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A cognitive radio (CR) is a promising technology to solve the emerging spectrum crisis, especially for applications where thousands of wireless sensor nodes are deployed. Since continuous spectrum sensing will greatly reduce the lifetime of a network composed of energy-restricted CR nodes, an accurate method for predicting spectrum occupancy is necessary to improve energy efficiency. This paper proposes a hidden Markov model (HMM)-based cooperative spectrum sensing (CSS) that predicts the status of a network environment. The traditional prediction algorithms for cooperative spectrum sensing assume that all CR nodes have the same network environment. However, the channel availability of various CR nodes can be quite different, and thus the traditional algorithms will lead to low prediction accuracy in a complex radio environment. The proposed methods learn the historical spectrum sensing results and help the network to make an energy-efficient spectrum sensing decision. More specifically, the hidden state of HMM is set to different areas, where primary users (PUs) perform different activities. A Baum-Welch (BW) algorithm is employed to estimate the parameters of the HMM based on the past spectrum sensing results, and then the parameters are fed to a forward algorithm for the predicting of PUs' activity. Based on the prediction, secondary users (SUs) are classified into either ”interfered by PU” or ”not interfered by PU. ” The nodes selected as ”interfered by PU” will not perform spectrum sensing to reduce unnecessary energy consumption. The performance of the proposed method is evaluated using the simulations under different traffic conditions. The simulation results show that, compared with the conventional HMM-based methods, the effectiveness of the proposed algorithm in energy efficiency and spectrum utilization improved by about 13% and 15%, respectively.

The cognitive radio technology enables secondary users (SUs) to occupy licensed bands when primary users (PUs) are not occupy them. Spectrum sensing is a key technology for SUs to detect PUs, and the sensing time is a critical parameter for spectrum sensing performance. Optimum sensing time tradeoffs between the spectrum sensing performance and the secondary throughput. This paper proposes a novel spectrum sensing scheme that performs spectrum sensing for either one period or two periods based on the previous sensing result. Due to the energy constraint in cognitive radio sensor networks, the energy efficiency is maximized by optimizing spectrum sensing time. In order to seek the optimal sensing time, the objective function is proven to be a concave function and the Golden Section Search method is employed. Our simulation study verifies that the proposed scheme improves the network energy efficiency, especially when PUs are more active.

The cognitive radio (CR) technology allows secondary users (SUs) to occupy the licensed bands opportunistically without causing interferences to primary users (PUs). SUs perform spectrum sensing to detect whether PUs are busy or idle. Therefore, spectrum sensing directly affects the performance of the PU protection and the secondary throughput. The sensing time is a critical parameter for spectrum sensing performance, and the optimum sensing time is a tradeoff between the spectrum sensing performance and the secondary throughput. In this paper, a novel spectrum sensing scheme is proposed to maximize both sensing accuracy and network energy efficiency. In order to provide a better protection for the PU, another spectrum sensing is adaptively performed according to the first sensing result. In other words, SU will perform spectrum sensing again to confirm that the PU is indeed idle when the first sensing result indicates the PU is idle. Due to the energy constraint in CR sensor networks, this adaptive sensing interval can also be adjusted according to the varying activity of the PU to maximize the network energy efficiency. Finally, our simulation study validates that the proposed scheme improves both the spectrum sensing performance and the energy efficiency compared with other existing methods.

In a wireless body area network (WBAN), high data reliability and long operating time are important requirements. The retransmission process of the default Automatic Repeat reQuest (ARQ) mechanism in IEEE 802.15.4 is a suitable method to ensure the data reliability of WBAN communications, where frame loss can occur frequently. However, retransmitting the entire DATA frame is energy inefficient due to the fact that the most of payload data within lost frames are only partially corrupted. Therefore, this paper proposes the automatic segment repeat request scheme for the IEEE 802.15.4-based WBANs. The proposed scheme partitions the data payload into segments when the channel condition is bad, and retransmits only the corrupted segment(s). This reduces the size of the retransmitted frames, which improves frame reception rate and decreases the amount of transmitted traffic, and thus energy consumption. Our experiments using a real IEEE 802.15.4-based WBAN test bed show that the proposed method provides higher transmission reliability and lower power consumption than the default IEEE 802.15.4 ARQ mechanism.

Cognitive radio technology can enable secondary users (SUs) to occupy licensed bands in a non-interference way. SUs perform spectrum sensing to determine the presence of primary user (PU). Spectrum sensing period and transmission time are the critical parameters for the sensing performance and the secondary throughput, respectively. In this paper, a joint optimization of sensing period and transmission time is proposed for cognitive radio sensor network (CRSNs). The joint optimization model is established by taking the network energy efficiency and interference caused by SU's traffic into consideration. By using the optimization model, we try to figure out the optimal sensing period and transmission time which can guarantee the network energy efficiency and maximize the network throughput jointly. The performance of the proposed optimization model is evaluated through a set of simulations, and the existence of the optimal sensing period and transmission time is verified.

Clustering schemes have been adopted as an efficient solution to prolong network lifetime and improve network scalability. In such clustering schemes cluster ratio is represented by the rate of the number of cluster heads and the number of total nodes, and affects the performance of clustering schemes. In this paper, we mathematically analyze an optimal clustering ratio in wireless sensor networks. We consider a multi-hop to one-hop transmission case and aim to provide the optimal cluster ratio to minimize the system hop-count and maximize packet reception ratio between nodes. We examine its performance through a set of simulations. The simulation results show that the proposed optimal cluster ratio effectively reduce transmission count and enhance energy efficiency in wireless sensor networks.

By cognitive radio technology, secondary users (SUs) can use licensed bands opportunistically without causing interferences to primary users (PUs). Spectrum sensing is performed by SUs to detect that PUs are present or not. Therefore, spectrum sensing is a key technology for cognitive radio (CR), and sensing time is a critical parameter for spectrum sensing performance. The longer sensing time can result in a higher sensing accuracy. However, it will lead to occupying data transmission period, and consuming higher energy in spectrum sensing. In this paper, a novel spectrum sensing scheme is proposed to guarantee both of the sensing accuracy and energy efficiency. In the proposed scheme, SU will dynamically decide to perform spectrum sensing one or two periods according to the sensing result of the current frame for guaranteeing the sensing accuracy. Furthermore, in order to guarantee the network energy efficiency, the spectrum sensing time is optimized through the mathematical model of the secondary network. Simulation results validate that the energy efficiency and miss detection probability of SU can be improved significantly by the proposed scheme.

This paper proposes a power management technique for video playback in mobile multimedia devices, which combines low-power video decoding and QoS-guaranteed algorithms: ILI (Interval-based Linear Interpolation) and QLB (QoS-guaranteed and Low-power Buffering). First, the proposed ILI algorithm precisely estimates the decoding time of video frames through interval-based linear interpolation. Second, the QLB algorithm has two modes of operation, and dynamically switches between the modes based on feedback of decoding statistics of recent frames. The combinations ILI and QLB algorithms allow the CPU voltage and clock frequency to be adjusted to the optimal setting. Our extensive experiments show that the proposed approach can achieve about 4.8%-13.7% more power savings compared with existing methods.

In this paper, we propose an routing algorithm for ultra low power and high reliable transmission in WBAN environment. This algorithm is to minimize energy consumption and to maximize the life and reliability for medical devices. Also, this algorithm is not only medical devices but also non-medical devices is to minimize energy consumption and to maximize the life of device. The combination of the distance from the previous node and residual energy calculates weight. The calculated weight is used to calculate the weight of full path by cumulative weights. The full path to the smallest of the weights are set to the path. Also this algorithm is able to select another path to avoid the error path by determining the link status between nodes, when occurs link error and congestion. In this paper, we show that WSN routing algorithm based on shortest hop count routing algorithm and EAR routing algorithm compared to ensure high reliability and low power characteristic of WBAN to be verified through simulations.

Reliable data transmission is desirable in wireless sensor networks due to the high packet loss rate during multi-hop transmissions. To reliably transmit data for event-driven applications, packet loss recovery mechanism is needed. For loss recovery, sensor nodes need to keep packets in their buffers until transmissions successfully complete. However, since sensor nodes have limited memory, packets cannot be buffered for a long period of time. This letter proposes an efficient buffer management technique that caches data packets for appropriate amount of time to minimize the resource requirements and at the same time provide reliable data transmission among sensor nodes.

Sensor network deployment is very challenging due to the hostile and unpredictable nature of environments. The field coverage of wireless sensor networks (WSNs) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of mobile sensors. In this paper, we introduce a comprehensive taxonomy for WSN self-deployment in which three sensor relocation algorithms are proposed to match the mobility degree of sensor nodes, particle swarm optimization based algorithm (PSOA), relay shift based algorithm (RSBA) and energy efficient fuzzy optimization algorithm (EFOA). PSOA regards the sensors in the network as a swarm, and reorganizes the sensors by the particle swarm optimization (PSO) algorithm, in the full sensor mobility case. RSBA and EFOA assume relatively limited sensor mobility, i.e., the movement distance is bounded by a threshold, to further reduce energy consumption. In the zero mobility case, static topology control or scheduling schemes can be used such as optimal cluster formation. Simulation results show that our approaches greatly improve the network coverage as well as energy efficiency compared with related works.

Applying cognitive radio technologies to wireless sensor networks (WSNs) explores new possibilities for network architectures. The cognitive radio technology enables opportunistic access of unlicensed sensor nodes to licensed bands without influencing primary users (PUs). Spectrum sensing is a key technology to identify the presence of PUs. Benefit from the diversity of different sensors, Cooperative spectrum sensing (CSS) can provide a more reliable sensing result compared with individual spectrum sensing. However, due to limited energy of batteries in cognitive radio sensor networks (CRSNs), more energy consumed by CSS will result in a short network lifetime. In this paper, a novel sensing nodes selection scheme is proposed to improve energy efficiency of cooperative spectrum sensing for cluster-based CRSNs. A novel optimization technique is employed to optimize the number of spectrum sensing nodes in a cluster. The optimized results can efficiently maximize the detection probability and minimize false alarm probability. In addition to this, the proposed scheme can efficiently balance the energy consumption among different sensors to some extent. Through a set of simulation, it is verified that the proposed scheme can obtain a good sensing performance with less sensor nodes and energy consumption, and thus, prolong the network lifetime.

In a wireless sensor network, sensor nodes are deployed in an ad hoc fashion and they deliver data packets using multihop transmission. However, transmission failures occur frequently in the multihop transmission over wireless media. Thus, a loss recovery mechanism is required to provide end-to-end reliability. In addition, because the sensor nodes are very small devices and have insufficient resources, energy-efficient data transmission is crucial for prolonging the lifetime of a wireless sensor network. This paper proposes a transmission power control mechanism for reliable data transmission, which satisfies communication reliability through recovery of lost packets. The proposed method calculates packet reception rate (PRR) of each hop to maintain end-to-end packet delivery rate (PDR), which is determined based on the desired communication reliability. Then, the transmission power is adjusted based on the PRR to reduce energy consumption. The proposed method was evaluated through extensive simulations, and the results show that it leads to more energy-efficient data transmission compared to existing methods.

Video can be encoded into multiple-resolution format in nature. A multi-resolution or scalable video stream is a video sequence encoded such that subsets of the full resolution video bit stream can be decoded to recreate lower resolution video streams. Employing scalable video enables a video server to provide multiple resolution services for a variety of clients with different decoding capabilities and network bandwidths connected to the server. The inherent advantages of the multi-resolution video server include: heterogeneous client support, storage efficiency, adaptable service, and interactive operations support. For designing a video server, several issues should be dealt with under a unified framework including data placement/retrieval, buffer management, and admission control schemes for deterministic service guarantee. In this paper, we present a general framework for designing a large-scale multi-resolution video server. First, we propose a general multi-resolution video stream model which can be implemented by various scalable compression techniques. Second, given the proposed stream model, we devise a hybrid data placement scheme to store scalable video data across disks in the server. The scheme exploits both concurrency and parallelism offered by striping data across the disks and achieves the disk load balancing during any resolution video service. Next, the retrieval of multi-resolution video is described. The deterministic access property of the placement scheme permits the retrieval scheduling to be performed on each disk independently and to support interactive operations (e.g. pause, resume, slow playback, fastforward and rewind) simply by reconstructing the input parameters to the scheduler. We also present an efficient admission control algorithm which precisely estimates the actual disk workload for the given resolution services and hence permits the buffer requirement to be much smaller. The proposed schemes are verified through detailed simulation and implementation.

Due to rapid development of embedded system technologies, miniaturization of low-power sensor nodes becomes possible. Based on these improvements of sensor nodes, several sensor applications can run on a single sensor hardware and all of the applications in sensor network should be guaranteed the real-time requirements and independence. To satisfy these requirements, the virtualization concept was proposed. However, the virtualization cannot be applied to sensor nodes because it requires powerful computing resources. To solve this problem, the partitioning scheme which is categorized into temporal partitioning and spatial partitioning has been proposed. In this paper, we design and implement temporal partitioning scheme for sensor nodes and validate that our implementation guarantees minimum execution time of each partition.

Coexistence problem occurs when a single WBAN(Wireless Body Area Network) locates in multiple WBANs environment. In that case, WBANs are suffered from serious channel interferences which may degrade the performance of each WBAN due to failure of data transmission. Because WBAN handles physical signal or emergency data affecting human life, WBAN requires the detection of coexistence condition to guarantee reliable communication continuously for each sensor node of WBAN. In this paper, we present a prediction algorithm to detect coexistence problem efficiently in multiple WBANs environment. The algorithm measures PRR(Packet Reception Ratio) and SINR(Signal to Interference and Noise Ratio) to detect interference reliably. In order to handle coexistence problem efficiently, the algorithm employs the naive Bayesian classifier which is one of machine learning techniques to classify the coexistence condition into four states. We conduct extensive simulations for coexistence detection with various packet transmit rates of sender node and speeds of mobile WBAN by using Castalia 3.2 simulator based on OMNet++ platform. Consequently, we demonstrate that the proposed algorithm provides more reliable and accurate performance than existing studies to detect coexistence in multiple WBANs environment.

High Definition video streaming over WLANs faces many challenges because video data requires not only data integrity but also frames have strict playout deadline. Traditional streaming methods that rely solely on either UDP or TCP have difficulties meeting both requirements because UDP incurs packet loss while TCP incurs delay. This paper proposed a new streaming method called Flexible Dual-TCP/UDP Streaming Protocol (FDSP) that utilizes the benefit of both UDP and TCP. The FDSP takes advantage of the hierarchical structure of the H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit non-important elements. The proposed FDSP is implemented and validated under different wireless network conditions. Both visual quality and delay results are compared against pure-UDP and pure-TCP streaming methods. Our results show that FDSP effectively achieves a balance between delay and visual quality, thus it has advantage over traditional pure-UDP and pure-TCP methods.

Clustering schemes have been adopted as efficient solutions to prolong network lifetime and improve network scalability. In such clustering schemes cluster ratio is represented by the rate of the number of cluster heads and the number of total nodes and affects the performance of clustering schemes. In this paper, we mathematically analyze relation between cluster ratio and path loss in a cluster and an optimal cluster ratio is developed to provide promising channel and minimize packet loss. We examine its performance through a set of simulations. The simulation results show that the proposed optimal cluster ratio effectively guarantees packet reception rate in WSN.

Simple, fast, low energy consumption time synchronization protocol is needed by sensor networks. Mutual interaction synchronization is highly attractive for wireless sensor network for its simplicity, self-organization and good scalability. The proposed selective coupling synchronicity algorithm (SCSA) addresses the problem of low efficiency caused by the phase swing actions in the original all pulse coupling model. The benefits are fast synchronizing speed and associated low energy consumption. The simulation results show that our algorithm performs better than the original model in most situations.

Sensor network deployment and its maintenance are very challenging due to hostile and unpredictable nature of environments. The field coverage of a wireless sensor network (WSN) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of sensors. In this paper, we propose an energy-efficient fuzzy optimization algorithm (EFOA) for movement assisted self-deployment of sensor networks based on three descriptors - energy, concentration and distance to neighbors. The movement of each sensor node is assumed relatively limited to further reduce energy consumption. The existing next-step move direction formulas are improved to be more realistic. We also propose a network maintenance strategy in the post-deployment phase based on the sensor node importance level ranking. Simulation results show that our approach not only achieves fast and stable deployment but also greatly improves the network coverage and energy efficiency as well as prolongs the lifetime.

Sensor deployment is an important issue in designing sensor networks. In this paper, particle swarm optimization (PSO) approach is applied to maximize the coverage based on a probabilistic sensor model in mobile sensor networks and to reduce cost by finding the optimal positions for the cluster-head nodes based on a well-known energy model. During the coverage optimization process, sensors move to form a uniformly distributed topology according to the execution of algorithm at base station. The simulation results show that PSO algorithm has faster convergence rate than genetic algorithm based method while achieving the goal of energy efficient sensor deployment.

In this paper, we propose a relay shift based approach to solve uneven sensor distribution problem due to the initial random dropping or the existence of faulty sensors. The distinguishing feature of our work is that the sensors in our model have limited mobility. After determining the optimal cluster head positions by particle swarm optimization (PSO) method, we use proposed Relay Shift Based Algorithm (RSBA) for movement assisted sensor deployment. Dijkstra’s algorithm is applied to find a shortest path from a redundant sensor to a virtual node point in an uncovered area, and each sensor moves along this path by relay shift based on the principle that evenly distributed sensors can provide better coverage. Simulation results show that our approach can provide high coverage within a short time and limited movement distance as well as ensuring connectivity and energy efficiency.

Coverage, energy efficiency and connectivity are important metrics for sensor network deployment. In this paper, we propose a relay shift based approach to eliminate coverage hole due to the initial random dropping or the existence of faulty sensors. The sensors in our model are assumed to have only limited mobility. Firstly, the optimal number of clusters are determined by energy function and the optimal cluster head positions are determined by Fuzzy C-mean (FCM). We use proposed Relay Shift Based Algorithm (A*-RSBA) for movement assisted sensor deployment. A* algorithm is applied to find a shortest path from a redundant sensor node to a virtual node point in an uncovered area, and each sensor moves along this path by relay shift based on the principle that evenly distributed sensors can provide better coverage. Simulation results show that our approach can improve coverage within a short time and limited movement distance as well as ensuring connectivity and energy efficiency.

In ad hoc sensor networks, sensor nodes have very limited energy resources, thus energy consuming operations such as data collection, transmission and reception must be kept at a minimum. This paper applies particle swarm optimization (PSO) approach to optimize the coverage in ad hoc sensor networks deployment and to reduce cost by clustering method based on a well-known energy model. Sensor nodes are assumed to be mobile, and during the coverage optimization process, they move to form a uniformly distributed topology according to the execution of algorithm at base station. The simulation results show that PSO algorithm has faster convergence rate than genetic algorithm based method while demonstrating good performance.

Core operations (e.g. TDMA scheduler, synchronized sleep period, data aggregation) of many proposed protocols for different layer of sensor network necessitate clock synchronization. Our paper mingles the scheme of dynamic clustering and diffusion based asynchronous averaging algorithm for clock synchronization in sensor network. Our proposed algorithm takes the advantage of dynamic clustering and then applies asynchronous averaging algorithm for synchronization to reduce number of rounds and operations required for converging time which in turn save energy significantly than energy required in diffusion based asynchronous averaging algorithm.