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

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