Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms

Effects of carbon nanotube arrays on nucleate pool boiling

The integration of wireless communication and control systems revealed wireless networked control systems (WNCSs). One fundamental problem in WNCSs is to have a wide coverage area. For the first time in the literature, we address this problem and we obtain the maximum coverage area by solving an optimization problem. In this technical note, we consider a WNCS where the output sensor measurements are transmitted over separate heterogeneous multi-hop wireless ad-hoc subnetworks. The observation process is divided into N parts and the system state is estimated using the Kalman filter. We present the critical arrival probability for a sensor measurement packet such that if the packet arrival probability is larger than the critical value, it is guaranteed that the estimator of the WNCS converges. We derive the maximum total coverage area of the heterogeneous wireless subnetworks having maximum cost-efficiency under the constraint of the convergence of the WNCS estimator.

Ongoing advancements in electromechanical, power electronics and wireless communications have made possible the use of wireless sensor networks in remote applications. A wide range of applications such as monitoring of environmental conditions, forest fire protection, disaster management, and target monitoring in military applications utilize these sensor networks to solve problems. This has made wireless sensor networks the hottest topic for research today. These remote and unattended applications require a huge number of sensor nodes working efficiently together for a common goal. Since sensor nodes have limited resources, like processing, power and sensing capacity, these resources need to be used in efficient manner in order to improve a network’s lifetime. One of the most important methods to achieve this is using clusters. Nodes may not have the same energy levels, which makes it even more important for these clusters to work efficiently together to save power. Wireless sensor networks are of homogeneous and heterogeneous type. This paper presents a survey of energy-efficient clustering mechanisms in heterogeneous wireless sensor networks. Nodes in homogeneous wireless sensor networks have similar resources, however heterogeneous networks have nodes with different energy levels that can be effectively used to enhance a network’s lifetime.

Wireless Sensor Network (WSN) had become almost an indispensible especially the demand for data acquisition from national security to disaster mitigation management, weather data to environmental changes and from many more agencies. The effectiveness and efficacy of WSN dependent on the strength and weakness of the deployment of the sensor nodes which collect and transmit the data. The success of data acquisition in any network depended upon the adequacy of coverage by the sensor nodes; which in turn depended on the method of deployment and redeployment. Since deterministic deployment of nodes could not always be done, random deployment was adopted as a compulsion rather than an option. The random deployment of sensors by nature provided poor network coverage and leading to unsatisfactory data acquisition. Therefore, a better method was sought-after to redeploy the sensors that were deployed earlier at random. Hence, the compelling need had resulted in the development of numerous algorithms for suitably moving the sensors for maximum coverage. Such algorithms were of standalone ones or hybrid/combination in nature. One such combination algorithm termed as Voronoi-Genetic Algorithm (V-GA) a combination/tandom application of Voronoi Vertex Averaging Algorithm (VVAA) and Genetic Algorithm (GA) was analyzed in this study. The displacement and coverage performance were studied, analyzed and compared with that of random deployment and redeployment by the earlier proposed algorithms namely VVAA and GA by the same researcher.

Code dissemination is currently a major research issue in wireless sensor networks (WSNs). Many studies focus on code dissemination in homogeneous WSNs, mainly using a broadcast approach to solve this problem; few studies on code dissemination in heterogeneous WSNs. Furthermore, broadcasting cannot readily be used to solve the heterogenous WSN code dissemination problem directly, which is where we have focused our attention. We transformed this problem into a minimum non-leaf nodes (MNN) Steiner tree problem. We designed a scalable multicast protocol, named Heterogeneous Sensor Networks Scalable Reprogramming Protocol (HSR) to solve the MNN problem. HSR can build different multicast trees according to different nodes or code modules to disseminate different codes to them. HSR is able to approximate the MNN tree problem to a ratio of ln|<italic>R</italic>|(<italic>R</italic> is the set of all destinations) best known lowest bound. Therefore, the communication cost is significantly decreased and the total energy required by WSNs is reduced. We further designed two scalable schemes, special routing log and hops-restricted local broadcast, which compress the multicast tree information and deliver the multicast messages without loss. We also designed a 3-stage pipeline to speed up the transmission of packets, which alleviated interference and hidden terminal issues. We evaluated our design through comprehensive simulations and prototype implementations on Mica2 motes. Experimental results demonstrate that HSR outperforms previous protocols including the most recent studies on Sprinkler and uCast.

A wireless sensor network is a correspondence framework which detects and accumulates data from a specific zone and sends it to the base station or sink node. WSN can be very exceptionally valuable for target detection and recognizable proof by military and ecological applications like agrarian farm monitoring and woodland fire supervising. A genetic algorithm is a meta-heuristic approach propelled by the process of natural selection that belongs to the class of evolutionary algorithms. It is utilized to create high-quality solutions to optimization and search problem by relying on bio-inspired operators such as mutation, crossover, and selection. The goal is deploying a minimum number of sensor element nodes without overlapping each other based on antenna height and separation between sensor nodes. The experimental result shows that better coverage in heterogeneous wireless sensor network using NS2 software simulation.

Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes’ resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach.

Since the emergence of WSNs (Wireless Sensor Networks), various middleware architectures have been proposed to achieve a suitable abstraction from the distribution and management tasks of sensor devices. This allows users to focus on application development. In the near future, WSNs will be more pervasive, common, and distributed. Programming on WSNs requires a novel programming paradigm and middleware, especially in distributed and heterogeneous WSNs. We propose Virtual Sensor Agent-Oriented Middleware (VSAM), a middleware system for distributed and heterogeneous WSNs, which provides data dissemination protocol-independent Application Programming Interface (API) and an integrated platform for sensor applications. VSAM makes it possible to integrate WSNs with traditional networks.

We develop a new algorithm for spatial field reconstruction in heterogeneous (mixed analog & digital sensors) wireless sensor networks (WSNs). We consider spatial physical phenomena which are observed by a heterogeneous WSN, meaning that it is partially consists of sparsely deployed high-quality sensors and partially of low-quality sensors. The high-quality sensors transmit their (continuous) noisy observations to the Fusion Centre (FC), while the low-quality sensors first perform a simple thresholding operation and then transmit their binary values over imperfect wireless channels to the FC. We develop a novel algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue.

In consequence of advances in wireless communication, digital systems and micro-electronic-mechanical system technologies wireless sensor networks (WSNs) have been developed and applied in lots of different real-world applications such as military, industrial, environmental, health, etc. Due to their limited resources and constraints, WSNs face with several problems such as energy consumption, node deployment, data aggregation and transmission. Node deployment affects other problem domains in a direct or indirect way. Therefore, in this study, we deal with this problem and try to increase the coverage area of WSN system with an organized deployment approach. This problem is an NP-Hard problem, and it is hard to solve with standard mathematical formulations. Also this problem is known as maximum coverage sensor deployment problem (MCSDP) in literature. In this paper, worked on the topology that consists from sensor nodes which have the different sensing ranges - heterogeneous. A novel GA is developed for deploying sensor nodes on 2 dimensional area. Studied problem, proposed genetic algorithm and novel local optimization algorithm are described deeply. The result of applying GA to initial deployment topology, increase of coverage are due to iteration count and results of experiments with local optimization algorithm are represented. Organized and random deployment techniques are described briefly and fitness function of developed GA is applied to test data. According to results of experiments; proposed GA is performed effective and got desired coverage.

Designing and development of energy effective path planning algorithm is very key research domain in order to tackle the issues of limited life-time for Wireless Sensor Networks (WSNs). So in WSNs, energy efficiency is major concern of researchers. Overall advancement in routing protocols prove that clustering is much better approach as compared to flat and location-based energy efficient routing protocols. Due to better performance, multiple energy efficient clustering routing protocols have been proposed. But existing clustering algorithms are centralized or distributed, which are not intelligent enough and do not produce hybrid cluster-head selection. In this paper, we propose a cluster structured path planning algorithm named, Hybrid Advance Distributed Centralized Clustering (HADCC) path planning energy efficient algorithm. HADCC proposed model is fascinated with hybrid cluster head selection algorithm. This hybrid algorithm makes decision of cluster head selection of nodes. In order to execute proposed model we have also proposed an advance network topology, in which the whole network region is divided into two physical levels. First physical level consists of a circular region, containing homogeneous normal nodes and all important Base Station. While, second physical level is outer region of the circle that contains advanced heterogeneous nodes. Simulation results indicate that HADCC prolongs the network lifetime as compared to existing advanced clustering routing protocols for both homogeneous and heterogeneous WSNs. HADCC outperforms in case of stability and network life time as compared to the existing clustering algorithms.

The technology of Heterogeneous Wireless Sensor Networks (HWSNs) is critical to the efficient operation and deployment of a variety of real-time Internet of Things (IoT) and Mobile Ad hoc Networks (MANETs). It is crucial for reducing overall energy dissipation and ensuring consistent energy distribution throughout the network. Bio-inspired hybrid optimization algorithms are emerging as a possible option for overcoming basic difficulties in Wireless Sensor Networks (WSNs), with a focus on sensor lifespan restrictions. A significant topic that must be considered prior to network configuration is attaining energy efficiency and optimal communication. Several papers have been published on the use of bio-inspired algorithms in WSNs. Few articles, however, addressed the hybrid strategy for routing and clustering in WSNs with communication. This research focuses on hybrid bio-inspired optimization algorithms and elaborates on their taxonomy and problem domains in WSNs. Furthermore, we explored and investigated the hybridization of the Whale Optimization Algorithm (WOA) with other meta-heuristic algorithms such as the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and others. This review can assist researchers in exploring the uses of such algorithms within and outside of this study area.

Wireless sensor networks consist of small nodes with sensing, computation and wireless communication capabilities. Various architectures and node deployment strategies have been developed for wireless sensor network, depending upon the requirement of application. Sensor networks are used in different applications e.g. environmental monitoring, habitat monitoring, home automation, military application etc. In this paper we present survey of state-of-the-art of architecture and node deployment in wireless sensor network. We present the characteristics of the environment in which the sensor networks may deploy. Node deployment in wireless sensor network is application dependent and can be either deterministic or randomized. But in both the cases coverage of interested area is the main issue. We also explain the routing protocols for wireless sensor network.

Coverage is one of the most important factors in evaluating the service quality of a wireless sensor network (WSN), which depends on the deployment of sensor nodes. Unreasonable deployment of sensor nodes will seriously affect the performance and reduce quality of service for WSN. Generally, sensor nodes are randomly distributed in the target area. This will result in overlapping coverage and weak coverage of sensor nodes to areas. In addition, the target area is complex and diverse where the size and shape of the target area are affected by the different application scenarios. This increases the difficulty for achieving maximum coverage. This paper proposes an optimization method of sensor nodes deployment based on a improved differential evolution algorithm for coverage area maximization in WSN. The improved differential evolution method uses two better individuals joint forces to pull the current individual's evolutionary direction where these two better individuals are selected through a tournament selection strategy and their influence scale for current individual evolution is controlled by two different scaling factors, which improves the search ability. In simulation, the results show that the improved method can better optimize the deployment of sensor nodes and achieve higher coverage rate.

A Graphical-based educational simulation tool for Wireless Sensor Networks