Electronic Engineer Essay Example
Electronic Engineer Essay Example

Electronic Engineer Essay Example

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  • Pages: 11 (2819 words)
  • Published: December 19, 2017
  • Type: Case Study
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The Wows are composed of small sensors with lopper devices and possess limited battery capacity. Current architectural and optimization studies concentrate on prolonging the network's lifespan. The sensor nodes' lifetime is influenced by the microprocessor, sensing module, and wireless transmitter/receiver. These studies take into account factors such as optimal deployment, topology, protocols, and other considerations. Additionally, recent research has investigated the impact of environmental factors on path loss for monitoring and assessment purposes.

Path loss is typically examined independently from higher layers like application and network. To achieve a more realistic assessment, it is crucial to integrate path loss calculations from the physical layer with data from upper layers like the application layer. This paper presents a simulation-based study that incorporates a path loss model and information from the application layer to anticipate network lifetime. The physical e

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nvironment is also taken into account. We demonstrate that when path loss is introduced, an increase in transmission power is necessary to minimize packet loss.

This presents a tradeoff between the residual energy and the successful transmission rate when more realistic settings are employed for simulation. It is a challenging task to optimism the transmission power of Wows, in presence of path loss, because although increasing the transmission power reduces the residual energy, it also reduces the number of retransmissions required. Index Terms? attenuation; path loss; wireless sensor networks; energy consumed; life time Evaluation tools Analytical modeling Simulators Test Beds Fig. 1. Performance evaluation methods l.

I INTRODUCTION

Recent advancements in wireless communications and electronics have facilitated the emergence of wireless sensor networks (Wows). These networks consist of numerous sensor nodes that are low cost, low power,

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and multifunctional, and are utilized for intelligent sensing tasks. A sensor network is a specific kind of network that typically consists of a data acquisition system and a data distribution system. Wows have distinct features in terms of data collection and energy limitations that differentiate them from other communication networks.

This text examines the various techniques used to evaluate performance, including analytical modeling, simulation, and benchmarking. Benchmarking is typically accomplished through test beds and measurements during real deployment. However, due to energy constraints, Wows face challenges in analyzing and managing such complex systems [1]. Wows are inherently complex with diverse characteristics like dynamic topology, wireless channel attributes, mobility, node density, etc. Analytical methods may not be appropriate as they require simplifications that can yield inaccurate results. Experimental studies are also impractical for evaluating systems with different architectures and conditions due to difficulties in deploying real systems.

Deploying multiple sensor nodes in the physical environment presents potential challenges, including programming and monitoring their behavior and the high costs of obtaining necessary instrumentation. Other factors to consider are fault tolerance, scalability, and the limited applicability of benchmarking results across different systems or environments. Consequently, testing and evaluating Wows' performance through analytical modeling, real deployment, and test beds can be a complex, inaccurate, time-consuming, and expensive process.

Simulation is widely used to analyze Wows, as it allows for quicker evaluation, optimization, and modifications of algorithms and protocols at different stages - design, development, and implementation. Several simulation tools with various features, models, architectures, and characteristics for performance evaluation in Wows are available. Packet level simulators offer optimization methods for free space scenarios and help avoid the effects of

path loss caused by obstacles.

The current studies on path loss for wireless communications (Wows) typically focus on upper layers such as network and application details. However, this paper introduces a new approach that combines path loss and packet level simulation. The study presents a case study that incorporates path loss, network, and application layer data to predict the network lifetime.

The new tool utilizes well-known path loss computation models to enable users to deploy sensors and introduce obstacles in a two-dimensional abstraction of the physical environment. This tool communicates with both a well-known Statistical package and MOMENT simulation environment. It presents the energy consumption of nodes, taking into account the impact of path loss for different transmission powers. Additionally, it illustrates the tradeoff between traditional performance measures, such as packet loss and residual energy.

The presented approach is a flexible and efficient tool for analyzing Wows and evaluating performance in terms of energy efficiency. It offers a realistic approach and allows for the use of various path loss models, including experimental ones. The paper is organized as follows: Section II discusses different types of simulators, Section III presents our approach, Section IV provides details of the chosen case study on home automation application, and Section V presents the numerical results.

The study demonstrates the impact of path loss on energy consumption in a Wireless Sensor Network (WSN) and explores the behavior of nodes under different transmission powers with the presence of path losses. The conclusion and future studies are presented in section VI. In the related work, existing simulators are discussed and classified into three categories: Instruction level, algorithm level, and packet level. Instruction

level simulators, such as TOSSES [8], Attempt [9], and Vapor [10], are considered emulators that model CPU execution at the instruction level.

TOSSES is the most commonly used emulator, but it is not the most precise one when compared to other emulators. TOSSES is a platform-specific simulator, specifically designed for Tiny mote simulation. It has the ability to compile any code written for Tiny into an executable file. Tiny serves as the basic GUI for TOSSES and allows for visualization and interaction with running simulations. However, TOSSES is specifically designed for Tiny applications on Mica totes sensors and does not include power models. On the other hand, Favor is a Javanese emulator that is used for programs specifically written for AVER microelectronics produced by Motel and the Mica sensor modes.

The Attempt platform provides low-level emulation of individual sensor nodes, and its unique feature is the ability to simulate a heterogeneous sensor network. It is scalable and serves as a presentation tool for sensor networks. Algorithm level simulators, such as Shawn [1 1], Alleghenies [1 2], and Signals [1 3], are well-known for their emphasis on logic, ATA structure, and algorithm presentation. They utilize graphical data structures to depict node communication and support distributed protocols and generic high-level algorithms.

Alleghenies specializes in analyzing algorithms such as localization, distributed routing, and flooding within a network context. Its main purpose is to support the implementation and assessment of new algorithms. On the other hand, Signals focuses on validating network algorithms and abstracting from the underlying layers. It also provides a message passing perspective of the network. Signals can be used for rapid prototyping and verification in customizable

network environments.

They are responsible for implementing the data link and physical layers in the OSI network layers. This includes radio models, 802.11b or newer MAC protocols, fading, collisions, noise, and wave diffractions. The Network Simulator (INS) is a discrete event simulator that combines C++ and TCL to function. TCL is an object-oriented scripting language specifically designed for networking research. It provides comprehensive support for simulating TCP, multicast protocols, and routing in wired and wireless networks. INS-2's success can largely be credited to its capability of producing and developing protocol implementations, which showcases its extensibility.

The INS-2 network simulator has an object-oriented design that facilitates the creation of new protocols. Its main features include battery models, hybrid simulation support, sensor channels, scenario generation tools, and a visualization tool. However, it has limitations such as scalability, lack of an application model, and limited customization options [3]. There are also commercial network simulator software like OPPONENT and Equaled, which offer powerful standard modules and provide a satisfactory simulation environment.

OPPONENT is a great option for simulating Gibe based networks as it implements the Gibe protocol and IEEE 802.15.4 MAC protocol. While Equaled is effective in simulating large-scale sensor networks due to its wireless simulation scalability, OPPONENT simulations take a long time when dealing with a large number of sensors. These simulators utilize basic radio/channel models [17]. Additionally, they are platform-specific and lack scalability, which makes them unsuitable for protocol design and testing purposes.

Moreover, none of the simulation packages provided have taken into account the environmental details and the impact of path loss. In our approach, as shown in Figure 2, we have developed a tool called Applicable.

Applicable consists of an environment editor where users can define the physical environment using a graphical editor. The environment can involve various obstacles and sensors. Each obstacle can possess different characteristics, including the material it is made of.

The Environment Editor Application Model Path loss Model is a widely used translation engine that determines how signal strength behaves in a path. It considers the impact of objects on signal attenuation, which can be influenced by factors like construction materials (e.g., wood, glass, and concrete) and object size. Table I presents db values for attenuation caused by different materials. A more detailed explanation on this topic will be given in the next section. The dependent path loss model can be represented as [21]: LAP = LO + egg(d) + empty witty Statistical Moment++ Fig. 2.

Applicable where, LAP represents the path loss between two nodes, d is the distance between the two nodes, LO is the path loss in free space environment, empty refers to the number of objects of the same type and witty is the loss in decibels attributed to that particular object.

The translation engine takes as an input the environment, application, and path loss models in order to produce simulation scripts. We use Statistical [22] as a simulation tool. Statistical is a WAS simulator used for initial testing of protocols and/or algorithms with a realistic node behavior, wireless channel and radio models.

Statistical is used to evaluate various platform characteristics due to its high tunability and ability to simulate a wide range of platforms. It incorporates an accurate radio model based on the work of the authors in [23] and includes a

physical process model that takes into account factors such as clock drift, sensor energy consumption, CPU energy consumption, and sensor bias. Simple simulators often overlook the unpredictability of the wireless channel, energy spent in transmission/receiving packets, performance degradation caused by duty cycles, and collisions.

However, Statistical includes all the important aspects related to the energy consumption of sensor nodes such as the micro-processor, sensor module, wireless transmitter/receiver, and path loss. It is important to note that while Statistical offers a comprehensive simulation platform at a low level, it does not offer any options to define the behavior of the application, environment, or path loss models. The application behavior is necessary to determine simulation parameters at the application level, while the environment and path loss models facilitate path loss calculations.

Our approach automatically derives the loss related parameters from high level models such as the environment and path loss, instead of assuming that the user provides those values, as Statistical does.

MOM AUTOMATION

The case study that is often considered is the monitoring and automatic control of the building environment. Home automation can consist of the following functionalities:

  1. heating, ventilation, and air conditioning (HAVE) systems;
  2. emergency control systems (fire alarms);
  3. centralized control lighting; and
  4. other systems, to provide comfort, energy efficiency and security.

The environment editor enables the validation of the material used and its size. It also provides the ability to specify the sensor position in the physical environment. The path loss is calculated using obstacles and sensor positions. The behavior of nodes is defined by an application model, which allows for

the derivation of various performance parameters like transmission and sensing rates. To achieve a more realistic approximation for the lifetime, Applicable incorporates information from other layers such as network, data, and physical layers.

Different protocols such as DVD [18] and ADS [19] can be specified at the network layer, along with static routing. These specifications can be easily defined on the environment model. In this case study, the Timeout MAC (T-MAC) protocol has been chosen as the data link layer access method. T-MAC is a contention-based MAC protocol that utilizes synchronized sleep schedules between nodes in a Wireless Sensor Network (WSN) to conserve energy [20]. It also provides collision avoidance and reliable transmission. Path loss refers to the attenuation in power density of an electromagnetic wave as it propagates.

Path loss is a result of various effects like free-space loss, refraction, diffraction, reflection, aperture-medium coupling loss, and absorption. It is also influenced by factors such as the propagation medium (dry or moist air), the distance between the transmitter and receiver, and the signal frequency. Failing to consider the effects of path loss can lead to an overly optimistic assessment of the underlying structure, as it doesn't account for issues such as retransmissions and the impact on energy consumption.

Our approach allows the user to define a path loss model, in conjunction with the physical environmental model, to determine the path loss between two nodes. This paper specifically examines indoor environments and employs a dependent path loss model [21]. The alarm system is a crucial part of our home automation system, comprised of temperature sensors and smoke detectors placed throughout the building. The energy consumption

of each node is influenced by the presence or absence of path loss.

Sprinkler actuators are also utilized for allowing water flow during fire incidents. Temperature sensors continuously monitor the temperature every 30 seconds. If a temperature sensor detects a value surpassing a predetermined threshold, it sends an alert message to the smoke detector. Upon receiving the alert, the smoke detector checks for smoke and raises an alarm if detected. Furthermore, in such scenarios, the smoke sensor triggers all the sprinklers. The automatic heating system consists of multiple temperature sensors, a base station, and various heaters.

The temperature sensors in a star topology send readings to the base station every 30 seconds. The base station calculates the average temperature and determines if the central heating system should be on or off. If the system is on and the average temperature exceeds the minimum threshold, it turns off. Conversely, if the average temperature is below the minimum threshold, the system turns on. The example depicted in Figure 3 illustrates a flat with five rooms (AY-AS).

Various obstacles, including wooden doors, walls, and glass partitions, are taken into account.

V. N NUMERICAL RESULTS AND DISCUSSIONS

The numerical results presented in this section demonstrate the usefulness and effectiveness of our approach to analyzing various factors that influence the energy consumption performance of Wows. The simulation parameters include the use of the COCCYX radio defined by Texas instruments, varying the output power of different transmission levels in the dam from O to -Dobb, and observing the corresponding energy consumption for each transmission level.

The power consumption for listening (receiving) is 62 maw and for transmission is 57.42 maw.

The packet rate remains at 250 Kbps, the radio bandwidth is 20 Much, and the simulation duration is 9000 sec. T-MAC serves as the MAC rotator, resulting in a frame period of 610 milliseconds for all nodes and a listen time out duration of 61 milliseconds. In our case study, we calculated the path loss caused by the material and explicitly specified our path loss map [21], [28]. Please refer to Figure 3 and Table I [21] to understand the contribution of each obstacle type to the path loss.

Node O represents the base station, while nodes 1,4,5,7, and 9 monitor temperature in areas AY ,AY, AY,AY, and AY respectively. Nodes 6 and 8 monitor smoke in the areas AY and AY respectively. Table II and Table Ill display the energy consumed by the nodes for the application scenario, considering the path-loss phenomenon and ignoring the path loss respectively. Furthermore, Figure 4 depicts the variation in energy consumption for each node in two different cases - one where path loss is ignored and another where path loss is considered.

The transmission power range varies from -25 dam to O dam and increasing the transmission power results in increased energy consumption of the nodes. Specifically for node 7, increasing the transmission power from -25 dam to ODBC also increases the energy consumed by the node from 80.1 Joules to 88.9 Joules. The relationship between traditional performance measures such as packet loss and residual energy is shown in Figure 6, where dotted lines indicate packets lost and straight lines represent energy consumption for each node. Decreasing the transmission power from O dam to -25 dam leads to

a gradual increase in the number of packets lost.

Decreasing the transmission power from O dam to -25 dam for node O results in an increase in lost packets to 370 (from 206) and an increase in energy consumption to 100 Joules (from 88 Joules). This increase in energy consumption is due to the need for retransmissions. However, it is important to note that increasing the transmission power does not necessarily extend the overall lifespan as there will be no retransmissions. Analyzing the tradeoff between packet loss and energy consumption reveals that the ideal transmission power should range between -15 and -5 dam. Within this range, energy consumption remains below 95 Joules and packet loss stays under 200 packets.

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