Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications

Wireless sensor networks (WSN) are widely used in various situations. Energy saving is one of the most important issues because of the limited power. Communication is the mainly energy consuming part of sensor nodes. Reducing the size of transmitted data can conserve the energy of nodes. Spatial and temporal correlation is ubiquitous in wireless sensor networks. By exploiting spatial and temporal correlation, only a subset of data need to be transmitted and the rest of the data can be estimated. Data aware clustering is an effective way to exploit spatial correlation among sensor nodes. In our SC-EEDC framework, fuzzy ART artificial neural network is used to measure the shape similarity among data sequences and the magnitude similarity is estimated by a magnitude similarity model. And two corresponding estimation algorithm are proposed. We propose Weighting based K-means clustering algorithm (WK-means), which considers multiple clustering factors in addition to the data similarity. The K-means algorithm structure is introduced in clustering algorithm to search the more energy-saving clustering topology. Anchor node based data collection strategy is proposed to measure the spatial correlation in real time and suppress the transmission of spatial redundant data at source node. Sleeping scheduling is introduced to dynamically adjust the spatial sampling rate and the temporal redundancy is reduced using Length Encoding. The cluster maintenance scheme keeps the cluster structure’s efficiency over the network lifetime. Simulation results show that our SC-EEDC achieves significant data reduction without affecting the accuracy of collected data, reduces the energy consumption in each round of data collection and effectively prolongs the network lifetime.

Directional antennas in wireless sensor networks (WSNs) have several advantages including the enhanced capacity of the network, longer range of transmission, better spatial reuse and lower interference. By enhancing the alleviating contention and the range of communication, the directional antennas improve the performance of WSNs. In common, various methods like localisation, power management, data aggregation and optimisation obtain these advantages in WSNs; however, the reliability requirements and the energy concerns make the antenna technology more advantageous. When compared with methods, in which the directional antennas provide advantages like the extended range of transmission, energy savings, better prevention of wormhole attack, long coverage and high interference tolerance. This review provides a detailed study of recent improvements and open research problems on directional antennas in WSN applications. First, the introduction and the background of directional antennas briefly based on their classification radiation patterns and advantages in directional communication in WSNs. During the past years, a number of directional antennas are designed for the applications of WSNs. In this study, recently developed directional antennas and their applications in WSNs are reviewed. The applications of directional antenna in WSNs are classified into security benefits, localisation, convergecast, contention reduction and energy efficiency.

Data compression is a technique used to save energy in Wireless Sensor Networks by reducing the quantity of data transmitted and the number of transmission. Actually, the main cause of energy consumption in WSN is data transmission. There exist critical applications such as delay constrained activities in which the data have to arrive quickly to the Sink for rapid analysis. In this article, we explore the use of data compression algorithms for delay constrained applications by evaluating a recent data compression algorithm for WSN named K-RLE with optimal parameters on an ultra-low power microcontroller from TI MSP430 series. The relevance of the parameter K for the lossy algorithm K-RLE led us to propose and compare two methods to characterize K: the Standard deviation and the Allan deviation. The last one allow us to control the percentage of data modified. Experimental results show that data compression is an energy efficient technique which can also perform in certain cases the global data transfer time (compression plus transmission time) compared to direct transmission.

In many applications of wireless sensor networks (WSNs), sensors are deployed in hostile environments where an adversary can eavesdrop communications. To secure communications in WSNs, the q-composite key predistribution scheme has been widely recognized as a suitable approach. In this paper, we investigate connectivity in secure WSNs operating under the q-composite scheme, in consideration of different link constraints: the unreliability of wireless links and the requirement that two sensors have to be within certain distance to have a link. We formally derive conditions on how to scale the model parameters so that the network is securely connected with high probability when the number of sensors becomes large. The results are given in the form of zero-one laws and provide useful guidelines for designing securely and reliably connected sensor networks.

Wireless industrial sensor networks are wireless sensor networks which have been adapted to industrial applications. Most techniques for wireless sensor networks can be applied to wireless industrial sensor networks. However, for industrial applications of wireless industrial sensor networks, new requirements such as real-time, reliable delivery need to be considered. In this paper, we propose EARQ, which is a novel routing protocol for wireless industrial sensor networks. It provides real-time, reliable delivery of a packet, while considering energy awareness. In EARQ, a node estimates the energy cost, delay and reliability of a path to the sink node, based only on information from neighboring nodes. Then, it calculates the probability of selecting a path, using the estimates. When packet forwarding is required, it randomly selects the next node. A path with lower energy cost is likely to be selected, because the probability is inversely proportional to the energy cost to the sink node. To achieve real-time delivery, only paths that may deliver a packet in time are selected. To achieve reliability, it may send a redundant packet via an alternate path, but only if it is a source of a packet. Experimental results show that EARQ is suitable for industrial applications, due to its capability for energy efficient, real-time, reliable communications.

The wireless sensor network is an emerging technology, which is used to sense and monitor the environment. As the nodes are deployed in an open environment, the security is one of the essential factors. The cryptography techniques can ensure confidentiality, integrity and authentication. However, wireless sensor network also needs to deal with inside and outside attackers. To deal with outside attackers, attacks by compromised or malicious nodes, trust management system is suggested by many researchers in the area of wireless sensor network. Trust management system can be implemented in various applications for security management such as secure data aggregation, secure cluster head selection, trusted routing, access control, etc. Many researchers provide different kind of solutions for these secure applications based on trust management. However, to incorporate, all such applications on a single sensor node in the network, it is essential to design and develop a trust management system, which considers various aspects and applications of wireless sensor network. As a result, in this paper, we would like to propose a parameter and trust factor based secure communication framework and design a trust management system for wireless sensor networks. Our main contribution is to identify various parameters and trust factors which influences on trust in wireless sensor network and developing a framework for a trust management system based on various parameters and trust factors. The working of the proposed model is shown by simulation experiments conducted in MATLAB for the application of secure communication, data aggregation and intrusion detection in wireless sensor networks.

In this article, we study a cooperative coding scheme for densely deployed wireless sensor networks (WSNs) where a number of sensors transmit data to a single destination with the help of a relay. The latter applies algebraic network coding to the source codewords and forwards only the additional redundancy to the destination that observes a product code matrix built based on source codewords and relay-generated redundancy. However, for such an application two types of correlation can be found between the different sensors' observations. The first type is due to the high density of the WSN that results in a correlation between observations delivered by neighbor sensors (spatial correlation). The nature of the measured physical phenomena induces also some correlation between successive observations of the same sensor (temporal correlation). Such correlations represent extra source information that was neglected in previous contributions dealing with cooperative communications. In this contribution, we investigate a joint source channel decoding scheme that exploits the source memory structure to improve the product code iterative decoding performance. Significant performance improvements are demonstrated depending on the spatial and temporal correlation level. A performance gain achieving 0.8 dB for the additive white Gaussian noise (AWGN) channel, and 1.5 dB for the fast Rayleigh fading channel are demonstrated.

During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect of the field. Therefore, WSNs are insecure because of this. With so much riding on WSN applications, accuracy in replies is paramount. Technology that can swiftly and continually analyse internet data streams is essential for spotting breaches and assaults. Without categorization, it is hard to simultaneously reduce processing time while maintaining a high level of detection accuracy. This paper proposed using a Multi-Layer Perceptron (MLP) to enhance the classification accuracy of a system. The proposed method utilises a feed-forward ANN model to generate a mapping for the training and testing datasets using backpropagation. Experiments are performed to determine how well the proposed MLP works. Then, the results are compared to those obtained by using the Hoeffding adaptive tree method and the Restricted Boltzmann Machine-based Clustered-Introduction Detection System. The proposed MLP achieves 98% accuracy, which is higher than the 96.33% achieved by the RBMC-IDS and the 97% accuracy achieved by the Hoeffding adaptive tree.

Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation.

Data collection plays an important role in wireless sensor networks. Recovery of spatio-temporal data from incomplete sensing data is vital to the network lifetime. Many works have utilized the spatial and temporal correlations to achieve satisfactory data recovery results. However, these methods introduce large computational overhead at the fusion center. In this paper, we develop an ADMM-Net framework for correlated spatio-temporal data recovery. Both the spatial correlation and temporal correlation of sensing data are considered in a convex optimization problem, which is solved by the alternating direction method of multipliers (ADMM) algorithm. We then unfold the ADMM algorithm into a fixed-length neural network that reduces the iterations dramatically and does not require additional location information of nodes. Experimental results on a realworld dataset demonstrate that the proposed method can achieve faster convergence speed than the baseline ADMM algorithm with slight accuracy loss.

Internet of Things (IOT) is experiencing a super-fast growing phase. Wireless Sensor Networks (WSN), as the “five sense organs” and teleneuron of IOT, is developing fast along. Owing to the application-oriented characteristics of WSN, WSN applications may differ enormously in implementation methods. The WSN developers may have to rewrite the code for each and every application. Along with the increasing demand to WSN applications, WSN developers may have heavier tasks of programming for these applications. Thanks to TinyOS and NesC, an operating system and programming language designed especially for WSN, raises the WSN application's programming to a higher level. Large number of supported sensor hardware plus system provided and user-de-fined libraries eases WSN application programmers' jobs. How-ever, the massive amount of nodes' communication and networking, and security protection slows down the development pace. A unified and empirical WSN application implementation platform is one of the available solutions to the problem. This paper introduces an empirical WSN application implementation platform, which avails the WSN developers build an application with unified code template and network packet generation and processing strategies.

Environmental monitoring is a typical application in wireless sensor networks (WSNs), the outlier detection of the sensor data streams is especially important. We put forward an outlier detection algorithm based on multidimensional kernel density estimation. Based on the hierarchical network model, the algorithm estimates the normal distribution model in the cluster head nodes with the latest data sample. Each distributed node computes the new data to identify the abnormal data by the kernel density estimation model. The proposed algorithm can compute the result online. It only spends little time to adjust the appropriate threshold to reduce its complexity. In addition, We also take the spatial and temporal correlation, multiple attribute correlation of sensor data into account, such that the result of outlier detection is very reliable. Theoretical analysis and simulation experimental results demonstrate that the outlier detection accuracy of the proposed algorithm is more than 98 % when the outlier rate p is within a reasonable range. With the increase of p, the outlier detection accuracy will decline gradually.

Multicast is essential for wireless sensor network (WSN) applications. Existing multicast protocols in WSNs are often designed in a P2P pattern, assuming small number of destination nodes and frequent changes in network topologies. In order to truly adopt multicast in WSNs, we propose a base-station model-based multicast, SenCast, to meet the general requirements of applications. SenCast is scalable and energy-efficient for large group communications in WSNs. Theoretical analysis shows that SenCast is able to approximate the Minimum Nonleaf Nodes (MNN) problem to a ratio of ln ∣R∣ (R is the set of all destinations), the best known lowest bound. We evaluate our design through comprehensive simulations and prototype implementations on Mica2 motes. Experimental results demonstrate that SenCast outperforms previous multicast protocols including the most recent work uCast.

Resource Management Approach to an Efficient Wireless Sensor Network

ABSTRACTIn the realm of wireless sensor networks (WSNs), where energy efficiency and communication reliability are paramount, this paper proposes an innovative approach named Fuzzy‐based hybrid Philippine eagle optimization and Tasmanian devil optimization (FPEO‐TDO). WSNs utilize sensor nodes across diverse domains like agriculture, military, medicine, and manufacturing to gather data and monitor environments over prolonged periods. Efficient data transmission is crucial to conserve energy reserves in sensor nodes. The FPEO‐TDO model integrates the Philippine eagle optimization (PEO) and Tasmanian devil optimization (TDO) algorithms to optimize energy efficiency in WSNs. PEO and TDO algorithms are renowned for their abilities to enhance optimization processes by mimicking the behaviors of Philippine eagles and Tasmanian devils, respectively. By fusing these algorithms, our approach aims to minimize energy usage during data transfer, thereby extending network's lifetime and enhancing overall performance metrics. To determine optimal clusters within the network, a fuzzy logic model is employed, facilitating intelligent decision‐making for efficient data aggregation and transmission. Performance evaluation measures such as WSN lifetime, end‐to‐end delay, and network stability period are utilized to assess effectiveness of FPEO‐TDO. Experimental results demonstrate significant improvements, with a 98% of network lifetime and low energy consumption of 78 J compared with the traditional approaches, highlighting the efficacy of our proposed approach in enhancing WSN performance and energy efficiency.