Minimizing sleep duration time for energy harvesting wireless sensor networks

Wireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS). In WSNs, due to the dense deployment, sensor readings can be spatially correlated and it is nonessential to transmit all their readings to the BS. Therefore, for more energy efficient, it is vital to choose which sensor node should report their sensed readings to the BS. In this paper, the event distortion-based clustering (EDC) algorithm is proposed for the spatially correlated sensor nodes. Here, the sensor nodes are assumed to harvest energy from ambient electromagnetic radiation source. The EDC algorithm allows the energy-harvesting sensor nodes to select and eliminate nonessential nodes while maintain an acceptable level of distortion at the BS. To measure the reliability, a theoretical framework of the distortion function is first derived for both single-hop and two-hop communication scenarios. Then, based on the derived theoretical framework, the EDC algorithm is introduced. Through extensive simulations, the performance of the EDC algorithm is evaluated in terms of achievable distortion level, number of alive nodes and harvested energy levels. As a result, EDC algorithm can successfully exploit both the spatial correlation and energy harvesting to improve the energy efficiency while preserving an acceptable level of distortion. Furthermore, the performance comparisons reveal that the two-hop communication model outperforms the single-hop model in terms of the distortion and energy-efficiency.

Designing routing protocols for Energy Harvesting- Wireless Sensor Network (EH-WSN) has two key challenges like improving harvested energy utilization and examination harvested energy utilization. However, the clustering and routing phases of the clustering-based routing protocols employed in EH-WSN need a lot of message overhead. Cluster Head Selection (CHS) is a crucial component of clustering that improves energy efficiency. An Enhanced Energy Harvesting Clustering (EEHC) protocol with two phases for cluster setup and data transmission is presented in this research. Sensor nodes are grouped into clusters during cluster construction, and CHS using the Gaussian Harris Hawks Optimizer (GHHO) algorithm is used for data transmission. Hawks chasing behaviours serve as the search agent in the GHHO algorithm, while prey serves as the ideal CH position. Data from each cluster node is collected by CHS, which then sends the aggregated data to the base station. Following CHS, connecting Cluster Member (CM) to a suitable CH is done using a variety of factors like energy, distance to neighbours, distance to the Base Station (BS), node density, and Signal-to-Noise Ratio (SNR). Each CM awakens during its designated working time slot during the DT stage, and transfers the data it has gathered towards the CH of the same cluster. A mobile sink is employed to enhance network performance and relay transmission distance is changed to match the amount of gathered energy. Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Network Lifetime, and average delay are metrics used to measure the results of clustering methods.

Renewable energy harvesting schemes in wireless sensor networks: A Survey

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols.

Energy harvesting aware topology control with power adaptation in wireless sensor networks

We study a multi-input-single-output (MISO) transmission system consisting of a multi-antenna basestation (BS) and a single-antenna user in the presence of multiple single-antenna radio frequency (RF) energy harvesting (EH) sensor nodes, where the user decodes information and multiple EH sensor nodes accumulate energy through the received signal from the BS. The sensor nodes coordinate their transmissions to perform virtual MISO beamforming, which requires sufficient individual and sum transmission power across multiple sensor nodes. For this reason, we consider simultaneously both the individual and sum EH constraints for SWIPT networks. Specifically, under both the individual and sum EH constraints, we propose beam selection of random unitary beamforming to maximize the transmission rate of the MISO system. Then we derive the exact rate-energy outage probability in closed-form for the system with identical average value of harvested energy across all sensor nodes and the exact probability in recursive form for the system with distinct average value of harvested energy at each sensor node. Numerical results confirm that the analyses are exact and the outage performance improves as the number of antennas at the BS increases and/or as the average received power at the user and sensor nodes increases.

Wireless Sensor Networking is an important area of research due to its wide and varied applications, especially in environmental monitoring and health monitoring. Battery-powered Wireless Sensor Networks (WSNs) face the problem of limited lifetime due to fast depletion of charge of typical batteries. The incorporation of energy harvesting in WSNs thus becomes crucial for extending the lifetime of battery-powered WSNs, while enhancing the network performance. The Medium Access Control (MAC) protocol has to be properly designed so as to ensure proper operation of the sensor nodes in WSNs with energy harvesting. This paper presents a MAC protocol for energy harvesting WSNs, based on IEEE 802.15.4 MAC, which takes into account the power consumption of the sensor nodes during operation and the battery discharge characteristics. This MAC protocol also employs duty cycle adaptation based on node energy.

The technology of wireless sensor networks (WSNs) is in constant development and it made great progress in many applications. One of the most popular problems in WSNs is the limited energy storage power at every sensor node. This paper aims to propose and develop a new distributed clustering algorithm for energy harvesting wireless sensor networks denoted by DEH-WSN (Energy Harvesting for Distributed Clustering Wireless Sensor Networks Protocol) that relies on matching between clustering and energy harvesting in a distributed topology. DEH-WSN uses initial and residual energy capacity of the nodes to choose cluster heads. Simulation results prove that the proposed method increases network lifetime and the effective throughput.

Wireless sensor networks (WSNs) are widely deployed in many fields nowadays. However, the limited energy storage ability of sensor nodes causes great detrimental effect on the performance of the WSNs. Energy harvesting has been proved as one of the promising ways to replenish the energy constrained sensors for prolonging WSNs lifetime. Another exacerbated problem introduced by non-uniform energy distribution and nonoptimal energy management in WSNs also causes great challenge to WSNs. For realizing the efficient energy utilization and improving the performance of energy harvesting enabled WSNs, two transmission management schemes, Markov decision based packets aggregation allocation (MDPA) and partial Kalman filter with best-match aggregation (PKBPA), are proposed respectively in this paper. MDPA focuses on the node's own energy capabilities to decide the packets number for aggregation, while PKBPA decides the number of packets to be aggregated and transmitted based on the estimated energy state information of the node and its next hop. Simulation results show that the PKBPA scheme outperforms other schemes, especially when the energy is insufficient for the packets transmission.

In this paper, the joint optimization of information transmission and energy utilization in energy harvesting wireless sensor networks (EH-WSNs) is studied. In EH-WSNs, the imbalance of energy harvesting and consumption affects the sensing and communication ability of the network. To solve this problem, a novel energy aware opportunistic routing protocol with long-short term memory (LSTM) based solar energy prediction is proposed. The protocol innovatively takes account of the nodes’ current residual energy and the harvesting solar energy in a short term which is predicted by a LSTM neural network as key factors in forwarding candidates election process of the opportunistic routing. Additionally, in order to jointly optimize the energy consumption and information transmission, a new metric with combined energy factor and relay efficiency for each node is proposed to assist candidate node selection where the relay priority considers the residual energy and working history of nodes. Simulation results show that the proposed protocol increases the network throughput by 12% and reduces retransmission rate by 15% compared to opportunistic routing based on geography. Simultaneously, it has stronger ability to balance energy consumption among sensor nodes.

Node placement for connected target coverage in wireless sensor networks with dynamic sinks

Over the past few years wireless sensor networks applications have evolved from a stage where these networks were designed in a technology-dependent manner to a stage where some conceptual understanding issues now exist. As wireless sensor networks are deployed, energy consumption emerges as a core requirement. Recent advances in energy harvesting technology for wireless devices have increased the lifetime and performance of wireless sensor node. It is useful to get a tradeoff between the duration of energy harvesting and network reliability. Previous investigations are mostly focused on duty cycle optimizing. An interrelation of energy harvesting schedule and network reliability has not been considered. To fill this gap we formulate optimizing problems for operation strategy of Energy Harvesting Wireless Sensor Networks. A problem solving approach is considered as well.

Cooperative Hybrid ARQ in Solar Powered Wireless Sensor Networks

When using multiple sensor nodes in wireless sensor networks (WSNs) for monitoring (or measuring) parameters of the target and sending the result to the base station (BS), data redundancy is an inevitable problem. The measured data often contains the same information, and sending redundant data to BS causes the waste of energy of sensor nodes and the risk of congestion. Multi-sensor data fusion in WSNs is a technology of gathering and processing data applied from node to BS. It improves the performance of surveillance systems by allowing the obtained sensed information from multiple sensor nodes aggregated to one unified format data packet to send to BS to make decision. In this paper, we propose a solution namely DF-SWin for sampling sensor node in the cluster to optimize the energy conservation of sensor nodes in clusters and cluster head.

A fast and accurate performance analysis of beaconless IEEE 802.15.4 multi-hop networks