Energy Harvesting for Sensor Nodes in the Internet of Things

Wireless sensor networks have an extensive range of applications in the real world. From military uses saving lives, to environmental applications monitoring the fauna and weather conditions, but also by checking the health of patients and even by automating our homes. This work presents a solution to implement an energy harvesting sensor network. By using solar energy to power a sensor node we can extend its lifetime beyond the one powered only by batteries. Moreover, this solution attempts to be energy efficient and to achieve a communication scheme in order to create a sensor network where nodes read environmental data and transmit back to a sink node. The communication scheme was successful to synchronize two nodes and transmit packets between them without collisions and avoiding loss of data due to lack of energy. Furthermore, the duty cycling algorithm allowed the node to operate at its maximum performance level, making the best use of its energy available without depleting it

Novel Concept for HEMS Apparatus

AbstractHEMS (Home Energy Management System) is widely recognized as the useful technology for saving energy. On the other hand HEMS is never influenced around common people. The reason is supposed coming from following three points. 1. High initial cost, 2. Location dependence (The present HEMS is mounted into the wall), 3. Personal unconcern for energy saving. Then I introduce the novel concept for HEMS apparatus. The idea is introducing sensor network technology taking place of the conventional wired system. In addition to that, by introducing energy harvesting technology into the power source of sensor network nodes, each node can continue to work without battery or AC power supply cable.In order to examine this new idea, following step by step study procedure is necessary.At first to examine the validity of sensor network HEMS, secondary to estimate consuming energy amount by using sensor network HEMS, thirdly to proof the continuity working sensor network node without battery.Then we’ve described above study procedure in the case of using solar cell in this paper. Still we’ve added future perspective for using many kinds of energy harvesting (thermal, vibration, wireless power transmission)

Scheduling for Cooperative Energy Harvesting Sensor Networks

In cooperative communication networks, the source node transmits its data to the destination either directly or cooperatively with a cooperating node. When using energy harvesting technology, where nodes collect their energy from the environment, the energy availability at the nodes becomes unpredictable due to the stochastic nature of energy harvesting processes. As a result, when the source has a transmission, it cannot immediately transmit its data cooperatively with the cooperating node. It first needs to determine whether the cooperating node has sufficient energy to forward its transmission or not. Otherwise, its transmitted data may get lost. Therefore, when using energy harvesting, the challenge is for the source to schedule its transmissions whether directly or cooperatively, such that the fraction of its events (sensed data) that are successfully reported to the destination is maximized. Hence, in this dissertation, we address the problem of cooperating node scheduling in energy harvesting sensor networks. We consider the problem for the case of a single cooperating node and the case of multiple cooperating nodes, as well as the scenarios of one-way and two-way cooperative communications. We propose a simple scheduling scheme, called feedback scheme, which enables the source to optimally schedule its transmissions whether directly or cooperatively. We show that the feedback scheme maximizes the system performance, but does not require auxiliary parameter optimization as does the-state-of-the-art scheme, i.e., the threshold-based scheme. However, the feedback scheme has the problem of overhead caused by transmitting the energy status of the cooperating node to the source. To overcome this burden, we introduce a statistical model that enables the source to estimate the energy status of the cooperating node. Because cooperation may result in the cooperating node performing worse than the source, we address this problem through fairness in the performance between the nodes in the network. In addition, we address the problem of scheduling for throughput maximization in a wireless energy harvesting uplink. We propose centralized and distributed algorithms that find the optimal solution, and we address complexity issues. Our algorithms are shown to have a linear or quadratic complexity compared to the exponential complexity of the brute force approach. Compared with cooperative transmission, our approach maximizes the network throughput such that no node\u27s throughput is adversely affected

Dynamic Resource Allocation for Multiple-Antenna Wireless Power Transfer

We consider a point-to-point multiple-input-single-output (MISO) system where a receiver harvests energy from a wireless power transmitter to power itself for various applications. The transmitter performs energy beamforming by using an instantaneous channel state information (CSI). The CSI is estimated at the receiver by training via a preamble, and fed back to the transmitter. The channel estimate is more accurate when longer preamble is used, but less time is left for wireless power transfer before the channel changes. To maximize the harvested energy, in this paper, we address the key challenge of balancing the time resource used for channel estimation and wireless power transfer (WPT), and also investigate the allocation of energy resource used for wireless power transfer. First, we consider the general scenario where the preamble length is allowed to vary dynamically. Taking into account the effects of imperfect CSI, the optimal preamble length is obtained online by solving a dynamic programming (DP) problem. The solution is shown to be a threshold-type policy that depends only on the channel estimate power. Next, we consider the scenario in which the preamble length is fixed. The optimal preamble length is optimized offline. Furthermore, we derive the optimal power allocation schemes for both scenarios. For the scenario of dynamic-length preamble, the power is allocated according to both the optimal preamble length and the channel estimate power; while for the scenario of fixed-length preamble, the power is allocated according to only the channel estimate power. The analysis results are validated by numerical simulations. Encouragingly, with optimal power allocation, the harvested energy by using optimized fixed-length preamble is almost the same as the harvested energy by employing dynamic-length preamble, hence allowing a low-complexity WPT system to be implemented in practice.Comment: 30 pages, 6 figures, Submitted to the IEEE Transactions on Signal Processin

ASURVEY ON CLUSTER BASED LOAD BALANCINGAPPROACHESFOR WIRELESSSENSOR NETWORK

Wireless sensor network (WSN) is becoming a very interesting field of research in recent days. It has wide area of research due to various issues caused by the hardware capabilities of sensing nodes such as memory, power, and computing capabilities. One of the major issues is to concentrate on the energy consumption of the sensing node which determines the lifetime of the network. One of such problem is called Hot-spot problem, in which the best channel to the sink are overloaded with traffic and thus causing the nodes to deplete their energy quicker than the energy of other nodes in the network. Clustering algorithms along with sink mobility widely support for equal distribution of the load in the network. In order to overcome this problem various load balancing algorithms are discussed for improving the lifetime of the network