Data gathering in wireless sensor networks with mobile collectors

In large-scale wireless sensor networks the data- gathering mechanism to introducing mobility into the network. . We consider the location service in a WSN, where each sensor needs to maintain its location information by 1) frequently updating its location information within its neighboring set of polling points in the network. In the previous work a mobile data collector, for convenience called an M-collector for gathering information from the sensor and send to sink node. M- collector could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, working like a mobile base station and gathering data while moving through the field. An M-collector starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range, then directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. Since data packets are directly gathered without relays and collisions, the lifetime of sensors is expected to be prolonged. In this paper, we mainly focus on the problem of minimizing the length of each data-gathering tour and remove the redundancy for data gathering. We develop a stochastic sequential decision framework to analyze this problem. Under a Markovian mobility model, the location update decision problem is modeled as a Markov Decision Process (MDP). For the work with strict distance/ time and energy constraints, we consider utilizing single and multiple M-collectors and propose a data- gathering algorithm where multiple M-collectors traverse through several shorter subtours concurrently to satisfy the distance/time and energy constraints. To identify the redundancy of data from the sensor using Pearson auto correlated technique. Our proposed system for mobile data gathering scheme can improve the scalability and balance the energy consumption among sensors.

Extending the network lifetime is a challenging problem in wireless sensor networks. Using additional mobile agents called data collectors in order to gather data from sensor nodes and carry them to the sink, reveals a convenient solution. Intuitively, a great sensor nodes energy conservation can be achieved by single hop data gathering strategies. However, these strategies may lead to significantly increase the data latency in the network. Previous studies propose to introduce local relay transmissions in order to reduce the data gathering time. Most of these strategies rely on a static parameter in order to choose the data collector trajectory. In this paper, we focus on a novel data gathering strategy that aims at dynamically selecting the data collector trajectory according to the required data delivery delay deadline. In other words, the data collector may change its trajectory according to the urgency of the gathered data. For this purpose, we propose an algorithm for coloring the network area into red and green disks, according to the delay deadline required by the sensor nodes. Only the red disks must be visited during a tour of the data collector. Simulation results demonstrate that the proposed scheme significantly outperforms static cluster based gathering strategies by efficiently adapting the tolerated data latency to the consumed energy rate.

Various communication services have been provided. They include environmental monitoring and/or control, ad-hoc communication between mobile nodes, and inter-vehicle communication in intelligent transport systems. As a means of facilitating the above advanced communication services, autonomous decentralized networks, such as wireless sensor networks (Akyildiz et al., 2002; Rajagopalan & Varshney, 2006), mobile ad-hoc networks (Perkins & Royer, 1999; Johnson et al., 2003; Clausen & Jaquet, 2003; Ogier et al., 2003), and wireless mesh networks (Yamamoto et al., 2009), have been intensively researched with great interests. Especially, a wireless sensor network, which is a key network to construct ubiquitous information environments, has great potential as a means of realizing a wide range of applications, such as natural environmental monitoring, environmental control in residential spaces or plants, object tracking, and precision agriculture (Akyildiz et al., 2002). Recently, there is growing expectation for a new network service by a wireless sensor network consisting of a lot of static sensor nodes arranged in a service area and a few mobile robots as a result of the strong desire for the development of advanced systems that can flexibly function in dynamically changing environments (Matsumoto et al., 2009). In this chapter, a large scale and dense wireless sensor network made up of many static sensor nodes with global positioning system, which is a representative network to actualize the above-mentioned sensor applications, is assumed. In a large scale and dense wireless sensor network, generally, hundreds or thousands of static sensor nodes limited resources, which are compact and inexpensive, are placed in a service area, and sensing data of each node is gathered to a sink node by inter-node wireless multi-hop communication. Each sensor node consists of a sensing function to measure the status (temperature, humidity, motion, etc.) of an observation point or object, a limited function of information processing, and a simplified wireless communication function, and it generally operates on a resource with a limited power-supply capacity such as a battery. Therefore, a data gathering scheme and/or a routing protocol capable of meeting the following requirements is mainly needed to prolong the lifetime of a large scale and dense wireless sensor network composed of hundreds or thousands of static sensor nodes limited resources. 1. Efficiency of data gathering 2. Balance of communication load among sensor nodes 3. Adaptability to network topology changes

Data gathering with distributed rateless coding based on enhanced online fountain codes over wireless sensor networks

In the era of New Generation Networks (NWGN) many emerging and future enable technologies accomplish the requirements of ubiquitous communication networks. Mobile wireless sensor network (mWSN) is one of the rising and emerging technologies for various application of NWGN, the enormous concerns of these networks are data gathering with energy efficiency. Routing protocol act as middleware, which is responsible to enhance the network performance with less energy consumption. In this paper we propose, analyze and validate mobile data collector (MDC) based LEACH routing protocol for environmental application which is based on multi-hop routing strategy. MDC based LEACH routing protocol utilizes self-organized sensor nodes with distributed cluster formation technique, randomly selection of cluster heads to equally balance the energy consumption among the sensor nodes and finally forward the data to base station by the support of MDC. Moreover, our approach shows the significant improvement in terms of energy consumption of sensor nodes, enhance network lifetime and data gathering at base station are outperform when compared with LEACH protocol. Key words: Mobile wireless sensor networks, wireless sensor networks, network protocol, cluster based routing, data gathering technique.

In this work, we address data gathering in Wireless Sensors Networks. Wireless sensor networks are composed of small and autonomous sensor devices. Typical applications include monitoring and recording physical conditions of the environment. Sensors count on limited processing power, small storage space, radio for short range communications, and limited battery. One major issue in wireless sensor networks is data gathering. Data gathering based on static sinks incur in premature energy exhaustion in nodes near the sink. By using mobile collectors, wireless sensors networks can save energy since the data routes tend to be smaller. In this article, we propose three data gathering algorithms. We analyze different strategies to either avoid packet collisions and to retransmit lost packets. The results indicate that packet collisions may substantially affect the network performance in wireless sensor networks data gathering.

Wireless Sensor Networks (WSNs) have become an indistinguishable part of any Internet of Things (IoT) based system. In an IoT-based system, WSN monitors different physical objects and forwards the collected data to the base station or gateway node for further processing. Existing routing protocols designed for WSNs are not suitable for IoT-based systems. An intelligent and high-performance routing protocol is essential for real-time data analysis and decision-making. Therefore, this paper proposes a mobile sink-based intelligent data routing scheme for WSNs. Initially, Particle Swarm Optimization is used for optimal cluster formation. After cluster formation, the optimal number of rendezvous points are selected, and a data-gathering tour is planned for the mobile sink. Furthermore, a mobile sink-based intelligent data gathering scheme is proposed to collect the data from the cluster heads. Extensive simulations and mathematical analysis are performed to verify the effectiveness of the proposed scheme. The simulation results are compared with state-of-the-art algorithms in terms of stability period, network lifetime, throughput, average energy consumption, fairness index, data transmission latency, and message overhead.

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.

A high-available and location predictive data gathering scheme with mobile sinks for wireless sensor networks

In this paper, we propose an optimal data forwarding strategy to minimize sleep duration time for one-dimensional wireless sensor networks with energy harvesting sensor nodes. Based on the values of remaining energy and harvested energy of sensor nodes, the base station (data sink) determines the sleep duration time and the data forwarding strategy for the next data gathering round. According to the proposed algorithm, each sensor node can act as a relay node that receives and forwards the data toward data sink. This strategy also guarantees that every sensor nodes do not reach the energy depletion while minimizing sleep duration time, resulting in the maximum data gathering frequency. From the mathematical analysis and the numerical simulation, the proposed strategy shows 2.5% to 50% smaller sleep duration time compared to the simple data forwarding algorithms such as single hop or multi-hop data forwarding only.

In wireless sensor networks (WSN), generally sensors closest to the sink or base station tend to deplete their battery energy faster than other sensors and create an energy hole around the sink. In order to overcome this problem, a mobile data gathering using PSO and minimum covering spanning tree (MDG-PSO-MCST) is proposed. In this technique, multiple sensors are arranged to form clusters. Two SenCars with multiple antennas are deployed in the cluster location and space division multiple access (SDMA) technique is applied for data gathering with energy efficiency. The anchor points are selected for intermediate data collection based on the connectivity and node degree, node compatibility and the distance between the sensors of two adjacent clusters parameters using particle swarm optimisation (PSO) technique. The visiting tour of each Sencars among the selected anchors is performed using the MCST algorithm. By simulation results, we show that the proposed technique minimises the energy consumption and delay, and enhances the network efficiency.

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 Wireless Sensor Network (WSN), nodes are usually powered by battery and the wireless links of the nodes suffer from packet loss. It is extremely significant to develop an energy-efficient reliable data gathering scheme for WSN applications. In this paper, the Enhanced Reed-Solomon (E-RS) code, together with the intra-segment coding and the inter-segment coding, are designed. The E-RS code based data gathering scheme called the E-RS scheme is presented that adopts the intra-segment coding or the inter-segment coding. The Overall Energy Consumption (OEC) of the E-RS scheme is derived and minimized in the Optimization Problem (OP) with the constraint of data gathering ratio. The OP is able to find the optimal number of segments each carrying original information symbols, the maximum number of retransmissions in IEEE 802.15.4 Medium Access Control (MAC) layer, and the redundant degree of the E-RS code, so that the E-RS scheme achieves the minimum energy consumption while keeping data gathering ratio over a preset threshold.

À Load-balancing and Self-adaptation Clustering for Lifetime Prolonging in Large Scale Wireless Sensor Networks