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
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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 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. 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. Spectrum sensing is an integral part of medium access control (MAC) in cognitive radio (CR) networks as its reliability determines the success of transmission. However, it is an energy-consuming operation that needs to be minimized for CR sensor networks (CRSNs) due to resource scarcity. In this paper, a cognitive adaptive MAC (CAMAC) protocol, which supports opportunistic transmission while addressing the issue of power limitation in CRSNs, is proposed. Energy conservation in CAMAC is achieved in three fronts: on-demand spectrum sensing, limiting the number of spectrum sensing nodes, and applying a duty cycle. Spectrum sensing is initiated on-demand when the nodes have data to transmit, and it also exploits a subset of spectrum sensing nodes to gather spectrum availability information for all the nodes. Furthermore, it defines an adaptive duty cycle for the CRSN nodes to periodically sleep and remains awake when data are available for transmission. Hence, CAMAC stands as an adaptive solution that employs the small number of spectrum sensing nodes with an adaptive sensing period yielding minimum energy consumption. Simulation results reveal the efficiency of CAMAC in terms of high throughput and less energy consumption, which is adaptive to primary users' traffic and duty cycle. Opportunistic routing is widely known to have substantially better performance than traditional unicast routing in wireless networks with lossy links. However, wireless sensor networks are heavily duty-cycled, i.e. they frequently enter deep sleep states to ensure long network life-time. This renders existing opportunistic routing schemes impractical, as they assume that nodes are always awake and can overhear other transmissions. In this paper, we introduce a novel opportunistic routing metric that takes duty cycling into account. By analytical performance modeling and simulations, we show that our routing scheme results in significantly reduced delay and improved energy efficiency compared to traditional unicast routing. The method is based on a new metric, EDC, that reflects the expected number of duty cycled wakeups that are required to successfully deliver a packet from source to destination. We devise distributed algorithms that find the EDC-optimal forwarding, i.e. the optimal subset of neighbors that each node should permit to forward its packets. We compare the performance of the new routing with ETX-optimal single path routing in both simulations and testbed-based experiments. ABSTRACT Traditionally, routing in wireless sensor networks consists of two steps: First, the routing protocol selects a next hop, and, second, the MAC protocol waits for the intended destination to wake up and receive the data. This design makes it difficult to adapt to link dynamics and introduces delays while waiting for the next hop to wake up. In this paper we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. In a duty-cycled setting, packets are addressed to sets of potential receivers and forwarded by the neighbor that wakes up first and successfully receives the packet. This reduces delay and energy consumption by utilizing all neighbors as potential forwarders. Furthermore, this increases resilience to wireless link dynamics by exploiting spatial diversity. Our results show that ORW reduces radio duty-cycles on average by 50 (up to 90 on individual nodes) and delays by 30 to 90 when compared to the state of the art. 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. 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. Environmental monitoring is one of the driving applications in the domain of sensor networks. The lifetime of such systems is envisioned to exceed several years. To achieve this longevity in unattended operation it is crucial to minimize energy consumption of the battery-powered sensor nodes. This paper proposes Dozer, a data gathering protocol meeting the requirements of periodic data collection and ultra-low power consumption. The protocol comprises MAC-layer, topology control, and routing all coordinated to reduce energy wastage of the communication subsystem. Using a tree-based network structure, packets are reliably routed towards the data sink. Parents thereby schedule precise rendezvous times for all communication with their children. In a deployed network consisting of 40 TinyOS- enabled sensor nodes, Dozer achieves radio duty cycles in the magnitude of 0.2 . Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment, (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint, and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58 more environmental energy compared to the case when harvesting-aware power management is not used. Wireless charging through directed radio frequency (RF) waves is an emerging technology that can be used to replenish the battery of a sensor node, albeit at the cost of data communi- cation in the network. This tradeoff between energy transfer and communication functions requires a fresh perspective on medium access control (MAC) protocol design for appropriately sharing the channel. Through an experimental study, we demonstrate how the placement, the chosen frequency, and number of the RF energy transmitters impact the sensor charging time. These studies are then used to design a MAC protocol called RF-MAC that optimizes energy delivery to sensor nodes, while minimizing disruption to data communication. In the course of the protocol design, we describe mechanisms for (i) setting the maximum energy charging threshold, (ii) selecting specific transmitters based on the collective impact on charging time, (iii) requesting and granting energy transfer requests, and (iv) evaluating the respective priorities of data communication and energy transfer. To the best of our knowl- edge, this is the first distributed MAC protocol for RF energy harvesting sensors, and through a combination of experimenta- tion and simulation studies, we observe 300 maximum network throughput improvement over the classical modified unslotted CSMA MAC protocol. A reinforcement learning (RL) method for Quality of Service (QoS)-aware power management (PM) of an energy harvesting wireless sensor network, named QoS-aware RLPM, is proposed in this paper. The RL environment for each sensor node is represented by the observable measurements of harvesting energy and residual energy stored in the battery. To achieve QoS-awareness, the proposed QoS-aware RLPM attempts to satisfy various QoS demands by adjusting the duty-cycle rate of sensor node according to the observable measurements. The outcomes of these interactions are evaluated by rewards that express how well the duty-cycle rate adjustments satisfy QoS requests under energy neutrality criteria [1]. The QoS-aware RLPM learns the adjustment strategy of the duty-cycle rate by interacting with the environment and at the same time the QoS of the sensor network is maintained. Experiment results show that the proposed method satisfies the QoS requests under the energy neutrality criteria and performs better than the adaptive duty-cycling method of [1]. Wireless transfer of energy will help realize perennially operating sensors, where dedicated transmitters replenish the residual node battery level through directed radio frequency (RF) waves. However, as this radiative transfer is in-band, it directly impacts data communication in the network, requiring a fresh perspective on medium access control (MAC) protocol design for appropriately sharing the channel for these two critical functions. Through an experimental study, we first demonstrate how the placement, the chosen frequency, and number of the RF energy transmitters affect the sensor charging time. These studies are then used to design a MAC protocol called RFMAC that optimizes energy delivery to desirous sensor nodes on request. To the best of our knowledge, this is the first distributed MAC protocol for RF energy harvesting sensors, and through a combination of experimentation and simulation studies, we demonstrate 112 average network throughput improvement over the modified unslotted CSMA MAC protocol. Although gossiping protocols for wireless sensor networks (sensornets) excel at minimizing the number of generated packets, they leave room for improvement when it comes to the end-to-end performance, namely energy efficiency. As a step in remedying this situation, we propose NarrowCast: a new primitive that can be provided by asynchronous duty-cycling link layers as a substitute for broadcasting for gossiping protocols. The principal idea behind the NarrowCast primitive is to allow a sensor node to transmit to a fraction of its neighbors, which enables controlling energy expenditures and reliability. We discuss methods of approximating the primitive in practice and integrating it with gossiping protocols. We also evaluate implementations of the approximations with Trickle, a state-of-the-art gossiping protocol, and X-MAC, a popular link layer based on low-power listening. The results show thatwithout sacrificing reliabilitygossiping using even the simplest approximations of NarrowCast can considerably outperform gossiping based on broadcasting in energy efficiency. In this paper, we investigate schemes for energy-efficient multi-hop broadcasting in large-scale dense Wireless Sensor Networks. We begin with an initial simplified study of the schemes for relay selection. Our first finding is that MPR-based (Multipoint Relay) mechanisms work poorly in a dense network while the recently proposed Multicast Protocol for Low power and Lossy Networks (MPL) protocol based on Trickle performs better. However, Trickle requires to overhear packet retransmissions in the vicinity, while sensor nodes try to avoid overhearing by periodically waking up and going to sleep to save energy. We propose Beacon-based Forwarding Tree (BFT), a new scheme that achieves similar performance to MPL, although it fits better the case of nodes with low radio duty cycling MACs of the type of beacon-enabled IEEE 802.15.4. Our scheme also guarantees network coverage and its optimized version results in the shortest path distance to the broadcast source at a cost of lesser load mitigation. We compare and discuss the measured performance of MPL on top of ContikiMAC and BFT over beacon-enabled 802.15.4 on a Contiki testbed. The experimental results of the comparisons show that BFT may achieve very good performance for a range of broadcast intensity, it has a predictable power consumption, a remarkable low power consumption for leaf nodes, and low loss rates. On the other hand, MPL over ContikiMAC can obtain very low duty cycles for low broadcast traffic. In this paper, we present a software control method that maximizes the sensing rate of wireless sensor networks (WSN) that are solely powered by ambient RF power. Different from all other energy harvesting WSN systems, RF powered systems present a new challenge for the energy management. A WSN node repeatedly charges and discharges at short intervals depending on the energy intake. A capacitor is used for an energy storage in the energy harvesting system because of its efficient charge and discharge performance and infinite recharge cycles. When this charging time is too short, the node is more likely to experience an energy shortage. On the contrary, if it is too long, more energy is lost because of the leakage in the capacitor. Therefore, we implemented an adaptive duty cycle control scheme that is optimized for RF energy harvesting. This method maximizes the sensing rate considering the leakage problem, a factor that has never previously been studied in this context. Our control scheme improves efficiency by aggregate evaluation of the operation reliability and leakage reduction. In this paper, we present an adaptive power manager for solar energy harvesting sensor nodes. We use a simplified model consisting of a solar panel, an ideal battery and a general sensor node with variable duty cycle. Our power manager uses Reinforcement Learning (RL), specifically SARSA(λ) learning, to train itself from historical data. Once trained, we show that our power manager is capable of adapting to changes in weather, climate, device parameters and battery degradation while ensuring near-optimal performance without depleting or overcharging its battery. Our approach uses a simple but novel general reward function and leverages the use of weather forecast data to enhance performance. We show that our method achieves near perfect energy neutral operation (ENO) with less than 6 root mean square deviation from ENO as compared to more than 23 deviation that occur when using other approaches. Wireless Sensor Networks (WSNs) allow for untethered sensing of the environment. It is anticipated that, within the next few years, sensors will be deployed in a variety of scenarios, ranging from environmental monitoring to health care, from public to private sector and other areas. This paper investigate the utilization of receiver-based Medium Access Control (MAC) protocol in enhancing the performance of routing protocols such as IETF ROLL's RPL. Receiver-Based MAC (RB-MAC) is a preamble-sampling MAC protocol which dynamically elects the next-hop among a number of potential relay neighbors, based on current channel conditions and status of the sensor nodes. The proposed scheme is resilient to lossy links by nature, and hence reduces the number of retransmissions. We show by analysis how it outperforms the state-of-the-art sender-based preamble sampling MAC protocols in terms of energy and delay. In this paper, we analyse the impact of the Contiki Operating System (OS), and its Carrier Sense Multiple Access and Collision Avoidance (CSMA-CA) implementation on an IEEE 802.15.4 node's throughput and wireless channel utilization. The analysis is based on Contiki's Rime networking protocol stack, and its target is to determine an upper bound for the stated metrics. We explain that in Contiki with CSMA-CA as a MAC layer protocol, a node's throughput is limited to 8.1 kbps, at maximum, even without power saving features. In order to maximize a node's transmission capability, we modified Contiki's CSMA-CA implementation. A number of simulations are performed, and it is observed that with our modifications node throughput reaches 45 kbps, at maximum. Simulation results for estimating the channel capacity with our modified CSMA-CA MAC layer protocol show that the average per-node delay is low when the offered data load remains below 100 kbps. For an offered load of 100 kbps, the channel drops almost 20 of packets. Going beyond 100 kbps results in large latencies and significant packet loss. Results presented in this paper can serve as basis for the available bandwidth estimation in Wireless Sensor Networks (WSNs), QoS-based routing, and design of congestion control algorithm. Neighbor discovery plays a crucial role in the formation of wireless sensor networks and mobile networks where the power of sensors (or mobile devices) is constrained. Due to the difficulty of clock synchronization, many asynchronous protocols based on wake-up scheduling have been developed over the years in order to enable timely neighbor discovery between neighboring sensors while saving energy. However, existing protocols are not fine-grained enough to support all heterogeneous battery duty cycles, which can lead to a more rapid deterioration of long-term battery health for those without support. Existing research can be broadly divided into two categories according to their neighbor-discovery techniques — the quorum based protocols and the co-primality based protocols. In this paper, we propose two neighbor discovery protocols, called Hedis and Todis, that optimize the duty cycle granularity of quorum and co-primality based protocols respectively, by enabling the finest-grained control of heterogeneous duty cycles. We compare the two optimal protocols via analytical and simulation results, which show that although the optimal co-primality based protocol (Todis) is simpler in its design, the optimal quorum based protocol (Hedis) has a better performance since it has a lower relative error rate and smaller discovery delay, while still allowing the sensor nodes to wake up at a more infrequent rate.