Radio optimization
Radio optimization focuses on energy conservation by enhancing various parameters like coding and modulation schemes, transmission power, and antenna direction This process can be categorized into four main strategies: modulation optimization, cooperative communication, transmission power control, and the use of directional antennas.
Modulation optimization focuses on refining modulation parameters to minimize radio energy consumption It effectively balances factors such as constellation size, information rate, transmission time, distance between nodes, and noise levels.
Cooperative communication schemes try to improve the quality of the received signal by collaborating several single antenna devices to create a virtual multi-antenna transmitter [74] [75]
Transmission power control schemes improve energy efficiency at the physical layer by regulating radio transmission power By reducing the communication range between nodes, these schemes minimize the power required for radio transmission.
Another idea is that a node with higher remaining energy may increase its transmission power, which enables other nodes to decrease their transmission power
Directional antenna schemes allow the signal to be sent and received in one direction at a time that allows the improvement of transmission range and throughput
[79] [80] To take advantage of directional antennas, new MAC protocols have been proposed [81] [82] In addition, some specific problems also have to be considered in in [83]
1.2.2 Sleep/wake- hemes up sc
Sleep/wake-up schemes aim to optimize node activity for energy savings by utilizing sleep mode for the radio Central to this strategy is the duty cycling scheme, which adjusts the radio's state based on network activity to reduce idle listening and enhance sleep mode efficiency While this method is highly energy-efficient, it can lead to sleep latency issues Additionally, broadcasting information to all neighbors may be challenging due to nodes not being simultaneously active Key parameters such as listening/sleeping periods, preamble length, and slot time are critical factors that can impact system performance For a comprehensive overview of duty cycling, refer to [84].
Routing significantly drains energy reserves, presenting a considerable challenge in network management Various routing paradigms exist to address this issue, including cluster architecture, the use of energy as a routing metric, and multipath routing strategies.
22 relay node placement An extensive review of energy-aware routing protocols can be found in [85] [86]
Cluster architecture organizes a network into clusters, each managed by a cluster head, to improve energy efficiency This approach reduces energy consumption by minimizing the communication range within clusters, which decreases transmission power It also limits the number of transmissions through data fusion at the cluster head, reduces energy-intensive computations by assigning them to the cluster head, allows for the power-off of certain nodes while the cluster head handles forwarding duties, and balances energy use through cluster head rotation.
Energy serves as a crucial metric during the setup phase of sensor networks, playing a significant role in extending their lifespan In this context, routing algorithms prioritize not only the shortest paths but also choose the next hop based on its remaining energy levels.
Multipath routing offers a more complex solution compared to single-path routing, which often leads to quicker energy depletion in the nodes along the chosen route By utilizing multiple paths, multipath routing effectively balances energy consumption among nodes by distributing the forwarding responsibilities For further insights into multipath routing protocols, additional surveys are available.
Premature depletion of nodes in various regions can lead to energy holes or network partitioning To mitigate this issue, optimizing node placements and incorporating relay nodes with enhanced capabilities is essential This approach enhances energy balance, prevents sensor hot-spots, and ensures comprehensive network coverage.
Energy consumption is closely linked to data transmission, making it essential to minimize the volume of data delivered to conserve energy Effective data reduction strategies can be categorized into three main types: data aggregation, adaptive sampling, and network coding.
Data aggregation techniques are essential for efficiently routing and combining data packets from various sensor nodes by utilizing extracted features and statistics These techniques include various aggregation functions tailored to specific application needs One common approach involves extracting the maximum, minimum, or average values from the aggregated data, which helps reduce communication data volume and power consumption However, this method may result in a significant loss of the original data structure.
The second type of aggregation technique is data compression Data compression techniques are further divided into distributed data compression [97]
Distributed data compression techniques offer optimal performance, although they are more complex than local data compression methods, which yield lower compression rates A comprehensive review of data compression in Wireless Sensor Networks (WSN) is available in the literature It's crucial to understand that these data compression techniques are most effective when applied to correlated data, making correlation a necessary factor for their successful implementation.
The representative aggregation technique involves selecting certain nodes to represent a group, allowing other nodes to enter a sleep mode to conserve energy The number of nodes that can be put to sleep depends on a predetermined level of distortion Similar to data compression, this method relies on the correlation of data among the nodes.
Adaptive sampling techniques optimize the sampling rate at each sensor to meet application requirements for information coverage and precision by leveraging spatial-temporal correlations in data This approach minimizes the number of samples taken, thereby decreasing the volume of transmitted data and conserving node energy Temporal analysis of sensed data is utilized, alongside spatial correlation, to enhance efficiency in data transmission.
[104] More details about adaptive sampling can be found in [105]
Network coding effectively minimizes traffic in broadcast scenarios by transmitting a linear combination of multiple data packets instead of sending each one individually This approach allows destination nodes to decode the information by solving linear equations Additionally, network coding leverages the balance between computation and communication, as communication is typically slower and more energy-intensive than computation.
Several recent types of research address energy harvesting and wireless charging techniques for WSNs as promising solutions because of recharge capability without human intervention