Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have attracted a great deal of attention. One significant problem linked with DSNs is target coverage, which primarily operate based on simultaneously observing a group of targets occurring in a set area, hence maximizing the network lifetime. As there are limitations to the directional sensors’ sensing angle and energy resource, designing new techniques for effectively managing the energy consumption of the sensors is crucial. In this study, two problems were addressed. First, a new learning automata-based algorithm is proposed to solve the target coverage problem, in cases where sensors have multiple power levels (i.e., sensors have multiple sensing ranges), by selecting a subset of sensor directions that is able to monitor all the targets. In real applications, targets may have different coverage quality requirements, which leads to the second; the priority-based target coverage problem, which has not yet been investigated in the field of study. In this problem, two newly developed algorithms based on learning automata and greedy are proposed to select a subset of sensor directions in a way that different coverage quality requirements of all the targets could be satisfied. All of the proposed algorithms were assessed for their performances via a number of experiments. In addition, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. All algorithms are successful in solving the problems; however, the learning automata-based algorithms are proven to be superior by up to 18% comparing with the greedy-based algorithms, when considering extending the network lifetime

Cobertura Fornecendo em Redes de Sensores Direcionais através de Algoritmos de Aprendizagem (Autômatos de Aprendizagem)

Today, wireless sensor networks due to application development are widely used. There are significant issues in these networks; they can be more effective if they would be fixed. One of these problems is the low coverage of these networks due to their low power. If coverage increases only by increasing the power of sending and receiving power, it can increase network consumption as a catastrophic disaster, while the lack of energy is one of the most important constraints on these networks. To do this, the antenna coverage is oriented in some sensor networks to cover the most important places. This method tries to improves the efficiency and coverage of directional sensor networks by providing a mechanism based on the learning algorithm of the machine called learning automata. Results show this method outperform the before methods at least 20%.Hoy en día, las redes de sensores inalámbricos debido al desarrollo de aplicaciones son ampliamente utilizadas. Hay problemas importantes en estas redes; pueden ser más efectivos si se solucionan. Uno de estos problemas es la baja cobertura de estas redes debido a su baja potencia. Si la cobertura aumenta solo elevando la potencia de envío y recepción de energía, puede aumentar el consumo de red como un desastre catastrófico, mientras que la falta de energía es una de las limitaciones más importantes de estas redes. Para hacer esto, la cobertura de la antena está orientada en algunas redes de sensores para cubrir los lugares más importantes. Este método intenta mejorar la eficiencia y la cobertura de las redes de sensores direccionales al proporcionar un mecanismo basado en el algoritmo de aprendizaje de la máquina denominado autómatas de aprendizaje. Los resultados muestran que este método supera los métodos anteriores al menos un 20%.Hoy en día, as redes de sensores inalámbricos debitaram o desenvolvimento de aplicações sonoras extensamente utilizadas. Obras do feno importantes nas redes; pueden ser más effectivos e se solucionan. Uns de esos protes es la baja cobertura de es redes debido a su baja potencia. Se a porta leva sozinho a aumentar a potência de envio e a recepção de energia, aumentar o consumo de energia como um desastre catastrófico, a falta de energia de energia é uma das limitações mais importantes destas redes. Para hacer esto, a cobertura da antena está orientada nas algunas redes de sensores para cubrir os lugares mais importantes. This method intenta mejor a eficiencia and the coverage of the networks of sensors directionals are provided in engine based on the algorithm of aprendizado of the machine denominado autómatas de aprendizaje. Los resultados muestran que este método supera os métodos anteriores a menos de 20%

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Using the Learning Coverage Model to Increase the Wireless Sensor Networks Lifetime

One of the most important issues of the wireless sensor networks is their energy consumption and life increase. In the sensor networks this is done by different guidelines, including creating planned coverage that causes all nodes would not be active all the time and act alternatively, while whole the network would always be under the sensor nodes coverage. Though there are different methods in this case, yet more effective ones are needed. In this research, a learning – based approach has been designed which decides about nodes being sleeping or awaking per round of nodes operation. Simulation shows that this method can work effectively and network life will increase at least 20 percent of past methods

Target coverage through distributed clustering in directional sensor networks

Maximum target coverage with minimum number of sensor nodes, known as an MCMS problem, is an important problem in directional sensor networks (DSNs). For guaranteed coverage and event reporting, the underlying mechanism must ensure that all targets are covered by the sensors and the resulting network is connected. Existing solutions allow individual sensor nodes to determine the sensing direction for maximum target coverage which produces sensing coverage redundancy and much overhead. Gathering nodes into clusters might provide a better solution to this problem. In this paper, we have designed distributed clustering and target coverage algorithms to address the problem in an energy-efficient way. To the best of our knowledge, this is the first work that exploits cluster heads to determine the active sensing nodes and their directions for solving target coverage problems in DSNs. Our extensive simulation study shows that our system outperforms a number of state-of-the-art approaches

A Deterministic Algorithm for the Deployment of Wireless Sensor Networks

Wireless sensor networks are made up by communicating sensor nodes that gather and elaborate information from real world in a distributed and coordinated way in order to deliver an intelligent support to human activities. They are used in many fields such as national security, surveillance, health care, biological detection, and environmental monitoring. However, sensor nodes are characterized by limited wireless communication and computing capabilities as well as reduced on-board battery power. Therefore, they have to be carefully deployed in order to cover the areas to be monitored without impairing network lifetime. This paper presents a new deterministic algorithm to solve the coverage problem of well-known areas by means of wireless sensor networks. The proposed algorithm depends on a small set of parameters and can control sensor deployment within areas even in the presence of obstacles. Moreover, the algorithm makes it possible to control the redundancy degree that can be obtained in covering a region of interest so as to achieve a network deployment characterized by a minimum number of wireless sensor nodes

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Cybernetic automata: An approach for the realization of economical cognition for multi-robot systems

The multi-agent robotics paradigm has attracted much attention due to the variety of pertinent applications that are well-served by the use of a multiplicity of agents (including space robotics, search and rescue, and mobile sensor networks). The use of this paradigm for most applications, however, demands economical, lightweight agent designs for reasons of longer operational life, lower economic cost, faster and easily-verified designs, etc. An important contributing factor to an agent’s cost is its control architecture. Due to the emergence of novel implementation technologies carrying the promise of economical implementation, we consider the development of a technology-independent specification for computational machinery. To that end, the use of cybernetics toolsets (control and dynamical systems theory) is appropriate, enabling a principled specifi- cation of robotic control architectures in mathematical terms that could be mapped directly to diverse implementation substrates. This dissertation, hence, addresses the problem of developing a technologyindependent specification for lightweight control architectures to enable robotic agents to serve in a multi-agent scheme. We present the principled design of static and dynamical regulators that elicit useful behaviors, and integrate these within an overall architecture for both single and multi-agent control. Since the use of control theory can be limited in unstructured environments, a major focus of the work is on the engineering of emergent behavior. The proposed scheme is highly decentralized, requiring only local sensing and no inter-agent communication. Beyond several simulation-based studies, we provide experimental results for a two-agent system, based on a custom implementation employing field-programmable gate arrays