Energy consumption and coverage are common design issues in Wireless Sensor Networks (WSNs).
For that reason, it is vital to consider network coverage and energy consumption in the design of WSN layouts.
Because selecting the optimal geographical positions of the nodes is usually a very complicated task, we propose a novel heuristic search technique to solve this problem.
Our approach is a multi-population search algorithm based on the Particle Swarm Optimization (PSO).
The goal of this algorithm is to search for sensor network layouts that maximize both the coverage and lifetime of the network.
Unlike traditional PSO, our algorithm assignes a swarm to each sensor in the network and a global network topology is used to evaluate the
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The Design of sensor layouts that account for both network coverage and power consumption is a difficult problem.
Because WSN may have large number of nodes, the task of selecting the optimal geographical positions of the nodes can be very complicated.
Therefore, we propose a cooperative particle swarm algorithm as a heuristic to address the problem of wireless sensor layout design.
In our approach we assign a swarm to each node in the network.
Each swarm will search for optimal $x$ and $y$ positions for its associated sensor.
A global network layout, consisiting of the coordinates found by the best particles, will be maintained by the algorithm.
The lifetime and coverage of this global layout will be used to measure the quality of each particle 's position.
We hypothesize that by splitting the swarms across the set of sensors, our alorithm with obtain a finer-grained credit assignment, and reduce the chance of neglecting a good solution for a specific portion of the solution vector.
We will verify this hypothesis by comparing our algorithm to several tradtional single-population search techniques.
section{Background}
subsection{Layout Optimization}
Layout optimization for wireless sensor networks consists of finding the coordinates for a set of sensors that maximizes the lifetime and coverage for the sensor network.
Placement of the sensors is bounded within a two-dimensional square region with an upper left
In order to implement the data measurement, storage, and actuation, communication paths must be set up between the sensors, actuators, processor, and storage. The communication paths use communication standards such at HTTP and Zigbee and custom protocols such as the protocol between the Gateway and Sensor Modules. The primary goal of these protocols is reliable communication. Each protocol uses acknowledgements to ensure the validity of the data. Using these protocols approximately 3.3KBytes are sent from the Sensor nodes to the Gateway per hour and approximately 1.3KBytes are sent from the Gateway and the Sensor nodes. This is a low date rate system designed to save power. Although the system network usually functions well there are noise levels with
In the location-based routing, sensor nodes are distributed randomly in an interesting area. They are positioned mostly by utilizing of Global position system. The distance among the sensor nodes is evaluated by the signal strength obtained from those nodes and coordinates are computed by interchanging information among neighbouring nodes. Location-based routing networks are;
Because the first deployment model can cause network slowdowns and affect the normal workflow, it doesn’t make a viable solution. The other deployments models, build in WIPS functionality in the access points and WIPS with dedicated sensors are more common, if not the most widely used and that why it will be my main focus for the analysis.
maximization of network lifetime [8]. This protocol is also divided into two phase: 1. Clustering and 2. Routing of aggregated data. In clustering phase, a fixed topological arrangement is done by sensor nodes. In the data aggregation phase, heuristic is proposed. The advantage is that it provides energy efficiency and network lifetime also be increased.
In this section, we present the details of proposed protocol. Our protocol implements the idea of probabilities for cluster heads selection based on initial energy and residual energy of sensor nodes as well as the average energy of the sensor network.
A group of wireless sensor nodes (devices) dynamically constructs a temporary network without the exercise of any pre-existing network infrastructure or centralized administration. The main goal of ad-hoc networking is multihop broadcasting in which packets are transferred from source node to destination node through the intermediate nodes (hops). The main function of multi hop WSN is to enable communication between two terminal devices through a bit of middle nodes, which are transferring information from one level to another level. On the foundation of network connectivity, it dynamically gets to determine that which nodes should get included in routing, each node involved in routing transmit the data to further
Phase 1—forwarding packets towards the target region: when a node receives a packet, it checks that any neighbor node is closer to the target region than itself. If there is more than one, the nearest neighbor to the target region is selected as
A WSN is a type of wireless networks that consists of collection sensor nodes which are tiny devices. Each sensor node of the network has different processing capability. It may contain multiple types of memory (program, data and flash memories), have a RF transceiver, have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion [13, 14]. Optimum need of each sensor node is to maximize its own utility function. Also the whole network requires resource assignments balance to perform in a useful and efficient way. This chapter presents a brief survey on WSNs showing its types, characterizing features, protocols and applications.
A Wireless Sensor Network is one kind of wireless network includes a large number of circulating, self-directed, minute, low powered devices named sensor nodes called motes. These networks certainly cover a huge number of spatially distributed, little, battery-operated, embedded devices that are networked to caringly collect, process, and transfer data to the operators, and it has controlled the capabilities of computing & processing. Nodes are the tiny computers, which work jointly to form the networks. The sensor node is a multi-functional, energy efficient wireless device. The applications of motes in industrial are widespread. A collection of sensor nodes collects the data from the surroundings to achieve specific
A sensor network consists of a large number of sensor nodes, which are arranged either inside the phenomenon to be monitored or very close to it. Sensor networks denote an important improvement over traditional sensor networks, which are deployed in two means as shown below [26]:
A complete topology layout for a network covers an end-to-end networking solution. The design of the network has to
This paper covers the online sensor selection in which the sensor network and relations between sensors dynamically change by time and a model which is working for selection of sensors at time may not work for the sensor selection at time , . The selection model of this paper is similar to model discussed in section 3 (i.e. Online distributed sensor selection) but it is based on a well defined special centralized algorithm (central server calculates the utility function and decides which sensors should be selected).
Keywords- Wireless sensor network, compressive sensing, distributed compressive sensing, fault tolerance, data fusion, robust averaging.
We propose some strategies like load balancing that balance the load of these sensor nodes and ensure maximum network lifetime. First, we formalize the network lifetime maximization problem using load balancing as well as we propose a solution which checks energy of every node while load balancing. Then we compare both existing solution and our solutions with most common strategies such as shortest-path and equi-proportional routing. We conclude that through the results of this work, load balancing as well as energy checking of every node with
There are a lot scenarios need different sensor nodes to collaborate and integrated on the air into networks to facilitate applications. The scaled-down censoring system presented below is an example of those wireless sensor networks (WSN). In this system sensors are different between corridors area and manufacturing area, since they need different precise level. Those sensors combined together by different wireless AP and provide services to different applications inside and outside the plant. This is where virtualization shines. Virtualization creates an uniformed environment in heterogeneous sensor networks. It bridge the gap between infrastructure and applications. And the network and sensor virtualization can achieved by the middleware. Middleware hide unnecessary details and complexity of the underlying heterogeneous infrastructures, like different sensor nodes and different wireless AP in the presented scenario.