Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC’s staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC.

Recent years, anomaly detection for time series in wireless sensor networks has attracted much research attention. Existing anomaly detection methods based on pattern representation ignore the trend features of an original sequence. This leads to low detection accuracy. This paper aims to solve the above problem with an adaptive sliding window for anomaly detection. The advantage of this method is that the size of the sliding window is changed according to the trend of the time series. Thus, the extracted trend feature points can accurately represent an original time series. The pattern representation is vertically divided into several subspaces with equal-size in the time domain. We employ four features which are extracted from each subspace to obtain a pattern matrix. The similarity among different patterns is measured according to the similarity matrix which is formed by pattern matrices. Finally, anomaly scores obtained from similarity matrices are compared with a threshold to detect anomaly. The proposed method is verified on two synthetic datasets and a real-world dataset. Experimental results showed that our method could improve the accuracy and reduce the specificity compared to three classic models.

With large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

IoT is widely used and plays an critical role in transforming traditional industries, leading emerging industries, improving people's lives, and providing national security. From the physical layer of communication, it is particularly important to ensure the reliability of transmission in the IoT. Polar code has outstanding performance, but its coding structure determines that the length of polar codes must be the power of 2. The structure of polar code is more flexible with the existing puncturing schemes, but the decoding performance of these schemes varies with the number of puncturing bits. The decoding performance of the puncturing scheme in C0 mode drops sharply when the number of puncturing bits is large, and the decoding performance of the puncturing scheme in C1 mode is poor when the number of puncturing bits is small. In this paper, an improved polar code puncturing scheme is proposed based on the forward sequential puncturing scheme and the bit reversal puncturing scheme in the C0 puncturing mode. Compared with the traditional puncturing scheme, the simulation results indicated that the proposed scheme solves the problem of decoding performance degradation when the number of puncturing bits is too large in C0 mode; compared with the scheme in C1 mode, the scheme in this paper has a decoding performance gain of about 0.2dB when the block error rate reaches 10(-3), which can also be achieved in high or low code rate.

Internet of Things(IoT) makes everything intelligently possible. In the IoT era, the application of products such as smart cities and smart homes will provide people with a more convenient and quality life. 5G is the prelude and foundation for achieving this. In the 5G mobile communication system, the base station performs adaptive coding and modulation based on the channel state information fed back by user, and improves the spectrum efficiency by selecting different combinations of modulation types and code rates. Therefore, the channel state information estimated by receiver side needs to be mapped to a value measuring the overall channel state and fed back to the base station. This paper first simulates the modulation and coding schemes(MCS) given in the 5G standard under Gaussian channels, and obtains the SNR-BLER curves of different MCS. Then, the scheme of SNR mapping to CQI is improved, and a new mapping relationship between SNR and CQI is obtained by performing region fitting. Finally, for the adjustment factor optimization process, a new adjustment factor is introduced and the calculation formula for the optimization factor is modified. The BLER is taken as the logarithm and then used Logarithmic results to find the mean square error, thereby reducing mapping errors Compared with the traditional scheme, the proposed scheme has higher throughput, so as to better promote IoT technology.

In the network of poor conditions and high error ratio, the stored data may be lost. If the lost data cannot be recovered effectively, it will make serious effects. Therefore, guaranteeing the availability and integrity of data is very important in any storage system, especially in cloud storage. The existing data redundancy strategies in cloud storage are unable to adapt to dynamic changes in the network environment. ADRS( Adaptive Data Redundancy Strategy), which combines fragment replication and LT(Luby Transform) code, is proposed. ADRS can adjust its parameters according to the current network state to optimize the performance of cloud storage networks. When the amount of source packets sent by the server nodes is smaller than the threshold, fragment replication will be the main storage mode and supplemented by LT code. When the amount of source packets is larger than the threshold, LT code is the main storage mode and supplemented by fragment replication. The simulation results show that ADRS fully integrates the advantages of fragment replication and LT code, which can reduce average delay and improve the reliability and stability of the system at the cost of increasing some storage space.

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

In challenging environment, sensory data must be stored inside the network in case of sink failures, we need to redistribute overflowing data items from the depleted storage source nodes to sensor nodes with available storage space and residual energy. We design a distributed energy efficient data storage algorithm named distributed data preservation with priority (D2P2). This algorithm takes both data redistribution costs and data retrieval costs into account and combines these two problems into a single problem. D2P2 can effectively realize data redistribution by using cooperative communication among sensor nodes. In order to solve the redistribution contention problem, we introduce the concept of data priority, which can avoid contention consultations between source nodes and reduce energy consumption. Finally, we verify the performance of the proposed algorithm by both theory and simulations. We demonstrate that D2P2's performance is close to the optimal centralized algorithm in terms of energy consumption and shows superiority in terms of data preservation time.

In remote and inaccessible environment, sensory data must be stored inside the network in case of sink failures. Since all sensor nodes have limited storage capacity and energy, so we need to ensure that the most important and urgent data can be stored and decoded first. In this paper, we studied the data storage problem in sink-failures sensor networks. Considering that most existing algorithms mainly focus on how to maximized number of stored data, which makes the loss of the most critical data, so we design a novel network coding data storage scheme based on priority named NCSP (Network Coding Storage with Priority). In this scheme, in order to prevent the loss of the most critical information, data in the networks are divided into independent priority groups. Different network coding schemes are used in different groups. Aggressiveness mechanism is also considered in this paper. Finally, MATLAB simulations results demonstrate that NCSP outperforms than other algorithms in terms of decoding priority.

Forward Error Correcting codes are the codes applied in error correction in the communications, by adding redundancies. Without a feedback channel, it is an attractive alternate of ARQ in long delay path and one-way transmission. FECs could also avoid multicast problems. However, in practice, FECs could be relative inefficient due to the expensive cost of computation and requirement of over-transmission sometimes. Hence, in the random error or the burst error channel, particular FEC scheme should be used correspondently.