LiReTa: A Lightweight Reliable Transmission Scheme for Wireless Sensor Networks Using Cross-Layer Information

In this paper, we study two problems of providing reliable data transmission and developing aggregation techniques for correlated data in wireless sensor networks. A system with forward error correction (FEC) can provide an objective reliability while using less transmission power than a system without FEC. Because of the additional parity bits and encoding/decoding energy consumptions, we study the effect of FEC on energy efficiency. We propose to use LDPC codes for FEC. We show that wireless sensor networks using LDPC codes are almost 45% more energy efficient than those that use BCH codes, which were shown to be 15% more energy efficient than the best performing convolutional codes. Then we study the problem of providing aggregation for two and three correlated nodes in wireless sensor networks. We propose to use LDPC codes in wireless sensor networks for source and channel coding to obtain a two-fold energy savings. For two correlated nodes, we study both distributed source coding and joint source-channel coding. While distributed source coding using LDPC codes was studied before, joint source-channel coding using LDPC codes is introduced for the first time. The difference between our work in distributed source coding using LDPC codes and the previous work lies in the LDPC code design procedure. The simulation results show that our proposed design criteria improves the performance of the source coding. The convergence of the non-uniform LDPC code of our design technique is almost 60% closer to the Slepian-Wolf limit. For three correlated nodes, we study distributed source coding using LDPC codes. We simplified the problem of design procedure to randomly punctured LDPC codes. This is a new approach for designing LDPC codes and the simulation results for code of length 1000 shows that the convergence of the LDPC code is achieved at compression rate 0.3174 which is only 0.08 away from the Slepian-Wolf limit.

The Internet of Things (IoT) stands as one of the emerging technologies because of its novel innovations and solutions for countless domains. As one of the enabling components for IoT, Wireless Sensor Network (WSN) is utilized for various real-world appliances to collect valuable raw sensed data. Here, valuable data from the sensor nodes are suitable to access with the help of a gateway node through the registered user. However, due to the resource constraints of sensor nodes and the exposed nature of the wireless frequency, security has turned out to a considerable task in WSN. Authentication is a primary security facility that can ensure the legitimacy of data access in WSN. There are numerous 2-factor verification protocols in related work based on a smartcard, public-key, and password. However, most of them have trouble reconciling proficiency and security necessities; hence, it is still challenging to implement a 2-factor authentication scheme to satisfy security topographies while preserving suitable proficiency. We designed an enhanced 2-factor verification scheme based on ECC and biometric information in this work. Our proposed scheme accomplishes distinct security features and protocol-designed logic verified with the AVISPA tool. Simulation results demonstrate that the projected scheme is robust, energy-efficient, and well-suited to WSN environments.

For the purpose of saving the resources of wireless sensor networks, it is necessary to fuse the existing data in wireless sensor networks. When using the current method to fuse data in wireless sensor networks, the fusing time is too long to remove the noise signal in wireless sensor networks effectively. In the process of fusing, data packet loss is easy to occur. There are some problems such as low fusion efficiency, poor denoising performance and high packet loss rate. A multi-path data hierarchical fusion method based on routing algorithm is proposed in this paper. The redundant node decision method based on mesh partitioning is combined with the grid algorithm and EECRS protocol to establish a routing protocol based on redundant node elimination, and the redundant data existing in the redundant nodes of the wireless sensor network is removed. Based on the statistical data of the data, the probability density function is deduced and analyzed, and the noise filtering matrix is constructed to optimize the measurement matrix and remove the noise existing in the wireless sensor network. The multi-path scheduling technology is used to synthesize the pheromone table and the data forwarding flow table, and the ant is used to actively monitor the link QOS real-time information in the path finding process to update the pheromone table and the flow table, and calculate the link idle rate according to the update result. According to the calculation result, the data transmission path is selected, and the multi-path data layer fusion is completed. The experimental results show that the proposed method has high fusion efficiency, good denoising performance and low packet loss rate.

Classification of sensory data is a major research problem in wireless sensor networks and it can be widely used in reducing the data transmission in wireless sensor networks effectively and also in process monitoring. In order to examine the huge size of data set in stream model generated by sensor network, it will be analyzed different sensor's output signal, topology of sensors network, number of sensor parameters and number of acquisition data. In our wind energy monitoring, sensor node monitors six attributes: speed, direction, temperature, pressure, humidity, and battery voltage. Every attribute value is set as four measures: average, instantaneous, minimum, and maximum. This paper presents several data mining techniques applied on the wireless sensor network's data considered: Naïve Bayes, k-nearest neighbor, decision trees, IF-THEN rules, and neural networks. Before classification, the data was clustered in order to be labeled. A similarity based algorithm, k-means, was selected in the clustering process for its simplicity and efficiency. A conclusion that decision trees are a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation.

Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of dense deployed sensor nodes. Due to the dense deployment, since sensor observations are spatially correlated with respect to spatial location of sensor nodes, it may not be necessary for every sensor node to transmit its data. Therefore, due to the resource constraints of sensor nodes it is needed to select the minimum number of sensor nodes to transmit the data to the sink. Furthermore, to achieve the application-specific distortion bound at the sink it is also imperative to select the appropriate reporting frequency of sensor nodes to achieve the minimum energy consumption. In order to address these needs, we propose the new Distributed Node and Rate Selection (DNRS) method which is based on the principles of natural immune system. Based on the B-cell stimulation in immune system, DNRS selects the most appropriate sensor nodes that send samples of the observed event, are referred to as designated nodes. The aim of the designated node selection is to meet the event estimation distortion constraint at the sink node with the minimum number of sensor nodes. DNRS enables each sensor node to distributively decide whether it is a designated node or not. In addition, to exploit the temporal correlation in the event data DNRS regulates the reporting frequency rate of each sensor node while meeting the application-specific delay bound at the sink. Based on the immune network principles, DNRS distributively selects the appropriate reporting frequencies of sensor nodes according to the congestion in the forward path and the event estimation distortion periodically calculated at the sink by Adaptive LMS Filter. Performance evaluation shows that DNRS provides the minimum number of designated nodes to reliably detect the event properties and it regulates the reporting frequency of designated nodes to exploit the temporal correlation in the event data whereby it provides the significant energy saving.

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

Wireless Sensor Networks (WSNs) are widely used for different applications such as monitoring and surveillance in both civilian and military domains. Mobile data collection has significant impact on the performance of WSN. Therefore it is essential to have mechanisms to improve it. In this paper we proposed a three-layered approach that is used to collect data in wireless sensor network. The three layers include sensor layer which is made up of collection of sensors, cluster head layer which is made up of all cluster heads, and the mobile collector layer. The proposed approach also takes care of distributed load balanced clustering. The aim of the proposed approach is to achieve scalable solution with low data collection latency and energy efficiency. While sensors are organized into clusters the distributed algorithm is applied so as to ensure that the efficiency of the network is achieved. We built a prototype application using Microsoft .NET which simulates the proposed approach. The empirical results revealed that the proposed layered approach is energy efficient, scalable and achieves low latency in data collection.

Wireless sensor networks (WSNs) consist of small sensor nodes with constrained energy, memory and computation capabilities. They are often deployed in the unattended and hostile environment. Therefore sensor nodes are vulnerable to attacks such as node capture and collusion attack by adversaries. To protect the whole network, cryptographic techniques are necessary that pairwise key establishment is the essential procedure. It enables that sensor nodes can communicate to each other securely. However, it is impractical to use traditional key management techniques such as public key cryptography and key distribution center (KDC) because of the resource constraints of sensor nodes. This paper proposes a key distribution scheme, based on exclusion-based systems (EBSs) and t-degree bivariate polynomials. It is an energy-efficient dynamic key management scheme which performs localized rekeying to minimize overhead.

In this paper propose an several concepts for optimal algorithm of aggregation of data in wireless sensor network. Given analysis of problems for information aggregation in wireless sensor network. Proposed optimise of structure the node and given basic concepts for reducing energy consumption. Also proposed network partitioning into piconet with next clustering. In final proposed are two modifications for ant algorithms to optimize data collection in wireless sensor network.

A Technique of Filtering Dirty Data Based on Temporal- Spatial Correlation in Wireless Sensor Network

Wireless sensor networks are a modern and advanced technology whose applications are fast developing in recent years. Despite being a fascinating topic with various visions of a more intelligent world, there still exist security issues to be resolved in order to make WSNs fully adoptable. Due to the resource constraints of sensor nodes, it is infeasible to use traditional key establishment techniques that find use in fixed communication systems. In this paper, the design of a new hybrid Authenticated Group Key Agreement (AGKA) protocol is described for WSNs. The AGKA protocol reduces the high cost public-key operations at the sensor side and replaces them with efficient symmetric-key based operations. The proposed AGKA protocol is not only efficient but also meets strong security requirements. In order to demonstrate the protocol is verifiably secure and trustworthy, a formal verification of the AGKA protocol is carried out. Furthermore, several experiments are conducted on MICAz and TelosB platforms in order to evaluate the performance of the proposed protocol. The evaluation results show that the AGKA protocol is well suited for use with resource-constrained sensor nodes.

Data collection is a fundamental and important issue in wireless sensor networks (WSNs). Recent research has shown that using mobile nodes to collect and carry data in WSNs has many advantages over static multi-hop routing. In this paper, we focus on the problem of data collection in WSNs with the minimum mobile nodes. A mobile node can pick up the data cached in a sensor node via one-hop wireless communications when it passes by a point that R meters away from this stationary sensor node. Since the storage capability of sensor node is limited, each mobile node must visit the sensor nodes, that assigned to it, every t seconds to avoid the overflow of sensor data. In order to reduce the cost of mobile nodes, we try to minimize the number of mobile nodes. We formally prove that the problem of minimizing the number of mobile nodes required by periodical data collection in WSNs is NP hard. We propose a path planning algorithm to minimize the number of mobile nodes. Our simulation results show that our approach can notably reduce the number of required mobile nodes as much as 55.6%.

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensor nodes to monitor physical or environmental conditions. These distributed autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. The distributed sensor nodes communicate wirelessly to a central gateway, which provides a connection to the wired world where you can collect, process, analyze, and present your measurement data. Due to the broadcast nature of the transmission media they use, sensor networks are vulnerable to various security attacks, such as eavesdropping, jamming and node capture attacks. In this paper, we provide a solution for node capture attack. Node capture attacks result from the combination of active, passive and physical attacks by an intelligent adversary. In order to initialize or set up a node capture attack, the adversary will collect information about the WSN by eavesdropping on message exchanges, either local to a single adversarial device or throughout the network with the aid of a number of adversarial devices deployed throughout the network. Hence in order to securely aggregate data in a wireless sensor network, we must not only provide protection against eavesdroppers, but we should also prevent intermediate sensors from having access to the data.

Wireless Sensor Network (WSN) technology has recently moved out of controlled laboratory settings to real-world deployments. Many of these deployments experience high rates of failure. Common types of failure include node failure, link failure, and node reboot. Due to the resource constraints of sensor nodes, existing techniques for fault detection in enterprise networks are not applicable. Previously proposed WSN fault detection algorithms either rely on periodic transmission of node status data or inferring node status based on passive information collection. The former approach significantly reduces network lifetime, while the latter achieves poor accuracy in dynamic or large networks. Herein, we propose Sequence-Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs. The framework exploits in-network packet tagging using the Fletcher checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the sink. The sink monitors the extracted packet paths to detect persistent path changes which are indicative of network failures. When a failure is suspected, the sink uses control messages to check the status of the affected nodes. SBFD was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The method was found to achieve a fault detection accuracy of 90.7% to 95.0% for networks of 25 to 400 nodes at the cost of 0.164% to 0.239% additional control packets and a 0.5% reduction in node lifetime due to in-network packet tagging. Finally, a comparative study was conducted with existing solutions.

With the limited resources, it is difficult for wireless sensor network to achieve a long time, continuous, high-speed acquisition of multimedia information and a real-time, reliable transmission of high-volume sampling data. This article proposed the novel data acquisition strategy based on compressed sensing theory, which can perfectly achieve the long time, real-time, reliable transmission of high-volume multimedia data in wireless sensor network. Reasonable experiments were designed to verify the effectiveness of the algorithms, and the experiment results show that: the proposed multimedia signal acquisition strategy is reasonable, practicable, and more suitable for the wireless multimedia sensor networks.