Battery In Wireless Sensor Networks Research Articles

AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS

Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.

A Performance Assessment of Power-Efficient Variants of Distributed Energy-Efficient Clustering Protocols in WSN

Wireless sensor networks (WSNs) have enabled an improved awareness in a range of purposes like battlefield surveillance, disaster management, industrial process monitoring and control, machine health monitoring, and medical applications. Accordingly, great numbers of sensor nodes (SNs) are arranged for usages that can function as an individual. Because of minute power of batteries in WSNs, proficient exploitation of this power is a significant aspect. Clustering is a capable method to expand the lifespan of WSNs by diminishing the power utilization. In the research work, the authors have compared resourceful power consumption protocols using different variants of distributed energy-efficient clustering (DEEC), namely developed DEEC (DDEEC), enhanced DEEC (EDEEC), and threshold DEEC (TDEEC). The simulation results demonstrate that out of three variants of DEEC, TDEEC performs better than other two protocols in respect of stability and WSN lifespan.

Distributed clustering and operational state scheduling in wireless rechargeable sensor networks

Replacement of exhausted batteries in wireless sensor networks might lead to temporal disruption of network operations. Wireless rechargeable sensor networks (WRSNs) can mitigate this problem by recharging nodes before they run out of batteries. However, because WSRN nodes cannot perform recharging and task monitoring simultaneously, it is challenging to ensure continuous network operations while maintaining low-cost and low processing-power requirements of nodes. It is necessary to design an effective and energy-efficient node scheduling scheme to schedule nodes to either monitoring or recharging state without adversely reducing the network lifetime and throughput. In this paper, we propose a distributed clustering and operational state scheduling algorithm (DCOS) to maximise the overall network lifetime. Nodes needing energy can be replenished within time intervals when they are not in the state of sensing and transmitting. We conducted extensive simulations to evaluate the performance of DCOS. The results show that DCOS outperforms most of the state-of-the-art methods.

A survey of energy-aware cluster head selection techniques in wireless sensor network

Recently, wireless sensor networks (WSNs) are becoming very famous as they are inexpensive and easy to maintain and manage. The network contains a group of sensor nodes, which are capable of sensing, computing, and transmitting. Energy efficiency is one of the most important challenging problems in WSN. Sensor nodes have inadequate energy and installed in remote areas. Hence, it is difficult to restore the batteries in WSN. Therefore, to maximize the network lifetime, appropriate clustering techniques and cluster head (CH) selection methods should be implemented. The main idea behind the clustering technique is that it clusters the sensor nodes and reduces the composed data simultaneously and then, it broadcasts the data. In this process, CH selection is an essential part. Therefore, this survey paper provides an overview of the clustering techniques for reducing energy consumption by reviewing several CH selection techniques in WSN that provide high energy efficiency. Several techniques have been employed for CH selection based on partitional clustering, optimization, low-energy adaptive clustering hierarchy, hierarchical, distributed, and other classification methods. Finally, an analysis is done based on the implementation tools, metrics employed, accuracy, and achievements of the considered CH selection techniques.

An Energy-Efficient Fail Recovery Routing in TDMA MAC Protocol-Based Wireless Sensor Network

Conventional IoT applications rely on seamless data collection from the distributed sensor nodes of Wireless Sensor Networks (WSNs). The energy supplied to the sensor node is limited and it depletes after each cycle of data collection. Therefore, data flow from the network to the base station may cease at any time due to the nodes with a dead battery. A replacement of the battery in WSNs is often challenging and requires additional efforts. To ensure the robust operation of WSNs, many fault recovery routing mechanisms have been proposed. Most of the previous fault recovery routing methods incur considerable delays in recovery and high overhead in either energy consumption or device cost. We propose an energy-efficient fail recovery routing method that is aimed to operate over a data aggregation network topology using a TDMA media access control (MAC). This paper introduces a novel fault recovery routing algorithm for TDMA-based WSNs. It finds an optimal neighbor backup parent (NBP) for each node in a way that reduces the energy consumption. The proposed method allows the NBPs to utilize the time slot of the faulty parent nodes, so it eliminates the overhead of TDMA rescheduling for NBPs. To evaluate the fault recovery performance and energy efficiency of the proposed method, we implemented it in C++ simulation program. Simulation experiments with an extensive set of network examples demonstrate that the proposed method can extend the network lifetime by 21% and reduce the energy consumption by 23% compared with the reference methods.

Procedure for Selecting a Transmission Mode Dependent on the State-of-charge and State-of-health of a Lithium-ion Battery in Wireless Sensor Networks with Energy Harvesting Devices

Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting.

HE-MAC: Harvest-Then-Transmit Based Modified EDCF MAC Protocol for Wireless Powered Sensor Networks

Energy transfer (ET) and energy harvesting (EH) through radio frequency (RF) signals are a promising technology that can reduce the dependency on batteries in wireless sensor networks. However, there is a tradeoff between the RF-based ET and data communication when they operate in the same frequency band. Therefore, a proper medium access control (MAC) protocol is needed in wireless powered sensor networks (WPSNs). However, a utilization degradation problem occurs when the distributed coordination function (DCF) MAC protocol of the IEEE 802.11 is applied to WPSNs. In order to overcome this problem, this paper extends the IEEE 802.11e enhanced DCF (EDCF) into a harvest-then-transmit-based modified EDCF MAC (HE-MAC) protocol. In addition, the HE-MAC’s Markov chain model and steady-state probabilities are derived and used in the performance analysis. Next, based on the steady-state conditions, optimization is conducted to maximize the EH rate, which satisfies the frame generation rate and transfers additional energy to achieve a self-sustained energy consumption profile. Finally, the simulation performance of EH protocols HE-MAC, RF-MAC, and DOS are compared, where the results show that HE-MAC provides in a superior performance for the range of interest.

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks.

It is necessary to improve the energy efficiency of batteries in wireless sensor networks (WSNs). The multiple-input multiple-output (MIMO) technique has become an important means to ameliorate WSNs, and interference management is the core of improving energy efficiency. A promising approach is interference alignment (IA), which effectively reduces the interference and improves the throughput of a system in the MIMO interference channels. However, the IA scheme requires perfect channel state information (CSI) at all transceivers in practice, which results in considerable feedback overhead. Thus, limited IA feedback has attracted much attention. In this paper, we analyze the throughput loss of the K-user MIMO interference channels when each transmitter delivers multiple streams in one slot, and derives the upper-bound of the system interference leakage and throughput loss. Then, to reduce the interference leakage and throughput loss for the MIMO interference alignment with limited feedback, a joint power and feedback bit allocation optimization scheme is proposed. The simulation results show that, compared with the conventional schemes, the presented optimal scheme achieves less residual interference and better performance in the system throughput.

A Performance Evaluation of Solar Energy Prediction Approaches for Energy-Harvesting Wireless Sensor Networks

Energy harvesting from the surrounding environment has been a superior way of eliminating the burden of having to replace depleted batteries in wireless sensor networks (WSNs), thereby achieving a perpetual lifetime. However, the ambient energy is highly time-variable and depends on the environmental conditions, which raises the need to design new approaches for predicting future energy availability. This paper presents a performance evaluation and comparison of three recently-proposed solar energy prediction algorithms for WSNs. In order to provide an accurate performance of the algorithms, real-world measurements obtained from a solar panel were considered. Also, the performance characteristics of the algorithms in four seasons –winter, spring, summer and autumn – were demonstrated. To do this, a month in each season was selected for performance comparison, discussing the performance of the algorithms in each season.

Piggery Environmental Control System Based on Wireless Sensor Networks with Energy Optimization

According to the characteristics of piggery, such as complex environment, numerous control objects, complex wiring, a piggery environment control system based on wireless sensor networks was designed. This system consisted of the wireless network composed of various devices and sensors in piggery, the control center with ARM-LINUX and the remote control center. The energy of sensor nodes was offered by batteries in wireless sensor networks so that the power source was limited. In order to strengthen the node energy management, the LEACH algorithm was combined with AODV routing protocol, and a new optimization algorithm called LEACH-A was proposed in this paper. The wireless sensor networks execution process was divided into two phases, including cluster building and data transmission. In the two phases, the node residual energy was calculated by a weighted function and the cluster head role was changed by the size of remaining energy. NS-2 simulation results show that the proposed algorithm prolonged the lifetime of the nodes and the networks, and realized the energy optimization management.

Energy Consumption Rate based Stable Election Protocol (ECRSEP) for WSNs

In recent few years Wireless Sensor Networks (WSNs) have seen an increased interest in various applications like border field security, disaster management and medical applications. So large number of sensor nodes are deployed for such applications, which can work autonomously. Due to small power batteries in WSNs, efficient utilization of battery power is an important factor. Clustering is an efficient technique to extend life time of sensor networks by reducing the energy consumption. In this paper, we propose a new protocol; Energy Consumption Rate based Stable Election Protocol (ECRSEP). Our CH selection scheme is based on the weighted election probabilities of each node according to the Energy Consumption Rate (ECR) of each node. We compare results of our proposed protocol with Low Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy Efficient Clustering (DEEC), Stable Election Protocol (SEP), and Enhanced SEP(ESEP). Our simulation results show that our proposed protocol, ECRSEP outperforms all these protocols in terms of network stability and network lifetime.

QuARES: A quality-aware renewable energy-driven sensing framework

Renewable energy technologies have become a promising solution to reduce energy concerns that arise due to limited battery in wireless sensor networks. While this enables us to prolong the lifetime of a sensor network (perpetually), the realization of sustainable sensor platforms is challenging due to the unstable nature of environmental energy sources. In this paper, we propose an adaptive energy harvesting management framework, QuARES, which exploits an application's tolerance to quality degradation to adjust data collection quality based on energy harvesting conditions. The proposed framework consists of two phases: an offline phase which uses prediction of harvested energy to allocate energy budget for time slots; and an online phase to tackle fluctuations in the time-varying energy harvesting profile. We formulate the energy budget allocation problem as a linear programming problem and implemented heuristics to minimize error in data quality at runtime. Our techniques are implemented in a network simulator, QualNet. In comparison with other approaches (e.g., [8]), our system offers improved sustainability (low energy consumption, no node deaths) during operation with data quality improvement ranging from 30 to 70%. QuARES is currently being deployed in a campus-wide pervasive space at UCI called Responsphere [11].