Solar-powered Wireless Sensor Networks Research Articles

Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks.

In a wireless sensor network (WSN) environment with frequent errors, forward error correction (FEC) is usually employed at the link layer to achieve reliable transmission. In the FEC scheme, the error correction rate varies depending on the length of parity used for the recovery of broken data. The longer the parity length, the higher the possible error correction rate. However, this also means that the energy consumption increases. Meanwhile, in a solar-powered WSN, the energy of each node can be periodically collected, but the amount of collected energy varies drastically depending on the harvesting environment, including factors such as the weather, season and time of day. Therefore, each node must control energy consumption according to the energy harvesting rate. The scheme proposed in this study executes this control by adaptively adjusting the parity length of FEC according to the given energy budget of a node for the next period. This means that the error recovery rate can be increased as much as possible without adversely affecting the blackout time. Simulation results show that the proposed scheme improves the amount of data collected from the entire network for each environment compared with previous schemes.

Cluster-Based Mobile Sink Location Management Scheme for Solar-Powered Wireless Sensor Networks

Cluster-Based Mobile Sink Location Management Scheme for Solar-Powered Wireless Sensor Networks

태양 에너지 수집형 무선 센서 네트워크에서 데이터 압축과 순방향 오류 정정을 이용한 에너지 활용 기법

본 논문에서는 태양 에너지를 이용하는 무선 센서 네트워크에서 남는 에너지를 데이터 압축이나 순방향 오류 정정에 사용함으로써, 싱크 노드에 도달하는 데이터를 증가시키는 기법을 소개한다. 이 기법에서 각 노드는 주기적으로 자신의 전송 데이터 한계를 결정하고, 자식 노드에게 전달하여 과도한 데이터가 전송되는 것을 막는다. 이로 인해 에너지가 남을 경우, 압축이나 순방향 오류 정정 중 기법을 선택하여 싱크 노드에 더 많은 데이터가 전달될 수 있게 한다. 시뮬레이션 결과 제안된 기법은 대부분의 상황에서 기존의 기법들에 비해 정전 노드의 발생을 억제했고, 싱크 노드에서 더 많은 데이터를 수집한 것을 알 수 있었다.

Adaptive Data Aggregation and Compression to Improve Energy Utilization in Solar-Powered Wireless Sensor Networks.

A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node.

Sensing and Compression Rate Selection with Energy-Allocation in Solar-Powered Wireless Sensor Networks

Solar-powered wireless sensor nodes can use extra energy to obtain additional data to increase the precision. However, if the amount of data sensed is increased indiscriminately, the overhead of relay nodes may increase, and their energy may be exhausted. In this paper, we introduce a sensing and compression rate selection scheme to increase the amount of data obtained while preventing energy exhaustion. In this scheme, the neighbor nodes of the sink node determine the limit of data to be transmitted according to the allocated energy and their descendant nodes, and the other nodes select a compression algorithm appropriate to the allocated energy and the limitation of data to be transmitted. A simulation result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes. We also found that it adapts better to changes in node density and the amount of energy harvested.

UD-WCMA: An energy estimation and forecast scheme for solar powered wireless sensor networks

Energy estimation and forecast represents an important role for energy management in solar-powered wireless sensor networks (WSNs). In general, the energy in such networks is managed over a finite time horizon in the future based on input solar power forecasts to enable continuous operation of the WSNs and achieve the sensing objectives while ensuring that no node runs out of energy. In this article, we propose a dynamic version of the weather conditioned moving average technique (UD-WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location to update the prediction model. The UD-WCMA scheme is based on adaptive weighting parameters depending on the weather changes which makes it flexible compared to the existing estimation schemes without any precalibration. A performance analysis has been performed considering real irradiance profiles to assess the UD-WCMA prediction accuracy. Comparative numerical tests to standard forecasting schemes (EWMA, WCMA, and Pro-Energy) shows the outperformance of the new algorithm. The experimental validation has proven the interesting features of the UD-WCMA in real time low power sensor nodes.

An Indoor Test Methodology for Solar-Powered Wireless Sensor Networks

Repeatable and accurate tests are important when designing hardware and algorithms for solar-powered wireless sensor networks (WSNs). Since no two days are exactly alike with regard to energy harvesting, tests must be carried out indoors. Solar simulators are traditionally used in replicating the effects of sunlight indoors; however, solar simulators are expensive, have lighting elements that have short lifetimes, and are usually not designed to carry out the types of tests that hardware and algorithm designers require. As a result, hardware and algorithm designers use tests that are inaccurate and not repeatable (both for others and also for the designers themselves). In this article, we propose an indoor test methodology that does not rely on solar simulators. The test methodology has its basis in astronomy and photovoltaic cell design. We present a generic design for a test apparatus that can be used in carrying out the test methodology. We also present a specific design that we use in implementing an actual test apparatus. We test the efficacy of our test apparatus and, to demonstrate the usefulness of the test methodology, perform experiments akin to those required in projects involving solar-powered WSNs. Results of the said tests and experiments demonstrate that the test methodology is an invaluable tool for hardware and algorithm designers working with solar-powered WSNs.

Lessons learned deploying an oyster farm monitoring auto-sustainable wireless sensor network and trial of a temperature and relative humidity–based transmission power control scheme

We present challenges faced deploying a solar-powered wireless sensor network base station and nodes, at a remote oyster farm. It involved installing the base station system and a data server at the shore of a shallow bay, where there is no electrical power available. To solve the problem, we set up a photovoltaic array with an energy monitoring node, from which performance metrics were recorded and plotted. At the water, we deployed two wireless sensor nodes on a raft, a kilometre away from the base station. One node was configured for sea water pH and water temperature ( Tw) measurements. The other node was configured for salinity and Tw measurements. Furthermore, both nodes measured air temperature and relative humidity, for a more complete characterization. At the salinity node, temperature and relative humidity knowledge was crucial to determine a gain factor for doing a trial of a transmission power control scheme, using a novel temperature and relative humidity algorithm. To enable a fair comparison, the pH nodes transmitter was configured with a fixed power level. The nodes performances were measured locally at the base station, recording metrics such as received signal strength indicator and packet received rates.

태양 에너지 기반 무선 센서 네트워크의 데이터 신뢰성 향상을 위한 에너지 적응형 Reed-Solomon 기법

OSI 네트워크 모델의 데이터 링크 계층에서는 노드와 노드 사이에 신뢰성 있는 통신을 목표로 하며, 오류 발생이 잦은 무선 센서 네트워크 환경에서는 FEC(forward error correction) 기법을 주로 사용한다. FEC 기법은 패리티의 길이에 따라 오류 정정률을 설정할 수 있는데 오류 정정률을 높게 설정할수록, 에너지 소모가 크다는 한계가 존재한다. 한편 태양 에너지 기반 센서 네트워크에서는 주기적으로 에너지가 충전되며, 이로 인해 노드가 기본적으로 동작하는 데 필요한 에너지보다 더 많은 양의 에너지가 존재할 가능성이 있다. 따라서 본 논문에서는 수집되는 에너지 중 여분의 에너지를 사용해 데이터 손실률을 감소시키는 에너지 적응형 Reed-Solomon 기법을 제안한다. 노드의 에너지 상태에 따라 FEC의 패리티 길이를 다르게 설정하여, 에너지와 데이터 손실률의 trade-off(이율배반) 관계를 이용하는 기법을 제안하고 시뮬레이션을 통해 성능을 검증한다.

Energy-aware determination of compression for low latency in solar-powered wireless sensor networks

There have been many studies performed about increasing network lifetime in wireless sensor networks that involve reducing data size, since the data transmission process takes up a large part of energy consumption. However, reducing data size results in increased delay time due to not only the compression computation time but also the waiting time to gather a sufficient amount of data for compression. Meanwhile, in solar-powered wireless sensor networks, the harvested energy may be surplus to the basic operations of sensor nodes. In this study, such surplus energy is utilized to reduce the delay time between nodes. Nodes with residual energy less than a certain threshold transfer data with compression in order to reduce energy consumption, and nodes with residual energy over the threshold (which means there is surplus energy) transfer data without compression to reduce the delay time between nodes by using the surplus energy. Simulation-based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay times compared with traditional methods.

Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics

The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment. The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions. To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span. A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration. The wireless monitoring system collects sensor data on a daily schedule and communicates the data to the Internet where it is stored in a curated data repository. Bridge response data in the repository are autonomously processed to extract truck load events using machine learning, compensate for environmental variations using nonlinear regression and to quantitatively assess anomalous bridge performance using statistical process control.

A Reinforcement Learning-Based Sleep Scheduling Algorithm for Desired Area Coverage in Solar-Powered Wireless Sensor Networks

In this paper, a reinforcement learning-based sleep scheduling for coverage (RLSSC) algorithm is proposed for sustainable time-slotted operation in rechargeable sensor networks. RLSSC is a two-stage sleep scheduling algorithm. It includes the precedence operator-based group formation algorithm and the Q learning-based active node selection algorithm. First, a precedence operator is designed in the group formation algorithm to ensure the desired area coverage. All the nodes are formed into groups. Then, Q learning algorithm is expanded into a multi-sensor cooperation Q learning group model. The learning and action selection strategy are designed in a group to direct nodes in collaborative learning of working modes selection while adapting to the dynamic environment. Through the role changes of active nodes, the algorithm accomplishes the entire team learning of sleep scheduling while scheduling others into sleep modes. Experiments of RLSSC on a solar-powered wireless sensor network for area surveillance tasks are presented. Compared with LEACH and a random algorithm, the results show that RLSSC can effectively adjust the working modes of nodes in a group by perceiving the environment. In addition, it achieves the energy consumption balance between nodes so as to prolong the network lifetime while maintaining the desired coverage.

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks.

Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner.

Power Adaptive Data Encryption for Energy-Efficient and Secure Communication in Solar-Powered Wireless Sensor Networks

Basic security of data transmission in battery-powered wireless sensor networks (WSNs) is typically achieved by symmetric-key encryption, which uses little energy; but solar-powered WSNs sometimes have sufficient energy to achieve a higher level of security through public-key encryption. However, if energy input and usage are not balanced, nodes may black out. By switching between symmetric-key and public-key encryption, based on an energy threshold, the level of security can be traded off against the urgency of energy-saving. This policy can also reduce the amount of energy used by some nodes in a WSN, since data encrypted using a public-key is simply relayed by intermediate nodes, whereas data encrypted using a symmetric-key must be decrypted and reencrypted in every node on its path. Through a simulation, we compared the use of either symmetric-key or public-key encryption alone with our scheme, which was shown to be more secure, to use energy more effectively, and to reduce the occurrence of node blackouts.

태양 에너지 수집형 센서 네트워크에서 모바일 싱크를 지원하기 위한 클러스터 기반 에너지 인지 데이터 공유 기법

배터리 기반 무선 센서 네트워크(Wireless Sensor Networks, WSN)는 고정된 자원으로 인해 제한된 수명을 갖지만, 태양 에너지 기반 WSN은 에너지가 주기적으로 계속 공급되어, 하드웨어적인 문제가 없는 한 영원히 동작할 수 있다. 한편, 모바일 싱크를 활용한 기법은 데이터 전송 경로를 단축하여 센서의 에너지 소모량을 감소시킬 수 있지만, 비효율적인 싱크의 이동은 에너지 낭비를 초래할 수 있다. 이에 따라 모바일 싱크와 클러스터링을 혼합한 기법들이 제안되고 있지만, 클러스터링은 에너지 불균형 문제로 인한 네트워크 수명 단축을 야기한다. 따라서 본 연구에서는 태양 에너지 기반 WSN에서 모바일 싱크를 효과적으로 지원하기 위한 CE-DSS를 제안한다. CE-DSS는 에너지를 효율적으로 활용해 각 노드의 정전시간을 최소화하면서, 각 클러스터의 데이터를 공유한다. 이로 인해 네트워크 신뢰도가 향상되고, 모바일 싱크의 이동 거리가 단축되어 싱크의 에너지 사용량이 감소된다.

Lessons learned on solar powered wireless sensor network deployments in urban, desert environments

The successful deployment of a large scale solar powered wireless sensor network in an urban, desert environment is a very complex task. Specific cities of such environments cause a variety of operational problems, ranging from hardware faults to operational challenges, for instance due to the high variability of solar energy availability. Even a seemingly functional sensor network created in the lab does not guarantee reliable long term operation, which is absolutely necessary given the cost and difficulty of accessing sensor nodes in urban environments. As part of a larger traffic flow wireless sensor network project, we conducted several deployments in the last two years to evaluate the long-term performance of solar-powered urban wireless sensor networks in a desert area. In this article, we share our experiences in all domains of sensor network operations, from the conception of hardware to post-deployment analysis, including operational constraints that directly impact the software that can be run. We illustrate these experiences using numerous experimental results, and present multiple unexpected operational problems as well as some possible solutions to address them. We also show that current technology is far from meeting all operational constraints for these demanding applications, in which sensor networks are to operate for years to become economically appealing.

The Adaptive Path Selection Mechanism for Solar-Powered Wireless Sensor Networks

In this work, we consider the normal large scale environment condition and the undulatory surface that support from solar power charging is limited. In this paper, we propose an adaptive path selection mechanism. The mechanism avoids sensor node having less lifetime issue when energy is insufficient by using neural network model to choose sensor, which has more remaining power and have stronger sunshine conditions (i.e., sunshine angle and time). The current study evaluates this approach's performance by conducting computer simulations. Simulation results reveal the strengths of the proposed adaptive path selection mechanism in terms of increased lifetime and reduced sensors average battery charging time for solar-powered wireless sensor networks.

Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks

Understanding the optimal usage of fluctuating renewable energy in wireless sensor networks (WSNs) is complex. Lexicographic max-min (LM) rate allocation is a good solution but is nontrivial for multihop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and offline, suffering from low scalability and large computational complexity—typically solving O(N2) linear programming problems forN-node WSNs. This article presents the first optimal distributed solution to this problem with much lower complexity. We apply it to solar-powered wireless sensor networks (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MICAz motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions.

DRS: Dynamic Region Scheduling for Solar-powered Wireless Sensor Networks

Energy restriction is crucial for large-scale application of Wireless Sensor Networks (WSNs). To achieve sustainable operations, the wireless sensor nodes need to harvest energy from the surrounding environment. This paper proposed the Dynamic Region Scheduling (DRS) for solar-powered WSNs. In DRS, all the wireless sensor nodes are allocated into different regions with different transmitting priorities and the collected data could only be delivered from lower priority region to higher or the same priority regions. Each sensor node would renew its region ID dynamically by changing its Mask-identifier (MI) and thus would help the data delivered directly to the sink without any redundancies. Compared with another two routing algorithm SEHRP and EAHC, the results shows that DRS has achieved a significant performance improvement of a higher data delivery rate, a lower energy consumptions as well as lower data loss ratio and data redundancy ratio, which would be greatly helpful to prolong the lifetime of WSN.

Distributed Networking in Autonomic Solar Powered Wireless Sensor Networks

Recent advances in solar harvesting technologies pave the way for sustainable environmental-monitoring applications in the emerging solar powered wireless sensor networks (SP-WSNs). The complexities associated with the low-resourced, highly-dynamic, and vulnerable sensor nodes operating in potentially unattended or hostile environments require a high degree of self-management and automation. Guided by autonomic communication principles, this paper presents AutoSP-WSN, a novel distributed framework to achieve sustainable data collection while also optimizing end-to-end network performance for SP-WSNs. Initially, we present the energy-aware support component that provides reliable energy monitoring and prediction. This drives the power management component, which is adaptive to time-varying solar power, avoiding battery exhaustion as well as maximizing the per-node utility. Finally, to demonstrate the key design issues of the network protocol component, we propose two self-adaptive network protocols, a routing protocol SP-BCP and a rate control scheme PEA-DLEX. We show that the individual components seamlessly highly integrate as a whole, and the AutoSP-WSN framework exhibits the properties of context-awareness, distributed operation, self-configuration, self-optimization, self-protection and self-healing. Through extensive experiments on a real SP-WSN platform, and hardware-driven simulations, we show that the proposed schemes achieve substantial improvements over previous work, in terms of reliability, sustainable operation, and network utility.