Energy Consumption Of Data Transmission Research Articles

EnergyBox: Disclosing the wireless transmission energy cost for mobile devices

While evolving mobile technologies bring millions of users closer to the vision of information anywhere-anytime, device battery depletions still hamper the quality of experience to a great extent. The energy consumption of data transmission is highly dependent on the traffic pattern, and we argue that designing energy efficient data transmissions starts by energy awareness. Our work proposes EnergyBox, a parametrised tool that facilitates accurate and repeatable energy consumption studies for 3G and WiFi transmissions at the user end using real traffic data.The tool takes as input the parameters of a network operator and the power draw for a given mobile device in the 3G and WiFi transmission states. It outputs an estimate of the consumed energy for a given packet trace, either synthetic or captured in a device using real applications. Using nine different applications with different data patterns the versatility and accuracy of the tool was evaluated. The evaluation was carried out for a modern and popular smartphone in the WiFi setting, a specific mobile broadband module for the 3G setting, and within the operating environment of a major mobile operator in Sweden. A comparison with real power traces indicates that EnergyBox is a valuable tool for repeatable and convenient studies. It exhibits an accuracy of 94–99% for 3G, and 95–99% for WiFi given the studied applications’ traces.Next the tool was deployed in a use case where a location sharing application was ran on top of two alternative application layer protocols (HTTP and MQTT) and with two different data exchange formats (JSON and Base64). The illustrative use case helped to identify the appropriateness of the pull and push strategies in sharing location data, and the benefit of EnergyBox in characterising where the breaking point lies for preferring one or the other protocol, under which network load, or exchange data format.

A Wireless Sensor Networks Monitoring Device Based on ZigBee

environmental monitoring should have real-time, reliability and sustainability,the article design a device which can meet the request of environmental monitoring. the wireless sensor networks device based on ZigBee is described in detailed, meanwhile, a new routing strategy based on LEACH algorithm is proposed,implement of the strategy is described in the thesis.The new routing strategy is suitable for the long time monitoring, and realized the establishment of network cluster head nodes, broadcasting, selection and scheduling. According to the distance and energy consumption of nodes make a decision that the node joining a cluster or as a separate node directly communicate with the control node.The simulation results show that the device can real-time collection and remote transmission environmental temperature, humidity and other information, reduce energy consumption of data transmission, Prolong the network life-time, improve the network quality, and ensure the wireless sensor networks stable wrok,realize the network optimization.

Efficient Communication of Sensors Monitoring Overhead Transmission Lines

It is foreseen that a future smart grid has to handle more dynamic and distributed electricity supply and consumption. In that case, a robust automation system becomes essential. To monitor the status of the power system, a large amount of sensors are deployed in both the transmission grid and distribution grid. The sensors generate massive amount of data periodically for automation. This paper studies how the data measured on transmission lines can be delivered efficiently to substations. It has been demonstrated that the traditional way of data transmission is not sufficient and direct wireless links should be used to reduce the delay in information delivery. Furthermore, optimal placement of these direct wireless links is studied aiming at minimizing the delay in information delivery. The associated energy consumption in data transmission is also investigated.

Energy-Efficient Delay-Constrained Transmission and Sensing for Cognitive Radio Systems

In this paper, we study energy-efficient transmission for Cognitive Radio (CR) that opportunistically operates on the primary user's channel through spectrum sensing. Spectrum sensing and compulsory idling (for incumbent protection) introduce energy overheads for Secondary User (SU) operations, and thus, an appropriate balance between energy consumption in data transmission and energy overheads is required. We formulate this problem as a discrete-time Markov decision process in which the SU aims at minimizing its average cost (including both energy consumption and delay cost) to finish a target traffic payload through an appropriate rate allocation. Based on certainty equivalent control, we propose a low-complexity rate-adaptation policy that achieves comparable performance with the optimal policy. With the low-complexity policy, we quantify the impact of energy overheads (including the power consumption for spectrum sensing and compulsory idling) on the SU transmission strategy. Specifically, the SU rate increases with the increase of energy overheads, whose marginal impact, however, diminishes. Moreover, the marginal impact of energy overheads is more significant for delay-insensitive traffic compared with that for delay-sensitive traffic. To mitigate the loss due to imperfect spectrum sensing, we quantify that the SU decreases (increases) its rate with a larger misdetection probability (false alarm probability).

Optimal energy allocation in heterogeneous wireless sensor networks for lifetime maximization

We consider the problem of optimal energy allocation and lifetime maximization in heterogeneous wireless sensor networks. We construct a probabilistic model for heterogeneous wireless sensor networks where sensors can have different sensing range, different transmission range, different energy consumption for data sensing, and different energy consumption for data transmission, and the stream of data sensed and transmitted from a sensor and the stream of data relayed by a sensor to a base station are all treated as Poisson streams. We derive the probability distribution and the expectation of the number of data transmissions during the lifetime of each sensor and the probability distribution and the expectation of the lifetime of each sensor. In all these analysis, energy consumption of data sensing and data transmission and data relay are all taken into consideration. We develop an algorithm to find an optimal initial energy allocation to the sensors such that the network lifetime in the sense of the identical expected sensor lifetime is maximized. We show how to deal with a large amount of energy budget that may cause excessive computational time by developing accurate closed form approximate expressions of sensor lifetime and network lifetime and optimal initial energy allocation. We derive the expected number of working sensors at any time. Based on such results, we can find the latest time such that the expected number of sensors that are still functioning up to that time is above certain threshold.

Tree‐clustered data gathering protocol (TCDGP) for wireless sensor networks

Wireless Sensor Networks (WSNs) consist of numerous low cost, low power, low volume, and short‐distance transmitting nodes. As sensor nodes are deployed in a sensing field they can help people to monitor and aggregate data. Researchers also try to find more efficient ways of utilizing limited sensor node energy to provide longer WSN lifetime. Therefore, finding a method to reduce node data transmission energy consumption has become a very important issue. The multi‐hop routing protocol is well known for power saving in data gathering. Recently, several researchers have used cluster‐based (e.g., LEACH), chain‐based (e.g., PEGASIS), and tree‐based (e.g., TREEPSI) methods to establish energy efficient routing protocols. In this paper, we propose a tree‐clustered data gathering protocol (TCDGP) to improve upon the LEACH and TREEPSI methods. This novel protocol can preserve the advantages of the LEACH and TREEPSI methods and further reduce power consumption. The simulation results show that our proposal gives better performance than other methods.

무선 센서 네트워크를 위한 에너지 효율적인 클러스터 기반 지역 멀티홉 라우팅 프로토콜

다수의 센서로 구성된 무선 센서 네트워크는 다양한 환경에서의 정보수집을 목적으로 하며 현재 다양한 분야에 응용 및 활용이 되고 있다. 센서 네트워크를 구성하는 각 센서 노드들은 한정된 전력의 배터리로 동작하므로 에너지 효율성 및 장시간의 네트워크 수명을 제공하는 것이 센서 네트워크의 중요한 연구 목표 중 하나이다. 본 논문에서는 무선 센서 네트워크의 에너지 효율성을 향상 시키고 데이터 신뢰성을 보장하기 위해 새로운 클러스터 기반의 지역 멀티홉 라우팅 프로토콜을 제안한다. 제안된 프로토콜은 클러스터 내의 멀티홉 형성으로 센서 노드들의 데이터 전송을 위한 에너지 소비를 최소화하며 지역 클러스터 헤드 순환 기법을 통해 기존 클러스터링 기법에서 빈번한 클러스터 구성으로 인한 에너지 낭비를 효율적으로 관리한다. 시뮬레이션을 통해 기존에 제안되었던 LEACH, LEACH-C, PEACH와 비교해 전체 노드의 에너지 소모를 균등하게 하여 에너지 효율성을 향상시키고 네트워크 수명을 연장하였음을 확인하였다. Wireless sensor networks (WSN) consisting of a largenumber of sensors aims to gather data in a variety of environments and is beingused and applied in many different fields. The sensor nodes composing a sensornetwork operate on battery of limited power and as a result, high energyefficiency and long network lifetime are major goals of research in the WSN. Inthis paper we propose a novel cluster-based local multi-hop routing protocolthat enhances the overall energy efficiency and guarantees reliability in thesystem. The proposed protocol minimizes energy consumption for datatransmission among sensor nodes by forming a multi-hop in the cluster.Moreover, through local cluster head rotation scheme, it efficiently manageswaste of energy caused by frequent formation of clusters which was an issue inthe existing methods. Simulation results show that our scheme enhances energyefficiency and ensure longer network time in the sensor network as comparedwith existing schemes such as LEACH, LEACH-C and PEACH.

A bio-inspired field estimation scheme for wireless sensor networks

This paper proposes and analyzes a bio-inspired field estimation scheme using wireless sensor networks. The proposed scheme exploits the temporal pattern of the sensed process to reduce the number of samples sent back to the sink by a sensor node and, as consequence, decrease the energy consumption in data transmission. The proposed scheme is orthogonal to the techniques that reduce the spatial density of collected samples deactivating nodes with similar measurements. Thus, the proposed scheme can be used along with these techniques. We present two variations of this scheme: a sample-bounded and an error-bounded. The sample-bounded limits the maximum number of samples sent back to the sink, while the error-bounded guarantees the observation of every event of interest. Results show that for very regular processes the scheme can reduce up to 90% the total amount of samples sent in the network and even for less regular processes the proposed scheme can reduce the total amount of samples sent from approximately 10 up to 20%, with small reconstruction errors.