Tree-based energy-efficient data gathering in wireless sensor networks deploying compressive sensing

Data gathering is a fundamental operation for various applications of wireless sensor networks (WSNs), where sensor nodes sense information and forward data to a sink node via multihop wireless communications. Typically, data in a WSN is relayed over a tree topology to the sink for effective data gathering. A number of tree-based data gathering schemes have been proposed in the literature, most of which aim at maximizing network lifetime. However, the timeliness and reliability of gathered data are also of great importance to many applications in WSNs. To achieve low-latency, high-reliability data gathering in WSNs, in this paper, we construct a data gathering tree based on a reliability model, schedule data transmissions for the links on the tree and assign transmitting power to each link accordingly. Since the reliability of a link is highly related to its signal to interference plus noise ratio (SINR), the SINR of all the currently used links on the data gathering tree should be greater than a threshold to guarantee high reliability. We formulate the joint problem of tree construction, link scheduling and power assignment for data gathering into an optimization problem, with the objective of minimizing data gathering latency. We show the problem is NP-hard and divide the problem into two subproblems: Construction of a low-latency data gathering tree; Jointly link scheduling and power assignment for the data gathering tree. We then propose a polynomial heuristic algorithm for each subproblem and conduct extensive simulations to verify the effectiveness of the proposed algorithms. Our simulation results show that the proposed algorithms achieve much lower data gathering latency than existing data gathering strategies while guaranteeing high reliability.

Data gathering is a fundamental operation in various applications of wireless sensor networks (WSNs), where sensor nodes sense information and forward data to a sink node via multi-hop wireless communications. Typically, data in a WSN is relayed over a tree topology to the sink for effective data gathering. A number of tree-based data gathering schemes have been proposed in the literature, most of which aim at maximizing network lifetime. However, the timeliness and reliability of gathered data are also of great importance to many applications in WSNs. To achieve low-latency, high-reliability data gathering in WSNs, in this paper, we construct a data gathering tree based on a reliability model, schedule data transmissions for the links on the tree and assign transmitting power to each link accordingly. Since the reliability of a link is highly related to its signal to interference plus noise ratio (SINR), the SINR of all the currently used links on the data gathering tree should be greater than a threshold to guarantee high reliability. We formulate the joint problem of tree construction, link scheduling and power assignment for data gathering into an optimization problem, with the objective of minimizing data gathering latency. We show the problem is NP-hard and divide the problem into two subproblems: Constructing a low-latency data gathering tree; Jointly link scheduling and power assignment for the data gathering tree. We then propose a polynomial heuristic algorithm for each subproblem and conduct extensive simulations to verify the effectiveness of the proposed algorithms. Our simulation results show that the proposed algorithms achieve much lower data gathering latency than existing data gathering strategies while guaranteeing high reliability. Moreover, the algorithms also have a comparable energy efficiency and network lifetime to other algorithms.

In this study, the authors present an energy-efficient data compression protocol for data collection in wireless sensor networks (WSNs). WSNs are essentially constrained by motes’ limited battery power and networks bandwidth. The authors focus on data compression algorithms and protocol development to effectively support data compression for data gathering in WSNs. Their design of compressed data-stream protocol (CDP) is generic in the sense that other lossless or lossy compression algorithms can be easily ‘plugged’ into the proposed protocol system without any changes to the rest of the CDP. This design intends to support various different WSN applications where users may prefer more specific compression algorithms, tailored to the sensing data characteristics in question, to their general algorithm. CDP is not only able to significantly reduce energy consumptions of data gathering in multi-hop WSNs, but also able to reduce sensor network traffic and thus avoid congestion accordingly. The proposed CDP is implemented on the tinyOS platform using the nesC programming language. To evaluate their work, the authors conduct simulations via TOSSIM and PowerTOSSIM-z with real-world sensor data. The results demonstrate the significance of CDP.

The chapter focuses on minimizing the amount of wireless transmission in sensory data gathering for correlated data field monitoring in wireless sensor networks (WSN), which is a major source of power consumption. Compressive sensing (CS) is a new in-node compression technique that is economically used for data gathering in an energy-constrained WSN. Among existing CS-based routing, cluster-based methods offer the most transmission-efficient architecture. Most CS-based clustering methods randomly choose nodes to form clusters, neglecting the topology structure. A novel base station (BS)-assisted cluster, spatially correlated cluster using compressive sensing (SCC_CS), is proposed to reduce number of transmissions in and form the cluster by exploiting spatial correlation based on geographical proximity. The proposed BS-assisted clustering scheme follows hexagonal deployment strategy. In SCC_CS, cluster heads are solely involved in data gathering and transmitting CS measurements to BS, saving intra-cluster communication cost, and thus, network life increases as proved by simulation.

Age of Information minimization in UAV-aided data collection for WSN and IoT applications: A systematic review

Wireless sensor networks (WSNs), now-a-days, are deployed in environmental data collection as well as in critical event monitoring. Successful data collection requires reliability while reliable event detection necessitates timeliness. Simultaneous data gathering and event monitoring is not well studied in literature. In this paper, we propose a system model that works on homogeneous data gathering WSNs. When an event occurs, an event cluster with a different transmission channel is formed and both data gathering and event monitoring are performed at the same time. The proposed model has a novel routing strategy with a built-in congestion control technique to provide timely delivery of event data. Experimental results show that the proposed method performs better than known similar techniques in terms of reliable data gathering and reliable timely event monitoring. It also enhances the network lifetime significantly compared to other existing methods.

Scheduling of data aggregation trees using Local Heuristics to enhance network lifetime in sensor networks

This paper presents a model for ensuring data integrity using anomalous node identification in non-homogeneous wireless sensor networks (WSNs). We propose the anomaly detection technique while collecting data using mobile data collectors (MDCs), which detect the malicious activities before sending to the base station (BS). Our technique also protects the leader nodes (LNs) from malicious activities to ensure data integrity between the MDC and the LNs. The proposed approach learns the data characteristics from each sensor node and passes it to the MDC, where detection engine identifies the victim node and eventually alarm the LNs in order to keep the normal behaviour in the network. Our empirical evidence shows the effectiveness our approach.

The advantages of deploying mobile sinks (MSs) in wireless sensor networks (WSNs) have attracted tremendous attention recently, because mobility of a sink provides an efficient method to improve data gathering performance in WSNs. However, in such kind of WSN, the efficiency of data dissemination is obviously affected by the trajectory of the MS. Therefore, optimal moving schemes of the MS have been studied in many researches. Nevertheless, in many cases, the trajectory of the MS is not able to change ideally as previous studies assumed but depending on the environmental conditions. In this paper, we focus on the efficiency of data gathering in WSN with a MS whose trajectory cannot change ideally and propose a new data diffusion scheme - implementing a new Pre-Diffusion Algorithm (PDA) in a hierarchical WSN aimed at improving data gathering efficiency. This hierarchical WSN is composed of several clusters, and datatransmission is done by higher powered cluster head (CH) node. Our scheme predetermines the cluster area where the MS is going to arrive in most probability in next period of time. MS fulfills data converging in the cluster head (CH)whose distance to the MS may the smallest compared with other CHs when data gathering starts. Thus more data can be collected with less trajectory length of MS. Simulation results show that our proposed scheme improves data gathering efficiency as compared to conventional scheme.

In wireless sensor networks (WSNs), data produced by sensors are usually routed through several intermediate nodes to reach the sink Base Station (BS). In fact, since a WSN is usually composed of low-cost and limited capability sensors, their transmission range prevents the establishment of a reliable communication with the sink. When an intermediate node fails, errors may occur and the message is not delivered to the sink. The reliability of the system can be increased by using Network Coding (NC) techniques. In this paper, we consider the problem of data gathering in WSNs and we propose a novel error correction mechanism using Low Rank Parity Check code (LRPC), which is known to be good at correcting burst errors, as an outer code and a convolutional code as an inner code to correct sparse errors. Furthermore, we investigate the performance of the proposed system in terms of the packet error probability and the decoding complexity. We also propose a theoretical approximation of the decoding probability for LRPC codes in the case of network coding for binary and non-binary fields. We show, through Matlab simulations, that the proposed concatenated code outperforms the proposed coding schemes in the literature for data gathering in terms of decoding rate and complexity.

Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. However, the total number of data transmissions required in CS method is still large. It is observed that there are many zero elements in the measurement matrix. In each round of data transmission in CS method, the sensor nodes corresponding to the zero elements in the measurement matrix do not have their own data to transmit. To further reduce the number of data transmissions in the network, we aim to compute a data gathering tree by taking advantages of these zero elements in the measurement matrix, such that the total number of data transmissions is minimized. We formulate the problem as linear programming with boolean variables. The problem is NP-hard. We propose heuristic algorithm to compute the Minimum Transmission Tree (MTT) for data gathering in CS methods. The MTT algorithm constructs a spanning tree by iteratively including the edge whose average incremental transmission cost is minimum. The simulation results demonstrate that our algorithm can reduce the number of transmissions significantly, compared with the methods using minimum spanning tree (MST), shortest path tree and the CS method with nonzero measurement coefficient using MST.

In Wireless Sensor Networks (WSNs), using mobile sinks facilitates the interconnection between the far distance nodes and the base station and save the energy by reducing the number of hops. However, in time-sensitive applications, where the mobile sinks face time limitations for collecting data, the length of the traveling path should be considered attentively to ensure that the collected data is useful after delivery. Although Timeliness Traveling Path Planning (TTPP) as one of the proposed approaches works well for timely data gathering in WSNs, it suffers from scalability because it is essentially based on using a single mobile sink that is applicable only in small scale networks. In this paper by considering multiple mobile sinks, a new version of the TTPP named Scalable Cluster-based Path Planning (SCPP) is introduced for using in the large scale networks. The results of the simulations indicate that the proposed method reduces the relay hop counts, enhances the network lifetime, and offers a better performance as compared to the TTPP method.

Huge data processing contributes many factors in wireless sensor network such as network traffic and energy constraint. Using compressive sensing a new technique in data acquisition which reduced the required sampling rate to reconstruct the original signal will therefore lessen the power consumption. This paper will implement the compressive sensing algorithm of the wireless sensor network installed in the greenhouse. The primary objective of the design is to reduce the power consumption on wireless system network by maximizing the data packet payloads while minimizing the transmission activity of the Wireless Sensor Network. The sensor and receiver node consumes more power when transmission of data is taking place. The main contributions of this paper are to apply the compressive sensing algorithm in the greenhouse monitoring system to lessen the power consumption of the WSN, to serve as a reference for the new design of analog to digital converter using sampling rate lower than the traditional Nyquist rate and to give ideas to other researchers that compressive sensing can be applied to other WSN applications. This study will do the compression of the measured data in the sensor node and transmit it over a specified time. Matlab will be used to simulate and recover the original signal for verification of the results. The results will compare the power consumption of the existing system of the Precision Agriculture group in our laboratory.

This paper considers energy efficient distributed data gathering methods for correlated data field monitoring in multi-hop wireless sensor networks (WSNs) via compressed sensing (CS). This signal compression and acquisition method can be used to exploit the inherent temporal and spatial correlation of the sensor signals. Many existing CS-based data gathering methods exploit correlation structures only in one signal dimension in WSNs. Thus, we propose a distributed method which utilizes the joint correlation pattern of a multi-dimensional WSN signal ensemble via Kronecker sparsity basis. Numerical results how that the proposed method can significantly reduce the data traffic in correlated data gathering multi-hop WSNs.

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.