We present our Zero Energy Network'' (ZEN) protocol stack for energy harvesting wireless sensor networks applications. The novelty in our work is @math fold: (1) Energy harvesting aware fully featured MAC layer. Carrier sensing, Backoff algorithms, ARQ, RTS CTS mechanisms, Adaptive Duty Cycling are either auto configurable or available as tunable parameters to match the available energy (b) Energy harvesting aware Routing Protocol. The multi-hop network establishes routes to the base station using a modified version of AODVjr routing protocol assisted by energy predictions. (c) Application of a time series called Holt-Winters'' for predicting the incoming energy. (d) A distributed smart application running over the ZEN stack which utilizes a multi parameter optimized perturbation technique to optimally use the available energy. The application is capable of programming the ZEN stack in an energy efficient manner. The energy harvested distributed smart application runs on a realistic solar energy trace with a three year seasonality database. We implement a smart application, capable of modifying itself to suit its own as well as the network's energy level. Our analytical results show a close match with the measurements conducted over EHWSN testbed.
At the data link layer, while there are several MAC protocols been designed for WSN, they are not optimized for energy harvesting sensor nodes. However there are quite a few MAC protocols designed for EHS, the work in @cite_22 proposes dynamically adapting the duty cycle of a node by observing deviations in energy input from an estimated model to ensure energy-neutral operation. In @cite_19 the authors propose to use adaptive control theory to formulate a linear-quadratic optimal tracking problem for maximizing task performance and minimal duty cycle variations. In @cite_30 the authors present analytical models and performance metrics for different MAC protocols. The work however is limited to simulation studies and over single-hop network. In @cite_5 , performance of various sleep and wakeup strategies based on channel state, battery state and environmental factors are analysed. The authors propose a optimal sleep wakeup strategy using game theory approach which provides trade-off between packet dropping and blocking probabilities.
@cite_30 @cite_19 @cite_5 @cite_22
2139644003 2128603408 2005435577 2051376838 1511092558 2001308229 1965360590 2124107362 1540457649 2121528570 2614312052 2140865819 2219395474 2069443635 2159238092 2145834571 2163190245 2097941894 2136340918 2101261257
Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis. A queuing analytical model is presented to investigate the performances of different sleep and wakeup strategies in a solar-powered wireless sensor mesh network where a solar cell is used to charge the battery in a sensor mesh node. While the solar radiation process (and, hence, the energy generation process in a solar cell) is modeled by a stochastic process (i.e., a Markov chain), a linear battery model with relaxation effect is used to model the battery capacity recovery process. Developed based on a multidimensional discrete-time Markov chain, the presented model is used to analyze the performances of different sleep and wakeup strategies in a sensor mesh node. The packet dropping and packet blocking probabilities at a node are the major performance metrics. The numerical results obtained from the analytical model are validated by extensive simulations. In addition, using the queuing model, based on a game-theoretic formulation, we demonstrate how to obtain the optimal parameters for a particular sleep and wakeup strategy. In this case, we formulate a bargaining game by exploiting the trade-off between packet blocking and packet dropping probabilities due to the sleep and wakeup dynamics in a sensor mesh node. The Nash solution is obtained for the equilibrium point of sleep and wakeup probabilities. The presented queuing model, along with the game-theoretic formulation, would be useful for the design and optimization of energy-efficient protocols for solar-powered wireless sensor mesh networks under quality-of-service (QoS) constraints This paper addresses the problem of finding outage- optimal power control policies for wireless energy harvesting sensor (EHS) nodes with automatic repeat request (ARQ)-based packet transmissions. The power control policy of the EHS specifies the transmission power for each packet transmission attempt, based on all the information available at the EHS. In particular, the acknowledgement (ACK) or negative acknowledgement (NACK) messages received provide the EHS with partial information about the channel state. We solve the problem of finding an optimal power control policy by casting it as a partially observable Markov decision process (POMDP). We study the structure of the optimal power policy in two ways. First, for the special case of binary power levels at the EHS, we show that the optimal policy for the underlying Markov decision process (MDP) when the channel state is observable is a threshold policy in the battery state. Second, we benchmark the performance of the EHS by rigorously analyzing the outage probability of a general fixed-power transmission scheme, where the EHS uses a predetermined power level at each slot within the frame. Monte Carlo simulation results illustrate the performance of the POMDP approach and verify the accuracy of the analysis. They also show that the POMDP solutions can significantly outperform conventional ad hoc approaches. This paper presents a novel Medium Access Control (MAC) protocol named ALOHA and Q-Learning based MAC with Informed Receiving (ALOHA-QIR) for Wireless Sensor Networks (WSNs). This scheme is implemented by combining frame based Slotted ALOHA and Q-Learning. ALOHA based schemes have the benefits of simplicity, low computation and overheads, but suffer collisions from the blind transmission strategy. By considering the previous transmission experience, Q-Learning offers nodes certain intelligence to access slots with a lower probability of collision. During data transmissions, receivers are informed by transmitters about their future slot selections so that the receivers can turn off their radio in other slots to save energy. Compared with basic Slotted ALOHA, ALOHA-QIR obtains significant improvements in throughput, delay and energy efficiency. Energy harvesting wireless sensor network (EHWSNs) are being actively studied in order to solve the problems faced by battery-operated WSNs, namely the cost for battery replacement and impacts on the environment. In EH-WSNs, each node harvests ambient energy, such as light, heat, vibration, and uses it for sensing, computations, and wireless communications, where the amount of harvested energy of each node varies depending on their surrounding environments. MAC protocols for EH-WSNs need to be designed to achieve high throughput and fairness, however, the conventional MAC protocols proposed for EH-WSNs do not adapt to the harvesting rate of each node, resulting in poor fairness. In this paper, we propose a fair MAC protocol based on polling scheme for EH-WSNs. The proposed scheme adjusts contention probability of each node according to the individual harvesting rate, thereby increasing throughput achieved by nodes with low harvesting rate. We evaluate throughput and fairness of the proposed fair polling scheme by computer simulations, and show that the proposed scheme can improve fairness with little degradation of the overall network throughput. Increasingly many wireless sensor network deployments are using harvested environmental energy to extend system lifetime. Because the temporal profiles of such energy sources exhibit great variability due to dynamic weather patterns, an important problem is designing an adaptive duty-cycling mechanism that allows sensor nodes to maintain their power supply at sufficient levels (energy neutral operation) by adapting to changing environmental conditions. Existing techniques to address this problem are minimally adaptive and assume a priori knowledge of the energy profile. While such approaches are reasonable in environments that exhibit low variance, we find that it is highly inefficient in more variable scenarios. We introduce a new technique for solving this problem based on results from adaptive control theory and show that we achieve better performance than previous approaches on a broader class of energy source data sets. Additionally, we include a tunable mechanism for reducing the variance of the node's duty cycle over time, which is an important feature in tasks such as event monitoring. We obtain reductions in variance as great as two-thirds without compromising task performance or ability to maintain energy neutral operation. The Point Coordination Function (PCF) of the IEEE 802.11 standard represents a well-known Medium Access Control (MAC) protocol providing Quality-of Service guarantees in Wireless Local Area Networks (WLANs). However, with the currently employed polling mechanism WLANs consume a significant amount of the energy resources from battery-powered user devices. To provide energy saving, an improved MAC protocol is presented in this paper, where bidirectional transmissions of fixed duration are incorporated into PCF in order to enable dynamic scheduling of real-time traffic. Based on this new strategy, wireless access points (APs) can estimate the proper duration of the Contention Free Period (CFP), in order to allow mobile stations to acknowledge any received data packet with a data packet equal to the received packet in size. Having this information, a mobile station, following the data exchange with the AP, can determine its wake-up timer and activate the sleep mode for the rest of the CFP interval. Comprehensive computer-based simulations demonstrate the feasibility of the proposed MAC improvements to achieve energy efficiency with negligible impact on packet delivery delay. We consider a frequency-flat fading multihop network with a single active source-destination pair terminals communicating over multiple hops through a set of intermediate relay terminals. We use Shannon-theoretic tools to analyze the tradeoff between energy efficiency and spectral efficiency (known as the power-bandwidth tradeoff) for a simple communication protocol based on time-division decode-and-forward relaying in meaningful asymptotic regimes of signal-to-noise ratio (SNR) under a system-wide power constraint on source and relay transmissions. The impact of multi-hopping and channel fading on the key performance measures of the high and low SNR regimes is investigated to shed new light on the possible enhancements in power bandwidth efficiency and link reliability. In contrast to the common belief that in fading environments communicating over multiple hops suffers significantly in performance due to the worst link limitation, our results indicate that hopping could significantly improve the outage behavior over slow-fading networks and stabilize links against the random channel fluctuations. In particular, we prove that there exists an optimal number of hops that minimizes the end-to-end outage probability and characterize the dependence of this optimal number on the fading statistics and target energy and spectral efficiencies. This paper analyzes the performance of a duty-cycled polling-based access mechanism that exploits the Transmission Opportunity Power Save Mode (TXOP PSM) defined in the IEEE 802.11ac to improve the energy efficiency of Wireless Local Area Networks (WLANs) based on the IEEE 802.11. The basic idea behind the proposed approach, named GreenPoll, is to enable contention free periods, based on polling with beacons, during which wireless stations can save energy by turning off their radio transceivers after exchanging data with the access point. The closed expression of energy efficiency of GreenPoll is formulated in this paper and is used to evaluate the performance of GreenPoll considering important parameters like the traffic load, packet length, data rate, and number of stations in the network. Both analytical and simulation results show the high energy efficiency of GreenPoll with gains of up to 330 and 110 when compared to the legacy Distributed Coordination Function (DCF) and the Point Coordination Function (PCF) defined in the IEEE 802.11, respectively. We propose the cross-layer based opportunistic multi-channel medium access control (MAC) protocols, which integrate the spectrum sensing at physical (PHY) layer with the packet scheduling at MAC layer, for the wireless ad hoc networks. Specifically, the MAC protocols enable the secondary users to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users. In our proposed protocols, each secondary user is equipped with two transceivers. One transceiver is tuned to the dedicated control channel, while the other is designed specifically as a cognitive radio that can periodically sense and dynamically use the identified un-used channels. To obtain the channel state accurately, we propose two collaborative channel spectrum-sensing policies, namely, the random sensing policy and the negotiation-based sensing policy, to help the MAC protocols detect the availability of leftover channels. Under the random sensing policy, each secondary user just randomly selects one of the channels for sensing. On the other hand, under the negotiation-based sensing policy, different secondary users attempt to select the distinct channels to sense by overhearing the control packets over the control channel. We develop the Markov chain model and the M GY 1-based queueing model to characterize the performance of our proposed multi-channel MAC protocols under the two types of channel-sensing policies for the saturation network and the non-saturation network scenarios, respectively. In the non-saturation network case, we quantitatively identify the tradeoff between the aggregate traffic throughput and the packet transmission delay, which can provide the insightful guidelines to improve the delay-QoS provisionings over cognitive radio wireless networks. Energy harvesting (EH) two-hop communications are considered. The transmitter and the relay harvest energy from the environment and use it exclusively for transmitting data. A data arrival process is assumed at the transmitter. At the relay, a finite data buffer is used to store the received data. We consider a realistic scenario in which the EH nodes have only local causal knowledge, i.e., at any time instant, each EH node only knows the current value of its EH process, channel state, and data arrival process. Our goal is to find a power allocation policy to maximize the throughput at the receiver. We show that because the EH nodes have local causal knowledge, the two-hop communication problem can be separated into two point-to-point problems. Consequently, independent power allocation problems are solved at each EH node. To find the power allocation policy, reinforcement learning with linear function approximation is applied. Moreover, to perform function approximation two feature functions which consider the data arrival process are introduced. Numerical results show that the proposed approach has only a small degradation as compared to the offline optimum case. Furthermore, we show that with the use of the proposed feature functions a better performance is achieved compared to standard approximation techniques. Energy consumption is a perennial issue in the design of wireless sensor networks (WSNs) which typically rely on portable sources like batteries for power. Recent advances in ambient energy harvesting technology have made it a potential and promising alternative source of energy for powering WSNs. By using energy harvesters with supercapacitors, WSNs are able to operate perpetually until hardware failure and in places where batteries are hard or impossible to replace. In this paper, we study the performance of different medium access control (MAC) schemes based on CSMA and polling techniques for WSNs which are solely powered by ambient energy harvesting using energy harvesters. We base the study on (i) network throughput (S), which is the rate of sensor data received by the sink, (ii) fairness index (F), which determines whether the bandwidth is allocated to each sensor node equally and (iii) inter-arrival time (@c) which measures the average time difference between two packets from a source node. For CSMA, we compare both the slotted and unslotted variants. For polling, we first consider identity polling. Then we design a probabilistic polling protocol that takes into account the unpredictability of the energy harvesting process to achieve good performance. Finally, we present an optimal polling MAC protocol to determine the theoretical maximum performance. We validate the analytical models using extensive simulations incorporating experimental results from the characterization of different types of energy harvesters. The performance results show that probabilistic polling achieves high throughput and fairness as well as low inter-arrival times. Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime. The energy consumption of the information and communication technology (ICT) industry, which has become a serious problem, is mostly due to the network infrastructure rather than the mobile terminals. In this paper, we focus on reducing the energy consumption of base stations (BSs) by adjusting their working modes (active or sleep). Specifically, the objective is to minimize the energy consumption while satisfying quality of service (QoS, e.g., blocking probability) requirement and, at the same time, avoiding frequent mode switching to reduce signaling and delay overhead. The problem is modeled as a dynamic programming (DP) problem, which is NP-hard in general. Based on cooperation among neighboring BSs, a low-complexity algorithm is proposed to reduce the size of state space as well as that of action space. Simulations demonstrate that, with the proposed algorithm, the active BS pattern well meets the time variation and the non-uniform spatial distribution of system traffic. Moreover, the tradeoff between the energy saving from BS sleeping and the cost of switching is well balanced by the proposed scheme. In order to adapt to time-varying wireless channels, various channel-adaptive schemes have been proposed to exploit inherent spatial diversity in mobile wireless ad hoc networks where there are usually alternate next-hop relays available at a given forwarding node. However, current schemes along this line are designed based on heuristics, implying room for performance enhancement. To seek a theoretical foundation for improving spatial diversity gain, we formulate the selection of the next-hop as a sequential decision problem and propose a general "optimal stopping relaying (OSR)" framework for designing such next-hop diversity schemes. As a particular example, assuming Rayleigh fading channels, we implement an OSR strategy to optimize information efficiency (IE) in a protocol stack consisting of greedy perimeter stateless routing (GPSR) and IEEE 802.11 MAC protocols. We present mathematical analysis of the proposed OSR together with other strategies in literature for a single forwarding node. In addition, we perform extensive simulations (using QualNet) to evaluate the end-to-end performance of these relaying strategies in a multi-hop network. Both the mathematical and simulation results demonstrate the superiority of OSR over other existing schemes. Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations. In this paper, a reinforcement learning-based throughput on demand (ToD) provisioning dynamic power management method (RLTDPM) is proposed for sustaining perpetual operation and satisfying the ToD requirements for today's energy harvesting wireless sensor node (EHWSN). The RLTDPM monitors the environmental state of the EHWS and adjusts their operational duty cycle under criteria of energy neutrality to meet the demanded throughput. Outcomes of these observation-adjustment interactions are then evaluated by feedback reward that represents how well the ToD requests are met; subsequently, the observation-adjustment-evaluation process, so-called reinforcement learning, continues. After the learning process, the RLTDPM is able to autonomously adjust the duty cycle for satisfying the ToD requirement, and in doing so, sustain the perpetual operation of the EHWSN. Simulations of the proposed RLTDPM on a wireless sensor node powered by a battery and solar cell for image sensing tasks were performed. Experimental results demonstrate that the achieved demanded throughput is improved 10.7 for the most stringent ToD requirement, while the residual battery energy of the RLTDPM is improved 7.4 compared with an existing DPM algorithm for EHWSN with image sensing purpose. Wireless Heterogeneous Sensor Network (WHSN) facilitates ubiquitous information acquisition for Ambient Intelligence (AmI) systems. It is of great importance of power management and topology control for WHSN to achieve desirable network performances, such as clustering properties, connectivity and power efficiency. This paper proposes a game theoretic model of topology control to analyze the decentralized interactions among heterogeneous sensors. We study the utility function for nodes to achieve desirable frame success rate and node degree, while minimizing the power consumption. Specifically, we propose a static complete-information game formulation for power scheduling and then prove the existence of the Nash equilibrium with simultaneous move. Because the heterogeneous sensors typically react to neighboring environment based on local information and the states of sensors are evolving over time, the power-scheduling problem in WHSN is further formulated into a more realistic incomplete-information dynamic game model with sequential move. We then analyze the separating equilibrium, one of the perfect Bayesian equilibriums resulted from the dynamic game, with the sensors revealing their operational states from their actions. The sufficient and necessary conditions for the existence of separating equilibrium are derived for the dynamic Bayesian game, which provide theoretical basis to the proposed power scheduling algorithms, NEPow and BEPow. The primary contributions of this paper include applying game theory to analyze the distributed decision-making process of individual sensor nodes and to analyze the desirable utilities of heterogeneous sensor nodes. Simulations are presented to validate the proposed algorithms and the results show their ability of maintaining reliable connectivity, reducing power consumption, while achieving desirable network performances. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. This paper considers the problem of power management and throughput maximization for energy neutral operation when using an energy harvesting sensor (EHS) to send data over a wireless link. The EHS is assumed to be able to harvest energy at a constant rate, and use a fixed part of the energy harvested in a slot for measuring the channel state. The rest of the energy harvested is available for transmission, however, it can be stored in an inefficient battery if it is not fully utilized. The key constraint that the EHS needs to satisfy is energy neutrality, i.e., the expected energy drawn from the battery should equal the expected energy deposited into the battery. In this scenario, two popular models for data transmission are contrasted: the constant bit rate (CBR) model and the variable bit rate (VBR) model. In the CBR model, it is assumed that the EHS are designed to transmit data at a constant rate (using a fixed modulation and coding scheme) but are power-controlled. In the VBR model, the EHS selects both the transmit power and the data rate of transmission in each slot based on the channel instantiation. A framework under which the system designer can optimize several parameters of the EHS that determine the average data rate performance when the channel is Rayleigh fading is developed. Using this framework, the two transmission schemes are contrasted. It is shown that, with the right choice of parameter settings, the CBR scheme can perform nearly as well as the VBR scheme at significantly lower complextiy. The usefulness and validity of the framework developed is illustrated through simulations for specific examples.