Wireless Sensor Network deployment for monitoring wildlife passages

Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks

The concept of the internet of things (IoT) motivates us to connect bulk isolated heterogeneous devices to automate report generation without human interaction. Energy-efficient routing algorithms help to prolong the network lifetime of these energy-restricted smart devices that are connected by means of wireless sensor networks (WSNs). Current vendor-level advancements enable algorithm-level flexibility to design protocols to concurrently collect multiple application data while enforcing the reduction of energy expenditure to gain commercial success in the industrial stage. In this paper, we propose a hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks. In our proposed model, homogeneous and heterogeneous nodes are deployed within specific regions. To reduce unnecessary data transmission, threshold-based conditions are presented to prevent unnecessary transmission when minor or no change is observed in the simulated and real-world applications. We further extend our proposed multi-hop model to achieve more network stability in dense and larger network areas. Our proposed model shows enhancement in terms of load balancing and end-to-end delay as compared to the other threshold-based energy-efficient routing protocols, such as the threshold-sensitive stable election protocol (TSEP), threshold distributed energy-efficient clustering (TDEEC), low-energy adaptive clustering hierarchy (LEACH), and energy-efficient sensor network (TEEN).

Stochastic control of ecological networks

The paper models the maintenance of ecological networks in forest environments, built from bioreserves, patches and corridors, when these grids are subject to random processes such as extreme natural events. It also outlines a management plan to support the optimized results. After presenting the random graph-theoretic framework, we apply the stochastic optimal control to the graph dynamics. Our results show that the preservation of the network architecture cannot be achieved, under stochastic control, over the entire duration. It can only be accomplished, at the cost of sacrificing the links between the patches, by increasing the usage of the control devices. This would have a negative effect on the species migration by causing congestion among the channels left at their disposal. The optimal scenario, in which the shadow price is at its lowest and all connections are well-preserved, occurs at half of the course, be it the only optimal stopping moment found on the stochastic optimal trajectories. In such a scenario, the optimal forestry management policy has to integrate agility, integrated response, and quicker response time.

Cooperative Localization Using Distance Measurements for Mobile Nodes

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

A recent trend in wireless sensor network (WSN) research is the deployment of a mobile element (ME) for transporting data from sensor nodes to the base station (BS). This helps to achieve significant energy savings as it minimizes the communications required among nodes. However, a major problem is the large data gathering latency. To address this issue, the ME (i.e., vehicle) should visit certain rendezvous points (i.e., nodes) to collect data before it returns to the BS to minimize the data gathering latency. In view of this, we propose a rendezvous-based approach where some certain nodes serve as rendezvous points (RPs). The RPs gather data using data compression techniques from nearby sources (i.e., affiliated nodes) and transfer them to a mobile element when the ME traverses their paths. This minimizes the number of nodes to be visited, thereby reducing data gathering latency. Furthermore, we propose a minimal constrained rendezvous point (MCRP) algorithm, which ensures the aggregated data are relayed to the RPs based on three constraints: (i) bounded relay hop, (ii) the number of affiliation nodes, and (iii) location of the RP. The algorithm is designed to consider the ME’s tour length and the shortest path tree (SPT) jointly. The effectiveness of the algorithm is validated through extensive simulations against four existing algorithms. Results show that the MCRP algorithm outperforms the compared schemes in terms of the ME’s tour length, data gathering latency, and the number of rendezvous nodes. MCRP exhibits a relatively close performance to other algorithms with respect to power algorithms.

Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage.

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring

Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper.

An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network

Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm.

Deployment-based lifetime optimization model for homogeneous Wireless Sensor Network under retransmission

Sensor-deployment-based lifetime optimization is one of the most effective methods used to prolong the lifetime of Wireless Sensor Network (WSN) by reducing the distance-sensitive energy consumption. In this paper, data retransmission, a major consumption factor that is usually neglected in the previous work, is considered. For a homogeneous WSN, monitoring a circular target area with a centered base station, a sensor deployment model based on regular hexagonal grids is analyzed. To maximize the WSN lifetime, optimization models for both uniform and non-uniform deployment schemes are proposed by constraining on coverage, connectivity and success transmission rate. Based on the data transmission analysis in a data gathering cycle, the WSN lifetime in the model can be obtained through quantifying the energy consumption at each sensor location. The results of case studies show that it is meaningful to consider data retransmission in the lifetime optimization. In particular, our investigations indicate that, with the same lifetime requirement, the number of sensors needed in a non-uniform topology is much less than that in a uniform one. Finally, compared with a random scheme, simulation results further verify the advantage of our deployment model.

Sensing solutions for collecting spatio-temporal data for wildlife monitoring applications: a review

Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.

An automatic weighting system for wild animals based in an artificial neural network: how to weigh wild animals without causing stress

This paper proposes a novel and autonomous weighing system for wild animals. It allows evaluating changes in the body weight of animals in their natural environment without causing stress. The proposed system comprises a smart scale designed to estimate individual body weights and their temporal evolution in a bird colony. The system is based on computational intelligence, and offers valuable large amount of data to evaluate the relationship between long-term changes in the behavior of individuals and global change. The real deployment of this system has been for monitoring a breeding colony of lesser kestrels (Falco naumanni) in southern Spain. The results show that it is possible to monitor individual weight changes during the breeding season and to compare the weight evolution in males and females.

The potential for harvesting energy from the movement of trees

Over the last decade, wireless devices have decreased in size and power requirements. These devices generally use batteries as a power source but can employ additional means of power, such as solar, thermal or wind energy. However, sensor networks are often deployed in conditions of minimal lighting and thermal gradient such as densely wooded environments, where even normal wind energy harvesting is limited. In these cases a possible source of energy is from the motion of the trees themselves. We investigated the amount of energy and power available from the motion of a tree in a sheltered position, during Beaufort 4 winds. We measured the work performed by the tree to lift a mass, we measured horizontal acceleration of free movement, and we determined the angular deflection of the movement of the tree trunk, to determine the energy and power available to various types of harvesting devices. We found that the amount of power available from the tree, as demonstrated by lifting a mass, compares favourably with the power required to run a wireless sensor node.

An asynchronous multi-sensor micro control unit for wireless body sensor networks (WBSNs)

In this work, an asynchronous multi-sensor micro control unit (MCU) core is proposed for wireless body sensor networks (WBSNs). It consists of asynchronous interfaces, a power management unit, a multi-sensor controller, a data encoder (DE), and an error correct coder (ECC). To improve the system performance and expansion abilities, the asynchronous interface is created for handshaking different clock domains between ADC and RF with MCU. To increase the use time of the WBSN system, a power management technique is developed for reducing power consumption. In addition, the multi-sensor controller is designed for detecting various biomedical signals. To prevent loss error from wireless transmission, use of an error correct coding technique is important in biomedical applications. The data encoder is added for lossless compression of various biomedical signals with a compression ratio of almost three. This design is successfully tested on a FPGA board. The VLSI architecture of this work contains 2.68-K gate counts and consumes power 496-μW at 133-MHz processing rate by using TSMC 0.13-μm CMOS process. Compared with the previous techniques, this work offers higher performance, more functions, and lower hardware cost than other micro controller designs.