DiNAMAC: A disruption tolerant, reinforcement learning-based Mac protocol for implantable body sensor networks
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Ongoing advancements in Body Sensor Networks (BSN) have enabled continuous health monitoring of chronically ill patients, with the use of implantable and body worn sensor nodes. Inevitable day-to-day activities such as walking, running, and sleeping cause severe disruptions in the wireless link among these sensor nodes, resulting in temporary shadowing of wireless signals. These disruptions in the wireless link not only reduce the reliability of the network but also increase the power consumption. Both signal disruption and power consumption must be reduced in order to achieve long term monitoring of physiological signals in chronic patients. In this paper we propose a MAC protocol called DiNAMAC (Disruption tolerant reiNforcement leArning-based MAC), which is not only aware of the wireless link quality but also is aware of network resource availability and application requirements. DiNAMAC uses reinforcement learning to adapt the scheduling based on channel conditions and to prioritize data transmission and availability according to the application requirements. In addition, we design DiNAMAC based on a model-free learning technique to make it more practical in real-world applications. Our simulation results show that DiNAMAC performs better than conventional MAC protocols in terms of latency and throughput even with when the wireless link quality is challenged by large temporal variations.Wireless sensor networks (WSNs) are networks of distributed autonomous devices and use large number of sensor nodes that contains a processor, memory, wireless communication capabilities, sensing capabilities and a power source (battery) on-board to form a network. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Localization of sensor nodes in wireless sensor network plays an important role in many applications. It is important to monitor the location of the data source and event occurrences to track the target and phenomena. Localization in wireless sensor networks means estimating the position or spatial coordinates of wireless sensor nodes. This paper mainly focuses on the localization of sensor nodes using centralized and distributed localization techniques.
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Balancing energy consumption of sensor nodes and increasing network lifetime are the two important objectives for wireless sensor networks. Sensor nodes distribution in a topology affect the energy consumption of individual nodes as well as the wireless sensor network because the nodes at different locations will have different energy loss due to different distances from the base station. This reported work focuses on energy efficient clustering for practical non-uniformly deployed sensor nodes in a wireless sensor network.
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Building a wireless sensor network first of all requires the constituting nodes to be developed and available. These nodes have to met the requirements that come from the specific requirements of a given application. They might be small, cheap (energy efficient), they have to be equipped with the right sensors, the necessary computation and memory resources, and they need adequate communication facilities, joining them into wireless sensor network. Event mobility is one of the mobility types in the wireless sensor network. In this paper a study of the sensor properties which have direct effect on the power of the sensor node itself and the total power of the network. Sensor Radius, Sensor Period and Sensor Energy Cost are important properties related to the sensor, these properties have different effects on the power consumption in the network. These properties also have the effect on the number of packets received by the observer in the network.
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Wireless sensor networks (WSN) consist of sensor nodes. These sensor nodes are usually used to monitor different physical or environmental conditions. Configuration of wireless sensor networks requires certain functions to be automatically assigned to sensor nodes. The most important research problem that needs to be addressed in order to support autonomous configuration of WSN is the assignment of roles to sensor nodes based on the properties of the network. These roles represent the task that a specific node is supposed to fulfill. The assignment of roles and task to the suitable sensor nodes would ensure the increase in performance and longevity of the network. This paper discusses the different role assignment algorithms that are used in wireless sensor networks.
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Localization is the subject matter that has allured the attention of many researchers in the field of wireless sensor network. It is the process of assigning or computing the location of the sensor nodes in a sensor network. Since the sensor nodes are randomly deployed, it is very important that they localize themselves, as the manual deployment of sensor node is not feasible. Thus in this paper, we find out the solution for localizing the sensor nodes. We have developed a novel model that first finds the distance of the sensor nodes then it finds the location of the unknown sensor nodes in power efficient manner.
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Power consumption is a significant problem in Wireless Sensor Networks. Most applications require wireless sensor networks to operate reliably over an extended period. It is essential to know the energy consumption of each sensor node as the life of the sensor network has a strong functional dependence on the life of the sensor node. Mechanisms for the more prolonged operation of wireless sensor networks by energy management are known. Or hardware solutions for sensor unit using renewable energy sources. The generation of an energy model for sensor nodes that can accurately reveal the energy consumption of nodes is a significant part of the development of protocols and the design of wireless sensor networks and assessment of performance in WSNs. Research shows that there is currently no standard research process to meet all the requirements of these networks.This article presents a model for determining the energy consumption of sensor nodes in the network using mesh topology, as energy is determined depending on the number of hops of sensor nodes, the size of the sensor communication range of sensors, and their electrical parameters. Determining the residual energy of the sensor nodes involved in the construction of the network topology allows us to predict the probability of failure of routes that build the communication in the network.
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In wireless sensor networks, the number of sensor nodes has direct relation to the cost of total wireless sensor networks, and at the same time, the problem is closely connected to wireless sensor networks' performance, such as robust, fault-tolerance, and furthermore, it is considered at first as wireless sensor networks are designed. Therefore, the research on the number of sensor nodes has significant meanings of theory and practice to design of wireless sensor networks. By computation and analysis, the sensor deployments in the form of equilateral triangle, as a rule, are better than those in the form of square, and the efficient coverage area ratios decrease with increasing number of sensor nodes.
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In wireless sensor networks, the number of sensor nodes has direct relation to the cost of total wireless sensor networks, and at the same time, the problem is closely connected to wireless sensor networks' performance, such as robust, fault-tolerance, and furthermore, it is considered at first as wireless sensor networks are designed. Therefore, the research on the number of sensor nodes has significant meanings of theory and practice to design of wireless sensor networks. By computation and analysis, the sensor deployments in the form of equilateral triangle, as a rule, are better than those in the form of square, and the efficient coverage area ratios decrease with increasing number of sensor nodes. Sometime information is incompletely monitored or undetected. This is coverage and connectivity problems. The coverage problem is also one of basic problem in wireless sensor networks. The paper analyzes several sensor deployments and computes their efficient coverage areas and their efficient coverage area ratios. In addition, the relation between the number of sensors and efficient coverage area ratio is discussed.
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