Mechanism For Wireless Sensor Networks Research Articles

Security threats Detection and Handling Mechanism in Wireless Sensor Networks using Machine Learning

Security threats Detection and Handling Mechanism in Wireless Sensor Networks using Machine Learning

Deterministic Static Sensor Node Placement in Wireless Sensor Network based on Territorial Predator Scent Marking Behaviour

An optimum sensor node placement mechanism for Wireless Sensor Network (WSN) is desirable in ensuring the location of sensor nodes offers maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of a territorial predator in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). The main objectives considered in this paper are to achieve maximum coverage and minimum energy consumption with guaranteed connectivity. A simulation study has been carried out to compare the performance of the proposed algorithm implemented in two different single objective approaches with an Integer Linear Programming based algorithm. The proposed single objective approaches of TPSMA studied in this paper are TPSMA with minimum energy and TPSMA with maximum coverage. Simulation results show that the WSN deployed using the proposed TPSMA sensor node placement algorithm is able to arrange the sensor nodes according to the objective required; TPSMA with maximum coverage offers the highest coverage ratio with fewer sensor nodes up to 100% coverage while TPSMA with minimum energy consumption utilized the lowest energy as low as around 4.85 Joules. Full connectivity is provisioned for all TPSMA approaches since the constraint of the optimization problem is to ensure the connectivity from all sensor nodes to the sink node.

Design and Performance Analysis of Underwater Acoustic Sensor Networks

Abstract: The underwater acoustic sensor network (UASN) is essential for exploration missions and observation in demanding environments. The UASN'S connection route is acoustic waves, which limits its usefulness in comparison to ground sensor networks. This is the case because to its limited capacity, latency, and significant route loss. This article provides comprehensive research of the characteristics of UASN. We explore the functionality of underwater acoustic ad-hoc networks in the presence of disruptions. RF signals are used as a communication mechanism in wireless sensor networks, both terrestrial and aerial. However, in a sub-sea setting, such as deep-sea research, detecting and transmitting data needs a completely different method to underwater communication. The fact that the seas cover 70% of the earth's surface and contain massive amounts of unexplored riches cannot be ignored. The aquatic environment has largely escaped the effects of recent breakthroughs in wireless sensor networks (WSNS) and their broad application in latest studies and economic progress. Research on underwater acoustic sensor networks (UASNS) is developing at a snail's pace due to the difficulties in transferring most of the state-of-the-art of land and air based WSNS to its aquatic equivalent. The bulk of underwater activities rely on acoustic communication and specialized sensors that can endure the harsh environment of the oceans. The purpose of this study is to investigate how UASN works in different situations. End-to-end latency and energy consumption are examined in response to a variety of factors. We also investigate how well underwater acoustic ad-hoc networks perform when nodes are dispersed, and the network is large. Keywords: Under water Acoustics, Sensor, Wireless Sensor Networks, Energy Consumptions.

TEO-MCRP: Thermal exchange optimization-based clustering routing protocol with a mobile sink for wireless sensor networks

Sensor nodes consume their energy quickly due to their continuous perception, routing, and internal circuit mechanisms in Wireless Sensor Networks (WSNs). Especially, the nodes that are close to the sink node consume their energy more quickly, causing the hot spot problem. In this direction, the development of robust clustering and adaptive mobile routing methods is extremely significant to ensure energy efficiency in WSNs. For this purpose, a new mobile clustering routing protocol based on Thermal Exchange Optimization (TEO) inspired by Newton's cooling law, called TEO-MCRP, is presented for heterogeneous WSNs. In the present protocol, two different algorithms are proposed for Cluster Head (CH) selection and Mobile Sink (MS) path detection with objective functions including independent fitness parameters. With the manner of minimizing these objective functions, effective CH selection and MS trajectory determination have been performed for each mobile round using the temperature equations in the TEO algorithm. In parallel, an MS node gathers data from all CHs and transmits all data to the Base Station (BS). The performance results strikingly illustrate that the proposed protocol is better than other algorithms. Due to minimizing packet conflicts and losses, the packet delivery rate was achieved at 99.784% in the network

Rate aware congestion control mechanism for wireless sensor networks

The sensor nodes in wireless sensor networks (WSNs). inherit low processing, shorter range and low power features with the miniature size to offer wireless transmission. Therefore, in order to fulfil the long-time data sensing need, several Quality of Service (QoS) parameters such as: lesser delay, higher throughput and packet delivery ratio (PDR) with minimum overhead must to be improved. In recent years extensive research efforts have been made to ameliorate these parameters to achieve optimum QoS. However, the rate adaptation and congestion control in WSNs are still least explored areas. The traffic congestion in WSNs is the main reason that results in higher delay and low throughput. In this paper, a new rate aware congestion control (RACC) mechanism has been proposed which defines three levels of congestion based on which the data rate, throughput, overhead and the delay. RACC at the transport layer, improves congestion by source rate regulation at the specific hotspot areas. Further, RACC has been applied to different modulation schemes like: 16 QAM (Quadrature Amplitude Modulation), BPSK (Binary Phase Shift Keying) and QPSK (Quadrature Phase Shift Keying) to test the optimum modulation scheme for the proposed approach. The testing is done to ensure the that the data can be sent to the longer distant sensors using appropriate modulation technique suitable for the congestion model (RACC). The simulation outcomes in NS2 tool confirms the improvement of RACC over existing techniques (Delay-aware congestion control protocol (DACC) and Joint energy replenishment and load balancing (J-ERLB)) in WSNs. The overall improvement for RACC over existing techniques follows an improvement percentage 17% for throughput parameter, for packet delivery ratio, it is 8.35%, while for normalized routing overhead it shows 0.56% and for MAC Overhead, average end to end delay, and average remaining energy it shows 0.64%, 2.04% and 59.28% improvement respectively.

Lifetime centric load balancing mechanism in wireless sensor network based IoT environment

Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.

Smart node relocation (SNR) and connectivity restoration mechanism for wireless sensor networks

Node failures are inevitable in wireless sensor networks (WSNs) because sensor nodes in WSNs are miniature and equipped with small and often irreplaceable batteries. Due to battery drainage, sensor nodes can fail at any instance. Moreover, WSNs operate in hostile environments and environmental factors may also contribute to nodes failure. Failure of nodes leads to disruption of inter-node connectivity and might also lead to network partitioning. Failure to communicate with each other and with the base station can compromise the basic operation of the sensor network. For restoration of connectivity, a robust recovery mechanism is required. The existing connectivity restoration mechanisms suffer from shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration. As a result, most of these mechanisms lead to the excessive mobility of nodes, which itself causes the utilization of excessive battery. In this work, we propose a novel technique called smart node relocation (SNR). SNR is capable of detecting and restoring the connectivity caused by either single or multiple node failures. For achieving energy efficiency, SNR relies on transmitting a lesser number of control packets. For achieving the goal of being coverage-aware, it tries to relocate only essential nodes while trying to restore connectivity. By performing extensive simulations, we prove that SNR outperforms the existing approaches concerning multiple performance metrics including but not limited to the total number of packets transmitted, total distance moved for connectivity restoration, the percentage reduction in field coverage.

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Wireless sensor networks (WSN) is considered as one of the exploring technology for its deployment of the massive number of dedicated sensor nodes which sense the environment and collect the data. The collected data are sent to the sink node through the intermediate nodes. Since the sensors node data are exposed to the internet, there is a possibility of vulnerability in the WSN. The common attack that affects most of the sensor nodes is the Distributed Denial of Services (DDoS) attack. This paper aims to identify the DDoS (Flooding) attack quickly and to recover the data of sensor nodes using the fuzzy logic mechanism. Fuzzy based DDoS attack Detection and Recovery mechanism (FBDR) uses type 1 fuzzy logic to detect the occurrence of DDoS attack in a node. Similarly fuzzy- type 2 is used for the recovery of data from the DDoS attack. Both the type 1 fuzzy-based rule and type 2 fuzzy-based rule perform well in terms of identifying the DDoS attack and recover the data under attack. It also helps to reduce the energy consumption of each node and improves the lifetime of the network. The proposed FBDR scheme is also compared with other related existing schemes. The proposed method saves energy usage by up to 20% compared with the related schemes. The experimental results represent that the FBDR method works better than other similar schemes.

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

AbstractIn this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM‐BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self‐organization, and self‐configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM‐BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy‐efficient clustering approach consolidated game theory as well as dual‐cluster‐head mode for WSNs energy‐aware clustering by cuckoo optimization approach (EECM‐COA), energy‐aware clustering‐based routing using multi‐path reliable transmission with routing and control board (EECM‐RCB‐MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM‐AR‐TORA‐FCS), passive multi‐hop clustering algorithm (EECM‐PMC), dynamic source routing protocol based on genetic algorithm‐bacterial foraging optimization (DSR‐GA‐BFO).

Deep Reinforcement Learning for Attacking Wireless Sensor Networks.

Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures.

Key Distribution Strategy of Wireless Sensor Network Based on Multi-Hash Chain

Key management is the basis of the security mechanism for wireless sensor networks and services, and random key pre-distribution is the most effective key management mechanism at present. However, there is a potential challenge to most current random key pre-distribution strategies: it is difficult to achieve both ideal network security connectivity and network survivability. In this paper, we present a novel random key pre-distribution scheme based on the hash chain. By adjusting certain system parameters, such as the hash chain length, the number of common auxiliary nodes and the number of hash chains, a sensor node only need to preload a few of keys, making it possible to establish the pairwise key with high probability among its neighboring nodes. The proposed scheme can still maintain strong network survivability even if there are many compromised nodes. The theoretical analysis and simulation experiments show that the proposed scheme is not only effective and secure, but also scalable.

Learning automata based energy efficient and reliable data delivery routing mechanism in wireless sensor networks

Wireless Sensor Networks (WSNs) emerged as major data gathering paradigm due to its wide variety of applications. Achieving energy efficient reliable data delivery is an important concern in WSNs. In a network, packet loss reduces the reliable data transmission rate. Retransmissions are required to increase the reliable data transmission rate. However, number of retransmissions also increase the network energy consumptions and packet delivery delay. Packet loss rate can be reduced by selecting a best successor node in a routing path. In this work, a Learning Automata based Routing mechanism is proposed for wireless sensor networks to achieve energy efficiency and reliable data delivery. In this paper, learning automata has been adopted to calculate next node’s selection probability in a routing path using node’s score, quality of link and previous selection probability. Further, an energy efficient reliable routing mechanism is proposed using combination of learning automata and A-star search algorithm. The proposed mechanism determines energy efficient and reliable routing paths in WSNs by favoring highest residual energy, good quality of link , more free buffer space and less distance. Finally, simulation results indicate that the proposed algorithm reduces the energy consumptions, data delivery delay, data transmissions and increases the network lifetime.

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism.

In the current Internet of Things era, digital devices form complex interconnections. The statuses of objects of interest are monitored using sensors, and distributed wireless sensor networks are formed from numerous sensor nodes. Many Byzantine fault tolerance mechanisms in wireless sensor networks (WSNs) were proposed from Byzantine agreement which even with a few faulty nodes in a sensor network, most healthy nodes can reach a consensus, perform data transmission tasks, and maintain network operation. In this study, this mechanism was utilized together with the majority function technique; in particular, the proposed method uses original sensor signals to define a threshold to assert a binary value of one or zero, thereby performing data judgment and aggregation. This approach reduces node energy consumption and enables the nodes to quickly reach a consensus. Moreover, the operating performance of the network can be maintained even when problems such as node failure and faults occur within the fault tolerance range. Compared with existing algorithms, the proposed data aggregation mechanism exhibits a better network life cycle and can effectively extend the flexibility of network operations.

A lightweight security and privacy-aware routing scheme for energy-constraint multi-hop wireless sensor networks

Unique constraints associated with wireless sensor networks notably, limited resources and physical exposure of sensor nodes have warranted the need for a lightweight and low energy demand security mechanisms for wireless sensor networks (WSNs). Most of the existing security schemes demand computational power beyond the computational capacity of WSNs making them unsuitable security schemes for WSNs' routing protocols. In this work, a lightweight security and privacy scheme for WSNs' routing protocol is developed. An elliptic curve cryptography, scalar blinding, symmetric encryption, and modified Diffie Hellman key exchange protocol are adopted to evolve an additive perturbation that ensures data integrity, and an effective authentication that ensure confidentiality during routing. The security analysis shows that the scheme is secured against possible known attacks and performs better than some of the considered state-of-the-art schemes used in WSNs. Both the analytical and experimental results not only show that the proposed scheme requires lower computational power but with increase level of security and speed.

A Lightweight Security and Privacy-Aware Routing Scheme for Energy-Constraint Multi-hop Wireless Sensor Networks

Unique constraints associated with wireless sensor networks notably, limited resources and physical exposure of sensor nodes have warranted the need for a lightweight and low energy demand security mechanisms for wireless sensor networks (WSNs). Most of the existing security schemes demand computational power beyond the computational capacity of WSNs making them unsuitable security schemes for WSNs' routing protocols. In this work, a lightweight security and privacy scheme for WSNs' routing protocol is developed. An elliptic curve cryptography, scalar blinding, symmetric encryption, and modified Diffie Hellman key exchange protocol are adopted to evolve an additive perturbation that ensures data integrity, and an effective authentication that ensure confidentiality during routing. The security analysis shows that the scheme is secured against possible known attacks and performs better than some of the considered state-of-the-art schemes used in WSNs. Both the analytical and experimental results not only show that the proposed scheme requires lower computational power but with increase level of security and speed.

Cluster-based routing protocols for wireless sensor networks

Routing technology at the network layer is pivotal in the architecture of wireless sensor networks. As an active branch of routing technology, cluster-based routing protocols excel in network topology management, energy minimization, data aggregation and so on. In this paper, cluster-based routing mechanisms for wireless sensor networks are analyzed. Cluster head selection, cluster formation and data transmission are three key techniques in cluster-based routing protocols. As viewed from the three techniques, recent representative cluster-based routing protocols are presented, and their characteristics and application areas are compared. Finally, the future research issues in this area are pointed out.

NRSM: node redeployment shrewd mechanism for wireless sensor network

Despite numerous advantages, the challenges for wireless sensor communication always remains open due to which a continuous effort is being applied to tackle the unavoidable conditions regarding wireless network coverage. Somehow, the uncouth deployment of the sensor nodes is making the tribulation queue longer day by day which eventually has great impact over sensor coverage range. To address the issues related with network coverage and uncouth energy wastage, a sensor node redeployment-based shrewd mechanism (NRSM) has been proposed where new intended positions for sensor node are rummaged out in the coverage area. The proposed algorithm operates in two phases; in first phase it locates the intended node positions through Dissimilitude Enhancement Scheme (DES) and moves the node to new position. While second phase is called a Depuration, when the moving distance between initial and intended node position is shrewdly reduced. Further, different variation factors of NRSM such as loudness, pulse emission rate, maximum frequency, and sensing radius have been explored and related optimized parameters are identified. The performance metric has been meticulously analyzed through simulation rounds in Matlab and compared with state of art algorithms like Fruit Fly Optimization Algorithm (FOA), Jenga-inspired optimization algorithm (JOA) and Bacterial Foraging Algorithm (BFA) in terms of mean coverage range, computation time, standard deviation and network energy diminution. The performance metrics vouches the effectiveness of the proposed algorithm as compared to the FOA, JOA and BFA.

A Smart Sensing and Routing Mechanism for Wireless Sensor Networks.

Wireless sensor networks (WSNs) have long been used for many applications. The efficiency of a WSN is subject to its monitoring accuracy and limited energy capacity. Thus, accurate detection and limited energy are two crucial problems for WSNs. Some studies have focused on building energy-efficient transmission mechanisms to extend monitoring lifetimes, and others have focused on building additional systems to support monitoring for enhanced accuracy. Herein, we propose a distributed cooperative mechanism where neighboring sensors mutually confirm event occurrences for improved monitoring accuracy. Moreover, the mechanism transmits events in a time- and energy-efficient manner by using smart antennae to extend monitoring lifetimes. The results of the simulations reveal that monitoring lifetime is extended and time for event notifications is shortened under the proposed mechanism. The evaluations also demonstrate that the monitoring accuracy of the proposed mechanism is much higher than that of other existing mechanisms.

SybilEye: Observer-Assisted Privacy-Preserving Sybil Attack Detection on Mobile Crowdsensing

Mobile crowdsensing is a data collection system using widespread mobile devices with various sensors. The data processor cannot manage all mobile devices participating in mobile crowdsensing. A malicious user can conduct a Sybil attack (e.g., achieve a significant influence through extortion or the generation of fake IDs) to receive an incentive or destroy a system. A mobile crowdsensing system should, thus, be able to detect and block a Sybil attack. Existing Sybil attack detection mechanisms for wireless sensor networks cannot apply directly to mobile crowdsensing owing to the privacy issues of the participants and detection overhead. In this paper, we propose an effective privacy-preserving Sybil attack detection mechanism that distributes observer role to the users. To demonstrate the performance of our mechanism, we implement a Wi-Fi-connection-based Sybil attack detection model and show its feasibility by evaluating the detection performance.

Fault‐tolerant mechanism for wireless sensor network

The study aims to understand the various types of faults occur in a wireless sensor network (WSN). The primary objective of the fault-tolerant technique is to enhance the robustness and self-healing capacity of sensor network. The resource constraint environment, remote deployment and harsh monitoring conditions frequently invite different types of faults in WSN. The fault can be handled in two ways such as fault tolerance and fault prevention method. The novelty of this state-of-art is, the authors propose two new algorithms named Uniform Energy Clustering for fault tolerance and Population-Based Clustering for fault prevention. The result of the simulation is satisfactory in comparison to existing algorithms such as Low Energy Adaptive Clustering Hierarchy, Stable Election Protocol, Virtual Area Partition - Energy and Energy-Aware Multi-Hop Multi-Path Hierarchical protocols. In addition to these new algorithms, they also propose a unique taxonomy on fault which is based on sensitivity issues of specific applications.