpensive, power-efficient, clustered, and scalable wireless sensor network (WSN) testbed. The testbed operates in a harsh environment in which neither GPS nor Internet connectivity are available. We use this testbed to collect real-time data during football games and other major events at Bobby Dodd stadium at Georgia Tech. The sensing devices in the testbed are synchronized without GPS or beacons, yet achieve sufficient accuracy to support modal analysis and detect if the stands are experiencing torsion. We have also developed a cognitive radio backhaul link to establish communication between the WSN in the stadium and a server in our lab. We present in detail the architecture, hardware components, and embedded software of the structural health monitoring platform. We demonstrate that crowd behavior, such as rhythmic stomping, can be detected during a game.