IoT-enabled Wireless Sensor Networks Research Articles

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks

The high-speed data routing in the Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs) is vital in improving network performance. Existing routing protocols face difficulties dealing with frequent node relocations, energy efficiency optimization, scalability, and dynamic network environments. The inadequacy of conventional routing algorithms in handling the issues provided by frequent node relocations, energy efficiency optimization, scalability, and dynamic network settings in IoT-enabled WSNs is identified as the problem. Traditional protocols are inflexible in the face of node relocations, whereas machine learning-based techniques frequently miss energy efficiency factors. Reinforcement learning-based techniques presume static topologies and the potential of federated learning in WSN routing is largely untapped. The goal is to provide adaptive and energy-efficient routing while considering message overhead, temporal complexity, data sum rate, communication delay, and scalability. The research adopts federated deep reinforcement learning (FDRL) to address these limitations for routing the high-speed data packets in IoT-enabled WSNs. The proposed research technique uses FDRL, which enables distributed decision-making and adaptive routing in dynamic network situations. The fundamental instant data load is separated into cluster pairs in the proposed technique, each with one strong and one weak sensor node. This split enables optimal network load balancing and resource use. The FDRL architecture is used to disperse the learning process across numerous nodes or access points, allowing for localized decision-making while taking advantage of global coordination. The simulations are conducted to validate the effectiveness of the FDRL Routing in terms of packet loss, latency, energy efficiency, and scalability. The results show that the proposed federated DRL-based intelligent data routing approach outperforms traditional protocols and other current routing strategies.

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

SummaryIn today's world, the Internet of things (IoT) and wireless sensor networks (WSNs) have been integrated into various applications to support reliable communication systems. The IoT nodes collect the sensed data with the aid of WSN and transfer them to the cloud environment. Nevertheless, these nodes become vulnerable when Denial of Service attacks occurred in the IoT‐enabled WSN (IWSN). The insecure route also affects the energy efficiency of IWSN. Therefore, robust intrusion detection along with secure routing is necessitated to protect the IWSN from various security threats. In this paper, a novel Artificial Intelligence‐based Energy‐aware Intrusion Detection and Secure Routing model has been proposed to develop a secured IWSN. Initially, the proposed model executes the intrusion detection system to identify the various attacks. Afterward, a game strategy‐based decision mechanism is integrated with the proposed intrusion detection model to decide whether protection is needed or not. In the last phase, an energy‐aware ad‐hoc on‐demand distance vector algorithm has been established to offer secured routing among multiple nodes. The performance results show that the proposed model achieves superior detection accuracy of 95% and reduced energy utilization of 38% as compared with the existing intrusion detection models.

Optimization Network Lifetime Through Residual Energy-Based Cluster Head Selection in IoT-Enabled WSNs

Wireless Sensor Networks (WSNs) have gained an emerging importance in different application domains especially in event tracking and monitoring. The sensor nodes in WSNs are observed to have shorter lifetime due to the continuous sensing and processing operations that result in quicker energy depletion. Small, inexpensive, low-power, multipurpose nodes that are connected to one another form the basis of WSNs. Efficiently gather & communicate data to a washbasin. Cluster Heads (CHs) are used in cluster-based approaches to effectively arrange WSNs for data collection and energy conservation. A CH collects data from cluster nodes and aggregates/compresses it before sending it to a sink. The node's greater responsibility does, however, result in a higher energy drain, which leads to uneven network deterioration. This is made up for by LEACH (Low Energy Adaptive Clustering Hierarchy), which probabilistically alternates CH roles among nodes with energy over a set threshold. CH selection in WSN is NP-Hard because optimal data aggregation with effective energy savings cannot be done in polynomial time. To improve system performance, the synchronous firefly approach, a modified firefly heuristic, is introduced in this paper. A thorough simulation shows that the suggested method performs better than LEACH and energy-efficient hierarchical clustering. In today's world of intelligent networks, the internet of things (IoT) and industrial IoT (IIoT) are extremely important, and they fundamentally use a wireless sensor network (WSN) as a perception layer to collect the necessary data. The difficulty here is the usage of minimal energy for processing and communication. This data is processed as information and sent to cloud servers through a base station. The lifespan of WSNs is increased by the dynamic generation of cluster heads and energy-conscious clustering strategies.

Fuzzy logic based nodes distributed clustering for energy efficient fault tolerance in IoT-enabled WSN

The Internet of Things (IoT) enabled wireless sensor network (WSN) is now widely employed in various sectors like smart city and vehicle transportation for their expanded capabilities such as data storage, access, and monitoring. The use of smart sensors that continuously collect data from the smart environment makes these possible. Furthermore, these facilitate the easy access of stored data over a secure IoT-gateway for mobile users. This device mobility that allows shifting to multiple locations, makes it challenging to route data across many access points. In this regard, it induces packet loss and improper node selection, which could result in connection failure and network unreliability. This study proposes a new data routing protocol called as Fuzzy Logic Nodes Distributed Clustering for Energy-Efficient Fault Tolerance (F-NDC-EEFT). It can be deployed on any network platform, including mobile and non-mobile nodes. It considers performance metrics such as delivery rate, withstand node aliveness, communication delay, and energy efficiency to find an optimized path for the better performance of IoT enabled WSNs. The clustering approach is applied to the instant data load, which divides it into the distinct node groups. When proposed algorithm is tested alongside existing routing protocols for performance, it is found to save energy, minimize the number of connection failures, boost the throughput, and increase the network’s lifetime.

Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure

This proposal examines the effect of grass vegetation elevation and density on path loss between sensors deployed in an IoT-enabled wireless sensor network (IoWSN) crop monitoring infrastructures. Observations via real-time measurement campaigns at different node heights and vegetation depths revealed that the sensor deployment made using free-space or tree vegetation based path loss model (PLM) experiences network disconnectivity due to variations in vegetation density in a cropping cycle. An empirical PLM is formulated to identify signal strength at different development phases of paddy and sugarcane medium grass vegetation. For this, the 2.4 GHz RF path loss coefficient (PLC) is estimated using data collected through measurement campaign over combinations of sensor height and vegetation density. Further, the formulated PLC is used to train multiple regression model to develop a generic PLM for all medium grass vegetation. Improvements in coverage and connectivity have been validated through proof of concept.

Intelligent Fault-Tolerance Data Routing Scheme for IoT-Enabled WSNs

Wireless sensor networks (WSNs) have become one of the essential components of the Internet of Things (IoT). In any IoT application, different sensor-based devices gather data from physical objects and transmit the sensed information to the base station (BS). The BS analyzes this information depending on the sensor location. A high-performance intelligent WSNs is essential for any IoT-based application. In this article, high-performance intelligent WSNs are referred to as IoT-enabled WSNs. In IoT-enabled WSNs, fault occurrence probability is much more than traditional networks. Faulty nodes and broken links affect the reliability of the IoT-enabled WSNs. Various fault-tolerance algorithms enhance the network’s reliability using multipath transmission, relay node placement, and backup node selection. However, these algorithms suffer from huge data transmission delays, packet overhead, and less detection accuracy. In this work, a multiobjective-deep reinforcement-learning (DRL)-based algorithm is proposed for fault tolerance in IoT-enabled WSNs. The main objective of this work is to detect the faulty nodes with high accuracy and less overhead. Furthermore, this work focuses on reliable data transmission after fault detection. Finally, a mobile sink (MS) is used for energy-efficient data gathering that significantly improves the network lifetime. Extensive simulations and theoretical analysis prove that the proposed algorithm outperformed as compared to the state-of-the-art algorithms in terms of fault detection accuracy (FDA), false alarm rate (FAR), false-positive rate (FPR), network lifetime, and throughput.

An integrated model for energy conservation in IoT-enabled WSN using adaptive regional clustering and monkey inspired optimization

The advancement of the Internet of Things (IoT) technologies will play a significant role in the evolution of the smart city, smart healthcare, and smart grid applications. The key objective of IoT is to allow the autonomous exchange of valuable data between invisibly embedded devices with the help of some prominent technologies. Wireless Sensor Network (WSN) is one of the emerged technologies used for sensing and data exchange processes in IoT-based applications. Network sustainability and energy stability are the most significant multi-objectives to attain an energy-efficient IoT-based WSN (IWSN). Consequently, in order to handle these multi-objectives, a novel Adaptive Regional Clustering (ARC) scheme has been proposed in this paper by exploiting two appropriate methodologies. Primarily, location-based modelling is employed to gather the location information from each sensor node in the IWSN environment. Thereafter, an effective hierarchical clustering can be carried out with the assist of the ARC algorithm. The cluster head will be chosen based on node capacity and node trust value by implementing the Enhanced Monkey Inspired Optimization (EMIO) algorithm. Finally, the optimal cluster head node acts as an energy-efficient local director for conducting inter-cluster connectivity, data transmission, and other duties. The effectiveness of the proposed ARC-EMIO scheme has been assessed using the NS-3 simulator and the results evident that the proposed scheme guarantees better performance with an improved network lifetime of 35% and energy efficiency of 22% when compared with the existing state-of-the-art clustering techniques.

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

SummaryThe advancement of the Internet of Things (IoT) technologies will play a vital role in the evolution of the smart city, smart healthcare, and smart grid applications. Wireless sensor network (WSN) is one of the futuristic technologies utilized for sensing and data exchange processes in IoT‐enabled applications. However, the hotspot problem, control packet overhead, and inefficient clustering are the most significant challenges to attaining an energy‐efficient IoT‐enabled WSN (IWSN) model. Most of the existing clustering schemes lagged to mitigate these aforesaid constraints since it consumes extra energy for data computation and forwarding tasks under any environmental conditions. In this article, a novel energy‐efficient auto clustering (EEAC) framework has been proposed to develop an effective IWSN model with enhanced quality of service. The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as master, sub‐master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01 J during a dense network and the network lifetime is prolonged up to 52% when compared with existing state‐of‐the‐art clustering models.

Emergency Evacuation System for Clogging-Free and Shortest-Safe Path Navigation With IoT-Enabled WSNs

Wireless sensor networks (WSNs) play a vital role in the development of emergency evacuation systems. The main objective of the emergency evacuation system is to find a safe path for every individual by using the Internet of Things (IoT)-based WSNs, whenever an emergency arises. The major drawback of the existing approaches is that they ignore the clogging of individuals taking the same route to exit. Therefore, this article proposes an emergency evacuation system for clogging-free and shortest-safe path navigation (ECSSN) with IoT-enabled WSNs. The ECSSN algorithm detects the hazards, such as fire or harmful gases and humans, which creates an emergency in the indoor monitoring environment. Furthermore, several safety layers around the dangerous zones are created using a multisource breadth-first search (MBFS) algorithm. Therefore, the individuals near or far away from the hazardous region are directed along the clogging-free and shortest-safe path to the destination exit. The ECSSN algorithm also considers the dynamic emergency by taking and solving the current scenario’s input from sensor nodes after every fixed time interval. The proposed algorithm is compared with state-of-the-art algorithms in terms of execution time, average path stretch, and navigation overhead to prove the efficacy and competence of the ECSSN algorithm.

Protecting Source Location Privacy in IoT-Enabled Wireless Sensor Networks: The Case of Multiple Assets

A major limitation to the use of wireless sensor networks (WSNs) in asset monitoring applications is security and privacy, particularly the privacy of source location information. In this article, we develop two phantom routing-based solutions to provide source location privacy for the case of multisource/asset scenario—the case that has received very little attention in the literature. The idea of phantom routing is to relay the packets to a distant node in a random fashion to obfuscate the traffic flows and confuse the attacker. The first technique phantom routing-based backward random walk (PRBRW) uses a combination of backward random walk (RW) and a greedy forwarding approach to route the packets to the base station (BS). Although the first approach has better performance improvements in terms of capture ratio and safety period it hampers the lifetime of the network and has a poor entropy metric. To better this problem, an improved phantom routing scheme phantom routing-based L-path RW (PRLPRW) is proposed. The second technique has three phases: 1) pure RW; 2) L-walk; and 3) greedy walk. This technique performs well in terms of capture ratio, safety period, and entropy metrics. The improvement in network lifetime is 10-folds and entropy is 477-folds when compared with PRBRW. The performance is evaluated using the developed analytical models and compared with the baseline protection-less scheme shortest path routing (SPR). It is observed that PRBRW and PRLPRW, respectively, have 60- and 73-fold improvements in terms of capture ratio when compared with SPR, whereas existing phantom routing-based pure RW and forward RW techniques, respectively, have only 54- and 34-fold improvements.

Cryptanalysis and enhancement of an authentication protocol for secure multimedia communications in IoT-enabled wireless sensor networks

Cryptanalysis and enhancement of an authentication protocol for secure multimedia communications in IoT-enabled wireless sensor networks

Secure Critical Data Reclamation Scheme for Isolated Clusters in IoT-Enabled WSN

Internet of Things (IoT) comprises of a huge number of connected devices that can communicate within the same network and across the networks. IoT-enabled wireless sensor networks (WSNs) are getting growing interest due to its wide applicability in healthcare, patient monitoring, transportation, and surveillance. The main issue is that the network is mostly deployed in hostile environments where an attacker may physically destroy the CHs or other technical fault may occur. It isolates the cluster and causes loss of sensitive data from that region. This article presents a critical data reclamation (CDR) protocol that provides secure data transmission for isolated clusters. We present the data transfer and data aggregation algorithms for sensing nodes and data receiving and extraction at CH and sink. We performed extensive simulations using NS-2.35. The results prove the dominance of CDR in contrast to counterparts in terms of communication cost, energy consumption, and resilience.

Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks

The Internet of Things (IoT) paradigm allows the integration of cyber and physical worlds and other emerging technologies. IoT-enabled wireless sensor networks (WSNs) are rapidly gaining interest due to their ability to aggregate sensing data and transmit it towards the central or intermediate repositories, such as computational clouds and fogs. This paper presents an efficient multi-hop routing protocol (EMRP) for efficient data dissemination in IoT-enabled WSNs where hierarchy-based energy-efficient routing is involved. It considers a rank-based next-hop selection mechanism. For each device, it considers the residual energy to choose the route for data exchange. We extracted the residual energy at each node and evaluated it based on the connection degree to validate the maximum rank. It allowed us to identify the time slots for measuring the lifetime of the network. We also considered the battery expiry time of the first node to identify the network expiry time. We validated our work through extensive simulations using Network Simulator. We also implemented TCL scripts and C language code to configure low-power sensing devices, cluster heads and sink nodes. We extracted results from the trace files by utilizing AWK scripts. Results demonstrate that the proposed EMRP outperforms the existing related schemes in terms of the average lifetime, packet delivery ratio, time-slots, communication lost, communication area, first node expiry, number of alive nodes and residual energy.

Light-Weight Secure Aggregated Data Sharing in IoT-Enabled Wireless Sensor Networks

Internet of Things (IoT) is a network of physical objects or things that can communicate and share information. In IoT-enabled Wireless Medical Sensor Network (WMSN), the smart sensing devices share remote patient monitoring data towards central repositories. During medical data aggregation and transmission, security is mandatory to guard against intruders. The main problem in existing base schemes is that complex multiplication operations are used for batch key creation. These schemes are computationally expensive and require huge memory space at the aggregator node (AN). This paper presents a lightweight Secure Aggregation and Transmission Scheme (SATS) for secure and lightweight data computation and transmission. SATS provides a lightweight XOR operation for obtaining batch keys instead of the expensive multiplication operation. Furthermore, the AN Receiving Message Algorithm (ARMA) is presented at the AN to aggregate data generated by sensor nodes. The Receiving Message Extractor (RME) algorithm is presented to decrypt the message and perform batch verification at the Fog-Server. SATS protects against several security threats such as denial of service attacks, the man in the middle attack, and reply attacks. The proposed SATS is simulated by using simulation tool NS 2.35. The results show that SATS provides lightweight data transmission by reducing computation and communication costs.The computation cost of the SATS scheme is 14%, 23% and 59% at AN, and at Fog-Node 6.5%, 21.5% and 51%, and Communication cost 6%, 3%, and 4% at Sensor Node, and at AN 6%, 9%, and 12% better than PPDAS, IDAP, and ASAS respectively. The proposed SATS is compared with relevant schemes and the results show that it provides better storage capacity, computations cost, communications cost, and energy consumption.

A Survey on Path Planning Techniques for Mobile Sink in IoT-Enabled Wireless Sensor Networks

Recently, the Internet of Things (IoT) had emerged very rapidly and became the most important technology in today’s era. In an IoT-based environment, every physical object is connected to the internet. It has many applications in the fields of home automation, healthcare, military, weather forecasting, and industrial monitoring. Wireless Sensor Networks (WSNs) acting as a backbone of any IoT-based system where IoT-enabled sensor nodes are deployed to collect real-time data from the monitoring environment. IoT-enabled WSNs require well-designed network architecture so that the overall lifetime of the network increases since the nodes are battery-operated. The overall performance of the architecture depends on how the data is transmitted from the source nodes to the Base Station efficiently and effectively by minimizing the data losses. Static Sinks and Mobile Sinks are used for data gathering in IoT-enabled WSNs. But the performance of Mobile Sink (MS) based data gathering approaches are more efficient as compared to Static Sink based data gathering approaches. However, MS-based data gathering approaches have several drawbacks and limitations. Therefore, a detailed study of the existing state-of-the-art MS-based data gathering approaches can help further development in this direction. In this paper, the working of the sensor network is explained with its application in various fields. It also discusses the various path selection algorithm used for the MS to gather the information in the shortest possible time by minimizing the energy dissipation of each node to enhance the overall lifetime of the whole network. Some other factors also affect the path planning for a MS which is discussed and explained in the paper.

Energy-Efficient Intelligent Routing Scheme for IoT-Enabled WSNs

Recently, the Internet of Things (IoT) has attracted much interest in its wide applications, such as smart healthcare, home automation, transportation, and smart city. In these IoT-based systems, wireless sensor networks (WSNs) are highly used to gather information needed by smart environments. However, due to huge heterogeneous data coming from different sensing devices, IoT-enabled WSNs face different challenges, such as high communication delay, low throughput, and poor network lifetime. In this article, a deep-reinforcement-learning (DRL)-based intelligent routing scheme is proposed for IoT-enabled WSNs that significantly reduce delay and increase network lifetime. The proposed algorithm divides the whole network into different unequal clusters depending on the current data load present in the sensor node that significantly prevents immature death of the network. An extensive experiment on the proposed algorithm is performed using ns3. The experimental results are compared with the state-of-the-art algorithms to demonstrate the efficiency of the proposed scheme in terms of the number of alive nodes, packet delivery, energy efficiency, and communication delay in the network.

Towards green computing: intelligent bio-inspired agent for IoT-enabled wireless sensor networks

With the emergence of the Internet of Things (IoT) and machine to machine (M2M) communications, massive growth in the IoT-enabled wireless sensor node deployment is expected in the near future. The critical challenges for the sensor network include energy efficiency, optimum route calculation, and the overall transmission cost. To avoid the bias toward one of the objectives and also to facilitate ease of position updating, we propose a novel multi-objective optimisation (MOO) agent based on particle swarm grey wolf optimisation (PSGWO) and inverse fuzzy ranking. We initially developed an enhanced PSGWO model, and then it is utilised for the development of population and multi-criteria based soft computing algorithm, called fuzzy PSGWO. The performance of the proposed algorithm is validated and compared with the well-known techniques; for the proposed algorithm, residual energy of the nodes is much higher than that of other algorithms, and save up to 48% energy along with smaller variation in the standard deviation. The results also demonstrate the smaller average values of fitness function and computationally efficient capabilities of the proposed algorithm.

Trust Based Scheme for IoT Enabled Wireless Sensor Networks

Many research in the literature address the problem of aggregating data in wireless sensor network (WSN) applications. Data aggregation techniques of WSN using external mobile elements (ME) are, recently, proposed. Only few of the proposed techniques consider the trust factor when selecting one network component to send the data to the base station. In this paper, we introduce a new trust scheme that assigns the task of sending the aggregated data to only the most reliable external MEs of the WSN, in order to reduce data loss. Experimental results show that the proposed scheme performs well in terms of reducing the data and energy loss while not negatively affecting the network coverage.

Efficient authentication protocol for secure multimedia communications in IoT-enabled wireless sensor networks

In current times, multimedia application includes integrated sensors, mobile networks and Internet-of-Things (IoT) services. In IoT services, if more devices are connected without much constrains, the problem of security, trust and privacy remain a challenge. For multimedia communications through Wireless Sensor Network (WSN), sensor nodes transmit confidential data to the gateway nodes via public channels. In such an environment, the security remains a serious issue from past many years. Only few works are available to support secure multimedia communications performed in IoT-enabled WSNs. Among the few works, Kumari and Om recently proposed an authentication protocol for multimedia communications in IoT-enabled WSNs, which is applicable in coal mine for safety monitoring. The authors claimed in their work that their contributory protocol strongly withstands several security threats such as, user impersonation attack, sensor node impersonation attack, sensor node anonymity issue and others technical design issues. However, this article proved that Kumari and Om’s protocol has some design flaws and is susceptible to various security attacks including, user and sensor node impersonation attacks. As a remedy, a robust authentication protocol using smartcard is constructed to solve the security issues found in Kumari and Om’s protocol. The proof of correctness of mutual authentication is performed using the BAN logic model. In addition, our further security investigation claimed strong protection against known security attacks. Our protocol is analyzed comprehensively and compared against the similar protocols and the results showed that it is efficient and robust than earlier protocols.