Power Wireless Sensor Networks Research Articles

Analysis and Optimization of Wireless-Powered Cooperative Jamming for Sensor Network Over Nakagami-$m$ Fading Channels

This letter studies the secrecy communication in a wireless-powered sensor network, where the access point (AP) first sends an energy signal to power the sensor node (S) and jammer (J) and, then, J exploits the harvested energy to send a jamming signal to interfere with the eavesdropper, in order to protect the uplink communication from S to the AP. Specifically, assuming that all the channels are subjected to Nakagami-m fading, we derive a closed-form expression of the exact secrecy outage probability (SOP). Furthermore, by exploiting the results about the approximations to the statistics of products of independent random variables, we provide approximate and asymptotic expressions of the SOP, and the latter enables us to obtain the secrecy diversity order (SDO), observe the impact of several parameters on the SOP, and provide a closed-form expression for the time switching (TS) ratio, which maximizes the secrecy throughput. Simulations present the accuracy of the derived SOP and SDO, the tightness of the approximation, and the effectiveness of the obtained TS ratio.

Secrecy Performance Analysis of Wireless Sensor Networks

The wireless sensor network (WSN) plays a fundamentally important role in the realization of Internet of Things and Industry 4.0. In this article, we study the physical layer security performance of a large-scale ad hoc and homogeneous WSN. To accurately evaluate the secrecy performance of the large-scale WSN, we take into account the randomness of the interfering nodes (both in terms of the number of nodes and their respective position) based on Poisson point process in our analysis. We derive novel expressions for the average secrecy capacity and secrecy outage probability, including the effects of fading, path loss, and the network's spatial randomness. The results demonstrate the impacts of several factors such as node intensity and transmission power in large-scale WSN on secrecy performance, which should be seriously taken into consideration while designing WSNs.

QoS adaptive and energy aware cross‐layer opportunistic routing protocol in wireless sensor networks

Wireless sensor network (WSN) can reduce human labour and monitoring difficulties in forest fire prevention and environment monitoring due to its low power consumption, small size, self-organising, flexible setting, and unattended operation. However, the limited WSN hardware resources and power capacity, the practical application requirements such as real-time monitoring and timely warning of emergencies, pose higher challenges to WSN on its lifespan, reliability, and real-time performance. This paper proposes a QoS adaptive and energy aware cross-layer opportunistic routing protocol (QE-COR). The protocol sets an adaptive QoS function Q i comprehensively considering multiple metrics, through which the best forwarding node can be selected, and the QoS of transmission can adaptively adjust according to the data's needs. The protocol can also select a standby node to start retransmission when forwarding errors occur. A RTS-QACK and an ASS-DATA-SACK response mechanisms are designed to improve the transmission efficiency and avoid message conflicts. The sleep mechanism of MAC layer related with node's working state enables dynamical switching between active and sleep modes, which improves the energy utilisation and the flexibility of the network. Compared with similar opportunistic routing protocols, QE-COR can improve energy utilisation and extend network lifetime, while ensuring transmission reliability and end-to-end delay performance.

A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks.

Innovative Internet of Things (IoT) applications with strict performance and energy consumption requirements and where the agile collection of data is paramount are arising. Wireless sensor networks (WSNs) represent a promising solution as they can be easily deployed to sense, process, and forward data. The large number of Sensor Nodes (SNs) composing a WSN are expected to be autonomous, with a node’s lifetime dictated by the battery’s size. As the form factor of the SN is critical in various use cases, minimizing energy consumption while ensuring availability becomes a priority. Moreover, energy harvesting techniques are increasingly considered as a viable solution for building an entirely green SN and prolonging its lifetime. In the process of building a SN and in the absence of a clear and well-rounded methodology, the designer can easily make unfounded and suboptimal decisions about the right hardware components, their configuration, and reliable data communication techniques, such as automatic repeat request (ARQ) and forward error correction (FEC). In this paper, a methodology to design, configure, and deploy a reliable ultra-low power WSNs is proposed. A comprehensive energy model and a realistic path-loss (PL) model of the sensor node are also established. Through estimations and field measurements it is proven that, following the proposed methodology, the designer can thoroughly explore the design space and the make most favorable decisions when choosing commercial off-the-shelf (COTS) components, configuring the node, and deploying a reliable and energy-efficient WSN.

Small-Area Radiofrequency-Energy-Harvesting Integrated Circuits for Powering Wireless Sensor Networks.

This study presents a radiofrequency (RF)-energy-harvesting integrated circuit (IC) for powering wireless sensor networks with a wireless transmitter with an industrial, scientific, and medical (ISM) of 915 MHz. The proposed IC comprises an RF-direct current (DC) rectifier, an over-voltage protection circuit, a low-power low-dropout (LDO) voltage regulator, and a charger control circuit. In the RF-DC rectifier circuit, a six-stage Dickson voltage multiplier circuit is used to improve the received RF signal to a DC voltage by using native MOS with a small threshold voltage. The over-voltage protection circuit is used to prevent a high-voltage breakdown phenomenon from the RF front-end circuit, particularly for near-field communication. A low-power LDO regulator is designed to provide stable voltage by using zero frequency compensation and a voltage-trimming feedback. Charging current is amplified N times by using a current mirror to rapidly and stably charge a battery in the proposed charger control circuit. The obtained results revealed that the maximum power conversion efficiency of the proposed RF-energy-harvesting IC was 40.56% at an input power of −6 dBm, an output voltage of 1.5 V, and a load of 30 kΩ. A chip area of the RF-energy-harvesting IC was 0.58 × 0.49 mm2, including input/output pads, and power consumption was 42 μW.

Average Age of Information in Wireless Powered Sensor Networks

In this letter, we deal with the age of information (AoI) for a sensor network with wireless power transfer (WPT) capabilities. Specifically, we study a simple network topology, where a sensor node harvests energy from radio frequency signals (transmitted by a dedicated energy source) to transmit real-time status updates. The sensor node generates an update when its capacitor/battery becomes fully charged and transmits by using all the available energy without further energy management. The average AoI performance of the considered greedy policy is derived in closed form and is a function of the capacitor’s size. The optimal value of the capacitor that maximizes the freshness of the information, corresponds to a simple optimization problem requiring a 1-D search. The derived theoretical results provide useful performance bounds for practical WPT networks.

A Low Power IoT Medium Access Control for Receiver-Assigned CDMA

Low power, low cost, and security-conscious wireless sensor networks are becoming increasingly pervasive in the internet of things (IoT). In these networks, receiver-assigned code division multiple access (RA-CDMA) offers benefits over existing multiple access techniques. RA-CDMA networks are asynchronous, robust against multipath interference, and offer resilience against collision. A lightweight medium access control (MAC) protocol is needed to facilitate communication in RA-CDMA networks between low power sensor nodes and access points. This article provides an overview of RA-CDMA and proposes elements of a new MAC protocol that could improve performance of certain wireless sensor networks. Key features of the proposed MAC design are introduced and compared to those of existing protocols, highlighting its simple and lightweight design. Through its compatibility with RA-CDMA, the MAC design eliminates significant overhead and complexity while meeting requirements for low power networks, which enables the implementation of dense IoT sensor networks.

A Study on the Impact of Packet Length on Communication in Low Power Wireless Sensor Networks Under Interference

Reliability is nowadays considered a key requirement in wireless sensor networks for their increasing diffusion in various Internet of Things applications. However, radio interference from various sources may heavily affect the performance of wirelessly connected embedded devices, resulting into increased packet collisions and congestions. There is therefore a widely recognized need of both theoretical and practical investigations capable of shedding light on the factors affecting the operativeness of sensor networks subject to interference. In this paper, we investigate the role of packet length into the reliability and energy efficiency of low-power medium access protocols. Specifically, we propose a mathematical model to explore the functional dependence of the reliability of a pair of sensor nodes under interference from packet length. We also present a wide range of experimental evaluations aimed at validating the model and at providing novel insights on dependability issues. In particular, we assess the performance of Contiki's default medium access control layer (together with that of an always listening receiver, as a baseline) in terms of packet loss rate and energy efficiency for varying payload lengths. Experimental results highlight the interplay between packet size and interference and in particular the tradeoff between the robustness against interference and the overhead imposed to communication as a function of the length of data packets. The Pareto curve describing the energy efficiency as a function of the packet loss rate, demonstrates the existence of intermediate packet size representing an optimal choice for balancing energy consumption and communication reliability, enabling adequate system dimensioning at design-level.

HYREP: A Hybrid Low-Power Protocol for Wireless Sensor Network

In this paper, a new hybrid routing protocol is presented for low power Wireless Sensor Networks (WSNs). The new system uses an integrated piezoelectric energy harvester to increase the network lifetime. Power dissipation is one of the most important factors affecting lifetime of a WSN. An innovative cluster head selection technique using Cuckoo optimization algorithm has been used in the designed protocol. The residual energy of the nodes and the distances to the sink were used in the threshold calculations, besides to take advantage of the relay node for communication. A hybrid method using the optimized routing protocol and the integrated energy harvester results in 100% increase in the network lifetime compared to recent clustering-based protocols. The simulations results using MATLAB indicate that energy consumption was been decreased by more than 40%.

Robust Distributed Collaborative Beamforming for Wireless Sensor Networks with Channel Estimation Impairments.

We propose a new collaborative beamforming (CB) solution robust (i.e., RCB) against major channel estimation impairments over dual-hop transmissions through a wireless sensor network (WSN) of K nodes. The source first sends its signal to the WSN. Then, each node forwards its received signal after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired power equal to unity. These weights depend on some channel state information (CSI) parameters. Hence, they have to be estimated locally at each node, thereby, resulting in channel estimation errors that could severely hinder CB performance. Exploiting an efficient asymptotic approximation at large K, we develop alternative RCB solutions that adapt to different implementation scenarios and wireless propagation environments ranging from monochromatic (i.e., scattering-free) to polychromatic (i.e., scattered) ones. Besides, in contrast to existing techniques, our new RCB solutions are distributed (i.e., DCB) in that they do not require any information exchange among nodes, thereby dramatically improving both WSN spectral and power efficiencies. Simulation results confirm that the proposed robust DCB (RDCB) techniques are much more robust in terms of achieved signal-to-noise ratio (SNR) against channel estimation errors than best representative CB benchmarks.

Optimal Power Allocation for Wireless Sensor Powered by Dedicated RF Energy Source

This paper studies a wireless-powered sensor network, where a sensor harvests energy from a dedicated radio-frequency (RF) energy source and transmits information to an information sink using the harvested energy. Two working modes are considered; One is the frequency division multiplexing (FDM) mode in which the sensor harvests RF energy and transmits information simultaneously over orthogonal frequency bands. The other is the time division multiplexing (TDM) mode in which energy harvesting and information transmission are implemented in the same frequency band but in different time slots. The energy harvesting channel and the information transmission channel are assumed to follow the Rician and the Rayleigh distributions, respectively, and are discretized and modeled as finite-state Markov chains. We formulate the process of energy harvesting and information transmission as an infinite-horizon discounted Markov decision process. The value iteration algorithm is used to find an asymptotically optimal energy harvesting and information transmission policy to optimize the long-term throughput. In the asymptotically optimal policy of the FDM mode, the energy transmitted from the sensor in one slot is proved to be non-decreasing with the battery state of the sensor. By contrast, such monotonicity between the transmitted energy and the battery state does not exist in the asymptotically optimal policy in the TDM mode. Simulation results verify the above findings and demonstrate that the proposed method outperforms the heuristic greedy method.

Analyzing Power Beacon Assisted Transmission with Imperfect CSI in Wireless Powered Sensor Networks.

This paper proposes the maximal ratio transmission (MRT) and maximal ratio combining (MRC) protocols for the power beacon (PB) assisted wireless powered sensor networks and analyzes the impact of the imperfect channel state information (CSI) on the performance using the Markov chain theory. The wireless powered sensor chooses to transmit information to the destination or harvest energy from the PB when its energy can or cannot supply a transmission, respectively. The energy arrival and departure of the sensor is characterized, and the analytical expressions of the network transmit probability, and effective and overall ergodic capacities are formulated and derived. We also optimize the sensor transmit power to maximize the overall ergodic capacity. Our results reveal that the transmit probability and the effective ergodic capacity can be greatly improved with increasing the number of antennas at the PB and the destination, and can also be significantly degraded by decreasing the channel correlation factors. We also demonstrate the effectiveness of the sensor transmit power optimization in improving the overall ergodic capacity.

Approximation algorithm for receiver interference problem in dual power Wireless Sensor Networks

The problem of assigning power levels to the nodes of a wireless sensor network from a given a set of two power levels is called Dual power management problem and the underlying network is called Dual power network. We consider the problem of minimizing the maximum receiver interference of such a network. The interference disrupts the communication and forces the data packets to be retransmitted. The motivation is to conserve the energy by minimizing the interference and maintaining the connectivity of the dual power network. Receiver interference problem is proved to be NP-hard. In this paper, an approximation algorithm is derived for minimizing the maximum receiver interference of a dual power network by utilizing the approximation algorithm for Dual Power Management Problem. The proposed algorithm is supported by the simulation results. We term this problem as Dual Power Receiver Interference Problem and show that it is NP-complete using a polynomial time reduction from Degree Constrained Minimum Spanning Tree problem. We also prove the NP-completeness of Dual Power Management Problem by a polynomial reduction from Vertex Cover Problem.

Energy Beamforming in Wireless Powered mmWave Sensor Networks

This paper studies the beneficial combination of wireless power transfer and millimeter-wave (mmWave) communications in wireless sensor networks. In particular, an mmWave wireless powered sensor network is considered, where the access point (AP) employs beamforming techniques to transfer energy to the sensors of a selected sector of the cell. The served sensors harvest and store energy from the received signal, and use it to power their uplink transmissions. We consider a random energy beamforming scheme but also propose several intelligent schemes that steer the beam to specific areas of the cell by considering the sensors' locations. This setup is investigated from a large-scale point-of-view where spatial randomness is considered with the aid of Poisson point processes. The performance of the network is described in terms of the energy outage probability and the beam outage probability. We show that, depending on the scenario, each of the considered energy beamforming schemes can provide significant gains to the network's performance. Finally, we study an event monitoring application of our theoretical framework, where the active sensors observe a random event in the network and the AP attempts to estimate it based on the received information; this scenario is evaluated in terms of the estimation's mean squared error.

THE INFLUENCE OF AMBIENT CONDITIONS ON THE PERFORMANCE OF THE THERMOELECTRIC WIRELESS SENSOR NETWORK NODE

This paper considers the effects of the ambient temperature and unducted air flow on the voltage generated by a thermoelectric generator used to power wireless sensor network node. Structure of the node is simulated using a fully coupled numerical electro--thermal model with convective correlations. Results show that the effect of the ambient temperature is negligible as long as the temperature difference between the hot surface of the node and the ambient is maintained. For natural convection, voltage dependence on the temperature difference can be determined from the open circuit conditions and this can be used to approximate the load conditions. For forced convection, an increase rate of the generated voltage is governed by the thermal resistance of the heatsink and characteristic parameters of the thermoelectric generator.

Efficient energy resource utilization in a wireless sensor system for monitoring water quality

In this paper, a new approach to energy harvesting and data transmission optimization in a heterogeneous-based multi-class and multiple resource wireless transmission wireless sensor network system that focus on monitoring water and its quality is presented. Currently, energy is a scarce resource in wireless sensor networks due to the limited energy budget of batteries, which are typically employed for powering sensors. Once the available energy of a particular sensor node battery is depleted, such sensor node becomes inactive in a network. As a consequence, such node may not be able to participate in the transmission of the application signal in the uplink stage of the network, resulting in a lack of ability to communicate vital signals in a timely manner. Energy scarcity has been a long standing problem in wireless sensor network applications. To address this problem, energy harvesting from intended radiofrequency power source is considered in this work. However, wirelessly powered wireless sensor network systems are confronted by unfairness in resource allocation problem, as well as interference problem in multiple energy resource transmissions. These problems adversely impact the performance of the system in the context of the harvested energy by the sensor nodes, sensors information transmission rates, and the overall system throughput rate. These problems are tackled in this paper by formulating a sum-throughput maximization problem to reduce system energy consumption and enhance the system overall throughput rate. The throughput optimization problem is formulated as a non-convex function. Through the exploitation of the problem structure, it is converted to a convex function. The mathematical models of the optimization problem are validated through numerical simulations. The simulation results reveal that the proposed wireless powered sensor network system outperforms an existing wireless powered sensor network system, by comparison of the numerical simulations of this work to the numerical simulations of the existing WPSN system, regardless of the distances of the sensor nodes to the IPS and the base station. Also, the newly proposed method performs efficiently using parameters that include path-loss exponent impact, performance comparison of systems, convergence based on iteration, comparison based on unequal network distances to the BS, transmission power impact on the attainable throughput and on fraction of energy consumed on information transmissions, and influence of different number of nodes in the network classes.

A Novel Hybrid Opportunistic Scalable Energy Efficient Routing Design for Low Power, Lossy Wireless Sensor Networks

Opportunistic Routing (OR) scheme increases the transmission reliability despite the lossy wireless radio links by exploiting the broadcast nature of the wireless medium. However, OR schemes in low power Wireless Sensor Network (WSN) leads to energy drain in constrained sensor nodes due to constant overhearing, periodic beaconing for Neighbourhood Management (NM) and increase in packet header length to append priority wise sorted Forwarding Candidates Set (FCS) prior to data transmission. The timer-based coordination mechanism incurs the least overhead to coordinate among the FCS that has successfully received the data packet for relaying the data in a multi-hop manner. This timer-based mechanism suffers from duplicate transmissions if the FCS is either not carefully selected or coordinated. The focus of this work is to propose a hybrid opportunistic energy efficient routing design for large scale, low power and lossy WSN. This design avoids periodic 'hello' beacons for NM, limits constant overhearing and increase in packet header length. There are two modes of operation i) opportunistic ii) unicast mode. The sender node adopts opportunistic forwarding for its initial data packet transmission and instead of pre-computing the FCS, it is dynamically computed in a completely distributed manner. The eligible nodes to be part of FCS will be neighbour nodes at lower corona level than the sender with respect to the sink and remaining energy above the minimum threshold. The nodes part of FCS based on crosslayered multi-metrics and fuzzy decision logic determines its priority level to compute Dynamic Holding Delay (DHD) for effective timer coordination. The differentiated back off implementation along with DHD enables the higher priority candidate that had received data packet to forward the packet first and facilitates others to cancel its timer upon overhearing. The sender node switches to unicast mode of forwarding for successive transmissions by choosing the forwarding node with maximum trust value as it denotes the stability of the temporally varying link with respect to the forwarder. The sender node will revert to opportunistic mode to increase transmission reliability in case of link-level transmission error or no trustworthy forwarders. Simulation results in NS2 show significant increase in Packet Delivery Ratio (PDR),decrease in both average energy consumption per node and Normalized Energy Consumption (NEC) per packet in comparison with existing protocols.

A Novel Block Encryption Algorithm Based on Chaotic S-Box for Wireless Sensor Network

In order to ensure the basic security of wireless sensor networks (WSNs), a block encryption algorithm based on chaotic substitution box (S-box) is proposed. In this paper, we generated a new S-box based on the compound chaotic map, sinusoidal chaotic map, Baker map, and linear congruence generator. In addition, the limited computing power and communication capability of WSN are also considered in this paper. The method of generating round subkeys and F function is constructed based on the S-box. The extensive security and performance tests show that the proposed encryption algorithm has high security and low resource consumption which is suitable for WSN.

Persistent energy harvesting in the harsh desert environment using a thermal resonance device: Design, testing, and analysis

Thermal fluctuations in the environment are a ubiquitous, yet untapped energy source for harvesting. Our laboratory has introduced the concept of a thermal resonance device for this purpose, with potential to power wireless sensor networks (WSNs), devices embedded into structural elements, and electronics deployed in relatively inaccessible areas. This approach has particular advantages for the desert environment, where conventional energy harvesting devices are severely confounded by frequent sandstorms, extreme temperature and high solar fluence. Herein, we design, fabricate, and test a thermal resonator for the conversion of diurnal temperature fluctuations to electrical power in a harsh desert environment using a thermally conductive phase change composite as a high thermal effusivity material, specifically tuned to the temperature fluctuating environment of Thuwal, Saudi Arabia (22.3095°N, 39.1047°E). The composite consists of a highly porous and thermally conductive nickel foam impregnated with eicosane as a phase change material for enhanced thermal capacity. The high thermal effusivity material is incorporated into a rationally-designed thermal resonance device, which is tested for a period of two weeks in Saudi Arabia, extracting as high as 2 mW from approximately 10 °C diurnal temperature fluctuations. These experimental data provide a test of the theoretical model for the design and operation of the device. Overall, these results highlight the opportunity of employing thermal resonance devices for energy harvesting, particularly in the desert environment.

Reinforcement Learning for Scheduling Wireless Powered Sensor Communications

In a wireless powered sensor network, a base station transfers power to sensors by using wireless power transfer (WPT). Inadequately scheduling WPT and data transmission causes fast battery drainage and data queue overflow of some sensors who could have potentially gained high data reception. In this paper, scheduling WPT and data transmission is formulated as a Markov decision process (MDP) by jointly considering sensors’ energy consumption and data queue. In practical scenarios, the prior knowledge about battery level and data queue length in MDP is not available at the base station. We study reinforcement learning at the sensors to find a transmission scheduling strategy, minimizing data packet loss. An optimal scheduling strategy with full-state information is also investigated, assuming that the complete battery level and data queue information are well known by the base station. This presents the lower bound of the data packet loss in wireless powered sensor networks. Numerical results demonstrate that the proposed reinforcement learning scheduling algorithm significantly reduces network packet loss rate by 60%, and increases network goodput by 67%, compared to existing non-MDP greedy approaches. Moreover, comparing the optimal solutions, the performance loss due to the lack of sensors’ full-state information is less than 4.6%.