Cooperative Wireless Sensor Networks Research Articles

Shrimp Pond Monitoring System using Cooperative Wireless Sensor Network Multi-Hop Technique based on Internet of Things

Water quality is a crucial factor in maintaining the survival and growth of shrimp. Manual water quality monitoring in shrimp ponds is no longer effective due to the need for periodic monitoring to maintain stable water quality. Therefore, online monitoring using various sensors installed in each pond is necessary. However, there are several challenges to overcome, such as the large expanse of the shrimp ponds, which may lead to data loss due to signal disruptions, and limited energy to power the sensors. To address these issues, this paper proposes the cooperative Wireless Sensor Network (WSN) technique with a multi-hop method for communication in the monitoring process. The system consists of five sensor nodes: temperature sensor, pH sensor, water level sensor, intake water flow sensor, and drain water flow sensor. The cooperative WSN multi-hop technique helps reduce energy consumption in the sensor nodes during measurement and data transmission, while also preventing data packet loss. This is achieved through the use of relay nodes that strengthen signals and forward data to the sink node. As a result, the battery life is extended, and energy usage in the monitoring process can be optimized. The system enables real-time online monitoring and can be accessed through a smartphone application. The results of this study show that the total energy consumption for data transmission in the sensor nodes is 9.64 J, while the total energy consumption for data forwarding in the relay nodes is 9.15 J. The total energy consumption in the transmit and receive processes is 18.79 J or 5.2 mWh. Therefore, it can be concluded that the energy savings of the proposed system is 4.3 mWh or approximately 45%, and is more efficient than the previous system.

MCEN: Maximum cooperative equilibrium WSN based on greedy prediction to reduce opposite transmission

In wireless sensor network (WSN) routing model design, energy efficiency is a key to WSN performance. Existing energy efficiency routing approaches transmit fused data packets through cluster head (CH) to save energy. However, some of these approaches do not make full use of WSNs’ energy because some sensor node transmitting paths are opposite to their transmission destination in these WSNs. Therefore, this paper proposes an approach named maximum cooperative equilibrium WSN (MCEN) to improve WSN energy utilization based on greedy policies and nodes energy consumption prediction. MCEN reconstructs the cluster structure to help nodes minimize their transmitting paths on which signals travel in the opposite direction to their destination. The energy-consumption prediction strategy in MCEN is to find the best cooperative partner for a node by predicting the energy consumption of the cooperative data transmission between the node and base station. CHs are voted to form clusters based on the maximum energy consumption prediction results. The approach can be embedded in other WSN clustering approaches, thereby further extending the overall lifetime of those WSNs. The simulation results show that the proposed method can greatly extend the lifetime of the entire network by more than 50%.

Real-time adaptive optimization of average peak age of information in SWIPT-based cooperative WSN deploying NOMA

This paper investigates the average peak age-of-information (APAoI) in a cooperative wireless sensor network (WSN), which deploys non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) techniques. To assist the poor source–destination direct link, the relay can utilize the harvested energy to decode the superposition-coded signal sent by the source and send its decoded information to the remote destination. With the aim of minimizing APAoI for long-term transmission, we formulate an energy allocation policy problem to trade off rate, energy, and time under certain constraints. To overcome the difficulties caused by time-varying parameters and non-convexity constraints, we perform a quasi-static transformation to obtain a simplified optimization model and some energy allocation criteria. Based on this work, we propose a heuristic APAoI real-time adaptive optimization (APRAO) algorithm that decomposes the original problem into multiple sub-problems for each transmission. During the sub-problem solving process, we introduce the static energy allocation criteria, which let the obtained solutions help our APRAO algorithm track the optimal solution as the transmission number increases. Finally, we evaluate and verify the performance of the proposed algorithm through numerical experiments.

Energy-Saving Deployment Optimization and Resource Management for UAV-Assisted Wireless Sensor Networks With NOMA

Energy-saving techniques are vital for the battery-powered sensor devices (SDs), which affect their lifetime. In this paper, we propose an air-and-ground cooperative wireless sensor network (AGWSN), wherein several UAVs are deployed as aerial access points (AAPs) to assist the terrestrial access point (TAP) for data collecting. The positions of the AAPs can be modified to approach the cell-edge SDs, therefore reducing the energy of the SDs expended in uploading data. To fully exploit the potential of the AGWSN, we formulate a joint AAP position optimization, channel allocation, and power control problem to minimize the total power consumption of all SDs subject to their decoding threshold. To solve the formulated problem, we first analyze the optimal user pairing rule in each cell and based on the rule propose a maximum-weighted-independent-set inspired algorithm for the AAP position optimization. Then, we remodel the channel allocation problem as an interference minimization problem and devise a K-CUT based algorithm to solve it. We further propose a low-complex iterative algorithm to obtain the optimal transmission power for each SD. The performance of the proposed algorithms is evaluated via theoretical analysis and numerical simulation. Simulation results indicate that if the intracell and intercell interference are not well coordinated, the superiorities of the AGWSN cannot be developed, and its performance is even worse than the traditional terrestrial network (TTN). Cooperated with our algorithms, the AGWSN significantly outperforms the TTN in terms of total power consumption and probability of successful decoding.

Energy Efficient Probabilistic Switching ON-OFF Operation in Multiantenna Cooperative Wireless Sensor Networks.

The effective deployment of Internet of Things (IoT) applications such as smart cities, smart farming and smart transport systems must ensure the network robustness, scalability and longevity. Therefore, guaranteeing the successful delivery of information and extending the lifetime of the nodes that make up a wireless sensor network (WSN) are two essential aspects for IoT applications. This work evaluates the performance of a cooperative WSN by adopting two multiantenna schemes: antenna selection (AS) and beamforming transmission using the singular value decomposition (SVD) technique. In addition, cooperation is established according to an ON–OFF probability, so that the RF receiving circuits of the relays are activated in a probabilistic way, aiming at reducing the energy consumption of the sensors, extending their useful lifetime. Our main goal is to increase the amount of information effectively transmitted by the network, keeping an outage probability constraint. The results show that, when both techniques are used, there is a significant gain in the amount of information effectively transmitted by the network, with emphasis on the AS scheme at short transmission distances. By increasing the number of antennas, it was found that a lower ON–OFF probability is required, i.e., a trade-off is established between the nodes’ hardware complexity and their need for cooperation.

Neuro‐fuzzy and fuzzy schemes for cooperative communication in wireless sensor network: a military battlefield scenario

In the military battlefield, maintaining high survivability of energy constraint wireless sensor network (WSN) when some nodes are destroyed by the foe troops is one of the prominent issues for the researchers. This study proposes the fuzzy and neuro-fuzzy based relay selection schemes for cooperative WSN which reduce the bit error rate (BER) while improving the network lifetime. Meanwhile, the proposed model maintains the uninterrupted communication between military nodes in a warfare situation. The proposed proposed strategies are compared with the maximum residual energy-based relay selection (MRERS), minimum energy consumption-based relay selection (MECRS), and random relay selection (RRS) strategies. The performance of the proposed and existing strategies is evaluated on the basis of BER, network lifetime, number of dead nodes, and average energy of nodes in the network. The simulation results demonstrate that proposed schemes has 13.19–19.28% improvement in BER than the existing strategies. Moreover, 76.00–90.88% network lifetime improves when compared with the MECRS and RRS strategies. However, 6–7.76% is less than MRERS strategy. Furthermore, the results show that network survivability and average network energy is also better when the number of dead nodes in the network increases.

A comprehensive review of cooperative MIMO WSN: its challenges and the emerging technologies

The distributed nodes of a Wireless Sensor Network (WSN) cooperate among themselves to emulate a virtual multi-antenna system to get similar benefits as the conventional MIMO systems. In WSN, such a system is known as the Cooperative Multiple Input Multiple Output (CMIMO) system. A CMIMO system has an enormous capacity to improve the performance of a WSN. But the increasing demand in the data rate and the desired Quality of Services (QoS), necessitates a revival in the way CMIMO was originally perceived in the early 1990s. Therefore, this article identifies several emerging supportive technologies such as the Compressive Sensing (CS), Simultaneous Wireless Information and Power Transfer (SWIPT), over the air computation, etc. which can be integrated to the CMIMO framework for enhancing the network performance in terms of throughput, delay, Energy Efficiency (EE), etc. The inclusion of these novel ideas necessitates further exploration of CMIMO in WSN along with its associated challenges and their possible solutions. This article provides a comprehensive overview of a CMIMO WSN along with its challenges which have been classified based on the layers of the protocol stack. Since energy efficiency is a key concern in a WSN having scarce energy resources, the energy-saving techniques have been discussed in detail. Further, challenges and open issues have also been highlighted which opens up new research direction in the area.

Cooperative Physical Layer Security Transmission Scheme Based on Energy Harvesting

Energy harvesting (EH) is introduced into the physical layer security transmission of wireless sensor networks. It takes the energy efficiency and security into account simultaneously. To improve the physical layer security of the cooperative wireless sensor network, an EH-based physical layer security transmission scheme called multi-relay time switching (MRTS) is proposed in this paper. In the multi-source single-destination network with eavesdroppers, multiple relay nodes with time switching (TS) protocol convert the received signals into energy and information according to the time slots, and the converted energy is used to amplify and forward the received information. The research shows the optimal time switching ratio, under which the maximum secrecy capacity is achievable with the optimal converted energy. The results prove that under the same conditions, the secure transmission scheme based on MRTS is better than power splitting-based relaying (PSR) scheme when the total power of sources is limited.

A Review of Relay Assignment Problem in the Cooperative Wireless Sensor Networks

The relay selection is a promising approach of maximizing the diversity gain achieved in the cooperative wireless sensor networks. In the cooperative networks, the proper selection of relay node is a challenging task. The proper selection of relay nodes not only improves the source–destination performance, but also maximizes the overall system performance. There are many factors involved in designing a relay selection algorithm. The different relay selection algorithms have different focus, criteria, objective, mechanism, and performance issues. In this article, several relay assignment algorithms have been analyzed to show the effectiveness of channel capacity, power allocation, coverage expansion and interference mitigation in term of proper relay selection schemes. Moreover, this paper discusses some relay selection schemes, their challenging issues, limitations, performance criteria, and mechanisms. This article also highlights the significant design issues of relay selection methods and compares them that are appropriate in the cooperative wireless sensor networks.

Energy Collaboration for Non-Homogeneous Energy Harvesting in Cooperative Wireless Sensor Networks

In wireless sensor networks, energy harvesting is developed as an effective way to solve energy drain problem of nodes which powered by battery, just like that cooperative communication is used to improve transmission reliability. In some energy harvesting networks, the nodes’ energy harvested from ambient environment is disequilibrium. For example, nodes exposed to sunlight collect more solar energy than those shaded. Impoverished energy will reduce transmission reliability because some nodes have not enough energy to send data or are forced to decrease transmission power to save energy. In this paper, energy collaboration is taken into consideration for non-homogeneous energy harvesting in cooperative wireless sensor networks, where all sensor nodes harvest solar energy and then store it in rechargeable battery. And an energy cooperative protocol is proposed for energy inhomogeneity to enhance transmission reliability among relay nodes through radio frequency, where the nodes with abundant energy share excess power to other nodes which undertake the forwarding task but store insufficient energy. By exploiting ACK/NACK frame fed back from the destination node and No Enough Energy (NEE) frame advertised by the forwarding relays in the active set, each candidate node forms its energy supply set when all of them are lack of energy for transmitting. For the relay selection, three strategies are presented for choosing the pair of the best forwarding relay and the optimal energy supply relay, which are respectively named Best Channel Strategy, Nearest Distance Strategy and Minimum Energy Sharing Strategy. Furthermore, the outage probability of cooperative transmission under energy collaboration is derived. Simulation results show that energy collaboration for cooperative wireless sensor networks can significantly reduce the outage probability of the system and improve transmission reliability of the network.

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Formulation of an Energy Efficient Routing Problem in Cooperative Wireless Sensor Networks

A brief comprehensive research review of the various advances in the development of energy efficient distributed cooperative routing protocols for the WSNs, i.e., wireless sensor network in the current scenario is taken into consideration for the formulation of a research problem and is presented in a nut shell in this research article. The research problem is finalized with the clear objectives & outcomes.

A fuzzy decision scheme for relay selection in cooperative wireless sensor network

SummaryIn energy‐constrained military wireless sensor networks, minimizing the bit error rate (BER) with little compromise on network lifetime is one of the most challenging issues. This paper presents a new relay selection based on fuzzy logic (RSFL) scheme which provides balance between these parameters. The proposed scheme considers node's residual energy and path loss of the relay‐destination link as the input parameters for the selection of the relay node. The relay node selection by fuzzy logic is based on prioritizing higher residual energy and minimum path loss. To evaluate the performance on wireless sensor network, we compare the proposed scheme with the three existing relay selection strategies, ie, random, maximum residual energy based relay selection (MaxRes), and minimum energy consumption based relay selection (MinEnCon). The simulation results of the proposed scheme in terms of network lifetime, BER, Network Survivability Index (NSI), and average energy of network nodes have been presented and compared with different relay selection schemes. The simulation results show that the proposed RSFL scheme has the lowest BER, moderate network lifetime, average energy, and NSI.

Low-Complexity Matrix-Based Conjugate Gradient Channel Estimation for Cooperative Wireless Sensor Networks

In this paper, we consider a general cooperative wireless sensor network (WSN) with multiple hops and the problem of channel estimation. A matrix-based conjugate gradient (CG) algorithm is first developed, which is then incorporated with the set-membership filtering framework to improve the performance of complex channel estimation for WSNs. Due to selective update, the proposed set-membership matrix-based CG (SM-MCG) algorithm can reduce the computational complexity significantly and extend the lifetime of WSN. In addition, by introducing an exponentially decaying bound and combining with SM-MCG algorithm, we design an updating criterion to accurately track the optimal maximum iteration number of each data window while reducing complexity further. Simulation results show good performance of our proposed algorithms in terms of convergence speed, steady state mean-square error, and indicate reduced complexity.

Performance analysis of cooperative wireless sensor network with index‐based modulation

Spatial modulation (SM) emerges as a potential technique for next generation wireless networks. Enhanced spectral efficiency and equitable system complexity led SM to various innovations. In SM only one radio-frequency chain is active during the transmission of the signal. The information is carried by the index of the active antenna. Throughput loss and reduced spectrum utility of higher-order modulation schemes can be overcome by activating more than one antenna. The shortcoming of SM is overcome by an enhanced SM in which index of an antenna and its constellation conveys the information. In the proposed wireless sensor network nodes are considered with two antennas. Based on the primary constellation the symbols are transmitted from a single active antenna and symbols from secondary constellation are transmitted from the two active antennas. The performance of the proposed network is compared to the network with SM.

Graphene-Grid Deployment in Energy Harvesting Cooperative Wireless Sensor Networks for Green IoT

Energy harvesting (EH) technology is an effective way to resolve the energy supply problem for green Internet of Things application. However, it is extremely vulnerable to the unpredictable environmental changes, and as a result, sensor nodes are charged in an uncontrollable way. In this paper, we focus on EH cooperative wireless sensor networks (EHC-WSNs), a new type of WSNs that integrates EH and wireless energy transfer technologies to provide the continuous and controllable energy supply. We propose the graphene-grid deployment strategy to guarantee energy coverage and network connectivity, whereby a Graphene-based Energy Cooperation Management (GECM) mechanism is designed under the energy-neutral operation. Furthermore, we divide GECM into two different phases, i.e., graphene-based energy cooperative charging strategy and graphene-based opportunistic cooperative routing algorithm, which are optimized according to the graphene-grid structure. Extensive simulations show that the proposed GECM can maximize the harvested energy utilization and prolong the network lifetime.

A Linear Estimator for Network Localization Using Integrated RSS and AOA Measurements

This letter addresses the problem of simultaneous localization of multiple targets in three-dimensional cooperative wireless sensor networks. To this end, integrated received signal strength and angle of arrival measurements are employed. By exploiting the convenient nature of spherical representation of the considered problem, the measurement models are linearized and a sub-optimal estimator is formulated. Unlike the maximum likelihood estimator, which is highly non-convex and difficult to tackle directly, the derived estimator is quadratic and has a closed-form solution. Its computational complexity is linear in the number of connections and its accuracy surpasses the accuracy of existing ones in all considered scenarios.

Training Power Allocation Based on MSE-Minimization for Multi-Relay Amplify-and-Forward Cooperation in Wireless Sensor Networks

In this paper, a training scheme for multi-relay clustered wireless sensor networks is proposed for the destination cluster head node to estimate the individual source-relay and relay-destination channels. Compared with an existing training scheme, the proposed one has shown improved efficiency with reduced power and computation time in the training of the source relay channels. Furthermore, the training power allocation problem is analyzed. Based on minimizing the total mean-square-error (MSE) of all channel estimates, two approximate solutions for the power allocation among all network nodes and all training links are derived. The first solution is adaptive to the instantaneous relay-destination channel estimates, thus requires feedback from the destination during the training process. The other solution depends on channel variances only and has lower complexity in implementation. Simulation results demonstrate that the proposed training scheme and power allocation solutions obtain lower total MSE and higher network throughput than existing schemes.

Decision Fusion in Distributed Cooperative Wireless Sensor Networks

In this paper, the distributed decision fusion (DF) problem of cooperative wireless sensor networks is considered, where a group of sensors send their local decisions to a fusion center via a set of relays using decode-and-forward relaying. The likelihood ratio test under Neyman–Pearson sense is used to derive the optimal DF rule (DFR). Moreover, three suboptimal fusion rules are derived to enable a broad range of complexity-performance tradeoffs. The performance of the optimal and suboptimal fusion schemes is evaluated in terms of probability of detection, false alarm, and fusion decision error where analytical expressions are derived for the suboptimal schemes, while Monte Carlo simulation is used for the optimal DFR. The obtained analytical and simulation results show that the decision error performance of the network is mainly determined by the number and accuracy of the local sensors, while the relaying process could be effective in mitigating the fading channel effects. Furthermore, no significant improvement is achieved by increasing the number of relays beyond three for each sensor.

RSS-AOA-Based Localization via Mixed Semi-Definite and Second-Order Cone Relaxation in 3-D Wireless Sensor Networks

In this paper, the target node localization problems based on hybrid RSS-AOA measurements in both noncooperative and cooperative three-dimensional (3-D) wireless sensor networks (WSNs) are discussed. By using novel error approximate expressions for both received signal strength (RSS) and angle-of-arrival (AOA) measurement models, new estimators based on the least squares (LS) criterion are proposed. These estimators can be transformed into mixed semi-definite programming (SDP) and second-order cone programming (SOCP) problems by applying convex relaxation techniques. In addition, the closed-form Cramer-Rao lower bound (CRLB) of the estimator on hybrid measurements in cooperative WSNs is also derived. Theoretical analysis and simulation results show that the Root Mean Square Error (RMSE) of the proposed hybrid RSS-AOA estimators is lower than that of the discussed estimators in both noncooperative and cooperative cases.