Ensuring information freshness in Internet of Things (IoT) networks is essential for real-time monitoring. Due to the limited transmission capability of low-power IoT devices and the rich information content of each status update, a single update may need to be segmented into multiple packets. To achieve timely updates under dynamic wireless and energy conditions, the payload length must be adaptively selected according to the channel state and available energy. This paper investigates an RF-powered ambient backscatter (AmBC) network in which a device alternates between harvesting energy and transmitting status updates. The goal is to jointly optimize energy harvesting, transmission decisions, and payload length to minimize the long-term average age of information (AoI). The problem is formulated as an average-cost Markov decision process. A theoretical lower bound is first derived to characterize the best achievable freshness. Structural analysis further reveals how energy harvesting, battery evolution, channel fading, and adaptive payload jointly determine the optimal scheduling policy and shows that it follows a threshold-based structure. Leveraging this property, we develop a low-complexity structure-aware value-iteration-based method suitable for low-power AmBC devices with limited computational capability, and further propose two scalable model-free solutions based on Q-learning and deep Q-networks to support online scheduling when model knowledge is unavailable or the state space becomes large. Simulation results show that the proposed algorithms outperform benchmark schemes and validate the analytical structure and properties of the optimal policy.

Vehicular edge computing (VEC) is a promising technique for handling computation-intensive and delay-sensitive tasks by offloading them to roadside units (RSUs) or base stations (BSs) equipped with edge computing servers. However, the uneven spatial-temporal distribution of vehicles causes load imbalances among edge servers. To address this challenge, we propose a two-layer collaborative VEC network paradigm that incorporates offloading modes such as vehicle-to-RSU (V2R), vehicle-to-BS (V2B), and RSU-to-RSU collaboration. Within this framework, task offloading and resource allocation are jointly optimized to maximize system utility, which integrates revenue, delay, and energy consumption, while ensuring that vehicular tasks meet their delay requirements. Given the problem’s complexity and scalability concerns, we introduce a distributed framework and propose the joint task offloading and resource allocation (JTORA) algorithm. This algorithm decomposes the original problem into two sub-problems: task offloading and resource allocation. The task offloading sub-problem is modeled as a potential game and solved using the multi-agent twin delayed deep deterministic policy gradient (MATD3) framework. Based on the offloading decisions, the resource allocation sub-problem is further divided into multiple convex optimization problems. The system utility, derived from task offloading and resource allocation decisions, serves as a reward to iteratively evaluate, train the learning model, and refine the offloading strategy. Theoretical analysis confirms that the JTORA algorithm converges to the Nash equilibrium (NE). Simulations using real traffic data validate the proposed algorithm’s effectiveness and superiority over existing methods. Specifically, the JTORA algorithm improves overall system utility by reducing task processing delays and energy consumption while increasing the task completion rate.

Symbiotic radio (SR), combining the advantages of cognitive radio and ambient backscatter communication (AmBC), stands as a promising solution for spectrum-and-energy-efficient wireless communications. In an SR network, backscatter devices (BDs) share the spectrum resources with the primary transmitter (PT) by utilizing the incident radio frequency (RF) signal from PT for uplink non-orthogonal multiple access (NOMA) transmission. The primary receiver (PR) decodes the signals of PT and BDs via the successive interference cancellation (SIC) technique. Our goal is to establish a long-term commensalistic relationship between PT and BDs. We address the problem of maximizing the long-term average sum rate of BDs while ensuring a minimum average rate for the PT by optimizing the power reflection coefficients of the BDs. We explicitly consider practical constraints such as the required power difference among signals for SIC decoding and the unknown future channel state information (CSI). We prove the NP-hardness of the offline version of the problem and subsequently employ the Lyapunov optimization technique to convert the original problem into a series of sub-problems in each individual time slot that can be solved in an online manner without relying on future CSI. We then utilize the successive convex optimization (SCO) technique to solve the non-convex sub-problems. Extensive simulations validate that our proposed Lyapunov-SCO algorithm achieves superior performance in terms of the average sum rate of BDs while ensuring PT’s required average rate. In addition, we provide discussions on extending the proposed solution to SR networks with multiple PT-PR pairs, high-mobility BDs, and enhancing fairness among BDs.

With the development of the Internet of Things (IoT), substantial research efforts have been devoted to extending the sensing capability of commercial off-the-shelf (COTS) radio-frequency identification (RFID) tags. State-of-the-art approaches either demand sophisticated hardware redesign or are constrained to specific sensing capability, leading to increased costs and limited scalability. In this paper, we present the design of a generic sensor tag (GS-Tag) based on RF switches and COTS RFID system for transmission of generic sensor data. RF switches are connected in parallel with the RFID chip, and the GS-Tag modulates the sensor data by controlling the RF switches. Specifically, with the RF switches turned on, the tag’s chip is short-circuited, rendering it unreadable; conversely, turning off the RF switches makes the tag readable. The reader demodulates the data through the compatible electronic product code (EPC) protocol. A subtle dual RF switch scheme is adopted to extend the communication range. In addition to the battery-powered solution, we integrate an RF energy-harvesting module, develop a high-efficiency energy management circuit, and design an efficient task scheduling strategy to enable the GS-Tag to operate in a battery-free mode. We implement a prototype of GS-Tag with COTS RFID devices. Comprehensive experiments demonstrate that our designed GS-Tag can achieve an average packet reception rate (PRR) exceeding 99%, exhibits robustness to environmental disturbance, and facilitates coexistence of six GS-Tags with an average PRR of over 91%. Due to the dual RF switch scheme, the communication range of GS-Tag extends to 12 m. Besides, GS-Tag has an extremely low power consumption of just 3.98 μ W. A practical application is developed to accurately monitor temperature and ambient light intensity in an office environment while maintaining low power consumption. Our designed GS-Tag presents a cost-effective and compatible solution for expanding the sensing capabilities of COTS RFID system.

The growing edge cloud computing paradigm allows flexible handling of latency-sensitive and computation-intensive applications operating on user devices as the Internet of Things and 5G technologies gain in popularity. Microservices based on container technology are regarded as a potential architecture when applied to edge computing because of their lightweight and layered image properties. However, many current studies on the combination of the two simply treat microservices as a replacement for traditional virtual machine architecture without fully utilizing its advantages. In addition to discussing the impact of image loading strategy on neighboring time slots, this paper also focuses on the advantages of microservices layered image sharing. Our research in this paper studies the microservice deployment and task offloading of a mobility-aware things-edge-cloud system, and a deep reinforcement learning-based algorithm is proposed in this work to make decisions that optimize the system’s long-term throughput and delay utility.

With the explosive increase in the Internet of Things (IoT) devices, there is a rising demand for seamless and intuitive interactions between users and smart devices. Existing solutions require either dedicated sensors with microcontroller and battery power or modification of the hardware. This article presents RF-Keypad to realize a battery-free and wireless touch input interaction solution via commercial off-the-shelf (COTS) radio frequency identification (RFID) devices. RF-Keypad can easily turn an ordinary rigid object into an interaction touch keypad by deploying a tag array on the surface of the object. RF-Keypad extracts the received signal strength (RSS) variation feature of the touch action and builds an RSS-based model to detect the touch events, which is robust to the tag position and orientation. To acquire better performance, best touch area that poses distinct RSS variation feature is studied and optimal placement of the tag array is also investigated to eliminate mutual coupling effect between adjacent tags. Two touch detection algorithms based on RSS variance (RV) and the variance of the RSS variance (VoRV) are proposed, respectively, where the RV-based algorithm suits the scenario of fixed antenna-tag distance and the VoRV-based algorithm is designed for the scenario of variable antenna-tag distance. We implement a prototype of RF-Keypad with commodity RFID devices. Extensive experiments show that RF-Keypad achieves a touch detection accuracy of higher than 96% under fixed antenna-tag scenario and higher than 91% under variable antenna-tag distance scenario. An intelligent door access application is developed to highlight the practicality and scalability of our solution.

With the widespread application of 5G and the Internet of things (IoT), edge computing and cloud computing have been collaboratively utilized for task offloading and processing. However, though the massive devices (e.g., smartphones) are organized into multi-cells, most of the existing works do not explore the computation offloading for edge-cloud computing under inter-cell interference. Thus, the offloading decisions may be inappropriate as the transmission rate is overestimated. To address this issue, we propose COMEC, a novel Computation Offloading scheme in Multi-cell networks with Edge-Cloud collaboration, which could minimize the total cost in terms of delay and energy consumption. Specifically, we first formulate COMEC as an optimization problem taking into account inter-cell interference. Then, considering the offloading decisions of all users are coupled, a non-cooperative game is formulated to minimize the total cost of each user in a distributed manner. We prove that this game is a general (ordinal) potential game and possesses a pure strategy Nash equilibrium (NE). Based on the finite improvement property of the potential game, we develop the corresponding computation offloading algorithm to achieve the NE. Finally, simulation results show that the proposed scheme can achieve superior performance in overall system cost compared with other baselines.

Commodity RFID tags backscatter stored electronic product code (EPC) to the reader, but do not have sensing capability. Existing works have made much effort on designing RFID-based sensing platform. But most of them either need intricate hardware design or rely on modification of the tag, which increases the cost or constrains the sensing capability. In this paper, we design a self-jamming identification and sensing platform (SJISP) consisting of SJISP nodes and a commodity RFID reader. A subtle design of the SJISP node is the adoption of a jammer radio module with the same frequency as the reader, controlled by the micro control unit (MCU) to decide whether to interfere with the query process of the RFID reader. The RFID tag is not readable if the jammer is turned on to generate interference signals. Otherwise, it is readable when the jammer is turned off. The sensing data is thus modulated by switching the jammer on and off for transmitting bit 0 and bit 1, respectively. The reader demodulates the data through the compatible EPC UHF Gen2 air interface protocol. To further save the energy of the SJISP node, we propose a prefix codebook based data delivery scheme, which leverages the difference of energy consumption (DEC) between transmitting bit 0 and bit 1. Our proposed scheme can save more than 50$\%$of the energy than common communication without codebook. Experimental results based on our prototyped system show that the designed SJISP can achieve an average packet reception rate (PRR) of over 99$\%$and is quite robust to environmental disturbance. Our designed platform provides a low-cost and compatible solution to extend the sensing capability of RFID system. A demo application with a temperature sensor and a light sensor embedded in two SJISP nodes respectively are developed to demonstrate how SJISP applies in real world scenario.

The detail of the function PEO(.) in Section IV-B for this article was not available at the time of publication. It appears in Section IV-B as follows.

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for interested tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bidding-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted. In this paper, we design novel privacy-preserving incentive mechanisms to protect users’ true bid information against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated bids to the platform. Two solutions are proposed for the platform to solve the winner selection problem with the obfuscated information, which is proved to be NP-hard. Moreover, we further propose a novel task-bid pair protection truthful incentive mechanism to further prevent privacy leakage from the set of interested tasks, where each user encrypts his interested tasks via homomorphic encryption locally, and an encrypted task clustering method is proposed to group users with the same interested tasks into the same cluster for winner selection with users’ encrypted task-bid pairs. Both of theoretical analysis and extensive experiments demonstrate the effectiveness of proposed mechanisms against the untrusted platform.

With the increasing popularity of the internet of things (IoT) and 5G, emerging things-edge-cloud computing (TEC) paradigm provides a flexible way for execution of delay-sensitive and computation-intensive applications running on the user equipment (UE). By offloading these workloads to the mobile edge computing (MEC) or mobile cloud computing (MCC) server, the quality of experience, e.g., the execution delay, could be greatly improved. Nevertheless, conventional battery-powered devices face the challenge of battery exhaustion for task offloading. Using renewable energy via energy harvesting (EH) technologies has become a promising way to power these devices. In this paper, we investigate a multi-user green TEC system with EH UEs, each has a task buffer with limited capacity. A joint offloading decision and resource allocation problem is formulated, which addresses the long-term average execution delay, the task dropping and the long-term average energy cost constraint. A low-complexity online algorithm is proposed leveraging Lyapunov optimization framework and matroid theory, which jointly decides the offloading decision, the MEC server CPU frequencies and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the arrival tasks, wireless channel state, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot. Simulation results show that our proposed algorithm makes a best trade-off between minimizing the long-term average generalized delay and satisfying the long-term average energy cost constraint. Impacts of various parameters on the delay and energy cost performance are also discussed.

The Network Function Virtualization (NFV) service chaining problem, which involves locating Virtual Network Functions (VNFs) in an NFV-enabled network and routing network demands through their required VNFs, is key to the success of NFV. Solving the chaining problem can efficiently reduce required network resources, and thus reducing capital expenditures (CAPEX) and operational expenditures (OPEX). Previous works mainly focus on finding heuristic solutions, rather than investigating the intrinsic features of the problem. In this paper, we investigate the features of the problem from both theoretical and numerical points of view, by shrinking the NFV service chaining problem into a particular version and conducting tests to study what makes the NFV service chaining problem fundamentally difficult to solve. Results reveal that the demand routing part of the problem has a significant impact on solving the mathematical formulated problem, i.e., finding a feasible routing can be time-consuming. We further propose constructive methods that improve upon the mathematical formulation, which make the time of finding the optimal solution be reduced in most cases.

Backscatter communications have been widely adopted in wireless networks for low-power IoT devices. For the devices which are powered by a battery or harvest energy from ambient signals, it is important to backscatter data in an energy-efficient manner. Inspired by the energy consumption disparity (ECD) between backscattering bit 0 and bit 1, we propose an energy-efficient multicodebook-based backscatter communication (MBBC) scheme, where multiple prefix codebooks, differentiated by multiple data rates, are meticulously designed and shared by the sender and the receiver. The sender backscatters the codewords in the corresponding codebooks, and the receiver recovers the original data by searching the corresponding codebooks. To design the energy-efficient multiple codebooks, we formulate the optimization problem as the minimization of the energy consumption of backscattering data. To address the optimization problem, we employ a forest to represent the multiple codebooks, where each codebook is represented by a binary tree. By conducting the pruning and expanding operations (PEOs) on the forest, we propose a heuristic algorithm to search the energy-efficient codebooks. Simulation results demonstrate that, compared with the other schemes, the proposed MBBC scheme significantly saves energy without sacrificing throughput.

To meet the real-time requirement of the edge computing applications, technologies of software defined network and network function virtualization are introduced to reconstruct the MEC system. On this basis, we consider the design of online computing and communication resource allocation solution, aiming at maximizing the long-term average rate of successfully processing the real-time tasks. The problem is formulated in a Markov decision process framework. Both Q-learning and deep reinforcement learning algorithms are proposed to obtain online resource allocation solutions with consideration of time-varying channel conditions and task loads. Simulation results show that both proposed algorithms converge quickly and the average real-time task processing success rate achieved by deep reinforcement learning algorithm is the highest among all the baseline algorithms.

To maximize the throughput of wireless powered communication (WPC), it is critical for the device to decide when to harvest energy, when to transmit data and what transmit power to use. In this paper, we consider a WPC system with a single device using harvest-store-transmit protocol and aim to maximize the longterm average throughput with optimal allocation of the energy harvesting time, data transfer time and the device’s transmit power. With the consideration of many practical constraints including finite battery capacity, time-varying channels and non-linear energy harvesting model, we propose both deep Q-learning (DQL) and actor-critic (AC) approaches to solve the problem and obtain fully online policies. Simulation results show that the performance of our proposed AC approach comes close to that achieved by value iteration and is superior to DQL and other baseline algorithm. Meanwhile, its space complexity is 2-3 orders of magnitude less than that required by value iteration.

Energy harvesting wireless sensor networks (EH-WSNs) intend to support sustainable operations. It is important to design a high-throughput data delivery scheme that adapts to the fluctuation in harvested energy in the EH-WSN nodes. In this article, the optimal superframe and data buffer scheme (OSDBS) is investigated to improve the throughput of IEEE 802.15.4 beacon-enabled EH-WSNs. A stochastic model is developed for OSDBS, which leads to the characterization of network throughput and packet delay. The OSDBS achieves the maximum throughput through setting the optimal superframe and buffer sizes of the nodes, which are the solution of the formulated optimization problem that maximizes the network throughput with consideration of energy-harvesting rate and data arrival rate. The simulation results show the OSDBS significantly outperforms the existing schemes in terms of throughput.

Truth discovery is an effective tool to unearth truthful answers in crowdsourced question answering systems. Incentive mechanisms are necessary in such systems to stimulate worker participation. However, most of existing incentive mechanisms only consider compensating workers' resource cost, while the cost incurred by potential privacy leakage has been rarely incorporated. More importantly, to the best of our knowledge, how to provide personalized payments for workers with different privacy demands remains uninvestigated thus far. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in crowdsourced question answering systems, named PINTION, which provides personalized payments for workers with different privacy demands as a compensation for privacy cost, while ensuring accurate truth discovery. The basic idea is that each worker chooses to sign a contract with the platform, which specifies a privacy-preserving level (PPL) and a payment, and then submits perturbed answers with that PPL in return for that payment. Specifically, we respectively design a set of optimal contracts under both complete and incomplete information models, which could maximize the truth discovery accuracy, while satisfying the budget feasibility, individual rationality and incentive compatibility properties. Experiments on both synthetic and real-world datasets validate the feasibility and effectiveness of PINTION.

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bid-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted (e.g., honest-but-curious). In this paper, we focus on the bid protection problem in mobile crowdsensing with an untrusted platform, and propose a novel privacy-preserving incentive mechanism to protect users' true bids against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated task-bid pairs to the platform. The winner selection problem with the obfuscated task-bid pairs is formulated as an integer linear programming problem and proved to be NP-hard. We consider the optimization problem at two different scenarios, and propose a solution based on Hungarian method for single measurement and a greedy solution for multiple measurements, respectively. The proposed incentive mechanism is proved to satisfy ε-differential privacy, individual rationality and γ-truthfulness. The extensive experiments on a real-world data set demonstrate the effectiveness of the proposed mechanism against the untrusted platform.

Radio‐frequency (RF) energy harvesting is one promising technology to power the nodes in wireless networks. This study focuses on large‐scale wireless powered communication networks having multiple RF energy transmitters (ETs) and sinks, which almost have not been investigated previously. The authors aim to optimise the throughput via optimizing the transmit power allocation of ETs subject to a total power budget. Specifically, for the sum‐throughput maximisation (STM) problem, they firstly formulate it to be a non‐linear optimisation problem, then prove its convexity and finally propose an efficient dual sub‐gradient algorithm to solve it. Owing to the throughput unfairness among nodes of the STM approach, they further consider the common‐throughput maximisation (CTM; i.e. the worst node's throughput) and propose a very efficient algorithm for it. This algorithm divides the CTM problem into a master problem and a subproblem. The subproblem of determining the feasibility of a given common‐throughput is solved by transforming it to a linear problem whose optimal solution indicates the feasibility. The master problem of determining the maximal common‐throughput is solved by using the bisection search method. Simulation results demonstrate the effectiveness of the CTM approach to mitigate the throughput unfairness problem at the cost of decreased sum‐throughput.

When deploying radio frequency (RF)-powered sensor networks for mission-critical applications such as security surveillance, it is often required to maximize or guarantee the quality of surveillance. Both placing and scheduling the charging/working modes of sensors are of key importance in order to continuously ensure a satisfying quality of surveillance. Traditionally, sensor placement and scheduling have been considered separately. The first decision regards where to place the sensors, and then when to activate them. In this paper, we study simultaneous sensor placement and charging/working scheduling problem for fusion-based detection in RF-powered sensor networks. The problem is formulated as a constrained optimization problem and proved to be NP-complete. Two greedy heuristic algorithms, joint optimization greedy algorithm with fixed fusion radius (JOGA-FFR) and joint optimization greedy algorithm with dynamic fusion radius (JOGA-DFR) based on fixed and dynamic fusion radiuses, respectively, are presented to solve the problem. We validate our approaches through extensive numerical simulations as well as simulations based on real data traces collected from a vehicle detection experiment. The results show that, our proposed algorithms always outperform two-stage greedy algorithm (TSGA), an algorithm that optimizes sensor placement and scheduling separately, in all the simulation scenarios, and are near optimal in small-scale networks. Besides, JOGA-DFR outperforms JOGA-FFR under certain specific sensing model settings, but more often has a comparable performance with JOGA-FFR. JOGA-FFR is thus more recommended for its lower complexity.

This paper introduces a novel hand-free human-computer interaction system called AirMouse, which turns a common pair of glasses into a mouse to enable the interaction between computers and humans, especially for disabled people. The basic idea is to simulate mouse operations with head activities without using hands. To this end, an embedded device is attached to a pair of glasses, which leverages the gyroscope to accurately detect head activities and map them to corresponding mouse operations on devices (e.g., computers and smart TVs). In particular, AirMouse uses activities to simulate mouse operations instead of tracking the gaze or the head movements in the real-time manner. This provides flexibility to users allowing them to control devices even far away or not at front of the devices. We implement a prototype of AirMouse with the personalized pretraining module and the motion detection module, which is featured with low-cost, accurate, easy-to-use and real-time interaction, and evaluate AirMouse with 20 volunteers. The experimental results show that AirMouse achieves accurate, reliable, and real-time activity recognition and interaction. Specially, the technique of AirMouse can be integrated into wearable devices (e.g., smart glasses) to enrich their interaction functionalities.

Radio frequency identification (RFID) systems can be applied to efficiently identify the missing items by attaching them with tags. Prior missing tag identification protocols concentrated on identifying all of the tags. However, there may be some scenarios in which we just care about the key tags instead of all tags, making it inefficient to merely identify the missing key tags due to the interference of replies from the ordinary tags (i.e., nonkey tags). In this paper, we propose to investigate the problem of efficiently and completely identifying the missing key tags for anonymous RFID systems in which the tag privacy is required to be well protected. First, we propose a vector-based missing key tag identification protocol called VEKI. Then we propose an improved protocol called iVEKI, which consists of two phases: 1) ordinary tag deactivation and 2) missing key tag identification. The parameters of the proposed VEKI and iVEKI protocols are theoretically optimized to maximize the time efficiency. Finally, we conduct extensive simulations to evaluate the proposed VEKI and iVEKI protocols and the simulation results illustrate that they outperform other existing protocols in terms of execution time.

Narrowband Internet of Things (NB-IoT) is a new cellular technology introduced by the third generation partnership (3GPP) providing low-power and wide-area coverage for IoT. In this paper, we consider the scenario that NB-IoT is deployed in an heterogeneous network and the quality of the direct link from the NB-IoT user equipment (TIE) to the serving base station (BS) cannot satisfy the quality of service requirement for transmission of vital sensing data. Thereupon, device-to-device (D2D) communication is adopted as a routing extension to NB-IoT systems, and thus, enables two-hop routes between NB-IoT TIE and the serving BS via a set of D2D relays. As the candidate TIE relays work in duty cycle to save energy, we derive a model to select a set of TIE relays and perform opportunistic D2D communication according to a working schedule. Two optimization problems are formulated aiming at achieving optimal expected delivery ratio (EDR) and expected two-hop delay, respectively. Dynamic programming-based algorithms are proposed to solve the optimization problems and obtain the optimal working schedule of the relays. Simulation results demonstrate that our proposed maxEDR and minEED algorithms improves the system performance compared with other state-of-the-art algorithms.

Recently, the newly emerging wireless powered communication network (WPCN) has drawn significant interests, where network nodes are powered by the energy harvested from the radio-frequency (RF) signal. This paper studies the WPCN where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested RF energy only. The transmission completion time (TCT) minimization for the uplink (UL) transmissions of a given number of bits per node is considered. First, we prove that the harvest-then-transmit (HTT) transmission strategy is one of the transmission strategies able to achieve the minimal TCT, where all nodes first harvest the RF energy broadcast by the H-sink in the downlink and then send their independent information to the H-sink in the UL by time-division multiple access. Then for the HTT transmission, we prove that in order to achieve the minimal TCT, each node must transmit with constant power and consume all available energy, which helps to simplify the considered TCT minimization problem to be the optimization of time allocated for the H-sink's wireless energy transfer and the nodes' wireless information transmissions, and we formulate the optimal time allocation problem as a nonlinear optimization problem. Finally, we prove that it is a convex optimization problem. Due to the inexistence of explicit closed-form expressions of optimal time allocations to minimize TCT, one efficient algorithm is presented to obtain the optimal time allocations. Simulation results show that, compared with the available transmission strategies, the designed TCT-minimized transmission achieves a significantly smaller TCT.

It is critical to design energy-efficient communication technologies for wireless nanosensor networks (WNSNs) as nanosensors are highly energy-constrained. This paper focuses on WNSNs adopting the on-off keying (OOK) modulator. So far, some existing low-weight (LW) codes with low average codeword weight (ACW) map source symbols into different codewords with fewer high bits so as to greatly reduce the transmission energy at the transmitter. However, the transmission energy reduction is achieved at the price of large reception energy at the receiver as the codeword lengths of LW codes are large, incurring their ineffectiveness in most scenarios. To remedy the problem, we design the fixed-length minimum-communication-energy (F-MCE) code and variable-length minimum-communication-energy (V-MCE) code to minimize the total energy consumption at the transmitter and receiver for point-to-point communication in OOK-based WNSNs. Specifically, the code design problems are formulated as integer nonlinear programming (INLP) problems, and the F-MCE and V-MCE codes are obtained by solving the INLP problems. The F-MCE and V-MCE codes are applicable in more scenarios than the LW code. Extensive experimental results show that the V-MCE always outperforms the LW code regarding the energy saving, while the F-MCE code achieves energy saving no less than that of the existing LW codes.

In a Wireless Sensor Network (WSN), energy saving is a key issue for prolonging its runtime. Usually, a real-time application of WSN requires that data be collected within a delay constraint. There exists a tradeoff between energy saving and delay satisfaction. In this paper, a Tree-based Energy and Delay Aware Scheme (TEDAS) is proposed, which is able to maximize the lifetime of WSN while delay bound is satisfied. Based on Expected Transmission Count (ETX) of link, the TEDAS initially creates the Minimum ETX Spanning Tree (MEST) of the WSN and then the MEST is gradually improved by the proposed Adjusting Tree Algorithm (ATA) so that the optimal data gathering tree is obtained. In addition, the lifetime optimization problem (LOP) is developed for the ATA to maximize network lifetime. Moreover, the complexity of the ATA is O(N3), where N is the number of the nodes in the WSN. Simulation results show that the proposed TEDAS outperforms some existing schemes in terms of network lifetime and the volume of valid data.

The deployment strategy for achieving differentiated coverage and probabilistic connectivity in wireless sensor networks is studied in this paper. A novel solution based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed. Simulation results show that NSGA-II based strategy can meet the desired coverage requirements and maintain connectivity in a probabilistic manner with a relatively small number of sensors. In addition, for the applications in which the coverage requirement varies in some subareas, a local genetic operation is more time efficient and needs less variation in the original disposal than a renewed global optimization.

A two-hop neighborhood information-based routing protocol is proposed for real-time wireless sensor networks. The approach of mapping packet deadline to a velocity is adopted as that in SPEED; however, our routing decision is made based on the novel two-hop velocity integrated with energy balancing mechanism. Initiative drop control is embedded to enhance energy utilization efficiency, while reducing packet deadline miss ratio. Simulation and comparison show that the new protocol has led to lower packet deadline miss ratio and higher energy efficiency than two existing popular schemes. The result has also indicated a promising direction in supporting real-time quality-of-service for wireless sensor networks.

Many routing protocols have been proposed for wireless sensor networks in recent years. For some special applications, not only energy aware but link reliable is needed. Historical link status should be captured while making routing decisions. In this paper, we design a reliable link quality estimation based routing protocol (LQER), which integrates the approach of minimum hop field and (m, k). The performance of LQER is evaluated by simulation experiments to be more energy-aware, with lower loss rate and better scalability than MHFR (Z. Ma and Y. Sun, 2004) and MCR (F. Ye et al., 2001). Thus the whole network may obtain longer lifetime and better link quality.