Sensor Nodes In Wireless Sensor Networks Research Articles

Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

In recent scenario of Wireless Sensor Networks (WSNs), there are many application developed for handling sensitive and private data such as military information, surveillance data, tracking, etc. Hence, the sensor nodes of WSNs are distributed in an intimidating region, which is non-rigid to attacks. The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats. In traditional models, the solution has been made for defending the networks, only to specific attacks. However, in real-time applications, the kind of attack that is launched by the adversary is not known. Additionally, on developing a security mechanism for WSN, the resource constraints of sensor nodes are also to be considered. With that note, this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism (IDRM) for defending the sensor network from various attacks. Moreover, for efficient model design, the work includes the part of feature evaluation of some general attacks of WSNs. The IDRM also includes determination of optimal secure paths and Node security for secure routing operations. The performance of the proposed model is evaluated with respect to several factors; it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications. It is proven that the proposed IDRM produces 74% of PDR in average and a minimized packet drop of 38% when comparing with the existing works.

An Enhanced Spatial Correlation Framework for Heterogenous Wireless Sensor Networks

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

OGWO-CH: Hybrid Opposition-Based Learning with Gray Wolf Optimization Based Clustering Technique in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. This approach gathers the sensors into clusters and selects the cluster heads (CHs). CHs accumulate the information from the cluster members and transfer the data to the base station (BS). Yet, the most challenging task is to select the optimal CHs and thereby to enhance the network lifetime. This article introduces an optimal cluster head selection framework using hybrid opposition-based learning with the gray wolf optimization algorithm. The hybrid technique dynamically trades off between the exploitation and exploration search strategies in selecting the best CHs. In addition, the four different metrics such as energy consumption, minimal distance, node centrality and node degree are utilized. This proposed selection mechanism enhances the network efficiency by selecting the optimal CHs. In addition, the proposed algorithm is experimented on MATLAB (2018a) and validated by different performance metrics such as energy, alive nodes, BS position, and packet delivery ratio. The obtained results of the proposed algorithm exhibit better outcome in terms of more alive nodes per round, maximum number of packets delivery to the BS, improved residual energy and enhanced lifetime. At last, the proposed algorithm has achieved a better lifetime of ≈20%, ≈30% and ≈45% compared to grey wolf optimization (GWO), Artificial bee colony (ABC) and Low-energy adaptive clustering hierarchy (LEACH) techniques.

A Novel Distributed Ledger Technology Structure for Wireless Sensor Networks Based on IOTA Tangle

Wireless Sensor Networks (WSNs) consist of many wireless sensor nodes for collecting and sensing information. Distributed Ledger Technologies (DLTs) such as Blockchain allow organizations to store and share data in a decentralized, immutable, and secure way through a network of distributed peer-to-peer users or computers. The application of DLT to the Internet of Things (IoT) can improve the efficiency of information transmission and network security. IOTA Tangle is a DLT developed for IoT to process transactions. WSN is a core technology for IoT, and the two have a lot in common in terms of applications. Many solutions for IoT applications can be implemented with WSNs. However, the sensor nodes in WSNs have limited processing speed, storage capacity, communication bandwidth, and energy consumption capabilities. Therefore, a lightweight solution needs to be designed according to the characteristics of WSNs, rather than directly applying Tangle. The similarities between IoT and WSNs determine that the Tangle can be an essential reference for designing new solutions. In this paper, we propose a new DLT structure based on Tangle named Fishing Net Topology (FNT). The aim is to meet the lightweight requirements of sensor nodes in WSNs. We compared FNT with Tangle in terms of the packet network structure and algorithm and also experimentally analyzed the waste rate in the FNT network. It is concluded that FNT can be used at a reasonable Rate based on the requirement of the WSN applications, and it can significantly reduce the computation while enhancing the security of WSNs. Due to its structural stability and algorithmic simplicity, FNT outperforms Tangle in WSNs.

Quality of service assessment routing protocols for performance in a smart building: A case study

ABSTRACT Wireless sensor network (WSN) with several sensors is used to group measurements of certain physical quantities or environmental conditions, including sound, temperature, pressure, vibration, motion, or pollution, at various locations and ranges. In WSNs, several protocols address the issue of routing. Sensor nodes in WSN usually have limited energy resources and storage capacities. Therefore, the issue of energy usage in sensors and protocols is very important. This paper analyzes and compares the quality of service (QoS) performances of three important routing protocols of the mobile ad-hoc network (MANET) including Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR), and destination sequenced distance vector (DSDV), in a smart building case study. The QoS evaluation metrics include residual energy of nodes, instant throughput (IT), average throughput (AT), packet delivery ratio (PDR), packet loss ratio (PLR), and route discovery latency (RDL) based on IEEE 802.15.4 MAC protocol standard. The simulation is carried out using NS2, and the WSN has 16 fixed and 4 mobility nodes with different speeds and paths. The simulation results illustrate that average throughput in AODV is 1.985118 (kbps), however, the figures for DSR and DSDV are 1.977780 and 1.720700 (kbps), respectively. PDR, also, in DSR stands at 1.0, but the figures for AODV and DSDV are lower with the range of 0.999572 and 0.997930, respectively. Overall, The DSR protocol provides a better performance compared to AODV and DSDV routing protocols in terms of PLR and PDR. Also, AODV has better efficiency in RDL and AT compared to other assumed protocols.

A novel hybrid range-free approach to locate sensor nodes in 3D WSN using GWO-FA algorithm

The precise node location of the sensor nodes is an essential requirement in wireless sensor networks (WSNs) to determine the place or event occurring at a particular instant of time. In WSN, existing localization schemes consider two-dimensional (2D) space, while in actual life, sensor nodes are placed in three-dimensional (3D) space. In 3D localization, there are many research challenges, such as higher computational complexity, poor location prediction, lesser coverage, and depending only on fewer anchor nodes. To address various research issues in a 3D environment we propose a range-free technique applied in an anisotropic scenario having degree of irregularity (DOI) as 0.01 using the concepts of a fuzzy logic system (FLS). Anisotropic properties of nodes are considered to determine the efficiency of Grey wolf with the Firefly algorithm. In our proposed scenario, the received signal strength (RSS) information is necessary among the target nodes and their corresponding anchor nodes for determining the location of target nodes using the information based on edge weights. These edge weights are further modeled using Hybrid Grey Wolf Optimization with Firefly Algorithm (GWO-FA) to estimate the location of target nodes. The proposed algorithm is energy efficient as a single location-aware node is used for localization. Further, the concept of virtual anchors is introduced that helps the algorithm to determine 3D positions.

Optimal cluster head selection for energy efficient wireless sensor network using hybrid competitive swarm optimization and harmony search algorithm

A Wireless Sensor Network (WSN) is a network made up of many sensor nodes that are geographically dispersed. Because the sensor nodes in WSN are battery-powered, energy efficiency is a major issue. As a result, it is critical to make effective use of available energy in order to avoid energy waste. The clustering system provides an energy-efficient method of data transfer. The existing clustering scheme utilises distinctive Low-Energy Adaptive Clustering Hierarchy (LEACH) and optimization approaches faces issues like complicated local search, trade off in exploration and exploitation. A combination of the Harmony Search Algorithm (HSA) and the Competitive Swarm Optimization (CSO) is developed for energy efficient selection of cluster head (CH) to provide a global search with quick convergence rate. The proposed method has a high HSA search efficiency and a dynamic CSO capability that extends the life of sensor nodes. The proportion of alive nodes, count of dead nodes, residual energy and throughput are used to measure the hybrid algorithm’s performance. In terms of throughput and residual energy, the suggested hybrid HSA-CSO algorithm surpasses the current methods.

Energy-Aware QoS MAC Protocol Based on Prioritized-Data and Multi-Hop Routing for Wireless Sensor Networks.

Wireless sensor networks (WSNs) have received considerable interest in recent years. These sensor nodes can gather information from the surrounding environment and transmit it to a designated location. Each sensor node in WSN typically has a battery with a limited capacity. Due to their large number and because of various environmental challenges, it is sometimes hard to replace this finite battery. As a result, energy-efficient communication is seen as a critical aspect in extending the lifespan of a sensor node. On the other hand, some applications that require large coverage and generate various sorts of data packets require multi-hop routing and quality of service (QoS) features. Therefore, in order to avoid network failure, these applications need an energy-efficient QoS MAC protocol that can support multiple levels of data packet priority and multi-hop routing features while focusing on energy conservation. An energy-aware QoS MAC protocol based on Prioritized Data and Multi-hop routing (EQPD-MAC) is proposed in this article. The EQPD-MAC protocol offers a simple yet effective cross-layer communication method. It provides timely delivery of multi-priority packets, uses an adaptive active time to limit idle listening, and integrates a robust routing protocol. Finally, the EQPD-MAC protocol’s performance was evaluated and compared to three other well-known QoS MAC protocols. The simulation findings show that the proposed protocol significantly decreases sensor node energy consumption by up to 30.3%, per-bit energy consumption by up to 29.6%, sink node energy consumption by up to 27.4% and increases throughput by up to 23.3%.

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics.

Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

Energy Efficient QoS Aware Cluster Based Multihop Routing Protocol for WSN

Wireless sensor networks (WSN) have become a hot research area owing to the unique characteristics and applicability in diverse application areas. Clustering and routing techniques can be considered as an NP hard optimization problem, which can be addressed by metaheuristic optimization algorithms. With this motivation, this study presents a chaotic sandpiper optimization algorithm based clustering with groundwater flow optimization based routing technique (CSPOC-GFLR). The goal of the CSOC-GFLR technique is to cluster the sensor nodes in WSN and elect an optimal set of routes with an intention of achieving energy efficiency and maximizing network lifetime. The CSPOC algorithm is derived by incorporating the concepts of chaos theory to boost the global optimization capability of the SPOC algorithm. The CSPOC technique elects an optimum set of cluster heads (CH) whereas the other sensors are allocated to the nearer CH. Extensive experimentation portrayed the promising performance of the CSPOC-GFLR technique by achieving reduced energy utilization, improved lifetime, and prolonged stability over the existing techniques.

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.

Piezoelectric Energy Harvester for IoT Sensor Devices

Limited battery power is a major challenge for wireless sensor network (WSN) in internet of things (IoT) applications, especially in hard-to-reach places that require periodic battery replacement. The energy harvesting application is intended as an alternative to maintain network lifetime by utilizing environmental energy. The proposed method utilized piezoelectricity to convert vibration or pressure energy into electrical energy through a modular piezoelectric energy harvesting design used to supply energy to sensor nodes in WSN. The module design consisted of several piezoelectric elements, of which each had a different character in generating energy. A bridge diode was connected to each element to reduce the feedback effect of other elements when pressure was exerted. The energy produced by the piezoelectric is an impulse so that the capacitor was used to quickly store the energy. The proposed module produced 7.436 μJ for each step and 297.4 μJ of total energy with pressure of a 45 kg load 40 times with specific experiments installed under each step. The energy could supply WSN nodes in IoT application with a simple energy harvesting system. This paper presents a procedure for measuring the energy harvested from a commonly available piezoelectric buzzer. The specific configurations of the piezoelectric and the experiment setups will be explained. Therefore, the output energy characteristics will be understood. In the end, the potentially harvested energy can be estimated. Therefore, the configuration of IoT WSN could be planned.

A secure and energy saving protocol for wireless sensor networks

The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.

Analysis of Epidemic Model for Performance Evaluation of Rechargeable Wireless Sensor Network

Wireless sensor network (WSN) is a decentralized network system which consists of sensor nodes, and these nodes are connected through wireless link. Due to decentralized network system and resource constraint WSN faces security threat. Malware (malicious signals, worm, Trajan horse, virus etc.) attacks on the sensor node of WSN and make them paralyze and steal information from the network. Malware attack also increases the energy consumption of Sensor nodes of WSN. It just begins to spread from an infected node, and spread across the entire WSN with the help of neighboring nodes. Therefore, security of WSN against attack of malware is an inescapable need. On the basis of earlier works and consideration of charging mechanism of sensor nodes, and considering the effect of coverage and connectivity, proposed a SILRD (Susceptible - Infectious – Low Energy – Recovered –Dead) model with vital dynamics. The propose model investigates the dynamics of malware propagation in WSN and also explain sensor node’s energy consumption. The system’s stability has analyzed in terms of local and global of malware-free and endemic equilibrium. For the investigation of system dynamics, the expression of basic reproduction number has computed, which is also utilized to analyze state of malware in WSN. The effect of charging, coverage and connectivity is explained in this paper.

A Hierarchical Energy Conservation Framework (HECF) of Wireless Sensor Networks by Temporal Association Rule Mining for Smart Buildings

The challenge of extending the sensor’s energy consumption is a key research issue in Wireless Sensor Networks (WSNs). Recently, association rule mining has proven to be a potential candidate to prolong the lifetime of sensor nodes in WSN. However, temporal correlations of the contextual values are not taken into account which is useful for the sensors to conserve their energy. Similarly, association rules mining at different tiers of the network has not been considered to reduce the number of transmission messages, by avoiding redundant data which is the major cause of the energy drain of sensors. In this paper, a novel Hierarchical Energy Conservation Framework (HECF) is proposed which aims to conserver’s energy at each layer of a network by using the Hierarchical Temporal Association Rule Mining in multistory buildings. In hierarchal setup, each floor of the building can conserve energy locally at the local sink and conserve entire network energy at the global sink by using temporal association rule mining at different tiers of the network. The HECF is ideal for large multistory buildings where energy conservation is a major issue along with effective monitoring and system performance. The result shows that HECF outperformed other classical energy conversation methods such as LEACH-C and RR-Schedule-Buffer in terms of energy consumption. It extends 16% network lifetime, also 20% less number of messages during data transmission, which is a remarkable improvement for sensors energy conservation.

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.

IPAWL: An integrated power aware Wireless sensor network and Location-Based social network for incidence reporting

One of the constraints in Wireless Sensor Network (WSN) is the limited power of distributed interconnected sensor nodes over the space. There are several proposed architectures for handling power consumption in a WSN. Traditional clustering and routing methods improve the Cluster Head (CH) selection of WSN internodes for balancing energy in the network. In such clustering methods, every message is transmitted from a source node to its CH, from the CH to other CHs, and finally to the BS (Base Station). Therefore, a high amount of energy should be consumed for routing when the source nodes are far from CHs or the BS. This paper proposes an integrated LBSN (Location-Based Social Network) and WSN routing and CH selection method for reducing routes from source nodes to BSs to reduce the energy consumption of WSN. In the integrated network, LBSN nodes can be added to the WSN as BS or WSN sensor nodes. In the first situation, the integrated network is called IPAWL I and the latter builds the IPAWL II network.To evaluate the method, we considered a scenario of smoke detection and reporting on the campus of the University of Calgary and generated a simulation dataset for the LBSN and WSN. Then, results of implementing IPAWL I, IPAWL II, and a stand-alone WSN on the dataset were compared in terms of network energy consumption and message transmission latency. Results demonstrated a 42% reduction in energy consumption in IPAWL I and 33% in IPAWL II. Moreover, a significant reduction of time latency was observed in bigger size networks.

Malware propagation model for cluster-based wireless sensor networks using epidemiological theory.

Due to limited resources, wireless sensor network (WSN) nodes generally possess weak defense capabilities and are often the target of malware attacks. Attackers can capture or infect specific sensor nodes and propagate malware to other sensor nodes in WSNs through node communication. This can eventually infect an entire network system and even cause paralysis. Based on epidemiological theory, the present study proposes a malware propagation model suitable for cluster-based WSNs to analyze the propagation dynamic of malware. The model focuses on the data-transmission characteristics between different nodes in a cluster-based network and considers the actual application parameters of WSNs, such as node communication radius, node distributed density, and node death rate. In addition, an attack and defense game between malware and defending systems is also established, and the infection and recovery rates of malware propagation under the mixed strategy Nash equilibrium condition are given. In particular, the basic reproductive number, equilibrium point, and stability of the model are derived. These studies revealed that a basic reproductive number of less than 1 leads to eventual disappearance of malware, which provides significant insight into the design of defense strategies against malware threats. Numerical experiments were conducted to validate the theory proposed, and the influence of WSN parameters on malware propagation was examined.

Temperature and Humidity Optimization of Air Conditioner for Saving Electrical Energy Using Wireless Sensor Network Method

The consumption of electrical energy from year to year continues to increase. This is caused by the use of electrical energy and electrical equipment that inefficient. Hence, it is necessary to make efforts to save electricity efficiently. One of the efforts that have been made is research on the PPC (Programmable Power Controller) which functions as a controller for the use of electrical energy in the AC (Air Conditioner) device. Optimization of AC is also an effort to save electrical energy. This research focuses on technological efforts in the form of a room condition monitoring system design (temperature and humidity monitoring), AC mode setting scenarios, and room characterization for efficient use of electrical energy using the WSN (Wireless Sensor Network) method. The performance of the WSN sensor node as a monitoring device for room conditions which includes temperature and humidity has been successfully created using a temperature sensor, humidity sensor, signal conditioning circuit, and a programmed microcontroller. WSN sensor node results show that the DHT21 temperature and humidity sensor, which has a temperature range is 15°C-90°C, minimum humidity of 20% RH, and a maximum of 90% RH, testing works well. Meanwhile, the average time needed to carry out the node selection process until it is connected to the selected node when there is a default node, and no default node is 9.068 seconds and 9.968 seconds. Moreover, the implementation result of this device in the room area is in general, during working hours from 09:00 to 17:00, humidity conditions range from 30% - 75%, and temperatures range from 20°C - 40°C close to normal limits according to ASHRAE standard 55. In the next research, we focus on using the WSN relay to completely aim the function of the WSN sensor as a tool to reduce the electrical energy for monitoring and controlling the temperature and humidity AC.

An Optimal Routing Protocol Using Multi-Objective Cultural Algorithm for Wireless Sensor Networks (ORPMCA)

Wireless Sensor Networks (WSNs) is one of the most important types of networks in the world where sensors collect information within a specific area and send the data to a central point called the Base Station (BS). Many researchers have attempted to develop or improve the performance of WSNs by trying to mitigate the limitations and challenges facing WSNs. The most important challenge is to reduce energy consumption in a limited battery power supply of sensor nodes. Energy is consumed by sensor nodes in WSNs to perform three significant functions namely data sensing, transmitting and relaying. Various energy-saving routing protocols have been proposed to solve this problem to maintain the lifetime of the network for longer periods. In this paper, we propose an optimal routing protocol using multi-objective cultural algorithm for wireless sensor networks (ORPMCA). The simulation results showed that the ORPMCA protocol extends the lifetime of the WSN compared to other protocols by 15%.