Source Sensor Node Research Articles

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy.

The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.

Detecting Anomaly Data for IoT Sensor Networks

The Internet of Things, or IoT, has been widely recognized as a new perception paradigm for interacting between the digital world and the physical one. Acting as the interface and integral part of the Internet of Things, sensors embedded within the network are the principal components that collect the unprocessed data, and these sensors are usually deployed in unattended, hostile, or harsh areas, which inevitably makes the sensor readings prone to faults and even anomalies. Therefore, the quality of sensor readings will ultimately affect the quality of various data-oriented IoT services, and the sensor data are of vital importance affecting the performance of the system. However, the data anomaly detection is a nontrivial task for IoT because sensors are usually resource-constrained devices with limited computing, communication, and capacity. Therefore, an efficient and lightweight detecting method is needed to meet the requirements. In this study, we deal with the anomaly data by detecting the source sensor nodes through combination methods of the local outlier factor and time series. Simulations show that the proposed method can effectively detect the anomaly data and presents a better normal data rate.

Novel Fuzzy Based Crow Search Optimization Algorithm for Secure Node-to-Node Data Transmission in WSN

Various sensor nodes are interconnected with each other in wireless sensor network (WSN). WSN communicates to every node within the network wirelessly to collect data regarding the surrounding environment and send the particular data to the destination/centralised location. The sensor nodes transmit the data and the source sensor node transmits data to destination sensor nodes through various intermediary sensor nodes. Thus, secure transmission is required in which WSN must be free from malicious nodes to achieve safe transfer. In this paper, our objective is to provide secure data transmission in an efficient manner from source to destination through a trust based path namely novel fuzzy based crow search optimization algorithm (F-CSO) and optimised link state routing protocol (OLSR) protocol. Through NS3 tool, nodes are deployed in the network area and novel F-CSO is performed with respect to three parameters such as distance, degree and transmission energy. The best nodes are considered for optimization process and it must satisfy the three conditions such as lower distance between each node for transmitting the data, higher degree and higher energy for transmission. A set of nodes that satisfies this condition are gathered by the fuzzy rule technique and then further optimised by crow search optimization technique leading to exact nodes through which trust based transmissions are performed. Further, routing can be achieved by the OLSR protocol. The performance of the proposed system is compared with existing studies with respect to the system for detecting the malicious nodes with and without utilizing proposed F-CSO in terms of execution time, throughput, and packet delivery ratio. The evaluation result has shown that malicious nodes is detected in an efficient manner through the use of F-CSO, showing better results with various parameters.

Creating Balance on Bandwidth Consumption using Network Coding in Wireless Sensor Networks

In recent years, Network Coding (NC) has been used to increase performance and efficiency in Wireless Sensor Networks (WSNs). In NC, Sensor Nodes (SNs) of network first store the received data as a packet, then process and combine them and eventually send them. Since the bandwidth of edges between SNs is limited, management and balancing bandwidth should be used for NS. In this paper, we present an optimization model for routing and balancing bandwidth consumption using NC and multicast flows in WSNs. This model minimizes the ratio of the total maximum bandwidth to the available bandwidth in network's edges and we use the dual method to solve this model. We also use the Karush–Kuhn–Tucker conditions (KKT) to calculate a lower bound and find the optimal solution and point in optimization model. For this purpose, we need to calculate the derivative of the Lagrangian function relative to its variables, in order to determine the condition as a multi-excited multi-equation device. But since the solution of equations KKT is centralized and for WSNs with a large number of SNs, it is very difficult and time consuming and almost impractical, we provide a distributed and repeatable algorithm for solving proposed model in which instead of deriving derivatives, combination Sub-gradient method and network flow separation method are used, thus allow each SN locally and based on the information of its neighboring nodes performs optimal routing and balances bandwidth consumption in the network. The effectiveness of the proposed optimization model and the proposed distributed algorithm with multiple runs of simulation in terms of the number of Source SNs (SSNs) and Lagrange coefficient and step size have been investigated. The results show that the proposed model and algorithm, due to informed routing and NC, can improve the parameters of the average required time to find the route optimal, the total amount of virtual flow in network’s edges, the average latency end-to-end of the network, the consumed bandwidth, the average lifetime of the network and the consumed energy, or not very weak compared to other models. The proposed algorithm also has great scalability, because computations are done distributed and decentralized, and there is an insignificant dependence between the SNs.

Data Distribution Optimization Strategy in Wireless Sensor Networks With Edge Computing

Resource-constrained wireless sensor networks (WSNs) impose a substantial challenge to the transmission and processing of non-redundant perceptual data. Moreover, breakthroughs in this area have opened up a new dimension that integrates edge computing technology in wireless sensor networks. However, achieving reliable data distribution in wireless sensor networks with edge computing (EWSN) and balancing the link bandwidth and node energy resources have become challenging problems. In this article, we propose a joint bandwidth allocation and energy consumption (JBAEC) algorithm for distributing different perceptual data to different sensor nodes of the EWSN by considering the available transmission links between the source sensor and destination nodes under the condition of early perceived energy. Therefore, we first establish the model of different data packet types and energy detection costs. Second, we describe the problem of effective bandwidth allocation and prove its NP-hard feature. In addition, we formulate the bandwidth allocation problem as an optimization problem that is divided into the two states of sufficient energy and lack of energy. Afterwards, we present an available node selection (ANS) algorithm for sorting the available relay nodes under the condition of different energy-sufficient nodes. The effective transmission network construction (ETNC) algorithm selects the available transmission links based on the set of available nodes. Finally, we implement the JBAEC algorithm by using the NS-2 simulator and present extensive simulation results to verify the data distribution efficiency of the JBAEC algorithm in the EWSN.

Ant-based delay-bounded and power-efficient data aggregation in wireless sensor networks

Purpose The purpose of this paper is to present a novel delay-bounded and power-efficient routing for in-network data aggregation, called DPIDA, which aims to ensure a compromise between the energy consumed during the collection of data sensed by a set of source sensor nodes and their timely delivery to the sink node. Design/methodology/approach Based on the ant-colony-optimization metaheuristic, the proposal establishes a routing structure that maximizes the number of overlapping routes and minimizes the total transmission power while ensuring delay-bounded paths and a symmetric transmission power assignment to reliably deliver the sensed data. Findings The proposal was extensively compared to two other known protocols regarding different keys factors. Simulation results, including topology snapshots, show the ability of DPIDA to ensure the energy–latency tradeoff. They also show the superiority of DPIDA compared to the two considered protocols. Originality/value This paper presents a novel ant-based protocol that uses in-network data aggregation and transmission power-adjustment techniques to conserve the energy of nodes while ensuring delay-bounded paths and a reliable deliverance of data which is ensured by providing a symmetric transmission power assignment.

Starfish routing for sensor networks with mobile sink

Wireless Sensor Networks (WSNs) with mobile sinks were proven to provide extended network lifetime and better data delivery services. This was achieved by minimizing routing costs and avoiding development of any hot-spot zones in the network. However, existing routing strategies in the literature have limitations to offer end-to-end data delivery delay and throughput required by the real-time sensing and monitoring applications. In this paper, following the principle of water vascular system of a Starfish, we have designed a routing backbone consisting of a central ring-canal and a number of radial-canals across the network. The radius of the ring-canal and the number of radial-canals are dynamically determined based on the transmission range of sensor nodes and size of the network. The proposed Starfish routing backbone guarantees that each source sensor node gets single-hop access to a backbone node, which in turn facilitates to reduce data delivery delay and increases fairness of energy consumption load distribution on network nodes. The results of the simulation experiments, carried out in NS-2, prove the efficiency of the proposed Starfish routing backbone in terms of end-to-end data delivery delay, throughput and energy consumption compared to state-of-the-art works.

Ticket-based QoS routing optimization using genetic algorithm for WSN applications in smart grid

Wireless sensor network (WSN) information network in Smart Grid is envisioned to handle diversified traffic such as real-time sensitive data and non-real-time traffic. Therefore, QoS routing protocol in smart grid network is essential. Ticket-based routing (TBR) protocol is a promising protocol because it can select routes based on several desired metrics, for example route cost and delay. However, the original TBR suffers the need for transmitting a huge number of tickets to probe the sensor network and discover the path cost and delay. Genetic algorithm can be used to minimize the number of tickets as well as discovery messages overhead. In this work, we implement genetic algorithm (GA-TBR) at the source sensor node to collect the state information inside the WSN environment of Smart Grid and hence optimize the selection of routes to ensure the required QoS. Extensive simulation experiments have been conducted to investigate the performance of GA-TBR. The simulation results have shown that with few tickets, the proposed algorithm is able to select routes with minimum possible delay and shows 28% improvement compared to ad hoc on demand distance vector routing (AODV) protocol.

Joint resource optimization for DF relaying SWIPT based cognitive sensor networks

Spectrum and energy are two important resources for wireless sensor networks (WSNs). Cognitive Radio (CR) can improve the efficiency of spectrum utilization by allowing secondary users (SUs) access to the licensed bands as long as they do not significantly affect the performance of primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) can harvest energy and decode information simultaneously from radio-frequency (RF) signal, which can prolong the life time of energy-constraint networks (e.g., WSNs). In this paper, we propose a joint resource optimization scheme in respect of transmission power and power splitting ratio for decode-and-forward (DF) relaying SWIPT based cognitive sensor networks (CSNs), where relay sensor node (RSN) harvests energy from the signal of source sensor node (SSN) by using power splitting (PS) receiver architecture and utilize the harvested energy to help forward SSNs information. We maximize the throughput of SUs while guaranteeing the interference to PUs caused by SUs is smaller than the interference threshold. Simulation results demonstrate the performance of the joint resource optimization scheme.

Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay.

Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN’s achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines.

In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.

Quality of service management for intelligent systems

A control application requirements currently used is very low, such as packet loss rate, minimum delay on sensor networks with quality of service (QoS) requirements some packet delivery guarantee. This paper is the sampling period at the end of the actuator and sensor data transfer related to the Miss ratio for each source sensor node, use the controller and the internal ANFIS. The proposed scheme has the advantages of simplicity, scalability, and General. Simulation results of the proposed scheme can provide QoS support in WSANs.

Cooperative Transmission in Mobile Wireless Sensor Networks with Multiple Carrier Frequency Offsets: A Double-Differential Approach

As a result of the rapidly increasing mobility of sensor nodes, mobile wireless sensor networks (MWSNs) would be subject to multiple carrier frequency offsets (MCFOs), which result in time-varying channels and drastically degrade the network performance. To enhance the performance of such MWSNs, we propose a relay selection (RS) based double-differential (DD) cooperative transmission scheme, termed RSDDCT, in which the best relay sensor node is selected to forward the source sensor node’s signals to the destination sensor node with the detect-and-forward (DetF) protocol. Assuming a Rayleigh fading environment, first, exact closed-form expressions for the outage probability and average bit error rate (BER) of the RSDDCT scheme are derived. Then, simple and informative asymptotic outage probability and average BER expressions at the large signal-to-noise ratio (SNR) regime are presented, which reveal that the RSDDCT scheme can achieve full diversity. Furthermore, the optimum power allocation strategy in terms of minimizing the average BER is investigated, and simple analytical solutions are obtained. Simulation results demonstrate that the proposed RSDDCT scheme can achieve excellent performance over fading channels in the presence of unknown random MCFOs. It is also shown that the proposed optimum power allocation strategy offers substantial average BER performance improvement over the equal power allocation strategy.

Qos Management in Wireless Sensor Networks using LFQMS

Wireless sensor networks (WSNs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSNs. In particular, quality-of-service (QoS) management in WSNs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a logic for QoS management (LFQMS) scheme for WSNs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSNs. It utilizes a logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the sensor. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The (LFQMS) has the advantages of generality, scalability, and simplicity. Simulation results show that the (LFQMS) can provide WSNs with QoS support.

Visualization of dynamic fault tolerance rerouting for data traffic in wireless sensor network

SUMMARYIn wireless sensor network environments, a phenomenon of data concentration may occur in one or certain sensor nodes in the process of transmitting sensing data from a source sensor node to a sink node. In this case, the overhead that occurs in the sensor node affects the performance of the entire sensor network. In addition, in the sensor network, excessive sensing data traffic or data loss may occur depending on the variability of the topology of the sensor network. In this paper, visualization for dynamic rerouting is designed and implemented, which visibly provides packet movement routes between sensor nodes and transmitted packet traffic transmission capacity to Geography Markup Language‐based maps having GPS coordinate information. A mechanism for the visualization for dynamic rerouting to detect sensing data overheads and sensor node faults occurring in sensor networks and dynamically rerouting of data is proposed. In addition, information on rerouting route paths from source sensors to sink nodes is visually provided. Copyright © 2012 John Wiley & Sons, Ltd.

Energy-Efficient and Reliable Routing Protocol for Wireless Sensor Networks

In WSNs routing of data consumes a lot of energy due to excessive flooding of packets. In the proposed EERP scheme routing is done on the basis of intensity of received signals. Unlike REEP, information packet is forwarded by only one source sensor node. The information packets are forwarded by the sensor node which has the maximum intensity of received signal from the object. Alternate routing paths are also maintained through local flooding. Theoretical analysis and simulation results show that the proposed routing protocol is more energy efficient as compared to other existing protocols such as Directed Diffusion and REEP.

Improving Data Confidentiality and Integrity for Data Aggregation in Wireless Sensor Networks

Since wireless sensor networks (WSNs) are resources-constrained, it is very essential to gather data efficiently from the WSNs so that their life can be prolonged. Data aggregation can conserve a significant amount of energy by minimizing transmission cost in terms of the number of data packets. Many applications require privacy and integrity protection of the sampled data while they travel from the source sensor nodes to a data collecting device, say a query server. However, the existing schemes suffer from high communication cost, high computation cost and data propagation delay. To resolve the problems, in this paper, we propose a new and efficient integrity protecting sensitive data aggregation scheme for WSNs. Our scheme makes use of the additive property of complex numbers to achieve sensitive data aggregation with protecting data integrity. With simulation results, we show that our scheme is much more efficient in terms of both communication and computation overheads, integrity checking and data propagation delay than the existing schemes for protecting integrity and privacy preserving data aggregation in WSNs.

Reliability Measure Model for Assistive Care Loop Framework Using Wireless Sensor Networks

Body area wireless sensor networks (BAWSNs) are time‐critical systems that rely on the collective data of a group of sensor nodes. Reliable data received at the sink is based on the collective data provided by all the source sensor nodes and not on individual data. Unlike conventional reliability, the definition of retransmission is inapplicable in a BAWSN and would only lead to an elapsed data arrival that is not acceptable for time‐critical application. Time‐driven applications require high data reliability to maintain detection and responses. Hence, the transmission reliability for the BAWSN should be based on the critical time. In this paper, we develop a theoretical model to measure a BAWSN′s transmission reliability, based on the critical time. The proposed model is evaluated through simulation and then compared with the experimental results conducted in our existing Active Care Loop Framework (ACLF). We further show the effect of the sink buffer in transmission reliability after a detailed study of various other co‐existing parameters.

Tier-Based Scalable and Secure Routing for Wireless Sensor Networks with Mobile Sinks

Multiple hop based routing in homogeneous sensor networks with a single sink suffers performance degradation and severe security threats with the increase of the size of sensor networks. Large-scale sensor networks need to be deployed with multiple powerful nodes as sinks and they should be scheduled to move to different places during the lifetime of the networks. Existing routing mechanisms lack of such supports for large-scale sensor networks. In this paper, we propose a heterogeneous network model where multiple mesh nodes are deployed in a sensor network, and sensed data are collected through two tiers: firstly from a source sensor node to the closest mesh node in a multiple-hop fashion (called sensor routing), and then from the mesh node to the base station through long-distance mesh routing (called mesh routing). Based on this network model, we propose an energy-efficient and secure protocol for the sensor routing that can work well in large-scale sensor networks and resist most of attacks. Experiments demonstrate that our routing protocol significantly reduces average hops for data transmission. Our lightweight security mechanism enables the routing protocol to defend most attacks against sensor networks.

Mobile data collector strategy for delay-sensitive applications over wireless sensor networks

Applications that require fast response time such as emergency preparedness and hostile environment surveillance pose challenging obstacles to wireless sensor network (WSN) protocols. A routing protocol must provide fast and reliable techniques for data propagation. Most routing solutions for WSNs utilize static sinks to collect data from the entire network. This approach results in high traffic load in the sink’s vicinity. The nodes located near the sink will be more requested than other nodes in the network. Therefore, these nodes will consume more energy and face high congestion in a large scale network. In this paper, we propose a solution to the problem of deploying mobile data collectors in order to alleviate the high traffic load and resulting bottleneck in a sink’s vicinity caused by static approaches. Our proposed MDC/PEQ protocol employs mobile data collectors (MDCs) that broadcast beacons periodically. Sensor nodes that receive the beacon will join the MDC’s cluster and update their routing information in order relay data packets to the MDC. Sensor nodes use the signal strength of the beacon in order to perform a simple but efficient route re-configuration (handoff). An extensive set of simulation experiments is conducted and results confirm that the introduction of mobile data collectors in wireless sensor networks reduces the bottleneck at the nodes closer to the sink. Our proposed mobility technique for data gathering introduces no traffic or energy overhead. In fact, it significantly reduces traffic and, consequently, packet delay and energy dissipation by reducing the average number of hops that data packets traverse from source sensor nodes to sinks or mobile data collectors.