Network Components
Wireless sensor networks utilize essential components to establish wireless connectivity, linking an application platform to multiple sensor or actuator devices throughout the network By incorporating specific elements like gateways and nodes, these networks create a seamless data path that bridges the application platform and the physical environment.
Wireless sensor networks facilitate the wireless exchange of information between an application platform and multiple sensor nodes As illustrated in Fig 2.1, the connection between the gateway and the sensor node exemplifies a single-hop network link.
To extend the range of a network or circumvent an obstacle, a wireless relay node can be added between a gateway and a leaf node, making a mesh network, as shown in Fig 2.2
This article illustrates a multi-hop data path where data packets are transmitted from one node to another until they reach their final destination, such as a gateway or another node A detailed examination of more complex network layouts will be provided in Section 2.4 Additionally, we will outline the functions of each network component depicted in Figure 2.2, including the gateway, relay node, leaf node, and sensor/actuator.
A gateway serves as the crucial interface connecting the application platform to the wireless nodes within a wireless sensor network It aggregates and manipulates data received from these nodes, such as translating network packet formats, before forwarding it to the application, which can operate on either a local or networked computer Conversely, when the application sends a command to a wireless node, the gateway facilitates the relay of this information back to the sensor network.
All gateways can perform protocol conversion to enable the wireless network to work with other industry or non-standard network protocols
Relay nodes, often referred to as routers, serve as full-function devices (FFDs) that enhance network coverage, navigate around obstacles, and offer backup routes during network congestion or device failures Additionally, these nodes can connect to sensors and actuators through analog and digital interfaces, delivering similar input/output capabilities as leaf nodes.
A leaf node, also known as a reduced-function device (RFD) or endpoint, serves as the physical interface between a wireless sensor network and its connected sensors or actuators.
Leaf nodes are usually equipped with one or more I/O connections for connecting to and communicating with analog or digital sensors or actuator devices
A sensor/actuator is a crucial device that facilitates interaction with physical systems intended for monitoring and control For instance, a temperature sensor can monitor room temperature and regulate air conditioning equipment accordingly.
Fig 2.1 Basic wireless sensor network components
Fig 2.2 Adding intermediate node (relay node)
2 Wireless Sensor Networks: Concepts and Components
Hardware Platforms
Wireless sensor networks (WSNs) consist of essential components including sensor, actuation, and computation nodes For effective WSN-based applications, these nodes must deliver fundamental functionalities.
• Signal conditioning and data acquisition for different sensors
• Storage of data (sample data and confi gurations)
• Analysis of the processed data for alert generation
• Scheduling and execution of the measurement tasks
• Management of node confi guration (e.g changing the sampling rate and repro- gramming of data processing algorithms)
• Reception, transmission and forwarding of data packets
• Scheduling and execution of communication and networking tasks
An FFD node can be an embedded device or a more powerful computer server or workstation, and it may need to provide any of the functionalities described above
A RFD is typically an embedded device and provides only part of the functionalities described above
Computer-based platforms primarily operate on Windows, Linux, or other systems designed for computer hardware, featuring standard LAN communication (IEEE 802.11) Their high processing power and communication bandwidth enable the use of advanced programming languages such as Java and C++, facilitating the development and implementation of software components Furthermore, these platforms support networking protocols like Internet Protocol (IP), which streamlines integration into enterprise systems.
While flexible in configuration and computing power, large platforms are unsuitable for every sensing and actuation location due to their high cost, size, and power requirements Instead, embedded devices are more appropriate for these scenarios, as they typically feature limited resources, compact designs, and may even be battery-operated, such as certain wireless devices Today, a variety of wireless devices, including MICAz, TelosB motes, Waspmote, and Econotag, are available for constructing Wireless Sensor Networks (WSNs), with new options continually emerging This diversity allows for the selection of platforms that best meet the specific needs of various applications.
Wireless devices often have limited processor, radio, and memory capabilities, which helps keep costs low The microcontroller unit (MCU) is commonly programmed in C, allowing for efficient code development that fits within restricted memory sizes While application developers have complete access to the hardware, they must also manage resource constraints effectively.
WSN software platforms are specifically designed for the limited hardware of nodes, distinguishing them from conventional operating systems like Windows or Linux Unlike full-fledged operating systems, they do not feature advanced capabilities such as powerful scheduling, comprehensive memory management, or sophisticated file system support.
Wireless Sensor Operating Software
TinyOS
TinyOS, developed using nesC—an extension of the C programming language—facilitates event-driven, component-based programming This approach involves breaking down programs into self-contained components that communicate via message exchanges through defined interfaces Events, which can arise from environmental factors, such as sensor readings exceeding a threshold, or from interactions with other components, trigger specific actions The primary benefit of this component-based structure is the enhanced reusability of components, promoting efficient software development.
The nesC language extension introduces several additional keywords to describe a TinyOS component and its interfaces NesC and TinyOS are both open-source projects supported by the research community
TinyOS serves as the native operating system for the Tmote platform and has been adapted for use on various other wireless sensor network (WSN) hardware Although TinyOS does not support dynamic loading of new executables without requiring a full image replacement and reboot, it remains the industry standard for WSN programming.
SOS
SOS enhances TinyOS by introducing dynamic memory allocation, loadable modules, and a kernel, allowing for more efficient application programming in C The kernel manages network messaging, memory allocation, and module operations Unlike TinyOS, which necessitates shipping the entire mote image for over-the-network reprogramming, SOS supports the dynamic loading of smaller modules, streamlining the update process.
2 Wireless Sensor Networks: Concepts and Components
Contiki
Contiki is an open-source operating system designed for memory-efficient operation in networked embedded devices It offers essential OS features such as proto-threads, timers, a random number generator, clock functionality, and file system support Additionally, Contiki includes an IPv6 stack that supports both TCP and UDP connections, along with the Rime radio communication stack.
Contiki operates on an event-driven kernel designed for efficiency and a small footprint Its lightweight event scheduler manages the dispatching of events to active processes while periodically invoking polling handlers Program execution in Contiki is initiated by events from the kernel or through a polling mechanism, and the kernel ensures that once an event handler is scheduled, it is not pre-empted.
It supports two kinds of events: asynchronous and synchronous.
MANTIS
MANTIS features pre-emptive thread scheduling, which is essential for ensuring fairness among threads and tasks It also supports multi-granular code injection, allowing modifications at the system level, individual threads, or even specific thread variables This capability enables dynamic and efficient runtime reprogrammability, even post-deployment of a network Additionally, MANTIS provides POSIX-style system calls in C and includes a remote interactive shell for enhanced usability.
SensorOS
SensorOS is a multithreading operating system designed for resource-constrained environments, featuring pre-emptive priority-based scheduling, fine-granularity timing, and message passing for inter-process communication It has been specifically implemented for the Tampere University of Technology Wireless Sensor Network (TUTWSN) nodes, utilizing a 2 MIPS PIC microcontroller unit The SensorOS kernel is efficient, requiring only 6,964 bytes of code.
115 bytes of data memory Compared to event-handler kernels, such as TinyOS or ContikiOS, SensorOS enables coexistence of multiple time critical application tasks.
MagnetOS
MagnetOS is a distributed operating system designed for ad hoc and sensor networks, aiming to facilitate the development of power-aware and adaptive networking applications It achieves these goals by offering a unified system image, enhancing ease of development and functionality in dynamic network environments.
The system efficiently manages ten virtual machines across a flexible network of nodes, automatically dividing applications into components It dynamically allocates these components to various nodes, optimizing energy consumption and enhancing the longevity of the system.
Nano-RK
Nano-RK is a real-time operating system (RTOS) designed for wireless sensor networks, featuring multi-hop networking capabilities It employs fixed-priority preemptive multitasking to ensure that task deadlines are consistently met, while also allowing for CPU and network bandwidth reservations This system enables tasks to define their resource requirements, ensuring timely and controlled access to CPU cycles and network packets, making it ideal for resource-constrained embedded sensor environments.
Nano-RK includes a lightweight wireless networking stack for packet forwarding, routing and TDMA-based network scheduling.
ERIKA
ERIKA Enterprise RTOS is a multiprocessor real-time operating system kernel designed for automotive embedded controllers, offering a suite of application programming interfaces (APIs) aligned with the OSEK/VDX standard With its advanced real-time scheduler and resource management capabilities, ERIKA enables optimal utilization of modern microcontrollers and multicore platforms.
ERIKA utilizes fixed and dynamic priorities for task scheduling and employs the Immediate Priority Ceiling Protocol for resource sharing, ensuring that interrupts can pre-empt running tasks to handle urgent peripheral operations The ERIKA Enterprise features the RT-Druid development environment, built on Eclipse, which facilitates the writing, compiling, and analysis of applications RT-Druid includes various plug-ins, such as a schedulability analysis tool that performs scheduling acceptance tests, sensitivity analysis, and task offset calculations, along with design tools for modeling, analyzing, and simulating the timing behavior of embedded real-time systems.
RETOS
RETOS is a resilient and expandable multithreaded operating system designed specifically for wireless sensor networks It utilizes a modular software architecture, allowing for distinct separation between kernel and user applications, which ensures their reliable performance on limited hardware Additionally, the RETOS kernel supports dynamic reconfiguration through a loadable kernel framework, enhancing its adaptability and efficiency.
2 Wireless Sensor Networks: Concepts and Components
To operate RETOS on constrained devices, the framework implements a software technique known as application code checking, which includes both static and dynamic checks This process involves accessing memory to ensure that it remains within legal boundaries, achieved by thoroughly inspecting all memory instructions.
LiteOS
LiteOS is an operating system that offers Unix-like abstractions for wireless sensor networks, effectively mapping these networks into a Unix-like file system Specifically designed for resource-constrained nodes like MICAz, LiteOS utilizes the C programming language and supports online debugging, making it a versatile choice for developers in the field.
LiteOS features a wireless node mounting system via a file system known as LiteFS, allowing LiteOS nodes to connect seamlessly to the root filesystem of nearby base stations Once connected, these nodes are represented as directories within the base station, creating a structured sensor network that resembles a higher-level directory of node-mapped directories The accompanying shell, LiteShell, supports Unix commands such as copy (cp) for managing these directories Additionally, LiteOS operates on a multi-threaded kernel, enabling concurrent execution of applications as threads, with LiteShell offering three commands—ps, exec, and kill—to manage thread behavior effectively.
Among the ten operating systems designed for wireless sensor networks (WSNs), TinyOS and Contiki are the most recognized by WSN programmers, providing high-level programming languages along with essential components and features for WSN development Additionally, Nano-RK and ERIKA Enterprise RTOS are also popular choices for real-time applications.
Network Topologies
Star Topology
A star topology is a single-hop system where all wireless sensor nodes communicate bi-directionally with a central gateway, facilitating easy connections in small networks However, once the maximum number of nodes is reached, a hierarchical structure with multiple subnetworks should be considered In this configuration, the failure of a non-central node does not impact other nodes, but if the central node fails, the entire network is affected Additionally, the star topology can feature multiple network segments radiating from the central node, allowing for the easy addition of new nodes without disrupting network operations.
A gateway, which can be a computer or a specialized embedded device, serves as a crucial communication hub for transmitting data and commands between nodes Instead of communicating directly with one another, nodes rely on the gateway to centralize all data flow and facilitate connections to applications or other networks, including the Internet.
Star topology is known for its low overall power consumption in wireless sensor networks, but it is constrained by the transmission distance of each node to the gateway, typically ranging from 10 to 100 meters, depending on the radio technology used Generally, greater transmission ranges lead to increased power consumption, a crucial consideration for autonomous, battery-operated devices.
2 Wireless Sensor Networks: Concepts and Components
Tree Topology
In a tree topology, the structure is organized with a root node at the top, serving as the primary communication router and interface for applications and larger networks Beneath the root node, relay nodes connect to multiple child nodes, creating a hierarchical arrangement This configuration allows for efficient data flow and management within the network, as illustrated in Fig 2.4.
This network configuration aims to enhance power efficiency while extending communication range To achieve this, data transmission and operations are scheduled, necessitating precise time synchronization among all nodes.
Mesh Topology
This network topology connects every node to all nearby nodes within its radio range, allowing for reduced power consumption compared to topologies that cover longer distances Each node can establish a radio link with its closest neighbors, optimizing energy use However, it is essential for nodes to remain awake at all times, as they may serve as intermediaries in the communication path between other nodes and the gateway.
A mesh network offers exceptional fault tolerance, as each sensor node can connect through multiple pathways to the gateway or neighboring nodes This multi-hop configuration significantly extends the operational range compared to traditional star topology systems.
Hybrid Topology
Some network topologies used for wireless sensor network applications use a hybrid combination of the previous topologies, to create larger networks consisting of hundreds, even thousands of nodes
A hybrid network integrates star and mesh topologies, creating a star-mesh network that leverages the simplicity and low power consumption of the star topology while benefiting from the extended range and self-healing capabilities of the mesh topology In this setup, nodes play a crucial role in sensing, extending network range, and ensuring fault tolerance.
In a network where nodes can connect with multiple other nodes, the system is designed to automatically reconfigure when a node fails or a radio link is disrupted, such as from interference or low battery.
2 Wireless Sensor Networks: Concepts and Components
Data Models
The data model defines the interaction between sensors and applications, differing from topology, which is determined by the network protocol Selecting the right data model for an application depends on its specific requirements Monitoring applications typically feature data flow from sensor nodes to gateways, while control applications involve more frequent data transmission from gateways back to sensor nodes Common data models for monitoring applications include various frameworks designed to optimize this one-way data flow.
Periodic sampling is essential for monitoring conditions like temperature in controlled environments or pressure in pipelines Sensor data is collected from multiple nodes and transmitted to a gateway at regular intervals The frequency of sampling is determined by the rate of change in the monitored conditions and the specific characteristics that need to be observed.
Sensor nodes must adapt their sampling rates to the changing dynamics of the monitored process over time This adaptation can occur automatically or through commands from the application, ensuring effective monitoring.
Event-driven systems are essential for monitoring critical variables immediately after specific events or conditions, such as fire alarms, pressure alarms, and door/window sensors To ensure efficient power usage and rapid response, sensor nodes must be designed for minimal power consumption during inactive periods and quick wake-up times when an event occurs Additionally, many applications benefit from integrating both event-driven and periodic sampling methods.
Store and Forward technology allows sensor nodes to capture, store, and process data before sending it to the gateway By aggregating and processing data rather than transmitting each sample immediately, this approach enhances network performance, optimizing power consumption and bandwidth efficiency.
Routing Techniques
This section outlines the pros and cons of various routing techniques tailored for wireless sensor networks Understanding how data is transmitted to the gateway is crucial after detailing the data models, as the routing algorithm within the network layer plays a vital role in discovering routes and efficiently delivering data packets from their sources.
Routing in wireless sensor networks is complex due to unique challenges that differentiate them from traditional communication networks Unlike classical IP-based protocols, global addressing and routing algorithms cannot be implemented effectively due to the sheer number of energy and processing-constrained sensor nodes Additionally, sensor network applications typically require data flow from various sources to a specific sink or gateway This data often contains significant redundancy, as multiple sensors may capture the same information, necessitating data and goal-oriented routing protocols to optimize energy and bandwidth usage Furthermore, sensor nodes face strict limitations in transmission power, energy, processing capacity, and storage, highlighting the need for careful resource management in routing strategies.
Recent advancements in routing algorithms for sensor networks address the unique characteristics of sensor nodes and the specific application and architecture requirements These protocols can be categorized into three main types: data-centric, hierarchical, and location-based, with additional approaches focusing on network flow and Quality of Service (QoS) awareness Data-centric protocols operate on a query basis, relying on data naming to minimize redundant transmissions Hierarchical protocols cluster nodes to enable cluster heads to aggregate and reduce data, thereby conserving energy Location-based protocols leverage positional information to direct data to specific regions instead of broadcasting across the entire network Lastly, some routing methods are grounded in network flow modeling and aim to fulfill QoS requirements alongside their routing functions.
This section delves into the recent advancements in routing mechanisms for sensor networks, aiming to enhance the understanding of current data-centric and goal-oriented routing protocols in wireless sensor networks Additionally, we will highlight open issues that present opportunities for further research in this field.
This section focuses on data and goal-oriented routing in Wireless Sensor Networks (WSNs), building on previous surveys that explored various characteristics, applications, and communication protocols While existing literature highlights design issues and physical constraints of sensor nodes, this review categorizes and examines recent protocols and algorithms for effective data delivery We begin with data-centric protocols that prioritize the data itself, followed by an analysis of hierarchical routing, which establishes structured paths to data collection points Additionally, we explore location-based protocols that utilize positional information for efficient data relay, and conclude with a discussion on Quality of Service (QoS)-aware protocols that enhance data transmission quality.
2 Wireless Sensor Networks: Concepts and Components
In this chapter, we examine various routing algorithms and classes that consider energy consumption and data quality, as outlined in Table 2.2 The network must meet specific Quality of Service (QoS) metrics, including delay, energy, and bandwidth, to ensure effective data delivery to the base station, which are critical for QoS-aware protocols.
To choose the best routing mechanism for a sensor application, it is essential to categorize routing protocols based on network and operational characteristics, along with a clear objective model that outlines the routing goals.
Table 2.3 organizes the routing protocols from Table 2.2 by incorporating additional factors such as the number of base stations, mobility characteristics, and the nature of transmission power—whether fixed or variable It also distinguishes between approaches that utilize data aggregation and those that are query-based Additionally, the table outlines the primary objectives of each alternative algorithm.
Table 2.2 Routing algorithms and classes
Routing Data centric SPIN SPIN-PP
SPIN-EC SPIN-BC SPIN-RL Direct Diffusion
Energy aware Reliable Energy Aware Routing (REAR) Rumor
MCFA Link quality estimation based Gradient based
EWC PEGASIS TEEN/APTEEN Energy-aware cluster based Self-organized
Minimum energy communication network Small minimum energy communication network Location based Geographic Adaptive Fidelity
Energy Aware Greedy Routing (EAGR) Geographic and Energy Aware QoS aware SPEED
MMSPEED Sequential Assignment Real-Time Power-Aware DCEERP
Energy Effi cient with Delay Guaranties (EEDG) 2.6 Routing Techniques
Routing algorithms can be classified based on various properties such as the number of base stations, mobility, transmission power, data aggregation, and query goals Data-centric protocols like SPIN and Direct Diffusion utilize a single base station and fixed transmission power, focusing on efficient communication paths and data aggregation to enhance network lifetime Energy-aware protocols and REAR also adopt a similar approach but allow for adjustable transmission power, optimizing data delivery and lifetime Rumor and MCF A protocols, while limited in mobility, aim to reduce network queries and ensure data delivery In contrast, hierarchical protocols like LEACH and EWC employ fixed base stations to form distributed clusters, enhancing network lifetime and guaranteeing data delivery Overall, these routing algorithms are designed to optimize data transmission and energy efficiency in various network scenarios.
2 Wireless Sensor Networks: Concepts and Components
The PEGASIS protocol optimizes lifetime and bandwidth in a hierarchical, fixed base station network without energy awareness In contrast, TEEN/APTEEN offers real-time, energy-aware clustering, allowing for adjustable configurations while maintaining fixed base stations The self-organized approach enhances fault tolerance and minimizes energy communication, while geographic adaptive fidelity improves network lifetime with an increasing number of nodes The EA GR algorithm focuses on optimizing geographic adaptive fidelity, reducing interest messages across the network QoS protocols like SPEED and MMSPEED provide real-time capabilities and lifetime improvements, with sequential assignment ensuring fixed configurations and real-time performance Additionally, the real-time power-aware QoS protocol and DCEERP guarantee deadlines and lifetime, further enhancing network efficiency.
In various applications, the presence of multiple gateways enhances data collection and reduces network delays When a single gateway is used, all messages are directed to the same destination node In contrast, multiple gateways allow routing algorithms to optimize message delivery based on specific objectives Despite this potential, many techniques fail to leverage the advantages offered by having more than one gateway.
Transmission power is a crucial element in sensor networks, which can be either fixed or dynamically adjustable With fixed transmission power, all sensor nodes transmit messages at the same energy level Conversely, when power is adjustable, each node can determine the optimal energy level for sending messages to neighboring nodes, often based on an inverse relationship with the cost assigned to those nodes.
Routing in various protocols involves directing queries from the gateway to the appropriate sensor that holds the requested data, and subsequently returning that data back to the gateway.
In hierarchical protocols, each node is assigned a specific level, allowing it to forward messages only from lower-level nodes Nodes may also aggregate incoming data before sending it to higher-level nodes, with the base station positioned at the top of the hierarchy Hierarchies can be either dynamic or static; in dynamic hierarchies, the aggregator role rotates among nodes, ensuring that all selected nodes send their data to the designated aggregator The primary goal of establishing these hierarchies is to extend the overall lifetime of the network.
Certain sensor applications primarily focus on the effective transmission of messages from a source to a destination In contrast, other applications demand additional assurances, such as real-time message delivery, extended network longevity, and enhanced fault tolerance.
Industrial Monitoring and Control
The significance of wireless networks is increasingly recognized by organizations seeking real-time access to critical information regarding their plants, processes, and equipment to avert disruptions These wireless solutions not only reduce system, infrastructure, and operational costs but also enhance product quality In process control, nodes play a vital role by collecting and transmitting real-time data to operators, while also detecting variations in processes Equipped with various sensors and actuators, these nodes can adjust motor speeds according to desired outputs By facilitating machine-to-machine communication through wireless distributed networks, the efficiency of factory processes can be significantly improved.
In industrial environments, equipment management relies on continuous monitoring to assess machine health and usage Sensor nodes installed on various machines measure key physical properties, including temperature, pressure, humidity, and vibrations These sensors communicate with one another and transmit data to a centralized network for processing When critical values are detected, the system promptly issues alerts, enabling predictive maintenance to prevent potential failures.
Table 3.1 System requirements of process control application scenarios
Time synchronization Second Localization Required (meter)
Addressing Address centric Fault tolerance Middle Heterogeneity Yes Traffi c characteristics Periodic, queried, event based End-to-end delay 1–3 s
Packet loss Occasional (