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In this paper, we consider underlay cognitive radio (CR) networks with one primary receiver, one cognitive transmitter–receiver pair, and one energy harvesting relay. The transmission power of the secondary source is opportunistically determined by its interference to the primary receiver, and the relay transmission is powered by the energy harvested from the radio-frequency observations at the relay. For the considered CR networks with simultaneous wireless information and power transfer (SWIPT), we derive analytical expressions for the outage probability, as well as their high signal-to-noise ratio (SNR) approximations in closed form. The developed analytical results demonstrate that the use of SWIPT will not cause any loss in diversity gain, but the outage probability achieved by the SWIPT-CR scheme asymptotically decays as \log \mboxSNR/\mboxSNR, whereas a decaying rate of \mbox1/\mboxSNR is achieved by a conventional CR network. Computer simulation results are also provided to demonstrate the accuracy of the presented analysis. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multiple-access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. To avoid consuming extra energy on competition for the channel, the users are supposed to share the channel via time division multiple access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for real-time information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first study the optimal transmission scheme in the case where each node has an infinite-capacity battery. By optimization theory, we show that, to maximize the average system throughput, all the users should transmit at an identical optimal power, which solely depends on the energy harvesting rate per time slot. We then study the equal-power TDMA scheme in the case where each node is equipped with a battery of finite capacity. The system is formulated as a polling system consisting of multiple energy queues and one server. By the Markov chain modeling method, we derive the performance of equal-power TDMA in this case, in terms of the energy loss ratio and average system throughput. In addition, we develop an algorithm to efficiently compute the optimal transmission power in the finite-capacity battery case. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user, and analyze its system performance. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, whereas equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities. [ABSTRACT FROM AUTHOR]

In this paper, we present a new power-allocation scheme for a decode-and-forward (DF) relaying-enhanced cooperative wireless system. While both the source node (SN) and relay node (RN) have limited energy storage, the SN can also draw power from the surrounding radio-frequency (RF) signals. In particular, we assume a deterministic RF energy-harvesting (EH) model under which the signals transmitted by the relay serve as the renewable energy source for the SN. The amount of harvested energy is known for a given transmission power value of the forwarding signal and channel condition between the SN and RN. To maximize the overall throughput while meeting the constraints imposed by the initially stored energy and the renewable RF energy source, an optimization problem is formulated and solved. Based on different harvesting efficiency values and channel conditions, closed-form solutions are derived to obtain the optimal joint source and relay power allocation. It is shown that, instead of demanding high on-grid power supply or high green energy availability, the system can achieve compatible or higher throughput by utilizing the harvested energy. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH) from background radio-frequency signals has recently started to be considered as a promising means of mitigating energy deficiencies in wireless sensors that have limited battery power. In this paper, to deal with the problem of energy shortage, we consider EH and energy efficiency, which is the ratio of the amount of data transferred to the energy dissipated within the system, at the same time. We formulate a nonconvex optimization problem for determining the training interval for channel estimation, user scheduling, and power allocation strategies to maximize the energy efficiency and transform the problem into a tractable convex problem. Based on the convexity of the problem, we propose an energy-efficient resource allocation strategy using an iterative method and provide the convergence of the proposed algorithm based on nonlinear fractional programming. The simulation results show that the proposed algorithm operates in an energy-efficient way, thereby achieving maximum energy efficiency while ensuring the data rate and harvested energy requirements. [ABSTRACT FROM AUTHOR]

In this correspondence paper, we consider the performance of a mixed free-space optical and radio frequency (FSO-RF) relaying system with energy harvesting in the existence of both atmospheric turbulence and pointing errors. More specially, we assume that the direct current component of the FSO signal, which is generally filtered out, can be used for energy harvesting at the relay. We derive the exact closed-form expression for the outage probability of this setup in terms of the bivariate Fox-H function. We also provide the asymptotic analysis to obtain the diversity order. Finally, Monte Carlo simulations are carried out to verify the correctness of our analytical results. [ABSTRACT FROM AUTHOR]

This paper investigates an integrated wireless communication system including non-orthogonal multiple access, full-duplex relaying, and energy harvesting techniques (named as EH-FD-NOMA). In this scheme, an energy-limited full-duplex relay harvests energy from a source at the first stage. Then, the relay detects the superimposed signal from the source and transmits the decoded signal to destination. Closed-form outage probabilities and ergodic rates at the relay and destination are derived. Numerical results verify the analytical results and show the superior performance of the EH-FD-NOMA if compared to its counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we study an optimal transmission problem in a point-to-point wireless communication system with energy harvesting and limited battery at its transmitter. Considering the non-availability of prior information about distribution on energy arrival process and channel coefficient, we propose a deep reinforcement learning (DRL) based optimal policy to allocate transmission power and adaptively adjust multi-ary modulation level according to the obtained causal information on harvested energy, battery state, and channel gain to achieve maximum throughput of the system. This optimization problem is formulated as a Markov decision process with unknown state transition probability. Applying the principle of the DRL, we use a deep Q-network to find the optimal solution in continuous state space, which provides rapid convergence since there is no additional memory required. Simulation results show that the proposed policy is effective and valid and it can improve the throughput of the system compared with Q-learning, greedy, random, and constant modulation level transmission policies. [ABSTRACT FROM AUTHOR]

Wireless energy harvesting has been recognized as a promising technique to prolong the lifetime of energy constrained relay nodes in wireless sensor networks. Its application and related performance study in three-step two-way decode-and-forward (DF) relay networks are of high interest but still lack sufficient study. In this paper, we propose a dynamic power-splitting (PS) scheme to minimize the system outage probability in a three-step two-way energy harvesting DF relay network and derive an analytical expression for the system outage probability with respect to the optimal dynamic PS ratios. In order to further improve the system outage performance, we propose an improved dynamic scheme where both the PS ratios and the power allocation ratio at the relay are dynamically adjusted according to instantaneous channel gains. The corresponding system performance with the improved dynamic scheme is also investigated. Simulation results show that our proposed schemes outperform the existing scheme in terms of the system outage performance and the improved dynamic scheme is superior to the dynamic PS scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we present a deep RL-based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency, and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL-based offloading scheme reduces the energy consumption, computation latency, and task drop rate, and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes. [ABSTRACT FROM AUTHOR]

This paper considers joint optimization of spectrum sensing, channel probing, and transmission power control for a single-channel secondary transmitter that operates with harvested energy from ambient sources. At each time slot, to maximize the expected secondary throughput, the transmitter needs to decide whether or not to perform the operations of spectrum sensing, channel probing, and transmission, according to energy status and channel fading status. First, we model this stochastic optimization problem as a two-stage continuous-state Markov decision process, with a sensing-and-probing stage and a transmit-power-control stage. We simplify this problem by a more useful after-state value function formulation. We then propose a reinforcement learning algorithm to learn the after-state value function from data samples when the statistical distributions of harvested energy and channel fading are unknown. Numerical results demonstrate learning characteristics and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

As an emerging energy replenishment technique, radio-frequency (RF) energy transfer has been considered as a promising solution to power the low-end wireless sensor networks (WSNs). However, in RF-powered orthogonal frequency-division multiple accessing (OFDMA) WSNs, both the energy provisioning and data transmission are easily impacted by the highly dynamic and unpredictable wireless channels, making channel assignment and energy management closely coupled. Furthermore, due to the lack of priori knowledge of stochastic channel conditions, network performance optimization becomes more challenging. In this paper, we investigate joint channel assignment and stochastic energy management to optimize the long-term network utility in RF-powered OFDMA WSNs, by jointly considering the stochasticity in RF energy harvesting rates and channel fading. We formulate the network utility optimization problem (NUOP) as a mixed-integer non-linear stochastic optimization problem. By leveraging Lyapunov optimization to decouple NUOP into two subproblems and address them separately, we propose an online algorithm, named JAMA, to adaptively adjust the sampling rates, data transmission power, transmission rate, and channel assignment according to the ever-changing channel conditions, while guarantee the network stability and sustainability. In addition, theoretical performance analysis is provided to prove that JAMA can achieve near-optimal network utility and satisfy the network stability and sustainability constraints. Extensive simulation results based on the experimental data of an RF-powered WSN testbed verify the effectiveness and efficiency of JAMA and evaluate the impacts of system parameters on network performance. [ABSTRACT FROM AUTHOR]

Integrating radio frequency energy harvesting into wireless communications to support simultaneous wireless information and power transfer (SWIPT) is a promising technique to extend lifetime of energy-limited wireless cooperative networks. In this paper, multiuser resource optimization for incremental amplify-and-forward orthogonal frequency division multiplexing relaying systems with SWIPT is investigated. To maximize the overall achievable rate, multi-dimensional degrees of freedom (e.g., power allocation, subcarrier assignment, incremental policy, and power splitting ratio) are jointly optimized, subject to the transmission power as well as the quality of service constraints. The optimization problem is formulated as a mixed-integer nonlinear programming and the corresponding solution entails prohibitively high computational complexity. This motivates us to find a suboptimal solution with the aim of realizing a better tradeoff between performance and complexity. Specifically, dual decomposition and subgradient algorithm are applied to achieve this goal and the essence of our proposal is that the variables to be optimized are sequentially and iteratively derived. Finally, complexity analysis and simulation results are shown to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper considers radio frequency energy harvesting wireless sensor networks where in each period multiple mobile chargers (MCs) simultaneously move along their respective fixed trajectories in the network area to wirelessly charge each sensor node's energy storage above a threshold. We aim to minimize the maximum moving time (i.e., charging completion time) of MCs consumed for passing through their respective trajectories via well-refined MCs’ velocity control. First, we transform this nonconvex and intractable velocity-control problem into a tractable one by discretizing these trajectories into segments and applying the charging power approximation strategy. Then we propose an efficient distributed algorithm to obtain the approximately optimal solution with the approximation ratio $(1+\epsilon)$. Simulation results demonstrate that our proposed velocity-control scheme can reduce the charging time by around 40% as compared with the baseline scheme using a constant speed. [ABSTRACT FROM AUTHOR]

In this correspondence paper, we investigate the security and efficiency of data transmission for overlay cognitive radio networks, which consist of a pair of primary users, multiple pairs of energy-limited secondary users and multiple eavesdroppers. Specifically, to achieve a better tradeoff between the security of primary transmission and the efficiency of secondary transmission, we propose an optimal relay selection (ORS) based on two energy harvesting (EH) protocols, i.e., the time switching relay (TSR) protocol and power splitting relay (PSR) protocol. Building in this, the exact closed-form expressions for the secrecy outage probability (SOP) of primary transmission and ergodic rate of secondary transmission are derived with the two EH protocols, respectively. Moreover, to deeply analyze the secrecy performance, two asymptotic expressions for SOP of primary transmission are also derived under high main-to-eavesdropper ratio and the infinity number of secondary user conditions, respectively. Through the simulation analysis, we find that the ORS based on EH protocols can achieve better performance than that of ORS without EH protocol in terms of tradeoff. Moreover, PSR protocol is superior to TSR protocol. [ABSTRACT FROM AUTHOR]

In this paper, we investigate nonorthogonal multiple access (NOMA) for a simultaneous wireless information and power transfer (SWIPT) system consisting of a battery powered access point and two energy harvesting users. In SWIPT, there exists an information-energy tradeoff. Since the adoption of NOMA may cause more energy consumption than the orthogonal multiple access (OMA), it is not known whether NOMA can always enhance the spectral efficiency in SWIPT systems compared to OMA. We prove that NOMA performs better than OMA when the decoding energy consumption is negligible. However, for the nonnegligible decoding energy consumption, we show that NOMA is not always superior to OMA. Interestingly enough, for the nonnegligible decoding energy consumption, OMA can outperform NOMA when the channel power gains of the two users are not sufficiently different. Moreover, we study the performance of cognitive radio inspired NOMA (CR-NOMA), in which the power is allocated to the user with poor channel condition such that its quality of service (QoS) requirement is met. Different from the battery powered CR-NOMA, the analytical results show that CR-NOMA can outperform OMA in SWIPT systems only if the channel power gains of users are sufficiently different and the QoS of the weak user is above a threshold. [ABSTRACT FROM AUTHOR]

This correspondence paper studies joint beamforming optimization and time allocation for a wireless powered two-way hybrid-relay network. As a general relaying architecture accommodating the existing relaying architectures in the literature as two special cases, we consider the two-way communication system where both energy-harvesting (EH) relays and non-EH relays exist. We aim to maximize the minimum rate by jointly optimizing the distributed beamforming vector at the relays, the energy beamforming matrix at the non-EH relays, and the EH time allocation with the energy constraints at the relays. The formulated problem is intractable to solve because the objective function and the energy constraints are highly nonconvex. We solve the optimization problem using the successive convex approximation. Simulations demonstrate that the proposed iterative algorithm considerably outperforms any existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, an analytical framework is proposed to explore the potential of wireless power transfer (WPT) for a millimeter wave (mmWave) network with large-scale antennas. Prior works mostly focus on WPT systems with a linear energy harvesting (EH) model, which cannot properly capture the power dependent EH efficiency. On the other hand, based on a nonlinear EH model, the effect of practical EH circuit specifications on the EH performance can be analyzed. By using stochastic geometry approach, analytical and asymptotic expressions for the energy coverage probability, average harvested energy, and achievable rate are derived under different base station (BS) configuration and deployment scenarios. Numerical results provide interesting design insights that the EH circuit specifications significantly affect the network performance. Increasing the BS density and number of BS antennas can improve the network performance. Due to saturation of EH circuits, the typical receiver can only achieve finite maximum amount of harvested energy and achievable rate even when the BS has a large number of antennas. [ABSTRACT FROM AUTHOR]

In this paper, we compare the performance of time-switching relaying (TSR) and power-splitting relaying (PSR) protocols in two-hop networks with a battery-assisted energy-harvesting (EH) amplify-and-forward relay. In this battery-assisted relay framework, the EH relay augments the harvested energy with energy drawn from a battery. Using expressions derived for throughput and average battery energy consumption, we investigate maximization of throughput for given battery energy availability per time slot and minimization of battery energy consumption for target throughput requirement. When only statistical channel knowledge is available, PSR results in better throughput performance at practical signal-to-noise ratios. However, in the typical low battery energy and low target throughput regime, it is TSR that is more battery energy efficient. When second-hop instantaneous channel knowledge is available, we observe dramatical energy savings that increases lifetime of nodes. Simulation results are presented to demonstrate accuracy of the derived expressions. [ABSTRACT FROM AUTHOR]

This paper considers a multiuser simultaneous wireless information and power transfer system with a nonlinear energy harvesting model, in which a multiantenna base station estimates the downlink channel state information (CSI) via uplink pilots. Each single-antenna user is equipped with a power splitter. Three crucial issues on resource management for this system include: 1) power-efficient improvement, 2) user-fairness guarantee, and 3) nonideal channel reciprocity effect mitigation. Potentially, a resource allocation scheme to address jointly such issues can be devised by using the framework of multiobjective optimization. However, the resulting problem might be complex to solve since the three issues hold different characteristics. Therefore, we propose a novel method to design the resource allocation scheme. In particular, the principle of our method relies on structuralizing mathematically the issues into a cross-layer multilevel optimization problem. On this basis, we then devise solving algorithms and closed-form solutions. Moreover, to instantly adapt the CSI changes in practice while reducing computational burdens, we propose a closed-form suboptimal solution to tackle the problem. Finally, we provide numerical results to show the achievable performance gains using the optimal and suboptimal solutions, and then validate the proposed resource allocation scheme. [ABSTRACT FROM AUTHOR]

In this paper, a two-hop nonregenerative multiple-input multiple-output relay system is investigated, where the relay node relies on harvesting the radio frequency energy transferred from the source node to forward information from source to destination. We consider the time switching (TS) protocol between wireless information and energy transfer. In particular, we propose a more general energy consumption constraint at the source node during the information and energy transfer, which includes the constant power constraints used in existing works as special cases. We study the joint optimization of the source precoding matrices, the relay amplifying matrix, and the TS factor to maximize the source–destination mutual information (MI). The optimal structure of the source and relay matrices is derived, which reduces the original transceiver optimization problem to a simpler power allocation problem. We propose a primal decomposition based algorithm and an upper bound based approach to efficiently solve the power allocation problem. The first algorithm achieves the global optimum, whereas the latter one has a lower computational complexity. Numerical simulations show that both proposed algorithms yield much higher system MI and better rate-energy tradeoff than existing approaches. [ABSTRACT FROM AUTHOR]

As mobile edge computing (MEC) emerges as a paradigm to meet the ever-increasing computation demands from real-time Internet of Things (IoT) applications in 5 G era, the development trends of which are mainly divided into two, with one being MEC with advanced computing architectures, and the other being MEC with high efficiency for sustainable operations. We are committed to taking advantage of these two trends to explore a novel multi-tier edge computing scenario with hierarchical task offloading and green energy provisioning via leveraging the energy harvesting (EH) technique. Specifically, we focus on the key problem of joint task offloading and energy scheduling in such green multi-tier edge computing systems. We aim to minimize the task execution cost by jointly considering the system cost that covers latency, energy consumption, and cloud rental fees. By formulating the problem as a stochastic optimization problem, we invoke the Lyapunov technique to decompose the long-term optimization problem into a series of one-slot optimization problems which only use the current system information. To solve the one-slot optimization problem which is a mixed-integer linear problem (MILP) proved to be NP-hard, we first relax the integer variables into real ones to obtain the optimal fractional solutions. Considering the capacity of the physical resources of each edge server, we propose a resource-constrained randomized dependent rounding algorithm to properly round up or down the fractional variables to get a feasible yet near-optimal solution. We conduct rigorous theoretical analysis and extensive simulations to verify the superior performance of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper considers a wireless powered communication network, where a multiantenna power beacon (PB) is employed to assist an energy constrained source to establish secure communications with a destination in the presence of multiple eavesdroppers. We propose a two-phase communication protocol termed as accumulate then transmit to enhance network secrecy. In the energy harvesting phase, the PB transfers radio frequency energy to charge the source; then in the information transmission phase, the PB acts as a friendly jammer to protect the source transmission against eavesdropping. We study the dynamic energy state transitions at the source using an energy discretization method and a finite-state Markov chain. Closed-form expressions are derived for connection outage probability, secrecy outage probability, and effective secrecy throughput, with the aim to evaluate both reliability and security of the proposed protocol. Furthermore, numerical results reveal the impacts of various system parameters on the secrecy performance and demonstrate the superior performance of our proposed protocol over an existing scheme. [ABSTRACT FROM AUTHOR]

Using renewable energy to power roadside units (RSUs) in vehicular ad hoc networks is a desirable green alternative to the conventional electric grid, since it lowers both the carbon footprint and the cost of deployment. This paper investigates the problem of scheduling the downlink communication from renewable energy-powered RSUs toward vehicles, with the objective of maximizing the number of served vehicles. First, an offline setting is considered where the RSUs are assumed to have advance knowledge of the incoming communication requests from vehicles and of the amount of energy to be harvested. The problem is formulated as an integer linear programming model that is shown to be NP-hard and two near-optimal solutions are proposed. The first one is a greedy heuristic that prioritizes communications based on their energy cost and the second is the particle swarm optimization metaheuristic. Then, the problem is considered in an online setting and two different solution approaches are investigated. The first one assumes distributed scheduling control between RSUs and two algorithms are proposed, one based on a stochastic model and the other on simple threshold-based selection of communication requests. The second approach assumes centralized scheduling and two algorithms are also investigated. The first one uses a greedy approach and the second one uses threshold-based selection again. Simulations compare the proposed schedulers and show their efficiency in terms of the number of vehicles served and the service delay. It is concluded that employing energy harvesting RSUs is a viable green alternative to grid-powered ones. [ABSTRACT FROM AUTHOR]

This paper develops an adaptive cooperative transmission scheme with relays harvesting wireless energy. Two relays are adopted to alternately forward source data to the destination using the two-path successive relaying (TPSR) protocol. When one relay forwards previous data along with source transmitting current data, using the power-splitting technique, the other relay will harvest energy and decode the current data from the received composite signal. If the TPSR cannot be performed due to the failure of data decoding at either relay, the decode-and-forward (DF) protocol will be performed by selecting a better relay. If one relay joins in the DF relaying, it will harvest energy and decode source data using the PS technique, while the other relay will always harvest energy from the received signal. If neither TPSR nor DF relaying can be performed, the source will transfer wireless power to both relays. The transmit powers of relays and the PS ratio are judiciously determined through analyzing the occurrence probability, average power fading, and success probability of each case. Numerical results show that our adaptive relaying scheme can achieve a much higher throughput than the single-relay DF scheme and the TPSR-only scheme. [ABSTRACT FROM PUBLISHER]

This paper investigates the tradeoff between information rate and harvested energy in a simultaneous wireless information and power transfer system under power splitting (PS) scheme and time switching (TS) scheme. A single-input-multiple-output transmitter–receiver pair is considered. For the channel from the transmitter to the receiver, only the mean and covariance matrix are known, but the instantaneous channel information or the channel gain distribution is unknown. We define robust outage capacity that applies for all possible channel distributions with the given mean and covariance matrix, and study the tradeoff between the robust outage capacity and harvested power level. By solving optimization problems, we find the PS ratio setting for PS scheme and TS ratio setting for TS scheme to achieve the outer boundary of robust outage capacity-energy region (which includes all possible pairs of achievable robust outage capacity and harvested power level). The PS and TS ratio setting are fixed over fading blocks, and thus can avoid communication overhead and computation overhead over each fading block, and facilitate a simple implementation in a practical system. We also prove that the robust outage capacity-energy regions in PS scheme and TS scheme are both convex. Further, we prove that the PS scheme outperforms the TS scheme in terms of the rate-energy tradeoff. Numerical results are also presented to verify the correctness of our theoretical results. [ABSTRACT FROM PUBLISHER]

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using wireless power transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, whereas the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes’ energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semidecentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance. [ABSTRACT FROM AUTHOR]

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to transfer information and energy signals at the same time and frequency utilizing the multi-input multi-output (MIMO) beamforming. In this correspondence, the SWIPT MIMO technique is realized in the multicell cooperative transmission environment, namely, network-MIMO where the signals from different base-stations (BSs) to all the mobile users can be jointly designed based on the perfect knowledge of the downlink channels and transmit messages. To develop an efficient precoding matrix, we adopt the weighted minimum mean squared error criterion that can be generally applied to the sum-utility maximization of each receiver's information rate. In the SWIPT network-MIMO, a number of constraints arise including the power constraint at each BS, namely, the per-BS power constraints and the energy constraint at the energy harvesting users, which makes the problem challenging. A main contribution of the paper is to propose a simple bisection-based algorithm that finds a solution. The efficiency of the proposed algorithm is verified via computer simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an energy harvesting cooperative network in which the cooperation is done in a random access environment. Although the cooperation provides an extra harvested energy supply for transmissions of the source, it causes probable collisions for the transmitted source packets. Thus, this kind of cooperation can improve or degrade the QoS of the source packets. We find the optimal policy in such a scenario to maximize the source throughput and derive the necessary and sufficient condition for no-cooperation policy to be throughput-optimal. Then, we prove that the maximum throughput is obtained by considering no-cooperation or full-cooperation policy depending on the network parameters. Also, we design optimal static and dynamic policies to minimize the average end-to-end delay for the source packets. In a static policy, the packets are accepted at the relay with a fixed probability while in a dynamic policy, the decision is made based on the status of data and energy buffers in the relay. We derive the necessary and sufficient condition under which the no-cooperation is the delay-optimal policy. When no-cooperation is not optimal, we model the scenario as a quasi-birth-death process and a constrained Markov decision process to derive static and dynamic delay-optimal policies, respectively. By several numerical results, we show the effect of tradeoff between cooperation and collision in the derived optimal policies in different network conditions. [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy-harvesting underlay cognitive radio (CR) system, which is comprised of multiple cognitive users (CUs), a common cognitive base station (CBS), and multiple passive eavesdroppers (Es). When CUs transmit to CBS, Es may overhear the confidential transmissions from CUs. In the aforementioned system, all CUs are considered to be equipped with energy harvesters to collect energy from their surrounding environments. Meanwhile, the transmit power of CUs shall be limited by a tolerable interference temperature to guarantee the quality-of-service of a primary user. In order to improve the physical-layer security of energy-harvesting CR systems, we propose an Energy-AwaRe mulTiuser scHeduling (EARTH) scheme, which takes into account both the interference temperature constraint and residual energy level harvested from the ambient surroundings. We consider the use of security-reliability tradeoff (SRT) as a metric to evaluate the performance of proposed EARTH scheme. To be specific, we derive closed-form expressions of outage probability (OP) and intercept probability (IP) for the proposed scheme over Rayleigh fading channels. For the purpose of comparison, the SRT analysis of round-robin scheduling (RRS) and conventional multiuser scheduling (CMUS) methods is also carried out. Numerical results show that the SRT of proposed EARTH scheme is better than that of RRS and CMUS methods, demonstrating the advantage of our proposed scheme against eavesdropping attacks. Additionally, it is illustrated that the sum of IP and OP can be minimized for RRS and CMUS as well as EARTH schemes through a time allocation between the energy harvesting and information transmission phases. [ABSTRACT FROM AUTHOR]

In this paper, we consider the sum secrecy rate maximization problem in multiuser multiple-input single-output (MU-MISO) systems in the presence of multiple energy harvesters (EHs) which also have potential to wire-tap the information users (IUs). To facilitate delivering secure information to the IUs and increase the total harvested energy by the EHs simultaneously, we optimize the transmit beamforming vectors to direct the information signals toward the IUs and artificial noise (AN) toward the EHs. We assume that each EH relies on itself to decode the information signal intended for an individual IU. Therefore, the corresponding problem is to maximize the worst-case sum secrecy rate under transmit power and energy harvesting constraints. The problem is optimally solved by transforming it into a convex iterative program using a change of variables, semi-definite relaxation (SDR), and linearization of quadratic terms. We prove that rank-one optimal solutions for the IUs beamforming covariance matrices can be obtained from the optimal relaxed unconstrained solution. Also, we provide three suboptimal solutions based on null space projection and power control of the beamforming vectors for the low and high harvested energy constrained regions. A special case of cooperative EHs in which the EHs can collaboratively cancel the signal of all IUs except the one they intend to eavesdrop is also investigated, and the optimal solution is derived in a comparable way as in noncooperative EHs case. Our simulation results reveal an understanding of how the tradeoff between the AN and information signal can jointly improve both the sum secrecy rate and the total harvested energy. We also show that, within the low total harvested energy region, the suboptimal solution in which the AN is projected in the null space of the IUs channels outperforms the suboptimal solution which ignores AN alignment at the IUs, and vice versa over the high total harvested energy region; and the suboptimal solution that combines both of them achieves close to optimal performance. [ABSTRACT FROM PUBLISHER]

Energy harvesting emerges as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we focus on radio frequency (RF) energy harvesting for multiuser multicarrier mobile wireless networks. Specifically, we propose joint data and energy transfer optimization frameworks for powering mobile wireless devices through RF energy harvesting. We introduce a power utility that captures the power consumption cost at the base station (BS) and the used power from the users’ batteries, and determine optimal power resource allocations that meet data rate requirements of downlink and uplink communications. Two types of harvesting capabilities are considered at each user: harvesting only from dedicated RF signals and hybrid harvesting from both dedicated and ambient RF signals. The developed frameworks increase the end users’ battery lifetime at the cost of a slight increase in the BS power consumption. Several evaluation studies are conducted in order to validate our proposed frameworks. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH)-powered wireless communications have attracted great attention from both industry and academia. However, the available energy, which relies on EH efficiency, will become another nonnegligible factor when we do research on EH-powered wireless communications. This paper studies the resource allocation problems in terms of spectrum and energy under EH-powered Device-to-Device (D2D) Communication underlaying Cellular Network (EH-DCCN). In this network, D2D pairs powered by EH are allowed to reuse the spectrum resources occupied by Cellular Users (CUs). To investigate the resource allocation problems, a sum-rate maximization problem of the whole cellular network with consideration of Quality of Service (QoS) and available energy constraints is formulated. The maximization problem is a nonconcave mixed-integer nonlinear programming (MINLP) problem, which has been proved to be NP-hard. To solve the problem, we first relax it with a concave lower bound on the original problem and then obtain the theoretical performance of the lower bound by Outer Approximation Algorithm. Moreover, a heuristic algorithm, an Energy-aware Space Matching approach (ESM), is proposed to acquire a suboptimal solution with low computational complexity. Finally, numerical simulation results indicate our considered resource allocation strategy is more effective than the strategy only based on channel state information under the EH-DCCN. Moreover, the performance in aspects of the sum rate and the matching probability shows that the ESM can approximately obtain the theoretical performance of the lower bound on the original problem under the scenarios with higher ratio of CU and EH-powered D2D numbers. [ABSTRACT FROM PUBLISHER]

In this paper, a collaborative energy-harvesting cognitive radio (CR) network is considered such that the transmitter of the secondary user (SU) is allowed to harvest signal energy of the primary user (PU) when the presence of the PU is detected. The harvested energy is converted to electrical power in order to supply the sensing and transmission energy of SUs. The time frame (time slot) is divided into two phases allocated to sensing (divided into two durations: spectrum sensing and results reporting) and transmission, respectively. The time spanned by the results reporting duration depends on the number of collaborative sensing users, while the time spent on spectrum sensing duration controls the number of sensing samples. A constrained convex optimization problem of the overall probability of error is formulated incorporating constraints on time and energy resources along with PU interference protection presented as a threshold on probability of collision. We use a soft decision rule scheme while considering two energy harvesting scenarios namely, energy surplus and energy deficit. In each scenario, the convexity of the optimization problem is established analytically and the global optimal solution is obtained. Simulation results are provided to demonstrate the impact of the different parameters on the overall system performance as well as to verify the deduced analytical results. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a novel energy-harvesting (EH) cognitive radio framework in which the primary source beamforms excess available energy to an EH secondary transmitter (ST) so as to enable it to share the spectrum. In the first phase of transmission, the multi-antenna primary source optimally beamforms symbols to its decode and forward relay while beamforming all excess energy to a selected EH ST. In the second phase while the relay transmits symbols to a selected primary receiver, the EH ST transmits symbols using underlay principles to its secondary receiver. Assuming peak interference constraints at the STs, we derive approximate expressions for outage probability and ergodic rate of the secondary link with the optimal beamformer. Using asymptotic expressions for outage and ergodic rate, we derive useful insights into performance of the system. We show that diversity equal to the number of secondary transmitters is achieved by the considered system. Computer simulations confirm accuracy of the derived expressions. [ABSTRACT FROM PUBLISHER]

This paper presents the design, modeling, bench experiments, and road tests for a retrofit regenerative shock absorber based on a permanent magnetic generator and a rack–pinion mechanism for the purposes of energy harvesting and vibration damping. Results show that variable damping coefficients and the asymmetric feature in jounce and rebound motions are achieved by controlling the electrical load of the shock absorber. Improved efficiency and reliability are achieved by utilizing a roller to guide the rack and preload on the gear transmission to reduce backlash and friction. A peak power of 68 W and average power of 19 W are attained from one prototype shock absorber when the vehicle is driven at 48 km/h (30 mi/h) on a fairly smooth campus road. [ABSTRACT FROM PUBLISHER]

We consider an energy-harvesting cognitive radio (CR) system coexisting with an unslotted primary user (PU) system. The secondary user (SU) either harvests energy from the PU transmissions or transmits when the PU is sensed to be idle. In this paper, we optimize the sensing intervals to balance between energy harvesting and spectrum access, considering the PU traffic statistics that affects both the energy arrival and consumption in addition to the opportunistic spectrum access. We propose to use different sensing intervals if the SU decides to transmit or harvest energy. The harvesting and transmission durations can be used to optimize the tradeoff between maximizing the utilization of PU spectrum opportunities and harvesting sufficient energy to transmit. We determine a closed-form expression for the probability of SU being active as a function of the PU traffic statistics and SU's access and harvest durations. Optimal transmission and harvesting durations have been found to maximize the CR throughput while protecting the PU. Furthermore, we enhance the system capability by considering that the SU can choose to purchase energy from the PU during the discovered PU idle periods in addition to the energy harvested opportunistically. It can further exploit the difference in the PU busy and idle durations as per its energy needs. The average energy cost spent in a time slot is minimized to achieve a given SU transmission rate while adhering to the PU collision constraint. [ABSTRACT FROM AUTHOR]

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy-constrained wireless networks. In this paper, we study the security issue of a nonregenerative multiple-input multiple-out (MIMO) relay system with SWIPT. When the full channel state information (CSI) of the eavesdropper is available, we aim to maximize the secrecy rate under the source and relay transmit power constraints and energy-harvesting (EH) constraints at the multiple EH receivers by jointly designing the source and relay beamforming matrices, which is a nonconvex optimization and is still an open problem. In order to enhance the secrecy rate effectively, we first transform the optimization into a difference of convex programming and propose a sequential parametric convex approximation-based iterative algorithm which is able to achieve a local optimum. To reduce the complexity, we also propose a singular value decomposition (SVD)-generalized SVD-based method to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization is employed to find a suboptimal solution of the joint power allocation problem. When the CSI of the eavesdropper is unknown, we propose an artificial-noise (AN) aided joint source and relay beamforming scheme, which can maximize the AN power to jam the eavesdropper under the limited source and relay transmit power and satisfy the quality of service of the receiver. Simulation results have shown the effectiveness of our proposed schemes. [ABSTRACT FROM PUBLISHER]

Recent years have witnessed many new promising technologies to power wireless sensor networks, which motivate some fundamental topics to be revisited. Different from energy harvesting, which generates dynamic energy supply, the mobile charger is able to provide a stable and reliable energy supply for sensor nodes and, thus, enables sustainable system operations. While previous mobile charging protocols focus on either the charger travel distance or the charging delay of sensor nodes, in this work, we propose a novel energy synchronized mobile charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of traveling salesman problem (TSP)-based solutions, when the nodes' energy consumption is diverse, we construct a set of nested TSP tours based on their energy consumption, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging request sequence of nodes with their sequence on the TSP tours. Experimentation and simulation demonstrate that ESync can reduce charger travel distance and nodes' charging delay by about 30% and 40%, respectively. [ABSTRACT FROM PUBLISHER]

The secure transmission in the multiple-input–single-output simultaneous wireless information and power transfer (SWIPT) system is an important issue. Considering channel uncertainties, we investigate the robust secure transmission scheme, which maximizes the worst-case secrecy rate under transmit power constraint and energy-harvesting (EH) constraint. The optimization problem is a nonconvex problem. Omitting the rank-one constraint on transmit covariance, it is transformed into a solvable semidefinite program (SDP) where the rank relaxation performance upper bound is obtained. Since the obtained rank relaxation transmit covariance may not be rank one, we propose a lower bound-based rank-one suboptimal (LB-Sub) solution by employing Charnes–Cooper transformation. We also propose the suboptimal Gaussian randomized (GR) solutions based on the rank relaxation upper bound and the lower bound, respectively. Simulation results have shown that our proposed LB-Sub solution and suboptimal GR solution based on the rank relaxation upper bound outperform the nonrobust scheme. [ABSTRACT FROM PUBLISHER]

We propose an interference-based wireless powered communication system in which an energy constrained source harvests energy from a single dominant co-channel interferer and then transmits information to a destination equipped with $N$ receive antennas. Assuming interference dominates noise power at the destination and the receive antenna that maximizes the signal-to-interference ratio is selected, we investigate the average throughput for the delay-limited and delay-tolerant transmission modes. In particular, we derive an analytical expression of the average throughput for the delay-limited transmission mode, and a precise lower bound on the average throughput for the delay-tolerant transmission mode. Furthermore, we derive simplified asymptotic expressions of the average throughput for both transmission modes assuming asymptotically large number of receive antennas (i.e., $N \rightarrow \infty$). We use the obtained asymptotic expressions to derive insightful closed-form expressions for the optimal energy harvesting time, which maximizes the average throughput, for both transmission modes. We show that the throughput-optimal energy harvesting time for the delay-limited and the delay-tolerant transmission scale like $O (\frac{1}{\sqrt{N}})$ and $ O (\frac{1}{ \log (N)})$ , respectively, for asymptotically large $N$. [ABSTRACT FROM AUTHOR]

In this article, a dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system is studied. With a splitting (PS) protocol, the relay node harvests the radio frequency (RF) energy in the signals sent by the source node and utilizes the harvested energy to forward signals from the source node to the destination node. We aim at maximizing the source-destination mutual information (MI) through a joint design of the source matrix, the relay matrix, and the PS ratios under the source node power constraint and the relay node harvested energy constraint. We consider a general sum energy constraint at the relay node with different PS ratios across relay antennas, which includes existing works based on uniform PS or per data stream energy constraint as special cases. Moreover, a practical nonlinear energy harvesting (EH) model is adopted, where the harvested energy is bounded as the incident RF signal power increases. We establish the structure of the source matrix and the relay matrix, which simplifies the complicated transceiver design problem with matrix variables to a power distribution problem with scalar variables. Three approaches are proposed to efficiently solve the optimal power distribution problem. In particular, the first proposed algorithm solves the original nonconvex power allocation problem using the sequential quadratic programming, while the other two algorithms convert the original problem to convex problems by exploiting a tight upper bound and a tight lower bound of the objective function, respectively. Numerical simulations demonstrate that when the EH circuit works in the linear region, the proposed algorithms have a larger system MI than existing PS and TS based MIMO AF relay systems. The peak harvest power constraint plays an important role in choosing the location of the relay node. [ABSTRACT FROM AUTHOR]

This article proposes a piezoelectric energy harvester to supply power for low-consumption devices in intelligent vehicle wheels. The harvester is embedded in the spoke and possesses the ability of self-tuning due to the centrifugal force in the rotational motion. A dynamic model suitable for design is derived through the Euler-Lagrange method and verified by experiments. In order to facilitate the design of the harvester, the concept of design factor is proposed and discussed. The wheel's rotational characteristics is measured by a road test and a harvester is designed for the R16 wheel. An experimental platform with Macro Fiber Composite is built and the output power of 0.61-6.28 mW is achieved at the wheel speeds from 40 to 120 kph on real road test. The results demonstrate that the proposed piezoelectric energy harvester can be applied to establish a wireless self-sufficient intelligent vehicle wheel system. [ABSTRACT FROM AUTHOR]

Integration of backscatter communication and wireless powered communication is a promising way to improve the performance of future Internet of Things (IoT) networks. However, this integration needs to be studied in practical scenarios with realistic models for the network elements. In this paper, we investigate optimal resource allocation in a backscatter assisted wireless powered communication network (BS-WPCN), where a piece-wise linear energy harvesting (EH) model is assumed at the users, which accounts for the saturation effect of practical energy harvesters. We study the problem of throughput maximization and apply a block coordinate descent (BCD) method to find the optimal allocation of resources for maximizing the total throughput. Numerical results demonstrate that the proposed scheme considerably improves the performance compared to the baseline scheme, which assumes linear EH model at the users. [ABSTRACT FROM AUTHOR]

The technologies of energy harvesting enable wireless sensors to provide a slow but steady transmission rate with very low deployment cost. To efficiently (re)configure settings, transmit data, or update system information to a group of energy-harvesting devices, broadcast/multicast schemes should be carefully designed. Currently, two main challenges to reliable broadcast transmission are known as transmission error and energy deficiency. The former comes from nonperfect channel conditions, and the latter is a result of power shortage, causing devices to work abnormally due to energy exhaustion. Considering the two challenges, we propose an erasure-based broadcast scheme for an energy-harvesting network to guarantee reliable broadcast transmission. We address the tradeoff between reliability and throughput, and based on this, we propose three policies to determine the broadcast period for different performance requirements. Simulation results show that the proposed schemes and policies can significantly improve the performance of broadcast transmission based on energy harvesting characteristics. [ABSTRACT FROM PUBLISHER]

This paper describes a new function for electrical-power-assisted steering (EPAS) systems applied for hybrid electric trucks (HETs). EPAS systems consume power to provide torque to steer the wheels. The required power must be equal to the power caused by the self-alignment torque that forces the wheels back into the aligned position. This paper describes, for the first time, how self-alignment torque is used to recover energy that is required to turn the wheels. This paper shows that, for a common steering maneuver, a maximum of 20\% of the energy required to complete the steering maneuver can be recovered. This energy saving relates to a fuel gain of 2094.5 gal over the lifetime of a truck of 500 000 mi. This paper also shows that the new EPAS function can be embedded in existing systems without changes to the hardware or the motor configuration. An EPAS model is developed in Dymola to show how steering at various vehicle speeds and various steering wheel angles contribute to the energy savings. An EPAS drive has been designed, constructed, and tested for an HET to validate the simulation results. [ABSTRACT FROM PUBLISHER]

Wireless-powered communication network (WPCN) is a key technology to power energy limited massive devices, such as on-board wireless sensors in autonomous vehicles, for Internet-of-Things applications. Conventional WPCNs rely only on dedicated downlink wireless power transfer (WPT), which is practically inefficient due to the significant energy loss in wireless signal propagation. Meanwhile, ambient energy harvesting is highly appealing as devices can scavenge energy from various existing energy sources (e.g., solar energy and cellular signals). Unfortunately, the randomness of the availability of these energy sources cannot guarantee stable communication services. Motivated by the above statements, we consider a generalized WPCN where the devices can not only harvest energy from a dedicated multiple-antenna power station (PS), but can also exploit stored energy stemming from ambient energy harvesting. Since the dedicated WPT consumes system resources, if the stored energy is sufficient, WPT may not be needed to maximize the weighted sum rate. To analytically characterize this phenomenon, we derive the condition for WPT activation and reveal how it is affected by the different system parameters. Subsequently, we further derive the optimal resource allocation policy for the cases that WPT is activated and deactivated, respectively. In particular, it is found that when WPT is activated, the optimal energy beamforming at the PS does not depend on the devices’ stored energy, which is shown to lead to a new unfairness issue. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed optimal resource allocation. [ABSTRACT FROM AUTHOR]

We consider a cognitive radio system, in which a secondary transmitter harvests energy from a primary transmitter's RF signal. The secondary transmitter, which provides decode-and-forward relaying service for the primary system, transmits its own data by using downlink non-orthogonal multiple access (NOMA). A time-switching protocol is used by the secondary transmitter to harvest energy and decode the primary transmitter's information. Our objective is to achieve maximal secondary throughput, by optimally selecting the time portion used for energy harvesting and the secondary transmitter's power allocation in NOMA transmission. In this paper, two optimization problems are formulated, in which the secondary receiver performs or does not perform successive interference cancellation (SIC), respectively. Although the two problems are nonconvex, we devise a method to transform the problems into equivalent problems under different cases. Then, we theoretically prove that the objective functions of the equivalent problems are quasiconcave, based on which we develop two-level bisection search algorithms to solve the equivalent problems. Interestingly, we show that performing SIC at the secondary receiver does not always guarantee a higher secondary throughput than the case without performing SIC. Computer simulation demonstrates that our algorithm performs better than an equal power allocation algorithm and an orthogonal multiple access algorithm. [ABSTRACT FROM AUTHOR]

Radio-frequency (RF) energy harvesting is one of the promising technologies in wireless networks. This paper considers wireless powered communication networks (WPCNs) where one hybrid access point (H-AP) coordinates RF energy/information transmissions to/from a set of nodes. For the important type of WPCNs where the nodes have throughput requirement, we aim to minimize H-AP's energy provision (EP) via jointly optimizing the time allocation of energy/information transmissions and H-AP's transmission power while satisfying node throughput requirement. More specifically, the half-duplex WPCN is considered first. We prove that H-AP's energy transmission power must be the maximum allowed value to achieve the minimal EP, then show that the problem is convex under this condition and finally design an algorithm applying golden section search and bisection search jointly to efficiently obtain the optimal time allocation. Second, the full-duplex WPCN is considered. Its EP minimization problem is proved convex. However, due to the hardness of solving it directly, we turn to solve the feasibility problem (FP) for a given EP per unit time. We construct a convex optimization problem based on this FP and design a dual subgradient algorithm for finding its optimal solution, which indicates whether this FP is feasible. The minimal EP per unit time and its corresponding optimal solution are found by using the bisection search method. Simulation results show that, the EP-minimized schemes are able to greatly reduce H-AP's EP as compared to the baseline schemes. [ABSTRACT FROM AUTHOR]

This paper considers a novel problem in rechargeable wireless sensor networks (WSNs), given a set of locations with one or more targets, determine the minimum number of sensor nodes to deploy in order to ensure a given coverage quality. This problem is significant as sensor nodes are often used to monitor one or more valuable assets or critical infrastructure. We formulate the problem as an integer linear program (ILP) and use it to compute the minimum number of sensor nodes required to monitor targets in small-scale WSNs. For large-scale WSNs, we relax the integer variables of the ILP and devise three approximation algorithms: greedy round node placement, target protection node placement, and energy efficient node placement (EENP). We prove the worst case performance bound of these algorithms. We also conducted simulation to compare these algorithms against the optimal solution produced by the ILP. Our results show that the solution computed by EENP is within one percentage point from the optimal solution. [ABSTRACT FROM AUTHOR]