Energy Data Transmission Research Articles

Energy efficient data collection in sparse sensor networks using multiple Mobile Data Patrons

Wireless sensor networks consist of sensor nodes that can sense the environment and forward the sensed data to the destination. It takes multiple hops for the data to get transmitted from the source to the destination node. Mobile Data Patron is a high energy mobile data collector that collects data from the sensors within sparse networks. In sparse networks, the sensors detect the physical phenomenon, and the MDP is used to collect data generated by the sensors. In existing models, a single MDP is used to collect data from sensors within sparse networks. It requires high amounts of energy for long-range data transmission to the base station. The proposed work uses multiple MDPs to solve the energy depletion problem. The research also focuses on improving the data transfer rate so that the MDPs lose minimal amounts of energy.

Survey: energy efficient protocols using radio scheduling in wireless sensor network

An efficient energy management scheme is crucial factor for design and implementation of any sensor network. Almost all sensor networks are structured with numerous small sized, low cost sensor devices which are scattered over the large area. To improvise the network performance by high throughput with minimum energy consumption, an energy efficient radio scheduling MAC protocol is effective solution, since MAC layer has the capability to collaborate with distributed wireless networks. The present survey study provides relevant research work towards radio scheduling mechanism in the design of energy efficient wireless sensor networks (WSNs). The various radio scheduling protocols are exist in the literature, which has some limitations. Therefore, it is require developing a new energy efficient radio scheduling protocol to perform multi tasks with minimum energy consumption (e.g. data transmission). The most of research studies paying more attention towards to enhance the overall network lifetime with the aim of using energy efficient scheduling protocol. In that context, this survey study overviews the different categories of MAC based radio scheduling protocols and those protocols are measured by evaluating their data transmission capability, energy efficiency, and network performance. With the extensive analysis of existing works, many research challenges are stated. Also provides future directions for new WSN design at the end of this survey.

Energy Efficient UAV Communication With Energy Harvesting

This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless communication system with energy harvesting, where the UAV transfers energy to the users in half duplex or full duplex, and the users harvest energy for data transmission to the UAV. We minimize the total energy consumption of the UAV while accomplishing the minimal data transmission requests of the users. The original optimization problem is decomposed into two subproblems: path planning subproblem and energy minimization subproblem with fixed path planning. For path planning subproblem, the optimal visiting order is obtained by using the dual method and the trajectory is optimized via the successive convex approximation technique. For energy minimization subproblem with fixed path planning, we firstly obtain the optimal portion of data transmission time within the entire procedure and the optimal transmission power of each user. Then, the the energy minimization subproblem is greatly simplified and it is efficiently solved via a one-dimensional search method. Simulation results are illustrated to verify the theoretical findings.

The Charging Strategy of Mobile Charging Vehicles in Wireless Rechargeable Sensor Networks With Heterogeneous Sensors

Energy shortage obstructs the applications of the wireless rechargeable sensor network (WRSN). With the development of the wireless energy transfer technology, the mobile wireless charging vehicle (WCV) becomes a promising solution to solve that problem. However, the importance of different sensor nodes in the data transmission and uneven energy consumptions are often ignored. In this paper, the charging strategy of the WCV is studied in the WRSN considering these two phenomena. According to the importance of the sensor node, which is associated with the distance to the base station, we divide sensor nodes into two types: sensor nodes in ring 0 and sensor nodes in outer ring. We propose a novel charging model, the WCV adopts different charging strategies for different sensor nodes. To make the charging more efficient, the WCV charges sensor nodes one by one in ring 0 first, and then charges multiple sensor nodes simultaneously in outer ring. To estimate the lifetime of the network, a new metric named as the normalized dead time is proposed. Maximizing the lifetime of the network is modeled as minimizing the sum normalized dead time, and an efficient algorithm is proposed to minimize the sum normalized dead time through searching the optimal charging timeslots sequences. Then, through reassigning charging timeslots of sensor nodes, the proposed minimum travel cost algorithm minimizes the travel distance of the WCV and guarantee the lifetime of the network. We further deploy a cluster head node which has larger battery capacity in each cluster and can charge other sensor nodes within a limited distance. An algorithm is proposed to pre-distribute energy of the cluster head node. At last, the performance of proposed algorithms is verified by MATLAB. The results indicate that the performance of the WRSN can be improved by our proposed algorithms.

Signal Strength-Aware Adaptive Offloading with Local Image Preprocessing for Energy Efficient Mobile Devices

To prolong battery life of mobile devices, image processing applications often exploit offloading techniques which run some or all of the computations on remote servers. Unfortunately, the existing offloading techniques do not consider the fact that data transmission time and energy consumption of wireless network interfaces exponentially increase when signal strength decreases. In this paper, we propose an adaptive offloading for image processing applications, which considers wireless signal strength. To improve performance and energy efficiency of offloading, we also propose to adaptively exploit local preprocessing (executing image preprocessing on local mobile devices), considering wireless signal strength; the local preprocessing usually reduces the size of transmission image in offloading. Our proposed technique estimates performance and energy consumption of the following three methods, depending on the wireless signal strength: 1) local execution (executing all the computations on the local mobile devices), 2) offloading without local preprocessing, and 3) offloading with local preprocessing. Based on the estimated performance and energy consumption, our technique employs one among the three methods, which is expected to result in the best performance or energy efficiency. In our evaluation on an off-the-shelf smartphone, when a user prefers performance to energy, our proposed technique improves performance by 27.1 percent, compared to the conventional offloading technique that does not consider the signal strength. On the other hand, when a user prefers energy to performance, our proposed technique saves system-wide (not just CPU nor wireless network interface) energy consumption by 26.3 percent, on average, compared to the conventional offloading technique.

Adaptive Scheduling Design for Time Slotted Channel Hopping Enabled Mobile Adhoc Network

Industrial Internet of things (IIOT) applications comprises the wearable sensor devices for human activity monitoring; these devices generate the continuous data at higher data rate and it is powered through the battery. Hence, it restricts the uses of wireless protocol such as IEEE 802.15.4 and BLE (Bluetooth Low Energy). Moreover, there are promising technologies such as TSCH (Time Slotted Channel Hoping) MAC (Medium Access Control) which can be deployed in the different environment, which are prone to interference. In this research work we focus on overcoming the issue for designing the AS (Adaptive Scheduling) for TSCH – MANET (Mobile Adhoc Network); furthermore it is very difficult to design scheduling technique considering the unpredictable nature of data source location and wireless link, this results in waste of reserve resource. Moreover proposed Adaptive Scheduling model allows the both slots i.e. shared and dedicated slots, it also allows the communicating device to active the assigned slots adaptively. Hence, our proposed AS model achieves the higher overall access fairness, minimal idle listening overhead, higher packet deliver rate; further to cope up with the higher traffic load MANET device can activate the additional slots dynamically. Moreover, the outcome of Adaptive Scheduling model shows the higher data transmission and lower energy consumption.

A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

Long-term electrocardiogram (ECG) monitoring requires high-ratio lossless compression techniques to reduce data transmission energy and data storage capacity. In this paper, we have proposed a high-ratio ECG compression system with low computational complexity. Firstly, as the morphologies of the ECG change over time, we divide the signal of each heartbeat cycle into two regions. To achieve high prediction accuracy, a 1 st order linear predictor and a combination of the template predictor and 3 rd order linear predictor are applied in the two regions respectively. Secondly, we introduce a context-based error modeling module to the system, which cancels the statistical bias of the prediction algorithm and further improves the prediction accuracy. Thirdly, we modify the Golomb-Rice encoding algorithm to adaptively encode the prediction errors, while preserving a code space for packaging the information that is necessary for prediction. We evaluate the proposed system by using the MIT-BIH Arrhythmia Database (ARRDB). The experimental results show that with memory requirements as low as 444 to 14556 total variables this system achieves a compression ratio (CR) from 2.975 to 3.040, suggesting that it is highly applicable to both the low-power design and the cloud.

Internet of Things transmission and network reliability in complex environment

With the realization of the Internet of Things from theory to practical application, the work of ensuring high reliability of the system has become the key to system design, and it is also the obstacle to further promotion of the Internet of Things. This paper makes an in-depth study on the Internet of Things transmission and network reliability in complex environments. In this paper, we use the artificial bee colony algorithm to get the shortest path analysis of each cluster head node. The simulation results show that the algorithm proposed in this paper can effectively reduce the amount of data transmitted to sink by the sensor nodes through cluster head node fusion, improve the efficiency of data collection, energy consumption balance and network reliability, and extend the network life cycle. In addition, the index model of Internet of things reliability index system is established, and the comprehensive evaluation method of using the index is explained. The simulation results show that the proposed algorithm reduces the amount of data transmission and network energy consumption, prolongs the network life cycle, improves the efficiency of data fusion and data transmission reliability. Aodv-sms (abc-pso) routing recovery protocol shows that compared with other routing recovery strategies, packet transmission delay time is less, and the gap between them is more and more large; aiming at the end-to-end reliability and the network capacity, the paper focuses on the analysis of the impact of the change of communication link on the network capacity under the mobile node.

Study on the Optimal Access Orbiter Selection Algorithm in Mars Automatic Relay Communications

In current Mars relay communications, the Consultative Committee for Space Data Systems (CCSDS) Proximity-1 protocol is simple and efficient but it exhibits low flexibility in optimal access orbiter selection. Once connected with the first successful hailing orbiter, the rover would not switch to other better orbiters until the end of that connection, which makes it difficult to provide more convenient and efficient data relay services. The entire system cost is essential for optimal access orbiter selection, which includes both the Proximity-1 links' transmission cost concerned with the data transmission energy consumption and the orbiters' storage cost concerned with the data buffering energy consumption, thus minimizing the system cost on unit data volume could reduce energy consumption and extend the deep space vehicle's life. We employ long-term simulations of the single rover, simultaneously tracking two orbiters utilizing the improved Hotelling oligopoly model, and reveal the potential improvements for the current “first-come first-serve” selection strategy. In this article, a complete solution is proposed to solve the optimal access orbiter selection issues, including the geometric model, orbit design, oligopoly model, storage cost, and optimal selection algorithms. Simulation results show that our new algorithm outperforms current CCSDS Proximity-1 protocols and distance-dependent selection algorithm in the system cost on unit data volume with the selection gains $36.14\%$ and $ 29.59\%$, respectively, which could be used as the rover's optimal access orbiter selection algorithm in Mars automatic relay communications.

Performance Analysis for MIMO SWIPT Systems with Space-Time Coding

Energy supply is a key factor affecting the normal operation of energy-constrained networks. Simultaneous wireless information and power transfer (SWIPT) technology can be used to obtain a stable and continuous energy source. But SWIPT need to take into account data transmission rate and energy harvesting performance, it is necessary to design a complex physical layer method to improve the energy efficiency of SWIPT system. The paper studies a multiple input multiple output (MIMO) SWIPT system based on space-time coding, the transmitter sends the information using the space-time encoder through multi-antenna transmission, the receiver uses the space-time decoder to decode the information and collect the energy. The expressions of channel capacity and energy harvesting under MIMO are given respectively. The energy harvesting optimization target equation under coding conditions is established. The simulation results show that the using of space-time coding can reduce the data transmission error rate of MIMO SWIPT system and improve the energy harvesting performance of the user terminals.

Energy-Aware CPU Frequency Scaling for Mobile Video Streaming

The energy consumed by video streaming includes the energy consumed for data transmission and CPU processing, which are both affected by the CPU frequency. High CPU frequency can reduce the data transmission time but it consumes more CPU energy. Low CPU frequency reduces the CPU energy but increases the data transmission time and then increases the energy consumption. In this paper, we aim to reduce the total energy of mobile video streaming by adaptively adjusting the CPU frequency. Based on real measurement results, we model the effects of CPU frequency on TCP throughput and system power. Based on these models, we propose an Energy-aware CPU Frequency Scaling (EFS) algorithm which selects the CPU frequency that can achieve a balance between saving the data transmission energy and CPU energy. Since the downloading schedule of existing video streaming apps is not optimized in terms of energy, we also propose a method to determine when and how much data to download. Through trace-driven simulations and real measurement, we demonstrate that the EFS algorithm can reduce 30 percent of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50 percent of energy than the default Youtube app.

Intelligent data fusion algorithm based on hybrid delay-aware adaptive clustering in wireless sensor networks

Data fusion can effectively reduce the amount of data transmission and network energy consumption in wireless sensor networks (WSNs). However the existing data fusion schemes lead to additional delay overhead and power consumptions. In order to improve the performance of WSNs, an intelligent data fusion algorithm based on hybrid delay-aware clustering (HDC) in WSNs is proposed, which combines the advantages of single-layer cluster structure and multi-layer cluster structure, and adaptive selects the clustering patterns of the cluster by the decision function to achieve the tradeoff between network delay and energy consumption. The network model of HDC is presented, and theoretical analysis of the delay and energy consumption of single-layer cluster and multi-layer cluster are provided. And the energy efficient clustering algorithm and the dynamic cluster head re-selection algorithm are proposed to optimize network energy consumption and load balancing of the network. Simulation results show that, compared with the existing delay-aware models, the proposed scheme can effectively reduce the network delay, network energy consumption, and extend the network lifetime simultaneously.

A Low-energy Data Aggregation Protocol Using an Emergency Efficient Hybrid Medium Access Control Protocol in Hierarchal Wireless Sensor Networks

Recent wireless sensor network focused on developing communication networks with minimal power and cost. To achieve this, several techniques have been developed to monitor a completely wireless sensor network. Generally, in the WSN network, communication is established between the source nodes and the destination node with an abundant number of hops, an activity which consumes much energy. The node existing between source and destination nodes consumes energy for transmission of data and maximize network lifetime. To overcome this issue, a new Emergency Efficient Hybrid Medium Access Control (EEHMAC) protocol is presented to reduce consumption of energy among a specific group of WSNs which will increase the network lifetime. The proposed model makes a residual battery is utilized for effective transmission of data with minimal power consumption. Compared with other models, the experimental results strongly showed that our model is not only able to reduce network lifetime but also to increase the overall network performance.

Performance Evaluation of Secure Routing in Mobile Ad Hoc Network Using Ad Hoc on-Demand Distance Vector Routing Protocol

In modern era, MANET i.e., Mobile Ad-hoc Network is a very powerful, interactive tool and useful resource for transmission/communication in computer networks. About composition of MANET, a collection of mobile nodes have the potential of creating self-maintaining and self-configurable network without having any centralized infrastructure i.e., MANET is a self-organized network without any infrastructure. Nodes in the network require energy for transmission of data after collapse of energy a node become inactive and it can’t participate in the communication. Due to exhaustation of energy the network becomes unstable. An unstable network requires extra routing process for coin out a route. In future security of MANET is a major challenge for smooth and efficient data communication. There are many protocols in MANET but in this paper, we discussed AODV routing protocol for data communication. In this paper a new algorithm is designed i.e., modified AODV with security constraint. This paper also gives the comparative study of original AODV and modified AODV.

An Energy-Efficient Dual Prediction Scheme Using LMS Filter and LSTM in Wireless Sensor Networks for Environment Monitoring

Environmental monitoring is a practical application where a wireless sensor network (WSN) may be utilized effectively. However, the energy consumption issues have become a major concern in using a WSN, particularly in remote locations without readily accessible electrical power supply. In general, the activities of data transmission among sensor nodes and the gateway (GW) can be a significant fraction of the total energy consumption within a WSN. Hence, reducing the number and the duration of transmissions as much as possible while maintaining a high level of data accuracy can be an effective strategy for saving energy. To achieve this objective, a least mean square (LMS) filter is used for a dual prediction scheme (DPS), in this paper. The DPS is data quality-based, allowing both the sensor nodes and the GW to predict the data simultaneously. Only when the error between the predicted data and the real sensed data exceeds a predefined threshold, the sensor nodes will send the sensed data to the GW/another node and consequently will update the coefficients of the filter. It is observed that, with this scheme, the total number of transmissions and their overall duration can be effectively reduced, and therefore, further energy savings can be realized. With the developed methodologies, at least 62.3% of the total energy for data transmission could be saved while achieving a 93.1% prediction accuracy.

Mobility Aware Clustering Routing Algorithm (MACRON) to improve lifetime of Wireless Sensor Network

In wireless sensor network energy consumption is the recent research trend. Sleep scheduling and clustering of nodes in mobile environment are two different approaches adopted to elongate the life of network. In this paper Mobility Aware Clustering routing algorithm (MACRON) is proposed by integrating two challenging approaches. In the sleep scheduling approach, the node takes the decision for their states independently in decentralized manner. Using this reward based approach; the node decides its own current state (sleep, wake, idle) and it also predict the state of neighbors without communicating with neighbors. The performance improvement of sleep scheduling algorithm is significant but, to enhance performance it should be integrated with clustering approaches. To support the mobility in wireless sensor network various MAC protocol has been proposed, but they consume huge energy for data transmission. The proposed MACRON algorithm works efficiently in both mobile and stationary network. The performance of proposed algorithm gives decent outcome in varying size of nodes ranging from 10 to 40 in ns-2.

Differential data processing technique to improve the performance of wireless sensor networks

A wireless sensor network is a network composed of various types of sensors for the detection of magnetic, thermal, infrared, and acoustic fields, in addition to earthquakes and radio detecting and ranging (radar), among others. The size of the data collected from the various sensors is significant, and it is utilized in various applications such as artificial intelligence, data prediction, and analyses. However, the hardware of a wireless sensor node has limited energy and consumes a significant amount of energy for data transmission. Several studies have been conducted on multi-hop communication, clustering, and the compression/merging of data to increase energy efficiency in data transmission. In this study, the differential data processing (DDP) method is employed to reduce the size of the transmission data and improve the performance of wireless sensor networks. We propose node identification (ID)-based DDP and cluster header (CH)-based DDP. The node ID-based DDP transmits the initial collected aggregate data to the CH at the start of the collection, compares the previous collected data with the currently collected data, and then transfers the difference value to the CH. The CH is transmitted to the base station based on the smallest value of the collected data. The CH-based DDP collects data from the CH, generates reference data for the difference, and transmits the reference data at the time of cluster broadcasting. The member node performs differential processing on the collected data using the reference data transmitted to the CH. The performances of low-energy adaptive clustering hierarchy and DDP were compared. The simulation results revealed that the performance of the wireless sensor networks was improved by efficiently using the energy of the sensor nodes and by decreasing energy consumption in data transmission, given the reduction in the data size.

Service level agreement and energy cooperative cyber physical system for quickest healthcare services

In this paper, typical application of Cyber Physical System (CPS) has been highlighted for the critical-healthcare data transmission services. Sensors of CPS are providing patient’s health information via a communication network to a medical practitioner at some distant place. Needs of development of dependable routing protocol for such type of applications in healthcare are increased day-by-day. This paper proposes a quickest, critical and energy efficient routing for the CPS based healthcare system. Simulations performed to convey the appropriateness of the quality of data transmission system according to the well-defined Service Level Agreement (SLA) formulation. Proposed energy and SLA cooperation in data transmission is beneficial for the tele-operated medical service. A medical practitioner is able to monitor a patient in real-time with the help of proposed dependable and energy efficient data transmission. The results shows that CPS with consideration of different constraints such as energy and SLAs have a severe effect on its performance parameters such as mean number of QSS s-t paths, average hop counts and average energy efficiency. In addition to this comparison with existing algorithms shows the improvement in consideration with all performance components.

A priority based energy harvesting scheme for charging embedded sensor nodes in wireless body area networks.

This research work proposes a novel priority aware schedule based charging algorithm that uses wireless power transfer (WPT) technique in order to charge embedded sensor nodes (SNs) in a wireless body area network (WBAN). Implanted sensor nodes in WBANs require energy for both information extraction and data transmission to the remote controller unit. Thus, energy shortage of these SNs deteriorates due to the data transmission process of the patient health monitoring system. However, continuous operation by means of electromagnetic induction for energy harvesting, obtained from ambient sources, reduces the overall efficiency of the primary unit. With this paradigm in sight, an algorithm demonstrating the modeling of a priority-based mechanism is proposed in order to ensure proper sensor voltage level and to reduce the transmission losses. Medium access control (MAC) protocols are used for inductive powering from the primary unit to the secondary unit in a collision-free centralized scheduling scheme. Therefore, the proposed wireless charging algorithm for implanted SNs in WBAN is designed as per carrier sense multiple access with collision avoidance (CSMA/CA) technique. Because of this, the overall power consumption of SNs for certain operation periods, successful charging probabilities for multiple SNs, and instantaneous power requirements are considered as key performance measures of analysis. It is assumed that proper energy storage in both transmitters and receivers can handle channel interference and traffic contention. Simulation results verify that a significant reduction in power consumption for the proposed priority aware algorithm will maintain almost similar output. For this reason, saturating class—C as well as class—E driver circuits have been used to justify the performance in two different circuit topologies. Effects of priority with respect to the full charge period have also been observed for the multi-node system. Furthermore, from performance analysis, it has been demonstrated that the scheduling scheme causes both single MOSFET composed saturating class—C and Lchoke modeled class—E associated driver circuits to be considerably more loss efficient than corresponding existing ones.

A Routing Protocol Based on Link Quality and Hop Count inWireless Sensor Networks

In wireless sensor networks, data transmission quality and energy efficiency are closely related to link quality indicator. In order to improve the performance of Multipath Routing protocol, this paper proposes a LqiRings Routing protocol based on link quality and hops between nodes. In the routing discovery stage, LqiRings Routing does not take hops as the only factor in routing decision-making, but considers hops and link quality as two factors, and determines the optimal path by adjusting the weights between them. The LqiRings Routing protocol, Multipath Routing protocol and Hop Counts Routing protocol are simulated and analyzed in Castalia simulator. The experimental results show that the LqiRings Routing protocol proposed in this paper has higher packet delivery rate and lower average energy consumption under the same environment.