Wireless Sensor Networks

📡Wireless Sensor Networks Unit 6 – Energy Efficiency in Wireless Sensor Networks

Energy efficiency is crucial in wireless sensor networks due to limited power resources. This unit explores key concepts like duty cycling, data aggregation, and energy harvesting techniques that help extend network lifetime. It also covers energy-efficient architectures and routing protocols. The unit delves into power management techniques, including dynamic voltage scaling and adaptive sampling. It examines data compression methods and energy harvesting from sources like solar and vibration. Performance metrics for evaluating energy efficiency in WSNs are also discussed.

Got a Unit Test this week?

we crunched the numbers and here's the most likely topics on your next test

Key Concepts and Terminology

  • Wireless Sensor Networks (WSNs) consist of spatially distributed autonomous sensors that cooperatively monitor physical or environmental conditions (temperature, sound, pressure)
  • Energy efficiency plays a crucial role in WSNs due to limited power resources, typically batteries or energy harvesting devices
  • Network lifetime represents the time duration from the deployment of the network until the first node or a certain percentage of nodes run out of energy
  • Duty cycling involves alternating between active and sleep modes to conserve energy
    • Nodes turn off their radio transceivers during idle periods to minimize energy consumption
  • Data aggregation combines data from multiple sensors to reduce the amount of data transmitted, thereby saving energy
  • Energy harvesting techniques enable WSNs to capture energy from the environment (solar, vibration, thermal) and convert it into electrical energy to power the nodes
  • Quality of Service (QoS) requirements in WSNs include data accuracy, latency, and reliability, which must be balanced with energy efficiency

Energy Consumption in WSNs

  • Communication subsystem consumes the most significant portion of energy in WSNs, primarily due to data transmission and reception
  • Sensing subsystem energy consumption depends on the type of sensors used and the sampling frequency
    • Higher sampling rates lead to increased energy consumption
  • Processing subsystem includes the microcontroller and memory, which consume energy during data processing and storage
  • Idle listening occurs when a node listens to an idle channel, waiting for potential transmissions, and consumes a considerable amount of energy
  • Collisions result in retransmissions, which increase energy consumption and latency
  • Overhearing happens when a node receives packets destined for other nodes, wasting energy in the process
  • Control packet overhead, such as routing updates and acknowledgments, also contributes to energy consumption

Energy-Efficient Network Architectures

  • Hierarchical architectures organize nodes into clusters, with cluster heads responsible for data aggregation and communication with the base station
    • Clustering reduces energy consumption by minimizing long-distance transmissions
  • Chain-based architectures arrange nodes in a chain, with each node forwarding data to its nearest neighbor until reaching the base station
    • PEGASIS (Power-Efficient Gathering in Sensor Information Systems) is an example of a chain-based protocol
  • Tree-based architectures construct a tree structure rooted at the base station, with nodes forming parent-child relationships for data forwarding
    • Tree-based architectures enable efficient data aggregation and minimize redundant transmissions
  • Grid-based architectures divide the network area into a grid, with nodes in each grid cell collaborating for data collection and forwarding
  • Hybrid architectures combine multiple architectures (hierarchical, chain-based, tree-based) to leverage their strengths and optimize energy efficiency based on network requirements

Power Management Techniques

  • Dynamic Voltage and Frequency Scaling (DVFS) adjusts the voltage and frequency of the processor based on the workload to save energy
    • Lower voltage and frequency are used during periods of low activity
  • Dynamic Power Management (DPM) selectively turns off or puts components (sensors, transceivers) into low-power modes when not in use
  • Adaptive sampling adjusts the sensing frequency based on the observed phenomenon's dynamics to reduce energy consumption
    • Lower sampling rates are used for slowly changing phenomena
  • Transmission power control adjusts the transmission power based on the distance between the sender and receiver to minimize energy consumption
  • Sleep scheduling coordinates the sleep/wake cycles of nodes to minimize idle listening and overhearing while maintaining network connectivity
  • Battery management techniques, such as battery level monitoring and load balancing, help optimize the use of available energy resources

Energy-Aware Routing Protocols

  • Energy-aware routing protocols consider the residual energy of nodes and the energy cost of transmissions when making routing decisions
  • Minimum Energy Routing (MER) selects routes that minimize the total energy consumption along the path from source to destination
  • Maximum Lifetime Routing (MLR) aims to maximize the network lifetime by balancing energy consumption across nodes
    • MLR avoids overloading nodes with low residual energy
  • Adaptive Transmission Power Routing adjusts the transmission power based on the link quality and distance to minimize energy consumption
  • Cluster-based routing protocols (LEACH, HEED) rotate the cluster head role among nodes to balance energy consumption
    • LEACH (Low-Energy Adaptive Clustering Hierarchy) is a popular cluster-based routing protocol
  • Multipath routing distributes traffic across multiple paths to balance energy consumption and improve fault tolerance
  • Opportunistic routing exploits the broadcast nature of wireless communication to dynamically select the best forwarding node based on link quality and residual energy

Data Aggregation and Compression Methods

  • Data aggregation combines data from multiple sensors to reduce the amount of data transmitted, saving energy
    • Aggregation functions include average, min, max, and sum
  • Temporal aggregation combines data from the same sensor over time to reduce the transmission frequency
  • Spatial aggregation combines data from multiple sensors in a specific region to eliminate redundant information
  • In-network processing performs data processing and aggregation at intermediate nodes to reduce the amount of data forwarded to the base station
  • Data compression techniques (lossless, lossy) reduce the size of the data transmitted, thereby saving energy
    • Lossless compression preserves the original data, while lossy compression allows for some data loss to achieve higher compression ratios
  • Distributed source coding exploits the spatial correlation among sensor data to compress data without explicit communication between nodes
  • Compressive sensing enables the reconstruction of sparse signals from a limited number of measurements, reducing the amount of data collected and transmitted

Energy Harvesting in WSNs

  • Energy harvesting captures energy from the environment and converts it into electrical energy to power sensor nodes
  • Solar energy harvesting uses photovoltaic cells to convert sunlight into electrical energy
    • Solar energy is abundant and widely available, making it a popular choice for outdoor WSNs
  • Vibration energy harvesting converts mechanical vibrations into electrical energy using piezoelectric, electromagnetic, or electrostatic transducers
  • Thermal energy harvesting exploits temperature gradients to generate electrical energy using thermoelectric generators
  • RF energy harvesting captures ambient radio frequency signals and converts them into electrical energy
  • Hybrid energy harvesting combines multiple energy sources (solar, vibration, thermal) to improve the reliability and efficiency of energy harvesting
  • Energy storage devices (rechargeable batteries, supercapacitors) store the harvested energy for later use when the energy source is unavailable

Performance Metrics and Evaluation

  • Network lifetime is a critical performance metric in WSNs, representing the time duration until the first node or a certain percentage of nodes run out of energy
  • Energy consumption per node or per data packet indicates the efficiency of the energy management techniques employed
  • Throughput measures the amount of data successfully delivered to the base station over time
  • Latency represents the time delay between data generation at the source node and reception at the base station
  • Packet delivery ratio (PDR) is the ratio of the number of packets successfully received at the base station to the total number of packets generated
  • Coverage and connectivity metrics evaluate the ability of the WSN to monitor the desired area and maintain communication between nodes
  • Scalability assesses the performance of the WSN as the number of nodes or the network size increases
  • Fault tolerance evaluates the ability of the WSN to continue functioning in the presence of node failures or communication link failures


© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.