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Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Botnet detection is one of the most imminent tasks for cyber security. Among popular botnet countermeasures, an intrusion detection system is the prominent mechanism. In the past, packet-based intrusion detection systems were popular. However, flow-based intrusion detection systems have been preferred in recent years due to their ability to adapt to modern high-speed networks. A collection of flows from an enterprise network usually contains both botnet traffic and normal traffic. To classify this traffic, supervised machine learning algorithms, i.e., classifications, have been applied and achieved a high accuracy. In an effort to improve the ability of intrusion detection systems against botnets, some studies have suggested partitioning flows into clusters before applying the classifications and this step could significantly reduce the complexity of a flow set. However, the instability of individual clustering algorithms is still a constraint for botnet detection.To overcome this bottleneck, we propose a novel method that combines individual partitions to become a strong learner through the use of a link-based algorithm. Our experiments show that our cluster ensemble model outperforms existing botnet detection mechanisms with a high reliability. We also determine the balance between accuracy and computer resources for botnet detection, and thereby propose a range for the maximum duration time of flows in botnet research.

An uninterruptible power supply (UPS) allows small companies and domestic users to cope with power outages; but existing designs lack flexibility of control and require expensive battery maintenance, with a cost proportional to the outage compensation time. We combine a compact synchronous generator with a battery, with 10% of the capacity that would otherwise be required, to obtain a UPS with reduced maintenance costs for the same performance. Any UPS must respond immediately to a power loss, and our uninterruptible generator supply (UGS) is therefore built around real-time scheduling of its internal operations; this also makes it suitable for integration into the industrial gateway. The UGS is based on a real-time operating system, with an integrated wireless module providing connectivity to a web server, for monitoring and management, which can be performed remotely on a mobile device.

Data compression involves a trade-off between delay time and data size. Greater delay times require smaller data sizes and vice versa. There have been many studies performed in the field of wireless sensor networks on increasing network life cycle durations by reducing data size to minimize energy consumption; however, reductions in data size result in increases of delay time due to the added processing time required for data compression. Meanwhile, as energy generation occurs periodically in solar energy-based wireless sensor networks, redundant energy is often generated in amounts sufficient to run a node. In this study, this excess energy is used to reduce the delay time between nodes in a sensor network consisting of solar energy-based nodes. The energy threshold value is determined by a formula based on the residual energy and charging speed. Nodes with residual energy below the threshold transfer data compressed to reduce energy consumption, and nodes with residual energy above the threshold transfer data without compression to reduce the delay time between nodes. Simulation based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay time compared with traditional methods.

In contrast with battery-based wireless sensor networks (WSNs), solar-powered WSNs can operate for a longtime assuming that there is no hardware fault. Meanwhile, a mobile sink can save the energy consumption of WSN, but its ineffective movement may incur so much energy waste of not only itself but also an entire network. To solve this problem, many approaches, in which a mobile sink visits only on clustering-head nodes, have been proposed. But, the clustering scheme also has its own problems such as energy imbalance and data instability. In this study, therefore, a cluster-based energy-aware data-sharing scheme (CE-DSS) is proposed to effectively support a mobile sink in a solar-powered WSN. By utilizing the redundant energy efficiently, CE-DSS shares the gathered data among cluster-heads, while minimizing the unexpected black-out time. The simulation results show that CE-DSS increases the data reliability as well as conserves the energy of the mobile sink.

Recently, the use of wireless sensor networks to collect video data for environmental observation or monitoring has been increasing, but they are not suitable for transmitting large video data due to the limitation of node energy. Energy-harvesting node could be used to overcome this limitation, but the harvested energy is also limited. Therefore, an efficient energy utilization method is required to transmit a large amount of video data. This paper proposes a scheme to reduce the number of blackout nodes and increase the amount of collected data by selecting an appropriate video encoding method according to energy condition of the node in a solar-powered wireless sensor network. To collect data continuously regardless of day or night, this scheme allocates the amount of energy that can be used by time, and selects a coding with a high-compression ratio if the allocated amount is large, or a coding with a low-compression ratio if it is small. In this way, the blackouts of relay nodes are reduced and data can be transmitted continuously, which increases the amount of data arriving at the sink node. The simulation results verified confirmed that the proposed scheme prevented the energy exhaustion of the relay nodes and collected more data compared to using one coding only.

We analyze the execution time of Reed-Solomon coding, which is the MAC-layer forward error correction scheme used in CDMA2000 1xEV-DO broadcast services, under different air channel ∙제1저자 : 강경태 ∙교신저자 : 노동건 ∙투고일 : 2014. 1. 16, 심사일 : 2014. 2. 10, 게재확정일 : 2014. 3. 2. * 한양대학교 컴퓨터공학과(Dept. of Computer Science & Engineering, Hanyang University) ** 숭실대학교 정보통신전자공학부(School of Electronic Engineering, Soongsil University) ※ 이 논문은 2012년도 정부(미래창조과학부)의 재원으로 한국연구재단-차세대정보・컴퓨팅기술개발사업의 지원을 받아 수행 된 연구임(No. 2012M3C4A7032182). 64 Journal of The Korea Society of Computer and Information March 2014 conditions. The results show that the time constraints of MPEG-4 cannot be guaranteed by Reed-Solomon decoding when the packet loss rate (PLR) is high, due to its long computation time on current hardware. To alleviate this problem, we propose three error control schemes. Our static scheme bypasses Reed-Solomon decoding at the mobile node to satisfy the MPEG-4 time constraint when the PLR exceeds a given boundary. Second, dynamic scheme corrects errors in a best-effort manner within the time constraint, instead of giving up altogether when the PLR is high; this achieves a further quality improvement. The third, video-aware dynamic scheme fixes errors in a similar way to the dynamic scheme, but in a priority-driven manner which makes the video appear smoother. Extensive simulation results show the effectiveness of our schemes compared to the original FEC scheme. ▸

Since solar energy can be harvested periodically, using solar power in wireless sensor networks (WSNs) requires a different approach to energy consumption from typical batterybased WSNs. Meanwhile, it is also challenging to supply enough energy required for heavy operations such as video data encoding and transferring in battery-based WSNs. Therefore, we address the problem of determining the quality of encoding sensory data on the solarpowered sensor node. Based on a simple energy model of the solar-powered node, proposed scheme controls the quality of encoding data adaptively in the way of using the harvested energy maximally. Experimental results demonstrate the efficiency of our scheme.

In recent years, much attention has been paid to the development of intelligent vehicles that integrate automotive technology into the information technology, with the aim of improving user friendliness and stability. The representative function is a autonomous driving and a cruise control. In designing such vehicles, it is critical to address the real-time issues (i.e., real-time vehicle control and timely response). However, previous research excluded the real-time scheduling. We develop a model car with unmanned cruise control, design the real-time scheduler using cyclic executive to easily adapt the model car, and provide some insight into potential solutions based on various experiments. ∙제1저자 : 이재면 ∙교신저자 : 노동건 ∙투고일 : 2013. 10. 05, 심사일 : 2013. 10. 25, 게재확정일 : 2013. 11. 15. * 한양대학교 컴퓨터공학과(Dept. of Computer Science and Engineering, Hanyang University) ** 숭실대학교 정보통신전자공학부(School of Electronic Engineering, Soongsil University) 본 논문은 2011년도 정부(교육과학기술부)의 재원으로 한국연구재단의 지원을 받아 수행된 과제임 (NRF-2011-0012996) 2 Journal of The Korea Society of Computer and Information January 2014 ▸

Energy-harvesting sensor nodes have been developed to solve the limited lifetime problem in wireless sensor networks. However, these energy-harvesting nodes have similar problems such as blackout time caused by excessive energy consumption and wasting of harvested energy that exceeds the charging capacity of their batteries. To solve this problem, this paper proposes a scheme that uses the surplus energy exceeding the charging capacity of each node's battery to compress data and/or expand transmission range, thereby reducing the workload of relay nodes and increasing the amount of data that reaches the sink node. In this scheme, a node selects the more efficient method between data compression and expansion of transmission range by estimating the amount of energy it harvested and the workload of the relay nodes to the sink. If surplus energy exists even after the aforementioned process, the node combines both methods. The experimental results indicates that the proposed scheme reduces the workload of relay nodes, thereby reducing the blackout time for all nodes and increasing data throughput.

The data-link layer takes charge of reliable inter-node communication in open systems interconnection (OSI) reference model. Generally, in the environment of wireless sensor network (WSN) where errors are frequently occurred, the forward error correction (FEC) method is mainly used at this data-link layer. However, the FEC method requires more amount of energy consumption as it has higher ability of error correction rate, thus reducing the network lifetime seriously. Meanwhile, as the energy is regularly recharged in the solar-powered WSN (in contrast with the battery-based WSN), a greater amount of energy than that required for fundamental operation of nodes might exist in the node. By utilizing this surplus energy efficiently, the proposed energy-aware FEC method can reduce the data loss rate without any decrement of network lifetime. This method uses a trade-off relationship between the energy and data loss rate by adjusting the parity length in the FEC method according to a state of energy in each node. The performance of the proposed scheme is verified through the simulation.

In energy-harvesting wireless sensor networks (WSNs), not only energy savings but also efficient energy utilization is required. This study suggests a scheme that indicates when to send data decided by predicting the remaining energy of a node, and aggregates sensed data to increase the amount of data arrived at the sink node. With this method, if the estimated remaining energy of a node is expected to run over the capacity, it transmits aggregated data or else it turns off its radio and only stores sensed data to decrease the blackout time of nodes. Simulation results show that the proposed scheme decreases the blackout time of nodes and increases the data collecting rate efficiently compared to both normal data sending and the specific amount of aggregated data sending cases.

The periodic nature of solar power requires a different approach to energy consumption in wireless sensor networks (WSNs) from battery-based WSNs. Based on the energy model of a solar-powered node, we develop efficient energy-aware topology-control and routing schemes which utilize a backbone network consisting of energy-rich nodes within the WSN. This backbone handles most of the traffic with low latency, while reconfiguring itself dynamically in response to changes in the availability of energy at each node. Simulation results demonstrate that our schemes can achieve a balance between latency and energy consumption.

Some of the most challenging issues in data outsourcing scenario are the enforcement of authorization policies and the support of policy updates. Ciphertext-policy attribute-based encryption is a promising cryptographic solution to these issues for enforcing access control policies defined by a data owner on outsourced data. However, the problem of applying the attribute-based encryption in an outsourced architecture introduces several challenges with regard to the attribute and user revocation. In this paper, we propose an access control mechanism using ciphertext-policy attribute-based encryption to enforce access control policies with efficient attribute and user revocation capability. The fine-grained access control can be achieved by dual encryption mechanism which takes advantage of the attribute-based encryption and selective group key distribution in each attribute group. We demonstrate how to apply the proposed mechanism to securely manage the outsourced data. The analysis results indicate that the proposed scheme is efficient and secure in the data outsourcing systems.

In this paper, we introduce an efficient energy management using a notion of virtual energy system for shared solar-powered sensor network. Virtual energy system is an abstraction that allows sensor network applications on a node to reserve their own fractions of the shared solar cell and the shared rechargeable battery, hence achieving logically partition of a shared renewable power source. Our results show that our design and implementation are reliable, lightweight and efficient, allowing proper isolation of energy consumption among applications.

Recently, solar energy emerged as a feasible supplement to battery power for wireless sensor networks (WSNs) which are expected to operate for long periods. Since solar energy can be harvested periodically and permanently, solar-powered WSNs can use the energy more efficiently for various network-wide performances than traditional battery-based WSNs of which aim is mostly to minimize the energy consumption for extending the network lifetime. However, using solar power in WSNs requires a different energy management from battery-based WSNs since solar power is a highly varying energy supply. Therefore, firstly we describe a time-slot-based energy allocation scheme to use the solar energy optimally, based on expectation model for harvested solar energy. Then, we propose a flow-control algorithm to maximize the amount of data collected by the network, which cooperates with our energy allocation scheme. Our algorithms run on each node in a distributed manner using only local information of its neighbors, which is a suitable approach for scalable WSNs. We implement indoor and outdoor testbeds of solar-powered WSN and demonstrate the efficiency of our approaches on them. Copyright © 2010 John Wiley & Sons, Ltd.

BCMCS (Broadcast and Multicast Services) are designed to provide media broadcast in cellular networks. In designing media broadcast protocols and systems, such as BCMCS, delay must be thoroughly considered because it is the key QoS parameter in multimedia applications. This paper investigate the forward error correction (FEC) delay using Reed-Solomon (RS) code related to the temporal behavior of Rayleigh fading. We implemented the evaluation framework for the RS decoding delay employing analytic and simulation-based approaches. An analytic method and a simulation approach are combined in a unified environment exploiting Java programming language, ARM9 processor simulator and Mathematica coupled together. This framework enables us to estimate various statistics required to ensure seamless multimedia services.

Data compression involves a trade-off between delay time and data size. More the delay time, lesser the data size and vice versa. There have been many studies performed in the field of wireless sensor networks about increasing network life cycle durations that involve minimizing energy consumption by reducing data size; however, reducing data size results in increased delay time due to the added processing time required for data compression. Meanwhile, as energy generation occurs periodically in solar energy based wireless sensor networks, the redundant energy is often generated that is sufficient to run a node. In this study, the excess energy is used to reduce the delay time between nodes in a sensor network consisting of solar energy based nodes. The energy threshold value is determined by a formula based on the residual energy and charging speed. Nodes with residual energy less than the threshold, transfer data with compression in order to reduce energy consumption, and nodes with residual energy over the threshold transfer data without compression to reduce the delay time between nodes. Simulation based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay time compared with traditional methods.

Most existing simulators for WSNs (wireless sensor networks) model battery-powered sensors and provide MAC and routing protocols designed for battery-powered WSNs. Recently, however, increasingly extensive studies of energy harvesting sensor systems require the development of appropriate simulators, but there are few related studies on such simulators. Unlike existing simulators, simulators for energy harvesting WSNs require a new energy model that is integrated with the energy harvesting, rechargeable battery, and energy consuming models. Additionally, the new model must enable applications of the well-known MAC and routing protocols designed for energy harvesting WSNs and have a convenient user-friendly interface. In this work, we design and implement a user-friendly simulator for solar energy harvesting WSNs.

In this paper, we propose an efficient call admission control scheme for ertPS, one of QoS services in IEEE 802.16, which is designed for Voice over IP. In ertPS, a user's unused spare bandwidth is temporally shared with others, which can achieve the efficient bandwidth usage. Since the bandwidth sharing happens among the connected calls, a BS should retain the appropriate number of connected calls. If it has too small number of calls, the unallocated spare BW will be waisted. Otherwise, the excessive number of calls accepted by the BS may cause cell-overloading and call-dropping. By using the statistical multiplexing and probabilistic guard channel reservation, the proposed scheme increases the bandwidth utilization. The results show that the analytical model and the simulation are the very close, and our scheme can achieve 10% less blocking probability of a new call and higher utilization of bandwidth than a conventional guard channel scheme.

Up to date, numerous studies from a wide range of fields on wireless sensor networks have been performed, and most of them utilize the information of the amount of residual energy on the battery directly or indirectly. However, it is tricky to obtain the information of the residual energy from the real-deployed physical sensor. The residual energy in a battery is usually estimated using a linear function of voltage. We propose a more accurate scheme, for use in wireless sensor nodes, based on the interpolation of lookup tables which allow for temperature and load characteristics, as well as battery voltage.

Solar energy is an inexhaustible supply which can potentially allow a sensor system to run forever, but there are several issues in optimally using the harvested energy, such as the periodic property of charging cycle and the uncertainty of available solar energy. Since solar energy can be harvested periodically, our aim is to use this energy for enhancing the routing performance of the sensor network, not just to minimize the energy consumption to extend the network lifetime. Therefore, we propose a node-level adaptive transmission range determination algorithm which can periodically and locally determines the topological knowledge range of each node based on an allocated energy budget for the timeslot. The proposed algorithm can help the node to find the better next-hop node to which the sensory data is routed, while using the harvested solar energy efficiently.

This paper proposes a reliable wildfire monitoring system based on a wireless sensor network (WSN) sparsely deployed in adverse conditions. The physical environment under consideration is characterized by asymmetric, irregular, and unreliable wireless links, inadequate Fresnel zone clearance, and routing problems, to name a few. We use reliable communication schemes on a fault-tolerant network topology, where sensory data are guaranteed to reach the base station with organized data storage and real-time visualization. Our approach has been validated experimentally for the case of peat-forest wildfire in southern Borneo where the fire breaks out frequently.

We explore the use of IEEE 802.11 wireless technology to support health monitoring application, by providing ubiquitous connectivity while allowing patients greater mobility. The acquisition of electrocardiogram (ECG) is used as an example to highlight the engineering choices, such as packetization and access control rules, that must be made in designing a wireless transport. The error-prone nature of wireless channels, together with the unpredictable delay and jitter caused by the non-deterministic nature of access to the wireless medium, need to be considered. We discuss how our design choice with respect to each issue affects the performance of the network in a high-quality ECG monitoring service with bounded delay. We evaluate our schemes using simulations with non-compressed 2-lead ECG data from the MIT-BIH arrhythmia database.

Troubleshooting unresponsive sensor nodes is a significant challenge in remote sensor network deployments. This paper introduces the tele-diagnostic powertracer, an in-situ troubleshooting tool that uses external power measurements to determine the internal health condition of an unresponsive host and the most likely cause of its failure. We developed our own low-cost power meter with low-bandwidth radio to report power measurements and findings, hence allowing remote (i.e., tele-) diagnosis. The tool was deployed and tested in a remote solar-powered sensing network for acoustic and visual environmental monitoring. It was shown to successfully distinguish between several categories of failures that cause unresponsive behavior including energy depletion, antenna damage, radio disconnection, system crashes, and anomalous reboots. It was also able to determine the internal health conditions of an unresponsive node, such as the presence or absence of sensing and data storage activities (for each of multiple sensors). The paper explores the feasibility of building such a remote diagnostic tool from the standpoint of economy, scale and diagnostic accuracy. To the authors' knowledge, this is the first paper that presents a remote diagnostic tool that uses power measurements to diagnose sensor system failures.

In this paper, we present AdaptSens: a reliable data collection and storage system for solar-powered sensor networks. Unlike battery-operated devices, solar-powered systems have a less predictable energy supply and their ability to harvest energy depends on past spending, thereby creating incentives for adaptive matching of energy supply and demand. Our storage system is novel in its layered architecture and its incremental layer activation mechanism. AdaptSens provides a set of functions, in separate layers, such as sensory data collection, replication (to prevent failure-induced data loss), and storage balancing (to prevent depletion-induced data loss). The mechanism utilizes surplus energy when available by activating more layers, and resorts to progressively more energy-efficient (partial hibernation) modes when energy is scarce. Best reliability is achieved when all layers are active but meaningful intermediate modes allow different degrees of energy conservation. The efficacy of AdaptSens in trading off reliability for energy is tested on both an outdoor system and an indoor testbed. Evaluation results show that AdaptSens minimizes the sum of all data losses when combining the energy, storage and node failure factors.

Broadcast and multicast services (BCMCS) in CDMA2000 wireless telecommunications networks restrict high- quality multimedia services to their intended recipients by encrypting content. The air specification of BCMCS includes the Advance Encryption Standard (AES) block cipher in the security layer of the broadcast protocol suite. We surveyed the AES block cipher algorithm and characterized the time it requires. We also profiled the execution time of each transformation that comprises the AES cipher. Drawing on these results, we proposed a analytic model that can predict the time required to decrypt encrypted contents at a mobile by considering cross-layer information, such as the characteristics of error control using Reed-Solomon coding in the MAC layer and varying conditions of the fading channel in the physical layer. This model will enable us to estimate the buffer size required to ensure seamless multimedia services. Index Terms—AES cipher, fading channel, timing analysis. I. INTRODUCTION The aim of BCMCS (1), (2) is to provide broadcast and multicast services to subscribers, who receive content from content sources (CSs), via a cellular serving system. The content may be IP multimedia messages, such as broadcast multimedia messages. A CS may be part of the serving net- work, or an independent entity. If access to BCMCS requires a subscription, then the content can be encrypted so that it can only be viewed or otherwise processed by authorized users (3). In order to prevent illegal access, the BCMCS access network encrypts the BCMCS content with the Advanced Encryption Standard (AES (4)-(6)), also known as Rijndael, which uses defined encryption procedures (7). The AES is an iterated block cipher with a fixed block size and a variable key length. It has been adopted as an encryption standard by the U.S. government, extensively analyzed and is now used worldwide. The AES was announced by the National Institute of Standards and Technology (NIST) as U.S. FIPS PUB 197 (FIPS 197) in 2001, after its predecessor, Data Encryption Standard (DES (8)), was found to be too weak, because of its small key size, in the context of advances in processor power. In this paper, we analyze the AES block cipher in detail, and characterize the execution time required by an AES cipher in the context of the flow diagram of the algorithm. Based on this fundamental analysis, we propose a method of analyzing the delay incurred by the operation of the security protocol in the BCMCS environment, and show how it depends on the configuration of the error control performed in the MAC layer. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2007-357-D00174). The BCMCS MAC protocol uses an error control based on forward error-correction (FEC) to serve all subscribers reliably (9), (10), even in the presence of poor channel con- ditions. This FEC mechanism uses Reed-Solomon (RS) (11), (12) codes on top of the turbo codes to reduce the packet error-rate at mobile receivers, which experience fading in the physical channel. A fading channel has a memory property, which allows state transitions to be represented by a Markov process (13)-(15), and this in turn permits a function to be constructed which can predict the number of errors in a particular RS codeword of a receiver, and hence the expected number of residual packet errors after RS decoding at the mobile. This allows us to formulate and evaluate a metric for the delay incurred by AES deciphering at the security layer of a receiving mobile, for different settings of the RS error control at the MAC layer, and for different conditions of fading on the broadcast physical channel.

Using solar power in wireless sensor networks (WSNs) requires a different approach to energy consumption from networks with battery-based nodes. Since solar energy can be harvested periodically, our aim is to use this harvested energy efficiently for specific network-wide performance goals, not just to minimize the energy consumption to sustain the network lifetime. Therefore, we propose a flow control algorithm to maximize the amount of data collected by the network while cooperating with our energy allocation scheme in order to efficiently use the available solar energy which varies from time to time. Our algorithm runs on each node in a distributed manner using only information about its neighbors. We demonstrate the efficacy of our approach on a real solar-powered WSN.

In this paper, we present a reliable storage service, called SolarStore, that adaptively trades-off storage reliability versus energy consumption in solar-powered sensor networks. SolarStore adopts a predominantly disconnected network model, where long-running data-collection experiments are conducted in the absence of a continuous connection to the outside world. SolarStore (i) replicates data in the network until the next upload opportunity, and (ii) adapts the degree of data replication dynamically depending on solar energy and storage availability. The goal is to maximize the amount of data that can eventually be retrieved from the network subject to energy and storage constraints. Maximization of retrievable data implies minimizing sensing blackouts due to energy depletion as well as minimizing loss due to node damage in harsh environmental conditions. We have deployed an outdoor solar-powered sensor network, on which SolarStore is implemented and tested. An indoor testbed is also set up for performance evaluation under environmental conditions not attained locally. Experiments show that SolarStore is successful in dynamically responding to variations in the environment in a manner that increases retrievable data under different node failure scenarios.

Using solar power in wireless sensor networks (WSNs) requires adaptation to a highly varying energy supply. From an application's perspective, however, it is often preferred to operate at a constant quality level as opposed to changing application behavior frequently. Reconciling the varying supply with the fixed demand requires good tools for predicting supply such that its average is computed and demand is fixed accordingly. In this paper, we describe a probabilistic observation-based model for harvested solar energy, which accounts for both long-term tendencies and temporary environmental conditions. Based on this model, we develop a time-slot-based energy allocation scheme to use the periodically harvested solar energy optimally, while minimizing the variance in energy allocation. Our algorithm is tested on both outdoor and indoor testbeds, demonstrating the efficacy of the approach.

A number of studies have been actively conducted to address limited energy resources in sensor systems over wireless sensor networks. Most of these studies are based on energy-aware schemes, which take advantage of the residual energy from the sensor system's own or neighboring nodes. However, existing sensor systems estimate residual energy based solely on voltage and current consumption, leading to inaccurate estimations because the residual energy in real batteries is affected by temperature and load. This misinformation makes a complete nonsense of existing energy-aware research, which is not allowed in reliable WSN applications. In this study, therefore, an efficient residual-energy estimation scheme is proposed in consideration of not only the voltage but also the temperature and load characteristics of batteries. The performance of the proposed scheme was verified through an experiment and simulations in the actual environment, and its effect gets more notable when the scale of the WSN goes larger.