Large Wireless Sensor Networks Research Articles

Lightweight Cryptography Using Pairwise Key Generation and Malicious Node Detection in Large Wireless Sensor Network

Lightweight Cryptography Using Pairwise Key Generation and Malicious Node Detection in Large Wireless Sensor Network

Categorization of crowd-sensing streaming data for contextual characteristic detection

The growing reliance on large wireless sensor networks, potentially consisting of hundreds of nodes, to monitor real-world phenomena inevitably results in large, complex datasets that become increasingly difficult to process using traditional methods. The inadvertent inclusion of anomalies in the dataset, resulting from the inherent characteristics of these networks, makes it difficult to isolate interesting events from erroneous measurements. Simultaneously, improvements in data science methods, as well as increased accessibility to powerful computers, lead to these techniques becoming more applicable to everyday data mining problems. In addition to being able to process large amounts of complex streaming data, a wide array of specialized data science methods enables complex analysis not possible using traditional techniques. Using real-world streaming data gathered by a temperature sensor network consisting of approximately 600 nodes, various data science methods were analyzed for their ability to exploit implicit dependencies embedded in unlabelled data to solve the complex task to identify contextual characteristics. The methods identified during this analysis were included in the construction of a software pipeline. The constructed pipeline reduced the identification of characteristics in the dataset to a trivial task, the application of which led to the detection of various characteristics describing the context in which sensors are deployed.

A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

Real-Time Auto Calibration for Heterogeneous Wireless Sensor Networks

The constant technological advances bring new devices to the market every day. Due to this, heterogeneous Wireless Sensor Network (WSN) are common scenarios in many applications. Neural Network (NN) based models may implement particular features provided by sensor hardware to collect surrounding environment information. Thus, a sensor can provide a specific group of features while others do not. In this perspective, it may be required, for some sensors in a WSN, to be trained and have their data manually categorized, which does not scale, particularly for large WSN setups. In light of this problem, this paper proposes a Real-time (RT) auto-calibration framework to allow WSN devices to collaborate in the training process to enable new uncalibrated devices to join the network. The method does not need previous knowledge about sensor features. Also, the proposal is validated by practical experiments evaluating its accuracy in image classification. The provided experimental results demonstrate the feasibility of the proposed method.

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

The Wireless Sensor Network (WSN) is a network of Sensor Nodes (SN) which adopt radio signals for communication amongst themselves. There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Data security, detection of faults, management of energy, collection and distribution of data, network protocol, network coverage, mobility of nodes, and network heterogeneity are some of the issues confronted by WSNs. There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data. Towards the goal of boosting the mobility-based WSNs’ network performance and energy, data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy (LEACH-M) and Energy Efficient Heterogeneous Clustered (EEHC) scheme have been examined in this work. A novel Artificial Bee Colony (ABC) algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station (BS) with least energy utilization. There is avoidance of the local optima problem at the time of solution space search in this proposed technique. Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.

DTAR: A Dynamic Threshold Adaptive Ranking-Based Energy-Efficient Routing Algorithm for WSNs

Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance ([Formula: see text]) and Residual Energy ([Formula: see text]). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency.

False Event Message Detection Robust to Burst Attacks in Wireless Sensor Networks

A sensor device can be used to detect target events at low cost. Moreover, there is a significant risk of sensor nodes being compromised or captured within large wireless sensor networks (WSNs). The transmission of valid event messages to users by a WSN can be hindered by network congestion due to false event messages in a compromised node. Several methods are available for detecting false event messages. However, they rely on long bits of authentication codes and hence do not provide a fundamental solution to prevent network congestion. In the proposed method, various hashing vectors, which are space-efficient data structures that can determine whether the given data are an element of a set, are created based on the correct combination of authentication codes and placed in each node in advance. An event message contains an XOR of the authentication codes, and each node verifies it based on its hashing vector. If a node is acquired illegally, the information of the hashing vector and the XOR information of the authentication codes assigned to the correct event message is compromised, so we propose an algorithm to update the information securely. Compared to existing research, the number of hops required to detect a false event message increases by only about one hop, but the amount of traffic that a malicious node can generate can be reduced by about 60% or more. In other words, the proposed method effectively reduces the amount of traffic an attacker can generate with false event messages, which also reduces the overall network congestion.

Efficient Multi-level Clustering for Very Large Wireless Sensor Networks with Gateways Support and Meta-heuristic Integration

Efficient Multi-level Clustering for Very Large Wireless Sensor Networks with Gateways Support and Meta-heuristic Integration

MINDS: Mobile Agent Itinerary Planning Using Named Data Networking in Wireless Sensor Networks

Mobile agents have the potential to offer benefits, as they are able to either independently or cooperatively move throughout networks and collect/aggregate sensory data samples. They are programmed to autonomously move and visit sensory data stations through optimal paths, which are established according to the application requirements. However, mobile agent routing protocols still suffer heavy computation/communication overheads, lack of route planning accuracy and long-delay mobile agent migrations. For this, mobile agent route planning protocols aim to find the best-fitted paths for completing missions (e.g., data collection) with minimised delay, maximised performance and minimised transmitted traffic. This article proposes a mobile agent route planning protocol for sensory data collection called MINDS. The key goal of this MINDS is to reduce network traffic, maximise data robustness and minimise delay at the same time. This protocol utilises the Hamming distance technique to partition a sensor network into a number of data-centric clusters. In turn, a named data networking approach is used to form the cluster-heads as a data-centric, tree-based communication infrastructure. The mobile agents utilise a modified version of the Depth-First Search algorithm to move through the tree infrastructure according to a hop-count-aware fashion. As the simulation results show, MINDS reduces path length, reduces network traffic and increases data robustness as compared with two conventional benchmarks (ZMA and TBID) in dense and large wireless sensor networks.

Over-the-Air Computation in Correlated Channels

Over-the-Air (OTA) computation is the problem of computing functions of distributed data without transmitting the entirety of the data to a central point. By avoiding such costly transmissions, OTA computation schemes can achieve a better-than-linear (depending on the function, often logarithmic or even constant) scaling of the communication cost as the number of transmitters grows. Among the most common functions computed OTA are linear functions such as weighted sums. In this work, we propose and analyze an analog OTA computation scheme for a class of functions that contains linear functions as well as some nonlinear functions such as $p$-norms of vectors. We prove error bound guarantees that are valid for fast-fading channels and all distributions of fading and noise contained in the class of sub-Gaussian distributions. This class includes Gaussian distributions, but also many other practically relevant cases such as Class A Middleton noise and fading with dominant line-of-sight components. In addition, there can be correlations in the fading and noise so that the presented results also apply to, for example, block fading channels and channels with bursty interference. We do not rely on any stochastic characterization of the distributed arguments of the OTA computed function; in particular, there is no assumption that these arguments are drawn from identical or independent probability distributions. Our analysis is nonasymptotic and therefore provides error bounds that are valid for a finite number of channel uses. OTA computation has a huge potential for reducing communication cost in applications such as Machine Learning (ML)-based distributed anomaly detection in large wireless sensor networks. We illustrate this potential through extensive numerical simulations.

Energy efficient zone‐based clustering algorithm using fuzzy inference system for wireless sensor networks

AbstractIn Wireless Sensor Networks (WSN), as sensor nodes have limited energy, the design of an energy‐efficient clustering technique is a critical challenge to be addressed. We propose an Energy‐Efficient Zone‐based Clustering algorithm for WSN that works efficiently in large area WSN as well. In the proposed method, the sensing field is divided into equal size zones, and one Zone Monitor is selected in each zone. The distance of a Zone Monitor from the Base Station is considered in the calculation of the number of desired Cluster Heads (CHs) in its zone for implementing Unequal clustering. The CHs are selected such that they are uniformly distributed inside each zone to conserve the energy of sensor nodes needed for intra‐cluster transmission. Also, for better load balancing among the selected CHs, we propose that cluster size should be uniform at zone level. Hence, after the CHs are selected, the remaining sensor nodes in a zone decide to become a cluster member of one of the selected CHs using a fuzzy‐based cluster formation technique. We compare the proposed algorithm with other approaches for three different network scenarios using parameters such as the number of alive nodes, the first node dies, half of the nodes die, the last node dies, the amount of data received at Base Station, and total remaining energy. Simulation results show that the proposed clustering algorithm provides better performance than other methods.

Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks.

The participating nodes in Wireless Sensor Networks (WSNs) are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. Energy is one of the major constraints which requires an efficient mechanism that takes into account the energy consumption of nodes to prolong the network lifetime. Particularly in the large scale heterogeneous WSNs, this challenge becomes more critical due to high data collection rate and increased number of transmissions. To this end, clustering is one of the most popular mechanisms which is being used to minimize the energy consumption of nodes and prolong the lifetime of the network. In this paper, therefore, we propose a robust clustering mechanism for energy optimization in heterogeneous WSNs. In the proposed scheme, nodes declare themselves as cluster head (CH) based on available resources such as residual energy, available storage and computational capability. The proposed scheme employs the multi criteria decision making technique named as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which allows the child nodes to select the optimal CH among several potential CH candidates. Moreover, we also propose mechanisms such as CH-acquaintanceship and CH-friendship in order to prolong the network lifetime. Simulation results show that our proposed scheme minimizes the control overhead, reduces the power consumption and enhances overall lifetime of the network by comparing with the most recent and relevant proposed protocol for WSNs.

Sum-rate maximization and data delivery for wireless seismic acquisition

Traditional seismic acquisition systems suffer from a number of difficulties related to telemetry cables that are used as a means of data transmission. Transforming the traditional seismic acquisition system to a wireless system has been considered as a potential solution to most of these difficulties. The wireless seismic acquisition system has to serve a huge aggregate data rate requirement as is usually the case in a large wireless sensor network. This paper considers the wireless acquisition system, and studies the maximum achievable transmission data rates from the geophones to the wireless gateways. Successive interference cancellation decoding is assumed to be used at the gateway nodes. We consider the problem of sum-rate maximization by optimizing the decoding process at each gateway node. The optimization searches for optimal decoding set at each gateway, i.e. which group of geophones will be decoded at each gateway. Various integer programming algorithms are proposed for solving the maximization problem. These optimization algorithms are simulated and compared among each other, where it is shown that the ant system algorithm achieves the highest sum-rate with lower computational complexity compared to other algorithms. Furthermore, the data delivery from the gateways to the data center is also considered. In this stage, two gateways with different buffer sizes are studied. For small-size buffers, two optimization problems are identified and solved. The first problem considers the minimization of the total power of the gateways, and the second problem considers power fairness between the gateways. For large-size buffers, the problem of maximizing the weighted sum rate of the gateways is solved.

Monitoring High Voltage Power Lines Using Efficient WSN

The power transmission line is one of most critical components in electrical power system. In order to enhance the reliability of the large power systems, an advanced and efficient Wireless Sensor Network (WSN) must be employed along power transmission line. However, time delay has emerged as one of the most critical issues in the design of WSN-based monitoring network of transmission lines. This paper proposes a new model for WSN for monitoring High Voltage Transmission Line (HVTL) which improves the needed time delay to transmit the measured quantity from transmission line towers to the control center. The proposed design considers the broadband power line communication (BPLC) technologies to transmit the collected data from group of towers to the nearest substation. To evaluate the performance of proposed model, the maximum time delay in data flow for the proposed model is determined in mathematical form and compared with other common models. The results of this paper demonstrate that the maximum time delay of proposed model less than the other common models, especially when advanced wireless communication, such as 4G and 5G, is used between the relay nodes. Based on the simulation results, the proposed WSN-based transmission line monitoring is efficient, powerful, reliable, and applicable to meet the smart grid requirements.

Proposed method: mean delays synchronization protocol (MDSP) for wireless sensor networks

PurposeMany synchronization approaches are based on low-level time capturing, causing a tight integration with the Media Access Control (MAC) layer. Alternatively, this study aims to present a hybrid approach combining both receiver–receiver and sender–receiver schemes to reduce the variation of two-way message exchange durations, in heavy-load networks. To achieve network-wide synchronization, a variant of Prim’s algorithm (Cormen et al., 2009) is used to build a spanning tree, guaranteeing the minimum number of ancestors and limited error propagation. The simulation results show that the proposed approach is very competitive with a set of the most-cited synchronization protocols. In addition, a new synchronization simulator SynSim was developed using C++ languageDesign/methodology/approachTo achieve network-wide synchronization, a variant of Prim's algorithm (Cormen et al., 2009) is used to build a spanning tree, guaranteeing the minimum number of ancestors and limited error propagation.FindingsSimulation results show that the proposed approach is very competitive with a set of the most-cited synchronization protocols. In addition, a new synchronization simulator SynSim was developed using in C++ language.Research limitations/implicationsIt can be concluded from the experiments that MDSP is suitable for WSNs especially if MAC layer timestamping is not possible. So, the mean delays synchronization protocol (MSDP) is suitable to achieve time synchronization in single-hop and multi-hop networks without the MAC layer timestamping in large wireless sensor network (WSN) deployments.Practical implicationsA future enhancement of MDSP could be switching between the traditional timestamping and the new proposed timestamping based on a given threshold, which is the number of nodes in the neighborhood and the load of the network. It will be also interesting to test it in a prototype. The proposed solution can be used in practice to implement the Time-division multiple access (TDMA) protocol in a WSN. In addition, the proposed simulator can be used in a computer network synchronization protocols course.Originality/valueTo the best of authors’ knowledge, this study’s contribution is original. In addition, the authors implemented a new synchronization simulator

Designing Optimal Path for Wireless Sensor Networks by Combining Energy and Security Components.

The data transferring using multi-directional in remote sensor systems (WSNs) offers little security against malicious attacks through proper acknowledgement. An enemy can use confidential information to attack events, also deteriorate proper functioning of routing protocols. This situation is also expands to mobile and hostile network conditions. In this novel, I would like to propose trustworthy and operational location based routing instructions which provides security and also helpful to extends this works for large wireless sensor networks. The proposed idea has been clearly provide the mechanism to finds out malicious attacks as well as to provide security. As per the statistics, There are constraints on storage, processing of data, battery resources and variation in frequency ranges deeply effects for implementation, in addition to this data propagation ,improper links between network components, requires extra care while choosing different routing paths.

On the topology design of large wireless sensor networks for distributed consensus with low power consumption

Sensor-based structural health monitoring systems are commonly used to provide real-time information and detect damage in complex structures. In particular, wireless structural health monitoring systems are of low cost but, since wireless sensors are powered with batteries, a low power consumption is critical. A common approach for wireless structural health monitoring is to use a distributed computation strategy, which is usually based on consensus algorithms. Power consumption in such wireless consensus networks depends on the number of connections of the network. If sensors are randomly connected, there is no control on the power consumption. In this article, we present a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies using the Kronecker product.

A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks

Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.

Design and Performance Analysis of Secure Multicasting Cooperative Protocol for Wireless Sensor Network Applications

This letter proposes a new security cooperative protocol, for dual phase amplify-and-forward large wireless sensor networks. In such a network, a portion of the ${K}$ relays can be potential eavesdroppers. The source agrees to share with the destination a given channel state information (CSI) of a source-trusted relay-destination link to encode the message. Then, in the first hop, the source will use this CSI to map the right message to a certain sector while transmitting fake messages to the other sectors using sectoral transmission. In the second hop, the relays retransmit their received signals to the destination, using the distributed beamforming technique. We derived the secrecy outage probability and demonstrated that the probability of receiving the right encoded information by an untrustworthy relay is inversely proportional to the number of sectors. We also showed that the aggressive behavior of the cooperating untrusted relays is not effective compared to the case where each untrusted relay is trying to intercept the transmitted message individually.

AREOR–Adaptive ranking based energy efficient opportunistic routing scheme in Wireless Sensor Network

Energy efficient and cluster based routing techniques are gaining popularity due to complexities associated with the power and maintenance factors of large Wireless Sensor Networks during transmission. An adaptive ranking based energy efficient opportunistic routing protocol- AREOR is researched in this paper. It identifies the most effective node to participate as a cluster head by using adaptive participatory criteria. A novel forwarder node selection method, based on adaptive ranking system is proposed. Node’s remaining energy and geographic position is utilized to compute the ranks. The proposed approach is verified through variable sized clustered networks to investigate the impact of opportunistic routing. Evaluation parameters such as Message Success Rate, Energy Consumption, End-to-End delay and Packet Delivery Ratio are used to demonstrate the effectiveness of the proposed energy optimization approach. AREOR reports reduction in energy consumption for the duration of the transmission. Adaptive ranking and energy optimal forwarder node selection has shown an edge over competitive peer techniques as per demonstrative simulations performed on realistic scenarios.