1 Introduction
Intermittently powered and battery-free sensing devices have recently emerged as a promising approach in building a more sustainable Internet of Things and enabling long-term and low-maintenance data gathering in smart farming [
22], health and urban monitoring [
49,
65], infrastructure sensing [
3], industrial control [
84], and other applications. These devices harvest energy from their surroundings—solar, thermal, kinetic, or RF—and store that energy in tiny capacitors to collect and process sensor data, then wirelessly transmit the results to other connected systems and services [
57]. Harvested energy varies, often unpredictably, and when energy becomes scarce, these devices often operate intermittently with periods of active processing intermixed with frequent power outages of varying length.
Intermittent operation presents new challenges [
33], and a range of language and runtime systems, tools, and techniques have been proposed [
4,
12,
16,
35,
37,
46,
51,
58,
76] to address many of them; however, effective wireless communication remains a critical gap due to inherent challenges like unpredictable power failures, limited energy storage, and timing inaccuracies [
33,
34]. The wireless technologies and protocols used in today’s low-power networks have utilized techniques like duty-cycling,
Wake-up Radios (WuR), and energy harvesting to extend network lifetimes [
73]. Simple duty-cycling (periodically turning off radios when not needed) reduces energy usage but suffers from latency and synchronization issues when nodes are intermittent. WuR-based
Medium Access Control (MAC) protocols that use both a main radio and ultra-low power WuR, promise to eliminate idle listening, overhearing costs, and duty cycle induced latency [
24,
68,
71]. However, these protocols are designed based on problematic assumptions that devices are reliably powered (using batteries or supercapacitors), can accurately keep time, or that a mains-powered base station compensates for the nodes’ shortcomings. Other energy harvesting-oriented protocols aim for
energy neutral operation (ENO) and adapt to avoid intermittence [
8,
69,
70], but avoiding power failures is not always an option. Additionally, most WuR-based MAC protocols have been evaluated only in simulation, under simplifying assumptions that often differ significantly from real deployment conditions, specifically when operating entirely on harvested energy [
8,
9,
10,
49,
54,
71,
72].
With few available options, many intermittent batteryless devices rely on best-effort transmission, allowing one-way data transmission without channel coordination or delivery guarantees, while requiring the sink node to listen constantly (at considerable energy cost) [
3]. In many sensing applications—especially those relying on mobile, ad hoc, or solar-powered receivers [
78,
80,
88,
92] or those located in remote locations [
62,
91]—the energy efficiency of the receiver impacts the overall cost, size, and flexibility of the system and limits how these networks can be deployed. Overall, it is critical to address the following research questions to enable reliable and robust wireless networking for intermittent batteryless systems: (1) What are the novel changes in traditional PHY and MAC layer protocols required to support constrained batteryless networks with intermittent connectivity?, (2) What kinds of platform and radio support are needed to enable intermittent batteryless networks that rely entirely on harvested energy?, and (3) How can we effectively employ existing tools for empirical evaluation of new batteryless network protocols in real-world environments?
In this article, we explore a unique approach, called Greentooth—a receiver-initiated MAC and PHY layer protocol designed for efficient, active, connection-oriented communication among battery-free, intermittently powered sensing devices. Unlike other asymmetric WuR MACs that use expensive ID-based wake-up and control packets, Greentooth adopts a synchronous approach for reliable congestion-free communication using an energy-efficient connection mechanism that tolerates power outages and outage-induced timing inaccuracies. Greentooth utilizes connections for two reasons. First, a single broadcast of a
wake-up packet (WuPkt) from an energy-constrained sink can synchronize multiple connected nodes simultaneously, eliminating the need to individually poll each node for data using costly addressed WuPkts. This approach minimizes latency and conserves energy at both intermittent nodes and the coordinating sink node. Second, employing
Time Division Multiple Access– (TDMA) style communication, where dedicated time slots are allocated to each node, enhances reliability and energy efficiency by ensuring collision-free transmissions. In general, Greentooth is well suited for scheduled sensing applications with high reliability long-term and low-maintenance requirements, such as active volcano monitoring [
91], smart agriculture [
22], battlefield surveillance [
90], infrastructure monitoring (protected archaeological site) [
3], and animal tracking [
19].
Specifically, Greentooth provides a robust and energy-efficient networking paradigm that supports emerging battery-free intermittent sensing applications requiring ad hoc deployment capabilities with untethered or mobile receivers that are energy constrained. A typical example is a livestock tracking application on a cattle farm where battery-free sensors are mounted on animals for sensing location and body temperature data, which are then collected using a mobile sink or smartphone by a farmer [
56]. Greentooth addresses the challenges of batteryless networking by implementing the following intermittent-aware solutions: (i)
Sol1 (enabling lightweight synchronization and connections using a simplified WuPkt broadcast technique), (ii)
Sol2 (minimizing timing issues on batteryless nodes using a timekeeping solution that works across power failures alongside an ACK-based drift correction mechanism), (iii)
Sol3 (minimizing collisions, overhearing, and discovery latency through a TDMA-style MAC protocol with novel adaptive neighbor discovery and dynamic slot recycling capabilities), (iv)
Sol4 (minimizing reconnection overhead by preserving connection state across multiple power failures), and (v)
Sol5 (adopting a configurable WuPkt transmission recurrence to further reduce receiver energy consumption). While there has been notable progress in research related to WuR technology, low-power MAC protocols, and energy harvesting, this work presents the first implementation of an intermittent-resilient networking protocol that uses reliable connections for real intermittent batteryless networks. This is significant as effectively tackling the challenges posed by intermittent connectivity is crucial for realizing the long-term and low-maintenance potential of batteryless systems. Our contributions in this regard include the following:
(1)
We present Greentooth, the first energy efficient MAC- and PHY-layer protocol for robust connection-oriented communication on intermittent battery-free sensor networks.
(2)
We employ a dual-radio configuration, integrating a low-power WuR with a commodity radio transceiver, to efficiently synchronize batteryless sensors and manage device-to-receiver connections.
(3)
We describe reference Greentooth hardware and software implementations, with the commitment to make these assets accessible to the research community upon the publication of our work.
(4)
We evaluate and compare the performance of Greentooth against key representatives of state-of-the-art (SoA) receiver-initiated WuR MAC protocols, through both synthetic and real world energy traces for indoor temperature sensing and soil moisture monitoring applications.
3 Greentooth
Greentooth is motivated by the need for a new networking paradigm capable of supporting long-lasting, battery-free intermittent sensing applications requiring untethered or mobile receivers (sinks) that are energy constrained. It enables reliable connection-oriented communication for intermittent energy-harvesting devices through a combination of MAC and PHY layer features that ensure scalable, robust, and energy-efficient batteryless networking. Greentooth uses lightweight connections initiated by a single WuPkt broadcast for synchronous, single-hop TDMA communication between intermittent sensor nodes and coordinating receiver, enabling the nodes to deliver reliable data to the sink without the concerns of packet collisions and poor quality of service. Using a single WuPkt broadcast to synchronize all nodes in the network eliminates the energy overhead and latency caused by continuous transmissions of expensive ID-based WuPkt for each individual node as in some prior protocols, such as
Asynchronous Wake-up on Demand MAC (AWD-MAC) [
49] and DoRa [
50]. We design Greentooth as a cross-layer protocol over the medium access and physical layers of the communication stack presented in Figure
3, which provides a unified interface for the application layer at the top of the stack.
The communication cycle is central to how Greentooth manages synchronous connection-oriented communication between an energy-constrained receiver and many intermittent sensor nodes. In a similar way to BLE’s connection interval [
11], the cycle defines the protocol’s communication pattern, which happens in three distinct phases:
Wake_and_Sync,
Discovery, and
Transmission. These phases are repeated continuously throughout Greentooth’s operation and are defined by a series of user-configurable parameters listed in Table
1. As shown in Figure
4, the communication cycle,
\(T_c\) , is the sum of the duration for the three phases,
\(T_w\) ,
\(T_d\) , and
\(T_t\) . The value of
\(T_c\) impacts network throughput, power consumption, and the total number of sensor nodes that the network can accommodate. For instance, a high
\(T_c\) value will increase the number of nodes the network can support but will also decrease the throughput of each node.
3.1 Wake_and_Sync
The communication cycle begins with a WuPkt broadcast from the receiver that wakes and synchronizes all batteryless nodes in the network. The WuPkt—transmitted by the receiver’s main radio—serves both as an advertisement packet for new nodes looking to pair and a synchronization packet for already paired nodes. The WuPkt transmission period, \(T_w\) , can also vary depending on whether pattern correlation is enabled (requires a longer wake-up pattern) or additional data is included in the WuPkt. Enabling pattern correlation is beneficial for asynchronous WuR MAC protocols that prioritize selective ID-based wake ups, as it reduces false wake-up events. After broadcasting the WuPkt, the receiver proceeds to the Discovery phase.
Prior to receiving a WuPkt, all nodes in the network are expected to either be in a sleep state or harvesting energy to startup (for nodes that just joined the network or have depleted their energy). After waking up, connected nodes synchronize their times with the receiver and return to sleep until the start of their time slots, while unconnected nodes proceed to the Discovery phase.
3.2 Discovery
The
Discovery phase provides an opportunity,
\(T_d\) , for unconnected nodes to connect. During this phase, the receiver actively listens (Rx) for new nodes. As described in Figure
5(a), a node that wants to connect and has enough energy will send a
Connection Request packet to the receiver after a small random delay (configurable via
\(dd_{t}\) ). The random delay along with clear channel assessment helps avoid collisions when two or more nodes try to pair with the receiver at the same time.
After receiving a Connection Request packet, the receiver adds the new node to the schedule by assigning it a transmission slot \(t_{i}\) and then sends back a Connection Response packet with time slot if the pairing process was successful. The node saves the received connection information, in non-volatile memory (FRAM) for use across power failures, and returns to sleep until the start of its time slot. If pairing fails either due to a collision or a lost packet, then the node will try to rediscover in the next communication cycle—provided it has harvested enough energy.
The discovery period ( \(T_d\) ) on the receiver presents a tradeoff between energy efficiency and responsiveness. Longer discovery periods allow more nodes to joint the network more quickly. Shorter discovery periods improve the energy efficiency of the receiver. To help developers tune their systems to specific application conditions and needs, we support two discovery modes: fixed and adaptive discovery.
Fixed discovery mode, shown in Figure
6, uses a constant
\(T_d\) value (which could be
High or
Low) every communication cycle throughout the entire deployment lifetime of the network. A
Low \(T_d\) value shortens the active discovery listening time, thereby conserving energy on the receiver. But this allows only a small number of new nodes to connect each cycle, and may extend the time it takes to completely discover all participating nodes (discovery duration). A
Low discovery duration is best when sensed data changes slowly, new nodes join the network less frequently, and the energy efficiency of the sink node is critical (i.e., a smart city air-quality sensing deployment with a mobile sink node or cellphone as the receiver). A
High \(T_d\) value allows more nodes to connect more quickly due to the extended discovery listening time, but this leads to an increase in the receiver’s power draw and reduces its battery life. This discovery mode is more suited for deployments where rapidly changing data must be collected as soon as new nodes join the network. Overall, the right settings for
\(T_d\) will depend on deployment requirements and application needs, which may change over time and may be difficult to predict before the network is deployed.
Adaptive discovery mode automatically sets (
\(T_d\) ) to match current network conditions at runtime. In this mode, Greentooth dynamically scales
\(T_d\) up or down depending on whether there are new nodes waiting to join the network or not respectively. This is aimed at increasing the likelihood of a node getting discovered in the first few discovery cycles. For instance, the value of
\(T_d\) increases incrementally (in step size defined by
\(d_{ss}\) ) up to the max discovery period as long as new discoveries were made or when collisions happen during discovery. Conversely,
\(T_d\) decreases gradually to the min discovery period (taken as
\(d_{ss}\) ) as soon as the nodes are completely discovered (no new discoveries). Increasing the
\(T_d\) value helps in achieving a more responsive network through faster discovery, while decreasing it helps optimize for overall power savings. So adaptive discovery benefits from the strengths of both the
High and
Low discovery modes without significant effort on the part of the developer. To ensure that batteryless nodes adapts their random delay threshold
\(dd_{t}\) in line with receiver’s discovery period, the nodes first set their
\(dd_{t}\) to
\(d_{ss}\) and then increase it incrementally (by
\(d_{ss}\) ) whenever a node experiences packet collision while trying to pair with the receiver.
3.3 Transmission
During the
Transmission phase, connected nodes have the opportunity to exchange messages with the receiver. This phase consists of
\(n_{max}\) transmission slots, of time
\(t_s\) , where
\(n_{max} = T_t/t_s\) . Each connected node is assigned to a single transmission slot. The slot duration
\(t_s\) and other parameters in Table
1 are customizable based on payload size and application needs.
As shown in Figure
5(b), if a connected node, assigned slot
i, has data to send and enough energy to send it, then that node wakes up from sleep after receiving the broadcasted WuPkt, activates its main radio, and transmits its data at time,
\(t_i = t_0 + (i-1)t_s\) , where
\(t_0\) is the start of the
transmission period and
\(t_i\) is the time delay before the start of each node’s time slot. After receiving a data packet, the receiver responds with an ACK that contains the receiver’s estimate of the node’s current time drift (how far beyond
\(t_i\) the packet was received) to allow the node to adjust its timing.
After communicating with all connected nodes, the receiver goes to sleep to conserve energy until the next communication cycle. Each sensor node also returns to sleep at the end of its time slot, turns off its main radio, and waits for the next communication cycle while listening for a WuPkt. If a WuPkt fails to arrive, then a backup timer wakes up the node at its assigned time slot and the communication cycle continues. If either the sensor node or receiver fails to communicate for a user-configurable amount of time ( \(CONNECTION\_TIMEOUT\) ), then the connection is considered lost. Nodes can also specifically end a connection by sending a connection termination message during their assigned time slots.
While the scalability of Greentooth depends on the maximum communication cycle period achievable and the duration of each allocated time slot, Greentooth is capable of handling up to 10 times more concurrent connections than existing protocols.
3.4 Key Features of Greentooth
To enable robust, reliable, and energy efficient connection-oriented networking for intermittent batteryless devices, Greentooth implements the following set of features along with the
adaptive discovery mode mechanism presented in Section
3.2. These key features are tailored distinctively to address the fundamental challenges of batteryless networking described in Section
2.1.
Lightweight synchronization and connections - Sol1: While existing synchronous protocols incur considerable energy overhead and complexity for neighbor discovery and synchronization [
21,
25,
48], Greentooth uses a simple broadcast WuPkt (about 32 times less payload compared to ID-based WuPkts) in conjunction with the dual-radio architecture to achieve efficient synchronization and significant power savings on battery-free sensor nodes. The nodes in the network usually remain in sleep mode (like LPM3 on an MSP430 MCU [
85]), with their main radios powered off, while using their WuR to listen for a WuPkt from the coordinating receiver—WuR listening costs 3–5 orders of magnitude less power than the main radio. When a WuPkt is received, each node wakes from sleep and aligns itself with the receiver’s global time. This efficient synchronization mechanism helps Greentooth maintain inexpensive but reliable connections between the sink and nodes that are plagued with unpredictable failures and timing inaccuracies. The resulting connection-oriented communication method helps mitigate congestion and overhearing problems and is significantly more energy efficient and achieves better throughput compared to asynchronous WuR MAC protocols that poll individual nodes using ID-based WuPkt.
Backup timing and drift resolution - Sol2: The wake-up radio may occasionally miss WuPkts, perhaps due to physical barriers, RF-interference, or node mobility. In the absence of a WuPkt, a connected Greentooth node uses a local timer to continue using its time slot during subsequent
communication cycles as long as it can. This local timer is configured to wake the node in a recurring fashion (every
\(T_c\) seconds) as soon as the node receives a
Connection Response packet at the end of the initial pairing process. So, WuPkts are always helpful, but only necessary during initial pairing or when a node completely drifts out of sync. The local timer on the batteryless nodes is prone to clock drift that can disrupt subsequent slot alignment with the receiver. Consequently, the receiver accounts for node clock drift by monitoring node timing errors at every connection event and provides corrective feedback containing the node ID and drift (
\(packet\ arrival\ time - slot\ start\ time\) ) in its ACKs. This allows sensor nodes to automatically compensate for time drift even when WuPkts are missed or transmitted less frequently. Power outages also introduce timing errors, as batteryless nodes lose track of global time right after reboot. Therefore, Greentooth nodes use a timekeeper to estimate outage duration, which is then used to stay on schedule if WuPkts are missed after reboots. The nodes can keep time with any remanence-based timekeeping method, but we use CuSTARD [
36] in our implementation because of its simplicity and low-power benefits. In general, remanence-based timekeeping techniques, like CusTARD, tend to be less precise compared to their counterparts that require active power.
Adaptive neighbor discovery and dynamic slot recycling - Sol3: In addition to the detailed description of the novel adaptive neighbor discovery mechanism provided in Section
3.2, Greentooth’s TDMA-style protocol also allows time slots to be managed dynamically, allowing sensor nodes to be added and removed from the network without disrupting existing connections or setup. For example, a node will be kicked off the network (disconnected) if it has been inactive for a period longer than
CONNECTION_TIMEOUT. This helps maintain efficient use of allocated time slots and energy conservation at the receiver, as the receiver goes to sleep during unused time slots, including those belonging to recently evicted nodes. These unused slots are assigned to newly connected nodes, or reconnected ones that were previously disconnected.
Preserving connection state across outages - Sol4: Frequent and erratic power failures can significantly impact the operation and quality-of-service of intermittent networks. Greentooth addresses this fundamental issue by preserving connection state information across power failures. Each node saves its connection state in persistent memory after successfully pairing with the receiver during the
discovery phase. This checkpointing technique is lightweight as writing and reading operations in the MSP430 MCUs (with built-in persistent memory) [
86] are very energy efficient. By preserving connection state in non-volatile memory (FRAM), a depleted node can resume communication with the receiver after each reboot without spending additional energy to establish a new connection.
Variable WuPkt transmission - Sol5: Sending a WuPkt every single communication cycle is often an overkill as the batteryless nodes can stay synchronized using their backup timers. Thus, Greentooth further reduces the energy consumption on both the batteryless nodes and the receiver by supporting two different WuPkt transmission modes: fixed mode and dynamic mode.
In fixed mode, WKUP_TX_REPS (a user defined variable) defines how often a WuPkt is broadcasted by the receiver. When WKUP_TX_REPS is set to 1, a WuPkt is transmitted at the start of every communication cycle, when set to 2, it is transmitted every other communication cycle, and so on. This flexibility allows programmers to define WuPkt transmission repetition that suits their application requirements. For example, a higher WKUP_TX_REPS value leads to exceptional power savings but may limit how quickly new nodes are discovered and added to the network. However, a small WKUP_TX_REPS value is crucial for poor harvesting conditions, where nodes are kicked off the network due to inactivity (exceeds CONNECTION_TIMEOUT period) or in conditions where nodes enter and leave the network frequently.
In dynamic mode, rather than defining the value of
WKUP_TX_REPS statically, the receiver dynamically adjusts the value at runtime based on
node_coverage—which tracks the percentage of connected nodes that actively interact with the receiver. This is critical as network conditions are subject to change over time because of the arbitrary nature of harvested energy. So, adapting
WKUP_TX_REPS at runtime in response to changes in network environment provides the benefits of faster discovery and power savings on the receiver. We compute
node_coverage every
communication cycle using
The connected_active_nodes is computed by recording the number of connected Greentooth nodes that delivered data to the receiver during a given communication cycle period. We utilize a circular buffer to keep track of the last few values of node_coverage. So, before inserting the current node_coverage in to the buffer, the value is compared with the minimum value in the buffer. This is to confirm whether batteryless nodes are maintaining their interaction with the receiver or dropping out of the network at a significant rate due to poor harvesting conditions. If node_coverage is greater than or equal to buffer min, then the WKUP_TX_REPS is doubled to save power, else it is set to 1—which signifies that some of the nodes are either unavailable or disconnected, thus requiring continuous transmission of WuPkts.
3.5 Greentooth Analytical Model
To mathematically characterize and analyze the behavior of Greentooth, we adopt a formal approach by treating the operation of the batteryless nodes as a stochastic process in discrete time and state. This enables us to model the high-level behavior using a Markov process, with state transition probability from state
i to state
j defined as
\(p_{ij} = P[X_{n+1} = j \mid X_n = i]\) , where
\(\lbrace X_0, X_1, X_2, \ldots , X_n\rbrace\) denotes a vector of discrete random variable at various timesteps. Figure
7 illustrates the representative Markov chain showing different states of the node and transition probabilities among the states. The following are the key states that each batteryless sensor node can be in the following:
•
Power failure – \(S_1\) : The sensor node losses power due to due to erratic and scarce harvesting conditions.
•
wake-up receiver (WuRx) sleep – \(S_2\) : The sensor node remains in low-power mode with wake-up radio actively listening for WuPkts.
•
Wake state – \(S_3\) : The sensor node is in active state after existing the sleep state due to WuPkt reception.
•
Discovery – \(S_4\) : The sensor node establishes a connection with the receiver if time slot is yet to be allocated.
•
Data transmission – \(S_5\) : The sensor node delivers data to the receiver during its allocated time slot.
Given the finite and countable nature of the Markov chain (Figure
7) with these five states, we consolidate the transition probabilities into a probability matrix denoted as
P. The matrix
P conveys the probabilities associated with transitioning from one state to another within a single timestep, and a transition probability of 0 denotes that a state is not accessible from another state.
Since the transition probabilities leaving a state are mutually exclusive and forms a universal set, each row of
P represents the transition probabilities departing from a state and add up to 1. For instance, all transition probabilities departing from the
\(Power\ failure\) state
\(S_1\) equals
\(p_{11} + p_{12} = 1\) . Conversely, each column represents the transition probabilities terminating at a state. Transitions terminating at the
\(WuRx\ sleep\) state
\(S_2\) is given as
\(p_{12} + p_{22} + p_{32} + p_{42} + p_{52}\) . In general, the Chapman–Kolmogorov equation given as
\(P_{ij}(n+m) = \sum _{k \in K} p_{ik}(n) p_{kj}(m)\) , allows us to recursively compute the transition probability of moving from state
i to state
j after
n steps, where
k is an intermediate state between
i and
j. Furthermore, the probability that the node is in state
i at timestep
n is given as
\(\pi _i(n) = P(X_n = i)\) , which can be grouped as a state probability vector
\(\boldsymbol {\pi }(n) = [ \pi _0(n), \pi _1(n), \pi _2(n), \ldots ]\) . So, we can represent the state probability vector of the node at timestep n as
\(\boldsymbol {\pi }(n) = \boldsymbol {\pi }(n-1) \mathbf {P} = \boldsymbol {\pi }(0) \mathbf {P}^n\) , where
\(\mathbf {P}^n\) is the
n-step transition probability matrix for moving between different states and
\(\boldsymbol {\pi }(0)\) is the state probability vector at the initial state (timestep 0, signifying when the batteryless node is initially introduced into the network). As
n continues to increase, the states of the nodes reaches a steady state i.e.,
\(P_{\text{ss}} = \lim _{{n \rightarrow \infty }} P^n\) , and we can compute the steady-state probability vector
\(\boldsymbol {\pi }\) using:
\(\boldsymbol {\pi } = \boldsymbol {\pi }(0) P_{\text{ss}}\) or
\(\boldsymbol {\pi } = \boldsymbol {\pi }P\) , through a system of linear equations derived from
We can solve the following equations to obtain the steady-state probabilities
\(\pi _1\) ,
\(\pi _2\) ,
\(\pi _3\) ,
\(\pi _4\) ,
\(\pi _5\) :
The computed steady-state probability vector
\(\boldsymbol {\pi } = [\pi _1\ \pi _2\ \pi _3\ \pi _4\ \pi _5]\) can be used to evaluate key network metrics like throughput, energy consumption, reliability, and so on.
Performance analysis using steady-state probability vector \(\boldsymbol {\pi }\) : We provide an analytical evaluation of the following network metrics using the derived steady-state probabilities \(\pi _1\) to \(\pi _5\) :
Throughput. To evaluate network throughput, we first identify the relevant state (
\(\mathbf {S_5}\) ) and transitions (
\(p_{12}, p_{35}, p_{45}, p_{52}\) ) in the Markov chain (Figure
7) that contribute to successful data transmission from each batteryless node to the receiver. For instance, a node will need to harvest enough energy to start up (
\(p_{12}\) ), be paired already (
\(p_{35}\) ) or just finish pairing (
\(p_{45}\) ) with the receiver, and be able to deliver its data to the receiver successfully (
\(p_{52}\) and
\(\pi _5\) ). We compute the average successful transmissions per
communication cycle,
\(T_c\) , as the product of the steady-state probability of being in the relevant state and the associated state transitions, i.e.,
\((p_{12} + p_{35} + p_{45}) \times \pi _5 \times p_{52}\) . The network throughput
\(T_p\) is further computed as follows:
The overall network throughput is then evaluated by averaging \(T_p\) with respect to the total number of batteryless nodes deployed in the network.
Energy consumption. For a batteryless node, the average energy consumption per successful transmission can be evaluated by considering the amount of energy consumed for data transmission given as
\(E_{\text{transmit}}\) or
\(E_{52}\) and the aggregate probability of successful transmission from all relevant state transitions
\(P_{\text{tx}} = (p_{12} + p_{35} + p_{45} + p_{52})\) . Hence,
This can also be extended to analyze the overall energy efficiency and lifetime of the receiver by considering the different states and transitions of the receiver Markov chain.
Reliability. The reliability (success probability) represents the likelihood of a successful data transmission from a batteryless node to the receiver, indicating network robustness. From the Markov chain, we consider the following states (
\(\mathbf {S_1, S_3, S_4, S_5}\) ) and transitions (
\(p_{12},\ p_{35},\ p_{45},\ p_{52}\) ) that contribute to the likelihood of a successful data transmission. Using the steady-state probabilities (
\(\pi _1,\ \pi _3,\ \pi _4,\ \pi _5\) ) corresponding to the identified states, we define the network reliability as
The overall network reliability can be generalized over the total number of sensor nodes deployed in the network.
6 Related Work
Wireless sensing systems have revolutionized data acquisition and monitoring applications in many fields; nonetheless, they usually experience limited lifetime due to the expensive nature of radio communication. Several MAC techniques [
2,
7,
66,
73] in the literature have been explored and used for extending network lifetime while optimizing for throughput, latency, and fairness.
First, duty-cycled MAC protocols [
38,
52,
68,
82] have been proposed as better alternatives to always-on protocols due to their superior energy efficiency and channel utilization. In contrast to always-on protocols—in which sensor nodes continuously listen for or transmit data packets, they operate by systematically putting the node’s main radio into sleep mode, which is later woken up briefly to either receive or transmit data. Synchronous duty-cycled MACs keep a common time reference among the nodes, which introduces time synchronization overhead and complexity, while the asynchronous counterparts utilize schemes like preamble sampling, random duty-cycling, or receiver-initiation to circumvent synchronization challenges [
13]. Notwithstanding, they are still susceptible to idle listening—energy consumed listening for data packet during active period without success, and other major issues like latency—time spent waiting for sleeping nodes to wake-up, and overhearing—energy consumed receiving data meant for another node [
68].
Advances in wake-up radios technology [
28,
39,
43,
59,
60,
71] have provided ways to resolve most of the challenges faced by duty-cycled MAC protocols. Wake-up radios are ultra-low power receivers with orders of magnitude lower power consumption compared to existing low-power radio transceivers. They are mostly used alongside the main radios (dual-radio architecture) for continuous monitoring of the wireless medium for wake-up signals while the main radio is off or in a deep sleep [
24,
69]. Unlike traditional MAC protocols with single radio transceivers, WuR MAC protocols utilize the dual-radio WuR architecture to minimize overhearing, idle listening, and latency issues. Transmitter initiated WuR MAC protocols [
1,
27,
47,
61,
63] allow a node to initiate communication with a receiver in a single hop on-demand fashion. While bi-directional WuR MAC protocols [
5,
15,
44,
45,
64,
72] enable both transmitters and receivers to initiate communication using WuPkt as they are both equipped with WuRs, thus making them suitable for multi-hop wireless communication. Receiver-initiated WuR MAC protocols [
19,
29,
49,
50,
79] enable receiving nodes (sinks) to initiate single hop communication by announcing their readiness to collect data using WuPkts.
Receiver-initiated WuR MACs are classified as either broadcast-based—where a single WuPkt wakes several nodes equipped with WuR, or addressed-based (ID-based)—in which nodes are activated individually based on the address information in a WuPkt [
79]. RI-LD-WuR MAC [
79], RI-WuR MAC, and RI-CPT-WuR MAC [
29] all utilize broadcast WuPkt transmission with CSMA/CA for asynchronous communication when multiple nodes compete for medium access utilization. However, channel access competition degrades performance as collision increases as the number of nodes increases. In contrast, AWD MAC [
49] and DoRa [
50] utilize ID-based WuPkt for polling individual node for data. This minimizes collisions and improves reliability, but also decreases the network throughput while increasing the overall energy consumption. Despite their potentials, these receiver-initiated WuR MAC protocols have only been designed and tested in simulation; without real-world empirical evaluations.
Unlike existing receiver-initiated WuR MACs whose operation is contingent on satisfying the ENO condition, Greentooth explores a broadcast-based synchronous mechanism for networking real battery-free intermittent sensor nodes with an energy constrained receiver. Time synchronization is crucial for neighbor discovery and TDMA communication in both battery-powered and batteryless networks. So, Find+Flync [
25] have explored mechanisms for speeding up neighbor discovery among battery-free nodes. Find employs randomized delays to minimize discovery latency, while Flync uses harvested powerline-induced brightness variations in indoor lighting to further speed up neighbor discovery. Recently, Bonito [
26] and FreeBie [
18] have also explored the use of connections to maintain data exchange, however, Find and Bonito still incur the expensive beacon transmission and listening costs prior to achieving initial encounter and after connection is lost. Also, Flync is limited to indoor applications while FreeBie employs supercapitors for storing harvested energy, which makes it operate on the ENO condition with little to no intermittency.
Furthermore, Ambient backscatter [
89] and Mesh [
54,
55] networking techniques have been proposed for low-power intermittent systems. Despite their potentials, the modulated ambient RF signals used in Ambient backscatter are dynamic, unpredictable, and uncontrollable that complicates design and deployment, and limits network performance and reliability. Mesh networking method was only validated in MATLAB simulations. Greentooth however leverages the ultra-low power capability of WuR to provide a robust and energy efficient way of networking real intermittent batteryless systems that are prone to frequent and unpredictable power failures and timing inaccuracies.