In this paper, we investigate radio frequency (RF) energy harvesting (EH) in wireless sensor networks (WSNs) using non-orthogonal multiple access (NOMA) uplink transmission with regard to a probable secrecy outage during the transmission between sensor nodes (SNs) and base station (BS) in the presence of eavesdroppers (EAVs). In particular, the communication protocol is divided into two phases: 1) first, the SNs harvest energy from multiple power transfer stations (PTSs), and then, 2) the cluster heads are elected to transmit information to the BS using the harvested energy. In the first phase, we derive a 2D RF energy model to harvest energy for the SNs. During the second phase, the communication faces multiple EAVs who attempt to capture the information of legitimate users; thus, we propose a strategy to select cluster heads and implement the NOMA technique in the transmission of the cluster heads to enhance the secrecy performance. For the performance evaluation, the exact closed-form expressions for the secrecy outage probability (SOP) at the cluster heads are derived. A nearly optimal EH time algorithm for the cluster head is also proposed. In addition, the impacts of system parameters, such as the EH time, the EH efficiency coefficient, the distance between the cluster heads and the BS, and the number of SNs as well as EAVs on the SOP, are investigated. Finally, Monte Carlo simulations are performed to show the accuracy of the theoretical analysis; it is also shown that the secrecy performance of NOMA in RF EH WSN can be improved using the optimal EH time.
The transmission of safety applications in VANET is clearly the top concern. However, the non-safety applications, which improve the quality of experience, as well as the efficiency of the traffic, also need to be paid more attention. In VANET, the safety applications are transmitted in control channel interval and the non-safety applications can be transmitted only in service channel interval. In this paper, we propose a priority-based multichannel Medium Access Control to support the non-safety applications in service channel interval in Vehicle-to-Infrastructure communication with the presence of roadside unit. The novel Medium Access Control also allocates service channel according to the priority and divides the vehicles, which have the same required service channel, into four priority groups to enhance the Enhanced Distributed Channel Access mechanism. We use the mathematical model and the simulation for evaluating the performance under the influence of velocity with a different number of vehicles in the network.
The deployment of sensor nodes (SNs) to form a network with coverage ability is one of the most important challenges of wireless sensor networks. In this paper, we study an efficient distributed deployment algorithm for barrier coverage improvement with mobile sensors, in which the SNs can be relocated after the initial deployment. To achieve the maximum number of barriers, we propose a distributed algorithm to construct k-barrier coverage by relocation of the SNs. Different from existing approaches, we propose a novel clustering technique based on the network area to reduce the information exchange messages. Then, based on the SNs clusters, we propose a heuristic method to assign the SNs evenly into each cluster with regard to the required number of SNs of each cluster and decide the moving SNs by computing the optimal relocation, considering moving distance minimization. The main goal of this approach is to relocate the SNs to form the maximum number of barriers with a minimum relocation cost, in terms of sensor energy consumption of communication and movement. The simulation results demonstrate the effectiveness of our algorithm when compared with other competing approaches.
In this paper, we investigate downlink cooperative multiple-input single-output wireless sensor networks with the nonorthogonal multiple access technique and simultaneous wireless information and power transfer over Nakagami-m fading. Specifically, the considered network includes a multiantenna sink node, an energy-limited relay cluster, a high-priority sensor node (SN) cluster, and a low-priority SN cluster. Prior to transmission, a transmit antenna, a relay, a high-priority SN, and a low-priority SN are selected. In this paper, we propose three antenna-relay-destination selection schemes, i.e., sink node-high-priority, sink node-relay, and sink node-low-priority. In each proposed scheme, we consider two relaying strategies, i.e., decode-and-forward and amplify-and-forward, and then, we derive the corresponding closed-form expressions of outage probability at the selected SNs. In addition, we introduce two algorithms: 1) the powersplitting ratio optimization algorithm and 2) the best antenna-relay-destination selection determination algorithm. Finally, we utilize the Monte Carlo simulations to verify our analytical results.
Network congestion is a key challenge in resource-constrained networks, particularly those with limited bandwidth to accommodate high-volume data transmission, which causes unfavorable quality of service, including effects such as packet loss and low throughput. This challenge is crucial in wireless sensor networks (WSNs) with restrictions and constraints, including limited computing power, memory, and transmission due to self-contained batteries, which limit sensor node lifetime. Determining a path to avoid congested routes can prolong the network. Thus, we present a path determination architecture for WSNs that takes congestion into account. The architecture is divided into 3 stages, excluding the final criteria for path determination: (1) initial path construction in a top-down hierarchical structure, (2) path derivation with energy-aware assisted routing, and (3) congestion prediction using exponential smoothing. With several factors, such as hop count, remaining energy, buffer occupancy, and forwarding rate, we apply fuzzy logic systems to determine proper weights among those factors in addition to optimizing the weight over the membership functions using a bat algorithm. The simulation results indicate the superior performance of the proposed method in terms of high throughput, low packet loss, balancing the overall energy consumption, and prolonging the network lifetime compared to state-of-the-art protocols.
Localization is one of the key challenges facing wireless sensor networks (WSNs), particularly in the absence of global positioning equipment such as GPS. However, equipping WSNs with GPS sensors entails the additional costs of hardware logic and increased power consumption, thereby lowering the lifetime of the sensor, which is normally operated on a non-rechargeable battery. Range-free-based localization schemes have shown promise compared to range-based approaches as preferred and cost-effective solutions. Typical range-free localization algorithms have a key advantage: simplicity. However, their precision must be improved, especially under varying node densities, sensing coverage conditions, and topology diversity. Thus, this work investigates the probable integration of two soft-computing techniques, namely, Fuzzy Logic (FL) and Extreme Learning Machines (ELMs), with the goal of enhancing the approximate localization precision while considering the above factors. In stark contrast to ELMs, FL methods yield high accuracy under low node density and limited coverage conditions. In addition, as a hybrid scheme, extra steps are integrated to compensate for the effects of irregular topology (i.e., noisy signal density due to obstacles). Signal and weight are normalized during the fuzzy states, while the ELM uses a deep learning concept to adjust the signal coverage, including the spring force error estimation enhancement. The performance of our hybrid scheme is evaluated via simulations that demonstrate the scheme’s effectiveness compared with other soft-computing-based range-free localization schemes.
Preserving coverage is one of the most essential functions to guarantee quality of service in wireless sensor networks. With this key constraint, the energy consumption of the sensors including their transmission behaviour is a challenging problem in term of how to efficiently use them while achieving good coverage performance. This research proposes a clustering protocol, point-coverage-aware (PCACP), based on point-coverage awareness with energy optimisations that focuses on a holistic view with respect to activation sensors, network clustering and multi-hop communication, to improve energy efficiency, i.e., network lifetime extension while preserving coverage and maximising the network coverage. The simulation results demonstrate the effectiveness of PCACP, which strongly improves the performance. Given a diversity of deployments with scalability concerns, PCACP outperformed other competitive protocols, i.e., low energy adaptive clustering hierarchy (LEACH), coverage-preserving clustering protocol (CPCP), energy-aware distributed clustering (EADC) and energy and coverage-aware distributed clustering (ECDC) in terms of conserving energy, sensing point coverage ratios and overall network lifetime.
Multimedia services over wireless networking environment have become increasingly popular, especially for the online video streaming services and applications. This research analyzes the performance of video transmission over IEEE 802.11n in term of throughput, delay, and peak signal to noise ratio (PSNR) to find the characteristics of video streaming over a wireless network and to also propose a method to improve the transmission performance. Videos on YouTube from various categories were employed as a video dataset for evaluation in this research. Video splitting, video blending, and optimized reconstruction were proposed as video pre-processing and video reconstruction techniques used for enhancing the transmission usage and the quality of the transmitted video. Results indicated that the approach can improve the PSNR to the desired level.
Radio irregularity and signal attenuation are common phenomena in wireless sensor networks (WSNs) caused by many factors, such as the impact of environmental characteristics, the non-isotropic path losses, and especially, the obstacle on the transmission (multi) paths. The diversity of these phenomena make difficulty for accurate evaluation of WSNs’ applications which specifically require high coverage and connectivity. Thus, in this paper, we investigated the radio irregularity and signal power attenuation, primarily due to the obstacle in WSNs. With empirical data obtained from experiments using a well-known sensor node,i.e., MICAz, we found that the signal strength attenuation is different in each case according to obstacle characteristics. Then, we proposed a radio model, called Radio Irregularity Obstacle-Aware Model (RIOAM). The results obtained from real measurements are also supported with regard to those from the simulation. Our model effectiveness is justified against a radio irregularity model (RIM) – higher precision with the existence of obstacles in WSNs.
In this paper, we study radio frequency energy harvesting (EH) in a wireless sensor network in the presence of multiple eavesdroppers (EAVs). Specifically, the sensor source and multiple sensor relays harvest energy from multiple power transfer stations (PTSs), and then, the source uses this harvested energy to transmit information to the base station (BS) with the help of the relays. During the transmission of information, the BS typically faces a risk of losing information due to the EAVs. Thus, to enhance the secrecy of the considered system, one of the relays acts as a jammer, using harvested energy to generate interference with the EAVs. We propose a best-relay-and-best-jammer scheme for this purpose and compare this scheme with other previous schemes. The exact closed-form expression for the secrecy outage probability (SOP) is obtained and is validated through Monte Carlo simulations. A near-optimal EH time algorithm is also proposed. In addition, the effects on the SOP of key system parameters such as the EH efficiency coefficient, the EH time, the distance between the relay and BS, the number of PTSs, the number of relays, and the number of EAVs are investigated. The results indicate that the proposed scheme generally outperforms both the best-relay-and-random-jammer scheme and the random-relay-and-best-jammer scheme in terms of the secrecy capacity.
Barrier coverage is one of the most important issues in wireless sensor networks (WSNs), and it has been a popular area of WSN research in recent years. Maximizing the number of barrier paths is one of the key factors in the design of barrier coverage algorithms for WSNs. This study proposes a novel concept to optimize the number of barrier paths by minimizing the total moving distance and the number of moving sensor nodes or sensors using two algorithms. After an initial random placement of the sensors, the first approach applies a heuristic method to move all sensors close to the optimal location to maximize the total number of barrier paths. The second approach is used to determine the barrier paths based on a coverage graph and then fill up the barrier gaps by moving the other sensors that do not belong to any available barrier path. The simulation results show that our two proposed algorithms exhibit improved barrier coverage. The results also demonstrate the effectiveness of our algorithms when compared with other competitive approaches, such as CBarrier and BCLD. The first approach is more suitable for a high degree of fault-tolerant coverage, while the second approach can achieve the optimal number of moving sensors and total moving distance.
In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks among the potential SNs to balance the energy, and, finally, adopting a multihop hierarchical routing scheme to reduce the long-distance transmission energy. Simulation results show that our scheme can prolong the overall network lifetime and achieve high throughput, in comparison with a traditional routing scheme and its state-of-the-art variants.
The broadcast nature of energy harvesting wireless sensor networks (EH-WSNs) allows sensor nodes (SNs) within the coverage range of a transmitter to capture its signals. However, an EH-WSN is vulnerable to eavesdropping and signal interception; therefore, security in the EH-WSNs is of significant interest, and this issue has been addressed over many years. However, no work has studied the existence of a friendly jammer to mitigate the security impact. Thus, this paper proposes a model and optimization scheme that uses a wirelessly powered friendly jammer to improve secrecy in EH-WSNs. The considered EH-WSN model includes multiple power stations, multiple SNs (sources) and their base station, a friendly jammer, and multiple passive eavesdroppers. We divide the model into two phases: 1) the power stations transfer RF energy to the source SNs and 2) the source SNs transmit information to their base station, while a friendly jammer generates jamming signals against multiple eavesdroppers. Using statistical characteristics of the signal-to-noise ratio, the closed-form expressions of the existence probability of the secrecy capacity and secrecy outage probability are derived. We also propose an optimal sensor scheduling scheme to enhance physical layer secrecy (i.e., best-node scheduling), and we demonstrate our method's superior performance compared with a conventional round-robin scheduling scheme. The analysis of the simulation results supports our hypothesis, which is in line with Monte Carlo simulations.
Coverage is a key metric in evaluating the monitoring capacity and quality of services in wireless sensor networks. The energy consumption of self-contained sensors is also a challenging problem for energy-efficient use while still achieving better coverage performance. Although techniques have been developed to mitigate the problem of area coverage, particularly together with efficient clustering methods, none focuses intensively on the sensor activation stage, which is used to maintain coverage while optimizing energy usage. In this research, we thus propose a cover set to find the minimum set of sensors that completely cover the sensing ranges within an interest area as a criterion for sensor activation. Our main goal is to select an optimal number of active sensors considering residual energy and the cover set and to keep alive the important sensors for the sensing coverage task as long as possible. Additionally, this research proposes an area coverage-aware clustering protocol (ACACP) with energy consumption optimization with respect to the activation sensor, network clustering, and multi-hop communication to improve overall network lifetime while preserving coverage. Throughout the intensive simulation, given a diversity of deployments with scalability concern, the results demonstrate the effectiveness of ACACP when compared with other competitive approaches such as ECDC and DECAR, including state-of-the-art clustering protocols such as LEACH, in terms of coverage ratio and overall network lifetime.
